From 08650c97ce50122c4a73cc09a9a9dac00c9840be Mon Sep 17 00:00:00 2001 From: Abolfazl Ziaeemehr Date: Mon, 17 Feb 2025 16:36:43 +0100 Subject: [PATCH 1/9] chore: Update GitHub Actions workflow to trigger on develop branch and include additional source files in packaging --- .github/workflows/publish.yml | 2 +- MANIFEST.in | 3 ++- pyproject.toml | 1 + 3 files changed, 4 insertions(+), 2 deletions(-) diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 2713b39..6c2b17c 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -3,7 +3,7 @@ name: Publish to PyPI on: push: branches: - - main + - develop tags: - "v*" diff --git a/MANIFEST.in b/MANIFEST.in index c4c0428..6bd4464 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,2 +1,3 @@ include LICENSE -graft docs \ No newline at end of file +graft docs +graft vbi/models/cpp/_src \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index f937184..debc24e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -64,6 +64,7 @@ include-package-data = true [tool.setuptools.package-data] vbi = [ "models/pytorch/data/*", # Include all files in the pytorch directory #!TODO: check this + "models/cpp/_src/*", # Include all files in the cpp directory ] From 3e9f4c5a98496a53361ac84f0129b7bed387eb76 Mon Sep 17 00:00:00 2001 From: Abolfazl Ziaeemehr Date: Mon, 17 Feb 2025 17:36:25 +0100 Subject: [PATCH 2/9] chore: Bump version to 0.1.3 and implement dynamic version retrieval --- pyproject.toml | 4 ++-- setup.py | 11 +++++++++-- vbi/__init__.py | 12 +++++++++++- vbi/_version.py | 2 +- 4 files changed, 23 insertions(+), 6 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index debc24e..9b75080 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -10,7 +10,7 @@ build-backend = "setuptools.build_meta" [project] name = "vbi" -version = "0.1.2" +version = "0.1.3" description = "Virtual brain inference." requires-python = ">=3.8" license = { text = "MIT" } @@ -64,7 +64,7 @@ include-package-data = true [tool.setuptools.package-data] vbi = [ "models/pytorch/data/*", # Include all files in the pytorch directory #!TODO: check this - "models/cpp/_src/*", # Include all files in the cpp directory + "models/cpp/_src/*", ] diff --git a/setup.py b/setup.py index b99b09c..f7112ea 100644 --- a/setup.py +++ b/setup.py @@ -4,6 +4,13 @@ from setuptools.command.build_ext import build_ext +def get_version(): + version_file = os.path.join(os.path.dirname(__file__), 'vbi', '_version.py') + with open(version_file) as f: + for line in f: + if line.startswith('__version__'): + return line.split('=')[1].strip().strip('"\'') + # Custom command to compile C++ and SWIG wrapper class CustomBuildExtCommand(build_ext): def run(self): @@ -54,10 +61,10 @@ def create_extension(model): # Setup function setup( name="vbi", - version="0.1.2", + version=get_version(), description="A Python package with C++ integration via SWIG", packages=find_packages(), # Use find_packages() to automatically discover packages - ext_modules=extensions, # Include all the C++ extensions + # ext_modules=extensions, # Include all the C++ extensions cmdclass={ "build_ext": CustomBuildExtCommand, # Override the default build_ext }, diff --git a/vbi/__init__.py b/vbi/__init__.py index cc6067d..01026cd 100644 --- a/vbi/__init__.py +++ b/vbi/__init__.py @@ -1,3 +1,4 @@ +import os from .tests.test_suite import tests from ._version import __version__ @@ -21,4 +22,13 @@ from .feature_extraction.utility import make_mask -from .utils import j2p, p2j \ No newline at end of file +from .utils import j2p, p2j + + + +def get_version(): + version_file = os.path.join(os.path.dirname(__file__), '_version.py') + with open(version_file) as f: + for line in f: + if line.startswith('__version__'): + return line.split('=')[1].strip().strip('"\'') diff --git a/vbi/_version.py b/vbi/_version.py index 78e47cc..3583411 100644 --- a/vbi/_version.py +++ b/vbi/_version.py @@ -1,4 +1,4 @@ -__version__ = 'v0.1.2' +__version__ = "v0.1.3" if __name__ == '__main__': print(__version__) From bbf4a5a5874ef5b963cd56c665800d7b4c5135d6 Mon Sep 17 00:00:00 2001 From: Abolfazl Ziaeemehr Date: Tue, 18 Feb 2025 19:03:35 +0100 Subject: [PATCH 3/9] docs: Add custom CSS styles and update documentation structure --- docs/_static/custom.css | 7 ++ docs/conf.py | 1 + docs/index.rst | 11 ++ docs/models.rst | 254 ++++++++++++++++++++++++++++++++++++++++ 4 files changed, 273 insertions(+) create mode 100644 docs/_static/custom.css create mode 100644 docs/models.rst diff --git a/docs/_static/custom.css b/docs/_static/custom.css new file mode 100644 index 0000000..97519d4 --- /dev/null +++ b/docs/_static/custom.css @@ -0,0 +1,7 @@ +.color-title th { + color: rgb(131, 94, 179); +} + +.color-caption caption { + color: rgb(139, 68, 206); +} \ No newline at end of file diff --git a/docs/conf.py b/docs/conf.py index 3c0bebb..3fb6f9f 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -31,6 +31,7 @@ html_theme = 'nature' pygments_style = "colorful" add_function_parentheses = True +html_static_path = ['_static'] # html_theme = "bootstrap" # html_theme_path = sphinx_bootstrap_theme.get_html_theme_path() diff --git a/docs/index.rst b/docs/index.rst index 4116d7c..cc2dae4 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -2,6 +2,10 @@ Virtual Brain Inference (VBI) ############################## +.. image:: _static/vbi_log.png + :alt: VBI Logo + :width: 300px + Installation ============ @@ -17,6 +21,13 @@ Installation # pip install -e .[all,dev,docs] +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + models + + Examples ========= diff --git a/docs/models.rst b/docs/models.rst new file mode 100644 index 0000000..eca485a --- /dev/null +++ b/docs/models.rst @@ -0,0 +1,254 @@ +.. raw:: html + + + +The Virtual Brain Models +======================== + +To build a virtual brain model the process begins with parcellating the brain into regions using anatomical data, typically derived from T1-MRI scans. Each region, represented as nodes in the network, is then equipped with a neural mass model to simulate the collective behavior of neurons within that area. These nodes are interconnected using a structural connectivity (SC) matrix, typically obtained from diffusion-weighted magnetic resonance imaging (DW-MRI). The entire network of interconnected nodes is then simulated using neuroinformatic tools, such as The Virtual Brain (``TVB``) [Sanz-Leon2015]_, replicating the intricate dynamics of brain activity and its associated brain imaging signals ((s)EEG/MEG/fMRI). This approach offers insights into both normal brain function and neurological disorders. In the following, we describe commonly used whole-brain network models corresponding to different types of neuroimaging recordings. + +.. image:: _static/Fig1.png + :alt: VBI Logo + :width: 800px + +Wilson-Cowan model +------------------ + +The Wilson-Cowan model [Wilson72]_ is a seminal neural mass model that describes the dynamics of connected excitatory and inhibitory neural populations, at cortical microcolumn level. It has been widely used to understand the collective behavior of neurons and simulate neural activities recorded by methods such as local field potentials (LFPs) and EEG. The model effectively captures phenomena such as oscillations, wave propagation, pattern formation in neural tissue, and responses to external stimuli, offering insights into various brain (dys)functions, particularly in Parkinson's disease [Duchet2021average]_, [Sermon2023sub]_. + + +We focused on a simplified model for generation of beta oscillation within the cortex-subthalamic nucleus-globus pallidus network [Pavlides2015]_. The model incorporates a closed-loop connection from the STN back to the cortex, represented by a single inhibitory connection with a time delay. However, it does not include feedback via the indirect pathway (cortex-striatum-GPe), as experimental evidence suggests this pathway is not essential for generating beta oscillations [Wei2015]_. Instead, the GPe receives a constant inhibitory input from the striatum, consistent with observations from Parkinson's disease models: + +.. math:: + + \tau_S \dot{S}(t) = F_S (w_{CS} E(t - T_{CS}) - w_{GS} G(t-T_{GS})) - S(t) + + \tau_G \dot{G}(t) = F_G (w_{SG} S(t - T_{SG}) - w_{GG} G(t-T_{GG}) - Str) - G(t) + + \tau_E \dot{E}(t) = F_E (-w_{SC} S(t - T_{SC}) - w_{CC} I(t-T_{CC}) + C) - E(t) + + \tau_I \dot{I}(t) = F_{I} (w_{CC} E(t-T_{CC})) - I(t) + + F_i (x) = \frac{M_i}{1+\big( \frac{M_i - B_i}{B_i} \big) \exp{\big(\frac{-4x}{M_i} \big)}}, \quad i \in \{S, G, E, I \} + +where the functions :math:`S`, :math:`G`, :math:`E`, and :math:`I` represent the firing rates of the STN, GPe, and the excitatory and inhibitory populations, respectively. The parameters :math:`T_{ij}` denote the synaptic connection time delays from population :math:`i` to population :math:`j`, while :math:`T_{ii}` represents the time delay of self-connections. The synaptic weights, :math:`w_{ij}`, follow the same subscript conventions as the time delays, indicating the influence of the presynaptic neuron's firing rate on the postsynaptic neuron. The membrane time constants are denoted by :math:`\tau_i`. A constant input, :math:`C`, is provided to the excitatory population in the cortex to account for a constant component of both extrinsic and intrinsic excitatory inputs, while :math:`Str` represents the constant inhibitory input from the striatum to the GPe. Lastly, :math:`F_{i}` are the activation functions. + +The nominal parameter values and the prior range for the target parameters are summarized in the following table. + + + +.. list-table:: Parameter descriptions for capturing whole-brain dynamics using **Wilson-Cowan** neural mass model. + :name: table:WCo + :header-rows: 1 + :class: color-caption + + * - Parameters + - Description + - Value + - Prior + * - T\ :sub:`SG` + - Delay from STN to GPe + - 6 ms + - + * - T\ :sub:`GS` + - Delay from GPe to STN + - 6 ms + - + * - T\ :sub:`GG` + - Self delay of GPe + - 4 ms + - + * - T\ :sub:`CS` + - Delay from cortex to STN + - 5.5 ms + - + * - T\ :sub:`SC` + - Delay from STN to cortex + - 21.5 ms + - + * - T\ :sub:`CC` + - Self delay of cortex + - 4.65 ms + - + * - τ\ :sub:`S` + - Time constant for STN + - 12.8 ms + - + * - τ\ :sub:`G` + - Time constant for GPe + - 20 ms + - + * - τ\ :sub:`E` + - Time constant for excitatory neurons + - 11.59 ms + - + * - τ\ :sub:`I` + - Time constant for inhibitory neurons + - 13.02 ms + - + * - M\ :sub:`S` + - Maximum firing rate of STN + - 300 spk/s + - + * - M\ :sub:`G` + - Maximum firing rate of GPe + - 400 spk/s + - + * - M\ :sub:`EI` + - Maximum firing rate of excitatory neurons + - 75.77 spk/s + - + * - M\ :sub:`I` + - Maximum firing rate of inhibitory neurons + - 205.72 spk/s + - + * - B\ :sub:`S` + - Baseline firing rate of STN + - 10 spk/s + - + * - B\ :sub:`G` + - Baseline firing rate of GPe + - 20 spk/s + - + * - B\ :sub:`EI` + - Baseline firing rate of excitatory neurons + - 17.85 spk/s + - + * - B\ :sub:`I` + - Baseline firing rate of inhibitory neurons + - 9.87 spk/s + - + * - C + - Excitatory input to cortex + - 172.18 spk/s + - + * - Str + - Inhibitory input from striatum to GPe + - 8.46 spk/s + - + * - w\ :sub:`GS` + - Synaptic weight from GPe to STN + - 1.33 + - U(0,10) + * - w\ :sub:`SG` + - Synaptic weight from STN to GPe + - 4.87 + - U(0,10) + * - w\ :sub:`GG` + - Self synaptic weight among GPe + - 0.53 + - U(0,20) + * - w\ :sub:`CS` + - Synaptic weight from cortex to STN + - 9.97 + - U(0,20) + * - w\ :sub:`SC` + - Synaptic weight from STN to cortex + - 8.93 + - U(0,10) + * - w\ :sub:`CC` + - Self synaptic weight among cortex + - 6.17 + - U(0,10) + +Jansen-Rit model +---------------- + +The Jansen-Rit neural mass model [Jansen1995]_ has been widely used to simulate physiological signals from various recording methods like intracranial LFPs, and scalp MEG/EEG recordings. For example, it has been shown to recreate responses similar to evoked-related potentials after a series of impulse stimulations [David2003]_, [David_etal06]_, generating high-alpha and low-beta oscillations (with added recurrent inhibitory connections and spike-rate modulation) [Moran2007]_, and also seizure patterns similar to those seen in temporal lobe epilepsy [Wendling2001]_. +This biologically motivated model comprises of three main populations of neurons: excitatory pyramidal neurons, inhibitory interneurons, and excitatory interneurons. These populations interact with each other through synaptic connections, forming a feedback loop that produces oscillatory activity governed by a set of nonlinear ordinary differential equations [JansenRit]_, [David2003]_, [Kazemi2022]_. + +.. math:: + + \dot{y}_{0i}(t) &=& y_{3i}(t); \quad \dot{y}_{1i}(t) = y_{4i}(t); \quad \dot{y}_{2i}(t) = y_{5i}(t) \\ + \dot{y}_{3i}(t) &=& A \, a\, \text{S}(y_{1i}(t)-y_{2i}(t)) - 2a \, y_{3i}(t) - a^2 y_{0i}(t) \\ + \dot{y}_{4i}(t) &=& A \, a\Big( P(t) + C_2 \, \text{S}(C_1 y_{0i}(t)) + G \, \text{H}_i \Big) + -2a y_{4i}(t) -a^2 y_{1i}(t) \\ + \dot{y}_{5i}(t) &=& B \, b \Big( C_4\, \text{S}(C_3 y_{0i}(t)) \Big) -2b \,y_{5i}(t) -b^2 y_{2i}(t) \\ + \text{S}(v) &=& \frac{v_{max}}{1+\exp(r(v_0-v))} \\ + \text{H}_{i} &=& \sum_{j=1}^{N} \text{SC}_{ij} \, \text{S} (y_{1j}-y_{2j}) + + +.. list-table:: Parameter descriptions for capturing whole-brain dynamics using **Jansen-Rit** neural mass model. + :name: table:JR + :header-rows: 1 + :class: color-caption + + * - **Parameters** + - **Description** + - **Value** + - **Prior** + * - *A* + - Excitatory PSPA + - 3.25 mV + - + * - *B* + - Inhibitory PSPA + - 22 mV + - + * - *1/a* + - Time constant of excitatory PSP (*a* = 100 s\ :sup:`-1`) + - + - + * - *1/b* + - Time constant of inhibitory PSP (*b* = 50 s\ :sup:`-1`) + - + - + * - C\ :sub:`1`, C\ :sub:`2` + - Average numbers of synapses between EP + - 1 C, 0.8 C + - + * - C\ :sub:`3`, C\ :sub:`4` + - Average numbers of synapses between IP + - 0.25 C + - + * - v\ :sub:`max` + - Maximum firing rate + - 5 Hz + - + * - v\ :sub:`0` + - Potential at half of maximum firing rate + - 6 mV + - + * - r + - Slope of sigmoid function at *v\ :sub:`0`* + - 0.56 mV\ :sup:`-1` + - + * - C + - Average numbers of synapses between neural populations + - 135 + - U(100, 500) + * - G + - Scaling the strength of network connections + - 1.5 + - U(0, 5) + +EP: excitatory populations, IP: inhibitory populations, PSP: post synaptic potential, PSPA: post synaptic potential amplitude. + +References +---------- + +.. [Wilson72] Wilson, H. R., & Cowan, J. D. (1972). Excitatory and inhibitory interactions in localized populations of model neurons. Biophysical Journal, 12(1), 1-24. + +.. [Duchet2021average] Duchet, B., & Others. (2021). Average neural activity in Parkinson's disease. *Neuroscience Journal*. + +.. [Sermon2023sub] Sermon, J., & Others. (2023). Subcortical effects of Parkinson's. *Brain Research*. + +.. [Sanz-Leon2015] Sanz-Leon, P., Knock, S. A., Spiegler, A., & Jirsa, V. K. (2015). Mathematical framework for large-scale brain network modeling in The Virtual Brain. *NeuroImage, 111*, 385-430. https://doi.org/10.1016/j.neuroimage.2015.01.002 + +.. [Pavlides2015] Pavlides, A., Hogan, S. J., & Bogacz, R. (2015). Computational models describing possible mechanisms for generation of excessive beta oscillations in Parkinson's disease. *PLoS Computational Biology, 11*(12)*, e1004609. https://doi.org/10.1371/journal.pcbi.1004609 + +.. [Wei2015] Wei, W., Wang, X., & Chen, X. (2015). The role of indirect pathway in beta oscillation of basal ganglia-thalamocortical circuitry in Parkinson's disease. *Frontiers in Computational Neuroscience, 9*, 63. https://doi.org/10.3389/fncom.2015.00063 + +.. [Jansen1995] Jansen, B. H., & Rit, V. G. (1995). Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns. *Biological Cybernetics*, 73(4), 357-366. + +.. [Moran2007] Moran, R. J., Kiebel, S. J., Stephan, K. E., Reilly, R. B., Daunizeau, J., & Friston, K. J. (2007). A neural mass model of spectral responses in electrophysiology. *NeuroImage*, 37(3), 706-720. https://doi.org/10.1016/j.neuroimage.2007.05.032. + +.. [Wendling2001] Wendling, F., Bartolomei, F., Bellanger, J.-J., & Chauvel, P. (2001). Interpretation of interdependencies in epileptic signals using a macroscopic physiological model of the EEG. *Clinical Neurophysiology*, 112(7), 1201-1218. + +.. [David2003] David, O., & Friston, K. J. (2003). A neural mass model for MEG/EEG: coupling and neuronal dynamics. *NeuroImage*, 20(3), 1743-1755. https://doi.org/10.1016/j.neuroimage.2003.07.015. + +.. [David_etal06] David, O., Kiebel, S. J., Harrison, L. M., Mattout, J., Kilner, J. M., & Friston, K. J. (2006). Dynamic causal modeling of evoked responses in EEG and MEG. *NeuroImage*, 30(4), 1255-1272. https://doi.org/10.1016/j.neuroimage.2005.10.045. + +.. [JansenRit] Jansen, B. H., & Rit, V. G. (1995). Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns. *Biological Cybernetics*, 73, 357-366. + +.. [Kazemi2022] Kazemi, S., & Jamali, Y. (2022). On the influence of input triggering on the dynamics of the Jansen-Rit oscillators network. *arXiv preprint arXiv:2202.06634*. From dbf8b55eaf9f7a773d45c5f42089d34045d4af5c Mon Sep 17 00:00:00 2001 From: Abolfazl Ziaeemehr Date: Wed, 19 Feb 2025 12:12:21 +0100 Subject: [PATCH 4/9] JR CuPy notebook example added. --- docs/examples/jansen_rit_sde_cupy.ipynb | 424 ++++++++++++++++++++++++ 1 file changed, 424 insertions(+) create mode 100644 docs/examples/jansen_rit_sde_cupy.ipynb diff --git a/docs/examples/jansen_rit_sde_cupy.ipynb b/docs/examples/jansen_rit_sde_cupy.ipynb new file mode 100644 index 0000000..f09a502 --- /dev/null +++ b/docs/examples/jansen_rit_sde_cupy.ipynb @@ -0,0 +1,424 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import pickle\n", + "import numpy as np\n", + "import networkx as nx\n", + "import sbi.utils as utils\n", + "import matplotlib.pyplot as plt\n", + "from sbi.analysis import pairplot\n", + "from helpers import plot_ts_pxx_jr\n", + "from vbi.inference import Inference\n", + "from vbi.models.cupy.jansen_rit import JR_sde\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from vbi import report_cfg\n", + "from vbi import extract_features_list\n", + "from vbi import get_features_by_domain, get_features_by_given_names" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "seed = 2\n", + "np.random.seed(seed)\n", + "torch.manual_seed(seed);" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "LABESSIZE = 12\n", + "plt.rcParams['axes.labelsize'] = LABESSIZE\n", + "plt.rcParams['xtick.labelsize'] = LABESSIZE\n", + "plt.rcParams['ytick.labelsize'] = LABESSIZE" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "nn = 6\n", + "num_sim = 100\n", + "num_workers = 10\n", + "weights = nx.to_numpy_array(nx.complete_graph(nn))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "par = {\n", + " \"weights\": weights,\n", + " \"t_cut\": 500,\n", + " \"t_end\": 2000,\n", + " \"noise_amp\": 0.05,\n", + " \"dt\": 0.02,\n", + " \"num_sim\": num_sim,\n", + " \"engine\": \"cpu\",\n", + " \"seed\": seed,\n", + " \"same_initial_state\": True,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "obj = Inference()\n", + "G_min, G_max = 0.0, 5.0\n", + "C1_min, C1_max = 135, 300\n", + "prior_min = [G_min, C1_min]\n", + "prior_max = [G_max, C1_max]\n", + "prior = utils.BoxUniform(low=torch.tensor(prior_min),\n", + " high=torch.tensor(prior_max))\n", + "theta = obj.sample_prior(prior, num_sim)\n", + "theta_np = theta.numpy().astype(float)\n", + "G = theta_np[:, 0]\n", + "C1 = theta_np[:, 1]\n", + "C1 = np.tile(C1, (nn, 1))\n", + "par['G'] = G\n", + "par['C1'] = C1" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "jr = JR_sde(par)\n", + "# print(jr())" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/100000 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if 1:\n", + " ts0 = data['x'][:, :, 0].T\n", + " data0 = {\"t\": t, \"x\": ts0}\n", + " info = np.isnan(ts0).sum()\n", + " print(t.shape, ts0.shape)\n", + " fig, ax = plt.subplots(1, 2, figsize=(10, 3))\n", + " plot_ts_pxx_jr(data0, par, ax, alpha=0.5)\n", + " plt.tight_layout()\n", + " plt.savefig(\"output/jr_ts_psd.png\", dpi=300)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Selected features:\n", + "------------------\n", + "■ Domain: statistical\n", + " ▢ Function: calc_std\n", + " ▫ description: Computes the standard deviation of the signal.\n", + " ▫ function : vbi.feature_extraction.features.calc_std\n", + " ▫ parameters : {'indices': None, 'verbose': False}\n", + " ▫ tag : all\n", + " ▫ use : yes\n", + " ▢ Function: calc_mean\n", + " ▫ description: Computes the mean of the signal.\n", + " ▫ function : vbi.feature_extraction.features.calc_mean\n", + " ▫ parameters : {'indices': None, 'verbose': False}\n", + " ▫ tag : all\n", + " ▫ use : yes\n" + ] + } + ], + "source": [ + "cfg = get_features_by_domain(domain=\"statistical\")\n", + "cfg = get_features_by_given_names(cfg, names=['calc_std', 'calc_mean'])\n", + "report_cfg(cfg)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(100, 6, 75000)\n" + ] + } + ], + "source": [ + "ts = data['x'] # [nt, nn, ns]\n", + "ts = ts.transpose(2, 1, 0) # [ns, nn, nt]\n", + "print(ts.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(100, 12)\n" + ] + } + ], + "source": [ + "from vbi import extract_features\n", + "stat_vec = extract_features(ts=ts,\n", + " cfg=cfg,\n", + " fs=1/par['dt']*1000,\n", + " n_workers=num_workers,\n", + " verbose=False).values\n", + "stat_vec = np.array(stat_vec)\n", + "print(stat_vec.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([100, 2]) torch.Size([100, 12])\n" + ] + } + ], + "source": [ + "scaler = StandardScaler()\n", + "stat_vec_st = scaler.fit_transform(stat_vec)\n", + "stat_vec_st = torch.tensor(stat_vec_st, dtype=torch.float32)\n", + "torch.save(theta, 'output/theta.pt')\n", + "torch.save(stat_vec_st, 'output/stat_vec_st.pt')\n", + "print(theta.shape, stat_vec_st.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Neural network successfully converged after 110 epochs.train Done in 0 hours 0 minutes 03.701637 seconds\n" + ] + } + ], + "source": [ + "posterior = obj.train(theta, stat_vec_st, prior, method='SNPE', density_estimator='maf')" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "with open('output/posterior.pkl', 'wb') as f:\n", + " pickle.dump(posterior, f)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "with open('output/posterior.pkl', 'rb') as f:\n", + " posterior = pickle.load(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "index = 0\n", + "theta_true = theta[index, :]\n", + "xo_st = stat_vec_st[index, :]" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "cb99cec649924ade974642326c5a4c46", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Drawing 10000 posterior samples: 0%| | 0/10000 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "limits = [[i, j] for i, j in zip(prior_min, prior_max)]\n", + "fig, ax = pairplot(\n", + " samples,\n", + " limits=limits,\n", + " figsize=(5, 5),\n", + " points=theta_true,\n", + " labels=[\"G\", \"C1\"],\n", + " offdiag='kde',\n", + " diag='kde',\n", + " fig_kwargs=dict(\n", + " points_offdiag=dict(marker=\"*\", markersize=10),\n", + " points_colors=[\"g\"]),\n", + " diag_kwargs={\"mpl_kwargs\": {\"color\": \"r\"}},\n", + " upper_kwargs={\"mpl_kwargs\": {\"cmap\": \"Blues\"}},\n", + ")\n", + "ax[0,0].tick_params(labelsize=14)\n", + "ax[0,0].margins(y=0)\n", + "plt.tight_layout()\n", + "fig.savefig(\"output/tri_jr_cupy.jpeg\", dpi=300)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From d3a6cfcdb2d26d550f454f48ba7f6cff0b14b14a Mon Sep 17 00:00:00 2001 From: Abolfazl Ziaeemehr Date: Wed, 19 Feb 2025 12:14:05 +0100 Subject: [PATCH 5/9] docs: Update API documentation and enhance CSS styles for better readability --- docs/API.rst | 71 + docs/_static/custom.css | 16 + docs/index.rst | 81 +- docs/references.bib | 16570 ++++++++++++++++++++++++++++++++ docs/requirements.txt | 3 +- vbi/models/cupy/jansen_rit.py | 84 +- vbi/models/cupy/utils.py | 47 +- 7 files changed, 16794 insertions(+), 78 deletions(-) create mode 100644 docs/API.rst create mode 100644 docs/references.bib diff --git a/docs/API.rst b/docs/API.rst new file mode 100644 index 0000000..899c7b8 --- /dev/null +++ b/docs/API.rst @@ -0,0 +1,71 @@ +.. raw:: html + + + + +API and documentation +===================== + + +C++ models +------------------------- + + +vbi.models.cpp.jansen_rit +~~~~~~~~~~~~~~~~~~~~~~~~~ + .. automodule:: vbi.models.cpp.jansen_rit + :members: + :undoc-members: + +vbi.models.cpp.km +~~~~~~~~~~~~~~~~~~~~~~~~~ + .. automodule:: vbi.models.cpp.km + :members: + :undoc-members: + +vbi.models.cpp.mpr +~~~~~~~~~~~~~~~~~~~~~~~~~ + .. automodule:: vbi.models.cpp.mpr + :members: + :undoc-members: + +vbi.models.cpp.vep +~~~~~~~~~~~~~~~~~~~~~~~~~ + .. automodule:: vbi.models.cpp.vep + :members: + :undoc-members: + + +vbi.models.cpp.wc +~~~~~~~~~~~~~~~~~~~~~~~~~ + .. automodule:: vbi.models.cpp.wc + :members: + :undoc-members: + + +Cupy models +------------------------- + +vbi.models.cupy.mpr +~~~~~~~~~~~~~~~~~~~~~~~~~ + .. automodule:: vbi.models.cupy.mpr + :members: + :undoc-members: + +vbi.models.cupy.ghb +~~~~~~~~~~~~~~~~~~~~~~~~~ + .. automodule:: vbi.models.cupy.ghb + :members: + :undoc-members: + +vbi.models.cupy.jansen_rit +~~~~~~~~~~~~~~~~~~~~~~~~~~~ + .. automodule:: vbi.models.cupy.jansen_rit + :members: + :undoc-members: + +vbi.models.cupy.utils +~~~~~~~~~~~~~~~~~~~~~~~~~ + .. automodule:: vbi.models.cupy.utils + :members: + :undoc-members: diff --git a/docs/_static/custom.css b/docs/_static/custom.css index 97519d4..7df68d6 100644 --- a/docs/_static/custom.css +++ b/docs/_static/custom.css @@ -4,4 +4,20 @@ .color-caption caption { color: rgb(139, 68, 206); +} + + +/* Change the color of module headers */ +.py.mod > .descclassname, .py.mod > .descname { + color: #FF5733; /* Orange */ +} + +/* Change the color of function signatures */ +.sig.sig-object.py { + color: #327219; /* Green */ +} + +/* Change the background color of code blocks */ +.highlight { + background-color: #F0F8FF; /* Light blue */ } \ No newline at end of file diff --git a/docs/index.rst b/docs/index.rst index cc2dae4..e543265 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -1,3 +1,8 @@ +.. raw:: html + + + + Virtual Brain Inference (VBI) ############################## @@ -47,6 +52,14 @@ Examples examples/ghb_sde_cupy + +.. toctree:: + :maxdepth: 2 + :caption: API Reference + + API + + Indices and tables ================== @@ -56,71 +69,3 @@ Indices and tables -API and documentation -===================== - -C++ models -------------------------- - -.. automodule:: vbi - :members: - :undoc-members: - -vbi.models.cpp.jansen_rit -------------------------- - .. automodule:: vbi.models.cpp.jansen_rit - :members: - :undoc-members: - -vbi.models.cpp.km -------------------------- - .. automodule:: vbi.models.cpp.km - :members: - :undoc-members: - -vbi.models.cpp.mpr -------------------------- - .. automodule:: vbi.models.cpp.mpr - :members: - :undoc-members: - -vbi.models.cpp.vep -------------------------- - .. automodule:: vbi.models.cpp.vep - :members: - :undoc-members: - - -vbi.models.cpp.wc -------------------------- - .. automodule:: vbi.models.cpp.wc - :members: - :undoc-members: - - -Cupy models -------------------------- - -vbi.models.cupy.mpr -------------------------- - .. automodule:: vbi.models.cupy.mpr - :members: - :undoc-members: - -vbi.models.cupy.ghb -------------------------- - .. automodule:: vbi.models.cupy.ghb - :members: - :undoc-members: - -vbi.models.cupy.jansen_rit ----------------------------- - .. automodule:: vbi.models.cupy.jansen_rit - :members: - :undoc-members: - -vbi.models.cupy.utils -------------------------- - .. automodule:: vbi.models.cupy.utils - :members: - :undoc-members: diff --git a/docs/references.bib b/docs/references.bib new file mode 100644 index 0000000..9d0e077 --- /dev/null +++ b/docs/references.bib @@ -0,0 +1,16570 @@ + +@article{Daunizeau2012, +itle = "Stochastic dynamic causal modelling of fMRI data: Should we care about neural noise?", +journal = "NeuroImage", +volume = "62", +number = "1", +pages = "464 - 481", +year = 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Daunizeau and K.E. Stephan and K.J. Friston", +} + + + +@article{Ostwald2014, +title = "A tutorial on variational {Bayes} for latent linear stochastic time-series models", +journal = "Journal of Mathematical Psychology", +volume = "60", +pages = "1 - 19", +year = "2014", +author = "Dirk Ostwald and Evgeniya Kirilina and Ludger Starke and Felix Blankenburg" +} + +@article{Ostwald2016, +title = "Probabilistic delay differential equation modeling of event-related potentials", +journal = "NeuroImage", +volume = "136", +pages = "227 - 257", +year = "2016", +author = "Dirk Ostwald and Ludger Starke" +} + + +@article{Razi2015, +title = "Construct validation of a DCM for resting state fMRI", +journal = "NeuroImage", +volume = "106", +pages = "1 - 14", +year = "2015", +issn = "1053-8119", +doi = "https://doi.org/10.1016/j.neuroimage.2014.11.027", +url = "http://www.sciencedirect.com/science/article/pii/S1053811914009446", +author = "Adeel Razi and Joshua Kahan and Geraint Rees and Karl J. 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Pelster", +title="A Thalamacortical Feedback Model to Explain EEG During Anesthesia", +bookTitle="Selforganization in Complex Systems: The Past, Present, and Future of Synergetics: Proceedings of the International Symposium, Hanse Institute of Advanced Studies, Delmenhorst, Germany, Novembe 2012", +year="2016", +publisher="Springer International Publishing", +pages="305--312" +} + + +@Inbook{Hutt_ch215, +author="Hutt, Axel +and Hashemi, Meysam +and beim Graben, Peter", +editor="Bhattacharya, Basabdatta Sen +and Chowdhury, Fahmida N.", +title="How to Render Neural Fields More Realistic", +bookTitle="Validating Neuro-Computational Models of Neurological and Psychiatric Disorders", +year="2015", +publisher="Springer International Publishing", +pages="141--159", + +} + + + + + +@article{Hashemi14, + author = {M. Hashemi and A. Hutt and J. Sleigh}, + title = {Anesthetic action on extra-synaptic receptors: effects in neural population models of {EEG} activity}, + journal = {J. Front. Syst. Neurosci.}, + volume = {8}, + number = {232}, + year = {2014} +} + +@article{Hashemi15, + author = {M. Hashemi and A. Hutt and J. Sleigh}, + title = {How the cortico-thalamic feedback affects the {EEG} power spectrum over frontal and occipital regions during propofol-induced sedation}, + journal = {J. Comput. Neurosc.}, + volume = {39}, + number = {2}, + pages = {155-179}, + year = {2015} +} + + + +@article{Hashemi17, + author = {M. Hashemi and A. Hutt and H. Darren and J. Sleigh}, + journal = {PLOS ONE}, + publisher = {Public Library of Science}, + title = {Anesthetic action on the transmission delay between cortex and thalamus explains the beta-buzz observed under propofol anesthesia}, + year = {2017}, + month = {06}, + volume = {12}, + pages = {1-29}, + number = {6} +} + +@article{Hashemi2018, +author="Hashemi, Meysam +and Hutt, Axel +and Buhry, Laure +and Sleigh, Jamie", +title="Optimal Model Parameter Estimation from EEG Power Spectrum Features Observed during General Anesthesia", +journal="Neuroinformatics", +year="2018", +month="Apr", +day="01", +volume="16", +number="2", +pages="231--251", +issn="1559-0089", +doi="10.1007/s12021-018-9369-x", +} + + + +@article{Hashemi2020, + title={The {Bayesian} Virtual Epileptic Patient: A probabilistic framework designed to infer the spatial map of epileptogenicity in a personalized large-scale brain model of epilepsy spread}, + author={Hashemi, Meysam and Vattikonda, AN and Sip, Viktor and Guye, Maxime and Bartolomei, Fabrice and Woodman, Michael Marmaduke and Jirsa, Viktor K}, + journal={NeuroImage}, + volume={217}, + pages={116839}, + year={2020}, + publisher={Elsevier} +} + + +@article{Hashemi2021, + title={On the influence of prior information evaluated by fully Bayesian criteria in a personalized whole-brain model of epilepsy spread}, + author={Hashemi, Meysam and Vattikonda, Anirudh N and Sip, Viktor and Diaz-Pier, Sandra and Peyser, Alexander and Wang, Huifang and Guye, Maxime and Bartolomei, Fabrice and Woodman, Marmaduke M and Jirsa, Viktor K}, + journal={PLoS computational biology}, + volume={17}, + number={7}, + pages={e1009129}, + year={2021}, + publisher={Public Library of Science San Francisco, CA USA} +} + + +@article{Sip2021, + doi = {10.1371/journal.pcbi.1008689}, + author = {Sip, Viktor AND Hashemi, Meysam AND Vattikonda, Anirudh N. AND Woodman, Marmaduke M. AND Wang, Huifang AND Scholly, Julia AND Medina Villalon, Samuel AND Guye, Maxime AND Bartolomei, Fabrice AND Jirsa, Viktor K.}, + journal = {PLOS Computational Biology}, + publisher = {Public Library of Science}, + title = {Data-driven method to infer the seizure propagation patterns in an epileptic brain from intracranial electroencephalography}, + year = {2021}, + month = {02}, + volume = {17}, + url = {https://doi.org/10.1371/journal.pcbi.1008689}, + pages = {1-31}, + abstract = {Surgical interventions in epileptic patients aimed at the removal of the epileptogenic zone have success rates at only 60-70%. This failure can be partly attributed to the insufficient spatial sampling by the implanted intracranial electrodes during the clinical evaluation, leading to an incomplete picture of spatio-temporal seizure organization in the regions that are not directly observed. Utilizing the partial observations of the seizure spreading through the brain network, complemented by the assumption that the epileptic seizures spread along the structural connections, we infer if and when are the unobserved regions recruited in the seizure. To this end we introduce a data-driven model of seizure recruitment and propagation across a weighted network, which we invert using the Bayesian inference framework. Using a leave-one-out cross-validation scheme on a cohort of 45 patients we demonstrate that the method can improve the predictions of the states of the unobserved regions compared to an empirical estimate that does not use the structural information, yet it is on the same level as the estimate that takes the structure into account. Furthermore, a comparison with the performed surgical resection and the surgery outcome indicates a link between the inferred excitable regions and the actual epileptogenic zone. The results emphasize the importance of the structural connectome in the large-scale spatio-temporal organization of epileptic seizures and introduce a novel way to integrate the patient-specific connectome and intracranial seizure recordings in a whole-brain computational model of seizure spread.}, + number = {2}, + +} + +@article{Vattikonda2021, + title={Identifying spatio-temporal seizure propagation patterns in epilepsy using Bayesian inference}, + author={Vattikonda, Anirudh N and Hashemi, Meysam and Sip, Viktor and Woodman, Marmaduke M and Bartolomei, Fabrice and Jirsa, Viktor K}, + journal={Communications biology}, + volume={4}, + number={1}, + pages={1--13}, + year={2021}, + publisher={Nature Publishing Group} +} + + +@article{Hayashi_etal07, + author = {K.~Hayashi and N.~Tsuda and T. 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Cybernetics}, + title = {A quantitative analysis of the local connectivity between pyramidal neurons in layers 2/3 of the rat visual cortex}, + volume = {82}, + pages = {11-121}, + year = {2000} +} + +@article{Hemmings_etal_TPS05, + author = {H.C. {Hemmings Jr.} and M.H. Akabas and P.A. Goldstein and J.R. Trudell and B.A. Orser and N.L. Harrison}, + journal = {Trends in Pharmacological Sciences}, + title = {Emerging molecular mechanisms of general anesthetic action}, + volume = {26}, + number = {10}, + pages = {503-510}, + year = {2005} +} + +@article{Henschel, + author = {O. Henschel and K.E. Gipson and A. Bordey}, + journal = {{CNS} Neurol Disord Drug Targets}, + title = {{$GABA_A$} Receptors, Anesthetics and Anticonvulsants in Brain Development}, + volume = {7}, + number = {2}, + pages = {211-224}, + year = {2008} +} + + +@article{Herculano99, + author = {S.~Herculano-Houzel and M. H. J.~Munk and + S.~Neuschwander and W.~Singer}, + journal = {J.~Neurosci.}, + title = "Precisely Synchronized Oscillatory Firing Patterns + Require Electroencephalographic Activation", + volume = "19", + number = "10", + pages = {3992%-4010%}, + year = "1999", +} + +@article{HerrmannEEG, + author = {C.~S.~Herrmann and A.~Mecklinger and E.~Pfeifer}, + title = {Gamma Response and {ERP}s in a Visual Classification + Task}, + journal = {Clin. Neurophysiol.}, + volume = {110}, + pages = {636-642}, + year = {1999} +} + +@article{HerrmannMEG, + author = {C.~S.~Herrmann and A.~Mecklinger}, + title = {Magnetoencephalographic responses to illusory + figures: Early evoked gamma is affected by + processing of stimulus features}, + journal = {Int.~J.~Psychophysiol.}, + volume = {38}, + number = {3}, + pages = {265-281}, + year = {2000} +} + +@article{Herrmannetal2016, + author = {C.~S.~Herrmann and M. Murray and S. Ionta and A. Hutt and J. Lefebvre}, + title = {Shaping Intrinsic Neural Oscillations with Periodic Stimulation}, + journal = {J.~Neurosci.}, + volume = {36}, + number = {19}, + pages = {5328-5337}, + year = {2016} +} + +@article{Herwig+Beckert00, + author = {H.~Herwig and K.~Beckert}, + title = {Experimental evidence about the controversy + concerning Fourier or non-Fourier heat conduction in + materials with a non-homogeneous inner structure}, + journal = {Heat and Mass Transfer}, + volume = {36}, + pages = {387-392}, + year = {2000} +} + + + +@article{Hindriks1, + author = {R. Hindriks and M. J. A. M. {van Putten}}, + title = {Meanfield modeling of propofol-induced changes in spontaneous {EEG} rhythms}, + journal = {Neuroimage}, + volume = {60}, + pages = {2323-2344}, + year = {2012} +} + + +@article{Hindriks2, + author = {R. Hindriks and M. J. A. M. {van Putten}}, + title = {Thalamo-cortical mechanisms underlying changes in amplitude and frequency of human alpha oscillations}, + journal = {Neuroimage}, + volume = {70}, + pages = {150-163}, + year = {2013} +} + +@article{Ho+Destexhe, + author = {N.~Ho and A.~Destexhe}, + title = {Synaptic background activity enhances the + responsiveness of neocortical pyramidal neurons}, + journal = {J.~Neurophysiol.}, + volume = {84}, + pages = {1488-1496}, + year = {2000} +} + +@article{HodgkinHuxley52, + author = {A.L.~Hodgkin and A.F.~Huxley}, + title = {A quantitative description of ion currents and its + application to conduction and excitation in nerve + membranes}, + journal = {J.~Physiol.}, + volume = {117}, + pages = {500-544}, + year = {1952} +} + +@BOOK{Holton, + editor = {J.R.~Holton}, + title = {An Introduction to Dynamic Meteorology}, + publisher = {Academic Press, New York}, + year = "1992" +} + +@article{Honeycutt92, + author = {R.L.~Honeycutt}, + title = {Stochastic {Runge-Kutta} algorithms, {I}. White noise}, + journal = {Phys.Rev.A}, + volume = {45}, + number = {2}, + pages = {600-603}, + year = {1992} +} + +@article{Hopfield84, + author = {J.~Hopfield}, + title = {Neurons with graded response have collective + computational properties like those of two-state + neurons}, + journal = {Proc.~Nat.~Acad.~Sci.~}, + volume = {81}, + pages = {3088-3092}, + year = {1984} +} + +@BOOK{Horsthemke, + author = {W.~Horsthemke and R.~Lefever}, + title = {Noise-induced transitions.}, + publisher = {Springer, Berlin}, + year = {1984} +} + +@article{Horton+Hubel81, + author = {J. C. Horton and D. H. Hubel}, + title = {Regular patchy distribution of cytochrome oxidase staining in primary visual cortex of macaque monkey}, + journal = {Nature}, + volume = {292}, + number = {5825}, + pages = {762-764}, + year = {1981} +} + + +@article{Horvath99, + author = {A.K.~Horvath and M.~Dolnik and A.P.~Munuzuri and A.M.~Zhabotinsky and I.R.~Epstein}, + title = {Control of {Turing} Structures by Periodic Illumination}, + journal = {Phys.Rev.Lett.}, + volume = {83}, + number = {15}, + pages = {2950-2952}, + year = {1999} +} + +@article{Houston_etal12, + author = {C.M. Houston and T.P. McGee and G. MacKenzie and K. Troyano-Cuturi and P. {Mateos Rodriguez} and E. Kutsarova and E. Diamanti and A.M. Hosie and N.P. Franks and S.G. Brickley}, + year = {2012}, + title = {Are extrasynaptic {GABA$_A$} receptors important targets for sedative/hypnotic drugs ?}, + journal = {J. Neurosci.}, + volume = {32}, + number = {11}, + pages = {3887-3897} +} + +@article{Huang, + author = {N.E. Huang and Z. Shen and S.R. Long and M.C. Wu and + H.H. Shih and Q. Zheng and N. Yen and C.C. Tung and + H.H. Liu}, + title={...}, + year = "1998", + journal = {Proc. R. Soc. Lond. A}, + volume = "454", + pages = "903" +} + +@article{Huang_etal_JN04, + author = {X. Huang and W.C. Troy and S.J. Schiff and Q. Yang and H. Ma and C.R. Laing and J.Y. Wu}, + title = {Spiral Waves in Disinhibited Mammalian Neocortex}, + journal = {J.Neurosc.}, + volume = {24}, + number = {44}, + pages = {9897-9902}, + year = {2004} +} + +@article{Hubel+Wiesel63, + author = {D.H.~Hubel and T.N.~Wiesel}, + title = {Receptive fields of cells in striate cortex of very + young, visually unexperienced kittens}, + journal = {TRENDS in Neurosciences}, + volume = {30}, + number={7}, + pages = {350--356}, + year = {1963} +} + + +@article{Huguenard, + author = {J.R. Huguenard and D. A. McCormick}, + title = {Thalamic synchrony and dynamic regulation of global forebrain oscillations Huguenard}, + journal = {J.~Physiol}, + volume = {26}, + pages = {994%-1002%}, + year = {2007} +} + + +@article{Hutt+Atay06, + author = {A.~Hutt and F.M.~Atay}, + title = {Effects of distributed transmission speeds on + propagating activity in neural populations}, + journal = {Phys.~Rev.~E}, + volume = {73}, + pages = {021906}, + year = {2006} +} + +@article{Hutt+Atay_CSF06, + author = {A.~Hutt and F.M.~Atay}, + title = {Spontaneous and evoked activity in extended neural populations with gamma-distributed spatial interactions and transmission delay}, + journal = {Chaos, Solitons and Fractals}, + volume = {32}, + pages = {547-560}, + year = {2007} +} + +@article{Hutt+Atay_PhysicaD05, + author = {A.~Hutt and F.M.~Atay}, + title = {Analysis of nonlocal neural fields for both general + and gamma-distributed connectivities}, + journal = {Physica D}, + volume = {203}, + pages = {30-54}, + year = {2005} +} + +@article{Hutt+Daffertshofer03, + author = {A.~Hutt and A.~Daffertshofer and U.~Steinmetz}, + title = {Detection of mutual phase synchronization in + multivariate signals and application to phase + ensembles and chaotic data}, + journal = {Phys.~Rev.~E}, + volume = {68}, + pages = {036219}, + year = {2003} +} + +@article{Hutt+Frank05, + author = {A.~Hutt and T.D.~Frank}, + title = {Critical fluctuations and 1/f -activity of neural + fields involving transmission delays}, + journal = {Acta Phys. Pol. A}, + volume = {108}, + number = {6}, + pages = {1021}, + year = {2005} +} + +@article{Hutt+Munk06, + author = {A.~Hutt and M.H.~Munk}, + title = {Mutual phase synchronization in single trial data}, + journal = {Chaos and Complexity Letters}, + volume = {2}, + number = {2}, + pages = {6}, + year = {2006} +} + +@article{Hutt+Riedel03, + author = {A.~Hutt and H.~Riedel}, + title = {Analysis and modeling of quasi-stationary + multivariate time series and their application to + middle latency auditory evoked potentials}, + journal = {Physica D}, + volume = {177}, + pages = {203%-232%}, + year = {2003} +} + +@article{Hutt+Schrauf07, + author = {A.~Hutt and M.~Schrauf}, + title = {Detection of transient synchronization in multivariate brain signals, application to event-related potentials}, + journal = {Chaos and Complexity Letters}, + volume = {3}, + number = {1}, + pages = {1-24}, + year = {2007} +} + +@article{HuttBTop02, + author = {A.~Hutt}, + title = {Spatiotemporal Modelling of {EEG}/MEG}, + journal = {Brain Topogr.}, + volume = {14}, + number = {4}, + year = {2002} +} + +@article{HuttCNS01, + author = {A.~Hutt and F.~Kruggel and C.S.~Herrmann}, + title = {Automatic Detection of ERP-components and their + modelling}, + journal = {J.~Cogn.~Neuroscience}, + volume = {Supplement 85}, + year = {2001} +} + +@BOOK{HuttDiss, + author = {A.~Hutt}, + title = {Methoden zur Untersuchung der Dynamik raumzeitlicher + Signale}, + publisher = {Max Planck-Institute of Cognitive Neuroscience, + Leipzig}, + series = {MPI series in cognitive neuroscience}, + volume = {15}, + note = {PhD thesis}, + year = {2001} +} + +@article{HuttIJBC04, + author = {A.~Hutt}, + title = {An Analytical Framework For Modeling Evoked and + Event-Related Potentials}, + journal = {Int.~J.~Bif.~Chaos}, + volume = {14}, + number = {2}, + pages = {653-666}, + year = {2004} +} + +@article{HuttPRE99, + author = {A.~Hutt and C.~Uhl and R.~Friedrich}, + title = {Analysis of spatio-temporal signals: A method based + on perturbation theory}, + journal = {Phys.Rev.E}, + volume = {60}, + number = {2}, + pages = {1350-1358}, + year = {1999} +} + +@article{HuttPRERC00, + author = {A.~Hutt and M.~Svensen and F.~Kruggel and + R.~Friedrich}, + title = {Detection of fixed points in spatiotemporal signals + by a clustering method}, + journal = {Phys.Rev.E}, + volume = {61}, + number = {5}, + pages = {R4691-R4693}, + year = {2000} +} + +@INCOLLECTION{HuttPamplona, + author = {A.~Hutt and F.~Kruggel}, + title = {Fixed Point Analysis: Dynamics of Non-stationary + Spatiotemporal Signals}, + booktitle = {Space-time Chaos: Characterization, Control and + Synchronization}, + editor = {S.~Boccaletti and H.L.~Mancini and W.~Gonzales-Vias + and J.~Burguete and D.L.~Valladares}, + pages = {29-44}, + publisher = {World Scientific, Singapore}, + year = {2001} +} + +@article{Hutt_Network03, + author = {A.~Hutt and M.~Bestehorn and T.~Wennekers}, + title = {Pattern formation in intracortical neuronal fields}, + journal = {Network:~Comput.~Neural Syst.}, + volume = {14}, + pages = {351-368}, + year = {2003} +} + +@article{Hutt_PLA08, + author = {A.~Hutt}, + title = {Local excitation-lateral inhibition interaction yields oscillatory instabilities in nonlocally interacting systems involving finite propagation delay}, + journal = {Phys.Lett.A}, + volume = {372}, + pages = {541-546}, + year = {2008} +} + +@article{Hutt_CP09, + author = {A.~Hutt}, + title = {Oscillatory activity in excitable neural systems}, + journal = {Contemp. Phys.}, + volume = {51}, + number = {1}, + pages = {3-16}, + year = {2009} +} + + +@article{Hutt_PRE07, + author = {A.~Hutt}, + title = {Generalization of the reaction-diffusion, {S}wift-{H}ohenberg, and {K}uramoto-{S}ivashinsky equations and effects of finite propagation speeds}, + journal = {Phys.~Rev.~E}, + volume = {75}, + pages = {026214}, + year = {2007} +} + +@article{Hutt_PREBR04, + author = {A.~Hutt}, + title = {Effects of nonlocal feedback on traveling fronts in + neural fields subject to transmission delay}, + journal = {Phys.~Rev.~E}, + volume = {70}, + pages = {052902}, + year = {2004} +} + + +@article{Hutt_etal_PRL07, + author = {A. Hutt and A. Longtin and L. Schimansky-Geier}, + title = {Additive global noise delays Turing bifurcations}, + journal = {Phys. Rev. Lett.}, + volume = {98}, + pages = {230601}, + year = {2007} +} + + +@article{Hutt_etal_PRE08, + author = {A.~Hutt and C.~Sutherland and A.~Longtin}, + title = {Driving neural oscillations with correlated spatial input and topographic feedback}, + journal = {Phys.Rev.E}, + volume = {78}, + pages = {021911}, + year = {2008} +} + +@article{Hutt_etal_PhysicaD08, + author = {A.~Hutt and A. Longtin and L. Schimansky-Geier}, + title = {Additive noise-induced Turing transitions in spatial systems with application to neural fields and the Swift-Hohenberg equation}, + journal = {Physica D}, + volume = {237}, + pages = {755-773}, + year = {2008} +} + +@article{Hutt_EPL08, + author = {A.~Hutt}, + title = {Additive noise may change the stability of nonlinear systems}, + journal = {Europhys.~Lett.}, + volume = {84}, + pages = {34003}, + year = {2008} +} + + +@article{Hutt+LSG_JBP08, + author = {A. Hutt and L. Schimansky-Geier}, + title = {Anesthetic-induced transitions by propofol modeled by nonlocal neural populations involving two neuron types}, + journal = {J. Biol. Phys.}, + volume = {34}, + number = {3-4}, + pages = {433-440}, + doi = {10.1007/s10867-008-9065-4}, + year = {2008} +} + + +@article{Schmidt_etal09, + author = {H. Schmidt and A.Hutt and L. Schimansky-Geier}, + title = {Wave fronts in inhomogeneous neural field models}, + journal = {Physica D}, + volume = {238}, + number = {14}, + pages = {1101-1112}, + year = {2009} +} + + +@article{Hutt_Longtin, + author = {A. Hutt and A. Longtin}, + title = {Effects of the anesthetic agent propofol on neural populations}, + journal = {Cogn. Neurodyn.}, + volume = {4}, + number = {1}, + pages = {37-59}, + year = {2009} +} + +@article{Hutt+RougierPRERC10, + author = {A. Hutt and N. Rougier}, + title = {Activity spread and breathers induced by finite transmission speeds in two-dimensional neural fields}, + journal = {Phys. Rev. E.}, + volume = {82}, + pages = {R055701}, + year = {2010} +} + +@article{Hutt_firing, + author = {A. Hutt}, + title = {The population firing rate in the presence of {GABA}ergic tonic inhibition in single neurons and application to general anaesthesia}, + journal = {Cogn. Neurodyn.}, + volume = {6}, + pages = {227-237}, + year = {2012} +} + + +@article{Hutt_frontiers, + author = {A. Hutt}, + title = {The anaesthetic propofol shifts the frequency of maximum spectral power in {EEG} during general anaesthesia: analytical insights from a linear model}, + journal = {Front. Comp. Neurosci.}, + volume = {7}, + pages = {2}, + year = {2013} +} + + + +@article{Hutt+Buhry13, + author = {A. Hutt and L. Buhry}, + title = {Study of {GABA}ergic extra-synaptic tonic inhibition in single neurons and neural populations by traversing neural scales: application to propofol-induced anaesthesia.}, + journal = {J. Comput. Neurosci.}, + volume = {in press}, + pages = {}, + year = {2014} +} + +@BOOK{SleepAnaesthesia11, + editor = {A. Hutt}, + title = {Sleep and Anesthesia: Neural Correlates in Theory and Experiment}, + publisher = {Springer, New York}, + series = {Springer Series in Computational Neuroscience}, + number = {15}, + year = {2011} +} + + +@article{Hutt+Zhang_13, + author = {A. Hutt and L. Zhang}, + title = {Distributed nonlocal feedback delays may destabilize fronts in neural fields, distributed transmission delays do not.}, + journal = {J. Math. Neurosci.}, + volume = {3}, + pages = {9}, + year = {2013} +} + +@article{Huttetal2016, + author = {A. Hutt and A. Mierau and J. Lefebvre}, + title = {Dynamic Control of Synchronous Activity in Networks of Spiking Neurons}, + journal = {PLoS One}, + volume = {11}, + number = {9}, + pages = {e0161488}, + year = {2016} +} + +@article{Lefebvre_etal12, + author = {J. Lefebvre and A. Hutt and V.G. LeBlanc and A. Longtin}, + title = {Reduced dynamics for delayed systems with harmonic or stochastic forcing}, + journal = {Chaos}, + volume = {22}, + pages = {043121}, + year = {2012} +} + +@article{Hutt_etal_EPL13, + author = {J. Lefebvre and A. Hutt}, + title = {Additive noise quenches delay-induced oscillations}, + journal = {Europhys. Lett.}, + volume = {102}, + pages = {60003}, + year = {2013} +} + +@article{Hutt_etal_EPL12, + author = {A. Hutt and J. Lefebvre and A. Longtin}, + title = {Delay stabilizes stochastic systems near an non-oscillatory instability}, + journal = {Europhysics Letters}, + volume = {98}, + pages = {20004}, + year = {2012} +} + +@article{Scholarpedia13, + author = {A. Hutt and J. Sleigh and A. Steyn-Ross and M. L. Steyn-Ross}, + title = {General anaesthesia}, + journal = {Scholarpedia}, + volume = {8}, + number = {8}, + pages = {30485}, + year = {2013} +} + +@article{Hutt+Buhry14, + author = {A. Hutt and L. Buhry}, + title = {Study of {GABA}ergic extra-synaptic tonic inhibition in single neurons and neural populations by traversing neural scales: application to propofol-induced anaesthesia}, + journal = {J. Comput. Neurosci.}, + volume = {in press}, + number = {}, + pages = {}, + year = {2014} +} + + +@article{Scholarpedia_NF, + author = {S. Coombes}, + title = {Neural {F}ields}, + journal = {Scholarpedia}, + volume = {1}, + number = {6}, + pages = {1373}, + year = {2006} +} + +@article{Hyman86, + author = {J.M. Hyman and B. Nicolaenko and S. Zaleski}, + title = {Order and complexity in the Kuramoto-Sivashinsky + model of weakly turbulent interfaces}, + journal = {Physica D}, + volume = {23}, + pages = {265}, + year = {1986} +} + +@article{Idiart+Abbott93, + author = {M.A.P. Idiart and L.F. Abbott}, + title = {Propagation of Excitation in Neural Network Models}, + journal = {Network: Comp. Neural Sys.}, + volume = "4", + pages = "285-294", + year = "1993", +} + +@article{Ihl, + author = {R.~Ihl and J.~Brinkmeyer}, + title={...}, + year = "1999", + journal = {Dement.~Geriatr.~Cogn.~Disord.}, + volume = "10", + number = "2", + pages = "64-69" +} + +@article{Ioannides_etal02, + author = {A.A.~Ioannides and G.K.~Kostopoulos and + N.A.~Laskaris and L.~Liu and T.~Shibata and + M.~Schellens and V.~Poghosyan and A.~Khurshudyan}, + title = {Timing and connectivity in the human somatosensory + cortex from single trial mass electrical activity}, + journal = {Human Brain Mapp.}, + volume = "15", + pages = "231-246", + year = "2002" +} + +@article{Isaacson93, + author = {S.H.~Isaacson and J.~Carr and A.J.~Rowan}, + title = {Cibroflocacin-induced complex partial status + epilepticus manifesting as an acute confusional + state}, + journal = {Neurol.}, + volume = {43}, + pages = {1619-1621}, + year = {1993} +} + +@article{Jabbari_etal07, + author = {S. {Jabbari-Farouji} and D. Mizuno and M. Atakhorrami and F.C. MacKintosh and C.F. Schmidt and E. Eiser and G.H. Wegdam and D. Bonn}, + title = {Fluctuation-dissipation theorem in an aging colloidal glass}, + journal = {Phys.Rev.Lett.}, + volume = {98}, + pages = {108302}, + year = {2007} +} + +@BOOK{Jackson, + author = {J.D. Jackson}, + title = {Classical Electrodynamics}, + publisher = {Wiley, New York}, + edition = {3}, + year = {1998} +} + +@article{JanckeEA99, + author = {D. Jancke and W. Erlhagen and H. R. Dinse and A. C. Akhavan and M. + Giese and A. Steinhage and G. Sch\"oner}, + title = {Parametric population representation of retinal location: neuronal + interaction dynamics in cat primary visual cortex}, + journal = {JNS}, + year = {1999}, + volume = {19}, + pages = {9016 -- 9028}, + number = {20}, + abstract = {Neuronal interactions are an intricate part of cortical information + processing generating internal representations of the environment + beyond simple one-to-one mappings of the input parameter space. Here + we examined functional ranges of interaction processes within ensembles + of neurons in cat primary visual cortex. Seven “elementary” stimuli + consisting of small squares of light were presented at contiguous + horizontal positions. The population representation of these stimuli + was compared to the representation of “composite” stimuli, consisting + of two squares of light at varied separations. Based on receptive + field measurements and by application of an Optimal Linear Estimator, + the representation of retinal location was constructed as a distribution + of population activation (DPA) in visual space. The spatiotemporal + pattern of the DPA was investigated by obtaining the activity of + each neuron for a sequence of time intervals. We found that the DPA + of composite stimuli deviates from the superposition of its components + because of distance-dependent (1) early excitation and (2) late inhibition. + (3) The shape of the DPA of composite stimuli revealed a distance-dependent + repulsion effect. We simulated these findings within the framework + of dynamic neural fields. In the model, the feedforward response + of neurons is modulated by spatial ranges of excitatory and inhibitory + interactions within the population. A single set of model parameters + was sufficient to describe the main experimental effects. Combined, + our results indicate that the spatiotemporal processing of visual + stimuli is characterized by a delicate, mutual interplay between + stimulus-dependent and interaction-based strategies contributing + to the formation of widespread cortical activation patterns.}, + eprint = {http://www.jneurosci.org/content/19/20/9016.full.pdf+html}, + file = {:Journals\\JNeurosci\\JanckeErlhagenEA.JNeuroSci19_20.pdf:PDF}, + url = {http://www.jneurosci.org/content/19/20/9016.abstract} +} + +@article{JansenRit, + author = {B.H. Jansen and V.G. Rit}, + title = {Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns}, + journal = {Biol. Cybern}, + volume = {73}, + pages = {357-366}, + year = { 1995} +} + + +@article{Jentzsch02, + author = {I.~Jentzsch}, + title = {Independent component analysis separates + sequence-sensitive {ERP} components}, + journal = {Int. J. Bif. Chaos}, + year = {this issue} +} + +@article{Jirsa+Kelso00, + author = {V.K. Jirsa and J.A.S. Kelso}, + title = {Spatiotemporal pattern formation in neural systems with heterogeneous connection topologies}, + journal = {Phys.Rev.E}, + volume = {62}, + number = {6}, + pages = {8462-8465}, + year = {2000} +} + +@article{Jirsa00, + author = {V.K. Jirsa and P. Fink and P. Foo and J.A.S. Kelso}, + title = {Parametric stabilization of biological coordination: + a theoretical model}, + journal = {J.~Biol.~Physics}, + volume = {26}, + pages = {85-112}, + year = {2000} +} + +@article{Jirsa02, + author = {V.K. Jirsa and K.J. Jantzen and A. Fuchs and J.A.S. Kelso}, + title = {Spatiotemporal forward solution of the {{EEG}} and {MEG} + using network modelling}, + journal = {IEEE Trans. Med. Imag.}, + volume = {21}, + number = {5}, + pages = {493-504}, + year = {2002} +} + +@article{Jirsa04_PRL, + author = {V.K. Jirsa and M.Z. 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Haken}, + title = {A Derivation of a Macroscopic Field Theory of the + Brain from the Quasi-microscopic Neural Dynamics}, + journal = {Physica D}, + volume = {99}, + pages = {503-526}, + year = {1997} +} + +@article{Jirsa2009, + author = {V.K. Jirsa }, + title = {Neural field dynamics with local and global connectivity and time delay. }, + journal = {Philos. Trans. R. Soc. A.}, + volume = {367}, + pages = {1131-1143}, + year = {2009} +} + +@article{John_etal99, + author = {Th. John and R. Stannarius and U. 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Nelson}, + year = {2006}, + pages = {2433--2438}, +} + +@article{Yi2, + title = {Solution of a System of Linear Delay Differential Equations Using the Matrix Lambert Function}, + journal = {Proceedings of the 2006 American Control Conference, Minnesota, USA}, + author = {S. Yi and A. G. Ulsoy }, + year = {2006}, + pages = {2433--2438}, +} + + +@article{Breda1, + title = { Pseudo spectral difference methods for characteristic roots of delay differential equation }, + volume = {24}, + number={1}, + journal = { SIMA J. Numer. Anal. }, + author = {D. Breda and S. Maset and R. Vermiglio}, + year = {2004}, + pages = {1--19}, +} + +@article{Breda2 , + title = { Trace-DDE, Tool for Robust Analysis Characteristic Equation of Delay Differential Equations}, + journal = {URL}, + author = {D. Breda and R. Vermiglio and S. Maset }, + year = {2006}, +} + +@article{Breda3, + title = { Pseudospectral approximation of eigenvalues of derivative operators with non-local boundary conditions}, + volume = {27}, + number={1}, + journal = { Appl. Numer. Math.}, + author = {D. Breda and S. Maset and R. Vermiglio}, + year = {2006}, + pages = {318--331}, +} + + +@article{Engelborghs1 , + title = { Numerical bifurcation analysis of delay differential equations }, + volume = {125}, + number={12}, + journal = {J. Comput. Appl. Math. }, + author = {K. Engelborghs and T. Luzyanina and D. Roose}, + year = {2000}, + pages = {265--275}, +} + + + +@article{Engelborghs2, + title = {On stability of LMS methods and characteristic roots of delay differential equations }, + volume = {40}, + number={2}, + journal = { SIAM J. Numer. Anal. }, + author = {K. Engelborghs and D. Roose}, + year = {2002}, + pages = {629--550}, +} + +@article{Engelborghs3 , + title = { Numerical bifurcation analysis of delay differential equations using DDE-BIFTOOL}, + volume = {28}, + number={1}, + journal = {ACM Trans. Math. Software}, + author = {K. Engelborghs and T. Luzyanina and D. Roose}, + year = {2002}, + pages = {1--24}, +} + +@article{Forde , + title = { Applications of Sturm sequences to bifurcation analysis of delay differential equation models}, + volume = {300}, + journal = {J. Math. Anal. Appl.}, + author = {J. Forde and P. Nelson}, + year = {2004}, + pages = {273--284}, +} + +@article{Pandur009, + author = {M. A. Panduro and C. A. Brizuela and L. I. Balderas and D. A. Acosta}, + title = { A comparison of genetic algorithms, particle swarm optimization and the differential evolution method for the design of scannable circular antenna arrays}, + journal = {Progress In Electromagnetics Research B}, + volume = {13}, + pages = {171-186}, + year = {2009} +} + +@article{Angeline05, + author = { P. J. Angeline }, + title = { Evolutionary optimization versus particle swarm optimization: Philosophy and performance differences }, + journal = {Evolutionary Programming VII}, + volume = {1447}, + pages = {601-610}, + year = {2005} +} + + + +@article{Vesterstram, + author = { J. Vesterstram and R. Thomsen }, + title = { A Comparative Study of Differential Evolution, Particle Swarm Optimization, and Evolutionary Algorithms on Numerical Benchmark Problems }, + journal = {IEEE Congress on Evolutionary Computation - CEC }, + pages = {1980-1987}, + year = {2004} +} + +@article { Canto2012, + title = "Global Optimization in Systems Biology: Stochastic Methods and Their + Applications", + year = "2012", + author = "E Balsa-Canto and J R Banga and J A Egea and A Fernandez-Villaverde and G M de Hijas-Liste", + journal = "Advances in Systems Biology (Advances in Experimental Medicine and Biology)", + volume = "736", + pages = "409-424", + } + + +@article {Villaverde2013, + author = { A. F. Villaverde and J.R. Banga }, + title = {Reverse engineering and identification in systems biology: strategies, perspectives and challenges}, + volume = {11}, + number = {91}, + year = {2013}, + journal = {Journal of The Royal Society Interface} +} + +@article {Green2014, + author = {Green, P. L. and Worden, K.}, + title = {{Bayesian} and Markov chain Monte Carlo methods for identifying nonlinear systems in the presence of uncertainty}, + volume = {373}, + number = {2051}, + year = {2015}, + journal = {Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences} +} + + +@article{Kramer2014, +author="Kramer, Andrei +and Calderhead, Ben +and Radde, Nicole", +title="Hamiltonian Monte Carlo methods for efficient parameter estimation in steady state dynamical systems", +journal="BMC Bioinformatics", +year="2014", +volume="15", +number="1", +pages="253", +} + + +@Book{Wilkinson2006, +author="Wilkinson, D. J.", +title="Stochastic Modelling for Systems Biology Mathematical and Computational Biology, vol. 11", +year="2006", +publisher="Hall/CRC", +address="London, UK" +} + + + +@article{Metropolis1953, +author="Metropolis, N. +and Rosenbluth, A. W. +and Rosenbluth, M. N. +and Teller, A. H. +and Teller, E.", +title="Equation of state calculations by fast computing machines", +journal="J Chem Phys", +year="1953", +volume="21", +doi="10.1063/1.1699114", +url="http://dx.doi.org/10.1063/1.1699114" +} + +@article{Kaderali2009, +author="Kaderali, L. +and Dazert, E. +and Zeuge, U. +and Frese, M. +and Bartenschlager, R.", +title="Reconstructing signaling pathways from RNAi data using probabilistic Boolean threshold networks", +journal="Bioinformatics", +year="2009", +volume="25", +doi="10.1093/bioinformatics/btp375", +url="http://dx.doi.org/10.1093/bioinformatics/btp375" +} + +@article{Bois2009, +author="Bois, F. Y.", +title="GNU MCSim: {Bayesian} statistical inference for SBML-coded systems biology models", +journal="Bioinformatics", +year="2009", +volume="25", +doi="10.1093/bioinformatics/btp162", +url="http://dx.doi.org/10.1093/bioinformatics/btp162" +} + +@Misc{Haario2006, +author="Haario, H. +and Laine, M. +and Mira, A. +and Saksman, E.", +title="DRAM: Efficient adaptive {MCMC}", +year="2006", +publisher="Springer", +address="Switzerland" +} + +@Book{Brooks2011, +author="Brooks, S. +and Gelman, A. +and Jones, G. L. +and Meng, X. -. L. (. E. d. s. ).", +title="Handbook of Markov Chain Monte Carlo. Handbooks of Modern Statistical Methods", +year="2011", +publisher="Hall/CRC", +address="London, UK", +doi="10.1201/b10905", +url="http://dx.doi.org/10.1201/b10905" +} + + + +@Book{Lawrence2010, +author="Lawrence, N. D. +and Girolami, M. +and Rattray, M. +and Sanguinetti, G. (. E. d. s. ).", +title="Learning and Inference in Computational Systems Biology. Computational Molecular Biology", +year="2010", +publisher="The MIT Press", +address="Cambridge, Massachusetts, London, England" +} + +@article{Rannala2002, +author="Rannala, B.", +title="Identifiability of parameters in {MCMC} {Bayesian} inference of phylogeny", +journal="Syst Biol", +year="2002", +volume="51", +doi="10.1080/10635150290102429", +url="http://dx.doi.org/10.1080/10635150290102429" +} + +@article{Duane1987, +author="Duane, S. +and Kennedy, A. D. +and Pendleton, B. J. +and Roweth, D.", +title="Hybrid Monte Carlo", +journal="Phys Lett B", +year="1987", +volume="195", +doi="10.1016/0370-2693(87)91197-X", +url="http://dx.doi.org/10.1016/0370-2693(87)91197-X" +} + +@article{Radde2011, +author="Radde, N.", +title="The role of feedback mechanisms in biological network models - a tutorial", +journal="Asian J Control", +year="2011", +volume="13", +doi="10.1002/asjc.376", +url="http://dx.doi.org/10.1002/asjc.376" +} + + + + + +@Misc{Calderhead2011, +author="Calderhead, B.", +title="Differential geometric {MCMC} methods and applications", +year="2011" +} + + + +@article{Weckesser2008, +author="Weckesser, W.", +title="Vfgen: A code generation tool", +journal="JNAIAM", +year="2008", +volume="3" +} + +@article{Wolff2004, +author="Wolff, U.", +title="Monte Carlo errors with less errors", +journal="Comput Phys Commun", +year="2004", +volume="156", +doi="10.1016/S0010-4655(03)00467-3", +url="http://dx.doi.org/10.1016/S0010-4655(03)00467-3" +} + +@article{Schmidt2006, +author="Schmidt, H. +and Jirstrand, M.", +title="Systems biology toolbox for matlab: a computational platform for research in systems biology", +journal="Bioinformatics", +year="2006", +volume="22", +doi="10.1093/bioinformatics/bti799", +url="http://dx.doi.org/10.1093/bioinformatics/bti799" +} + + +@article{Brannmark2010, +author="Br{\"a}nnmark, C. +and Palmer, R. +and Glad, S. T. +and Cedersund, G. +and Stralfors, P.", +title="Mass and information feedbacks through receptor endocytosis govern insulin signaling as revealed using a parameter-free modeling framework", +journal="J Biol C", +year="2010", +volume="285", +doi="10.1074/jbc.M110.106849", +url="http://dx.doi.org/10.1074/jbc.M110.106849" +} + + + + + + +@article{Hansen2001, +author = {N. Hansen and A. Ostermeier }, +title = {Completely derandomized self-adaptation in evolution strategies}, +journal = {Evolutionary Computation}, +volume={9}, +number={2}, +year = {2001} +} + + +@article{Hansen2003, +author = {N. Hansen and S.D. Muller and P. Koumoutsakos }, +title = {Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES)}, +journal = {Evolutionary Computation}, +volume={11}, +number={1}, +year = {2003} +} + +@article{Hansen2006, +author = {N. Hansen }, +title = {The CMA evolution strategy: a comparing review}, +journal = {Towards a new evolutionary computation}, +volume={192}, +pages={75-102}, +year = {2006} +} + + + + +@BOOK{Goldberg, + author = {D.E. Goldberg }, + title = {Genetic Algorithms in Search, Optimization +and Machine Learning}, + publisher = {Addison-Wesley, Reading, MA.}, + year = {1989}, +} + + +@BOOK{Kelley, + author = {C. T. Kelley }, + title = { Iterative Methods for Optimization }, + publisher = {North Carolina State University, Raleigh, North Carolina}, + year = {1999}, +} + + +@article{Kaupe, +author = {A. Kaupe }, +title = { Algorithm 178: Direct Search, Communications of the ACM }, +journal = {Communications of the ACM}, +volume={12}, +number={6}, +pages={313}, +year = {1963} +} + +@article{Sleigh2010, +author="Sleigh, James W. and Leslie, Kate and Voss, Logan", +title="The effect of skin incision on the electroencephalogram during general anesthesia maintained with propofol or desflurane", +journal="Journal of Clinical Monitoring and Computing", +year="2010", +volume="24", +number="4", +pages="307--318"} + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + + + + +@article{Carpenter2017, + author = {Bob Carpenter and Andrew Gelman and Matthew Hoffman and Daniel Lee and Ben Goodrich and Michael Betancourt and Marcus Brubaker and Jiqiang Guo and Peter Li and Allen Riddell}, + title = {Stan: A Probabilistic Programming Language}, + journal = {Journal of Statistical Software, articles}, + volume = {76}, + number = {1}, + year = {2017}, + pages = {1--32}, + doi = {10.18637/jss.v076.i01}, +} + + + +@software{cmdstan, + title = {{CmdStan}: the command-line interface to Stan, Version 2.18.0.}, + author = {{Stan Development Team}}, + year = {2018}, + version = {2.18.0.}, + url = {http://mc-stan.org/}, +} + + +@software{Stan, + title = {Stan Modeling Language Users Guide and Reference Manual, Version 2.18.0.}, + author = {{Stan Development Team}}, + year = {2018}, + version = {2.18.0.}, + url = {http://mc-stan.org/}, +} + + +@article{PyMC, +author = {A. Patil and D. Huard and C. J. Fonnesbeck}, +title = {PyMC: {Bayesian} stochastic modelling in Python}, +journal = {J. Stat. Softw}, +year = {2010} +} + + +@article{PyMC3, +author = {J. Salvatier and T.V. Wiecki and C. Fonnesbeck}, +title = {Probabilistic programming in Python using PyMC3}, +journal = {PeerJ Computer Science}, +year = {2016}, +doi={10.7717/peerj-cs.55}, +} + + +@article{Pyro, +author = {Bingham, Eli and Chen, Jonathan P. and Jankowiak, Martin and Obermeyer, Fritz and + Pradhan, Neeraj and Karaletsos, Theofanis and Singh, Rohit and Szerlip, Paul and + Horsfall, Paul and Goodman, Noah D.}, +title = {{Pyro: Deep Universal Probabilistic Programming}}, +journal = {Journal of Machine Learning Research}, +year = {2018} +} + + +@article{Edvard, + author = {Dustin Tran and Alp Kucukelbir and Adji B. Dieng and Maja Rudolph and Dawen Liang and David M. Blei}, + title = {{Edward: A library for probabilistic modeling, inference, and criticism}}, + journal = {arXiv preprint arXiv:1610.09787}, + year = {2016} +} + + +@article{Turing, +author = {Hong Ge and + Kai Xu and + Zoubin Ghahramani}, +title = {{Turing: a language for flexible probabilistic inference}}, +journal = {International Conference on Artificial Intelligence and Statistics, + {AISTATS} 2018, 9-11 April 2018, Playa Blanca, Lanzarote, Canary Islands, + Spain}, +pages = {1682--1690}, + year = {2018}, + url = {http://proceedings.mlr.press/v84/ge18b.html}, + biburl = {https://dblp.org/rec/bib/conf/aistats/GeXG18},} + +@Book{Gelman, +author="Gelman, A. +and Carlin, J. B. +and Stern, H. S. +and Rubin, D. B.", +title="{Bayesian} Data Analysis, Texts in Statistical Science", +year="2004", +publisher="Hall, CRC", +address="London, UK" +} + + +@Book{Aster, +title="Parameter Estimation and Inverse Problems", +editor = "Richard C. Aster and Brian Borchers and Clifford H. Thurber", +publisher = "Academic Press", +edition = "Second Edition", +address = "Boston", +year = "2013", +doi = "https://doi.org/10.1016/B978-0-12-385048-5.00030-6", +} + +@Book{BDA, + author = {Andrew Gelman and Christian Robert and Nicolas Chopin and Judith Rousseau}, + title = {{Bayesian} Data Analysis}, + publisher="CRC Press", + year = {1995} +} + + +@article{VanSchoot2021, + title={Bayesian statistics and modelling}, + author={van de Schoot, Rens and Depaoli, Sarah and King, Ruth and Kramer, Bianca and M{\"a}rtens, Kaspar and Tadesse, Mahlet G and Vannucci, Marina and Gelman, Andrew and Veen, Duco and Willemsen, Joukje and others}, + journal={Nature Reviews Methods Primers}, + volume={1}, + number={1}, + pages={1--26}, + year={2021}, + publisher={Nature Publishing Group} +} + + +@Book{Groetsch, +author="Groetsch, C.W.", +title="Inverse Problems: Activities for Undergraduates", +year="1999", +publisher="Cambridge University Press", +} + + + +@Book{Bishop, + author = "Christopher M. Bishop", + title = "Pattern Recognition and Machine Learning", + publisher = "Springer", + year = "2006", +} + + +@article{Girolami2011, +author="Girolami, M. +and Calderhead, B.", +title="Riemann manifold langevin and hamiltonian monte carlo methods", +journal="Journal of the Royal Statistical Society Series B: StatisticalMethodology", +year="2011", +volume="73", +doi="10.1111/j.1467-9868.2010.00765", +} + +@article{Betancourt2013a, +author="M. Betancourt", +title="Generalizing the no-U-turn sampler to Riemannian manifolds", +journal="arXiv:1304.1920", +year="2013", +} + + +@article{Betancourt2013b, +author="M. Betancourt and M. Girolami", +title="Hamiltonian Monte Carlo for hierarchical models", +journal="arXiv:1312.0906", +year="2013", +} + + +@article{Betancourt2014, +author="M. Betancourt and S. Byrne and S. Livingstone and M. Girolami", +title="The geometric foundations of Hamiltonian Monte Carlo", +journal="arXiv:1410.5110", +year="2014", +} + +@article{Betancourt2016a, +author="M. Betancourt", +title="Diagnosing suboptimal cotangent disintegrations in Hamiltonian Monte Carlo", +journal="arXiv:1604.00695", +year="2016", +} + + +@article{Betancourt2016b, +author="M. Betancourt", +title="Identifying the optimal integration time in Hamiltonian Monte Carlo", +journal="arXiv:1601.00225", +year="2016", +} + + +@article{Betancourt2018, +author="M. Betancourt", +title="A Conceptual Introduction to Hamiltonian Monte Carlo", +journal="arXiv:701.02434", +year="2014", +} + + +@article{Betancourt2018b, +author="M. Betancourt", +title=" Calibrating Model-Based Inferences and Decisions", +journal="arXiv:1803.08393", +year="2014", +} + + +@article{Vehtari2002, +author = {Vehtari, Aki and Lampinen, Jouko}, +title = {{Bayesian} Model Assessment and Comparison Using Cross-Validation Predictive Densities}, +journal = {Neural Computation}, +volume = {14}, +number = {10}, +pages = {2439-2468}, +year = {2002}, +doi = {10.1162/08997660260293292}, +} + + +@article{Vehtari2017, +author="Vehtari, Aki +and Gelman, Andrew +and Gabry, Jonah", +title="Practical {Bayesian} model evaluation using leave-one-out cross-validation and {{WAIC}}", +journal="Statistics and Computing", +year="2017", +month="Sep", +day="01", +volume="27", +number="5", +pages="1413--1432", +doi="10.1007/s11222-016-9696-4", +} + + +@article{Vehtari2017b, +author = {Vehtari, Aki and Gelman, Andrew and and Gabry, Jonah }, +title = {Pareto smoothed importance sampling}, +journal = {arXiv:1507.02646}, +year="2017", +} + +@article{Vehtari2016, + title={{Bayesian} Leave-One-Out Cross-Validation Approximations for Gaussian Latent Variable Models}, + author={Aki Vehtari and Tommi Mononen and Ville Tolvanen and Tuomas Sivula and Ole Winther}, + journal={Journal of Machine Learning Research}, + year={2016}, + volume={17}, + pages={103:1-103:38} +} + + + + + +@software{loo, + title = {loo: Efficient leave-one-out cross-validation +and {{WAIC}} for {Bayesian} models}, + author = {Vehtari, Aki and Gelman, Andrew and and Gabry, Jonah}, + year = {2016}, + version = {R package version 0.1.6}, + url = {https://github.com/stan-dev/loo}, +} + + +@InProceedings{Yao2018, + title = {Yes, but Did It Work?: Evaluating Variational Inference}, + author = {Yao, Yuling and Vehtari, Aki and Simpson, Daniel and Gelman, Andrew}, + booktitle = {Proceedings of the 35th International Conference on Machine Learning}, + pages = {5581--5590}, + year = {2018}, + volume = {80}, + series = {Proceedings of Machine Learning Research}, + publisher = {PMLR}, +} + + + + + + +@article{Watanabe, +author = {S. Watanabe}, +title = {Asymptotic equivalence of {Bayes} cross validation and widely applicable information criterion in singular learning theory}, +journal = {J. Mach. Learn. Res.}, +volume = {11}, +pages = {3571-3594}, +year = {2010}, +} + + +@article{Rubin1992, +author = {A. Gelman and D.B. Rubin}, +title = {Inference from iterative simulation using multiple sequences}, +journal = {Statistical Science}, +volume = {7}, +pages = {457-511}, +year = {1992}, +} + +@article{Brooks1997, +author = { Stephen P. Brooks and Andrew Gelman }, +title = {General methods for monitoring convergence of iterative simulations}, +journal = {Journal of Computational and Graphical Statistics}, +volume = {7}, +number = {4}, +pages = {434-455}, +year = {1998}, +publisher = {Taylor & Francis}, +doi = {10.1080/10618600.1998.10474787}, +} + + +@article{Kucukelbir2015, +author="A. Kucukelbir and R. Ranganath and A. Gelman and D. M. Blei", +title="Automatic variational +inference in Stan", +journal="arXiv:1506.03431", +year="2015", +} + +@article{Kucukelbir2017, + author = {Alp Kucukelbir and Dustin Tran and Rajesh Ranganath and Andrew Gelman and David M. Blei}, + title = {Automatic Differentiation Variational Inference}, + journal = {Journal of Machine Learning Research}, + year = {2017}, + volume = {18}, + number = {14}, + pages = {1-45}, + url = {http://jmlr.org/papers/v18/16-107.html} +} + + +@article{Blei2017, +author = {David M. Blei and Alp Kucukelbir and Jon D. McAuliffe}, +title = {Variational Inference: A Review for Statisticians}, +journal = {Journal of the American Statistical Association}, +volume = {112}, +number = {518}, +pages = {859-877}, +year = {2017}, +publisher = {Taylor & Francis}, +doi = {10.1080/01621459.2017.1285773}, +} + + +@article{Hoffman2014, +author = {Hoffman, Matthew D. and Gelman, Andrew}, + title = {The No-U-turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo}, + journal = {J. Mach. Learn. Res.}, + issue_date = {January 2014}, + volume = {15}, + number = {1}, + month = jan, + year = {2014}, + issn = {1532-4435}, + pages = {1593--1623}, + numpages = {31}, + url = {http://dl.acm.org/citation.cfm?id=2627435.2638586}, + publisher = {JMLR.org}, +} + + +@Book{Neal2011, +author="A. Neal", +title="{MCMC} using Hamiltonian dynamics", +year="2011", +publisher="Chapman and Hall/CRC", +address="London, UK" +} + + +@article{Duane87, +title = "Hybrid Monte Carlo", +journal = "Physics Letters B", +volume = "195", +number = "2", +pages = "216 - 222", +year = "1987", +doi = "https://doi.org/10.1016/0370-2693(87)91197-X", +author = "Simon Duane and A.D. Kennedy and Brian J. Pendleton and Duncan Roweth" +} + + +@article{Monnahan2016, +author = {Monnahan, Cole C. and Thorson, James T. and Branch, Trevor A.}, +title = {Faster estimation of {Bayesian} models in ecology using Hamiltonian Monte Carlo}, +journal = {Methods in Ecology and Evolution}, +volume = {8}, +number = {3}, +pages = {339-348}, +year="2016", +doi = {10.1111/2041-210X.12681}, +} + +@article{Annis2017, +author="Annis, Jeffrey +and Miller, Brent J. +and Palmeri, Thomas J.", +title="{Bayesian} inference with Stan: A tutorial on adding custom distributions", +journal="Behavior Research Methods", +year="2017", +month="Jun", +day="01", +volume="49", +number="3", +pages="863--886", +doi="10.3758/s13428-016-0746-9", +} + + + +@article {Gopalan2013, + author = {Gopalan, Prem K. and Blei, David M.}, + title = {Efficient discovery of overlapping communities in massive networks}, + volume = {110}, + number = {36}, + pages = {14534--14539}, + year = {2013}, + doi = {10.1073/pnas.1221839110}, + publisher = {National Academy of Sciences}, + journal = {Proceedings of the National Academy of Sciences} +} + + +@article {Gopalan2016, + Title = {Scaling probabilistic models of genetic variation to millions of humans}, + Author = {Gopalan, Prem and Hao, Wei and Blei, David M and Storey, John D}, + DOI = {10.1038/ng.3710}, + Number = {12}, + Volume = {48}, + Month = {December}, + Year = {2016}, + Journal = {Nature genetics}, + ISSN = {1061-4036}, + Pages = {1587—1590}, + URL = {http://europepmc.org/articles/PMC5127768}, +} + + + +@article{Stefan2018, +title = "A generative model of whole-brain effective connectivity", +journal = "NeuroImage", +volume = "179", +pages = "505 - 529", +year = "2018", +issn = "1053-8119", +doi = "https://doi.org/10.1016/j.neuroimage.2018.05.058", +url = "http://www.sciencedirect.com/science/article/pii/S1053811918304762", +author = "Stefan Fr{\"a}ssle and Ekaterina I. Lomakina and Lars Kasper and Zina M. Manjaly and Alex Leff and Klaas P. Pruessmann and Joachim M. Buhmann and Klaas E. Stephan", +keywords = "{Bayesian} regression, Dynamic causal modeling, Generative model, Effective connectivity, Sparsity, Connectomics" +} + + +@article{Stefan2017, +title = {Regression DCM for fMRI}, +journal = {NeuroImage}, +volume = {155}, +pages = {406-421}, +year = {2017}, +issn = {1053-8119}, +doi = {https://doi.org/10.1016/j.neuroimage.2017.02.090}, +url = {https://www.sciencedirect.com/science/article/pii/S105381191730201X}, +author = {Stefan Fr{\"a}ssle and Ekaterina I. Lomakina and Adeel Razi and Karl J. Friston and Joachim M. Buhmann and Klaas E. Stephan}, +keywords = {Bayesian regression, Dynamic causal modeling, Variational Bayes, Generative model, Effective connectivity, Connectomics}, +abstract = {The development of large-scale network models that infer the effective (directed) connectivity among neuronal populations from neuroimaging data represents a key challenge for computational neuroscience. Dynamic causal models (DCMs) of neuroimaging and electrophysiological data are frequently used for inferring effective connectivity but are presently restricted to small graphs (typically up to 10 regions) in order to keep model inversion computationally feasible. Here, we present a novel variant of DCM for functional magnetic resonance imaging (fMRI) data that is suited to assess effective connectivity in large (whole-brain) networks. The approach rests on translating a linear DCM into the frequency domain and reformulating it as a special case of Bayesian linear regression. This paper derives regression DCM (rDCM) in detail and presents a variational Bayesian inversion method that enables extremely fast inference and accelerates model inversion by several orders of magnitude compared to classical DCM. Using both simulated and empirical data, we demonstrate the face validity of rDCM under different settings of signal-to-noise ratio (SNR) and repetition time (TR) of fMRI data. In particular, we assess the potential utility of rDCM as a tool for whole-brain connectomics by challenging it to infer effective connection strengths in a simulated whole-brain network comprising 66 regions and 300 free parameters. Our results indicate that rDCM represents a computationally highly efficient approach with promising potential for inferring whole-brain connectivity from individual fMRI data.} +} + + + + + + + + +@article{Jirsa2017, +title = "The Virtual Epileptic Patient: Individualized whole-brain models of epilepsy spread", +journal = "NeuroImage", +volume = "145", +pages = "377 - 388", +year = "2017", +note = "Individual Subject Prediction", +issn = "1053-8119", +doi = "https://doi.org/10.1016/j.neuroimage.2016.04.049", +url = "http://www.sciencedirect.com/science/article/pii/S1053811916300891", +author = "V.K. Jirsa and T. Proix and D. Perdikis and M.M. Woodman and H. Wang and J. Gonzalez-Martinez and C. Bernard and C. B\'enar and M. Guye and P. Chauvel and F. Bartolomei" +} + + +@article{Bansal2018, +title = "Personalized brain network models for assessing structure-function relationships", +journal = "Current Opinion in Neurobiology", +volume = "52", +pages = "42 - 47", +year = "2018", +note = "Systems Neuroscience", +issn = "0959-4388", +doi = "https://doi.org/10.1016/j.conb.2018.04.014", +url = "http://www.sciencedirect.com/science/article/pii/S0959438818300059", +author = "Kanika Bansal and Johan Nakuci and Sarah Feldt Muldoon" +} + +@article{BernardJirsa, +title = "Virtual Brain for neurological disease modeling", +journal = "Drug Discovery Today: Disease Models", +volume = "19", +pages = "5 - 10", +year = "2016", +note = "Computational Models of Neurological Disorder", +issn = "1740-6757", +doi = "https://doi.org/10.1016/j.ddmod.2017.05.001", +url = "http://www.sciencedirect.com/science/article/pii/S1740675717300221", +author = "Christophe Bernard and Viktor Jirsa" +} + + +@article{Cooray2015, +title = "Characterising seizures in anti-NMDA-receptor encephalitis with dynamic causal modelling", +journal = "NeuroImage", +volume = "118", +pages = "508 - 519", +year = "2015", +issn = "1053-8119", +doi = "https://doi.org/10.1016/j.neuroimage.2015.05.064", +url = "http://www.sciencedirect.com/science/article/pii/S1053811915004541", +author = "Gerald K. Cooray and Biswa Sengupta and Pamela Douglas and Marita Englund and Ronny Wickstrom and Karl Friston", +keywords = "Anti-NMDA-R encephalitis, EEG, Dynamical causal modelling (DCM), Seizures" +} + + +@article{Cooray2016, +title = "Dynamic causal modelling of electrographic seizure activity using {Bayesian} belief updating", +journal = "NeuroImage", +volume = "125", +pages = "1142 - 1154", +year = "2016", +issn = "1053-8119", +doi = "https://doi.org/10.1016/j.neuroimage.2015.07.063", +url = "http://www.sciencedirect.com/science/article/pii/S1053811915006837", +author = "Gerald K. Cooray and Biswa Sengupta and Pamela K. Douglas and Karl Friston", +keywords = "Dynamic causal modelling (DCM), {Bayesian} belief updating, Epilepsy, Seizure activity, EEG/ECoG" +} + + +@article {Proix2014, + author = {Proix, Timoth{\'e}e and Bartolomei, Fabrice and Chauvel, Patrick and Bernard, Christophe and Jirsa, Viktor K.}, + title = {Permittivity Coupling across Brain Regions Determines Seizure Recruitment in Partial Epilepsy}, + volume = {34}, + number = {45}, + pages = {15009--15021}, + year = {2014}, + doi = {10.1523/JNEUROSCI.1570-14.2014}, + publisher = {Society for Neuroscience}, + issn = {0270-6474}, + URL = {http://www.jneurosci.org/content/34/45/15009}, + journal = {Journal of Neuroscience} +} + +@article{Proix2016, + author = {Timoth{\'{e}}e Proix and Andreas Spiegler and Michael Schirner and Simon Rothmeier and Petra Ritter and Viktor K. Jirsa}, + title = {How do parcellation size and short-range connectivity affect dynamics in large-scale brain network models?}, + journal = {{NeuroImage}}, + year = {2016}, + volume = {142}, + month = {nov}, + pages = {135--149}, + doi = {10.1016/j.neuroimage.2016.06.016}, + publisher = {Elsevier {BV}}, +} + +@article{Proix2017, +author = {Proix, Timoth\'ee and Bartolomei, Fabrice and Guye, Maxime and Jirsa, Viktor K.}, +title = {Individual brain structure and modelling predict seizure propagation}, +journal = {Brain}, +volume = {140}, +number = {3}, +pages = {641-654}, +year = {2017}, +doi = {10.1093/brain/awx004}, +URL = {http://dx.doi.org/10.1093/brain/awx004}, +} + +@article{Proix2018, + title={Predicting the spatiotemporal diversity of seizure propagation and termination in human focal epilepsy}, + author={Proix, Timoth{\'e}e and Jirsa, Viktor K and Bartolomei, Fabrice and Guye, Maxime and Truccolo, Wilson}, + journal={Nature Communications}, + volume={9}, + number={1}, + pages={1088}, + year={2018}, + publisher={Nature Publishing Group}, + doi={https://doi.org/10.1038/s41467-018-02973-y}, +} + +@article{Kenza2015, + title = {Seizures, refractory status epilepticus, and depolarization block as endogenous brain activities}, + author = {El Houssaini, Kenza and Ivanov, Anton I. and Bernard, Christophe and Jirsa, Viktor K.}, + journal = {Phys. Rev. E}, + volume = {91}, + issue = {1}, + pages = {010701}, + numpages = {5}, + year = {2015}, + month = {Jan}, + publisher = {American Physical Society}, + doi = {10.1103/PhysRevE.91.010701}, + url = {https://link.aps.org/doi/10.1103/PhysRevE.91.010701} +} + + +@article{Jirsa2014, +author = {Jirsa, Viktor K. and Stacey, William C. and Quilichini, Pascale P. and Ivanov, Anton I. and Bernard, Christophe}, +title = {On the nature of seizure dynamics}, +journal = {Brain}, +volume = {137}, +number = {8}, +pages = {2210-2230}, +year = {2014}, +doi = {10.1093/brain/awu133}, +} + + +@article{Papadopoulou2015, +title = "Tracking slow modulations in synaptic gain using dynamic causal modelling: Validation in epilepsy", +journal = "NeuroImage", +volume = "107", +pages = "117 - 126", +year = "2015", +issn = "1053-8119", +doi = "https://doi.org/10.1016/j.neuroimage.2014.12.007", +url = "http://www.sciencedirect.com/science/article/pii/S1053811914009999", +author = "Margarita Papadopoulou and Marco Leite and Pieter van Mierlo and Kristl Vonck and Louis Lemieux and Karl Friston and Daniele Marinazzo", +keywords = "Dynamical causal modelling, Neural mass models, Seizure onset, Dynamical connectivity, Electroencephalography, Epilepsy" +} + + +@article{Papadopoulou2017, +title = "Dynamic causal modelling of seizure activity in a rat model", +journal = "NeuroImage", +volume = "146", +pages = "518 - 532", +year = "2017", +issn = "1053-8119", +doi = "https://doi.org/10.1016/j.neuroimage.2016.08.062", +url = "http://www.sciencedirect.com/science/article/pii/S1053811916304542", +author = "Margarita Papadopoulou and Gerald Cooray and Richard Rosch and Rosalyn Moran and Daniele Marinazzo and Karl Friston" +} + +@article{Sengupta2015, +title = "Gradient-free {MCMC} methods for dynamic causal modelling", +journal = "NeuroImage", +volume = "112", +pages = "375 - 381", +year = "2015", +issn = "1053-8119", +doi = "https://doi.org/10.1016/j.neuroimage.2015.03.008", +url = "http://www.sciencedirect.com/science/article/pii/S1053811915001846", +author = "Biswa Sengupta and Karl J. Friston and Will D. Penny" +} + + +@article{scikit-learn, + title={Scikit-learn: Machine Learning in {P}ython}, + author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. + and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. + and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and + Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, + journal={Journal of Machine Learning Research}, + volume={12}, + pages={2825--2830}, + year={2011} +} + +@inproceedings{sklearn_api, + author = {Lars Buitinck and Gilles Louppe and Mathieu Blondel and + Fabian Pedregosa and Andreas Mueller and Olivier Grisel and + Vlad Niculae and Peter Prettenhofer and Alexandre Gramfort + and Jaques Grobler and Robert Layton and Jake VanderPlas and + Arnaud Joly and Brian Holt and Ga{\"{e}}l Varoquaux}, + title = {{API} design for machine learning software: experiences from the scikit-learn + project}, + booktitle = {ECML PKDD Workshop: Languages for Data Mining and Machine Learning}, + year = {2013}, + pages = {108--122}, +} + + +@article{harris2020array, + title = {Array programming with {NumPy}}, + author = {Charles R. Harris and K. Jarrod Millman and St{'{e}}fan J. + van der Walt and Ralf Gommers and Pauli Virtanen and David + Cournapeau and Eric Wieser and Julian Taylor and Sebastian + Berg and Nathaniel J. Smith and Robert Kern and Matti Picus + and Stephan Hoyer and Marten H. van Kerkwijk and Matthew + Brett and Allan Haldane and Jaime Fern{'{a}}ndez del + R{'{\i}}o and Mark Wiebe and Pearu Peterson and Pierre + G{'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and + Warren Weckesser and Hameer Abbasi and Christoph Gohlke and + Travis E. Oliphant}, + year = {2020}, + month = sep, + journal = {Nature}, + volume = {585}, + number = {7825}, + pages = {357--362}, + doi = {10.1038/s41586-020-2649-2}, + publisher = {Springer Science and Business Media {LLC}}, + url = {https://doi.org/10.1038/s41586-020-2649-2} +} + +@article{Fawcett, +ttitle = "An introduction to ROC analysis", +journal = "Pattern Recognition Letters", +volume = "27", +number = "8", +pages = "861 - 874", +year = "2006", +note = "ROC Analysis in Pattern Recognition", +issn = "0167-8655", +doi = "https://doi.org/10.1016/j.patrec.2005.10.010", +url = "http://www.sciencedirect.com/science/article/pii/S016786550500303X", +author = "Tom Fawcett", +} + +@article{Powers, + author = {Powers, D. M. W.}, + citeulike-article-id = {12882259}, + journal = {Journal of Machine Learning Technologies}, + keywords = {evaluation, metrics, nlp}, + number = {1}, + pages = {37--63}, + posted-at = {2013-12-26 21:07:18}, + priority = {2}, + title = {{Evaluation: From precision, recall and f-measure to roc., informedness, markedness \& correlation}}, + volume = {2}, + year = {2011} +} + + + + +@article{Sanz-Leon2013, +AUTHOR={Sanz Leon, Paula and Knock, Stuart and Woodman, M. and Domide, Lia and Mersmann, Jochen and McIntosh, Anthony and Jirsa, Viktor}, +TITLE={The Virtual Brain: a simulator of primate brain network dynamics}, +JOURNAL={Frontiers in Neuroinformatics}, +VOLUME={7}, +PAGES={10}, +YEAR={2013}, +URL={https://www.frontiersin.org/article/10.3389/fninf.2013.00010}, +DOI={10.3389/fninf.2013.00010}, +ISSN={1662-5196}, +} + + + +@article{Sanz-Leon2015, +title = "Mathematical framework for large-scale brain network modeling in The Virtual Brain", +journal = "NeuroImage", +volume = "111", +pages = "385 - 430", +year = "2015", +issn = "1053-8119", +doi = "https://doi.org/10.1016/j.neuroimage.2015.01.002", +url = "http://www.sciencedirect.com/science/article/pii/S1053811915000051", +author = "Paula Sanz-Leon and Stuart A. Knock and Andreas Spiegler and Viktor K. Jirsa", +} + + +@Misc{stan-dev, +title = {shinystan: Interactive Visual and Numerical Diagnostics +and Posterior Analysis for {Bayesian} Models.}, +author = {{Stan Development Team}}, +year = {2017}, +note = {R package version 2.3.0}, +url = {http://mc-stan.org/}, +} + + +@article{Zimmermann2018, +title = "Differentiation of Alzheimer's disease based on local and global parameters in personalized Virtual Brain models", +journal = "NeuroImage: Clinical", +volume = "19", +pages = "240 - 251", +year = "2018", +issn = "2213-1582", +doi = "https://doi.org/10.1016/j.nicl.2018.04.017", +author = "J. Zimmermann and A. Perry and M. Breakspear and M. Schirner and P. Sachdev and W. Wen and N.A. Kochan and M. Mapstone and P. Ritter and A.R. McIntosh and A. Solodkin" +} + + +@article{Schirner2015, +title = "An automated pipeline for constructing personalized virtual brains from multimodal neuroimaging data", +journal = "NeuroImage", +volume = "117", +pages = "343 - 357", +year = "2015", +issn = "1053-8119", +doi = "https://doi.org/10.1016/j.neuroimage.2015.03.055", +author = "Michael Schirner and Simon Rothmeier and Viktor K. Jirsa and Anthony Randal McIntosh and Petra Ritter", +keywords = "The Virtual Brain, Connectome, Tractography, Diffusion MRI, Multimodal imaging, Computational modeling", +} + +@article{Schirner2023, + title={Learning how network structure shapes decision-making for bio-inspired computing}, + author={Schirner, Michael and Deco, Gustavo and Ritter, Petra}, + journal={Nature Communications}, + volume={14}, + number={1}, + pages={2963}, + year={2023}, + publisher={Nature Publishing Group UK London} +} + +@article{Falcon2016, +title = "Functional Mechanisms of Recovery after Chronic Stroke: Modeling with the Virtual Brain", +journal = "eNeuro", +volume = "3", +year = "2016", +number="2", +doi = "10.1523/ENEURO.0158-15.2016", +author = "MI. Falcon and J.D. Riley and V. Jirsa and A.R. McIntosh and E. Elinor Chen and A. Solodkin " +} + + +@article{Stephan2007, +title = "Comparing hemodynamic models with DCM", +journal = "NeuroImage", +volume = "38", +number = "3", +pages = "387 - 401", +year = "2007", +issn = "1053-8119", +doi = "https://doi.org/10.1016/j.neuroimage.2007.07.040", +url = "http://www.sciencedirect.com/science/article/pii/S1053811907006489", +author = "Klaas Enno Stephan and Nikolaus Weiskopf and Peter M. Drysdale and Peter A. Robinson and Karl J. Friston", +} + + +@article{Penny2004, +title = "Comparing dynamic causal models", +journal = "NeuroImage", +volume = "22", +number = "3", +pages = "1157 - 1172", +year = "2004", +issn = "1053-8119", +doi = "https://doi.org/10.1016/j.neuroimage.2004.03.026", +url = "http://www.sciencedirect.com/science/article/pii/S1053811904001648", +author = "W.D. Penny and K.E. Stephan and A. Mechelli and K.J. Friston", +} + + +@article{Penny2004b, +title = "Modelling functional integration: a comparison of structural equation and dynamic causal models", +journal = "NeuroImage", +volume = "23", +pages = "S264 - S274", +year = "2004", +note = "Mathematics in Brain Imaging", +issn = "1053-8119", +doi = "https://doi.org/10.1016/j.neuroimage.2004.07.041", +url = "http://www.sciencedirect.com/science/article/pii/S1053811904003891", +author = "W.D. Penny and K.E. Stephan and A. Mechelli and K.J. Friston", +} + +@article{Penny2010, + author = {Penny, Will D. AND Stephan, Klaas E. AND Daunizeau, Jean AND Rosa, Maria J. AND Friston, Karl J. AND Schofield, Thomas M. AND Leff, Alex P.}, + journal = {PLOS Computational Biology}, + publisher = {Public Library of Science}, + title = {Comparing Families of Dynamic Causal Models}, + year = {2010}, + month = {03}, + volume = {6}, + url = {https://doi.org/10.1371/journal.pcbi.1000709}, + pages = {1-14}, + number = {3}, + doi = {10.1371/journal.pcbi.1000709} +} + + +@article{Penny2012, +title = "Comparing Dynamic Causal Models using AIC, BIC and Free Energy", +journal = "NeuroImage", +volume = "59", +number = "1", +pages = "319 - 330", +year = "2012", +note = "Neuroergonomics: The human brain in action and at work", +issn = "1053-8119", +doi = "https://doi.org/10.1016/j.neuroimage.2011.07.039", +url = "http://www.sciencedirect.com/science/article/pii/S1053811911008160", +author = "W.D. Penny", +} + +@article{Stephan2010, +title = "Ten simple rules for dynamic causal modeling", +journal = "NeuroImage", +volume = "49", +number = "4", +pages = "3099 - 3109", +year = "2010", +issn = "1053-8119", +doi = "https://doi.org/10.1016/j.neuroimage.2009.11.015", +url = "http://www.sciencedirect.com/science/article/pii/S1053811909011999", +author = "K.E. Stephan and W.D. Penny and R.J. Moran and H.E.M. den Ouden and J. Daunizeau and K.J. Friston", +} + +@article{Daunizeau2011, +title = "Dynamic causal modelling: A critical review of the biophysical and statistical foundations", +journal = "NeuroImage", +volume = "58", +number = "2", +pages = "312 - 322", +year = "2011", +issn = "1053-8119", +doi = "https://doi.org/10.1016/j.neuroimage.2009.11.062", +url = "http://www.sciencedirect.com/science/article/pii/S1053811909012488", +author = "J. Daunizeau and O. David and K.E. Stephan", +} + + +@article{Soch2018, +title = "MACS- a new SPM toolbox for model assessment, comparison and selection", +journal = "Journal of Neuroscience Methods", +volume = "306", +pages = "19 - 31", +year = "2018", +issn = "0165-0270", +doi = "https://doi.org/10.1016/j.jneumeth.2018.05.017", +author = "Joram Soch and Carsten Allefeld", +} + + + +@article{Schwarz1978, + author = {Schwarz, Gideon}, + doi = {10.1214/aos/1176344136}, + journal = {The Annals of Statistics}, + number = 2, + pages = {461--464}, + title = {Estimating the dimension of a model}, + volume = 6, + year = 1978 +} + + +@article{Akaike1974, + author = {H. Akaike}, + doi = {10.1109/TAC.1974.1100705 }, + journal = {IEEE Transactions on Automatic Control}, + number = 6, + pages = {716-723}, + title = {A New Look at the Statistical Model Identification}, + volume = {AC-19}, + year = 1974 +} + + +@article{Sugiura78, +author = { Nariaki Sugiura }, +title = {Further analysts of the data by {Akaike}' s information criterion and the finite corrections}, +journal = {Communications in Statistics - Theory and Methods}, +volume = {7}, +number = {1}, +pages = {13-26}, +year = {1978}, +publisher = {Taylor & Francis}, +doi = {10.1080/03610927808827599}, +} + + +@article{Hurvich1989, +author = {Hurvich, C. 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Eddy}, + journal = {Journal of the American Statistical Association}, + number = {365}, + pages = {153--160}, + publisher = {[American Statistical Association, Taylor & Francis, Ltd.]}, + title = {A Predictive Approach to Model Selection}, + volume = {74}, + year = {1979} +} + +@article{Stone77, + author = {M. Stone}, + journal = {Journal of the Royal Statistical Society}, + number = {36}, + pages = {44--47}, + title = {An asymptotic equivalence of choice of model cross-validation and {Akaike}'s +criterion}, + volume = {B}, + year = {1977} +} + +@article{Gelman1992, +author = "Gelman, Andrew and Rubin, Donald B.", +doi = "10.1214/ss/1177011136", +journal = "Statist. Sci.", +month = "11", +number = "4", +pages = "457--472", +publisher = "The Institute of Mathematical Statistics", +title = "Inference from Iterative Simulation Using Multiple Sequences", +volume = "7", +year = "1992" +} + + + + +@article{Gelman2014, +author="Gelman, Andrew +and Hwang, Jessica +and Vehtari, Aki", +title="Understanding predictive information criteria for {Bayesian} models", +journal="Statistics and Computing", +year="2014", +month="Nov", +day="01", +volume="24", +number="6", +pages="997--1016", +issn="1573-1375", +doi="10.1007/s11222-013-9416-2", +} + +@article{Luo2018, +author = {Yong Luo and Hong Jiao}, +title ={Using the Stan Program for {Bayesian} Item Response Theory}, +journal = {Educational and Psychological Measurement}, +volume = {78}, +number = {3}, +pages = {384-408}, +year = {2018}, +doi = {10.1177/0013164417693666}, +} + + + + +@article{Kass1995, +author = { Robert E. Kass and Adrian E. Raftery }, +title = {Bayes Factors}, +journal = {Journal of the American Statistical Association}, +volume = {90}, +number = {430}, +pages = {773-795}, +year = {1995}, +publisher = {Taylor & Francis}, +doi = {10.1080/01621459.1995.10476572}, +} + +@article{Hoeting99, + author = {Jennifer A. Hoeting and David Madigan and Adrian E. Raftery and Chris T. Volinsky}, + title = {Bayesian Model Averaging: A Tutorial}, + journal = {STATISTICAL SCIENCE}, + year = {1999}, + volume = {14}, + number = {4}, + pages = {382--417} +} + +@TECHREPORT{Madigan1993, + author = {David Madigan and Adrian E. Raftery}, + title = {Model selection and accounting for model uncertainty in graphical models using Occam's window}, + institution = {}, + year = {1993} +} + + + +@MISC{Raftery1995, + author = {Adrian Raftery}, + title = {Bayesian Model Selection in Social Research (with Discussion by Andrew Gelman \& Donald B. Rubin, and Robert M. Hauser, and a Rejoinder)}, + year = {1995} +} + +% Encoding: UTF-8 + +@article{ProixEtAl17, + author = {Timoth{\'{e}}e Proix and Fabrice Bartolomei and Maxime Guye and Viktor K. Jirsa}, + title = {Individual brain structure and modelling predict seizure propagation}, + journal = {Brain}, + year = {2017}, + volume = {140}, + number = {3}, + month = {feb}, + pages = {641--654}, + doi = {10.1093/brain/awx004}, + publisher = {Oxford University Press ({OUP})}, +} + +@article{ProixEtAl16, + author = {Timoth{\'{e}}e Proix and Andreas Spiegler and Michael Schirner and Simon Rothmeier and Petra Ritter and Viktor K. Jirsa}, + title = {How do parcellation size and short-range connectivity affect dynamics in large-scale brain network models?}, + journal = {{NeuroImage}}, + year = {2016}, + volume = {142}, + month = {nov}, + pages = {135--149}, + doi = {10.1016/j.neuroimage.2016.06.016}, + publisher = {Elsevier {BV}}, +} + +@article{Fischl12, + author = {Bruce Fischl}, + title = {{FreeSurfer}}, + journal = {{NeuroImage}}, + year = {2012}, + volume = {62}, + number = {2}, + month = {aug}, + pages = {774--781}, + doi = {10.1016/j.neuroimage.2012.01.021}, + publisher = {Elsevier {BV}}, +} + +@article{JenkinsonEtAl02, + author = {Mark Jenkinson and Peter Bannister and Michael Brady and Stephen Smith}, + title = {Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images}, + journal = {{NeuroImage}}, + year = {2002}, + volume = {17}, + number = {2}, + month = {oct}, + pages = {825--841}, + doi = {10.1006/nimg.2002.1132}, + publisher = {Elsevier {BV}}, +} + +@article{TournierEtAl12, + author = {J-Donald Tournier and Fernando Calamante and Alan Connelly}, + title = {{MRtrix}: Diffusion tractography in crossing fiber regions}, + journal = {International Journal of Imaging Systems and Technology}, + year = {2012}, + volume = {22}, + number = {1}, + month = {feb}, + pages = {53--66}, + doi = {10.1002/ima.22005}, + publisher = {Wiley-Blackwell}, +} + +@article{TournierEtAl07, + author = {J-Donald Tournier and Fernando Calamante and Alan Connelly}, + title = {Robust determination of the fibre orientation distribution in diffusion {MRI}: Non-negativity constrained super-resolved spherical deconvolution}, + journal = {{NeuroImage}}, + year = {2007}, + volume = {35}, + number = {4}, + month = {may}, + pages = {1459--1472}, + doi = {10.1016/j.neuroimage.2007.02.016}, + publisher = {Elsevier {BV}}, +} + + +@article{DesikanEtAl06, + author = {Rahul S. Desikan and Florent S{\'{e}}gonne and Bruce Fischl and Brian T. Quinn and Bradford C. Dickerson and Deborah Blacker and Randy L. Buckner and Anders M. Dale and R. Paul Maguire and Bradley T. Hyman and Marilyn S. Albert and Ronald J. Killiany}, + title = {An automated labeling system for subdividing the human cerebral cortex on {MRI} scans into gyral based regions of interest}, + journal = {{NeuroImage}}, + year = {2006}, + volume = {31}, + number = {3}, + month = {jul}, + pages = {968--980}, + doi = {10.1016/j.neuroimage.2006.01.021}, + publisher = {Elsevier {BV}}, +} + +@InProceedings{TournierEtAl10, + author = {Tournier, J Donald and Calamante, Fernando and Connelly, Alan}, + title = {Improved probabilistic streamlines tractography by 2nd order integration over fibre orientation distributions}, + booktitle = {Proceedings of the international society for magnetic resonance in medicine}, + year = {2010}, + volume = {18}, + pages = {1670}, +} + +@article{TournierEtAl13, + author = {J.-Donald Tournier and Fernando Calamante and Alan Connelly}, + title = {Determination of the appropriatebvalue and number of gradient directions for high-angular-resolution diffusion-weighted imaging}, + journal = {{NMR} in Biomedicine}, + year = {2013}, + volume = {26}, + number = {12}, + month = {aug}, + pages = {1775--1786}, + doi = {10.1002/nbm.3017}, + publisher = {Wiley}, +} + +@article{Piironen2017, +author="Piironen, Juho +and Vehtari, Aki", +title="Comparison of {Bayesian} predictive methods for model selection", +journal="Statistics and Computing", +year="2017", +month="May", +day="01", +volume="27", +number="3", +pages="711--735", +doi="10.1007/s11222-016-9649-y", +} + + +@book{Burnham2002, + author = {Burnham, Kenneth P. and Anderson, David R.}, + edition = {2}, + isbn = {0387953647}, + keywords = {AIC}, + pages = {1--488}, + posted-at = {2009-04-28 23:05:15}, + priority = {2}, + publisher = {Springer}, + title = {{Model selection and multimodel inference: a practical information-theoretic approach}}, + year = {2002} +} + +@article{Burnham2004, +author = {Burnham, Kenneth P. and Anderson, David R}, +title ={Multimodel Inference: Understanding {AIC} and {BIC} in Model Selection}, +journal = {Sociological Methods \& Research}, +volume = {33}, +number = {2}, +pages = {261-304}, +year = {2004}, +doi = {10.1177/0049124104268644}, +} + + + +@article{Deco2013, + author = {Deco, Gustavo and Hagmann, Patric and Hudetz, Anthony G. and Tononi, Giulio}, + title = "{Modeling Resting-State Functional Networks When the Cortex Falls Asleep: Local and Global Changes}", + journal = {Cerebral Cortex}, + volume = {24}, + number = {12}, + pages = {3180-3194}, + year = {2013}, + month = {07}, + issn = {1047-3211}, + doi = {10.1093/cercor/bht176}, +} + +@article {Deco2014, + author = {Deco, Gustavo and McIntosh, Anthony R. and Shen, Kelly and Hutchison, R. Matthew and Menon, Ravi S. and Everling, Stefan and Hagmann, Patric and Jirsa, Viktor K.}, + title = {Identification of Optimal Structural Connectivity Using Functional Connectivity and Neural Modeling}, + volume = {34}, + number = {23}, + pages = {7910--7916}, + year = {2014}, + doi = {10.1523/JNEUROSCI.4423-13.2014}, + publisher = {Society for Neuroscience}, + issn = {0270-6474}, + URL = {http://www.jneurosci.org/content/34/23/7910}, + journal = {Journal of Neuroscience} +} + + +@article{Hansen2015, +title = "Functional connectivity dynamics: Modeling the switching behavior of the resting state", +journal = "NeuroImage", +volume = "105", +pages = "525 - 535", +year = "2015", +issn = "1053-8119", +doi = "https://doi.org/10.1016/j.neuroimage.2014.11.001", +author = "Enrique C.A. Hansen and Demian Battaglia and Andreas Spiegler and Gustavo Deco and Viktor K. Jirsa", +} + +@article{Olmi2019, + author = {Olmi, Simona AND Petkoski, Spase AND Guye, Maxime AND Bartolomei, Fabrice AND Jirsa, Viktor}, + journal = {PLOS Computational Biology}, + publisher = {Public Library of Science}, + title = {Controlling seizure propagation in large-scale brain networks}, + year = {2019}, + month = {02}, + volume = {15}, + pages = {1-23}, + number = {2}, + doi = {10.1371/journal.pcbi.1006805} +} + + +@article{Scheinost2019, +title = "Ten simple rules for predictive modeling of individual differences in neuroimaging", +journal = "NeuroImage", +year = "2019", +issn = "1053-8119", +doi = "https://doi.org/10.1016/j.neuroimage.2019.02.057", +author = "Dustin Scheinost and Stephanie Noble and Corey Horien and Abigail S. Greene and Evelyn MR. Lake and Mehraveh Salehi and Siyuan Gao and Xilin Shen and David O'Connor and Daniel S. Barron and Sarah W. Yip and Monica D. Rosenberg and R. Todd Constable", +keywords = "Machine learning, Connectome, Classification, Cross-validation, Neural networks", +} + +@article{JUWELS, +author = {{J\"{u}lich Supercomputing Centre}}, +title = {{JUWELS: Modular Tier-0/1 Supercomputer at the J\"{u}lich +Supercomputing Centre}}, +journal = {Journal of large-scale research facilities}, +number = {A135}, +volume = {5}, +doi = {10.17815/jlsrf-5-171}, +url = {http://dx.doi.org/10.17815/jlsrf-5-171}, +year = {2019} +} + + +@article{mahani2015simd, + title={SIMD parallel MCMC sampling with applications for big-data Bayesian analytics}, + author={Mahani, Alireza S and Sharabiani, Mansour TA}, + journal={Computational Statistics \& Data Analysis}, + volume={88}, + pages={75--99}, + year={2015}, + publisher={Elsevier} +} + + + + + +@article{angelikopoulos2012bayesian, + title={Bayesian uncertainty quantification and propagation in molecular dynamics simulations: a high performance computing framework}, + author={Angelikopoulos, Panagiotis and Papadimitriou, Costas and Koumoutsakos, Petros}, + journal={The Journal of chemical physics}, + volume={137}, + number={14}, + pages={144103}, + year={2012}, + publisher={AIP} +} + +@article{jagiella2017parallelization, + title={Parallelization and high-performance computing enables automated statistical inference of multi-scale models}, + author={Jagiella, Nick and Rickert, Dennis and Theis, Fabian J and Hasenauer, Jan}, + journal={Cell systems}, + volume={4}, + number={2}, + pages={194--206}, + year={2017}, + publisher={Elsevier} +} + +@article{zhang2016guide, + title={Guide to automatic vectorization with Intel AVX-512 instructions in Knights Landing processors}, + author={Zhang, Bonan}, + journal={Colfax International}, + year={2016} +} + +@article{amiri2020simd, + title={SIMD programming using Intel vector extensions}, + author={Amiri, Hossein and Shahbahrami, Asadollah}, + journal={Journal of Parallel and Distributed Computing}, + volume={135}, + pages={83--100}, + year={2020}, + publisher={Elsevier} +} + + +@article{vehtari2019rank, + title={Rank-normalization, folding, and localization: An improved widehat{ R} for assessing convergence of MCMC}, + author={Aki Vehtari and Andrew Gelman and Daniel Simpson and Bob Carpenter and Paul-Christian Burkner}, + journal={arXiv:1903.08008}, + year={2019} +} + + +@article{McIntoshJirsa2019, +author = {McIntosh, Anthony R. and Jirsa, Viktor K.}, +title = {The hidden repertoire of brain dynamics and dysfunction}, +journal = {Network Neuroscience}, +volume = {3}, +number = {4}, +pages = {994-1008}, +year = {2019}, +doi = {10.1162/netn\_a\_00107}, +URL = {https://doi.org/10.1162/netn_a_00107}, +} + + +@article {Melozzi2019, + author = {Melozzi, Francesca and Bergmann, Eyal and Harris, Julie A. and Kahn, Itamar and Jirsa, Viktor and Bernard, Christophe}, + title = {Individual structural features constrain the mouse functional connectome}, + volume = {116}, + number = {52}, + pages = {26961--26969}, + year = {2019}, + doi = {10.1073/pnas.1906694116}, + publisher = {National Academy of Sciences}, + abstract = {The structural connectome is a key determinant of brain function and dysfunction. The connectome-based model approach aims to understand the functional organization of the brain by modeling the brain as a dynamical system, then studying how the functional architecture rises from the underlying structural skeleton. Here, taking advantage of mice studies, we systematically investigated the informative content of different structural features in explaining the emergence of the functional ones. We demonstrate that individual variations define a specific structural fingerprint with a direct impact upon the functional organization of individual brains stressing the importance of using individualized models to understand brain function. We show how limitations of connectome reconstruction with the diffusion-MRI method restrict our comprehension of the structural{\textendash}functional relation.Whole brain dynamics intuitively depend upon the internal wiring of the brain; but to which extent the individual structural connectome constrains the corresponding functional connectome is unknown, even though its importance is uncontested. After acquiring structural data from individual mice, we virtualized their brain networks and simulated in silico functional MRI data. Theoretical results were validated against empirical awake functional MRI data obtained from the same mice. We demonstrate that individual structural connectomes predict the functional organization of individual brains. Using a virtual mouse brain derived from the Allen Mouse Brain Connectivity Atlas, we further show that the dominant predictors of individual structure{\textendash}function relations are the asymmetry and the weights of the structural links. Model predictions were validated experimentally using tracer injections, identifying which missing connections (not measurable with diffusion MRI) are important for whole brain dynamics in the mouse. Individual variations thus define a specific structural fingerprint with direct impact upon the functional organization of individual brains, a key feature for personalized medicine.}, + issn = {0027-8424}, + URL = {https://www.pnas.org/content/116/52/26961}, + journal = {Proceedings of the National Academy of Sciences} +} + +@article {Saggio2020, +article_type = {journal}, +title = {A taxonomy of seizure dynamotypes}, +author = {Saggio, Maria Luisa and Crisp, Dakota and Scott, Jared M and Karoly, Philippa and Kuhlmann, Levin and Nakatani, Mitsuyoshi and Murai, Tomohiko and Dümpelmann, Matthias and Schulze-Bonhage, Andreas and Ikeda, Akio and Cook, Mark and Gliske, Stephen V and Lin, Jack and Bernard, Christophe and Jirsa, Viktor and Stacey, William C}, +editor = {Skinner, Frances K and Frank, Michael J and Van Drongelen, Wim and Valiante, Taufik A}, +volume = 9, +year = 2020, +month = {jul}, +pub_date = {2020-07-21}, +pages = {e55632}, +citation = {eLife 2020;9:e55632}, +doi = {10.7554/eLife.55632}, +url = {https://doi.org/10.7554/eLife.55632}, +abstract = {Seizures are a disruption of normal brain activity present across a vast range of species and conditions. We introduce an organizing principle that leads to the first objective Taxonomy of Seizure Dynamics (TSD) based on bifurcation theory. The ‘dynamotype’ of a seizure is the dynamic composition that defines its observable characteristics, including how it starts, evolves and ends. Analyzing over 2000 focal-onset seizures from multiple centers, we find evidence of all 16 dynamotypes predicted in TSD. We demonstrate that patients’ dynamotypes evolve during their lifetime and display complex but systematic variations including hierarchy (certain types are more common), non-bijectivity (a patient may display multiple types) and pairing preference (multiple types may occur during one seizure). TSD provides a way to stratify patients in complement to present clinical classifications, a language to describe the most critical features of seizure dynamics, and a framework to guide future research focused on dynamical properties.}, +keywords = {epilepsy, seizure, dynamics, bifurcation, intracranial EEG}, +journal = {eLife}, +issn = {2050-084X}, +publisher = {eLife Sciences Publications, Ltd}, +} + + +@article {Kuhlmann2018, +article_type = {journal}, +title = {Seizure prediction — ready for a new era}, +author = {Kuhlmann, Levin and Lehnertz, Klaus and Richardson, Mark P and Schelter, Bjorn and Zaveri, Hitten P. }, +volume = 14, +year = 2018, +pages = {618--630}, +doi = {10.1038/s41582-018-0055-2}, +url = {https://doi.org/10.1038/s41582-018-0055-2}, +journal = {Nat Rev Neurol}, +} + +@article{Bartolomei2008, + author = {Bartolomei, Fabrice and Chauvel, Patrick and Wendling, Fabrice}, + title = "{Epileptogenicity of brain structures in human temporal lobe epilepsy: a quantified study from intracerebral {EEG}}", + journal = {Brain}, + volume = {131}, + number = {7}, + pages = {1818-1830}, + year = {2008}, + month = {06}, + abstract = "{The identification of brain regions generating seizures (‘epileptogenic zone†EZ) in patients with refractory partial epilepsy is crucial prior to surgery. During pre-surgical evaluation, this identification can be performed from the analysis of intracerebral EEG. In particular, the presence of high-frequency oscillations, often referred to as ‘rapid discharges†has long been recognized as a characteristic electrophysiological pattern of the EZ. However, to date, there has been no attempt to make use of this specific pattern to quantitatively evaluate the degree of epileptogenicity in recorded structures. A novel quantitative measure that characterizes the epileptogenicity of brain structures recorded with depth electrodes is presented. This measure, called ‘Epileptogenicity Indexâ€(EI), is based on both spectral (appearance of fast oscillations replacing the background activity) and temporal (delay of appearance with respect to seizure onset) properties of intracerebral EEG signals. EI values were computed in mesial and lateral structures of the temporal lobe in a group of 17 patients with mesial temporal lobe epilepsy (MTLE). Statistically high EI values corresponded to structures involved early in the ictal process and producing rapid discharges at seizure onset. In all patients, these high values were obtained in more than one structure of the temporal lobe region. In the majority of patients, highest EI values were computed from signals recorded in mesial structures. In addition, when averaged over patients, EI values gradually decreased from structure to structure. For lateral neocortex, higher EI values were found in patients with normal MRI, in contrast with patients with hippocampal sclerosis. In this former sub-group of patients, a greater number of epileptogenic structures was also found. A statistically significant correlation was found between the duration of epilepsy and the number of structures disclosing high epileptogenicity suggesting that MTLE is a gradually evolving process in which the epileptogenicity of the temporal lobe tends to increase with time.}", + issn = {0006-8950}, + doi = {10.1093/brain/awn111}, +} + + + +@article{Bartolomei2017, +author = {Bartolomei, Fabrice and Lagarde, Stanislas and Wendling, Fabrice and McGonigal, Aileen and Jirsa, Viktor and Guye, Maxime and B\'enar, Christian}, +title = {Defining epileptogenic networks: Contribution of SEEG and signal analysis}, +journal = {Epilepsia}, +volume = {58}, +number = {7}, +pages = {1131-1147}, +keywords = {Stereoelectroencephalography, Signal processing, Brain networks, Focal epilepsies, Functional connectivity}, +doi = {https://doi.org/10.1111/epi.13791}, +url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/epi.13791}, +eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/epi.13791}, +abstract = {Summary Epileptogenic networks are defined by the brain regions involved in the production and propagation of epileptic activities. In this review we describe the historical, methodologic, and conceptual bases of this model in the analysis of electrophysiologic intracerebral recordings. In the context of epilepsy surgery, the determination of cerebral regions producing seizures (i.e., the “epileptogenic zone”) is a crucial objective. In contrast with a traditional focal vision of focal drug-resistant epilepsies, the concept of epileptogenic networks has been progressively introduced as a model better able to describe the complexity of seizure dynamics and realistically describe the distribution of epileptogenic anomalies in the brain. The concept of epileptogenic networks is historically linked to the development of the stereoelectroencephalography (SEEG) method and subsequent introduction of means of quantifying the recorded signals. Seizures, and preictal and interictal discharges produce clear patterns on SEEG. These patterns can be analyzed utilizing signal analysis methods that quantify high-frequency oscillations or changes in functional connectivity. Dramatic changes in SEEG brain connectivity can be described during seizure genesis and propagation within cortical and subcortical regions, associated with the production of different patterns of seizure semiology. The interictal state is characterized by networks generating abnormal activities (interictal spikes) and also by modified functional properties. The introduction of novel approaches to large-scale modeling of these networks offers new methods in the goal of better predicting the effects of epilepsy surgery. The epileptogenic network concept is a key factor in identifying the anatomic distribution of the epileptogenic process, which is particularly important in the context of epilepsy surgery.}, +year = {2017} +} + + + +@article{Lambert2018, +author = {Lambert, Isabelle and Roehri, Nicolas and Giusiano, Bernard and Carron, Romain and Wendling, Fabrice and Benar, Christian and Bartolomei, Fabrice}, +title = {Brain regions and epileptogenicity influence epileptic interictal spike production and propagation during NREM sleep in comparison with wakefulness}, +journal = {Epilepsia}, +volume = {59}, +number = {1}, +pages = {235-243}, +keywords = {co-occurrence, focal epilepsy, interictal spikes, NREM sleep, stereo-electroencephalography}, +doi = {10.1111/epi.13958}, +url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/epi.13958}, +abstract = {Summary Objective Non¨Crapid eye movement (NREM) sleep is known to be a brain state associated with an activation of interictal epileptic activity. The goal of this work was to quantify topographic changes occurring during NREM sleep in comparison with wakefulness. Method We studied intracerebral recordings of 20 patients who underwent stereo-electroencephalography (SEEG) during presurgical evaluation for pharmacoresistant focal epilepsy. We measured the number of interictal spikes (IS) and quantified the co-occurrence of IS between brain regions during 1 hour of NREM sleep and 1 hour of wakefulness. Co-occurrence is a method to estimate IS networks based on a temporal concordance between IS of different brain regions. Each studied region was labeled as ¡°seizure-onset zone¡± (SOZ), ¡°propagation zone¡± (PZ), or ¡°not involved region¡± (NIR). Results During NREM sleep, the number of interictal spikes significantly increased in all regions (mean of 68\%). This increase was higher in medial temporal regions than in other regions, whether involved in the SOZ. Spike co-occurrence increased significantly in all regions during NREM sleep in comparison with wakefulness but was greater in neocortical regions. Spike co-occurrence in medial temporal regions was not higher than in other regions, suggesting that the increase of the number of spikes in this region was in great part a local effect. Significance This study demonstrated that medial temporal regions show a greater propensity to spike production or propagation during NREM sleep compared to other brain regions, even when the medial temporal lobe is not involved in the SOZ.}, +year = {2018} +} + + +@article{Goodfellow2016, + title={Estimation of brain network ictogenicity predicts outcome from epilepsy surgery}, + author={Goodfellow, M and Rummel, Christian and Abela, Eugenio and Richardson, MP and Schindler, K and Terry, JR}, + journal={Scientific reports}, + volume={6}, + pages={29215}, + year={2016}, + publisher={Nature Publishing Group}, + issn = "2045-2322", + doi = "10.1038/srep29215", + url = {https://doi.org/10.1038/srep29215}, + +} + + +@article{Talairach1996, + title={Lesion, "Irritative" Zone and Epileptogenic Focus}, + author={Talairach, J and Bancaud, J }, + journal={Confin Neurol}, + volume={27}, + pages={91-94}, + year={1966}, + doi = "10.1159/000103937", + url = {https://www.karger.com/DOI/10.1159/000103937}, +} + + +@article{Pizzo2019, + author = {Pizzo, F and Roehri, N and Medina Villalon, S and Trebuchon, A and Chen, S and Lagarde, S and Carron, R and Gavaret, M and Giusiano, B and McGonigal, A and Bartolomei, F and Badier, J. M and Bnar, C. G}, + title = {Deep brain activities can be detected with magnetoencephalography}, + journal = {Nat Commun}, + volume = {10}, + number = {971}, + doi = {10.1038/s41467-019-08665-5}, + year = {2019} +} + + + +@article{Sunnaker2013, + author = {Sunnaker, Mikael AND Busetto, Alberto Giovanni AND Numminen, Elina AND Corander, Jukka AND Foll, Matthieu AND Dessimoz, Christophe}, + journal = {PLOS Computational Biology}, + publisher = {Public Library of Science}, + title = {Approximate Bayesian Computation}, + year = {2013}, + month = {01}, + volume = {9}, + url = {https://doi.org/10.1371/journal.pcbi.1002803}, + pages = {1-10}, + abstract = {Approximate Bayesian computation (ABC) constitutes a class of computational methods rooted in Bayesian statistics. In all model-based statistical inference, the likelihood function is of central importance, since it expresses the probability of the observed data under a particular statistical model, and thus quantifies the support data lend to particular values of parameters and to choices among different models. For simple models, an analytical formula for the likelihood function can typically be derived. However, for more complex models, an analytical formula might be elusive or the likelihood function might be computationally very costly to evaluate. ABC methods bypass the evaluation of the likelihood function. In this way, ABC methods widen the realm of models for which statistical inference can be considered. ABC methods are mathematically well-founded, but they inevitably make assumptions and approximations whose impact needs to be carefully assessed. Furthermore, the wider application domain of ABC exacerbates the challenges of parameter estimation and model selection. ABC has rapidly gained popularity over the last years and in particular for the analysis of complex problems arising in biological sciences (e.g., in population genetics, ecology, epidemiology, and systems biology).}, + number = {1}, + doi = {10.1371/journal.pcbi.1002803} +} + + +@article {Beaumont2002, + author = {Beaumont, Mark A. and Zhang, Wenyang and Balding, David J.}, + title = {Approximate Bayesian Computation in Population Genetics}, + volume = {162}, + number = {4}, + pages = {2025--2035}, + year = {2002}, + publisher = {Genetics}, + abstract = {We propose a new method for approximate Bayesian statistical inference on the basis of summary statistics. The method is suited to complex problems that arise in population genetics, extending ideas developed in this setting by earlier authors. Properties of the posterior distribution of a parameter, such as its mean or density curve, are approximated without explicit likelihood calculations. This is achieved by fitting a local-linear regression of simulated parameter values on simulated summary statistics, and then substituting the observed summary statistics into the regression equation. The method combines many of the advantages of Bayesian statistical inference with the computational efficiency of methods based on summary statistics. A key advantage of the method is that the nuisance parameters are automatically integrated out in the simulation step, so that the large numbers of nuisance parameters that arise in population genetics problems can be handled without difficulty. Simulation results indicate computational and statistical efficiency that compares favorably with those of alternative methods previously proposed in the literature. We also compare the relative efficiency of inferences obtained using methods based on summary statistics with those obtained directly from the data using MCMC.}, + issn = {0016-6731}, + URL = {https://www.genetics.org/content/162/4/2025}, + eprint = {https://www.genetics.org/content/162/4/2025.full.pdf}, + journal = {Genetics} +} + + +@article {Courtiol2020, + author = {Courtiol, Julie and Guye, Maxime and Bartolomei, Fabrice and Petkoski, Spase and Jirsa, Viktor K.}, + title = {Dynamical Mechanisms of Interictal Resting-State Functional Connectivity in Epilepsy}, + volume = {40}, + number = {29}, + pages = {5572--5588}, + year = {2020}, + doi = {10.1523/JNEUROSCI.0905-19.2020}, + publisher = {Society for Neuroscience}, + abstract = {Drug-resistant focal epilepsy is a large-scale brain networks disorder characterized by altered spatiotemporal patterns of functional connectivity (FC), even during interictal resting state (RS). Although RS-FC-based metrics can detect these changes, results from RS functional magnetic resonance imaging (RS-fMRI) studies are unclear and difficult to interpret, and the underlying dynamical mechanisms are still largely unknown. To better capture the RS dynamics, we phenomenologically extended the neural mass model of partial seizures, the Epileptor, by including two neuron subpopulations of epileptogenic and nonepileptogenic type, making it capable of producing physiological oscillations in addition to the epileptiform activity. Using the neuroinformatics platform The Virtual Brain, we reconstructed 14 epileptic and 5 healthy human (of either sex) brain network models (BNMs), based on individual anatomical connectivity and clinically defined epileptogenic heatmaps. Through systematic parameter exploration and fitting to neuroimaging data, we demonstrated that epileptic brains during interictal RS are associated with lower global excitability induced by a shift in the working point of the model, indicating that epileptic brains operate closer to a stable equilibrium point than healthy brains. Moreover, we showed that functional networks are unaffected by interictal spikes, corroborating previous experimental findings; additionally, we observed higher excitability in epileptogenic regions, in agreement with the data. We shed light on new dynamical mechanisms responsible for altered RS-FC in epilepsy, involving the following two key factors: (1) a shift of excitability of the whole brain leading to increased stability; and (2) a locally increased excitability in the epileptogenic regions supporting the mixture of hyperconnectivity and hypoconnectivity in these areas.SIGNIFICANCE STATEMENT Advances in functional neuroimaging provide compelling evidence for epilepsy-related brain network alterations, even during the interictal resting state (RS). However, the dynamical mechanisms underlying these changes are still elusive. To identify local and network processes behind the RS-functional connectivity (FC) spatiotemporal patterns, we systematically manipulated the local excitability and the global coupling in the virtual human epileptic patient brain network models (BNMs), complemented by the analysis of the impact of interictal spikes and fitting to the neuroimaging data. Our results suggest that a global shift of the dynamic working point of the brain model, coupled with locally hyperexcitable node dynamics of the epileptogenic networks, provides a mechanistic explanation of the epileptic processes during the interictal RS period. These, in turn, are associated with the changes in FC.}, + issn = {0270-6474}, + URL = {https://www.jneurosci.org/content/40/29/5572}, + eprint = {https://www.jneurosci.org/content/40/29/5572.full.pdf}, + journal = {Journal of Neuroscience} +} + +@article {Schirner2018, +article_type = {journal}, +title = {Inferring multi-scale neural mechanisms with brain network modelling}, +author = {Schirner, Michael and McIntosh, Anthony Randal and Jirsa, Viktor and Deco, Gustavo and Ritter, Petra}, +editor = {Schroeder, Charles E}, +volume = 7, +year = 2018, +month = {jan}, +pub_date = {2018-01-08}, +pages = {e28927}, +citation = {eLife 2018;7:e28927}, +doi = {10.7554/eLife.28927}, +url = {https://doi.org/10.7554/eLife.28927}, +abstract = {The neurophysiological processes underlying non-invasive brain activity measurements are incompletely understood. Here, we developed a connectome-based brain network model that integrates individual structural and functional data with neural population dynamics to support multi-scale neurophysiological inference. Simulated populations were linked by structural connectivity and, as a novelty, driven by electroencephalography (EEG) source activity. Simulations not only predicted subjects' individual resting-state functional magnetic resonance imaging (fMRI) time series and spatial network topologies over 20 minutes of activity, but more importantly, they also revealed precise neurophysiological mechanisms that underlie and link six empirical observations from different scales and modalities: (1) resting-state fMRI oscillations, (2) functional connectivity networks, (3) excitation-inhibition balance, (4, 5) inverse relationships between α-rhythms, spike-firing and fMRI on short and long time scales, and (6) fMRI power-law scaling. These findings underscore the potential of this new modelling framework for general inference and integration of neurophysiological knowledge to complement empirical studies.}, +keywords = {Brain modeling, connectomics, resting-state networks, fMRI, EEG, alpha rhythm}, +journal = {eLife}, +issn = {2050-084X}, +publisher = {eLife Sciences Publications, Ltd}, +} + + +@article{Sora2019, + author = {An, Sora AND Bartolomei, Fabrice AND Guye, Maxime AND Jirsa, Viktor}, + journal = {PLOS Computational Biology}, + publisher = {Public Library of Science}, + title = {Optimization of surgical intervention outside the epileptogenic zone in the Virtual Epileptic Patient ({VEP})}, + year = {2019}, + month = {06}, + volume = {15}, + pages = {1-25}, + number = {6}, + url = {https://doi.org/10.1371/journal.pcbi.1007051}, + doi = {10.1371/journal.pcbi.1007051} +} + +@book{Sisson2018, + title={Handbook of Approximate Bayesian Computation}, + author={Sisson, S.A. and Fan, Y. and Beaumont, M.}, + isbn={9781439881507}, + lccn={2018010970}, + series={Chapman \& Hall/CRC handbooks of modern statistical methods}, + year={2018}, + publisher={CRC Press, Taylor \& Francis Group} +} + + +@article{Baydin2017, + author = {Baydin, At\il\im G\"{u}nes and Pearlmutter, Barak A. and Radul, Alexey Andreyevich and Siskind, Jeffrey Mark}, + title = {Automatic Differentiation in Machine Learning: A Survey}, + journal = {J. Mach. Learn. Res.}, + issue_date = {January 2017}, + volume = {18}, + number = {1}, + month = jan, + year = {2018}, + issn = {1532-4435}, + number = {153}, + pages = {1-43}, + acmid = {3242010}, + publisher = {JMLR.org}, + url = {http://jmlr.org/papers/v18/17-468.html}, + keywords = {backpropagation, differentiable programming}, +} + +@article{Margossian2018, + title={A Review of automatic differentiation and its efficient implementation}, + author={Charles C. Margossian}, + journal={Wiley Interdiscip. Rev. Data Min. Knowl. Discov.}, + year={2019}, + volume={9} +} + +@article {Cloppenborg2016, + author = {Cloppenborg, Thomas and May, Theodor W and Bl{\"u}mcke, Ingmar and Grewe, Philip and Hopf, Lena J and Kalbhenn, Thilo and Pf{\"a}fflin, Margarete and Polster, Tilman and Schulz, Reinhard and Woermann, Friedrich G and Bien, Christian G}, + title = {Trends in epilepsy surgery: stable surgical numbers despite increasing presurgical volumes}, + volume = {87}, + number = {12}, + pages = {1322--1329}, + year = {2016}, + doi = {10.1136/jnnp-2016-313831}, + publisher = {BMJ Publishing Group Ltd}, + abstract = {Introduction Despite the success of epilepsy surgery, recent reports suggest a decline in surgical numbers. We tested these trends in our cohort to elucidate potential reasons.Patients and methods Presurgical, surgical and postsurgical data of all patients undergoing presurgical evaluation in between 1990 and 2013 were retrospectively analysed. Patients were grouped according to the underlying pathology.Results A total of 3060 patients were presurgically studied, and resective surgery was performed in 66.8\% (n=2044) of them: medial temporal sclerosis (MTS): n=675, 33.0\%; benign tumour (BT): n=408, 20.0\%; and focal cortical dysplasia (FCD): n=284, 13.9\%. Of these, 1929 patients (94.4\%) had a follow-up of 2 years, and 50.8\% were completely seizure free (Engel IA). Seizure freedom rate slightly improved over time. Presurgical evaluations continuously increased, whereas surgical interventions did not. Numbers for MTS, BT and temporal lobe resections decreased since 2009. The number of non-lesional patients and the need for intracranial recordings increased. More evaluated patients did not undergo surgery (more than 50\% in 2010{\textendash}2013) because patients were not suitable (mainly due to missing hypothesis: 4.5\% in 1990{\textendash}1993 up to 21.1\% in 2010{\textendash}2013, total 13.4\%) or declined from surgery (maximum 21.0\% in 2010{\textendash}2013, total 10.9\%). One potential reason may be that increasingly detailed information on chances and risks were given over time.Conclusions The increasing volume of the presurgical programme largely compensates for decreasing numbers of surgically remediable syndromes and a growing rate of informed choice against epilepsy surgery. Although comprehensive diagnostic evaluation is offered to a larger group of epilepsy patients, surgical numbers remain stable.}, + issn = {0022-3050}, + URL = {https://jnnp.bmj.com/content/87/12/1322}, + eprint = {https://jnnp.bmj.com/content/87/12/1322.full.pdf}, + journal = {Journal of Neurology, Neurosurgery \& Psychiatry} +} + + +@article{Uhlmann2018, +author = {Uhlmann, Jeffrey}, +title = {A Generalized Matrix Inverse That Is Consistent with Respect to Diagonal Transformations}, +journal = {SIAM Journal on Matrix Analysis and Applications}, +volume = {39}, +number = {2}, +pages = {781-800}, +year = {2018}, +doi = {10.1137/17M113890X}, + +URL = { + https://doi.org/10.1137/17M113890X + +}, +eprint = { + https://doi.org/10.1137/17M113890X + +} + +} + + +@inproceedings{Rao1972, + title={Generalized inverse of a matrix and its applications}, + author={Rao, C Radhakrishna and Mitra, Sujit Kumar and others}, + booktitle={Proceedings of the Sixth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Theory of Statistics}, + year={1972}, + organization={The Regents of the University of California} +} + + + +@article{Jordan1999, +author = {Jordan, Michael I. and Ghahramani, Zoubin and Jaakkola, Tommi S. and Saul, Lawrence K.}, +title = {An Introduction to Variational Methods for Graphical Models}, +year = {1999}, +issue_date = {Nov.1.1999}, +publisher = {Kluwer Academic Publishers}, +address = {USA}, +volume = {37}, +number = {2}, +issn = {0885-6125}, +doi = {10.1023/A:1007665907178}, +journal = {Mach. Learn.}, +month = nov, +pages = {183-233}, +numpages = {51}, +keywords = {Bayesian networks, approximate inference, probabilistic inference, belief networks, mean field methods, neural networks, hidden Markov models, Boltzmann machines, variational methods, graphical models} +} + + +@article{Wainwright2008, +year = {2008}, +volume = {1}, +journal = {Foundations and Trends in Machine Learning}, +title = {Graphical Models, Exponential Families, and Variational Inference}, +doi = {10.1561/2200000001}, +issn = {1935-8237}, +number = {1-2}, +pages = {1-305}, +author = {Martin J. Wainwright and Michael I. Jordan} +} + + + +@book{pearl_2009, +place={Cambridge}, edition={2}, +title={Causality}, DOI={10.1017/CBO9780511803161}, +publisher={Cambridge University Press}, +author={Pearl, Judea}, +year={2009} +} + + +@article{Pearl2009, +author = {Judea Pearl}, +title = {{Causal inference in statistics: An overview}}, +volume = {3}, +journal = {Statistics Surveys}, +number = {none}, +publisher = {Amer. Statist. Assoc., the Bernoulli Soc., the Inst. Math. Statist., and the Statist. Soc. Canada}, +pages = {96 -- 146}, +keywords = {causal effects, causes of effects, confounding, counterfactuals, graphical methods, mediation, policy evaluation, potential-outcome, structural equation models}, +year = {2009}, +doi = {10.1214/09-SS057}, +URL = {https://doi.org/10.1214/09-SS057} +} + + +@article{Lopez2020, +author = {Lopez, Romain and Gayoso, Adam and Yosef, Nir}, +title = {Enhancing scientific discoveries in molecular biology with deep generative models}, +journal = {Molecular Systems Biology}, +volume = {16}, +number = {9}, +pages = {e9198}, +keywords = {deep generative models, molecular biology, neural networks}, +doi = {https://doi.org/10.15252/msb.20199198}, +url = {https://www.embopress.org/doi/abs/10.15252/msb.20199198}, +eprint = {https://www.embopress.org/doi/pdf/10.15252/msb.20199198}, +abstract = {Abstract Generative models provide a well-established statistical framework for evaluating uncertainty and deriving conclusions from large data sets especially in the presence of noise, sparsity, and bias. Initially developed for computer vision and natural language processing, these models have been shown to effectively summarize the complexity that underlies many types of data and enable a range of applications including supervised learning tasks, such as assigning labels to images; unsupervised learning tasks, such as dimensionality reduction; and out-of-sample generation, such as de novo image synthesis. With this early success, the power of generative models is now being increasingly leveraged in molecular biology, with applications ranging from designing new molecules with properties of interest to identifying deleterious mutations in our genomes and to dissecting transcriptional variability between single cells. In this review, we provide a brief overview of the technical notions behind generative models and their implementation with deep learning techniques. We then describe several different ways in which these models can be utilized in practice, using several recent applications in molecular biology as examples.}, +year = {2020} +} + + +@article{Haken1997, + title={Synergetics}, + author={Haken, Herman}, + journal={Physics Bulletin}, + volume={28}, + number={9}, + pages={412}, + year={1977}, + publisher={IOP Publishing} +} + + +@article{JirsaHaken1997, + title={A derivation of a macroscopic field theory of the brain from the quasi-microscopic neural dynamics}, + author={Jirsa, Viktor K and Haken, Hermann}, + journal={Physica D: Nonlinear Phenomena}, + volume={99}, + number={4}, + pages={503--526}, + year={1997}, + publisher={Elsevier} +} + + +@article{Jafarian2020, + title={Comparing dynamic causal models of neurovascular coupling with fMRI and EEG/MEG}, + author={Jafarian, Amirhossein and Litvak, Vladimir and Cagnan, Hayriye and Friston, Karl J and Zeidman, Peter}, + journal={NeuroImage}, + volume={216}, + pages={116734}, + year={2020}, + publisher={Elsevier} +} + + +@article{Jafarian2021, + title={Adiabatic dynamic causal modelling}, + author={Jafarian, Amirhossein and Zeidman, Peter and Wykes, Rob C and Walker, Matthew and Friston, Karl J}, + journal={NeuroImage}, + pages={118243}, + year={2021}, + publisher={Elsevier} +} + + +@article{Tabak2013, + title={A family of nonparametric density estimation algorithms}, + author={Tabak, Esteban G and Turner, Cristina V}, + journal={Communications on Pure and Applied Mathematics}, + volume={66}, + number={2}, + pages={145--164}, + year={2013}, + publisher={Wiley Online Library} +} + +@inproceedings{Rezende2015, + title={Variational inference with normalizing flows}, + author={Rezende, Danilo and Mohamed, Shakir}, + booktitle={International conference on machine learning (ICML)}, + pages={1530--1538}, + year={2015}, + organization={PMLR} +} + +@inproceedings{Papamakarios2016, + title={Fast $\varepsilon$-free inference of simulation models with bayesian conditional density estimation}, + author={Papamakarios, George and Murray, Iain}, + booktitle={Advances in Neural Information Processing Systems}, + pages={1028--1036}, + year={2016} +} + + +@article{Papamakarios2017, + title={Masked autoregressive flow for density estimation}, + author={Papamakarios, George and Pavlakou, Theo and Murray, Iain}, + journal={Advances in Neural Information Processing Systems}, + year={2017} +} + + +@article{Papamakarios2019, + title={Normalizing flows for probabilistic modeling and inference}, + author={Papamakarios, George and Nalisnick, Eric and Rezende, Danilo Jimenez and Mohamed, Shakir and Lakshminarayanan, Balaji}, + journal={arXiv preprint arXiv:1912.02762}, + year={2019} +} + + +@inproceedings{Papamakarios2019b, + title={Sequential neural likelihood: Fast likelihood-free inference with autoregressive flows}, + author={Papamakarios, George and Sterratt, David and Murray, Iain}, + booktitle={The 22nd International Conference on Artificial Intelligence and Statistics}, + pages={837--848}, + year={2019}, + organization={PMLR} +} + + + +@article{Kobyzev2020, + title={Normalizing flows: An introduction and review of current methods}, + author={Kobyzev, Ivan and Prince, Simon and Brubaker, Marcus}, + journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, + year={2020}, + publisher={IEEE} +} + + +@inproceedings{Germain2015, + title={Made: Masked autoencoder for distribution estimation}, + author={Germain, Mathieu and Gregor, Karol and Murray, Iain and Larochelle, Hugo}, + booktitle={International Conference on Machine Learning}, + pages={881--889}, + year={2015}, + organization={PMLR} +} + + +@inproceedings{Dinh2015, + title={NICE: non-linear independent components estimation 3rd Int}, + author={Dinh, L and Krueger, D and Bengio, Y}, + booktitle={Conf. on Learning Representations, ICLR (San Diego, CA, USA,) Workshop Track Proc}, + year={2015} +} + +@article{Dinh2017, + title={Density estimation using real NVP}, + author={Dinh, Laurent and Sohl-Dickstein, Jascha and Bengio, Samy}, + journal={International Conference on Learning Representations}, + year={2017} +} + + +@inproceedings{VanOord2016Pixel, + title={Pixel recurrent neural networks}, + author={Van Oord, Aaron and Kalchbrenner, Nal and Kavukcuoglu, Koray}, + booktitle={International Conference on Machine Learning}, + pages={1747--1756}, + year={2016}, + organization={PMLR} +} + + +@article{VanOord2016Wavenet, + title={Wavenet: A generative model for raw audio}, + author={Oord, Aaron van den and Dieleman, Sander and Zen, Heiga and Simonyan, Karen and Vinyals, Oriol and Graves, Alex and Kalchbrenner, Nal and Senior, Andrew and Kavukcuoglu, Koray}, + journal={arXiv preprint arXiv:1609.03499}, + year={2016} +} + + +@inproceedings{Greenberg2019, + title={Automatic posterior transformation for likelihood-free inference}, + author={Greenberg, David and Nonnenmacher, Marcel and Macke, Jakob}, + booktitle={International Conference on Machine Learning}, + pages={2404--2414}, + year={2019}, + organization={PMLR} +} + + +@inproceedings{Hermans2020, + title={Likelihood-free mcmc with amortized approximate ratio estimators}, + author={Hermans, Joeri and Begy, Volodimir and Louppe, Gilles}, + booktitle={International Conference on Machine Learning}, + pages={4239--4248}, + year={2020}, + organization={PMLR} +} + +@article{Durkan2019, + title={Neural spline flows}, + author={Durkan, Conor and Bekasov, Artur and Murray, Iain and Papamakarios, George}, + journal={Advances in Neural Information Processing Systems}, + volume={32}, + pages={7511--7522}, + year={2019} +} + +@inproceedings{Durkan2020, + title={On contrastive learning for likelihood-free inference}, + author={Durkan, Conor and Murray, Iain and Papamakarios, George}, + booktitle={International Conference on Machine Learning}, + pages={2771--2781}, + year={2020}, + organization={PMLR} +} + + +@article{Dockhorn2020, + title={Density Deconvolution with Normalizing Flows}, + author={Dockhorn, Tim and Ritchie, James A and Yu, Yaoliang and Murray, Iain}, + journal={arXiv preprint arXiv:2006.09396}, + year={2020} +} + + + + +@article{Cranmer2020, + title={The frontier of simulation-based inference}, + author={Cranmer, Kyle and Brehmer, Johann and Louppe, Gilles}, + journal={Proceedings of the National Academy of Sciences}, + volume={117}, + number={48}, + pages={30055--30062}, + year={2020}, + publisher={National Acad Sciences} +} + +@article{Brehmer2020, + title={Mining gold from implicit models to improve likelihood-free inference}, + author={Brehmer, Johann and Louppe, Gilles and Pavez, Juan and Cranmer, Kyle}, + journal={Proceedings of the National Academy of Sciences}, + volume={117}, + number={10}, + pages={5242--5249}, + year={2020}, + publisher={National Acad Sciences} +} + + +@article{Goncalves2020, + title={Training deep neural density estimators to identify mechanistic models of neural dynamics}, + author={Gon{\c{c}}alves, Pedro J and Lueckmann, Jan-Matthis and Deistler, Michael and Nonnenmacher, Marcel and {\"O}cal, Kaan and Bassetto, Giacomo and Chintaluri, Chaitanya and Podlaski, William F and Haddad, Sara A and Vogels, Tim P and others}, + journal={Elife}, + volume={9}, + pages={e56261}, + year={2020}, + publisher={eLife Sciences Publications Limited} +} + +@article{Deistler2021, + title={Disparate energy consumption despite similar network activity}, + author={Deistler, Michael and Macke, Jakob H and Gon{\c{c}}alves, Pedro J}, + journal={bioRxiv}, + year={2021} +} + + + +@article{Boelts2022, + title={Flexible and efficient simulation-based inference for models of decision-making}, + author={Boelts, Jan and Lueckmann, Jan-Matthis and Gao, Richard and Macke, Jakob H}, + journal={Elife}, + volume={11}, + pages={e77220}, + year={2022}, + publisher={eLife Sciences Publications Limited} +} + + +@article{Tejero-Cantero2020, + doi = {10.21105/joss.02505}, + url = {https://doi.org/10.21105/joss.02505}, + year = {2020}, + publisher = {The Open Journal}, + volume = {5}, + number = {52}, + pages = {2505}, + author = {Alvaro Tejero-Cantero and Jan Boelts and Michael Deistler and Jan-Matthis Lueckmann and Conor Durkan and Pedro J. Gonçalves and David S. Greenberg and Jakob H. Macke}, + title = {sbi: A toolkit for simulation-based inference}, + journal = {Journal of Open Source Software} +} + + +@article {Bittner2021, +article_type = {journal}, +title = {Interrogating theoretical models of neural computation with emergent property inference}, +author = {Bittner, Sean R and Palmigiano, Agostina and Piet, Alex T and Duan, Chunyu A and Brody, Carlos D and Miller, Kenneth D and Cunningham, John}, +editor = {Huguenard, John R and O'Leary, Timothy and Goldman, Mark S}, +volume = 10, +year = 2021, +month = {jul}, +pub_date = {2021-07-29}, +pages = {e56265}, +citation = {eLife 2021;10:e56265}, +doi = {10.7554/eLife.56265}, +journal = {eLife}, +issn = {2050-084X}, +publisher = {eLife Sciences Publications, Ltd}, +} + + + +@article{Beaumont2010, + title={Approximate Bayesian computation in evolution and ecology}, + author={Beaumont, Mark A}, + journal={Annual review of ecology, evolution, and systematics}, + volume={41}, + pages={379--406}, + year={2010}, + publisher={Annual Reviews} +} + + +@article{Lueckmann2017, + title={Flexible statistical inference for mechanistic models of neural dynamics}, + author={Lueckmann, Jan-Matthis and Goncalves, Pedro J and Bassetto, Giacomo and {\"O}cal, Kaan and Nonnenmacher, Marcel and Macke, Jakob H}, + journal={Advances in Neural Information Processing Systems}, + year={2017} +} + +@inproceedings{Lueckmann2019, + title={Likelihood-free inference with emulator networks}, + author={Lueckmann, Jan-Matthis and Bassetto, Giacomo and Karaletsos, Theofanis and Macke, Jakob H}, + booktitle={Symposium on Advances in Approximate Bayesian Inference}, + pages={32--53}, + year={2019}, + organization={PMLR} +} + + +@inproceedings{Lueckmann2021, + title={Benchmarking Simulation-Based Inference}, + author={Lueckmann, Jan-Matthis and Boelts, Jan and Greenberg, David and Goncalves, Pedro and Macke, Jakob}, + booktitle={International Conference on Artificial Intelligence and Statistics}, + pages={343--351}, + year={2021}, + organization={PMLR} +} + + + +@article{Wood2010, + title={Statistical inference for noisy nonlinear ecological dynamic systems}, + author={Wood, Simon N}, + journal={Nature}, + volume={466}, + number={7310}, + pages={1102--1104}, + year={2010}, + publisher={Nature Publishing Group} +} + + +@article{Wrede2021, + title={Robust and integrative Bayesian neural networks for likelihood-free parameter inference}, + author={Wrede, Fredrik and Eriksson, Robin and Jiang, Richard and Petzold, Linda and Engblom, Stefan and Hellander, Andreas and Singh, Prashant}, + journal={arXiv preprint arXiv:2102.06521}, + year={2021} +} + + +@article{Alsing2018, + title={Massive optimal data compression and density estimation for scalable, likelihood-free inference in cosmology}, + author={Alsing, Justin and Wandelt, Benjamin and Feeney, Stephen}, + journal={Monthly Notices of the Royal Astronomical Society}, + volume={477}, + number={3}, + pages={2874--2885}, + year={2018}, + publisher={Oxford University Press} +} + + +@article{Zhao2022, + title={Simulation-based Inference of Reionization Parameters from 3D Tomographic 21 cm Light-cone Images}, + author={Zhao, Xiaosheng and Mao, Yi and Cheng, Cheng and Wandelt, Benjamin D}, + journal={The Astrophysical Journal}, + volume={926}, + number={2}, + pages={151}, + year={2022}, + publisher={IOP Publishing} +} + + +@article{Brehmer2018, + title={Constraining effective field theories with machine learning}, + author={Brehmer, Johann and Cranmer, Kyle and Louppe, Gilles and Pavez, Juan}, + journal={Physical review letters}, + volume={121}, + number={11}, + pages={111801}, + year={2018}, + publisher={APS} +} + +@article{Brehmer2021, + title={Simulation-based inference in particle physics}, + author={Brehmer, Johann}, + journal={Nature Reviews Physics}, + volume={3}, + number={5}, + pages={305--305}, + year={2021}, + publisher={Nature Publishing Group} +} + + + +@article{Baso2021, + title={Bayesian Stokes inversion with Normalizing flows}, + author={Baso, CJ and Ramos, A Asensio and Rodr{\'\i}guez, J}, + journal={arXiv preprint arXiv:2108.07089}, + year={2021} +} + + + +@software{nflows, + author = {Conor Durkan and + Artur Bekasov and + Iain Murray and + George Papamakarios}, + title = {{nflows}: normalizing flows in {PyTorch}}, + month = nov, + year = 2020, + publisher = {Zenodo}, + version = {v0.14}, + url = {https://github.com/bayesiains/nflows,} +} + + +@article{Hoffman2019, + title={NeuTra-lizing bad geometry in hamiltonian monte carlo using neural transport}, + author={Hoffman, Matthew and Sountsov, Pavel and Dillon, Joshua V and Langmore, Ian and Tran, Dustin and Vasudevan, Srinivas}, + journal={arXiv preprint arXiv:1903.03704}, + year={2019} +} + + +@article{Wu2020, + title={Stochastic normalizing flows}, + author={Wu, Hao and K{\"o}hler, Jonas and No{\'e}, Frank}, + journal={arXiv preprint arXiv:2002.06707}, + year={2020} +} + + +@article{Beghi2020, + title={The epidemiology of epilepsy}, + author={Beghi, Ettore}, + journal={Neuroepidemiology}, + volume={54}, + number={2}, + pages={185--191}, + year={2020}, + publisher={Karger Publishers} +} + + +@article{Kwan2010, + title={Erratum: Definition of drug resistant epilepsy. Consensus proposal by the ad hoc task force of the ILAE commission on therapeutic strategies (Epilepsia (2010) 51 (1069-77))}, + author={Kwan, P and Arzimanoglou, A and Berg, AT and Brodie, MJ and Hauser, W Allen and Mathern, G and Mosh{\'e}, SL and Perucca, E and Wiebe, S and French, J}, + journal={Epilepsia}, + volume={51}, + number={9}, + pages={1922}, + year={2010}, + publisher={Wiley-Blackwell} +} + + +@article{Bancaud1970, + title={Functional stereotaxic exploration (SEEG) of epilepsy.}, + author={Bancaud, J and Angelergues, R and Bernouilli, C and Bonis, A and Bordas-Ferrer, M and Bresson, M and Buser, P and Covello, L and Morel, P and Szikla, G and others}, + journal={Electroencephalography and clinical neurophysiology}, + volume={28}, + number={1}, + pages={85--86}, + year={1970} +} + + +@article{Cardinale2016, + title={Implantation of stereoelectroencephalography electrodes: a systematic review}, + author={Cardinale, Francesco and Casaceli, Giuseppe and Raneri, Fabio and Miller, Jonathan and Russo, Giorgio Lo}, + journal={Journal of Clinical Neurophysiology}, + volume={33}, + number={6}, + pages={490--502}, + year={2016}, + publisher={LWW} +} + + +@article{Cardinale2019, + title={Stereoelectroencephalography: retrospective analysis of 742 procedures in a single centre}, + author={Cardinale, Francesco and Rizzi, Michele and Vignati, Elena and Cossu, Massimo and Castana, Laura and d'Orio, Piergiorgio and Revay, Martina and Costanza, Martina Della and Tassi, Laura and Mai, Roberto and others}, + journal={Brain}, + volume={142}, + number={9}, + pages={2688--2704}, + year={2019}, + publisher={Oxford University Press} +} + + + +@article{Sohrabpour2020, + title={Noninvasive electromagnetic source imaging of spatiotemporally distributed epileptogenic brain sources}, + author={Sohrabpour, Abbas and Cai, Zhengxiang and Ye, Shuai and Brinkmann, Benjamin and Worrell, Gregory and He, Bin}, + journal={Nature communications}, + volume={11}, + number={1}, + pages={1--15}, + year={2020}, + publisher={Nature Publishing Group} +} + + +@article{Caune2014, + title={Evaluating dipolar source localization feasibility from intracerebral SEEG recordings}, + author={Caune, Vairis and Ranta, Radu and Le Cam, Steven and Hofmanis, Janis and Maillard, Louis and Koessler, Laurent and Louis-Dorr, Val{\'e}rie}, + journal={NeuroImage}, + volume={98}, + pages={118--133}, + year={2014}, + publisher={Elsevier} +} + + +@inproceedings{Gershman2014, + title={Amortized inference in probabilistic reasoning}, + author={Gershman, Samuel and Goodman, Noah}, + booktitle={Proceedings of the annual meeting of the cognitive science society}, + volume={36}, + number={36}, + year={2014} +} + + +@article{Fengler2021, + title={Likelihood approximation networks (LANs) for fast inference of simulation models in cognitive neuroscience}, + author={Fengler, Alexander and Govindarajan, Lakshmi N and Chen, Tony and Frank, Michael J}, + journal={Elife}, + volume={10}, + pages={e65074}, + year={2021}, + publisher={eLife Sciences Publications Limited} +} + + +@article{Muller2020, + title={Diffuse neural coupling mediates complex network dynamics through the formation of quasi-critical brain states}, + author={M{\"u}ller, Eli J and Munn, Brandon R and Shine, James M}, + journal={Nature communications}, + volume={11}, + number={1}, + pages={1--11}, + year={2020}, + publisher={Nature Publishing Group} +} + + +@misc{WHO, + author = {{World Health Organization}}, + howpublished = {\url{https://www.who.int/health-topics/epilepsy}}, + year = 2020, + note = {Accessed: July 7, 2020}, +} + + + + +@article{Prando2020, +title = {Sparse DCM for whole-brain effective connectivity from resting-state fMRI data}, +journal = {NeuroImage}, +volume = {208}, +pages = {116367}, +year = {2020}, +issn = {1053-8119}, +doi = {https://doi.org/10.1016/j.neuroimage.2019.116367}, +author = {Giulia Prando and Mattia Zorzi and Alessandra Bertoldo and Maurizio Corbetta and Marco Zorzi and Alessandro Chiuso}, +keywords = {effective connectivity, fMRI, Sparsity, Dynamic causal modelling, Resting-state}, +abstract = {Contemporary neuroscience has embraced network science and dynamical systems to study the complex and self-organized structure of the human brain. Despite the developments in non-invasive neuroimaging techniques, a full understanding of the directed interactions in whole brain networks, referred to as effective connectivity, as well as their role in the emergent brain dynamics is still lacking. The main reason is that estimating brain connectivity requires solving a formidable large-scale inverse problem from indirect and noisy measurements. Building on the dynamic causal modelling framework, the present study offers a novel method for estimating whole-brain effective connectivity from resting-state functional magnetic resonance data. To this purpose sparse estimation methods are adapted to infer the parameters of our novel model, which is based on a linearized, region-specific haemodynamic response function. The resulting algorithm, referred to as sparse DCM, is shown to compare favorably with state-of-the art methods when tested on both synthetic and real data. We also provide a graph-theoretical analysis on the whole-brain effective connectivity estimated using data from a cohort of healthy individuals, which reveals properties such as asymmetry in the connectivity structure as well as the different roles of brain areas in favoring segregation or integration.} +} + + + + + +@article{Zarghami2020, +title = {Dynamic effective connectivity}, +journal = {NeuroImage}, +volume = {207}, +pages = {116453}, +year = {2020}, +issn = {1053-8119}, +doi = {https://doi.org/10.1016/j.neuroimage.2019.116453}, +author = {Tahereh S. Zarghami and Karl J. Friston}, +keywords = {Dynamic causal modelling, Spectral DCM, Effective connectivity, Dynamic functional connectivity, Metastability, Itinerancy, Transient dynamics, Stable heteroclinic cycle, Hidden Markov model, Bayesian, Free energy, Resting state, fMRI}, +abstract = {Metastability is a key source of itinerant dynamics in the brain; namely, spontaneous spatiotemporal reorganization of neuronal activity. This itinerancy has been the focus of numerous dynamic functional connectivity (DFC) analyses – developed to characterize the formation and dissolution of distributed functional patterns over time, using resting state fMRI. However, aside from technical and practical controversies, these approaches cannot recover the neuronal mechanisms that underwrite itinerant (e.g., metastable) dynamics—due to their descriptive, model-free nature. We argue that effective connectivity (EC) analyses are more apt for investigating the neuronal basis of metastability. To this end, we appeal to biologically-grounded models (i.e., dynamic causal modelling, DCM) and dynamical systems theory (i.e., heteroclinic sequential dynamics) to create a probabilistic, generative model of haemodynamic fluctuations. This model generates trajectories in the parametric space of EC modes (i.e., states of connectivity) that characterize functional brain architectures. In brief, it extends an established spectral DCM, to generate functional connectivity data features that change over time. This foundational paper tries to establish the model’s face validity by simulating non-stationary fMRI time series and recovering key model parameters (i.e., transition probabilities among connectivity states and the parametric nature of these states) using variational Bayes. These data are further characterized using Bayesian model comparison (within and between subjects). Finally, we consider practical issues that attend applications and extensions of this scheme. Importantly, the scheme operates within a generic Bayesian framework – that can be adapted to study metastability and itinerant dynamics in any non-stationary time series.} +} + + + + +@article{Gilson2020, + author = {Gilson, Matthieu and Zamora-L\`{o}pez, Gorka and Pallar{\'e}s, Vicente and Adhikari, Mohit H. and Senden, Mario and Campo, Adri{\`a} Tauste and Mantini, Dante and Corbetta, Maurizio and Deco, Gustavo and Insabato, Andrea}, + title = "{Model-based whole-brain effective connectivity to study distributed cognition in health and disease}", + journal = {Network Neuroscience}, + volume = {4}, + number = {2}, + pages = {338-373}, + year = {2020}, + abstract = "{Neuroimaging techniques are now widely used to study human cognition. The functional associations between brain areas have become a standard proxy to describe how cognitive processes are distributed across the brain network. Among the many analysis tools available, dynamic models of brain activity have been developed to overcome the limitations of original connectivity measures such as functional connectivity. This goes in line with the many efforts devoted to the assessment of directional interactions between brain areas from the observed neuroimaging activity. This opinion article provides an overview of our model-based whole-brain effective connectivity to analyze fMRI data, while discussing the pros and cons of our approach with respect to other established approaches. Our framework relies on the multivariate Ornstein-Uhlenbeck (MOU) process and is thus referred to as MOU-EC. Once tuned, the model provides a directed connectivity estimate that reflects the dynamical state of BOLD activity, which can be used to explore cognition. We illustrate this approach using two applications on task-evoked fMRI data. First, as a connectivity measure, MOU-EC can be used to extract biomarkers for task-specific brain coordination, understood as the patterns of areas exchanging information. The multivariate nature of connectivity measures raises several challenges for whole-brain analysis, for which machine-learning tools present some advantages over statistical testing. Second, we show how to interpret changes in MOU-EC connections in a collective and model-based manner, bridging with network analysis. Our framework provides a comprehensive set of tools that open exciting perspectives to study distributed cognition, as well as neuropathologies.Brain connectivity measures have been increasingly used to study cognition and neuropathologies with neuroimaging data. Many methods have been developed with particular objectives, in particular predictability to obtain biomarkers (i.e., which brain regions exhibit changes in their interactions across conditions) and interpretability (relating the connectivity measures back to the biology). In this article we present our framework for whole-brain effective connectivity that aims to find the equilibrium between these two desired properties, relying on a dynamic model that can be fitted to fMRI time series. Meanwhile, we compare it with previous work. Concretely, we show how machine learning can be used to extract biomarkers and how network-oriented analysis can be used to interpret the model estimates in terms of brain dynamics.}", + issn = {2472-1751}, + doi = {10.1162/netn_a_00117}, +} + + + +@article{Montbrio2015, + title={Macroscopic description for networks of spiking neurons}, + author={Montbri{\'o}, Ernest and Paz{\'o}, Diego and Roxin, Alex}, + journal={Physical Review X}, + volume={5}, + number={2}, + pages={021028}, + year={2015}, + publisher={APS} +} + + +@article{Rabuffo2021, + title={Neuronal cascades shape whole-brain functional dynamics at rest}, + author={Rabuffo, Giovanni and Fousek, Jan and Bernard, Christophe and Jirsa, Viktor}, + journal={ENeuro}, + volume={8}, + number={5}, + year={2021}, + publisher={Society for Neuroscience} +} + + +@article{Fousek2022, + title={The structured flow on the brain's resting state manifold}, + author={Fousek, Jan and Rabuffo, Giovanni and Gudibanda, Kashyap and Sheheitli, Hiba and Jirsa, Viktor and Petkoski, Spase}, + journal={bioRxiv}, + year={2022}, + publisher={Cold Spring Harbor Laboratory} +} + +@article{Fousek2024, + title={Symmetry breaking organizes the brain's resting state manifold}, + author={Fousek, Jan and Rabuffo, Giovanni and Gudibanda, Kashyap and Sheheitli, Hiba and Petkoski, Spase and Jirsa, Viktor}, + journal={Scientific Reports}, + volume={14}, + number={1}, + pages={31970}, + year={2024}, + publisher={Nature Publishing Group UK London} +} + + +@article{Lavanga2022, + title={The virtual aging brain: a model-driven explanation for cognitive decline in older subjects}, + author={Lavanga, Mario and Stumme, Johanna and Yalcinkaya, Bahar Hazal and Fousek, Jan and Jockwitz, Christiane and Sheheitli, Hiba and Bittner, Nora and Hashemi, Meysam and Petkoski, Spase and Caspers, Svenja and others}, + journal={bioRxiv}, + year={2022}, + publisher={Cold Spring Harbor Laboratory} +} + + +@article{Hashemi2023, + title={Amortized Bayesian inference on generative dynamical network models of epilepsy using deep neural density estimators}, + author={Hashemi, Meysam and Vattikonda, Anirudh N and Jha, Jayant and Sip, Viktor and Woodman, Marmaduke M and Bartolomei, Fabrice and Jirsa, Viktor K}, + journal={Neural Networks}, + volume={163}, + pages={178--194}, + year={2023}, + publisher={Elsevier} +} + + +@article{Hashemi2024, +doi = {10.1088/2632-2153/ad6230}, +year = {2024}, +month = {jul}, +publisher = {IOP Publishing}, +volume = {5}, +number = {3}, +pages = {035019}, +author = {Meysam Hashemi and Abolfazl Ziaeemehr and Marmaduke M Woodman and Jan Fousek and Spase Petkoski and Viktor K Jirsa}, +title = {Simulation-based inference on virtual brain models of disorders}, +journal = {Machine Learning: Science and Technology}, +} + + + +@article{Gabrie2022, + title={Adaptive Monte Carlo augmented with normalizing flows}, + author={Gabri{\'e}, Marylou and Rotskoff, Grant M and Vanden-Eijnden, Eric}, + journal={Proceedings of the National Academy of Sciences}, + volume={119}, + number={10}, + pages={e2109420119}, + year={2022}, + publisher={National Acad Sciences} +} + + +@article{Jha2022, + title={Fully Bayesian estimation of virtual brain parameters with self-tuning Hamiltonian Monte Carlo}, + author={Jha, Jayant and Hashemi, Meysam and Vattikonda, Anirudh Nihalani and Wang, Huifang and Jirsa, Viktor}, + journal={Machine Learning: Science and Technology}, + volume={3}, + number={3}, + pages={035016}, + year={2022}, + publisher={IOP Publishing} +} + + + +@book{Box2011, + title={Bayesian inference in statistical analysis}, + author={Box, George EP and Tiao, George C}, + year={2011}, + publisher={John Wiley \& Sons} +} + + +@article{Andrieu2003, + title={An introduction to MCMC for machine learning}, + author={Andrieu, Christophe and De Freitas, Nando and Doucet, Arnaud and Jordan, Michael I}, + journal={Machine learning}, + volume={50}, + number={1}, + pages={5--43}, + year={2003}, + publisher={Springer} +} + + +@article{Ghahramani2015, + title={Probabilistic machine learning and artificial intelligence}, + author={Ghahramani, Zoubin}, + journal={Nature}, + volume={521}, + number={7553}, + pages={452--459}, + year={2015}, + publisher={Nature Publishing Group} +} + + +@book{Murphy2022, + title={Probabilistic machine learning: an introduction}, + author={Murphy, Kevin P}, + year={2022}, + publisher={MIT press} +} + + +@book{Mcelreath2020, + title={Statistical rethinking: A Bayesian course with examples in R and Stan}, + author={McElreath, Richard}, + year={2020}, + publisher={Chapman and Hall/CRC} +} + + +@article{Papamakarios2021, + title={Normalizing Flows for Probabilistic Modeling and Inference.}, + author={Papamakarios, George and Nalisnick, Eric T and Rezende, Danilo Jimenez and Mohamed, Shakir and Lakshminarayanan, Balaji}, + journal={J. Mach. Learn. Res.}, + volume={22}, + number={57}, + pages={1--64}, + year={2021} +} + + + +@article{Cabral2022, + title={Metastable oscillatory modes emerge from synchronization in the brain spacetime connectome}, + author={Cabral, Joana and Castaldo, Francesca and Vohryzek, Jakub and Litvak, Vladimir and Bick, Christian and Lambiotte, Renaud and Friston, Karl and Kringelbach, Morten L and Deco, Gustavo}, + journal={Communications Physics}, + volume={5}, + number={1}, + pages={1--13}, + year={2022}, + publisher={Nature Publishing Group} +} + + +@article{Kong2021, + title={Sensory-motor cortices shape functional connectivity dynamics in the human brain}, + author={Kong, Xiaolu and Kong, Ru and Orban, Csaba and Wang, Peng and Zhang, Shaoshi and Anderson, Kevin and Holmes, Avram and Murray, John D and Deco, Gustavo and van den Heuvel, Martijn and others}, + journal={Nature communications}, + volume={12}, + number={1}, + pages={1--15}, + year={2021}, + publisher={Nature Publishing Group} +} + + +@article{Wang2019, + title={Inversion of a large-scale circuit model reveals a cortical hierarchy in the dynamic resting human brain}, + author={Wang, Peng and Kong, Ru and Kong, Xiaolu and Li{\'e}geois, Rapha{\"e}l and Orban, Csaba and Deco, Gustavo and Van Den Heuvel, Martijn P and Thomas Yeo, BT}, + journal={Science advances}, + volume={5}, + number={1}, + pages={eaat7854}, + year={2019}, + publisher={American Association for the Advancement of Science} +} + + +@article{Lu2022, + title={The human digital twin brain in the resting state and in action}, + author={Lu, Wenlian and Zheng, Qibao and Xu, Ningsheng and Feng, Jianfeng and Consortium, DTB}, + journal={arXiv preprint arXiv:2211.15963}, + year={2022} +} + + +@article{petkoski2023white, + title={White-matter degradation and dynamical compensation support age-related functional alterations in human brain}, + author={Petkoski, Spase and Ritter, Petra and Jirsa, Viktor K}, + journal={Cerebral Cortex}, + volume={33}, + number={10}, + pages={6241--6256}, + year={2023}, + publisher={Oxford University Press} +} + + +@article{Wang2023, + title={Delineating epileptogenic networks using brain imaging data and personalized modeling in drug-resistant epilepsy}, + author={Wang, Huifang E and Woodman, Marmaduke and Triebkorn, Paul and Lemarechal, Jean-Didier and Jha, Jayant and Dollomaja, Borana and Vattikonda, Anirudh Nihalani and Sip, Viktor and Medina Villalon, Samuel and Hashemi, Meysam and others}, + journal={Science Translational Medicine}, + volume={15}, + number={680}, + pages={eabp8982}, + year={2023}, + publisher={American Association for the Advancement of Science} +} + +@article{Jirsa2023, + title={Personalised virtual brain models in epilepsy}, + author={Jirsa, Viktor and Wang, Huifang and Triebkorn, Paul and Hashemi, Meysam and Jha, Jayant and Gonzalez-Martinez, Jorge and Guye, Maxime and Makhalova, Julia and Bartolomei, Fabrice}, + journal={The Lancet Neurology}, + volume={22}, + number={5}, + pages={443--454}, + year={2023}, + publisher={Elsevier} +} + +@article{Sip2023, + title={Characterization of regional differences in resting-state fMRI with a data-driven network model of brain dynamics}, + author={Sip, Viktor and Hashemi, Meysam and Dickscheid, Timo and Amunts, Katrin and Petkoski, Spase and Jirsa, Viktor}, + journal={Science Advances}, + volume={9}, + number={11}, + pages={eabq7547}, + year={2023}, + publisher={American Association for the Advancement of Science} +} + + +@article{Yalcinkaya2023, + title={Personalized virtual brains of Alzheimer's Disease link dynamical biomarkers of fMRI with increased local excitability}, + author={Yalcinkaya, Bahar Hazal and Ziaeemehr, Abolfazl and Fousek, Jan and Hashemi, Meysam and Lavanga, Mario and Solodkin, Ana and McIntosh, Randy and Jirsa, Viktor and Petkoski, Spase}, + journal={medRxiv}, + pages={2023--01}, + year={2023}, + publisher={Cold Spring Harbor Laboratory Press} +} + + +@article{Bassett2017, + title={Network neuroscience}, + author={Bassett, Danielle S and Sporns, Olaf}, + journal={Nature neuroscience}, + volume={20}, + number={3}, + pages={353--364}, + year={2017}, + publisher={Nature Publishing Group US New York} +} + + + + + +@article{Triebkorn2022, + title={Brain simulation augments machine-learning--based classification of dementia}, + author={Triebkorn, Paul and Stefanovski, Leon and Dhindsa, Kiret and Diaz-Cortes, Margarita-Arimatea and Bey, Patrik and B{\"u}lau, Konstantin and Pai, Roopa and Spiegler, Andreas and Solodkin, Ana and Jirsa, Viktor and others}, + journal={Alzheimer's \& Dementia: Translational Research \& Clinical Interventions}, + volume={8}, + number={1}, + pages={e12303}, + year={2022}, + publisher={Wiley Online Library} +} + + + +@article{Antonenko2013, + title={Healthy aging by staying selectively connected: a mini-review}, + author={Antonenko, Daria and Fl{\"o}el, Agnes}, + journal={Gerontology}, + volume={60}, + number={1}, + pages={3--9}, + year={2013}, + publisher={S. Karger AG} +} + + +@article{Damoiseaux2017, + title={Effects of aging on functional and structural brain connectivity}, + author={Damoiseaux, Jessica S}, + journal={Neuroimage}, + volume={160}, + pages={32--40}, + year={2017}, + publisher={Elsevier} +} + + +@article{Zuo2017, + title={Human connectomics across the life span}, + author={Zuo, Xi-Nian and He, Ye and Betzel, Richard F and Colcombe, Stan and Sporns, Olaf and Milham, Michael P}, + journal={Trends in cognitive sciences}, + volume={21}, + number={1}, + pages={32--45}, + year={2017}, + publisher={Elsevier} +} + + +@article{Puxeddu2020, + title={The modular organization of brain cortical connectivity across the human lifespan}, + author={Puxeddu, Maria Grazia and Faskowitz, Joshua and Betzel, Richard F and Petti, Manuela and Astolfi, Laura and Sporns, Olaf}, + journal={NeuroImage}, + volume={218}, + pages={116974}, + year={2020}, + publisher={Elsevier} +} + + +@article{Honey2009, + title={Predicting human resting-state functional connectivity from structural connectivity}, + author={Honey, Christopher J and Sporns, Olaf and Cammoun, Leila and Gigandet, Xavier and Thiran, Jean-Philippe and Meuli, Reto and Hagmann, Patric}, + journal={Proceedings of the National Academy of Sciences}, + volume={106}, + number={6}, + pages={2035--2040}, + year={2009}, + publisher={National Acad Sciences} +} + + +@article{Greicius2003, + title={Functional connectivity in the resting brain: a network analysis of the default mode hypothesis}, + author={Greicius, Michael D and Krasnow, Ben and Reiss, Allan L and Menon, Vinod}, + journal={Proceedings of the national academy of sciences}, + volume={100}, + number={1}, + pages={253--258}, + year={2003}, + publisher={National Acad Sciences} +} + + + + + +@article{Bullmore2009, + title={Complex brain networks: graph theoretical analysis of structural and functional systems}, + author={Bullmore, Ed and Sporns, Olaf}, + journal={Nature reviews neuroscience}, + volume={10}, + number={3}, + pages={186--198}, + year={2009}, + publisher={Nature Publishing Group UK London} +} + +@book{Sporns2016, + title={Networks of the Brain}, + author={Sporns, Olaf}, + year={2016}, + publisher={MIT press} +} + + +@article{Sporns2005, + title={The human connectome: a structural description of the human brain}, + author={Sporns, Olaf and Tononi, Giulio and K{\"o}tter, Rolf}, + journal={PLoS computational biology}, + volume={1}, + number={4}, + pages={e42}, + year={2005}, + publisher={Public Library of Science San Francisco, USA} +} + + +@article{Zalesky2014, + title={Time-resolved resting-state brain networks}, + author={Zalesky, Andrew and Fornito, Alex and Cocchi, Luca and Gollo, Leonardo L and Breakspear, Michael}, + journal={Proceedings of the National Academy of Sciences}, + volume={111}, + number={28}, + pages={10341--10346}, + year={2014}, + publisher={National Acad Sciences} +} + +@article{Lurie2020, + title={Questions and controversies in the study of time-varying functional connectivity in resting fMRI}, + author={Lurie, Daniel J and Kessler, Daniel and Bassett, Danielle S and Betzel, Richard F and Breakspear, Michael and Kheilholz, Shella and Kucyi, Aaron and Li{\'e}geois, Rapha{\"e}l and Lindquist, Martin A and McIntosh, Anthony Randal and others}, + journal={Network neuroscience}, + volume={4}, + number={1}, + pages={30--69}, + year={2020}, + publisher={MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info~…} +} + + +@article{Fornito2015, + title={The connectomics of brain disorders}, + author={Fornito, Alex and Zalesky, Andrew and Breakspear, Michael}, + journal={Nature Reviews Neuroscience}, + volume={16}, + number={3}, + pages={159--172}, + year={2015}, + publisher={Nature Publishing Group UK London} +} + + +@article{Friston1994, + title={Functional and effective connectivity in neuroimaging: a synthesis}, + author={Friston, Karl J}, + journal={Human brain mapping}, + volume={2}, + number={1-2}, + pages={56--78}, + year={1994}, + publisher={Wiley Online Library} +} + + +@article{Friston2014, + title={A DCM for resting state fMRI}, + author={Friston, Karl J and Kahan, Joshua and Biswal, Bharat and Razi, Adeel}, + journal={Neuroimage}, + volume={94}, + pages={396--407}, + year={2014}, + publisher={Elsevier} +} + + + +@article{Frassle2017, + title={Regression DCM for fMRI}, + author={Fr{\"a}ssle, Stefan and Lomakina, Ekaterina I and Razi, Adeel and Friston, Karl J and Buhmann, Joachim M and Stephan, Klaas E}, + journal={Neuroimage}, + volume={155}, + pages={406--421}, + year={2017}, + publisher={Elsevier} +} + + + +@book{Pearl2009, + title={Causality}, + author={Pearl, Judea}, + year={2009}, + publisher={Cambridge university press} +} + + +@book{Gelman2013, + title={Bayesian data analysis}, + author={Gelman, Andrew and Carlin, John B and Stern, Hal S and Dunson, David B and Vehtari, Aki and Rubin, Donald B}, + year={2013}, + publisher={CRC press} +} + + +@book{Vapnik1999, + title={The nature of statistical learning theory}, + author={Vapnik, Vladimir}, + year={1999}, + publisher={Springer science \& business media} +} + + +@book{Hastie2009, + title={The elements of statistical learning: data mining, inference, and prediction}, + author={Hastie, Trevor and Tibshirani, Robert and Friedman, Jerome H and Friedman, Jerome H}, + volume={2}, + year={2009}, + publisher={Springer} +} + + + +@book{Mackay2003, + title={Information theory, inference and learning algorithms}, + author={MacKay, David JC}, + year={2003}, + publisher={Cambridge university press} +} + + +@book{Anderson2008, + title={Model based inference in the life sciences: a primer on evidence}, + author={Anderson, David Raymond}, + volume={31}, + year={2008}, + publisher={Springer} +} + + + +@article{Van2012, + title={The Human Connectome Project: a data acquisition perspective}, + author={Van Essen, David C and Ugurbil, Kamil and Auerbach, Edward and Barch, Deanna and Behrens, Timothy EJ and Bucholz, Richard and Chang, Acer and Chen, Liyong and Corbetta, Maurizio and Curtiss, Sandra W and others}, + journal={Neuroimage}, + volume={62}, + number={4}, + pages={2222--2231}, + year={2012}, + publisher={Elsevier} +} + +@article{Hagmann2007, + title={Mapping human whole-brain structural networks with diffusion MRI}, + author={Hagmann, Patric and Kurant, Maciej and Gigandet, Xavier and Thiran, Patrick and Wedeen, Van J and Meuli, Reto and Thiran, Jean-Philippe}, + journal={PloS one}, + volume={2}, + number={7}, + pages={e597}, + year={2007}, + publisher={Public Library of Science San Francisco, USA} +} + + +@article{Hagmann2010, + title={MR connectomics: principles and challenges}, + author={Hagmann, Patric and Cammoun, Leila and Gigandet, Xavier and Gerhard, Stephan and Grant, P Ellen and Wedeen, Van and Meuli, Reto and Thiran, Jean-Philippe and Honey, Christopher J and Sporns, Olaf}, + journal={Journal of neuroscience methods}, + volume={194}, + number={1}, + pages={34--45}, + year={2010}, + publisher={Elsevier} +} + + +@article{Griffa2013, + title={Structural connectomics in brain diseases}, + author={Griffa, Alessandra and Baumann, Philipp S and Thiran, Jean-Philippe and Hagmann, Patric}, + journal={Neuroimage}, + volume={80}, + pages={515--526}, + year={2013}, + publisher={Elsevier} +} + + +@article{Ozdemir2020, + title={Individualized perturbation of the human connectome reveals reproducible biomarkers of network dynamics relevant to cognition}, + author={Ozdemir, Recep A and Tadayon, Ehsan and Boucher, Pierre and Momi, Davide and Karakhanyan, Kelly A and Fox, Michael D and Halko, Mark A and Pascual-Leone, Alvaro and Shafi, Mouhsin M and Santarnecchi, Emiliano}, + journal={Proceedings of the National Academy of Sciences}, + volume={117}, + number={14}, + pages={8115--8125}, + year={2020}, + publisher={National Acad Sciences} +} + + +@article{Menardi2021, + title={Heritability of brain resilience to perturbation in humans}, + author={Menardi, Arianna and Reineberg, Andrew E and Vallesi, Antonino and Friedman, Naomi P and Banich, Marie T and Santarnecchi, Emiliano}, + journal={NeuroImage}, + volume={235}, + pages={118013}, + year={2021}, + publisher={Elsevier} +} + + +@article{Stam2014, + title={Modern network science of neurological disorders}, + author={Stam, Cornelis J}, + journal={Nature Reviews Neuroscience}, + volume={15}, + number={10}, + pages={683--695}, + year={2014}, + publisher={Nature Publishing Group UK London} +} + + + +@article{Bonomi2016, + title={Metainference: A Bayesian inference method for heterogeneous systems}, + author={Bonomi, Massimiliano and Camilloni, Carlo and Cavalli, Andrea and Vendruscolo, Michele}, + journal={Science advances}, + volume={2}, + number={1}, + pages={e1501177}, + year={2016}, + publisher={American Association for the Advancement of Science} +} + + +@article{Khalvati2019, + title={Modeling other minds: Bayesian inference explains human choices in group decision-making}, + author={Khalvati, Koosha and Park, Seongmin A and Mirbagheri, Saghar and Philippe, Remi and Sestito, Mariateresa and Dreher, Jean-Claude and Rao, Rajesh PN}, + journal={Science advances}, + volume={5}, + number={11}, + pages={eaax8783}, + year={2019}, + publisher={American Association for the Advancement of Science} +} + + +@article{Guimera2020, + title={A Bayesian machine scientist to aid in the solution of challenging scientific problems}, + author={Guimer{\`a}, Roger and Reichardt, Ignasi and Aguilar-Mogas, Antoni and Massucci, Francesco A and Miranda, Manuel and Pallar{\`e}s, Jordi and Sales-Pardo, Marta}, + journal={Science advances}, + volume={6}, + number={5}, + pages={eaav6971}, + year={2020}, + publisher={American Association for the Advancement of Science} +} + + +@article{Broderick2023, + title={Toward a taxonomy of trust for probabilistic machine learning}, + author={Broderick, Tamara and Gelman, Andrew and Meager, Rachael and Smith, Anna L and Zheng, Tian}, + journal={Science Advances}, + volume={9}, + number={7}, + pages={eabn3999}, + year={2023}, + publisher={American Association for the Advancement of Science} +} + + + +@article{Bates_Watts, + title = {Relative curvature measures of nonlinearity}, + volume = {42}, + number = {1}, + journal = {J R Stat Soc Ser B (Methodological)}, + author = {D.M. Bates and D.G. Watts}, + year = {1980}, + pages = {1--25}, +} + +@article{Rodriguez2006, + title = {Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems}, + volume = {7}, + journal = {BMC bioinformatics}, + author = {Rodriguez-Fernandez, Maria and Egea, Jose A. and Banga, Julio R.}, + year = {2006}, + pages = {483}, +} + + +@book{Constantine2015, + title={Active subspaces: Emerging ideas for dimension reduction in parameter studies}, + author={Constantine, Paul G}, + year={2015}, + publisher={SIAM} +} + + +@article{Schirner2020, +title = {Brain simulation as a cloud service: The Virtual Brain on EBRAINS}, +journal = {NeuroImage}, +volume = {251}, +pages = {118973}, +year = {2022}, +issn = {1053-8119}, +doi = {https://doi.org/10.1016/j.neuroimage.2022.118973}, +url = {https://www.sciencedirect.com/science/article/pii/S1053811922001021}, +author = {Michael Schirner and Lia Domide and Dionysios Perdikis and Paul Triebkorn and Leon Stefanovski and Roopa Pai and Paula Prodan and Bogdan Valean and Jessica Palmer and Chlo\^e Langford and Andr\'e Blickensd\"orfer and Michiel {van der Vlag} and Sandra Diaz-Pier and Alexander Peyser and Wouter Klijn and Dirk Pleiter and Anne Nahm and Oliver Schmid and Marmaduke Woodman and Lyuba Zehl and Jan Fousek and Spase Petkoski and Lionel Kusch and Meysam Hashemi and Daniele Marinazzo and Jean-Fran\c{c}ois Mangin and Agnes Fl\"oel and Simisola Akintoye and Bernd Carsten Stahl and Michael Cepic and Emily Johnson and Gustavo Deco and Anthony R. McIntosh and Claus C. Hilgetag and Marc Morgan and Bernd Schuller and Alex Upton and Colin McMurtrie and Timo Dickscheid and Jan G. Bjaalie and Katrin Amunts and Jochen Mersmann and Viktor Jirsa and Petra Ritter}, +} + + +@article{Baldy2023, + title={Hierarchical Bayesian Pharmacometrics Analysis of Baclofen for Alcohol Use Disorder}, + author={Baldy, Nina and Simon, Nicolas and Jirsa, Viktor and Hashemi, Meysam}, + journal={Machine Learning: Science and Technology}, + year={2023} +} + + +@article{Arbabyazd2020, + title={Dynamic functional connectivity as a complex random walk: definitions and the dFCwalk toolbox}, + author={Arbabyazd, Lucas M and Lombardo, Diego and Blin, Olivier and Didic, Mira and Battaglia, Demian and Jirsa, Viktor}, + journal={MethodsX}, + volume={7}, + pages={101168}, + year={2020}, + publisher={Elsevier} +} + + + + + +@article{Stumme2022, + title={Interrelating differences in structural and functional connectivity in the older adult's brain}, + author={Stumme, Johanna and Kr{\"a}mer, Camilla and Miller, Tatiana and Schreiber, Jan and Caspers, Svenja and Jockwitz, Christiane}, + journal={Human Brain Mapping}, + volume={43}, + number={18}, + pages={5543--5561}, + year={2022}, + publisher={Wiley Online Library} +} + +@article{Kramer2023, + title={Classification and prediction of cognitive performance differences in older age based on brain network patterns using a machine learning approach}, + author={Kr{\"a}mer, Camilla and Stumme, Johanna and da Costa Campos, Lucas and Rubbert, Christian and Caspers, Julian and Caspers, Svenja and Jockwitz, Christiane}, + journal={Network Neuroscience}, + volume={7}, + number={1}, + pages={122--147}, + year={2023}, + publisher={MIT Press} +} + +@article{Jockwitz2023, + title={Differential predictability of cognitive profiles from brain structure in older males and females}, + author={Jockwitz, Christiane and Kr{\"a}mer, Camilla and Dellani, Paulo and Caspers, Svenja}, + journal={GeroScience}, + pages={1--18}, + year={2023}, + publisher={Springer} +} + + +@article{Ghosh2008, + title={Noise during rest enables the exploration of the brain's dynamic repertoire}, + author={Ghosh, Anandamohan and Rho, Y and McIntosh, Anthony Randal and K{\"o}tter, Rolf and Jirsa, Viktor K}, + journal={PLoS computational biology}, + volume={4}, + number={10}, + pages={e1000196}, + year={2008}, + publisher={Public Library of Science San Francisco, USA} +} + + +@article{Lavanga2023, + title={The virtual aging brain: Causal inference supports interhemispheric dedifferentiation in healthy aging}, + author={Lavanga, Mario and Stumme, Johanna and Yalcinkaya, Bahar Hazal and Fousek, Jan and Jockwitz, Christiane and Sheheitli, Hiba and Bittner, Nora and Hashemi, Meysam and Petkoski, Spase and Caspers, Svenja and others}, + journal={NeuroImage}, + volume={283}, + pages={120403}, + year={2023}, + publisher={Elsevier} +} + + +@article{Baldy2024, + title={Inference on the Macroscopic Dynamics of Spiking Neurons}, + author={Baldy, Nina and Breyton, Martin and Woodman, Marmaduke M and Jirsa, Viktor K and Hashemi, Meysam}, + title = "{Inference on the Macroscopic Dynamics of Spiking Neurons}", + journal = {Neural Computation}, + pages = {1-43}, + year = {2024}, + month = {08}, + issn = {0899-7667}, + doi = {10.1162/neco_a_01701}, + url = {https://doi.org/10.1162/neco\_a\_01701}, +} + + + +@article{Arbabyazd2023, + title={State switching and high-order spatiotemporal organization of dynamic Functional Connectivity are disrupted by Alzheimer's Disease}, + author={Arbabyazd, Lucas and Petkoski, Spase and Breakspear, Michael and Solodkin, Ana and Battaglia, Demian and Jirsa, Viktor}, + journal={medRxiv}, + pages={2023--02}, + year={2023}, + publisher={Cold Spring Harbor Laboratory Press} +} + +@article{Kazemi2022, + title={On the influence of input triggering on the dynamics of the Jansen-Rit oscillators network}, + author={Kazemi, Sheida and Jamali, Yousef}, + journal={arXiv preprint arXiv:2202.06634}, + year={2022} +} + +@article{Wong2006, + title={A recurrent network mechanism of time integration in perceptual decisions}, + author={Wong, Kong-Fatt and Wang, Xiao-Jing}, + journal={Journal of Neuroscience}, + volume={26}, + number={4}, + pages={1314--1328}, + year={2006}, + publisher={Soc Neuroscience} +} + + +@article{Deco2013b, + title={Resting-state functional connectivity emerges from structurally and dynamically shaped slow linear fluctuations}, + author={Deco, Gustavo and Ponce-Alvarez, Adri{\'a}n and Mantini, Dante and Romani, Gian Luca and Hagmann, Patric and Corbetta, Maurizio}, + journal={Journal of Neuroscience}, + volume={33}, + number={27}, + pages={11239--11252}, + year={2013}, + publisher={Soc Neuroscience} +} + +@article{Deco2017, + title={The dynamics of resting fluctuations in the brain: metastability and its dynamical cortical core}, + author={Deco, Gustavo and Kringelbach, Morten L and Jirsa, Viktor K and Ritter, Petra}, + journal={Scientific reports}, + volume={7}, + number={1}, + pages={3095}, + year={2017}, + publisher={Nature Publishing Group UK London} +} + + +@article{Kong2021, + title={Sensory-motor cortices shape functional connectivity dynamics in the human brain}, + author={Kong, Xiaolu and Kong, Ru and Orban, Csaba and Wang, Peng and Zhang, Shaoshi and Anderson, Kevin and Holmes, Avram and Murray, John D and Deco, Gustavo and van den Heuvel, Martijn and others}, + journal={Nature communications}, + volume={12}, + number={1}, + pages={6373}, + year={2021}, + publisher={Nature Publishing Group UK London} +} + +@article{Glasser2011, + title={Mapping human cortical areas in vivo based on myelin content as revealed by T1-and T2-weighted MRI}, + author={Glasser, Matthew F and Van Essen, David C}, + journal={Journal of neuroscience}, + volume={31}, + number={32}, + pages={11597--11616}, + year={2011}, + publisher={Soc Neuroscience} +} + +@inproceedings{Gershman2014, + title={Amortized inference in probabilistic reasoning}, + author={Gershman, Samuel and Goodman, Noah}, + booktitle={Proceedings of the annual meeting of the cognitive science society}, + volume={36}, + number={36}, + year={2014} +} + +@article{Sorrentino2024, + title={The virtual multiple sclerosis patient}, + author={Sorrentino, P and Pathak, A and Ziaeemehr, A and Lopez, E Troisi and Cipriano, L and Romano, A and Sparaco, M and Quarantelli, M and Banerjee, A and Sorrentino, G and others}, + journal={iScience}, + volume={27}, + number={7}, + year={2024}, + publisher={Elsevier} +} + + +@article{Angiolelli2024, + title={The Virtual Parkinsonian Patient}, + author={Angiolelli, Marianna and Depannemaecker, Damien and Agouram, Hasnae and Regis, Jean and Carron, Romain and Woodman, Marmaduke and Chiodo, Letizia and Triebkorn, Paul and Ziaeemehr, Abolfazl and Hashemi, Meysam and others}, + journal={medRxiv}, + pages={2024--07}, + year={2024}, + publisher={Cold Spring Harbor Laboratory Press} +} + +@article{Wang2024, + title={Virtual brain twins: from basic neuroscience to clinical use}, + author={Wang, Huifang E and Triebkorn, Paul and Breyton, Martin and Dollomaja, Borana and Lemarechal, Jean-Didier and Petkoski, Spase and Sorrentino, Pierpaolo and Depannemaecker, Damien and Hashemi, Meysam and Jirsa, Viktor K}, + journal={National Science Review}, + volume={11}, + number={5}, + pages={nwae079}, + year={2024}, + publisher={Oxford University Press} +} + + +@article{Rabuffo2023, + title={On Global Brain Reconfiguration after Local Manipulations}, + author={Rabuffo, Giovanni and Lokossou, Houefa-Armelle and Li, Zengmin and Ziaee-Mehr, Abolfazl and Hashemi, Meysam and Quilichini, Pascale P and Ghestem, Antoine and Arab, Ouafae and Esclapez, Monique and Verma, Parul and others}, + journal={bioRxiv}, + pages={2023--09}, + year={2023}, + publisher={Cold Spring Harbor Laboratory} +} + + +@article{Deco2014b, + title={Great expectations: using whole-brain computational connectomics for understanding neuropsychiatric disorders}, + author={Deco, Gustavo and Kringelbach, Morten L}, + journal={Neuron}, + volume={84}, + number={5}, + pages={892--905}, + year={2014}, + publisher={Elsevier} +} + +@article{Lord2017, + title={Understanding principles of integration and segregation using whole-brain computational connectomics: implications for neuropsychiatric disorders}, + author={Lord, Louis-David and Stevner, Angus B and Deco, Gustavo and Kringelbach, Morten L}, + journal={Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences}, + volume={375}, + number={2096}, + pages={20160283}, + year={2017}, + publisher={The Royal Society Publishing} +} + + + +@article{Perl2023, + title={Model-based whole-brain perturbational landscape of neurodegenerative diseases}, + author={Perl, Yonatan Sanz and Fittipaldi, Sol and Campo, Cecilia Gonzalez and Moguilner, Sebasti{\'a}n and Cruzat, Josephine and Fraile-Vazquez, Matias E and Herzog, Rub{\'e}n and Kringelbach, Morten L and Deco, Gustavo and Prado, Pavel and others}, + journal={Elife}, + volume={12}, + pages={e83970}, + year={2023}, + publisher={eLife Sciences Publications Limited} +} + + +@article{Perl2023b, + title={Low-dimensional organization of global brain states of reduced consciousness}, + author={Perl, Yonatan Sanz and Pallavicini, Carla and Piccinini, Juan and Demertzi, Athena and Bonhomme, Vincent and Martial, Charlotte and Panda, Rajanikant and Alnagger, Naji and Annen, Jitka and Gosseries, Olivia and others}, + journal={Cell Reports}, + volume={42}, + number={5}, + year={2023}, + publisher={Elsevier} +} + + +@article{Mazzara2024, + title={Mapping brain lesions to conduction delays: the next step for personalized brain models in Multiple Sclerosis.}, + author={Mazzara, Camille and Ziaeemehr, Abolfazl and Troisi, Emahnuel Lopez and CIPRIANO, Lorenzo and Angiolelli, Marianna and Sparaco, Maddalena and Quarantelli, Mario and Granata, Carmine and Sorrentino, Giuseppe and Hashemi, Meysam and others}, + journal={medRxiv}, + pages={2024--07}, + year={2024}, + publisher={Cold Spring Harbor Laboratory Press} +} + + +@article{Deco2015, + title={Rethinking segregation and integration: contributions of whole-brain modelling}, + author={Deco, Gustavo and Tononi, Giulio and Boly, Melanie and Kringelbach, Morten L}, + journal={Nature Reviews Neuroscience}, + volume={16}, + number={7}, + pages={430--439}, + year={2015}, + publisher={Nature Publishing Group UK London} +} + + +@article{Idesis2022, + title={Inferring the dynamical effects of stroke lesions through whole-brain modeling}, + author={Idesis, Sebastian and Favaretto, Chiara and Metcalf, Nicholas V and Griffis, Joseph C and Shulman, Gordon L and Corbetta, Maurizio and Deco, Gustavo}, + journal={NeuroImage: Clinical}, + volume={36}, + pages={103233}, + year={2022}, + publisher={Elsevier} +} + +@article{Allegra2020, + title={Experimental and computational study on motor control and recovery after stroke: toward a constructive loop between experimental and virtual embodied neuroscience}, + author={Allegra Mascaro, Anna Letizia and Falotico, Egidio and Petkoski, Spase and Pasquini, Maria and Vannucci, Lorenzo and Tort-Colet, N{\'u}ria and Conti, Emilia and Resta, Francesco and Spalletti, Cristina and Ramalingasetty, Shravan Tata and others}, + journal={Frontiers in systems neuroscience}, + volume={14}, + pages={31}, + year={2020}, + publisher={Frontiers Media SA} +} + + +@article{Jung2023, + title={Whole-brain dynamical modelling for classification of Parkinson's disease}, + author={Jung, Kyesam and Florin, Esther and Patil, Kaustubh R and Caspers, Julian and Rubbert, Christian and Eickhoff, Simon B and Popovych, Oleksandr V}, + journal={Brain communications}, + volume={5}, + number={1}, + pages={fcac331}, + year={2023}, + publisher={Oxford University Press US} +} + +@article{Iravani2021, + title={Whole-brain modelling of resting state fMRI differentiates ADHD subtypes and facilitates stratified neuro-stimulation therapy}, + author={Iravani, Behzad and Arshamian, Artin and Fransson, Peter and Kaboodvand, Neda}, + journal={Neuroimage}, + volume={231}, + pages={117844}, + year={2021}, + publisher={Elsevier} +} + + +@article{Zhang2024, + title={In vivo whole-cortex marker of excitation-inhibition ratio indexes cortical maturation and cognitive ability in youth}, + author={Zhang, Shaoshi and Larsen, Bart and Sydnor, Valerie J and Zeng, Tianchu and An, Lijun and Yan, Xiaoxuan and Kong, Ru and Kong, Xiaolu and Gur, Ruben C and Gur, Raquel E and others}, + journal={Proceedings of the National Academy of Sciences}, + volume={121}, + number={23}, + pages={e2318641121}, + year={2024}, + publisher={National Acad Sciences} +} + +@article{Luppi2022, + title={Whole-brain modelling identifies distinct but convergent paths to unconsciousness in anaesthesia and disorders of consciousness}, + author={Luppi, Andrea I and Mediano, Pedro AM and Rosas, Fernando E and Allanson, Judith and Pickard, John D and Williams, Guy B and Craig, Michael M and Finoia, Paola and Peattie, Alexander RD and Coppola, Peter and others}, + journal={Communications biology}, + volume={5}, + number={1}, + pages={384}, + year={2022}, + publisher={Nature Publishing Group UK London} +} + +@article{Barttfeld2015, + title={Signature of consciousness in the dynamics of resting-state brain activity}, + author={Barttfeld, Pablo and Uhrig, Lynn and Sitt, Jacobo D and Sigman, Mariano and Jarraya, B{\'e}chir and Dehaene, Stanislas}, + journal={Proceedings of the National Academy of Sciences}, + volume={112}, + number={3}, + pages={887--892}, + year={2015}, + publisher={National Acad Sciences} +} + +@article{SBI-VBMs, + author={Hashemi, Meysam and Ziaeemehr, Abolfazl and Woodman, Marmaduke M. and Fousek, Jan and Petkoski, Spase and Jirsa, Viktor}, + title={Simulation-based Inference on Virtual Brain Models of Disorders}, + journal={Machine Learning: Science and Technology}, + url={http://iopscience.iop.org/article/10.1088/2632-2153/ad6230}, + year={2024}, + abstract={Connectome-based models, also known as Virtual Brain Models (VBMs), have been well established in network neuroscience to investigate pathophysiological causes underlying a large range of brain diseases. The integration of an individual's brain imaging data in VBMs has improved patient-specific predictivity, although Bayesian estimation of spatially distributed parameters remains challenging even with state-of-the-art Monte Carlo sampling. VBMs imply latent nonlinear state space models driven by noise and network input, necessitating advanced probabilistic machine learning techniques for widely applicable Bayesian estimation. Here we present Simulation-based Inference on Virtual Brain Models (SBI-VBMs), and demonstrate that training deep neural networks on both spatio-temporal and functional features allows for accurate estimation of generative parameters in brain disorders. The systematic use of brain stimulation provides an effective remedy for the non-identifiability issue in estimating the degradation limited to smaller subset of connections. By prioritizing model structure over data, we show that the hierarchical structure in SBI-VBMs renders the inference more effective, precise and biologically plausible. This approach could broadly advance precision medicine by enabling fast and reliable prediction of patient-specific brain disorders.} +} + +@article{Breakspear2017, + title={Dynamic models of large-scale brain activity}, + author={Breakspear, Michael}, + journal={Nature neuroscience}, + volume={20}, + number={3}, + pages={340--352}, + year={2017}, + publisher={Nature Publishing Group} +} + +@article{Ziaeemehr2020, + title={Frequency-dependent organization of the brain's functional network through delayed-interactions}, + author={Ziaeemehr, Abolfazl and Zarei, Mina and Valizadeh, Alireza and Mirasso, Claudio R}, + journal={Neural Networks}, + volume={132}, + pages={155--165}, + year={2020}, + publisher={Elsevier} +} + +@article{Ziaeemehr2021, + title={Frequency-resolved functional connectivity: role of delay and the strength of connections}, + author={Ziaeemehr, Abolfazl and Valizadeh, Alireza}, + journal={Frontiers in Neural Circuits}, + volume={15}, + pages={608655}, + year={2021}, + publisher={Frontiers Media SA} +} + + + +@article{Fulcher2017, + title={hctsa: A computational framework for automated time-series phenotyping using massive feature extraction}, + author={Fulcher, Ben D and Jones, Nick S}, + journal={Cell systems}, + volume={5}, + number={5}, + pages={527--531}, + year={2017}, + publisher={Elsevier} +} + +@article{Cliff2023, + title={Unifying pairwise interactions in complex dynamics}, + author={Cliff, Oliver M and Bryant, Annie G and Lizier, Joseph T and Tsuchiya, Naotsugu and Fulcher, Ben D}, + journal={Nature Computational Science}, + volume={3}, + number={10}, + pages={883--893}, + year={2023}, + publisher={Nature Publishing Group US New York} +} + +@article{Barandas2020, + title={TSFEL: Time series feature extraction library}, + author={Barandas, Mar{\'\i}lia and Folgado, Duarte and Fernandes, Let{\'\i}cia and Santos, Sara and Abreu, Mariana and Bota, Patr{\'\i}cia and Liu, Hui and Schultz, Tanja and Gamboa, Hugo}, + journal={SoftwareX}, + volume={11}, + pages={100456}, + year={2020}, + publisher={Elsevier} +} + +@article{Amunts2022, + title={Linking brain structure, activity, and cognitive function through computation}, + author={Amunts, Katrin and DeFelipe, Javier and Pennartz, Cyriel and Destexhe, Alain and Migliore, Michele and Ryvlin, Philippe and Furber, Steve and Knoll, Alois and Bitsch, Lise and Bjaalie, Jan G and others}, + journal={Eneuro}, + volume={9}, + number={2}, + year={2022}, + publisher={Society for Neuroscience} +} + +@article{Honey2010, + title={Can structure predict function in the human brain?}, + author={Honey, Christopher J and Thivierge, Jean-Philippe and Sporns, Olaf}, + journal={Neuroimage}, + volume={52}, + number={3}, + pages={766--776}, + year={2010}, + publisher={Elsevier} +} + +@article{Suarez2020, + title={Linking structure and function in macroscale brain networks}, + author={Su{\'a}rez, Laura E and Markello, Ross D and Betzel, Richard F and Misic, Bratislav}, + journal={Trends in cognitive sciences}, + volume={24}, + number={4}, + pages={302--315}, + year={2020}, + publisher={Elsevier} +} + + +@article{Sisson2007, + title={Sequential monte carlo without likelihoods}, + author={Sisson, Scott A and Fan, Yanan and Tanaka, Mark M}, + journal={Proceedings of the National Academy of Sciences}, + volume={104}, + number={6}, + pages={1760--1765}, + year={2007}, + publisher={National Acad Sciences} +} + +@article{Beaumont2009, + title={Adaptive approximate Bayesian computation}, + author={Beaumont, Mark A and Cornuet, Jean-Marie and Marin, Jean-Michel and Robert, Christian P}, + journal={Biometrika}, + volume={96}, + number={4}, + pages={983--990}, + year={2009}, + publisher={Oxford University Press} +} + + +@article{Luppi2023, + title={Computational modelling in disorders of consciousness: Closing the gap towards personalised models for restoring consciousness}, + author={Luppi, Andrea I and Cabral, Joana and Cofre, Rodrigo and Mediano, Pedro AM and Rosas, Fernando E and Qureshi, Abid Y and Kuceyeski, Amy and Tagliazucchi, Enzo and Raimondo, Federico and Deco, Gustavo and others}, + journal={NeuroImage}, + volume={275}, + pages={120162}, + year={2023}, + publisher={Elsevier} +} + +@article{Singh2020, + title={Estimation and validation of individualized dynamic brain models with resting state fMRI}, + author={Singh, Matthew F and Braver, Todd S and Cole, Michael W and Ching, ShiNung}, + journal={NeuroImage}, + volume={221}, + pages={117046}, + year={2020}, + publisher={Elsevier} +} + + + +@article{Liu2016, + title={Classification of Alzheimer's disease using whole brain hierarchical network}, + author={Liu, Jin and Li, Min and Lan, Wei and Wu, Fang-Xiang and Pan, Yi and Wang, Jianxin}, + journal={IEEE/ACM transactions on computational biology and bioinformatics}, + volume={15}, + number={2}, + pages={624--632}, + year={2016}, + publisher={IEEE} +} + + +@article{Patow2023, + title={Whole-brain modeling of the differential influences of amyloid-beta and tau in Alzheimer's disease}, + author={Patow, Gustavo and Stefanovski, Leon and Ritter, Petra and Deco, Gustavo and Kobeleva, Xenia and Alzheimer's Disease Neuroimaging Initiative}, + journal={Alzheimer's Research $\&$ Therapy}, + volume={15}, + number={1}, + pages={210}, + year={2023}, + publisher={Springer} +} + + +@article{Cook2022, + title={Neural Field Models: A mathematical overview and unifying framework}, + author={Cook, Blake J and Peterson, Andre DH and Woldman, Wessel and Terry, John R}, + journal={Mathematical Neuroscience and Applications}, + volume={2}, + year={2022}, + publisher={Episciences. org} +} + + +@article{Feng2024, + title={Spatial and temporal pattern of structure--function coupling of human brain connectome with development}, + author={Feng, Guozheng and Wang, Yiwen and Huang, Weijie and Chen, Haojie and Cheng, Jian and Shu, Ni}, + journal={eLife}, + volume={13}, + pages={RP93325}, + year={2024}, + publisher={eLife Sciences Publications Limited} +} + + +@article{Bazinet2023, + title={Towards a biologically annotated brain connectome}, + author={Bazinet, Vincent and Hansen, Justine Y and Misic, Bratislav}, + journal={Nature reviews neuroscience}, + volume={24}, + number={12}, + pages={747--760}, + year={2023}, + publisher={Nature Publishing Group UK London} +} + +@article{Tanner2024, + title={A multi-modal, asymmetric, weighted, and signed description of anatomical connectivity}, + author={Tanner, Jacob and Faskowitz, Joshua and Teixeira, Andreia Sofia and Seguin, Caio and Coletta, Ludovico and Gozzi, Alessandro and Mi{\v{s}}i{\'c}, Bratislav and Betzel, Richard F}, + journal={Nature Communications}, + volume={15}, + number={1}, + pages={5865}, + year={2024}, + publisher={Nature Publishing Group UK London} +} + + + +@article{Liu2023, + title={Parameter estimation for connectome generative models: Accuracy, reliability, and a fast parameter fitting method}, + author={Liu, Yuanzhe and Seguin, Caio and Mansour, Sina and Oldham, Stuart 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of cortico-cortical evoked potentials}, + author={Lemar{\'e}chal, Jean-Didier and Jedynak, Maciej and Trebaul, Lena and Boyer, Anthony and Tadel, Fran{\c{c}}ois and Bhattacharjee, Manik and Deman, Pierre and Tuyisenge, Viateur and Ayoubian, Leila and Hugues, Etienne and others}, + journal={Brain}, + volume={145}, + number={5}, + pages={1653--1667}, + year={2022}, + publisher={Oxford University Press} +} + +@article{Sorrentino2022, + title={Whole-brain propagation delays in multiple sclerosis, a combined tractography-magnetoencephalography study}, + author={Sorrentino, Pierpaolo and Petkoski, Spase and Sparaco, Maddalena and Lopez, E Troisi and Signoriello, Elisabetta and Baselice, Fabio and Bonavita, Simona and Pirozzi, Maria Agnese and Quarantelli, Mario and Sorrentino, Giuseppe and others}, + journal={Journal of Neuroscience}, + volume={42}, + number={47}, + pages={8807--8816}, + year={2022}, + publisher={Soc Neuroscience} +} + + +@article{Sermon2023sub, + title={Sub-harmonic entrainment of cortical gamma oscillations to deep brain stimulation in Parkinson's disease: model based predictions and validation in three human subjects}, + author={Sermon, James J and Olaru, Maria and Anso, Juan and Cernera, Stephanie and Little, Simon and Shcherbakova, Maria and Bogacz, Rafal and Starr, Philip A and Denison, Timothy and Duchet, Benoit}, + journal={Brain Stimulation}, + volume={16}, + number={5}, + pages={1412--1424}, + year={2023}, + publisher={Elsevier} +} + + +@article{Duchet2021average, + title={Average beta burst duration profiles provide a signature of dynamical changes between the ON and OFF medication states in Parkinson's disease}, + author={Duchet, Benoit and Ghezzi, Filippo and Weerasinghe, Gihan and Tinkhauser, Gerd and K{\"u}hn, Andrea A and Brown, Peter and Bick, Christian and Bogacz, Rafal}, + journal={PLoS computational biology}, + volume={17}, + number={7}, + pages={e1009116}, + year={2021}, + publisher={Public Library of Science San Francisco, CA USA} +} + + +@article{byrne2020next, + title={Next-generation neural mass and field modeling}, + author={Byrne, {\'A}ine and O'Dea, Reuben D and Forrester, Michael and Ross, James and Coombes, Stephen}, + journal={Journal of neurophysiology}, + volume={123}, + number={2}, + pages={726--742}, + year={2020}, + publisher={American Physiological Society Bethesda, MD} +} + + +@article{Breyton2023, + title={Spatiotemporal brain complexity quantifies consciousness outside of perturbation paradigms}, + author={Breyton, M and Fousek, J and Rabuffo, G and Sorrentino, P and Kusch, L and Massimini, M and Petkoski, S and Jirsa, V}, + journal={bioRxiv}, + pages={2023--04}, + year={2023}, + publisher={Cold Spring Harbor Laboratory} +} + + +@article{Hashemi2024VBT, + title={Principles and operation of virtual brain twins}, + author={Hashemi, Meysam and Depannemaecker, Damien and Saggio, Marisa and Triebkorn, Paul and Rabuffo, Giovanni and Fousek, Jan and Ziaeemehr, Abolfazl and Sip, Viktor and Athanasiadis, Anastasios and Breyton, Martin and others}, + journal={bioRxiv}, + pages={2024--10}, + year={2024}, + publisher={Cold Spring Harbor Laboratory} +} + + +@article{Park2013, + title={Structural and functional brain networks: from connections to cognition}, + author={Park, Hae-Jeong and Friston, Karl}, + journal={Science}, + volume={342}, + number={6158}, + pages={1238411}, + year={2013}, + publisher={American Association for the Advancement of Science} +} + + +@article{Jirsa2010, + title={Towards the virtual brain: network modeling of the intact and the damaged brain}, + author={Jirsa, Viktor and Sporns, Olaf and Breakspear, Michael and Deco, Gustavo and McIntosh, Anthony Randal}, + journal={Archives italiennes de biologie}, + volume={148}, + number={3}, + pages={189--205}, + year={2010} +} + + +@article{Triebkorn2022, + title={Brain simulation augments machine-learning--based classification of dementia}, + author={Triebkorn, Paul and Stefanovski, Leon and Dhindsa, Kiret and Diaz-Cortes, Margarita-Arimatea and Bey, Patrik and B{\"u}lau, Konstantin and Pai, Roopa and Spiegler, Andreas and Solodkin, Ana and Jirsa, Viktor and others}, + journal={Alzheimer's \& Dementia: Translational Research \& Clinical Interventions}, + volume={8}, + number={1}, + pages={e12303}, + year={2022}, + publisher={Wiley Online Library} +} + +@article{Stefanovski2019, + title={Linking molecular pathways and large-scale computational modeling to assess candidate disease mechanisms and pharmacodynamics in Alzheimer's disease}, + author={Stefanovski, Leon and Triebkorn, Paul and Spiegler, Andreas and Diaz-Cortes, Margarita-Arimatea and Solodkin, Ana and Jirsa, Viktor and McIntosh, Anthony Randal and Ritter, Petra and Alzheimer's Disease Neuroimaging Initiative}, + journal={Frontiers in computational neuroscience}, + volume={13}, + pages={54}, + year={2019}, + publisher={Frontiers Media SA} +} diff --git a/docs/requirements.txt b/docs/requirements.txt index 422a95d..54a2ca9 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -9,4 +9,5 @@ pandoc sphinx_rtd_theme myst_parser sphinx-autoapi -sphinxcontrib-napoleon \ No newline at end of file +sphinxcontrib-napoleon +sphinxcontrib-bibtex \ No newline at end of file diff --git a/vbi/models/cupy/jansen_rit.py b/vbi/models/cupy/jansen_rit.py index 3bc6a87..39e1f95 100644 --- a/vbi/models/cupy/jansen_rit.py +++ b/vbi/models/cupy/jansen_rit.py @@ -5,14 +5,11 @@ class JR_sde: - valid_parameters = [ - "weights", "delays", "dt", "t_end", "G", "A", "a", "B", "b", "mu", "SC", - "t_cut", "noise_amp", "C0", "C1", "C2", "C3", "dtype", "same_initial_state", - "C_vec", "decimate", "method", "same_noise_per_sim", - "vmax", "r", "v0", "output", "seed", "num_sim", "engine"] + def __init__(self, par: dict = {}): + self.valid_parameters = list(self.get_default_parameters().keys()) self.check_parameters(par) self._par = self.get_default_parameters() self._par.update(par) @@ -41,6 +38,19 @@ def __call__(self): return self._par def check_parameters(self, par): + """ + Check if the provided parameters are valid. + + Parameters + ---------- + par : dict + Dictionary of parameters to check. + + Raises + ------ + ValueError + If any parameter in `par` is not valid. + """ for key in par.keys(): if key not in self.valid_parameters: raise ValueError("Invalid parameter: " + key) @@ -116,9 +126,43 @@ def prepare_input(self): assert (self.t_cut < self.t_end), "t_cut must be smaller than t_end" def S_(self, x): + """ + Compute the sigmoid function. + + This function calculates the sigmoid of the input `x` using the parameters + `vmax`, `r`, and `v0`. + + Parameters + ---------- + x : float or array-like + The input value(s) for which to compute the sigmoid function. + + Returns + ------- + float or array-like + The computed sigmoid value(s). + + """ return self.vmax / (1.0 + self.xp.exp(self.r*(self.v0-x))) def f_sys(self, x0, t): + """ + Compute the derivatives of the Jansen-Rit neural mass model. + + Parameters + ---------- + x0 : array_like + Initial state vector of the system. It should have a shape of (6*nn, ns), where nn is the number of neurons and ns is the number of simulations. + t : float + Current time point (not used in the computation but required for compatibility with ODE solvers). + + Returns + ------- + dx : array_like + Derivatives of the state vector. It has the same shape as `x0`. + + The function computes the derivatives of the state vector based on the Jansen-Rit model equations. + """ nn = self.nn ns = self.num_sim @@ -161,6 +205,21 @@ def f_sys(self, x0, t): return dx def euler(self, x0, t): + """ + Perform one step of the Euler-Maruyama method for stochastic differential equations. + + Parameters + ---------- + x0 : array_like + The initial state of the system. + t : float + The current time. + + Returns + ------- + array_like + The updated state of the system after one Euler step. + """ _xp = self.xp nn = self.nn @@ -180,6 +239,21 @@ def euler(self, x0, t): return x0 def heun(self, x0, t): + """ + Perform a single step of the Heun's method for stochastic differential equations. + + Parameters + ---------- + x0 : ndarray + The initial state of the system. + t : float + The current time. + + Returns + ------- + ndarray + The updated state of the system after one Heun step. + """ nn = self.nn ns = self.num_sim diff --git a/vbi/models/cupy/utils.py b/vbi/models/cupy/utils.py index 14b88ea..f66ae17 100644 --- a/vbi/models/cupy/utils.py +++ b/vbi/models/cupy/utils.py @@ -7,11 +7,33 @@ def get_module(engine="gpu"): - ''' - to switch engine between gpu and cpu - ''' + """ + Switches the computational engine between GPU and CPU. + + Parameters + ---------- + engine : str, optional + The computational engine to use. Can be either "gpu" or "cpu". + Default is "gpu". + + Returns + ------- + module + The appropriate array module based on the specified engine. + If "gpu", returns the CuPy module. If "cpu", returns the NumPy module. + + Raises + ------ + ValueError + - If the specified engine is not "gpu" or "cpu". + - If CuPy is not installed. + """ + if engine == "gpu": - return cp.get_array_module(cp.array([1])) + if cp is None: + raise ValueError("CuPy is not installed.") + else: + return cp.get_array_module(cp.array([1])) else: return np # return cp.get_array_module(np.array([1])) @@ -139,6 +161,23 @@ def prepare_vec(x, ns, engine, dtype="float"): def get_(x, engine="cpu", dtype="f"): + """ + Parameters + ---------- + x : array-like + The input array to be converted. + engine : str, optional + The computation engine to use. If "gpu", the array is transferred from GPU to CPU. Defaults to "cpu". + dtype : str, optional + The desired data type for the output array. Defaults to "f". + + Returns + ------- + array-like + The converted array with the specified data type. + + """ + if engine == "gpu": return x.get().astype(dtype) else: From c27ea64fbe9683e7b720c1a889aa356bfc856613 Mon Sep 17 00:00:00 2001 From: Abolfazl Ziaeemehr Date: Wed, 19 Feb 2025 14:57:12 +0100 Subject: [PATCH 6/9] docs: Enhance Jansen-Rit model documentation and improve CSS styles --- docs/_static/custom.css | 8 ++ docs/models.rst | 134 ++++++++++++++++++++++--- vbi/models/cupy/jansen_rit.py | 178 +++++++++++++++++++++++++--------- 3 files changed, 264 insertions(+), 56 deletions(-) diff --git a/docs/_static/custom.css b/docs/_static/custom.css index 7df68d6..78b2dad 100644 --- a/docs/_static/custom.css +++ b/docs/_static/custom.css @@ -20,4 +20,12 @@ /* Change the background color of code blocks */ .highlight { background-color: #F0F8FF; /* Light blue */ +} + +.section { + background-color: #f0f8ff; /* Light blue background for sections */ +} + +.descclassname, .descname { + color: #327219; /* Green color for class and function names */ } \ No newline at end of file diff --git a/docs/models.rst b/docs/models.rst index eca485a..d2fd3ad 100644 --- a/docs/models.rst +++ b/docs/models.rst @@ -224,31 +224,141 @@ This biologically motivated model comprises of three main populations of neurons EP: excitatory populations, IP: inhibitory populations, PSP: post synaptic potential, PSPA: post synaptic potential amplitude. + +Montbri\'o model +---------------- + +The exact macroscopic dynamics of a specific brain region (represented as a node in the network) can be analytically derived in the thermodynamic limit of infinitely all-to-all coupled spiking neurons [Montbrio2015]_ or :math:`\Theta` neuron representation [Byrne2020next]_. By assuming a Lorentzian distribution on excitabilities in large ensembles of quadratic integrate-and-fire neurons with synaptic weights :math:`J` and a half-width :math:`\Delta` centered at :math:`\eta`, the macroscopic dynamics has been derived in terms of the collective firing activity and mean membrane potential [Montbrio2015]_. Then, by coupling the brain regions via an additive current (e.g., in the average membrane potential equations), the dynamics of the whole-brain network can be described as follows [Rabuffo2021]_, [Fousek2022]_: + +.. math:: + :label: eq:MPR + + \begin{aligned} + \tau\dot{r_i}(t) &= 2 r_i(t) v_i(t) + \dfrac{\Delta}{\pi \tau} \\[1ex] + \tau \dot{v_i}(t) &= v_i^2(t) - (\pi \tau r_i(t))^2 + J \tau r_i(t) + \eta + G \sum_{j=1}^{N} \text{SC}_{ij} r_{j}(t) + I_{\text{stim}}(t)+ \xi(t), + \end{aligned} + +where :math:`v_i` and :math:`r_i` are the average membrane potential and firing rate, respectively, at the :math:`i_{\text{th}}` brain region, and parameter :math:`G` is the network scaling parameter that modulates the overall impact of brain connectivity on the state dynamics. The :math:`\text{SC}_{ij}` denotes the connection weight between :math:`i_{\text{th}}` and :math:`j_{\text{th}}` regions, and the dynamical noise :math:`\xi(t) \sim \mathcal{N}(0, {\sigma}^2)` follows a Gaussian distribution with mean zero and variance :math:`\sigma^2`. + +The model parameters are tuned so that each decoupled node is in a bistable regime, exhibiting a down-state stable fixed point (low-firing rate) and an up-state stable focus (high-firing rate) in the phase-space [Montbrio2015]_, [Baldy2024]_. The bistability is a fundamental property of regional brain dynamics to ensure a switching behavior in the data (e.g., to generate FCD), that has been recognized as representative of realistic dynamics observed empirically [Rabuffo2021]_, [Breyton2023]_, [Fousek2024]_. + +The solution of the coupled system yields a neuroelectric dataset that describes the evolution of the variables :math:`(r_i(t), v_i(t))` in each brain region :math:`i`, providing measures of macroscopic activity. The surrogate BOLD activity for each region is then derived by filtering this activity through the Balloon-Windkessel model [Friston2000nonlinear]_. The input current :math:`I_{\text{stim}}` represents the stimulation to selected brain regions, which increase the basin of attraction of the up-state in comparison to the down-state, while the fixed points move farther apart [Rabuffo2021]_, [Breyton2023]_, [Fousek2024]_. + +The nominal parameter values and the prior range for the target parameters are summarized in the following table. + +.. list-table:: Parameter descriptions for capturing whole-brain dynamics using Montbri\'o model. + :widths: 25 25 15 15 + :header-rows: 1 + :name: table:MPR + :class: color-caption + + * - **Parameter** + - **Description** + - **Nominal value** + - **Prior** + * - :math:`\tau` + - Characteristic time constant + - 1 ms + - + * - :math:`J` + - Synaptic weight + - 14.5 :math:`\text{ms}^{-1}` + - + * - :math:`\Delta` + - Spread of the heterogeneous noise distribution + - 0.7 :math:`\text{ms}^{-1}` + - + * - :math:`I_{\text{stim}}(t)` + - Input current representing stimulation + - 0.0 + - + * - :math:`\sigma` + - Gaussian noise variance + - 0.037 + - + * - :math:`\eta` + - Excitability + - -4.6 + - :math:`\mathcal{U}(-6,-3.5)` + * - :math:`G` + - Scaling the strength of network connections + - 0.56 + - :math:`\mathcal{U}(0,1)` + + +Epileptor model +--------------- + +In personalized whole-brain network modeling of epilepsy spread [Jirsa2017]_, the dynamics of each brain region are governed by the Epileptor model [Jirsa2014]_. The Epileptor model provides a comprehensive description of epileptic seizures, encompassing the complete taxonomy of system bifurcations to simultaneously reproduce the dynamics of seizure onset, progression, and termination [Saggio2020]_. The full Epileptor model comprises five state variables that couple two oscillatory dynamical systems operating on three different time scales [Jirsa2014]_. Then motivated by Synergetic theory [Haken1997]_, [JirsaHaken1997]_ and under time-scale separation [Proix2014]_, the fast variables rapidly collapse on the slow manifold [McIntoshJirsa2019]_, whose dynamics is governed by the slow variable. This adiabatic approximation yields the 2D reduction of whole-brain model of epilepsy spread, also known as the Virtual Epileptic Patient (VEP) as follows: + +.. math:: + :label: eq:ReducednetVep + + \begin{aligned} + \dot{x_{i}} &= 1 - x_{i}^3 - 2 x_{i}^2 - z_{i} + I_{i} \\ + \dot{z_i} &= \dfrac{1}{\tau}(4 (x_{i} - \eta_{i}) - z_{i} - G \sum_{j=1}^{N} \text{SC}_{ij}(x_{j}-x_{i})), + \end{aligned} + +where :math:`x_i` and :math:`z_i` indicate the fast and slow variables corresponding to :math:`i_{\text{th}}` brain region, respectively, and the set of unknown :math:`\eta_i` is the spatial map of epileptogenicity to be estimated. In real-world epilepsy applications [Hashemi2021]_, [Hashemi2023]_, [Wang2023]_, we compute the envelope function from sEEG data to perform inference. The nominal parameter values and the prior range for the target parameters are summarized in the following table. + +.. list-table:: Parameter descriptions for capturing whole-brain dynamics using 2D Epileptor neural mass model. + :widths: 25 25 15 15 + :header-rows: 1 + :name: table:vep_parameters + + * - **Parameter** + - **Description** + - **Value** + - **Prior** + * - :math:`I` + - Input electric current + - 3.1 + - + * - :math:`\tau` + - System time constant + - 90 ms + - + * - :math:`\eta_i` + - Spatial map of epileptogenicity + - -3.65 + - :math:`\mathcal{U}(-5,-1)` + * - :math:`G` + - Global scaling factor on network connections + - 1.0 + - :math:`\mathcal{U}(0,2)` + + References ---------- .. [Wilson72] Wilson, H. R., & Cowan, J. D. (1972). Excitatory and inhibitory interactions in localized populations of model neurons. Biophysical Journal, 12(1), 1-24. - .. [Duchet2021average] Duchet, B., & Others. (2021). Average neural activity in Parkinson's disease. *Neuroscience Journal*. - .. [Sermon2023sub] Sermon, J., & Others. (2023). Subcortical effects of Parkinson's. *Brain Research*. - .. [Sanz-Leon2015] Sanz-Leon, P., Knock, S. A., Spiegler, A., & Jirsa, V. K. (2015). Mathematical framework for large-scale brain network modeling in The Virtual Brain. *NeuroImage, 111*, 385-430. https://doi.org/10.1016/j.neuroimage.2015.01.002 - .. [Pavlides2015] Pavlides, A., Hogan, S. J., & Bogacz, R. (2015). Computational models describing possible mechanisms for generation of excessive beta oscillations in Parkinson's disease. *PLoS Computational Biology, 11*(12)*, e1004609. https://doi.org/10.1371/journal.pcbi.1004609 - .. [Wei2015] Wei, W., Wang, X., & Chen, X. (2015). The role of indirect pathway in beta oscillation of basal ganglia-thalamocortical circuitry in Parkinson's disease. *Frontiers in Computational Neuroscience, 9*, 63. https://doi.org/10.3389/fncom.2015.00063 - .. [Jansen1995] Jansen, B. H., & Rit, V. G. (1995). Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns. *Biological Cybernetics*, 73(4), 357-366. - .. [Moran2007] Moran, R. J., Kiebel, S. J., Stephan, K. E., Reilly, R. B., Daunizeau, J., & Friston, K. J. (2007). A neural mass model of spectral responses in electrophysiology. *NeuroImage*, 37(3), 706-720. https://doi.org/10.1016/j.neuroimage.2007.05.032. - .. [Wendling2001] Wendling, F., Bartolomei, F., Bellanger, J.-J., & Chauvel, P. (2001). Interpretation of interdependencies in epileptic signals using a macroscopic physiological model of the EEG. *Clinical Neurophysiology*, 112(7), 1201-1218. - .. [David2003] David, O., & Friston, K. J. (2003). A neural mass model for MEG/EEG: coupling and neuronal dynamics. *NeuroImage*, 20(3), 1743-1755. https://doi.org/10.1016/j.neuroimage.2003.07.015. - .. [David_etal06] David, O., Kiebel, S. J., Harrison, L. M., Mattout, J., Kilner, J. M., & Friston, K. J. (2006). Dynamic causal modeling of evoked responses in EEG and MEG. *NeuroImage*, 30(4), 1255-1272. https://doi.org/10.1016/j.neuroimage.2005.10.045. - .. [JansenRit] Jansen, B. H., & Rit, V. G. (1995). Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns. *Biological Cybernetics*, 73, 357-366. - .. [Kazemi2022] Kazemi, S., & Jamali, Y. (2022). On the influence of input triggering on the dynamics of the Jansen-Rit oscillators network. *arXiv preprint arXiv:2202.06634*. +.. [Montbrio2015] Montbrio, E., et al. (2015). *Macroscopic description for networks of spiking neurons*. Physical Review X, 5(2), 021028. +.. [Byrne2020next] Byrne, A., et al. (2020). *Next generation neural mass models*. Journal of Neuroscience Methods, 340, 108746. +.. [Rabuffo2021] Rabuffo, Giovanni; Fousek, Jan; Bernard, Christophe; Jirsa, Viktor (2021). *Neuronal cascades shape whole-brain functional dynamics at rest*. ENeuro, 8(5), Society for Neuroscience. +.. [Fousek2022] Fousek, Jan; Rabuffo, Giovanni; Gudibanda, Kashyap; Sheheitli, Hiba; Jirsa, Viktor; Petkoski, Spase (2022). *The structured flow on the brain's resting state manifold*. bioRxiv, Cold Spring Harbor Laboratory. +.. [Baldy2024] Baldy, Nina; Breyton, Martin; Woodman, Marmaduke M; Jirsa, Viktor K; Hashemi, Meysam (2024). *Inference on the Macroscopic Dynamics of Spiking Neurons*. Neural Computation, 1-43, doi:10.1162/neco_a_01701. +.. [Breyton2023] Breyton, M; Fousek, J; Rabuffo, G; Sorrentino, P; Kusch, L; Massimini, M; Petkoski, S; Jirsa, V (2023). *Spatiotemporal brain complexity quantifies consciousness outside of perturbation paradigms*. bioRxiv, 2023-04, Cold Spring Harbor Laboratory. +.. [Fousek2024] Fousek, Jan; Rabuffo, Giovanni; Gudibanda, Kashyap; Sheheitli, Hiba; Petkoski, Spase; Jirsa, Viktor (2024). *Symmetry breaking organizes the brain's resting state manifold*. Scientific Reports, 14(1), 31970, Nature Publishing Group UK London. +.. [Friston2000nonlinear] Friston, Karl J; Mechelli, Andrea; Turner, Robert; Price, Cathy J (2000). *Nonlinear responses in fMRI: the Balloon model, Volterra kernels, and other hemodynamics*. NeuroImage, 12(4), 466-477, Elsevier. +.. [Jirsa2017] Jirsa, V.K.; Proix, T.; Perdikis, D.; Woodman, M.M.; Wang, H.; Gonzalez-Martinez, J.; Bernard, C.; Bénar, C.; Guye, M.; Chauvel, P.; Bartolomei, F. (2017). *The Virtual Epileptic Patient: Individualized whole-brain models of epilepsy spread*. NeuroImage, 145, 377-388, doi:https://doi.org/10.1016/j.neuroimage.2016.04.049. +.. [Saggio2020] Saggio, Maria Luisa; Crisp, Dakota; Scott, Jared M; Karoly, Philippa; Kuhlmann, Levin; Nakatani, Mitsuyoshi; Murai, Tomohiko; Dümpelmann, Matthias; Schulze-Bonhage, Andreas; Ikeda, Akio; Cook, Mark; Gliske, Stephen V; Lin, Jack; Bernard, Christophe; Jirsa, Viktor; Stacey, William C (2020). *A taxonomy of seizure dynamotypes*. eLife, 9, e55632, doi:10.7554/eLife.55632. +.. [Jirsa2014] Jirsa, Viktor K.; Stacey, William C.; Quilichini, Pascale P.; Ivanov, Anton I.; Bernard, Christophe (2014). *On the nature of seizure dynamics*. Brain, 137(8), 2210-2230, doi:10.1093/brain/awu133. +.. [Haken1997] Haken, Herman (1977). *Synergetics*. Physics Bulletin, 28(9), 412. +.. [JirsaHaken1997] Jirsa, Viktor K; Haken, Hermann (1997). *A derivation of a macroscopic field theory of the brain from the quasi-microscopic neural dynamics*. Physica D: Nonlinear Phenomena, 99(4), 503-526. +.. [McIntoshJirsa2019] McIntosh, Anthony R.; Jirsa, Viktor K. (2019). *The hidden repertoire of brain dynamics and dysfunction*. Network Neuroscience, 3(4), 994-1008, doi:10.1162/netn_a_00107. +.. [Proix2014] Proix, Timothée; Bartolomei, Fabrice; Chauvel, Patrick; Bernard, Christophe; Jirsa, Viktor K. (2014). *Permittivity Coupling across Brain Regions Determines Seizure Recruitment in Partial Epilepsy*. Journal of Neuroscience, 34(45), 15009-15021, doi:10.1523/JNEUROSCI.1570-14.2014. +.. [Hashemi2021] Hashemi, Meysam; Vattikonda, Anirudh N; Sip, Viktor; Diaz-Pier, Sandra; Peyser, Alexander; Wang, Huifang; Guye, Maxime; Bartolomei, Fabrice; Woodman, Marmaduke M; Jirsa, Viktor K (2021). *On the influence of prior information evaluated by fully Bayesian criteria in a personalized whole-brain model of epilepsy spread*. PLoS computational biology, 17(7), e1009129. +.. [Hashemi2023] Hashemi, Meysam; Vattikonda, Anirudh N; Jha, Jayant; Sip, Viktor; Woodman, Marmaduke M; Bartolomei, Fabrice; Jirsa, Viktor K (2023). *Amortized Bayesian inference on generative dynamical network models of epilepsy using deep neural density estimators*. Neural Networks, 163, 178-194. +.. [Wang2023] Wang, Huifang E; Woodman, Marmaduke; Triebkorn, Paul; Lemarechal, Jean-Didier; Jha, Jayant; Dollomaja, Borana; Vattikonda, Anirudh Nihalani; Sip, Viktor; Medina Villalon, Samuel; Hashemi, Meysam; et al. (2023). *Delineating epileptogenic networks using brain imaging data and personalized modeling in drug-resistant epilepsy*. Science Translational Medicine, 15(680), eabp8982. \ No newline at end of file diff --git a/vbi/models/cupy/jansen_rit.py b/vbi/models/cupy/jansen_rit.py index 39e1f95..3666ca3 100644 --- a/vbi/models/cupy/jansen_rit.py +++ b/vbi/models/cupy/jansen_rit.py @@ -4,8 +4,92 @@ class JR_sde: - + """ + Jansen-Rit model cupy implementation. + .. list-table:: Parameters + :widths: 25 50 25 + :header-rows: 1 + + * - Name + - Explanation + - Default Value + * - `A` + - Excitatory post synaptic potential amplitude. + - 3.25 + * - `B` + - Inhibitory post synaptic potential amplitude. + - 22.0 + * - `1/a` + - Time constant of the excitatory postsynaptic potential. + - a: 0.1 (1/a: 10.0) + * - `1/b` + - Time constant of the inhibitory postsynaptic potential. + - b: 0.05 (1/b: 20.0) + * - `C0`, `C1` + - Average numbers of synapses between excitatory populations. If array-like, it should be the shape of (`num_nodes`, `num_sim`). + - C0: 1.0 * 135.0, C1: 0.8 * 135.0 + * - `C2`, `C3` + - Average numbers of synapses between inhibitory populations. If array-like, it should be the shape of (`num_nodes`, `num_sim`). + - C2: 0.25 * 135.0, C3: 0.25 * 135.0 + * - `vmax` + - Maximum firing rate + - 0.005 + * - `v` + - Potential at half of maximum firing rate + - 6.0 + * - `r` + - Slope of sigmoid function at `v_0` + - 0.56 + * - `G` + - Scaling the strength of network connections. If array-like, it should of length `num_sim`. + - 1.0 + * - `mu` + - Mean of the noise + - 0.24 + * - `noise_amp` + - Amplitude of the noise + - 0.01 + * - `weights` + - Weight matrix of shape (`num_nodes`, `num_nodes`) + - None + * - `num_sim` + - Number of simulations + - 1 + * - `dt` + - Time step + - 0.01 + * - `t_end` + - End time of simulation + - 1000.0 + * - `t_cut` + - Cut time + - 500.0 + * - `engine` + - "cpu" or "gpu" + - "cpu" + * - `method` + - "heun" or "euler" method for integration + - "heun" + * - `seed` + - Random seed + - None + * - `initial_state` + - Initial state of the system of shape (`num_nodes`, `num_sim`) + - None + * - `same_initial_state` + - If True, all simulations have the same initial state + - False + * - `same_noise_per_sim` + - If True, all simulations have the same noise + - False + * - `decimate` + - Decimation factor for the output time series + - 1 + * - `dtype` + - Data type to use for the simulation, `float` for `float64` or `f` for `float32`. + - "float" + """ def __init__(self, par: dict = {}): @@ -24,15 +108,14 @@ def __init__(self, par: dict = {}): self.xp.random.seed(self.seed) def __str__(self) -> str: - print ("Jansen-Rit Model") - print ("----------------") + print("Jansen-Rit Model") + print("----------------") for item in self._par.items(): name = item[0] value = item[1] - print (f"{name} = {value}") + print(f"{name} = {value}") return "" - def __call__(self): print("Jansen-Rit Model") return self._par @@ -57,11 +140,16 @@ def check_parameters(self, par): def set_initial_state(self): self.initial_state = set_initial_state( - self.nn, self.num_sim, self.engine, self.seed, - self.same_initial_state, self.dtype) + self.nn, + self.num_sim, + self.engine, + self.seed, + self.same_initial_state, + self.dtype, + ) def get_default_parameters(self) -> dict: - ''' + """ Default parameters for the Jansen-Rit model Parameters @@ -73,7 +161,7 @@ def get_default_parameters(self) -> dict: ------- params : dict default parameters - ''' + """ params = { "G": 1.0, "A": 3.25, @@ -81,7 +169,7 @@ def get_default_parameters(self) -> dict: "v": 6.0, "r": 0.56, "v0": 6.0, - 'vmax': 0.005, + "vmax": 0.005, "C0": 1.0 * 135.0, "C1": 0.8 * 135.0, "C2": 0.25 * 135.0, @@ -103,15 +191,14 @@ def get_default_parameters(self) -> dict: "initial_state": None, "same_initial_state": False, "same_noise_per_sim": False, - } return params def prepare_input(self): - '''Prepare input parameters for the Jansen-Rit model.''' + """Prepare input parameters for the Jansen-Rit model.""" self.G = self.xp.array(self.G) - assert (self.weights is not None), "weights must be provided" + assert self.weights is not None, "weights must be provided" self.weights = self.xp.array(self.weights).T # ! check this self.weights = move_data(self.weights, self.engine) self.nn = self.num_nodes = self.weights.shape[0] @@ -123,7 +210,7 @@ def prepare_input(self): self.C1 = prepare_vec(self.C1, self.num_sim, self.engine, self.dtype) self.C2 = prepare_vec(self.C2, self.num_sim, self.engine, self.dtype) self.C3 = prepare_vec(self.C3, self.num_sim, self.engine, self.dtype) - assert (self.t_cut < self.t_end), "t_cut must be smaller than t_end" + assert self.t_cut < self.t_end, "t_cut must be smaller than t_end" def S_(self, x): """ @@ -143,7 +230,7 @@ def S_(self, x): The computed sigmoid value(s). """ - return self.vmax / (1.0 + self.xp.exp(self.r*(self.v0-x))) + return self.vmax / (1.0 + self.xp.exp(self.r * (self.v0 - x))) def f_sys(self, x0, t): """ @@ -185,22 +272,23 @@ def f_sys(self, x0, t): S = self.S_ x = x0[:nn, :] - y = x0[nn:2*nn, :] - z = x0[2*nn:3*nn, :] - xp = x0[3*nn:4*nn, :] - yp = x0[4*nn:5*nn, :] - zp = x0[5*nn:6*nn, :] + y = x0[nn : 2 * nn, :] + z = x0[2 * nn : 3 * nn, :] + xp = x0[3 * nn : 4 * nn, :] + yp = x0[4 * nn : 5 * nn, :] + zp = x0[5 * nn : 6 * nn, :] - dx = _xp.zeros((6*nn, ns)) - couplings = S(SC.dot(y-z)) + dx = _xp.zeros((6 * nn, ns)) + couplings = S(SC.dot(y - z)) dx[0:nn, :] = xp - dx[nn:2*nn, :] = yp - dx[2*nn:3*nn, :] = zp - dx[3*nn:4*nn, :] = Aa * S(y-z) - 2 * a * xp - aa * x - dx[4*nn:5*nn, :] = (Aa * (mu + C1 * S(C0 * x) + G * - couplings) - 2 * a * yp - aa * y) - dx[5*nn:6*nn, :] = Bb * C3 * S(C2 * x) - 2 * b * zp - bb * z + dx[nn : 2 * nn, :] = yp + dx[2 * nn : 3 * nn, :] = zp + dx[3 * nn : 4 * nn, :] = Aa * S(y - z) - 2 * a * xp - aa * x + dx[4 * nn : 5 * nn, :] = ( + Aa * (mu + C1 * S(C0 * x) + G * couplings) - 2 * a * yp - aa * y + ) + dx[5 * nn : 6 * nn, :] = Bb * C3 * S(C2 * x) - 2 * b * zp - bb * z return dx @@ -234,7 +322,7 @@ def euler(self, x0, t): dW = sqrt_dt * noise_amp * dW x0 = x0 + dt * self.f_sys(x0, t) - x0[4*nn:5*nn, :] += dW + x0[4 * nn : 5 * nn, :] += dW return x0 @@ -271,15 +359,15 @@ def heun(self, x0, t): k1 = self.f_sys(x0, t) * dt x1 = x0 + k1 - x1[4*nn:5*nn, :] += dW + x1[4 * nn : 5 * nn, :] += dW k2 = self.f_sys(x1, t + dt) * dt x0 = x0 + (k1 + k2) / 2.0 - x0[4*nn:5*nn, :] += dW + x0[4 * nn : 5 * nn, :] += dW return x0 def run(self, x0=None): - ''' + """ Simulate the Jansen-Rit model. Parameters @@ -297,11 +385,11 @@ def run(self, x0=None): x: array [n_step, num_nodes, num_sim] y1-y2 time series - ''' + """ self.prepare_input() x = self.initial_state if x0 is None else x0 - self.integrator = self.euler if self.method == 'euler' else self.heun + self.integrator = self.euler if self.method == "euler" else self.heun dt = self.dt _xp = self.xp nn = self.nn @@ -323,7 +411,7 @@ def run(self, x0=None): x_ = get_(x, self.engine, "f") if (i >= i_cut) and (i % decimate == 0): - y[ii, :, :] = x_[nn:2*nn, :] - x_[2*nn:3*nn, :] + y[ii, :, :] = x_[nn : 2 * nn, :] - x_[2 * nn : 3 * nn, :] ii += 1 t = get_(tspan[tspan >= t_cut][::decimate], self.engine, "f") @@ -331,8 +419,10 @@ def run(self, x0=None): return {"t": t, "x": y} -def set_initial_state(nn, ns, engine, seed=None, same_initial_state=False, dtype="float"): - ''' +def set_initial_state( + nn, ns, engine, seed=None, same_initial_state=False, dtype="float" +): + """ set initial state for the Jansen-Rit model Parameters @@ -355,18 +445,18 @@ def set_initial_state(nn, ns, engine, seed=None, same_initial_state=False, dtype y0: array [nn, ns] initial state - ''' + """ if seed is not None: np.random.seed(seed) if same_initial_state: - y0 = np.random.uniform(-1, 1, (nn,1)) - y1 = np.random.uniform(-500, 500, (nn,1)) - y2 = np.random.uniform(-50, 50, (nn,1)) - y3 = np.random.uniform(-6, 6, (nn,1)) - y4 = np.random.uniform(-20, 20, (nn,1)) - y5 = np.random.uniform(-500, 500, (nn,1)) + y0 = np.random.uniform(-1, 1, (nn, 1)) + y1 = np.random.uniform(-500, 500, (nn, 1)) + y2 = np.random.uniform(-50, 50, (nn, 1)) + y3 = np.random.uniform(-6, 6, (nn, 1)) + y4 = np.random.uniform(-20, 20, (nn, 1)) + y5 = np.random.uniform(-500, 500, (nn, 1)) y = np.vstack((y0, y1, y2, y3, y4, y5)) y = np.tile(y, (1, ns)) y = move_data(y, engine) From 55a892199a540315429ffc41719a3374fd086067 Mon Sep 17 00:00:00 2001 From: Abolfazl Ziaeemehr Date: Wed, 19 Feb 2025 15:01:07 +0100 Subject: [PATCH 7/9] docs: Add markdown header to Jansen-Rit CuPy example and update index --- docs/examples/jansen_rit_sde_cupy.ipynb | 7 +++++++ docs/index.rst | 1 + 2 files changed, 8 insertions(+) diff --git a/docs/examples/jansen_rit_sde_cupy.ipynb b/docs/examples/jansen_rit_sde_cupy.ipynb index f09a502..173c5be 100644 --- a/docs/examples/jansen_rit_sde_cupy.ipynb +++ b/docs/examples/jansen_rit_sde_cupy.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# [Jansen-Rit whole brain CuPy implementation](https://github.com/Ziaeemehr/vbi_paper/blob/main/docs/examples/jansen_rit_cupy.ipynb)\n" + ] + }, { "cell_type": "code", "execution_count": 11, diff --git a/docs/index.rst b/docs/index.rst index e543265..a90dd75 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -48,6 +48,7 @@ Examples examples/mpr_sde_cupy examples/mpr_sde_numba examples/jansen_rit_sde_cpp + examples/jansen_rit_sde_cupy examples/ww_sde_torch_kong examples/ghb_sde_cupy From f98a4fce796a0d8aa286dd7b8d7a56abbdb57fe0 Mon Sep 17 00:00:00 2001 From: Abolfazl Ziaeemehr Date: Wed, 19 Feb 2025 16:19:24 +0100 Subject: [PATCH 8/9] docs: Update CSS styles and modify index layout for improved presentation --- docs/_static/custom.css | 3 +- docs/index.rst | 10 +- docs/models.rst | 260 +++++++++++++++++++++++++++++++++------- 3 files changed, 225 insertions(+), 48 deletions(-) diff --git a/docs/_static/custom.css b/docs/_static/custom.css index 78b2dad..07c571c 100644 --- a/docs/_static/custom.css +++ b/docs/_static/custom.css @@ -28,4 +28,5 @@ .descclassname, .descname { color: #327219; /* Green color for class and function names */ -} \ No newline at end of file +} + diff --git a/docs/index.rst b/docs/index.rst index a90dd75..a11ec3f 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -9,7 +9,15 @@ Virtual Brain Inference (VBI) .. image:: _static/vbi_log.png :alt: VBI Logo - :width: 300px + :width: 200px + :align: center + +Workflow +======== + +.. image:: _static/Fig1.png + :alt: VBI Logo + :width: 800px Installation ============ diff --git a/docs/models.rst b/docs/models.rst index d2fd3ad..fa64119 100644 --- a/docs/models.rst +++ b/docs/models.rst @@ -46,110 +46,110 @@ The nominal parameter values and the prior range for the target parameters are s - Description - Value - Prior - * - T\ :sub:`SG` + * - :math:`T_{SG}` - Delay from STN to GPe - 6 ms - - * - T\ :sub:`GS` + * - :math:`T_{GS}` - Delay from GPe to STN - 6 ms - - * - T\ :sub:`GG` + * - :math:`T_{GG}` - Self delay of GPe - 4 ms - - * - T\ :sub:`CS` + * - :math:`T_{CS}` - Delay from cortex to STN - 5.5 ms - - * - T\ :sub:`SC` + * - :math:`T_{SC}` - Delay from STN to cortex - 21.5 ms - - * - T\ :sub:`CC` + * - :math:`T_{CC}` - Self delay of cortex - 4.65 ms - - * - τ\ :sub:`S` + * - :math:`\tau_{S}` - Time constant for STN - 12.8 ms - - * - τ\ :sub:`G` + * - :math:`\tau_{G}` - Time constant for GPe - 20 ms - - * - τ\ :sub:`E` + * - :math:`\tau_{E}` - Time constant for excitatory neurons - 11.59 ms - - * - τ\ :sub:`I` + * - :math:`\tau_{I}` - Time constant for inhibitory neurons - 13.02 ms - - * - M\ :sub:`S` + * - :math:`M_{S}` - Maximum firing rate of STN - 300 spk/s - - * - M\ :sub:`G` + * - :math:`M_{G}` - Maximum firing rate of GPe - 400 spk/s - - * - M\ :sub:`EI` + * - :math:`M_{EI}` - Maximum firing rate of excitatory neurons - 75.77 spk/s - - * - M\ :sub:`I` + * - :math:`M_{I}` - Maximum firing rate of inhibitory neurons - 205.72 spk/s - - * - B\ :sub:`S` + * - :math:`B_{S}` - Baseline firing rate of STN - 10 spk/s - - * - B\ :sub:`G` + * - :math:`B_{G}` - Baseline firing rate of GPe - 20 spk/s - - * - B\ :sub:`EI` + * - :math:`B_{EI}` - Baseline firing rate of excitatory neurons - 17.85 spk/s - - * - B\ :sub:`I` + * - :math:`B_{I}` - Baseline firing rate of inhibitory neurons - 9.87 spk/s - - * - C + * - :math:`C` - Excitatory input to cortex - 172.18 spk/s - - * - Str + * - :math:`Str` - Inhibitory input from striatum to GPe - 8.46 spk/s - - * - w\ :sub:`GS` + * - :math:`w_{GS}` - Synaptic weight from GPe to STN - 1.33 - - U(0,10) - * - w\ :sub:`SG` + - :math:`U(0,10)` + * - :math:`w_{SG}` - Synaptic weight from STN to GPe - 4.87 - - U(0,10) - * - w\ :sub:`GG` + - :math:`U(0,10)` + * - :math:`w_{GG}` - Self synaptic weight among GPe - 0.53 - - U(0,20) - * - w\ :sub:`CS` + - :math:`U(0,20)` + * - :math:`w_{CS}` - Synaptic weight from cortex to STN - 9.97 - - U(0,20) - * - w\ :sub:`SC` + - :math:`U(0,20)` + * - :math:`w_{SC}` - Synaptic weight from STN to cortex - 8.93 - - U(0,10) - * - w\ :sub:`CC` + - :math:`U(0,10)` + * - :math:`w_{CC}` - Self synaptic weight among cortex - 6.17 - - U(0,10) + - :math:`U(0,10)` Jansen-Rit model ---------------- @@ -177,50 +177,50 @@ This biologically motivated model comprises of three main populations of neurons - **Description** - **Value** - **Prior** - * - *A* + * - :math:`A` - Excitatory PSPA - 3.25 mV - - * - *B* + * - :math:`B` - Inhibitory PSPA - 22 mV - - * - *1/a* + * - :math:`1/a` - Time constant of excitatory PSP (*a* = 100 s\ :sup:`-1`) - - - * - *1/b* + * - :math:`1/b` - Time constant of inhibitory PSP (*b* = 50 s\ :sup:`-1`) - - - * - C\ :sub:`1`, C\ :sub:`2` + * - :math:`C_1, C_2` - Average numbers of synapses between EP - 1 C, 0.8 C - - * - C\ :sub:`3`, C\ :sub:`4` + * - :math:`C_3, C_4` - Average numbers of synapses between IP - 0.25 C - - * - v\ :sub:`max` + * - :math:`v_{max}` - Maximum firing rate - 5 Hz - - * - v\ :sub:`0` + * - :math:`v_0` - Potential at half of maximum firing rate - 6 mV - - * - r + * - :math:`r` - Slope of sigmoid function at *v\ :sub:`0`* - 0.56 mV\ :sup:`-1` - - * - C + * - :math:`C` - Average numbers of synapses between neural populations - 135 - - U(100, 500) - * - G + - :math:`U(100, 500)` + * - :math:`G` - Scaling the strength of network connections - 1.5 - - U(0, 5) + - :math:`U(0, 5)` EP: excitatory populations, IP: inhibitory populations, PSP: post synaptic potential, PSPA: post synaptic potential amplitude. @@ -327,6 +327,162 @@ where :math:`x_i` and :math:`z_i` indicate the fast and slow variables correspon - 1.0 - :math:`\mathcal{U}(0,2)` +Wong-Wang model +--------------- + +Another commonly used whole-brain model for simulation of neural activity is the so-called parameterized dynamics mean-field (pDMF) model [Hansen2015]_, [Kong2021]_, [Deco2013b]_. At each region, it comprises a simplified system of two non-linear coupled differential equations, motivated by the attractor network model, which integrates sensory information over time to make perceptual decisions, known as Wong-Wang model [Wong2006]_. +This biophysically realistic cortical network model of decision making then has been simplified further into a single-population model [Deco2013b]_, which has been widely used to understand the mechanisms underpinning brain resting state dynamics [Kong2021]_, [Deco2021]_, [Zhang2024]_. The pDMF model has been also used to study whole-brain dynamics in various brain disorders, including Alzheimer's disease [Monteverdi2023]_, schizophrenia [klein2021brain]_, and stroke [Klein2021]_. +The pDMF model equations are given as: + +.. math:: + + \frac{dS_i(t)}{dt} &= -\frac{S_i}{\tau_s} + (1 - S_i) \gamma H(x_i) + \sigma \xi_i(t) \\ + H(x_i) &= \frac{a x_i - b}{1 - \exp(-d(a x_i - b))} \\ + x_i &= w J S_i + GJ \sum_{j=1}^{N} \text{SC}_{ij} S_j + I + + +where :math:`H(x_i)` and :math:`S_i`, and :math:`x_i` denote the population firing rate, the average synaptic gating variable, and the total input current at the :math:`i_{th}` brain region, respectively. +:math:`\xi_i(t)` is uncorrelated standard Gaussian noise and the noise amplitude is controlled by :math:`\sigma`. +The nominal parameter values and the prior range for the target parameters are summarized in the following table. + +According to recent studies [Kong2021]_, [Zhang2024]_, we can parameterize the set of :math:`w`, :math:`I` and :math:`\sigma` as linear combinations of group-level T1w/T2w myelin maps [Glasser2011]_ and the first principal gradient of functional connectivity: + +.. math:: + + w_i &= a_w \textbf{Mye}_i + b_w \textbf{Grad}_i + c_w \\ + I_i &= a_I \textbf{Mye}_i + b_I \textbf{Grad}_i + c_I \\ + \sigma_i &= a_{\sigma} \textbf{Mye}_i + b_{\sigma} \textbf{Grad}_i + c_{\sigma} + + +.. list-table:: Parameter descriptions for capturing whole-brain dynamics using **Wong-Wang** model. + :widths: auto + :header-rows: 1 + :class: color-caption + + * - **Parameter** + - **Description** + - **Value** + - **Prior** + * - :math:`a` + - Max feeding rate of `H(x)` + - 270 n/C + - + * - :math:`b` + - Half saturation of `H(x)` + - 108 Hz + - + * - :math:`d` + - Control the steepness of curve of `H(x)` + - 0.154 s + - + * - :math:`\gamma` + - Kinetic parameter + - 0.641/1000 + - + * - :math:`\tau_s` + - Synaptic time constant + - 100 ms + - + * - :math:`J` + - Synaptic coupling + - 0.2609 nA + - + * - :math:`w` + - Local excitatory recurrence + - 0.6 + - :math:`\mathcal{U}(0,1)` + * - :math:`I` + - Overall effective external input + - 0.3 nA + - :math:`\mathcal{U}(0, 0.5)` + * - :math:`G` + - Scaling the strength of network connections + - 6.28 + - :math:`\mathcal{U}(1,10)` + * - :math:`\sigma` + - Noise amplitude + - 0.005 + - :math:`\mathcal{U}(0.0005, 0.01)` + + +The Balloon-Windkessel model +----------------------------- + +The Balloon-Windkessel model is a biophysical framework that links neural activity to the BOLD signals detected in fMRI. This is not a neuronal model but rather a representation of neurovascular coupling, describing how neural activity influences hemodynamic responses. The model is characterized by two state variables: venous blood volume (:math:`v`) and deoxyhemoglobin content (:math:`q`). The system's input is blood flow (:math:`f_{in}`), and the output is the BOLD signal (:math:`y`): + +.. math:: + + y(t) &= \lambda(v, q, E_0) = V_0 \big(k_1(1-q) + k_2(1-\frac{q}{v}) + k_3(1-v)\big) \\ + k_1 &= 4.3 \vartheta_0 E_0\, \mathit{TE} \\ + k_2 &= \varepsilon r_0 E_0 \, \mathit{TE} \\ + k_3 &= 1 - \varepsilon + +where :math:`V_0` represents the resting blood volume fraction, :math:`E_0` is the oxygen extraction fraction at rest, :math:`\epsilon` is the ratio of intra- to extravascular signals, :math:`r_0` is the slope of the relationship between the intravascular relaxation rate and oxygen saturation, :math:`\vartheta_0` is the frequency offset at the surface of a fully deoxygenated vessel at 1.5T, and :math:`\mathit{TE}` is the echo time. The dynamics of venous blood volume :math:`v` and deoxyhemoglobin content :math:`q` are governed by the Balloon model's hemodynamic state equations: + +.. math:: + + \tau_0 \frac{dv}{dt} &= f(t) - v(t)^{1/\alpha} \\ + \tau_0 \frac{dq}{dt} &= f(t) \frac{1-(1-E_0)^{1/f}}{E_0} - v(t)^{1/\alpha} q(t) + +where :math:`\tau_0` is the transit time of blood flow, :math:`\alpha` reflects the resistance of the venous vessel (stiffness), and :math:`f(t)` denotes blood inflow at time :math:`t`, given by + +.. math:: + + \frac{df}{dt} = s, + +where :math:`s` is an exponentially decaying vasodilatory signal defined by + +.. math:: + + \frac{ds}{dt} = \epsilon x - \frac{s}{\tau_s} - \frac{(f-1)}{\tau_f} + +where, :math:`\epsilon` represents the efficacy of neuronal activity :math:`x(t)` (i.e., integrated synaptic activity) in generating a signal increase, :math:`\tau_s` is the time constant for signal decay, and :math:`\tau_f` is the time constant for autoregulatory blood flow feedback [Friston2000nonlinear]_. For parameter values, see the following table, taken from [Friston2000nonlinear]_, [stephan2007comparing]_, [stephan2008nonlinear]_. The resulting time series is downsampled to match the `TR` value in seconds. + +.. list-table:: Parameter descriptions for the **Balloon-Windkessel** model to map neural activity to the BOLD signals detected in fMRI. + :widths: auto + :header-rows: 1 + :class: color-caption + + * - **Parameter** + - **Description** + - **Value** + * - :math:`\tau_s` + - Rate constant of vasodilatory signal decay in seconds + - 1.5 + * - :math:`\tau_f` + - Time of flow-dependent elimination in seconds + - 4.5 + * - :math:`\alpha` + - Grubb's vessel stiffness exponent + - 0.2 + * - :math:`\tau_0` + - Hemodynamic transit time in seconds + - 1.0 + * - :math:`\epsilon` + - Efficacy of synaptic activity to induce signal + - 0.1 + * - :math:`r_0` + - Slope of intravascular relaxation rate in Hertz + - 25.0 + * - :math:`\vartheta_0` + - Frequency offset at outer surface of magnetized vessels + - 40.3 + * - :math:`\varepsilon` + - Ratio of intra- and extravascular BOLD signal at rest + - 1.43 + * - :math:`V_0` + - Resting blood volume fraction + - 0.02 + * - :math:`E_0` + - Resting oxygen extraction fraction + - 0.8 + * - :math:`TE` + - Echo time, 1.5T scanner + - 0.04 + +.. _table:balloon: + + References ---------- @@ -361,4 +517,16 @@ References .. [Proix2014] Proix, Timothée; Bartolomei, Fabrice; Chauvel, Patrick; Bernard, Christophe; Jirsa, Viktor K. (2014). *Permittivity Coupling across Brain Regions Determines Seizure Recruitment in Partial Epilepsy*. Journal of Neuroscience, 34(45), 15009-15021, doi:10.1523/JNEUROSCI.1570-14.2014. .. [Hashemi2021] Hashemi, Meysam; Vattikonda, Anirudh N; Sip, Viktor; Diaz-Pier, Sandra; Peyser, Alexander; Wang, Huifang; Guye, Maxime; Bartolomei, Fabrice; Woodman, Marmaduke M; Jirsa, Viktor K (2021). *On the influence of prior information evaluated by fully Bayesian criteria in a personalized whole-brain model of epilepsy spread*. PLoS computational biology, 17(7), e1009129. .. [Hashemi2023] Hashemi, Meysam; Vattikonda, Anirudh N; Jha, Jayant; Sip, Viktor; Woodman, Marmaduke M; Bartolomei, Fabrice; Jirsa, Viktor K (2023). *Amortized Bayesian inference on generative dynamical network models of epilepsy using deep neural density estimators*. Neural Networks, 163, 178-194. -.. [Wang2023] Wang, Huifang E; Woodman, Marmaduke; Triebkorn, Paul; Lemarechal, Jean-Didier; Jha, Jayant; Dollomaja, Borana; Vattikonda, Anirudh Nihalani; Sip, Viktor; Medina Villalon, Samuel; Hashemi, Meysam; et al. (2023). *Delineating epileptogenic networks using brain imaging data and personalized modeling in drug-resistant epilepsy*. Science Translational Medicine, 15(680), eabp8982. \ No newline at end of file +.. [Wang2023] Wang, Huifang E; Woodman, Marmaduke; Triebkorn, Paul; Lemarechal, Jean-Didier; Jha, Jayant; Dollomaja, Borana; Vattikonda, Anirudh Nihalani; Sip, Viktor; Medina Villalon, Samuel; Hashemi, Meysam; et al. (2023). *Delineating epileptogenic networks using brain imaging data and personalized modeling in drug-resistant epilepsy*. Science Translational Medicine, 15(680), eabp8982. +.. [Hansen2015] Enrique C.A. Hansen, Demian Battaglia, Andreas Spiegler, Gustavo Deco, and Viktor K. Jirsa. "Functional connectivity dynamics: Modeling the switching behavior of the resting state." *NeuroImage*, 105:525-535, 2015. +.. [Kong2021] Xiaolu Kong, Ru Kong, Csaba Orban, Peng Wang, Shaoshi Zhang, Kevin Anderson, Avram Holmes, John D. Murray, Gustavo Deco, Martijn van den Heuvel, et al. "Sensory-motor cortices shape functional connectivity dynamics in the human brain." *Nature Communications*, 12(1):1-15, 2021. +.. [Deco2013b] Gustavo Deco, Adrián Ponce-Alvarez, Dante Mantini, Gian Luca Romani, Patric Hagmann, and Maurizio Corbetta. "Resting-state functional connectivity emerges from structurally and dynamically shaped slow linear fluctuations." *Journal of Neuroscience*, 33(27):11239-11252, 2013. +.. [Wong2006] Kong-Fatt Wong and Xiao-Jing Wang. "A recurrent network mechanism of time integration in perceptual decisions." *Journal of Neuroscience*, 26(4):1314-1328, 2006. +.. [Deco2021] Gustavo Deco, Morten L Kringelbach, Aurina Arnatkeviciute, Stuart Oldham, Kristina Sabaroedin, Nigel C Rogasch, Kevin M Aquino, Alex Fornito. "Dynamical consequences of regional heterogeneity in the brain's transcriptional landscape." *Science Advances*, 7(29):eabf4752, 2021. +.. [Zhang2024] Shaoshi Zhang, Bart Larsen, Valerie J Sydnor, Tianchu Zeng, Lijun An, Xiaoxuan Yan, Ru Kong, Xiaolu Kong, Ruben C Gur, Raquel E Gur, et al. "In vivo whole-cortex marker of excitation-inhibition ratio indexes cortical maturation and cognitive ability in youth." *Proceedings of the National Academy of Sciences*, 121(23):e2318641121, 2024. +.. [Monteverdi2023] Anita Monteverdi, Fulvia Palesi, Michael Schirner, Francesca Argentino, Mariateresa Merante, Alberto Redolfi, Francesca Conca, Laura Mazzocchi, Stefano F Cappa, Matteo Cotta Ramusino, et al. "Virtual brain simulations reveal network-specific parameters in neurodegenerative dementias." *Frontiers in Aging Neuroscience*, 15:1204134, 2023. +.. [Klein2021] Pedro Costa Klein, Ulrich Ettinger, Michael Schirner, Petra Ritter, Dan Rujescu, Peter Falkai, Nikolaos Koutsouleris, Lana Kambeitz-Ilankovic, Joseph Kambeitz. "Brain network simulations indicate effects of neuregulin-1 genotype on excitation-inhibition balance in cortical dynamics." *Cerebral Cortex*, 31(4):2013-2025, 2021. +.. [Glasser2011] Matthew F Glasser, David C Van Essen. "Mapping human cortical areas in vivo based on myelin content as revealed by T1-and T2-weighted MRI." *Journal of Neuroscience*, 31(32):11597-11616, 2011. +.. [stephan2007comparing] Klaas Enno Stephan, Nikolaus Weiskopf, Peter M Drysdale, Peter A Robinson, Karl J Friston. "Comparing hemodynamic models with DCM." *Neuroimage*, 38(3):387-401, 2007. +.. [stephan2008nonlinear] Klaas Enno Stephan, Lars Kasper, Lee M Harrison, Jean Daunizeau, Hanneke EM den Ouden, Michael Breakspear, Karl J Friston. "Nonlinear dynamic causal models for fMRI." *Neuroimage*, 42(2):649-662, 2008. +.. [klein2021brain] Pedro Costa Klein, Ulrich Ettinger, Michael Schirner, Petra Ritter, Dan Rujescu, Peter Falkai, Nikolaos Koutsouleris, Lana Kambeitz-Ilankovic, Joseph Kambeitz. "Brain network simulations indicate effects of neuregulin-1 genotype on excitation-inhibition balance in cortical dynamics." *Cerebral Cortex*, 31(4):2013-2025, 2021. \ No newline at end of file From 3bbcdbd48eca6421f86fdc6f1be6acfa913ba368 Mon Sep 17 00:00:00 2001 From: Abolfazl Ziaeemehr Date: Wed, 19 Feb 2025 16:21:48 +0100 Subject: [PATCH 9/9] ci: Comment out publish workflow steps in GitHub Actions --- .github/workflows/publish.yml | 82 +++++++++++++++++------------------ 1 file changed, 41 insertions(+), 41 deletions(-) diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 6c2b17c..cc14161 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -1,41 +1,41 @@ -name: Publish to PyPI - -on: - push: - branches: - - develop - tags: - - "v*" - -jobs: - deploy: - runs-on: ubuntu-latest - - steps: - - name: Checkout repository - uses: actions/checkout@v4 - - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: "3.10" - - - name: Install build dependencies - run: | - python -m pip install --upgrade pip - pip install build twine - - - name: Compile C++ code - run: | - cd vbi/models/cpp/_src - make - - - name: Build package - run: python -m build - - - name: Publish to PyPI - if: startsWith(github.ref, 'refs/tags/v') - env: - TWINE_USERNAME: __token__ - TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }} - run: twine upload dist/* +# name: Publish to PyPI + +# on: +# push: +# branches: +# - develop +# tags: +# - "v*" + +# jobs: +# deploy: +# runs-on: ubuntu-latest + +# steps: +# - name: Checkout repository +# uses: actions/checkout@v4 + +# - name: Set up Python +# uses: actions/setup-python@v5 +# with: +# python-version: "3.10" + +# - name: Install build dependencies +# run: | +# python -m pip install --upgrade pip +# pip install build twine + +# - name: Compile C++ code +# run: | +# cd vbi/models/cpp/_src +# make + +# - name: Build package +# run: python -m build + +# - name: Publish to PyPI +# if: startsWith(github.ref, 'refs/tags/v') +# env: +# TWINE_USERNAME: __token__ +# TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }} +# run: twine upload dist/*