adhd200_pubs.bib

@article{Wang2017,
author={Wang, Jian-Bao and Zheng, Li-Jun and Cao, Qing-Jiu and Wang, Yu-Feng and Sun, Li and Zang, Yu-Feng and Zhang, Hang},
title={Inconsistency in Abnormal Brain Activity across Cohorts of ADHD-200 in Children with Attention Deficit Hyperactivity Disorder},
journal={Frontiers in Neuroscience},
volume={11},
pages={320},
doi={10.3389/fnins.2017.00320},
issn={1662-453X},
url={http://journal.frontiersin.org/article/10.3389/fnins.2017.00320},
year={2017},
}
@article{Milham2017,
author={Milham, Michael P. and Craddock, R. Cameron and Klein, Arno },
title={Clinically useful brain imaging for neuropsychiatry: How can we get there?},
journal={Depression and Anxiety},
volume={34},
number={7},
pages={578–-587},
doi={10.1002/da.22627},
issn={1662-453X},
url={http://dx.doi.org/10.1002/da.22627},
year={2017},
}
@article{Macko2017,
author={Macko, Marek and Szczepanski, Zbigniew and Mikolajewska, Emilia and Nowak, Joanna and Mikolajewski, Dariusz},
title={Repository of 3D images for education and everyday clinical practice purposes},
journal={Bio-Algorithms and Med-Systems},
volume={13},
number={2},
pages={111--116},
doi={https://doi.org/10.1515/bams-2017-0007},
issn={1896-530X},
url={https://www.degruyter.com/view/j/bams.2017.13.issue-2/bams-2017-0007/bams-2017-0007.xml},
year={2017},
}
@article{Abraham2017,
author={Alexandre Abraham and Michael P. Milham and Adriana Di Martino and R. Cameron Craddock and Dimitris Samaras and Bertrand Thirion and Gael Varoquaux},
title={Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example},
journal={NeuroImage},
volume={147},
pages={736--745},
doi={http://dx.doi.org/10.1016/j.neuroimage.2016.10.045},
issn={1053-8119},
url={http://www.sciencedirect.com/science/article/pii/S1053811916305924},
year={2017},
}
@inproceedings{7965985,
author={B. Zhang and H. Zhou and L. Wang and C. Sung},
booktitle={2017 International Joint Conference on Neural Networks (IJCNN)},
title={Classification based on neuroimaging data by tensor boosting},
year={2017},
pages={1174-1179},
keywords={Boosting;Electroencephalography;Feature extraction;Magnetic resonance imaging;Neuroimaging;Principal component analysis;Tensile stress},
doi={10.1109/IJCNN.2017.7965985},
month={May},}
@article{Bellec2017,
title = "The Neuro Bureau ADHD-200 Preprocessed repository",
journal = "NeuroImage",
volume = "144, Part B",
number = "",
pages = "275 - 286",
year = "2017",
note = "Data Sharing Part \{II\}",
issn = "1053-8119",
doi = "http://dx.doi.org/10.1016/j.neuroimage.2016.06.034",
url = "http://www.sciencedirect.com/science/article/pii/S105381191630283X",
author = "Pierre Bellec and Carlton Chu and François Chouinard-Decorte and Yassine Benhajali and Daniel S. Margulies and R. Cameron Craddock",
keywords = "Preprocessed fMRI",
keywords = "Data sharing",
keywords = "Neuroimaging competition "
}


@mastersthesis{VanGalenLast2011,
author = {{van Galen Last}, Niels Arjan},
file = {:Users/cameron.craddock/Documents/papers/van Galen Last - 2011 - Cortical Parcellation and Classification using PageRank Clustering and the Small-Worldness of ADHD.pdf:pdf},
keywords = {Data Science},
mendeley-tags = {Data Science},
pages = {105},
school = {Delft University of Technolgy},
title = {{Cortical Parcellation and Classification using PageRank Clustering and the Small-Worldness of ADHD}},
type = {Master's Thesis},
url = {uuid:f352d7fb-5316-448b-ab58-8aeb3a45e8e5},
year = {2011}
}
@preprint{Ji2011,
abstract = {To uncover the underlying mechanisms of mental disorders such as attention deficit hyperactivity disorder (ADHD) for improving both early diagnosis and therapy, it is increasingly recognized that we need a better understanding of how the brain's functional connections are altered. A new brain wide association study (BWAS) has been developed and used to investigate functional connectivity changes in the brains of patients suffering from ADHD using resting state fMRI data. To reliably find out the most significantly altered functional connectivity links and associate them with ADHD, a meta-analysis on a cohort of ever reported largest population comprising 249 patients and 253 healthy controls is carried out. The greatest change in ADHD patients was the increased coupling of the saliency network involving the anterior cingulate gyrus and anterior insula. A voxel-based morphometry analysis was also carried out but this revealed no evidence in the ADHD patients for altered grey matter volumes in the regions showing altered functional connectivity. This is the first evidence for the involvement of the saliency network in ADHD and it suggests that this may reflect increased sensitivity over the integration of the incoming sensory information and his/her own thoughts and the network as a switch is bias towards to the central executive network.},
archivePrefix = {arXiv},
arxivId = {1112.3496},
author = {Ji, Xiaoxi and Cheng, Wei and Zhang, Jie and Ge, Tian and Sun, Li and Wang, Yufeng and Feng, Jianfeng},
eprint = {1112.3496},
journal = {ArXiv e-prints},
keywords = {arxiv},
mendeley-tags = {Neurobiology},
month = {dec},
title = {{Increased Coupling in the Saliency Network is the main cause/effect of Attention Deficit Hyperactivity Disorder}},
url = {http://arxiv.org/abs/1112.3496},
year = {2011}
}
@phdthesis{Zhang2012,
author = {Zhang, Bo},
file = {:Users/cameron.craddock/Documents/papers/Zhang - 2012 - Dimension Reduction and Classification for High Dimensional Complex Data.pdf:pdf},
pages = {108},
mendeley-tags = {Data Science},
school = {North Carolina State University},
title = {{Dimension Reduction and Classification for High Dimensional Complex Data}},
type = {PhD Dissertation},
url = {http://www.lib.ncsu.edu/resolver/1840.16/9022},
year = {2012}
}
@article{Takahashi2012,
abstract = {The brain's structural and functional systems, protein-protein interaction, and gene networks are examples of biological systems that share some features of complex networks, such as highly connected nodes, modularity, and small-world topology. Recent studies indicate that some pathologies present topological network alterations relative to norms seen in the general population. Therefore, methods to discriminate the processes that generate the different classes of networks (e.g., normal and disease) might be crucial for the diagnosis, prognosis, and treatment of the disease. It is known that several topological properties of a network (graph) can be described by the distribution of the spectrum of its adjacency matrix. Moreover, large networks generated by the same random process have the same spectrum distribution, allowing us to use it as a "fingerprint". Based on this relationship, we introduce and propose the entropy of a graph spectrum to measure the "uncertainty" of a random graph and the Kullback-Leibler and Jensen-Shannon divergences between graph spectra to compare networks. We also introduce general methods for model selection and network model parameter estimation, as well as a statistical procedure to test the nullity of divergence between two classes of complex networks. Finally, we demonstrate the usefulness of the proposed methods by applying them to (1) protein-protein interaction networks of different species and (2) on networks derived from children diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) and typically developing children. We conclude that scale-free networks best describe all the protein-protein interactions. Also, we show that our proposed measures succeeded in the identification of topological changes in the network while other commonly used measures (number of edges, clustering coefficient, average path length) failed.},
author = {Takahashi, Daniel Yasumasa and Sato, João Ricardo and Ferreira, Carlos Eduardo and Fujita, Andr{\'{e}}},
issn = {1932-6203},
journal = {PloS one},
keywords = {Attention Deficit Disorder with Hyperactivity,Attention Deficit Disorder with Hyperactivity: dia,Attention Deficit Disorder with Hyperactivity: met,Child,Cluster Analysis,Computational Biology,Computational Biology: methods,Computer Graphics,General Science,Humans,Magnetic Resonance Imaging,Protein Interaction Maps,ROC Curve,adhd200 preprc},
mendeley-tags = {General Science},
month = {jan},
number = {12},
pages = {e49949},
title = {{Discriminating different classes of biological networks by analyzing the graphs spectra distribution}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3526608{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {7},
year = {2012}
}
@inproceedings{Solmaz2012,
abstract = {Attention Deficit Hyperactivity Disorder (ADHD) is receiving lots of attention nowadays mainly because it is one of the common brain disorders among children and not much information is known about the cause of this disorder. In this study, we propose to use a novel approach for automatic classification of ADHD conditioned subjects and control subjects using functional Magnetic Resonance Imaging (fMRI) data of resting state brains. For this purpose, we compute the correlation between every possible voxel pairs within a subject and over the time frame of the experimental protocol. A network of voxels is constructed by representing a high correlation value between any two voxels as an edge. A Bag-of-Words (BoW) approach is used to represent each subject as a histogram of network features; such as the number of degrees per voxel. The classification is done using a Support Vector Machine (SVM). We also investigate the use of raw intensity values in the time series for each voxel. Here, every subject is represented as a combined histogram of network and raw intensity features. Experimental results verified that the classification accuracy improves when the combined histogram is used. We tested our approach on a highly challenging dataset released by NITRC for ADHD-200 competition and obtained promising results. The dataset not only has a large size but also includes subjects from different demography and edge groups. To the best of our knowledge, this is the first paper to propose BoW approach in any functional brain disorder classification and we believe that this approach will be useful in analysis of many brain related conditions.},
author = {Solmaz, Berkan and Dey, Soumyabrata and Rao, A. Ravishankar and Shah, Mubarak},
booktitle = {Medical Imaging 2012: Image Processing. Edited by Haynor},
editor = {Haynor, David R. and Ourselin, S{\'{e}}bastien},
keywords = {Data Science,adhd200 preprc},
mendeley-tags = {Data Science},
month = {feb},
pages = {83144T},
title = {{ADHD classification using bag of words approach on network features}},
url = {http://adsabs.harvard.edu/abs/2012SPIE.8314E.164S},
volume = {8314},
year = {2012}
}
@article{Sato2012a,
abstract = {Attention Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder, being one of the most prevalent psychiatric disorders in childhood. The neural substrates associated with this condition, both from structural and functional perspectives, are not yet well established. Recent studies have highlighted the relevance of neuroimaging not only to provide a more solid understanding about the disorder but also for possible clinical support. The ADHD-200 Consortium organized the ADHD-200 global competition making publicly available, hundreds of structural magnetic resonance imaging (MRI) and functional MRI (fMRI) datasets of both ADHD patients and typically developing (TD) controls for research use. In the current study, we evaluate the predictive power of a set of three different feature extraction methods and 10 different pattern recognition methods. The features tested were regional homogeneity (ReHo), amplitude of low frequency fluctuations (ALFF), and independent components analysis maps (resting state networks; RSN). Our findings suggest that the combination ALFF+ReHo maps contain relevant information to discriminate ADHD patients from TD controls, but with limited accuracy. All classifiers provided almost the same performance in this case. In addition, the combination ALFF+ReHo+RSN was relevant in combined vs. inattentive ADHD classification, achieving a score accuracy of 67{\%}. In this latter case, the performances of the classifiers were not equivalent and L2-regularized logistic regression (both in primal and dual space) provided the most accurate predictions. The analysis of brain regions containing most discriminative information suggested that in both classifications (ADHD vs. TD controls and combined vs. inattentive), the relevant information is not confined only to a small set of regions but it is spatially distributed across the whole brain.},
author = {Sato, João Ricardo and Hoexter, Marcelo Queiroz and Fujita, Andr{\'{e}} and Rohde, Luis Augusto},
doi = {10.3389/fnsys.2012.00068},
file = {:Users/cameron.craddock/Documents/papers/Sato et al. - 2012 - Evaluation of pattern recognition and feature extraction methods in ADHD prediction.pdf:pdf},
issn = {1662-5137},
journal = {Frontiers in systems neuroscience},
keywords = {Neurobiology,adhd200 preprc},
mendeley-tags = {Neurobiology},
month = {jan},
pages = {68},
pmid = {23015782},
title = {{Evaluation of pattern recognition and feature extraction methods in ADHD prediction}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3449288{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {6},
year = {2012}
}
@article{Sato2012,
abstract = {Studies based on functional magnetic resonance imaging (fMRI) during the resting state have shown decreased functional connectivity between the dorsal anterior cingulate cortex (dACC) and regions of the Default Mode Network (DMN) in adult patients with Attention-Deficit/Hyperactivity Disorder (ADHD) relative to subjects with typical development (TD). Most studies used Pearson correlation coefficients among the BOLD signals from different brain regions to quantify functional connectivity. Since the Pearson correlation analysis only provides a limited description of functional connectivity, we investigated functional connectivity between the dACC and the posterior cingulate cortex (PCC) in three groups (adult patients with ADHD, n=21; TD age-matched subjects, n=21; young TD subjects, n=21) using a more comprehensive analytical approach - unsupervised machine learning using a one-class support vector machine (OC-SVM) that quantifies an abnormality index for each individual. The median abnormality index for patients with ADHD was greater than for TD age-matched subjects (p=0.014); the ADHD and young TD indices did not differ significantly (p=0.480); the median abnormality index of young TD was greater than that of TD age-matched subjects (p=0.016). Low frequencies below 0.05 Hz and around 0.20 Hz were the most relevant for discriminating between ADHD patients and TD age-matched controls and between the older and younger TD subjects. In addition, we validated our approach using the fMRI data of children publicly released by the ADHD-200 Competition, obtaining similar results. Our findings suggest that the abnormal coherence patterns observed in patients with ADHD in this study resemble the patterns observed in young typically developing subjects, which reinforces the hypothesis that ADHD is associated with brain maturation deficits.},
author = {Sato, João Ricardo and Hoexter, Marcelo Queiroz and Castellanos, Xavier Francisco and Rohde, Luis A},
doi = {10.1371/journal.pone.0045671},
editor = {Fan, Yong},
file = {:Users/cameron.craddock/Documents/papers/Sato et al. - 2012 - Abnormal Brain Connectivity Patterns in Adults with ADHD A Coherence Study.pdf:pdf},
issn = {1932-6203},
journal = {PloS one},
keywords = {Adolescent,Adult,Age Factors,Artificial Intelligence,Attention Deficit Disorder with Hyperactivity,Attention Deficit Disorder with Hyperactivity: phy,Automated,Brain,Brain Mapping,Brain Mapping: methods,Brain: pathology,Child,Computer-Assisted,Female,General Science,Gyrus Cinguli,Gyrus Cinguli: physiology,Humans,Image Processing,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Male,Middle Aged,Models,Neural Pathways,Neural Pathways: physiology,Pattern Recognition,Reproducibility of Results,Statistical,Support Vector Machines,Young Adult,adhd200 preprc},
mendeley-tags = {General Science},
month = {jan},
number = {9},
pages = {e45671},
pmid = {23049834},
publisher = {Public Library of Science},
title = {{Abnormal brain connectivity patterns in adults with ADHD: a coherence study}},
url = {http://dx.plos.org/10.1371/journal.pone.0045671},
volume = {7},
year = {2012}
}
@article{Olivetti2012,
abstract = {The Attention Deficit Hyperactivity Disorder (ADHD) affects the school-age population and has large social costs. The scientific community is still lacking a pathophysiological model of the disorder and there are no objective biomarkers to support the diagnosis. In 2011 the ADHD-200 Consortium provided a rich, heterogeneous neuroimaging dataset aimed at studying neural correlates of ADHD and to promote the development of systems for automated diagnosis. Concurrently a competition was set up with the goal of addressing the wide range of different types of data for the accurate prediction of the presence of ADHD. Phenotypic information, structural magnetic resonance imaging (MRI) scans and resting state fMRI recordings were provided for nearly 1000 typical and non-typical young individuals. Data were collected by eight different research centers in the consortium. This work is not concerned with the main task of the contest, i.e., achieving a high prediction accuracy on the competition dataset, but we rather address the proper handling of such a heterogeneous dataset when performing classification-based analysis. Our interest lies in the clustered structure of the data causing the so-called batch effects which have strong impact when assessing the performance of classifiers built on the ADHD-200 dataset. We propose a method to eliminate the biases introduced by such batch effects. Its application on the ADHD-200 dataset generates such a significant drop in prediction accuracy that most of the conclusions from a standard analysis had to be revised. In addition we propose to adopt the dissimilarity representation to set up effective representation spaces for the heterogeneous ADHD-200 dataset. Moreover we propose to evaluate the quality of predictions through a recently proposed test of independence in order to cope with the unbalancedness of the dataset.},
author = {Olivetti, Emanuele and Greiner, Susanne and Avesani, Paolo},
issn = {1662-5137},
journal = {Frontiers in systems neuroscience},
keywords = {Neurobiology,adhd200 preprc},
mendeley-tags = {Neurobiology},
month = {jan},
pages = {70},
title = {{ADHD diagnosis from multiple data sources with batch effects}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3465911{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {6},
year = {2012}
}
@article{Lifshitz2012,
author = {Lifshitz, Michael and Margulies, Daniel S. and Raz, Amir},
issn = {2150-7740},
journal = {AJOB Neuroscience},
keywords = {editorial},
language = {en},
mendeley-tags = {Editorial},
month = {oct},
number = {4},
pages = {48--50},
publisher = {Taylor {\&} Francis Group},
title = {{Lengthy and Expensive? Why the Future of Diagnostic Neuroimaging May Be Faster, Cheaper, and More Collaborative Than We Think}},
url = {http://www.tandfonline.com/doi/abs/10.1080/21507740.2012.721466?journalCode=uabn20},
volume = {3},
year = {2012}
}
@inproceedings{Liang2012,
author = {Liang, Sheng-Fu and Hsieh, Tsung-Hao and Chen, Pin-Tzu and Wu, Ming-Long and Kung, Chun-Chia and Lin, Chun-Yu and Shaw, Fu-Zen},
booktitle = {2012 International conference on Fuzzy Theory and Its Applications (iFUZZY2012)},
doi = {10.1109/iFUZZY.2012.6409719},
isbn = {978-1-4673-2056-6},
keywords = {adhd200 preprc},
language = {English},
mendeley-tags = {Data Science},
month = {nov},
pages = {294--298},
publisher = {IEEE},
title = {{Differentiation between resting-state fMRI data from ADHD and normal subjects: Based on functional connectivity and machine learning}},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6409719 http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6409719},
year = {2012}
}
@inproceedings{Lavoie-Courchesne2012b,
author = {Lavoie-Courchesne, S. and Rioux, P. and Chouinard-Decorte, F. and Sherif, T. and Rousseau, M. -E and Das, S. and Adalat, R. and Doyon, J. and Craddock, C. and Margulies, D. and Chu, Carlton and Lyttelton, O. and Evans, A. C. and Bellec, P.},
booktitle = {Journal of Physics: Conference Series},
keywords = {HPC,cbrain,pipeline,psom},
mendeley-tags = {High Performance Computing},
number = {1},
pages = {012032+},
title = {{Integration of a neuroimaging processing pipeline into a pan-canadian computing grid}},
url = {http://dx.doi.org/10.1088/1742-6596/341/1/012032},
volume = {341},
year = {2012}
}
@article{Eloyan2012,
abstract = {Successful automated diagnoses of attention deficit hyperactive disorder (ADHD) using imaging and functional biomarkers would have fundamental consequences on the public health impact of the disease. In this work, we show results on the predictability of ADHD using imaging biomarkers and discuss the scientific and diagnostic impacts of the research. We created a prediction model using the landmark ADHD 200 data set focusing on resting state functional connectivity (rs-fc) and structural brain imaging. We predicted ADHD status and subtype, obtained by behavioral examination, using imaging data, intelligence quotients and other covariates. The novel contributions of this manuscript include a thorough exploration of prediction and image feature extraction methodology on this form of data, including the use of singular value decompositions (SVDs), CUR decompositions, random forest, gradient boosting, bagging, voxel-based morphometry, and support vector machines as well as important insights into the value, and potentially lack thereof, of imaging biomarkers of disease. The key results include the CUR-based decomposition of the rs-fc-fMRI along with gradient boosting and the prediction algorithm based on a motor network parcellation and random forest algorithm. We conjecture that the CUR decomposition is largely diagnosing common population directions of head motion. Of note, a byproduct of this research is a potential automated method for detecting subtle in-scanner motion. The final prediction algorithm, a weighted combination of several algorithms, had an external test set specificity of 94{\%} with sensitivity of 21{\%}. The most promising imaging biomarker was a correlation graph from a motor network parcellation. In summary, we have undertaken a large-scale statistical exploratory prediction exercise on the unique ADHD 200 data set. The exercise produced several potential leads for future scientific exploration of the neurological basis of ADHD.},
author = {Eloyan, Ani and Muschelli, John and Nebel, Mary Beth and Liu, Han and Han, Fang and Zhao, Tuo and Barber, Anita D and Joel, Suresh and Pekar, James J and Mostofsky, Stewart H and Caffo, Brian},
issn = {1662-5137},
journal = {Frontiers in systems neuroscience},
keywords = {Neurobiology,adhd200 preprc},
mendeley-tags = {Neurobiology},
month = {jan},
pages = {61},
title = {{Automated diagnoses of attention deficit hyperactive disorder using magnetic resonance imaging}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3431009{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {6},
year = {2012}
}
@article{Dey2012,
abstract = {Attention Deficit Hyperactive Disorder (ADHD) is a common behavioral problem affecting children. In this work, we investigate the automatic classification of ADHD subjects using the resting state functional magnetic resonance imaging (fMRI) sequences of the brain. We show that brain can be modeled as a functional network, and certain properties of the networks differ in ADHD subjects from control subjects. We compute the pairwise correlation of brain voxels' activity over the time frame of the experimental protocol which helps to model the function of a brain as a network. Different network features are computed for each of the voxels constructing the network. The concatenation of the network features of all the voxels in a brain serves as the feature vector. Feature vectors from a set of subjects are then used to train a PCA-LDA (principal component analysis-linear discriminant analysis) based classifier. We hypothesized that ADHD related differences lie in some specific regions of brain and using features only from those regions are sufficient to discriminate ADHD and control subjects. We propose a method to create a brain mask which includes the useful regions only and demonstrate that using the feature from the masked regions improves classification accuracy on the test data set. We train our classifier with 776 subjects, and test on 171 subjects provided by the Neuro Bureau for the ADHD-200 challenge. We demonstrate the utility of graph-motif features, specifically the maps that represent the frequency of participation of voxels in network cycles of length 3. The best classification performance (69.59{\%}) is achieved using 3-cycle map features with masking. Our proposed approach holds promise in being able to diagnose and understand the disorder.},
author = {Dey, Soumyabrata and Rao, A Ravishankar and Shah, Mubarak},
issn = {1662-5137},
journal = {Frontiers in systems neuroscience},
keywords = {Neurobiology,adhd200 preprc},
language = {English},
mendeley-tags = {Neurobiology},
month = {jan},
pages = {75},
publisher = {Frontiers},
title = {{Exploiting the brain's network structure in identifying ADHD subjects}},
url = {http://www.frontiersin.org/Journal/10.3389/fnsys.2012.00075/abstract},
volume = {6},
year = {2012}
}
@article{Dai2012,
abstract = {Attention deficit/hyperactivity disorder (ADHD) is one of the most common diseases in school-age children. To date, the diagnosis of ADHD is mainly subjective and studies of objective diagnostic method are of great importance. Although many efforts have been made recently to investigate the use of structural and functional brain images for the diagnosis purpose, few of them are related to ADHD. In this paper, we introduce an automatic classification framework based on brain imaging features of ADHD patients and present in detail the feature extraction, feature selection, and classifier training methods. The effects of using different features are compared against each other. In addition, we integrate multimodal image features using multi-kernel learning (MKL). The performance of our framework has been validated in the ADHD-200 Global Competition, which is a world-wide classification contest on the ADHD-200 datasets. In this competition, our classification framework using features of resting-state functional connectivity (FC) was ranked the 6th out of 21 participants under the competition scoring policy and performed the best in terms of sensitivity and J-statistic.},
author = {Dai, Dai and Wang, Jieqiong and Hua, Jing and He, Huiguang},
issn = {1662-5137},
journal = {Frontiers in systems neuroscience},
keywords = {Neurobiology,adhd200 preprc},
mendeley-tags = {Neurobiology},
month = {jan},
pages = {63},
title = {{Classification of ADHD children through multimodal magnetic resonance imaging}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3432508{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {6},
year = {2012}
}
@phdthesis{Colby2012a,
author = {Colby, John Benjamin},
mendeley-tags = {Data Science},
month = {jan},
pages = {191},
school = {University of California at Los Angeles},
title = {{Development of human brain connectivity in health and disease}},
type = {PhD Dissertation},
url = {http://escholarship.org/uc/item/2p3471tj{\#}page-2},
year = {2012}
}
@article{Colby2012,
abstract = {Attention deficit hyperactivity disorder (ADHD) currently is diagnosed in children by clinicians via subjective ADHD-specific behavioral instruments and by reports from the parents and teachers. Considering its high prevalence and large economic and societal costs, a quantitative tool that aids in diagnosis by characterizing underlying neurobiology would be extremely valuable. This provided motivation for the ADHD-200 machine learning (ML) competition, a multisite collaborative effort to investigate imaging classifiers for ADHD. Here we present our ML approach, which used structural and functional magnetic resonance imaging data, combined with demographic information, to predict diagnostic status of individuals with ADHD from typically developing (TD) children across eight different research sites. Structural features included quantitative metrics from 113 cortical and non-cortical regions. Functional features included Pearson correlation functional connectivity matrices, nodal and global graph theoretical measures, nodal power spectra, voxelwise global connectivity, and voxelwise regional homogeneity. We performed feature ranking for each site and modality using the multiple support vector machine recursive feature elimination (SVM-RFE) algorithm, and feature subset selection by optimizing the expected generalization performance of a radial basis function kernel SVM (RBF-SVM) trained across a range of the top features. Site-specific RBF-SVMs using these optimal feature sets from each imaging modality were used to predict the class labels of an independent hold-out test set. A voting approach was used to combine these multiple predictions and assign final class labels. With this methodology we were able to predict diagnosis of ADHD with 55{\%} accuracy (versus a 39{\%} chance level in this sample), 33{\%} sensitivity, and 80{\%} specificity. This approach also allowed us to evaluate predictive structural and functional features giving insight into abnormal brain circuitry in ADHD.},
author = {Colby, John B and Rudie, Jeffrey D and Brown, Jesse A and Douglas, Pamela K and Cohen, Mark S and Shehzad, Zarrar},
issn = {1662-5137},
journal = {Frontiers in systems neuroscience},
keywords = {Neurobiology,adhd200 preprc},
language = {English},
mendeley-tags = {Neurobiology},
month = {jan},
pages = {59},
publisher = {Frontiers},
title = {{Insights into multimodal imaging classification of ADHD}},
url = {http://www.frontiersin.org/Journal/10.3389/fnsys.2012.00059/abstract},
volume = {6},
year = {2012}
}
@article{Cheng2012,
abstract = {Accurate classification or prediction of the brain state across individual subject, i.e., healthy, or with brain disorders, is generally a more difficult task than merely finding group differences. The former must be approached with highly informative and sensitive biomarkers as well as effective pattern classification/feature selection approaches. In this paper, we propose a systematic methodology to discriminate attention deficit hyperactivity disorder (ADHD) patients from healthy controls on the individual level. Multiple neuroimaging markers that are proved to be sensitive features are identified, which include multiscale characteristics extracted from blood oxygenation level dependent (BOLD) signals, such as regional homogeneity (ReHo) and amplitude of low-frequency fluctuations. Functional connectivity derived from Pearson, partial, and spatial correlation is also utilized to reflect the abnormal patterns of functional integration, or, dysconnectivity syndromes in the brain. These neuroimaging markers are calculated on either voxel or regional level. Advanced feature selection approach is then designed, including a brain-wise association study (BWAS). Using identified features and proper feature integration, a support vector machine (SVM) classifier can achieve a cross-validated classification accuracy of 76.15{\%} across individuals from a large dataset consisting of 141 healthy controls and 98 ADHD patients, with the sensitivity being 63.27{\%} and the specificity being 85.11{\%}. Our results show that the most discriminative features for classification are primarily associated with the frontal and cerebellar regions. The proposed methodology is expected to improve clinical diagnosis and evaluation of treatment for ADHD patient, and to have wider applications in diagnosis of general neuropsychiatric disorders.},
author = {Cheng, Wei and Ji, Xiaoxi and Zhang, Jie and Feng, Jianfeng},
issn = {1662-5137},
journal = {Frontiers in systems neuroscience},
keywords = {Neurobiology,adhd200 preprc},
mendeley-tags = {Neurobiology},
month = {jan},
pages = {58},
title = {{Individual classification of ADHD patients by integrating multiscale neuroimaging markers and advanced pattern recognition techniques}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3412279{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {6},
year = {2012}
}
@article{Chang2012,
abstract = {The ADHD-200 Global Competition provides an excellent opportunity for building diagnostic classifiers of Attention-Deficit/Hyperactivity Disorder (ADHD) based on resting-state functional MRI (rs-fMRI) and structural MRI data. Here, we introduce a simple method to classify ADHD based on morphological information without using functional data. Our test results show that the accuracy of this approach is competitive with methods based on rs-fMRI data. We used isotropic local binary patterns on three orthogonal planes (LBP-TOP) to extract features from MR brain images. Subsequently, support vector machines (SVM) were used to develop classification models based on the extracted features. In this study, a total of 436 male subjects (210 with ADHD and 226 controls) were analyzed to show the discriminative power of the method. To analyze the properties of this approach, we tested disparate LBP-TOP features from various parcellations and different image resolutions. Additionally, morphological information using a single brain tissue type (i.e., gray matter (GM), white matter (WM), and CSF) was tested. The highest accuracy we achieved was 0.6995. The LBP-TOP was found to provide better discriminative power using whole-brain data as the input. Datasets with higher resolution can train models with increased accuracy. The information from GM plays a more important role than that of other tissue types. These results and the properties of LBP-TOP suggest that most of the disparate feature distribution comes from different patterns of cortical folding. Using LBP-TOP, we provide an ADHD classification model based only on anatomical information, which is easier to obtain in the clinical environment and which is simpler to preprocess compared with rs-fMRI data.},
author = {Chang, Che-Wei and Ho, Chien-Chang and Chen, Jyh-Horng},
issn = {1662-5137},
journal = {Frontiers in systems neuroscience},
keywords = {Neurobiology,adhd200 preprc},
language = {English},
mendeley-tags = {Neurobiology},
month = {jan},
pages = {66},
publisher = {Frontiers},
title = {{ADHD classification by a texture analysis of anatomical brain MRI data}},
url = {http://www.frontiersin.org/Journal/10.3389/fnsys.2012.00066/abstract},
volume = {6},
year = {2012}
}
@article{Bohland2012,
abstract = {Brain imaging methods have long held promise as diagnostic aids for neuropsychiatric conditions with complex behavioral phenotypes such as Attention-Deficit/Hyperactivity Disorder. This promise has largely been unrealized, at least partly due to the heterogeneity of clinical populations and the small sample size of many studies. A large, multi-center dataset provided by the ADHD-200 Consortium affords new opportunities to test methods for individual diagnosis based on MRI-observable structural brain attributes and functional interactions observable from resting-state fMRI. In this study, we systematically calculated a large set of standard and new quantitative markers from individual subject datasets. These features (>12,000 per subject) consisted of local anatomical attributes such as cortical thickness and structure volumes, and both local and global resting-state network measures. Three methods were used to compute graphs representing interdependencies between activations in different brain areas, and a full set of network features was derived from each. Of these, features derived from the inverse of the time series covariance matrix, under an L1-norm regularization penalty, proved most powerful. Anatomical and network feature sets were used individually, and combined with non-imaging phenotypic features from each subject. Machine learning algorithms were used to rank attributes, and performance was assessed under cross-validation and on a separate test set of 168 subjects for a variety of feature set combinations. While non-imaging features gave highest performance in cross-validation, the addition of imaging features in sufficient numbers led to improved generalization to new data. Stratification by gender also proved to be a fruitful strategy to improve classifier performance. We describe the overall approach used, compare the predictive power of different classes of features, and describe the most impactful features in relation to the current literature.},
author = {Bohland, Jason W and Saperstein, Sara and Pereira, Francisco and Rapin, J{\'{e}}r{\'{e}}my and Grady, Leo},
issn = {1662-5137},
journal = {Frontiers in systems neuroscience},
keywords = {Neurobiology,adhd200 preprc},
mendeley-tags = {Neurobiology},
month = {jan},
pages = {78},
title = {{Network, anatomical, and non-imaging measures for the prediction of ADHD diagnosis in individual subjects}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3527894{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {6},
year = {2012}
}
@article{Bellec2012,
abstract = {The analysis of neuroimaging databases typically involves a large number of inter-connected steps called a pipeline. The pipeline system for Octave and Matlab (PSOM) is a flexible framework for the implementation of pipelines in the form of Octave or Matlab scripts. PSOM does not introduce new language constructs to specify the steps and structure of the workflow. All steps of analysis are instead described by a regular Matlab data structure, documenting their associated command and options, as well as their input, output, and cleaned-up files. The PSOM execution engine provides a number of automated services: (1) it executes jobs in parallel on a local computing facility as long as the dependencies between jobs allow for it and sufficient resources are available; (2) it generates a comprehensive record of the pipeline stags and the history of execution, which is detailed enough to fully reproduce the analysis; (3) if an analysis is started multiple times, it executes only the parts of the pipeline that need to be reprocessed. PSOM is distributed under an open-source MIT license and can be used without restriction for academic or commercial projects. The package has no external dependencies besides Matlab or Octave, is straightforward to install and supports of variety of operating systems (Linux, Windows, Mac). We ran several benchmark experiments on a public database including 200 subjects, using a pipeline for the preprocessing of functional magnetic resonance images (fMRI). The benchmark results showed that PSOM is a powerful solution for the analysis of large databases using local or distributed computing resources.},
author = {Bellec, Pierre and Lavoie-Courchesne, S{\'{e}}bastien and Dickinson, Phil and Lerch, Jason P and Zijdenbos, Alex P and Evans, Alan C},
doi = {10.3389/fninf.2012.00007},
issn = {1662-5196},
journal = {Frontiers in neuroinformatics},
keywords = {Matlab,Neuroinformatics,Octave,high-performance computing,matlab,octave,open-source,p,parallel computing,pipeline,workflow},
mendeley-tags = {Neuroinformatics},
month = {jan},
number = {April},
pages = {7},
pmid = {22493575},
title = {{The pipeline system for Octave and Matlab (PSOM): a lightweight scripting framework and execution engine for scientific workflows}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3318188{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {6},
year = {2012}
}
@article{KadkhodaeianBakhtiari2012,
abstract = {The resting brain has been extensively investigated for low frequency synchrony between brain regions, namely Functional Connectivity (FC). However the other main stream of brain connectivity analysis that seeks causal interactions between brain regions, Effective Connectivity (EC), has been little explored. Inherent complexity of brain activities in resting-state, as observed in BOLD (Blood Oxygenation-Level Dependant) fluctuations, calls for exploratory methods for characterizing these causal networks. On the other hand, the inevitable effects that hemodynamic system imposes on causal inferences in fMRI data, lead us toward the methods in which causal inferences can take place in latent neuronal level, rather than observed BOLD time-series. To simultaneously satisfy these two concerns, in this paper, we introduce a novel state-space system identification approach for studying causal interactions among brain regions in the absence of explicit cognitive task. This algorithm is a geometrically inspired method for identification of stochastic systems, purely based on output observations. Using extensive simulations, three aspects of our proposed method are investigated: ability in discriminating existent interactions from non-existent ones, the effect of observation noise, and downsampling on algorithm performance. Our simulations demonstrate that Subspace-based Identification Algorithm (SIA) is sufficiently robust against above-mentioned factors, and can reliably uncover the underlying causal interactions of resting-state fMRI. Furthermore, in contrast to previously established state-space approaches in Effective Connectivity studies, this method is able to characterize causal networks with large number of brain regions. In addition, we utilized the proposed algorithm for identification of causal relationships underlying anti-correlation of default-mode and Dorsal Attention Networks during the rest, using fMRI. We observed that Default-Mode Network places in a higher order in hierarchical structure of brain functional networks compared to Dorsal Attention Networks.},
author = {Bakhtiari, Shahab Kadkhodaeian and Hossein-Zadeh, Gholam-Ali},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Algorithms,Brain,Brain: physiology,Child,Female,Humans,Magnetic Resonance Imaging,Male,Nerve Net,Nerve Net: physiology,Neuroimaging,Rest,Rest: physiology,adhd200 preprc},
mendeley-tags = {Neuroimaging},
month = {apr},
number = {2},
pages = {1236--49},
title = {{Subspace-based Identification Algorithm for characterizing causal networks in resting brain}},
url = {http://www.sciencedirect.com/science/article/pii/S105381191200016X},
volume = {60},
year = {2012}
}
@article{Yao2013,
abstract = {We use entropy to characterize intrinsic ageing properties of the human brain. Analysis of fMRI data from a large dataset of individuals, using resting state BOLD signals, demonstrated that a functional entropy associated with brain activity increases with age. During an average lifespan, the entropy, which was calculated from a population of individuals, increased by approximately 0.1 bits, due to correlations in BOLD activity becoming more widely distributed. We attribute this to the number of excitatory neurons and the excitatory conductance decreasing with age. Incorporating these properties into a computational model leads to quantitatively similar results to the fMRI data. Our dataset involved males and females and we found significant differences between them. The entropy of males at birth was lower than that of females. However, the entropies of the two sexes increase at different rates, and intersect at approximately 50 years; after this age, males have a larger entropy.},
author = {Yao, Y and Lu, W L and Xu, B and Li, C B and Lin, C P and Waxman, D and Feng, J F},
issn = {2045-2322},
journal = {Scientific reports},
keywords = {General Science},
language = {en},
mendeley-tags = {General Science},
month = {jan},
pages = {2853},
publisher = {Nature Publishing Group},
title = {{The increase of the functional entropy of the human brain with age}},
url = {http://www.nature.com/srep/2013/131009/srep02853/full/srep02853.html},
volume = {3},
year = {2013}
}
@article{Wang2013a,
abstract = {PURPOSE: Investigating the discriminative brain map for patients with attention-deficit/hyperactivity disorder (ADHD) based on feature selection and classifier; and identifying patients with ADHD based on the discriminative model. MATERIALS AND METHODS: A dataset of resting state fMRI contains 23 patients with ADHD and 23 healthy subjects were analyzed. Regional homogeneity (ReHo) was extracted from resting state fMRI signals and used as model inputs. Raw ReHo features were ranked and selected in a loop according to their p values. Selected features were trained and tested by support vector machines (SVM) in a cross validation procedure. Cross validation was repeated in feature selection loop to produce optimized model. RESULTS: Optimized discriminative map indicated that the ADHD brains exhibit more increased activities than normal controls in bilateral occipital lobes and left front lobe. The altered brain regions included portions of basal ganglia, insula, precuneus, anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), thalamus, and cerebellum. Correlation coefficients indicated significant positive correlation of inattentive scores with bilateral cuneus and precuneus, and significant negative correlation of hyperactive/impulsive scores with bilateral insula and claustrum. Additionally, the optimized model produced total accuracy of 80{\%} and sensitivity of 87{\%}. CONCLUSION: ADHD brain regions were more activated than normal controls during resting state. Linear support vector classifier can provide useful discriminative information of altered ReHo patterns for ADHD; and feature selection can improve the performances of classification.},
author = {Wang, Xunheng and Jiao, Yun and Tang, Tianyu and Wang, Hui and Lu, Zuhong},
issn = {1872-7727},
journal = {European journal of radiology},
keywords = {Adult,Attention Deficit Disorder with Hyperactivity,Attention Deficit Disorder with Hyperactivity: dia,Attention Deficit Disorder with Hyperactivity: phy,Automated,Automated: methods,Brain,Brain Mapping,Brain Mapping: methods,Brain: physiopathology,Female,Humans,Magnetic Resonance Imaging,Magnetic Resonance Imaging: methods,Male,Nerve Net,Nerve Net: physiopathology,Pattern Recognition,Radiology,Reproducibility of Results,Sensitivity and Specificity,Support Vector Machines},
mendeley-tags = {Radiology},
month = {sep},
number = {9},
pages = {1552--7},
title = {{Altered regional homogeneity patterns in adults with attention-deficit hyperactivity disorder}},
url = {http://www.sciencedirect.com/science/article/pii/S0720048X13002040},
volume = {82},
year = {2013}
}
@mastersthesis{Wang2013,
author = {Wang, Peng},
keywords = {adhd200 preprc},
mendeley-tags = {Data Science},
school = {Auburn University},
title = {{Machine Learning Approaches for Disease State Classification from Neuroimaging Data}},
type = {Masters Thesis, Data Science},
url = {http://etd.auburn.edu/etd/handle/10415/3623},
year = {2013}
}
@article{Subramanian2013,
abstract = {A meta-cognitive interval type-2 neuro-fuzzy inference system (McIT2FIS) based classifier and its pro- jection based learning algorithm is presented in this paper. McIT2FIS consists of two components, namely, a cognitive component and a meta-cognitive component. The cognitive component is an interval type-2 neuro-fuzzy inference system (IT2FIS) represented as a six layered adaptive network realizing Takagi-Sugeno-Kang type inference mechanism. A self-regulatory learning mechanism forms the meta-cognitive component. IT2FIS begins with zero rules, and rules are added and updated depending on the prediction error and relative knowledge contained the current sample. As each sample is presented to the net- work, the meta-cognitive component monitors the hinge- loss error and class-specific spherical potential of the cur- rent sample to decide what-to-learn, when-to-learn and how-to-learn them, efficiently. When a new rule is added or when an existing rule is updated, a projection based learning algorithm computes the optimal output weights with least computational effort by finding analytical min- ima of the nonlinear energy function. It uses class specific criterion and sample overlap criterion to estimate the net- work parameters corresponding to the minimum energy point of the error function. Moreover, consistently under - performing rules are pruned from the network leading to a compact network. The performance of McIT2FIS is first evaluated on a set of benchmark classification problems from UCI machine learning repository. A tenfold cross validation based performance comparison with other state- of-the-art approaches indicates its improved performance. Next, its performance is evaluated on detection of attention deficiency hyperactivity disorder (ADHD) in children. The aim of this study is to classify a child as having typically developing controls or as an ADHD patient. Voxel based features extracted from amygdala region of the brain is employed in this study. The network is trained and tested on samples obtained from ADHD-200 consortium dataset consisting of 941 subjects. The performance comparison with standard support vector machine shows that McIT2- FIS has superior classification ability than SVM in diag- nosing ADHD.},
author = {Subramanian, Kartick and Das, Ankit Kumar and Sundaram, Suresh and Ramasamy, Savitha},
doi = {10.1007/s12530-013-9102-9},
file = {:Users/cameron.craddock/Documents/papers/Subramanian et al. - 2013 - A meta-cognitive interval type-2 fuzzy inference system and its projection based learning algorithm.pdf:pdf},
isbn = {1253001391},
issn = {1868-6478},
journal = {Evolving Systems},
number = {4},
pages = {219--230},
keywords = {Data Science,attention deficiency,based learning {\'{a}},hyperactivity disorder {\'{a}} projection,interval type-2 fuzzy systems,meta-cognition {\'{a}} self-regulation {\'{a}},{\'{a}}},
mendeley-tags = {Data Science},
title = {{A meta-cognitive interval type-2 fuzzy inference system and its projection based learning algorithm}},
volume = {5},
url = {http://link.springer.com/10.1007/s12530-013-9102-9},
year = {2013}
}
@article{Sato2013,
abstract = {The application of graph analysis methods to the topological organization of brain connectivity has been a useful tool in the characterization of brain related disorders. However, the availability of tools, which enable researchers to investigate functional brain networks, is still a major challenge. Most of the studies evaluating brain images are based on centrality and segregation measurements of complex networks. In this study, we applied the concept of graph spectral entropy (GSE) to quantify the complexity in the organization of brain networks. In addition, to enhance interpretability, we also combined graph spectral clustering to investigate the topological organization of sub-network's modules. We illustrate the usefulness of the proposed approach by comparing brain networks between attention deficit hyperactivity disorder (ADHD) patients and the brain networks of typical developing (TD) controls. The main findings highlighted that GSE involving sub-networks comprising the areas mostly bilateral pre and post central cortex, superior temporal gyrus, and inferior frontal gyri were statistically different (p-value=0.002) between ADHD patients and TD controls. In the same conditions, the other conventional graph descriptors (betweenness centrality, clustering coefficient, and shortest path length) commonly used to identify connectivity abnormalities did not show statistical significant difference. We conclude that analysis of topological organization of brain sub-networks based on GSE can identify networks between brain regions previously unobserved to be in association with ADHD.},
author = {Sato, João Ricardo and Takahashi, Daniel Yasumasa and Hoexter, Marcelo Queiroz and Massirer, Katlin Brauer and Fujita, Andr{\'{e}}},
issn = {1095-9572},
journal = {NeuroImage},
keywords = {Attention Deficit Disorder with Hyperactivity,Attention Deficit Disorder with Hyperactivity: phy,Brain,Brain Mapping,Brain Mapping: methods,Brain: physiopathology,Child,Computer-Assisted,Computer-Assisted: methods,Entropy,Female,Humans,Image Interpretation,Magnetic Resonance Imaging,Male,Nerve Net,Nerve Net: physiopathology,Neuroimaging,adhd200 preprc},
mendeley-tags = {Neuroimaging},
month = {aug},
pages = {44--51},
title = {{Measuring network's entropy in ADHD: a new approach to investigate neuropsychiatric disorders}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23571416},
volume = {77},
year = {2013}
}
@inproceedings{Mahanand2013,
abstract = {This paper presents a pilot study on the development of an automated diagnostic tool for Attention Deficiency Hyperactivity Disorder (ADHD) based on regional anatomy of the child brain. For the pilot study, amygdala and cerebellar vermis are chosen from magnetic resonance images obtained from ADHD-200 consortium data set. These regions play a vital role in the control of emotional response and behavior/locomotion, respectively. The images are preprocessed, registered by transforming each image to the space of the population average. The gray matter tissue probability values of amygdala and cerebellar vermis are obtained by applying a region-of-interest mask. These values are then used to train a Projection Based Learning algorithm for a Meta-cognitive Radial Basis Function Network (PBL-McRBFN) for the diagnosis of ADHD and prediction of its subtype. Performance results show that the PBL-McRBFN diagnoses ADHD and predicts its subtypes based on these regions with an accuracy of approx. 65{\%} and 62{\%}, respectively.},
address = {Cham},
author = {Mahanand, B. S. and Savitha, R. and Suresh, S.},
booktitle = {AI 2013: Advances in Artificial Intelligence},
doi = {10.1007/978-3-319-03680-9},
editor = {Cranefield, Stephen and Nayak, Abhaya},
isbn = {978-3-319-03679-3},
pages = {386--395},
publisher = {Springer International Publishing},
series = {Lecture Notes in Computer Science},
title = {{Computer Aided Diagnosis of ADHD Using Brain Magnetic Resonance Images}},
url = {http://link.springer.com/10.1007/978-3-319-03680-9},
volume = {8272},
year = {2013},
mendeley-tags = {Data Science}
}

@article{Li2018,
  doi = {10.1007/s12561-018-9215-6},
  url = {https://doi.org/10.1007/s12561-018-9215-6},
  year = {2018},
  month = mar,
  publisher = {Springer Science and Business Media {LLC}},
  volume = {10},
  number = {3},
  pages = {520--545},
  author = {Xiaoshan Li and Da Xu and Hua Zhou and Lexin Li},
  title = {Tucker Tensor Regression and Neuroimaging Analysis},
  journal = {Statistics in Biosciences}
}

@inproceedings{Kong2013,
abstract = {Mining discriminative features for graph data has attracted much attention in recent years due to its important role in constructing graph classifiers, generating graph indices, etc. Most measurement of interestingness of discriminative subgraph features are defined on certain graphs, where the structure of graph objects are certain, and the binary edges within each graph represent the "presence" of linkages among the nodes. In many real-world applications, however, the linkage structure of the graphs is inherently uncertain. Therefore, existing measurements of interestingness based upon certain graphs are unable to capture the structural uncertainty in these applications effectively. In this paper, we study the problem of discriminative subgraph feature selection from uncertain graphs. This problem is challenging and different from conventional subgraph mining problems because both the structure of the graph objects and the discrimination score of each subgraph feature are uncertain. To address these challenges, we propose a novel discriminative subgraph feature selection method, DUG, which can find discriminative subgraph features in uncertain graphs based upon different statistical measures including expectation, median, mode and phi-probability. We first compute the probability distribution of the discrimination scores for each subgraph feature based on dynamic programming. Then a branch-and-bound algorithm is proposed to search for discriminative subgraphs efficiently. Extensive experiments on various neuroimaging applications (i.e., Alzheimer's Disease, ADHD and HIV) have been performed to analyze the gain in performance by taking into account structural uncertainties in identifying discriminative subgraph features for graph classification.},
address = {Philadelphia, PA},
archivePrefix = {arXiv},
arxivId = {1301.6626},
author = {Kong, Xiangnan and Yu, Philip S. and Wang, Xue and Ragin, Ann B.},
booktitle = {Proc of the Thirteenth SIAM International Conference on Data Mining (SDM 2013)},
eprint = {1301.6626},
file = {::},
mendeley-tags = {Data Science},
month = {jan},
pages = {12},
title = {{Discriminative Feature Selection for Uncertain Graph Classification}},
url = {http://arxiv.org/abs/1301.6626},
year = {2013}
}
@inproceedings{He2013,
address = {Philadelphia, PA},
author = {He, Lifang and Kong, Xiangnan and Yu, Philip S. and Ragin, Ann B. and Hao, Zhifeng and Yang, Xiaowei},
booktitle = {Proc of the Thirteenth SIAM International Conference on Data Mining (SDM 2013)},
doi = {http://epubs.siam.org/doi/abs/10.1137/1.9781611973440.15},
keywords = {Data Science},
language = {en},
mendeley-tags = {Data Science},
pages = {127--135},
title = {{DuSK: A Dual Structure-preserving Kernel for Supervised Tensor Learning with Applications to Neuroimages}},
url = {http://epubs.siam.org/doi/abs/10.1137/1.9781611973440.15},
year = {2013}
}
@patent{Dey2013,
abstract = {Systems and methods for processing image data are provided. A computer implemented method for processing image data, comprises gathering 4-D image data from a subject, extracting time series data, and spatial and degree data of each voxel of the subject, deriving at least one feature from the time series data, deriving at least one feature from the spatial and degree data, combining the at least one feature from the time series data, and the at least one feature from the spatial and degree data to generate combined data, and inputting the combined data to a classifier, wherein the classifier outputs a classification based on the combined data.},
author = {Dey, Soumyabrata and Rao, Ravishankar and Shah, Mubarak and Solmaz, Berkan},
keywords = {patent},
mendeley-tags = {Data Science},
month = {aug},
title = {{Method and system for modeling and processing fmri image data using a bag-of-words approach}},
url = {http://www.google.com/patents/US20130211229},
year = {2013}
}
@mastersthesis{Vidal2014,
address = {São Paulo},
author = {Vidal, Maciel Calebe},
file = {:Users/cameron.craddock/Documents/papers/Vidal - 2014 - Análise da estrutura de clusterização das redes de conectividade funcional do cérebro para investigar as bases das de.pdf:pdf},
pages = {64},
mendeley-tags = {Data Science},
school = {Universidade de São Paulo},
title = {Análise da estrutura de clusterizaçáo das redes de conectividade funcional do cérebro para investigar as bases das desordens do espectro autista},
type = {Masters Thesis},
year = {2014}
}
@inproceedings{Tabas2014,
author = {Tabas, Alejandro and Balaguer-Ballester, Emili and Igual, Laura},
booktitle = {2014 International Workshop on Pattern Recognition in Neuroimaging},
doi = {10.1109/PRNI.2014.6858546},
isbn = {978-1-4799-4149-0},
mendeley-tags = {Data Science},
keywords = {ADHD,Algorithm design and analysis,Brain,Computer architecture,Data mining,Fisher linear discriminant,Independent component analysis,Noise,Standards,biomedical MRI,brain,brain imaging technique,connectivity patterns,data extraction,disorders,extracted patterns,feature extraction,functional connectivity,independent component analysis,interclass differences,medical disorders,neurophysiology,point-of-interest,resting-state-fMRI characterisation,spatial discriminant independent component analysi,standard statistical tests,statistical analysis},
language = {English},
month = {jun},
pages = {1--4},
publisher = {IEEE},
title = {{Spatial discriminant ICA for RS-fMRI characterisation}},
url = {http://ieeexplore.ieee.org.proxy.wexler.hunter.cuny.edu/articleDetails.jsp?arnumber=6858546 http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6858546},
year = {2014}
}
@article{She2014,
author = {She, Yiyuan and He, Yuejia and Wu, Dapeng},
doi = {10.1109/TSP.2014.2358956},
issn = {1053-587X},
journal = {IEEE Transactions on Signal Processing},
keywords = {Data Science,Dynamical systems,Estimation,Lyapunov methods,Lyapunov sense,Lyapunov stability,Mathematical model,Network topology,Signal processing algorithms,Stability analysis,Terrorism,Topology,associated nonlinear dynamical system,convergence guarantee,direct cardinality control,large recurrent neural networks,learning dynamics,learning systems,learning topology,multivariate sparse sigmoidal regression,network topology identification,neurocontrollers,node observation sequence,nonlinear dynamical systems,parameter estimation,physical mechanisms,progressive recurrent network screening,quantile variant,recurrent networks,recurrent neural nets,regression analysis,shrinkage estimation,simple-to-implement network learning algorithms,sparse network learning,sparsity-promoting penalty forms,stability,stability constraints,system parameter estimation,topology learning,variable selection},
language = {English},
mendeley-tags = {Data Science},
month = {nov},
number = {22},
pages = {5881--5891},
publisher = {IEEE},
title = {{Learning Topology and Dynamics of Large Recurrent Neural Networks}},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6914572},
volume = {62},
year = {2014}
}
@inproceedings{Rangarajan2014,
author = {Rangarajan, B and Suresh, S. and Mahanand, B. S.},
booktitle = {2014 13th International Conference on Control Automation Robotics {\&} Vision (ICARCV)},
doi = {10.1109/ICARCV.2014.7064272},
file = {:Users/cameron.craddock/Documents/papers/Rangarajan, Suresh, Mahanand - 2014 - Identification of potential biomarkers in the hippocampus region for the diagnosis of ADHD using P.pdf:pdf},
isbn = {978-1-4799-5199-4},
month = {dec},
pages = {17--22},
publisher = {IEEE},
title = {{Identification of potential biomarkers in the hippocampus region for the diagnosis of ADHD using PBL-McRBFN approach}},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7064272},
volume = {2},
year = {2014},
mendeley-tags = {Data Science}
}
@article{Olivetti2014,
author = {Olivetti, Emanuele and Greiner, Susanne and Avesani, Paolo},
doi = {10.1007/s40708-014-0007-6},
file = {:Users/cameron.craddock/Documents/papers/Olivetti, Greiner, Avesani - 2014 - Statistical independence for the evaluation of classifier-based diagnosis.pdf:pdf},
issn = {2198-4018},
journal = {Brain Informatics},
keywords = {Neuroinformatics},
mendeley-tags = {Neuroinformatics},
month = {mar},
number = {1},
pages = {13--19},
title = {{Statistical independence for the evaluation of classifier-based diagnosis}},
url = {http://link.springer.com/10.1007/s40708-014-0007-6},
volume = {2},
year = {2014}
}
@article{Fujita2014,
abstract = {Statistical inference of functional magnetic resonance imaging (fMRI) data is an important tool in neuroscience investigation. One major hypothesis in neuroscience is that the presence or not of a psychiatric disorder can be explained by the differences in how neurons cluster in the brain. Therefore, it is of interest to verify whether the properties of the clusters change between groups of patients and controls. The usual method to show group differences in brain imaging is to carry out a voxel-wise univariate analysis for a difference between the mean group responses using an appropriate test and to assemble the resulting 'significantly different voxels' into clusters, testing again at cluster level. In this approach, of course, the primary voxel-level test is blind to any cluster structure. Direct assessments of differences between groups at the cluster level seem to be missing in brain imaging. For this reason, we introduce a novel non-parametric statistical test called analysis of cluster structure variability (ANOCVA), which statistically tests whether two or more populations are equally clustered. The proposed method allows us to compare the clustering structure of multiple groups simultaneously and also to identify features that contribute to the differential clustering. We illustrate the performance of ANOCVA through simulations and an application to an fMRI dataset composed of children with attention deficit hyperactivity disorder (ADHD) and controls. Results show that there are several differences in the clustering structure of the brain between them. Furthermore, we identify some brain regions previously not described to be involved in the ADHD pathophysiology, generating new hypotheses to be tested. The proposed method is general enough to be applied to other types of datasets, not limited to fMRI, where comparison of clustering structures is of interest. Copyright © 2014 John Wiley {\&} Sons, Ltd.},
author = {Fujita, Andr{\'{e}} and Takahashi, Daniel Y and Patriota, Alexandre G and Sato, João R},
journal = {Statistics in medicine},
month = {sep},
title = {{A non-parametric statistical test to compare clusters with applications in functional magnetic resonance imaging data}},
volume = {33},
number = {28},
issn = {1097-0258},
url = {http://www.ncbi.nlm.nih.gov/pubmed/25185759},
doi = {10.1002/sim.6292},
pages = {4949--4962},
year = {2014}
}
@article{DosSantosSiqueira2014,
author = {{dos Santos Siqueira}, Anderson and {Biazoli Junior}, Claudinei Eduardo and Comfort, William Edgar and Rohde, Luis Augusto and Sato, João Ricardo},
doi = {10.1155/2014/380531},
file = {::},
issn = {2314-6133},
journal = {BioMed Research International},
keywords = {Biotechnology},
mendeley-tags = {Data Science},
pages = {1--10},
title = {{Abnormal Functional Resting-State Networks in ADHD: Graph Theory and Pattern Recognition Analysis of fMRI Data}},
url = {http://www.hindawi.com/journals/bmri/2014/380531/},
volume = {2014},
year = {2014}
}
@article{Dey2014,
abstract = {Attention Deficit Hyperactive Disorder (ADHD) is getting a lot of attention recently for two reasons. First, it is one of the most commonly found childhood disorders and second, the root cause of the problem is still unknown. Functional Magnetic Resonance Imaging (fMRI) data has become a popular tool for the analysis of ADHD, which is the focus of our current research. In this paper we propose a novel framework for the automatic classification of the ADHD subjects using their resting state fMRI (rs-fMRI) data of the brain. We construct brain functional connectivity networks for all the subjects. The nodes of the network are constructed with clusters of highly active voxels and edges between any pair of nodes represent the correlations between their average fMRI time series. The activity level of the voxels are measured based on the average power of their corresponding fMRI time-series. For each node of the networks, a local descriptor comprising of a set of attributes of the node is computed. Next, the Multi-Dimensional Scaling (MDS) technique is used to project all the subjects from the unknown graph-space to a low dimensional space based on their inter-graph distance measures. Finally, the Support Vector Machine (SVM) classifier is used on the low dimensional projected space for automatic classification of the ADHD subjects. Exhaustive experimental validation of the proposed method is performed using the data set released for the ADHD-200 competition. Our method shows promise as we achieve impressive classification accuracies on the training (70.49{\%}) and test data sets (73.55{\%}). Our results reveal that the detection rates are higher when classification is performed separately on the male and female groups of subjects.},
author = {Dey, Soumyabrata and Rao, A. Ravishankar and Shah, Mubarak},
issn = {1662-5110},
journal = {Frontiers in Neural Circuits},
keywords = {Attention Deficit Hyperactive Disorder,Attributed Graph,Neurobiology,Support vector machine,functional magnetic resonance imaging,multidimensional scaling},
language = {English},
mendeley-tags = {Neurobiology},
month = {jun},
pages = {64},
publisher = {Frontiers},
title = {{Attributed graph distance measure for automatic detection of attention deficit hyperactive disordered subjects}},
url = {http://journal.frontiersin.org/Journal/10.3389/fncir.2014.00064/abstract},
volume = {8},
year = {2014}
}
@phdthesis{Dey2014,
author = {Dey, Soumyabrata},
file = {:Users/cameron.craddock/Documents/papers/Dey - 2014 - Automatic Detection of Brain Functional Disorder Using Imaging Data.pdf:pdf},
school = {University of Central Florida},
title = {{Automatic Detection of Brain Functional Disorder Using Imaging Data}},
mendeley-tags = {Data Science},
type = {PhD Dissertation, Data Science},
year = {2014}
}
@article{Anderson2014,
abstract = {In the multimodal neuroimaging framework, data on a single subject are collected from inherently different sources such as functional MRI, structural MRI, behavioral and/or phenotypic information. The information each source provides is not independent; a subset of features from each modality maps to one or more common latent dimensions, which can be interpreted using generative models. These latent dimensions, or "topics," provide a sparse summary of the generative process behind the features for each individual. Topic modeling, an unsupervised generative model, has been used to map seemingly disparate features to a common domain. We use Non-Negative Matrix Factorization (NMF) to infer the latent structure of multimodal ADHD data containing fMRI, MRI, phenotypic and behavioral measurements. We compare four different NMF algorithms and find that the sparsest decomposition is also the most differentiating between ADHD and healthy patients. We identify dimensions that map to interpretable, recognizable dimensions such as motion, default mode network activity, and other such features of the input data. For example, structural and functional graph theory features related to default mode subnetworks clustered with the ADHD-Inattentive diagnosis. Structural measurements of the default mode network (DMN) regions such as the posterior cingulate, precuneus, and parahippocampal regions were all related to the ADHD-Inattentive diagnosis. Ventral DMN subnetworks may have more functional connections in ADHD-I, while dorsal DMN may have less. ADHD topics are dependent upon diagnostic site, suggesting diagnostic differences across geographic locations. We assess our findings in light of the ADHD-200 classification competition, and contrast our unsupervised, nominated topics with previously published supervised learning methods. Finally, we demonstrate the validity of these latent variables as biomarkers by using them for classification of ADHD in 730 patients. Cumulatively, this manuscript addresses how multimodal data in ADHD can be interpreted by latent dimensions. © 2013 Elsevier Inc. All rights reserved.},
author = {Anderson, Ariana and Douglas, Pamela K. and Kerr, Wesley T. and Haynes, Virginia S. and Yuille, Alan L. and Xie, Jianwen and Wu, Ying Nian and Brown, Jesse a. and Cohen, Mark S.},
doi = {10.1016/j.neuroimage.2013.12.015},
file = {:Users/cameron.craddock/Documents/papers/Anderson et al. - 2014 - Non-negative matrix factorization of multimodal MRI, fMRI and phenotypic data reveals differential changes in d.pdf:pdf},
issn = {10538119},
journal = {NeuroImage},
volume = {102},
number = {1},
keywords = {ADHD,Attention deficit,Biomarkers,Default mode,Latent variables,MRI,Machine learning,Multimodal data,NMF,Neuroimaging,Phenotype,Sparsity,Topic modeling,fMRI},
mendeley-tags = {Neuroimaging},
pmid = {24361664},
publisher = {Elsevier Inc.},
title = {{Non-negative matrix factorization of multimodal MRI, fMRI and phenotypic data reveals differential changes in default mode subnetworks in ADHD}},
pages = {207-219},
url = {http://dx.doi.org/10.1016/j.neuroimage.2013.12.015},
year = {2014}
}


@article{Yang2015,
abstract = {In this paper, we consider the problem of estimating multiple graphical models simultaneously using the fused lasso penalty, which encourages adjacent graphs to share similar structures. A motivating example is the analysis of brain networks of Alzheimer's disease using neuroimaging data. Specifically, we may wish to estimate a brain network for the normal controls (NC), a brain network for the patients with mild cognitive impairment (MCI), and a brain network for Alzheimer's patients (AD). We expect the two brain networks for NC and MCI to share common structures but not to be identical to each other; similarly for the two brain networks for MCI and AD. The proposed formulation can be solved using a second-order method. Our key technical contribution is to establish the necessary and sufficient condition for the graphs to be decomposable. Based on this key property, a simple screening rule is presented, which decomposes the large graphs into small subgraphs and allows an efficient estimation of multiple ...},
author = {Yang, Sen and Lu, Zhaosong and Shen, Xiaotong and Wonka, Peter and Ye, Jieping},
doi = {10.1137/130936397},
issn = {1052-6234},
journal = {SIAM Journal on Optimization},
keywords = {62J10,65K05,90C22,90C25,90C47,Data Science,fused multiple graphical lasso,screening,second-order method},
language = {en},
mendeley-tags = {Data Science},
month = {may},
number = {2},
pages = {916--943},
publisher = {Society for Industrial and Applied Mathematics},
title = {{Fused Multiple Graphical Lasso}},
url = {http://epubs.siam.org/doi/abs/10.1137/130936397},
volume = {25},
year = {2015}
}

@article{Reiss2014,
author = {Reiss, Philip T and Huo, Lan and Zhao, Yihong and Kelly, Clare and Ogden, R. Todd},
doi = {10.1214/15-AOAS829},
file = {:Users/cameron.craddock/Documents/papers/Reiss et al. - 2015 - Wavelet-domain regression and predictive inference in psychiatric neuroimaging.pdf:pdf},
issn = {1932-6157},
journal = {The Annals of Applied Statistics},
keywords = {Statistics},
mendeley-tags = {Statistics},
month = {jun},
number = {2},
pages = {1076--1101},
title = {{Wavelet-domain regression and predictive inference in psychiatric neuroimaging}},
url = {http://works.bepress.com/phil{\_}reiss/29 http://projecteuclid.org/euclid.aoas/1437397124},
volume = {9},
year = {2015}
}
@inproceedings{Rangarajan2015,
author = {Rangarajan, B. and Subramaian, K. and Suresh, S.},
booktitle = {2015 International Conference on Cognitive Computing and Information Processing(CCIP)},
doi = {10.1109/CCIP.2015.7100722},
file = {:Users/cameron.craddock/Documents/papers/Rangarajan, Subramaian, Suresh - 2015 - Importance of phenotypic information in ADHD diagnosis.pdf:pdf},
isbn = {978-1-4799-7171-8},
month = {mar},
pages = {1--6},
title = {{Importance of phenotypic information in ADHD diagnosis}},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7100722},
year = {2015},
mendeley-tags = {Data Science}
}
@inproceedings{Nunez-Garcia2015,
abstract = {Resting state fMRI is a powerful method of functional brain imaging, which can reveal information of functional connectivity between regions during rest. In this paper, we present a novel method, called Functional-Anatomical Discriminative Regions (FADR), for selecting a discriminative subset of functional-anatomical regions of the brain in order to characterize functional connectivity abnormalities in mental disorders. FADR integrates Independent Component Analysis with a sparse feature selection strategy, namely Elastic Net, in a supervised framework to extract a new sparse representation. In particular, ICA is used for obtaining group Resting State Networks and functional information is extracted from the subject-specific spatial maps. Anatomical information is incorporated to localize the discriminative regions. Thus, functional-anatomical information is combined in the new descriptor, which characterizes areas of different networks and carries discriminative power. Experimental results on the public database ADHD-200 validate the method being able to automatically extract discriminative areas and extending results from previous studies. The classification ability is evaluated showing that our method performs better than the average of the teams in the ADHD-200 Global Competition while giving relevant information about the disease by selecting the most discriminative regions at the same time.},
author = {Nuñez-Garcia, Marta and Simpraga, Sonja and Jurado, Maria Angeles and Garolera, Maite and Pueyo, Roser and Igual, Laura},
booktitle = {Machine Learning in Medical Imaging},
doi = {10.1007/978-3-319-24888-2{\_}8},
editor = {Zhou, Luping and Wang, Li and Wang, Qian and Shi, Yinghuan},
keywords = {Classification,Data Science,Elastic Net,Feature selection,Independent Component Analysis,Resting-state fMRI,athena},
mendeley-tags = {Data Science},
pages = {61--68},
publisher = {Springer International Publishing},
title = {{FADR: Functional-Anatomical Discriminative Regions for Rest fMRI Characterization}},
url = {http://link.springer.com/10.1007/978-3-319-24888-2{\_}8},
year = {2015}
}
@article{Liu2015,
abstract = {This paper proposes a new method for estimating sparse precision matrices in the high dimensional setting. It has been popular to study fast computation and adaptive procedures for this problem. We propose a novel approach, called Sparse Column-wise Inverse Operator, to address these two issues. We analyze an adaptive procedure based on cross validation, and establish its convergence rate under the Frobenius norm. The convergence rates under other matrix norms are also established. This method also enjoys the advantage of fast computation for large-scale problems, via a coordinate descent algorithm. Numerical merits are illustrated using both simulated and real datasets. In particular, it performs favorably on an HIV brain tissue dataset and an ADHD resting-state fMRI dataset.},
author = {Liu, Weidong and Luo, Xi},
issn = {0047-259X},
journal = {Journal of multivariate analysis},
mendeley-tags = {Data Science},
keywords = {62F12,62H12,Adaptivity,Convergence rates,Coordinate descent,Cross validation,Gaussian graphical models,Lasso},
month = {mar},
pages = {153--162},
title = {{Fast and Adaptive Sparse Precision Matrix Estimation in High Dimensions}},
url = {http://www.sciencedirect.com/science/article/pii/S0047259X14002607},
volume = {135},
year = {2015}
}
@preprint{Li2015,
abstract = {Aiming at abundant scientific and engineering data with not only high dimensionality but also complex structure, we study the regression problem with a multidimensional array (tensor) response and a vector predictor. Applications include, among others, comparing tensor images across groups after adjusting for additional covariates, which is of central interest in neuroimaging analysis. We propose parsimonious tensor response regression adopting a generalized sparsity principle. It models all voxels of the tensor response jointly, while accounting for the inherent structural information among the voxels. It effectively reduces the number of free parameters, leading to feasible computation and improved interpretation. We achieve model estimation through a nascent technique called the envelope method, which identifies the immaterial information and focuses the estimation based upon the material information in the tensor response. We demonstrate that the resulting estimator is asymptotically efficient, and it enjoys a competitive finite sample performance. We also illustrate the new method on two real neuroimaging studies.},
archivePrefix = {arXiv},
arxivId = {1501.07815},
author = {Li, Lexin and Zhang, Xin},
doi = {arXiv:1501.07815},
eprint = {1501.07815},
file = {:Users/cameron.craddock/Documents/papers/Li, Zhang - 2015 - Parsimonious Tensor Response Regression.pdf:pdf},
journal = {ArXiv e-prints},
title = {{Parsimonious Tensor Response Regression}},
year = {2015},
url = {https://arxiv.org/abs/1501.07815}
}

@article{Kyeong2015,
abstract = {BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is currently diagnosed by a diagnostic interview, mainly based on subjective reports from parents or teachers. It is necessary to develop methods that rely on objectively measureable neurobiological data to assess brain-behavior relationship in patients with ADHD. We investigated the application of a topological data analysis tool, Mapper, to analyze the brain functional connectivity data from ADHD patients. METHODS: To quantify the disease severity using the neuroimaging data, the decomposition of individual functional networks into normal and disease components by the healthy state model (HSM) was performed, and the magnitude of the disease component (MDC) was computed. Topological data analysis using Mapper was performed to distinguish children with ADHD (n = 196) from typically developing controls (TDC) (n = 214). RESULTS: In the topological data analysis, the partial clustering results of patients with ADHD and normal subjects were shown in a chain-like graph. In the correlation analysis, the MDC showed a significant increase with lower intelligence scores in TDC. We also found that the rates of comorbidity in ADHD significantly increased when the deviation of the functional connectivity from HSM was large. In addition, a significant correlation between ADHD symptom severity and MDC was found in part of the dataset. CONCLUSIONS: The application of HSM and topological data analysis methods in assessing the brain functional connectivity seem to be promising tools to quantify ADHD symptom severity and to reveal the hidden relationship between clinical phenotypic variables and brain connectivity.},
author = {Kyeong, Sunghyon and Park, Seonjeong and Cheon, Keun-Ah and Kim, Jae-Jin and Song, Dong-Ho and Kim, Eunjoo},
issn = {1932-6203},
journal = {PloS one},
keywords = {General Science},
mendeley-tags = {General Science},
month = {jan},
number = {9},
pages = {e0137296},
publisher = {Public Library of Science},
title = {{A New Approach to Investigate the Association between Brain Functional Connectivity and Disease Characteristics of Attention-Deficit/Hyperactivity Disorder: Topological Neuroimaging Data Analysis}},
url = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0137296},
volume = {10},
year = {2015}
}
@inproceedings{Hou2015,
author = {Hou, Ming and Chaib-draa, Brahim},
booktitle = {2015 IEEE International Conference on Image Processing (ICIP)},
doi = {10.1109/ICIP.2015.7351019},
isbn = {978-1-4799-8339-1},
mendeley-tags = {Data Science},
keywords = {Approximation methods,Brain Imaging,Brain modeling,Data models,Estimation,Hierarchical Tucker Decomposition,Imaging,MRI images,Magnetic Resonance Image,Parameter estimation,Tensile stress,Tensor Regression,biomedical MRI,brain,brain imaging data analysis,dimension tree structure,generalized linear tensor regression model,hierarchical Tucker decomposition,hierarchical Tucker tensor regression,medical image processing,regression analysis,regression coefficient arrays,synthetic data,tensor-variate inputs,tensors},
language = {English},
month = {sep},
pages = {1344--1348},
publisher = {IEEE},
title = {{Hierarchical tucker tensor regression: Application to brain imaging data analysis}},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=7351019 http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7351019},
year = {2015}
}
@inproceedings{Han2015,
author = {Han, Xiaobing and Zhong, Yanfei and He, Lifang and Yu, Philip S. and Zhang, Liangpei},
booktitle = {Brain Informatics and Health},
doi = {10.1007/978-3-319-23344-4{\_}16},
editor = {Guo, Yike and Friston, Karl and Aldo, Faisal and Hill, Sean and Peng, Hanchuan},
keywords = {Deep learning,Hierarchical convolutional sparse auto-encoder (HC,Neuroimaging classification,Neuroinformatics,Sparse auto-encoder (SAE)},
mendeley-tags = {Neuroinformatics},
pages = {156--166},
publisher = {Springer International Publishing},
title = {{The Unsupervised Hierarchical Convolutional Sparse Auto-Encoder for Neuroimaging Data Classification}},
url = {http://link.springer.com/10.1007/978-3-319-23344-4{\_}16},
year = {2015}
}

@article{FUJITA201776,
title = {Correlation between graphs with an application to brain network analysis},
journal = {Computational Statistics & Data Analysis},
volume = {109},
pages = {76-92},
year = {2017},
issn = {0167-9473},
doi = {https://doi.org/10.1016/j.csda.2016.11.016},
url = {https://www.sciencedirect.com/science/article/pii/S0167947316302900},
author = {André Fujita and Daniel Yasumasa Takahashi and Joana Bisol Balardin and Maciel Calebe Vidal and João Ricardo Sato},
keywords = {Network, Correlation, fMRI, Functional brain network, Autism},
abstract = {The global functional brain network (graph) is more suitable for characterizing brain states than local analysis of the connectivity of brain regions. Therefore, graph-theoretic approaches are natural methods to use for studying the brain. However, conventional graph theoretical analyses are limited due to the lack of formal statistical methods of estimation and inference. For example, the concept of correlation between two vectors of graphs has not yet been defined. Thus, the introduction of a notion of correlation between graphs becomes necessary to better understand how brain sub-networks interact. To develop a framework to infer correlation between graphs, one may assume that they are generated by models and that the parameters of the models are the random variables. Then, it is possible to define that two graphs are independent when the random variables representing their parameters are independent. In the real world, however, the model is rarely known, and consequently, the parameters cannot be estimated. By analyzing the graph spectrum, it is shown that the spectral radius is highly associated with the parameters of the graph model. Based on this, a framework for correlation inference between graphs is constructed and the approach illustrated on functional magnetic resonance imaging data on 814 subjects comprising 529 controls and 285 individuals diagnosed with autism spectrum disorder (ASD). Results show that correlations between the default-mode and control, default-mode and somatomotor, and default-mode and visual sub-networks are higher in individuals with ASD than in the controls.}
}

@article{Deshpande2015,
author = {Deshpande, Gopikrishna and Wang, Peng and Rangaprakash, D. and Wilamowski, Bogdan},
doi = {10.1109/TCYB.2014.2379621},
file = {:Users/cameron.craddock/Documents/papers/Deshpande et al. - 2015 - Fully Connected Cascade Artificial Neural Network Architecture for Attention Deficit Hyperactivity Disorder Cl.pdf:pdf},
issn = {2168-2267},
journal = {IEEE Transactions on Cybernetics},
keywords = {Data Science},
mendeley-tags = {Data Science},
month = {dec},
number = {12},
pages = {2668--2679},
title = {{Fully Connected Cascade Artificial Neural Network Architecture for Attention Deficit Hyperactivity Disorder Classification From Functional Magnetic Resonance Imaging Data}},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7001645},
volume = {45},
year = {2015}
}
@article{Chen2016,
abstract = {An important problem in contemporary statistics is to understand the relationship among a large number of variables based on a dataset, usually with p, the number of the variables, much larger than n, the sample size. Recent efforts have focused on modeling static covariance matrices where pairwise covariances are considered invariant. In many real systems, however, these pairwise relations often change. To characterize the changing correlations in a high dimensional system, we study a class of dynamic covariance models (DCMs) assumed to be sparse, and investigate for the first time a unified theory for understanding their non-asymptotic error rates and model selection properties. In particular, in the challenging high dimension regime, we highlight a new uniform consistency theory in which the sample size can be seen as n4/5 when the bandwidth parameter is chosen as h∝n−1/5 for accounting for the dynamics. We show that this result holds uniformly over a range of the variable used for modeling the dynamic...},
author = {Chen, Ziqi and Leng, Chenlei},
doi = {10.1080/01621459.2015.1077712},
issn = {0162-1459},
journal = {Journal of the American Statistical Association},
volume = {111},
number = {515},
keywords = {Covariance model,Dynamic covariance,Functional connectivity,High Dimensionality,Marginal independence,Rate of convergence,Sparsity,Uniform consistency},
language = {en},
month = {aug},
pages = {1196--1207},
publisher = {Taylor \& Francis},
title = {{Dynamic Covariance Models}},
url = {http://dx.doi.org/10.1080/01621459.2015.1077712},
year = {2016}
}
@article{Carmona2015,
abstract = {We sought to determine whether functional connectivity streams that link sensory, attentional, and higher-order cognitive circuits are atypical in attention-deficit/hyperactivity disorder (ADHD). We applied a graph-theory method to the resting-state functional magnetic resonance imaging data of 120 children with ADHD and 120 age-matched typically developing children (TDC). Starting in unimodal primary cortex-visual, auditory, and somatosensory-we used stepwise functional connectivity to calculate functional connectivity paths at discrete numbers of relay stations (or link-step distances). First, we characterized the functional connectivity streams that link sensory, attentional, and higher-order cognitive circuits in TDC and found that systems do not reach the level of integration achieved by adults. Second, we searched for stepwise functional connectivity differences between children with ADHD and TDC. We found that, at the initial steps of sensory functional connectivity streams, patients display significant enhancements of connectivity degree within neighboring areas of primary cortex, while connectivity to attention-regulatory areas is reduced. Third, at subsequent link-step distances from primary sensory cortex, children with ADHD show decreased connectivity to executive processing areas and increased degree of connections to default mode regions. Fourth, in examining medication histories in children with ADHD, we found that children medicated with psychostimulants present functional connectivity streams with higher degree of connectivity to regions subserving attentional and executive processes compared to medication-na{\`i}ve children. We conclude that predominance of local sensory processing and lesser influx of information to attentional and executive regions may reduce the ability to organize and control the balance between external and internal sources of information in ADHD.},
author = {Carmona, Susana and Hoekzema, Elseline and Castellanos, Francisco X and García-García, David and Lage-Castellanos, Agustín and Van Dijk, Koene R A and Navas-Sánchez, Francisco J and Martínez, Kenia and Desco, Manuel and Sepulcre, Jorge},
issn = {1097-0193},
journal = {Human brain mapping},
keywords = {Neuroimaging},
mendeley-tags = {Neuroimaging},
month = {jul},
number = {7},
pages = {2544--57},
title = {{Sensation-to-cognition cortical streams in attention-deficit/hyperactivity disorder}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4484811{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {36},
year = {2015}
}
@article{Ahn2015,
author = {Ahn, Mihye and Shen, Haipeng and Lin, Weili and Zhu, Hongtu},
doi = {10.5705/ss.2013.232w},
file = {:Users/cameron.craddock/Documents/papers/Ahn et al. - 2015 - A Sparse Reduced Rank Framework for Group Analysis of Functional Neuroimaging Data.pdf:pdf},
issn = {10170405},
journal = {Statistica Sinica},
volume = {25},
number = {1},
title = {{A Sparse Reduced Rank Framework for Group Analysis of Functional Neuroimaging Data}},
pages = {295--312},
url = {http://www3.stat.sinica.edu.tw/statistica/J25N1/J25N117/J25N117.html},
year = {2015}
}

@article{nachamai2016sub,
  title={Sub-Type Discernment of Attention Deficit Hyperactive Disorder in Children using a Cluster Partitioning Algorithm},
  author={Nachamai, M},
  journal={Indian Journal of Science and Technology},
  volume={9},
  number={8},
  pages = {},
  year={2016}
}

@article{Brown20161238,
title = "Connected brains and minds—The {UMCD} repository for brain connectivity matrices",
journal ="NeuroImage",
volume = "124, Part B",
number = "",
pages = "1238--1241",
year = "2016",
note = "Sharing the wealth: Brain Imaging Repositories in 2015",
issn = "1053-8119",
doi = "http://dx.doi.org/10.1016/j.neuroimage.2015.08.043",
url = "http://www.sciencedirect.com/science/article/pii/S1053811915007624",
author = "Jesse A. Brown and John D. Van Horn",
abstract = "Abstract We describe the \{USC\} Multimodal Connectivity Database (http://umcd.humanconnectomeproject.org), an interactive web-based platform for brain connectivity matrix sharing and analysis. The site enables users to download connectivity matrices shared by other users, upload matrices from their own published studies, or select a specific matrix and perform a real-time graph theory-based analysis and visualization of network properties. The data shared on the site span a broad spectrum of functional and structural brain connectivity information from humans across the entire age range (fetal to age 89), representing an array of different neuropsychiatric and neurodegenerative disease populations (autism spectrum disorder, ADHD, and APOE-4 carriers). An analysis combining 7 different datasets shared on the site illustrates the diversity of the data and the potential for yielding deeper insight by assessing new connectivity matrices with respect to population-wide network properties represented in the UMCD. "
}

@article{Yu2016,
title = "Partial functional linear quantile regression for neuroimaging data analysis",
journal = "Neurocomputing",
volume = "195",
number = "",
pages = "74--87",
year = "2016",
note = "",
issn = "0925-2312",
doi = "http://dx.doi.org/10.1016/j.neucom.2015.08.116",
url = "http://www.sciencedirect.com/science/article/pii/S0925231216001181",
author = "Dengdeng Yu and Linglong Kong and Ivan Mizera",
keywords = "Functional linear quantile regression",
keywords = "Partial quantile covariance",
keywords = "PQR basis",
keywords = "SIMPQR",
keywords = "ADHD",
keywords = "ADNI",
abstract = "Abstract We propose a prediction procedure for the functional linear quantile regression model by using partial quantile covariance techniques and develop a simple partial quantile regression (SIMPQR) algorithm to efficiently extract partial quantile regression (PQR) basis for estimating functional coefficients. We further extend our partial quantile covariance techniques to functional composite quantile regression (CQR) defining partial composite quantile covariance. There are three major contributions. (1) We define partial quantile covariance between two scalar variables through linear quantile regression. We compute \{PQR\} basis by sequentially maximizing the partial quantile covariance between the response and projections of functional covariates. (2) In order to efficiently extract \{PQR\} basis, we develop a \{SIMPQR\} algorithm analog to simple partial least squares (SIMPLS). (3) Under the homoscedasticity assumption, we extend our techniques to partial composite quantile covariance and use it to find the partial composite quantile regression (PCQR) basis. The \{SIMPQR\} algorithm is then modified to obtain the \{SIMPCQR\} algorithm. Two simulation studies show the superiority of our proposed methods. Two real data from ADHD-200 sample and \{ADNI\} are analyzed using our proposed methods. "
}
@inproceedings{Liu2016,
title="Ordinal Patterns for Connectivity Networks in Brain Disease Diagnosis",
author="Liu, Mingxia and Du, Junqiang and Jie, Biao and Zhang, Daoqiang",
booktitle="Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part I",
pages="1--9",
doi="10.1007/978-3-319-46720-7_1",
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url="http://dx.doi.org/10.1007/978-3-319-46720-7_1",
year="2016",
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year = {2016}
}
@preprint{Brown2016,
archivePrefix = {arXiv},
arxivId = {1611.08699},
author = {Brown, Colin J and Hamarneh, Ghassan},
eprint = {1512.06830},
journal = {ArXiv e-prints},
keywords = {arxiv},
month = {nov},
title = {{Machine Learning on Human Connectome Data from MRI}},
url = {https://arxiv.org/abs/1611.08699},
year = {2016},
}
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  title={FCNet: A Convolutional Neural Network for Calculating Functional Connectivity from functional MRI},
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  pages={70--78},
  year={2017},
  organization={Springer}
}

@article{phinyomark2017resting,
  title={Resting-State fMRI Functional Connectivity: Big Data Preprocessing Pipelines and Topological Data Analysis},
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  number={4},
  pages={415--428},
  year={2017},
  publisher={IEEE}
}

@inproceedings{alonso2017fcnet,
  title={FCNet: A Convolutional Neural Network for Calculating Functional Connectivity from Functional MRI},
  author={Alonso, Eduardo and Dima, Danai and Corr, Philip and Slabaugh, Greg},
  booktitle={Connectomics in NeuroImaging: First International Workshop, CNI 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, Proceedings},
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  pages={70},
  year={2017},
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@article{riaz2018115,
title = "Fusion of fMRI and non-imaging data for ADHD classification",
journal = "Computerized Medical Imaging and Graphics",
volume = "65",
pages = "115 - 128",
year = "2018",
note = "Advances in Biomedical Image Processing",
issn = "0895-6111",
doi = "https://doi.org/10.1016/j.compmedimag.2017.10.002",
url = "http://www.sciencedirect.com/science/article/pii/S0895611117300988",
author = "Atif Riaz and Muhammad Asad and Eduardo Alonso and Greg Slabaugh",
keywords = "ADHD, Density clustering, Affinity propagation, Elastic net, Non-imaging data"
}

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  organization={IEEE}
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% 30441592 
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   Year="2018",
   Volume="2018",
   Pages="5541--5544",
   Month="Jul"
}

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}

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  year={2019},
  publisher={Radiological Society of North America}
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@preprint{bathelt2020more,
  title={More similarities than differences between ADHD and ASD in functional brain connectivity},
  author={Bathelt, Joe and Caan, Matthan and Geurts, Hilde},
  year={2020},
  journal={PsyArXiv}
}

@article{zhang2020separated,
  title={Separated Channel Attention Convolutional Neural Network (SC-CNN-Attention) to Identify ADHD in Multi-Site Rs-fMRI Dataset},
  author={Zhang, Tao and Li, Cunbo and Li, Peiyang and Peng, Yueheng and Kang, Xiaodong and Jiang, Chenyang and Li, Fali and Zhu, Xuyang and Yao, Dezhong and Biswal, Bharat and others},
  journal={Entropy},
  volume={22},
  number={8},
  pages={893},
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  publisher={Multidisciplinary Digital Publishing Institute}
}

@article{pmid32539526,
   Author="Zhan, Y.  and Wei, J.  and Liang, J.  and Xu, X.  and He, R.  and Robbins, T. W.  and Wang, Z. ",
   Title="{{D}iagnostic {C}lassification for {H}uman {A}utism and {O}bsessive-{C}ompulsive {D}isorder {B}ased on {M}achine {L}earning {F}rom a {P}rimate {G}enetic {M}odel}",
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   Title="{{I}dentifying {A}{D}{H}{D} {I}ndividuals {F}rom {R}esting-{S}tate {F}unctional {C}onnectivity {U}sing {S}ubspace {C}lustering and {B}inary {H}ypothesis {T}esting}",
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}