Created as part of an university project "Innovationlab Big Data Science" at LMU Munich. Homepage: https://innolab.ifi.lmu.de/
Alzheimer's disease (AD) is a progressive neurologic disorder that causes the brain to shrink (atrophy) and brain cells to die - resulting in reductions of synaptical relations between brain areas (connectivity loss) [1][2]. With over 10 million people estimated to have dementia in Europe with costs ranging up to 175000 US$ per patient [3][4], research in Alzheimer disease will play an distinct role in modern healthcare systems. The aim of this project was therefore two-fold:
- Prediction and
- Explanation
of Alzheimer Diagnosis based on connectivity matrices utilizing the Brainnetome Atlas [5]. The results of the presented endeavours include a pipeline for processing connectivity matrices in order to predict and explain a patients AD status. This pipeline enables the user to automate the training, evaluation and interpretation for several models as well as for several dataset options, e.g. aggregated connectivity matrices, connectivity matrices and graph metrics applied to human brain connectivity data. The applicable models in the pipeline include Elastic Net, Random Forest, Gradient Boosting as well as 2D Convoutional Neural Networks. For Evaluation of the results, the Accuracy, AUC, Precision, Recall and F1 values were compared with the following effects of the Elastic Net (Conn data) as an example: (i) Accuracy: 0.8 (ii) AUC: 0.86 (iii) Precision: 0.88 (iv) F1: 0.82. These results suggest, that the models perform well on the preprocessed connectivity matrices. In a last step, the following brain subregions were identified from the connectivity matrices for their key importance for AD: Caudal Temporal Thalamus, TE1.0 and TE1.2, Dorsal Agranular Insular, Caudal Hippocampus, Rostral Area 7, Posterior Parietal Thalamus, Posterior Parahippocampal Gyrus.
[1] Mayoclinic.org (N.A.). Alzheimer's disease. Retrieved from: https://www.mayoclinic.org/diseases-conditions/alzheimers-disease/symptoms-causes/syc-20350447#:~:text=Alzheimer's%20disease%20is%20a%20progressive,person's%20ability%20to%20function%20independently (01.03.22)
[2] Smith, M.A., 1998. Alzheimer disease. International review of neurobiology, 42, pp.1-54.
[3] Alzheimer Europe.org (2019). Dementia in Europe Yearbook 2019. Retrieved from: https://www.alzheimer-europe.org/sites/default/files/alzheimer_europe_dementia_in_europe_yearbook_2019.pdf (20.02.22)
[4] Alzheimer Association´s (2020). Costs of Alzheimer's to Medicare and Medicaid. Retrieved from: https://act.alz.org/site/DocServer/2012_Costs_Fact_Sheet_version_2.pdf?docID=7161 (22.02.22)
[5] Fan, L., Li, H., Zhuo, J., Zhang, Y., Wang, J., Chen, L., Yang, Z., Chu, C., Xie, S., Laird, A.R. and Fox, P.T., 2016. The human brainnetome atlas: a new brain atlas based on connectional architecture. Cerebral cortex, 26(8), pp.3508-3526.
Follow the following steps to start the pipeline locally.
- Install python 3.9.x or above
- From either:
- https://www.python.org/downloads/
- or: https://www.anaconda.com/
Recommended: Create a virtual environment before installing the package. If you choose not to, you can skip to the Installation
- Open the command prompt
- Navigate to the project directory or create a new one:
mkdir path/to/python/project
- Create a virtual environment within the project folder
Linux:
python3 -m venv connectome_env
Windows:
python -m venv connectome_env
- Activate virtual environment
Linux:
source connectome_env/bin/activate
Windows:
connectome\Scripts\activate
- Go to installation
- Windows/Linux
git clone https://github.com/JanaGauss/Connectome.git cd Connectome pip install .
This is the beginning to our Connectome Pipeline. To use our pipeline just open Connectome Pipeline
Important: The excel sheet must contain the column "ConnID" as an identifier for merging with the matlab files.
To start preprocessing the data for analysis, you need a folder with matlab files of connectivity matrices and an excel sheet with subject information. Afterwards, just specify the folder path and your good to go:
matlab_dir = r"./path/to/matlab/files" # Enter the directory for the matlab files
excel_path = r"./path/to/excel/sheet.xlsx" # Enter the directory for the corresponding excel sheet with xlsx at the end
For further the further steps check out the demo video or read our full documentation here: https://likai97.github.io/Conncetome-Documentation/
The following video displays the full pipeline from preprocessing to model outputs:
Pipeline_Connectome.mp4
Visualizations for a gradient boosting model - based on 246x246 connectivity matrices and graph metrics. As an example, the variable 95_216 represents the connectivity between the intermediate lateral area 20 and the rostral hippocampus.
Distributed under the MIT License. See LICENSE.txt for more information.
Authors:
- Leo Schaabner
- Kai Becker
- Jana Gauß
- Katharina Brenner
- Jonas Klingele
Clinical supervisor: Boris Rauchmann