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GCN_SCZ_Classification
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GCN_SCZ_Classification
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# GCN_SCZ_Classification This repository provides core codes and toolboxes for analysis in the paper entitled "Graph convolutional networks reveal network-level functional dysconnectivity in schizophrenia" by Lei et al. Please see the paper for the description of data analysis. # Overview Content includes demo data and source code for the implementation of graph convolutional network (GCN), linear support vector machine (SVM) and non-linear SVM with radial basis function (RBF) kernel on a large multi-site schizophrenia fMRI dataset. All custom codes were tested on Linux Ubuntu 20.04 LTS PC. # Requirements - Python (>= 3.5) - Scikit-Learn - Pytorch - Pytorch-geometric - Scipy - Numpy - Pandas # Toolboxes All other toolboxes and codes used in our study for image preprocessing, harmonization, ancillary analysis and visualization are shown below: - [SPM12](https://www.fil.ion.ucl.ac.uk/spm/software/spm12/) - [NeuroComBat-sklearn](https://github.com/Warvito/neurocombat_sklearn) - [Neuroharmony](https://github.com/garciadias/Neuroharmony) - [GraphSaliencyMap](https://github.com/sarslancs/graph_saliency_maps) - [GRETNA](https://www.nitrc.org/projects/gretna/) - [BrainNetViewer](https://www.nitrc.org/projects/bnv/)
