There are 2 repositories under resting-state-fmri topic.
rsHRF: A Toolbox for Resting State HRF Deconvolution and Connectivity Analysis (MATLAB)
Repository for the Brainhack School 2020 team working with fMRI and ABIDE data to train machine learning models.
Easylearn is designed for machine learning mainly in resting-state fMRI, radiomics and other fields (such as EEG). Easylearn is built on top of scikit-learn, pytorch and other packages. Easylearn can assist doctors and researchers who have limited coding experience to easily realize machine learning, e.g., (MR/CT/PET/EEG)imaging-marker- or other biomarker-based disease diagnosis and prediction, treatment response prediction, disease subtyping, dimensional decoding for transdiagnostic psychiatric diseases or other diseases, disease mechanism exploration and etc.
Graph saliency maps through spectral convolutional networks for brain mapping
A Python software package for BWAS
BIDS App for resting state signal extraction using nilearn.
System segregation (see Chan et al. 2014 PNAS) and RSFC brain network tools
Documentation and MATLAB code for test-retest functional MRI studies.
:octocat: This repository contains the notes, matlab toolbox and implement document of the brant. Enjoy!
A Python software package for sKPCR
Inter-Regional High-level Relation Learning from Functional Connectivity via Self-Supervision - PyTorch Implementation (MICCAI 2021)
Resting-state Functional Correlation (RSFC) node set (Chan et al. 2014 PNAS)
Computing Temporal ICA from HCP resting state data using Functional Modes DiFuMo atlas.
some of the work I've done with resting-state fMRI
Tools for mapping human motor circuits - with implications for Parkinson's Disease
Topological data analysis of brain networks through order statistics
This is a Work In Progress (WIP) project. It is part of my undergraduate studies in Biomedical Engineering at Ostbayerische Technische Hochschule in Germany. This repository incorporates the code and results of my participation in the course Data Science Projects: Train you own Machine Learning Model.
Scripts used in the "Dynamic Functional Connectivity in Autism Spectrum Disorder" project.
Scripts for the paper: A supervoxel-based method for groupwise whole brain parcellation with resting-state fMRI data.