Code used in Glerean et al. 2015 "Reorganization of functionally connected brain subnetworks in high-functioning autism" (in press: http://onlinelibrary.wiley.com/doi/10.1002/hbm.23084/abstract)
This is part of the code used for the article mentioned above.
Preprocessing and head motion quality control
The BraMiLa Matlab tools were used for further preprocessing and head motion quality control https://git.becs.aalto.fi/bml/bramila/ specifically
- bramila_clean_signal.m (to further clean the fMRI data as described in Power et al. 2014 http://www.sciencedirect.com/science/article/pii/S1053811913009117)
- bramila_diagnostics.m (for quality control)
Graph-theoretical analysis (macro-level, mesoscopic, micro-level)
Tools for graph-theoretical analysis in Python 2.7 are available at: https://git.becs.aalto.fi/rmkujala/brainnets
The scripts are using the extensive library developed by the Complex Networks group at the Neuroscience and Biomedical Engineering department of Aalto University https://git.becs.aalto.fi/complex-networks/verkko/tree/master http://becs.aalto.fi/en/research/complex_networks/
Please refere to the readme of the subfolder ABIDE
The following MATLAB functions and toolboxes were used for permutation based statistics:
- bramila_ttest_np.m from https://git.becs.aalto.fi/bml/bramila/ ..* Used to efficiently compute difference of the means using permutations. It uses Matlab parallel computing toolbox.
- bramila_mantel.m from https://git.becs.aalto.fi/bml/bramila/ (a copy available also in the ABIDE subfolder) ..* Used to perform Mantel test (correlation between two distance/similarity matrices).
- Micro-level statistics were computed with http://bia.korea.ac.kr/people/~cheolhan/software/