Graph Theory GLM (GTG) MATLAB Toolbox. This MATLAB toolbox calculates & runs a GLM on graph theory properties derived from brain networks. The GLM accepts continuous & categorical between-participant predictors & categorical within-participant predictors. Significance is determined via non-parametric permutation tests, including correction for multiple comparisons. Both fully connected & thresholded networks are tested. The toolbox also provides a data processing path for resting state & task fMRI data. Options for partialing nuisance signals include: local & total white matter signal (Jo et al., 2013), PCA of white matter/ventricular signal (Muschelli et al., 2014), Saad et al. (2013)’s GCOR, & Chen et al. (2012)’s GNI. In addition, Power et al. (2014)’s motion scrubbing method & Patel et al. (2014)’s WaveletDespike are available.
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References in zbMATH (referenced in 1 article )
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- Lea Waller; Anastasia Brovkin; Lena Dorfschmidt; Danilo Bzdok; Henrik Walter; Johann Daniel Kruschwitz: GraphVar 2.0: A user-friendly toolbox for machine learning on functional connectivity measures (2018) arXiv