Tools

This Matlab toolbox calculates & runs a GLM on graph theoretic 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. Both fully connected & thresholded networks are tested. GTG uses the Brain Connectivity Toolbox to calculate graph properties.

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.

See the NITRC page to download the toolbox: www.nitrc.org/projects/metalab_gtg/

Related Publications:

Conference abstract on toolbox:
Spielberg, J.M. (2014). Graph theoretic general linear model (GTG): a MATLAB toolbox. Brain Connectivity, 4, A1-A158. doi:10.1089/brain.2014.1501.abstracts

Resting state pathway & graph theory analysis:
Spielberg, J.M., McGlinchey, R.E., Milberg, W.P., & Salat, D.H. (2015). Brain network disturbance related to posttraumatic stress & traumatic brain injury in veterans. Biological Psychiatry, 78, 210-216. doi:10.1016/j.biopsych.2015.02.013

Block-design task pathway & graph theory analysis:
Spielberg, J.M., Miller, G.A., Heller, W., & Banich, M.T. (2015). Flexible brain network reconfiguration supporting inhibitory control. Proceedings of the National Academy of Sciences, 112, 10020-10025. doi:10.1073/pnas.1500048112

–Random Scripts–

EZdiff_4choice.m – Calculates diffusion model parameters based on Wagenmakers et al. (2007)’s EZ-diffusion model with one change to make the calculations more appropriate for 4-option tasks.

simbin.m – Computes 106 measures of similarity and dissimilarity (distance) between two binary matrices.