{"id":29,"date":"2014-07-28T16:28:47","date_gmt":"2014-07-28T20:28:47","guid":{"rendered":"https:\/\/sites.bu.edu\/metalab\/?page_id=29"},"modified":"2016-08-29T19:46:00","modified_gmt":"2016-08-29T23:46:00","slug":"tools","status":"publish","type":"page","link":"https:\/\/sites.bu.edu\/metalab\/tools\/","title":{"rendered":"Tools"},"content":{"rendered":"
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Dr. Spielberg has moved to the University of Delaware: \u00a0http:\/\/sites.udel.edu\/jmsp\/<\/a><\/h1>\n

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–Software Packages–<\/h1>\n

Graph Theoretic GLM (GTG)<\/h2>\n<\/div>\n

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<\/a> to calculate graph properties.<\/p>\n

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)\u2019s GNI. In addition, Power et al. (2014)’s motion scrubbing method & Patel et al. (2014)’s WaveletDespike are available.<\/p>\n

See the NITRC page to download the toolbox: www.nitrc.org\/projects\/metalab_gtg\/<\/a><\/p>\n

Related Publications:<\/h3>\n

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

Resting state pathway & graph theory analysis<\/em>:
\nSpielberg, J.M., McGlinchey, R.E., Milberg, W.P., & Salat, D.H. (2015). <\/span>Brain network disturbance related to posttraumatic stress & traumatic brain injury in veterans.<\/a> <\/span>Biological Psychiatry, <\/em>78, 210-216<\/span>.<\/em>\u00a0doi:10.1016\/j.biopsych.2015.02.013<\/span><\/p>\n

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

–Random Scripts–<\/h1>\n

EZdiff_4choice.m<\/a>\u00a0– 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.<\/p>\n

simbin.m<\/a> –\u00a0Computes 106 measures of similarity and dissimilarity (distance) between two binary matrices. <\/span><\/p>\n