BSMac: A MATLAB toolbox implementing a Bayesian spatial model for brain activation and connectivity

Lijun Zhang, Sanjay Agravat, Gordana Derado, Shuo Chen, Belinda J. McIntosh, F. Du Bois Bowman

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

We present a statistical and graphical visualization MATLAB toolbox for the analysis of functional magnetic resonance imaging (fMRI) data, called the Bayesian Spatial Model for activation and connectivity (BSMac). BSMac simultaneously performs whole-brain activation analyses at the voxel and region of interest (ROI) levels as well as task-related functional connectivity (FC) analyses using a flexible Bayesian modeling framework (Bowman et al., 2008). BSMac allows for inputting data in either Analyze or Nifti file formats. The user provides information pertaining to subgroup memberships, scanning sessions, and experimental tasks (stimuli), from which the design matrix is constructed. BSMac then performs parameter estimation based on Markov Chain Monte Carlo (MCMC) methods and generates plots for activation and FC, such as interactive 2D maps of voxel and region-level task-related changes in neural activity and animated 3D graphics of the FC results. The toolbox can be downloaded from http://www.sph.emory.edu/bios/CBIS/. We illustrate the BSMac toolbox through an application to an fMRI study of working memory in patients with schizophrenia.

Original languageEnglish (US)
Pages (from-to)133-143
Number of pages11
JournalJournal of Neuroscience Methods
Volume204
Issue number1
DOIs
StatePublished - Feb 15 2012

All Science Journal Classification (ASJC) codes

  • Neuroscience(all)

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