TY - JOUR
T1 - BSMac
T2 - A MATLAB toolbox implementing a Bayesian spatial model for brain activation and connectivity
AU - Zhang, Lijun
AU - Agravat, Sanjay
AU - Derado, Gordana
AU - Chen, Shuo
AU - McIntosh, Belinda J.
AU - Bowman, F. Du Bois
N1 - Funding Information:
This research was supported by NIH grants R01-MH079251 (Bowman) and T32-GM074909-01 (Derado).
PY - 2012/2/15
Y1 - 2012/2/15
N2 - 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.
AB - 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.
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U2 - 10.1016/j.jneumeth.2011.10.025
DO - 10.1016/j.jneumeth.2011.10.025
M3 - Article
C2 - 22101143
AN - SCOPUS:82455219079
SN - 0165-0270
VL - 204
SP - 133
EP - 143
JO - Journal of Neuroscience Methods
JF - Journal of Neuroscience Methods
IS - 1
ER -