Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2012 Feb 15;204(1):133-143.
doi: 10.1016/j.jneumeth.2011.10.025. Epub 2011 Nov 10.

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

Lijun Zhang  1 , Sanjay Agravat  2 , Gordana Derado  3 , Shuo Chen  3 , Belinda J McIntosh  4 , F DuBois Bowman  3
Affiliations

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

Lijun Zhang et al. J Neurosci Methods. .

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.

PubMed Disclaimer

Figures

Fig. 1
Fig. 1
BSMac GUI interface.
Fig. 2
Fig. 2
Interface for loading images.
Fig. 3
Fig. 3
Contrast matrix for the second-level analysis of the fBIRN data.
Fig. 4
Fig. 4
Basic summary plots: (a) selected region labels, (b) regional posterior mean plot, (c) regional posterior probabilities, (d) voxel-level posterior probabilities, (e) intra-regional correlations, and (f) inter-regional correlations.
Fig. 5
Fig. 5
Voxel-level activity map. Region labels are obtained from the GUI and then added manually to this figure.
Fig. 6
Fig. 6
Functional connectivity: functional connectivity in (a) schizophrenia patients and (b) healthy controls.
See this image and copyright information in PMC

References

    1. Barch DM, Sheline YI, Csernansky JG, Snyder AZ. Working memory and prefrontal cortex dysfunction: specificity to schizophrenia compared with major depression. Biol Psychiatry. 2003;53(5):376–84. - PubMed
    1. Bassett D, Bullmore E, Verchinski B, Mattay V, Weinberger D, Meyer-Lindenberg A. Hierarchical organization of human cortical networks in health and schizophrenia. Neuroscience. 2008;28(37):9239–48. - PMC - PubMed
    1. Bluhm R, Miller J, Lanius R, Osuch E, Boksman K, Neufeld R, et al. Spontaneous low frequency fluctuations in the BOLD signal in schizophrenic patients: anomalies in the default network. Schizophr Bull. 2007;33(4):1004–12. - PMC - PubMed
    1. Bowman FD. Spatiotemporal modeling of localized brain activity. Biostatistics. 2005;6(4):558–75. - PubMed
    1. Bowman FD, Caffo B, Bassett SS, Kilts C. A Bayesian hierarchical framework for spatial modeling of fMRI data. Neuroimage. 2008;39:146–56. - PMC - PubMed

Publication types