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Related Concept Videos

Brain Imaging01:14

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

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Related Experiment Video

Updated: May 27, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
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Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

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

Lijun Zhang1, Sanjay Agravat2, Gordana Derado1

  • 1Department of Biostatistics and Bioinformatics, The Rollins School of Public Health, Emory University, Atlanta, GA 30322, United States.

Journal of Neuroscience Methods
|November 22, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces BSMac, a MATLAB toolbox for analyzing functional magnetic resonance imaging (fMRI) data. BSMac offers advanced statistical and graphical visualization for brain activation and functional connectivity (FC) analysis.

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STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Statistical Modeling

Background:

  • Functional magnetic resonance imaging (fMRI) is crucial for understanding brain activity.
  • Analyzing complex fMRI data requires sophisticated statistical and visualization tools.
  • Existing methods may not fully integrate activation and connectivity analyses.

Purpose of the Study:

  • To present the Bayesian Spatial Model for activation and connectivity (BSMac) toolbox.
  • To provide a unified framework for whole-brain activation and functional connectivity (FC) analysis in fMRI.
  • To facilitate advanced statistical and graphical visualization of fMRI data.

Main Methods:

  • Development of a MATLAB toolbox (BSMac) utilizing a Bayesian modeling framework.
  • Implementation of Markov Chain Monte Carlo (MCMC) methods for parameter estimation.
  • Support for Analyze and Nifti file formats with user-defined design matrices.

Main Results:

  • BSMac enables simultaneous whole-brain activation and task-related functional connectivity (FC) analyses.
  • The toolbox generates interactive 2D maps of neural activity and animated 3D graphics of FC.
  • Demonstrated application to fMRI data from a working memory study in schizophrenia patients.

Conclusions:

  • BSMac offers a flexible and comprehensive approach to fMRI data analysis.
  • The toolbox enhances the visualization and statistical analysis of brain activation and connectivity.
  • BSMac is a valuable resource for researchers in neuroimaging and related fields.