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Proceedings Paper

Novel information theoretic and Bayesian approach for fMRI data analysis
Author(s): Chandan K. Reddy; Alejandro Terrazas
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Paper Abstract

Functional Magnetic Resonance Imaging (fMRI) is a powerful technique for studying the working of the human brain. This overall goals of the project are to devlop a novel method for the analysis of fMRI data in order to discover the activation of a network of regions involving most likely the hippocampus, parietal cortex and cerebellum as a person is navigating in a virtual environment. Spatially sensitive voxels are extracted by selecting voxels that have high mutual information. Each of these extracted voxels is then used to create a response curve for the stimulus of interest, in this case spatial location. Following the voxel extraction stage, the set of extracted voxel time series would be treated as a population and used to predict the location of the subject at any randomly selected time in the experiment. The population of voxels essentially "votes" with their current activity. The approach used for prediction is the Bayesian reconstruction method. The ability to predict the location of a subject in the virtual environment based on brain signals will be useful in developing a physiological understanding of spatial cognition in virtual environments.

Paper Details

Date Published: 2 May 2003
PDF: 9 pages
Proc. SPIE 5031, Medical Imaging 2003: Physiology and Function: Methods, Systems, and Applications, (2 May 2003); doi: 10.1117/12.480433
Show Author Affiliations
Chandan K. Reddy, Michigan State Univ. (United States)
Alejandro Terrazas, Michigan State Univ. (United States)
VRSciences (United States)


Published in SPIE Proceedings Vol. 5031:
Medical Imaging 2003: Physiology and Function: Methods, Systems, and Applications
Anne V. Clough; Amir A. Amini, Editor(s)

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