DocumentCode :
54236
Title :
Voxel Selection Framework in Multi-Voxel Pattern Analysis of fMRI Data for Prediction of Neural Response to Visual Stimuli
Author :
Chun-An Chou ; Kampa, Kittipat ; Mehta, Sonya H. ; Tungaraza, Rosalia F. ; Chaovalitwongse, Wanpracha A. ; Grabowski, Thomas J.
Author_Institution :
Dept. of Syst. Sci. & Ind. Eng., Binghamton Univ., Vestal, NY, USA
Volume :
33
Issue :
4
fYear :
2014
fDate :
Apr-14
Firstpage :
925
Lastpage :
934
Abstract :
Multi-voxel pattern analysis (MVPA) of functional magnetic resonance imaging (fMRI) data is an emerging approach for probing the neural correlates of cognition. MVPA allows cognitive states to be modeled as distributed patterns of neural activity and classified according to stimulus conditions. In practice, building a robust, generalizable classification model can be challenging because the number of voxels (features) far exceeds the number of stimulus instances/data observations. To avoid model overfitting, there is a need to select informative voxels before building a classification model. In this paper, we propose a robust feature (voxel) selection framework using mutual information (MI) and partial least square regression (PLS) to establish an informativeness index for prioritizing selection of voxels based on the degree of their association to the experimental conditions. We evaluated the robustness of our proposed framework by assessing performance of standard classification algorithms, when combined with our feature selection approach, in a publicly-available fMRI dataset of object-level representation widely used to benchmark MVPA performance (Haxby, 2001). The computational results suggest that our feature selection framework based on MI and PLS drastically improves the classification accuracy relative to those previously reported in the literature. Our results also suggest that highly informative voxels may provide meaningful insight into the functional-anatomic relationship of brain activity and stimulus conditions.
Keywords :
biomedical MRI; brain; cognition; feature selection; image classification; image representation; least squares approximations; medical image processing; neurophysiology; regression analysis; PLS; benchmark MVPA performance; brain activity; cognitive states; feature selection approach; functional magnetic resonance imaging data; functional-anatomic relationship; generalizable classification model; multivoxel pattern analysis; mutual information; neural activity; object-level representation; partial least square regression; publicly-available fMRI dataset; visual stimuli; voxel selection framework; Accuracy; Brain; Educational institutions; Feature extraction; Indexes; Pattern analysis; Support vector machines; Classification; feature selection; functional magnetic resonance imaging (fMRI); information theory; multi-voxel pattern analysis (MVPA); partial least square (PLS); pattern recognition;
fLanguage :
English
Journal_Title :
Medical Imaging, IEEE Transactions on
Publisher :
ieee
ISSN :
0278-0062
Type :
jour
DOI :
10.1109/TMI.2014.2298856
Filename :
6705701
Link To Document :
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