Voxel selection framework in multi-voxel pattern analysis of FMRI data for prediction of neural response to visual stimuli

IEEE Transactions on Medical Imaging
Chun-An ChouThomas J Grabowski

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 fr...Continue Reading

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Citations

Oct 18, 2016·Brain Informatics·Xinpei MaWanpracha Art Chaovalitwongse
Jun 4, 2016·Journal of X-ray Science and Technology·Yanbin WangPeipeng Liang
Apr 7, 2017·Biomedizinische Technik. Biomedical Engineering·Savita V Raut, Dinkar M Yadav
Jan 29, 2020·Social Cognitive and Affective Neuroscience·Haroon PopalIngrid R Olson
Mar 23, 2018·Neuroinformatics·James Deraeve, William H Alexander
Jul 31, 2020·Data Mining and Knowledge Discovery·Wei ZhangJr-Shin Li

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