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1 Computer Science Department SUNY 2 Mount Sinai School of Medicine 3 SBU - Stony Brook University The State University of New York 4 GALEN - Organ Modeling through Extraction, Representation and Understanding of Medical Image Content Inria Saclay - Ile de France, Ecole Centrale Paris 5 CVN - Centre de vision numérique 6 CSAIL - Computer Science and Artificial Intelligence Laboratory Cambridge

Abstract : Sparsity regularization allows handling the curse of dimensionality, a problem commonly found in fMRI data. In this paper, we compare LASSO L1 regularization and the recently introduced k-support norm on their ability to predict real valued variables from brain fMRI data for cocaine addiction, in a principled model selection setting. Furthermore, in the context of those two regularization methods, we compare two loss functions: squared loss and absolute loss. With the squared loss function, k-support norm outperforms LASSO in predicting real valued behavioral variables measured in an inhibitory control task given fMRI data from a different task, designed to capture emotionally-salient reward. The absolute loss function leads to significantly better predictive performance for both methods in almost all cases and the k-support norm leads to more interpretable and more stable solutions often by an order of magnitude. Our results support the use of the k-support norm for fMRI analysis and the generalizability of the I-RISA model of cocaine addiction.

Author: Michail Misyrlis - Anna Konova - Matthew Blaschko - Jean Honorio - Nelly Alia-Klein - Rita Goldstein - Dimitris Samaras -



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