Predicting cross-task behavioral variables from fMRI data using the k-support norm

Michail Misyrlis 1, 2 Anna Konova 2, 3 Matthew Blaschko 4, 5 Jean Honorio 6 Nelly Alia-Klein 2 Rita Goldstein 2 Dimitris Samaras 3
1 Computer Science Department [SUNY]
2 Mount Sinai School of Medicine
3 SBU - Stony Brook University [SUNY]
4 GALEN - Organ Modeling through Extraction, Representation and Understanding of Medical Image Content
Ecole Centrale Paris, Inria Saclay - Ile de France
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.
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Michail Misyrlis, Anna Konova, Matthew Blaschko, Jean Honorio, Nelly Alia-Klein, et al.. Predicting cross-task behavioral variables from fMRI data using the k-support norm. Sparsity Techniques in Medical Imaging (STMI), Sep 2014, Boston, United States. ⟨hal-01026303⟩

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