Mapping individual differences in cortical architecture using multi-view representation learning

• URL: http://arxiv.org/abs/2004.02804v1
• Date: Wed, 1 Apr 2020 09:01:25 GMT
• Title: Mapping individual differences in cortical architecture using multi-view representation learning
• Authors: Akrem Sellami (QARMA, LIS, INT), Fran\c{c}ois-Xavier Dup\'e (QARMA, LIS), Bastien Cagna (INT), Hachem Kadri (QARMA, LIS), St\'ephane Ayache (QARMA, LIS), Thierry Arti\`eres (QARMA, LIS, ECM), Sylvain Takerkart (INT)
• Abstract summary: We introduce a novel machine learning method which allows combining the activation-and connectivity-based information respectively measured through task-fMRI and resting-state fMRI. It combines a multi-view deep autoencoder which is designed to fuse the two fMRI modalities into a joint representation space within which a predictive model is trained to guess a scalar score that characterizes the patient.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In neuroscience, understanding inter-individual differences has recently emerged as a major challenge, for which functional magnetic resonance imaging (fMRI) has proven invaluable. For this, neuroscientists rely on basic methods such as univariate linear correlations between single brain features and a score that quantifies either the severity of a disease or the subject's performance in a cognitive task. However, to this date, task-fMRI and resting-state fMRI have been exploited separately for this question, because of the lack of methods to effectively combine them. In this paper, we introduce a novel machine learning method which allows combining the activation-and connectivity-based information respectively measured through these two fMRI protocols to identify markers of individual differences in the functional organization of the brain. It combines a multi-view deep autoencoder which is designed to fuse the two fMRI modalities into a joint representation space within which a predictive model is trained to guess a scalar score that characterizes the patient. Our experimental results demonstrate the ability of the proposed method to outperform competitive approaches and to produce interpretable and biologically plausible results.

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