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

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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Submitted on : Tuesday, March 31, 2020 - 11:50:38 AM
Last modification on : Friday, November 27, 2020 - 3:08:54 AM


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  • HAL Id : hal-02520673, version 1
  • ARXIV : 2004.02804



Akrem Sellami, François-Xavier Dupé, Bastien Cagna, Hachem Kadri, Stéphane Ayache, et al.. Mapping individual differences in cortical architecture using multi-view representation learning. IJCNN 2020 - International Joint Conference on Neural Networks, Jul 2020, Glasgow, United Kingdom. ⟨hal-02520673⟩



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