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

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 an...

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Vydané v:Proceedings of ... International Joint Conference on Neural Networks s. 1 - 8
Hlavní autori: Sellami, Akrem, Dupe, Francois-Xavier, Cagna, Bastien, Kadri, Hachem, Ayache, Stephane, Artieres, Thierry, Takerkart, Sylvain
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.07.2020
Predmet:
Brain modeling
Feature extraction
Functional magnetic resonance imaging
individual differences
Manifolds
multi-view representation learning
multimodal deep autoencoder
Predictive models
Task analysis
trace regression
ISSN:2161-4407
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Shrnutí: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.
ISSN:2161-4407
DOI:10.1109/IJCNN48605.2020.9206887