Apr 25, 2020

Functional Annotation of Human Cognitive States using Deep Graph Convolution

BioRxiv : the Preprint Server for Biology
Y. ZhangPierre Bellec

Abstract

A key goal in neuroscience is to understand the brain mechanisms of cognitive functions. An emerging approach is brain decoding, which consists of inferring a set of experimental conditions performed by a participant, using pattern classification of brain activity. Few works so far have attempted to train a brain decoding model that would generalize across many different cognitive tasks drawn from multiple cognitive domains. To tackle this problem, we proposed a domain-general brain decoder that automatically learns the spatiotemporal dynamics of brain response within a short time window using a deep learning approach. By leveraging our prior knowledge on network organization of human brain cognition, we constructed deep graph convolutional neural networks to annotate cognitive states by first mapping the task-evoked fMRI response onto a brain graph, propagating brain dynamics among interconnected brain regions and functional networks, and generating state-specific representations of recorded brain activity. We evaluated the decoding model on a large population of 1200 participants, under 21 different experimental conditions spanning 6 different cognitive domains, acquired from the Human Connectome Project task-fMRI database. U...Continue Reading

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Mentioned in this Paper

Genes
IFI6 protein, human
Oligonucleotide Primers
Species
RNA, Ribosomal, 16S
Bacterial 16S RNA
Full Supernumerary Rib

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