A deep learning based approach identifies regions more relevant than resting‐state networks to the prediction of general intelligence from resting‐state fMRI

• Type of Material: Article
• Authors: Bruno Hebling Vieira, Universidade de São Paulo; Julien Dubois, Cedars‐Sinai Medical Center; Vince D Calhoun, Emory University; Carlos Ernesto Garrido Salmon, Universidade de São Paulo
• Language: English
• Date: 2021-09-29
• Publisher: Wiley
• Publication Version: Final Published Version
• Copyright Statement: © 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1002/hbm.25656
• Title of Journal or Parent Work: HUMAN BRAIN MAPPING
• Volume: 42
• Issue: 18
• Start Page: 5873
• End Page: 5887
• Grant/Funding Information: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Grant/Award Number: Finance Code 001; Fundação de Amparo à Pesquisa do Estado de São Paulo, Grant/Award Numbers: 2017/02752‐0, 2018/11881‐1
• Keywords: intelligence; deep learning; brain‐behavior; fMRI; resting‐state
• Research Categories: Engineering, Electronics and Electrical

Abstract

Prediction of cognitive ability latent factors such as general intelligence from neuroimaging has elucidated questions pertaining to their neural origins. However, predicting general intelligence from functional connectivity limit hypotheses to that specific domain, being agnostic to time‐distributed features and dynamics. We used an ensemble of recurrent neural networks to circumvent this limitation, bypassing feature extraction, to predict general intelligence from resting‐state functional magnetic resonance imaging regional signals of a large sample (n = 873) of Human Connectome Project adult subjects. Ablating common resting‐state networks (RSNs) and measuring degradation in performance, we show that model reliance can be mostly explained by network size. Using our approach based on the temporal variance of saliencies, that is, gradients of outputs with regards to inputs, we identify a candidate set of networks that more reliably affect performance in the prediction of general intelligence than similarly sized RSNs. Our approach allows us to further test the effect of local alterations on data and the expected changes in derived metrics such as functional connectivity and instantaneous innovations.