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

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-distribute...

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Bibliographic Details
Main Authors:	Calhoun, V.D (Author), Dubois, J. (Author), Garrido Salmon, C.E (Author), Hebling Vieira, B. (Author)
Format:	Article
Language:	English
Published:	John Wiley and Sons Inc 2021
Subjects:	adult article brain Brain brain-behavior connectome Connectome controlled study deep learning Deep Learning diagnostic imaging feature extraction female fMRI functional connectivity functional magnetic resonance imaging human human experiment Humans intelligence Intelligence Magnetic Resonance Imaging major clinical study male nerve cell network Nerve Net nuclear magnetic resonance imaging physiology prediction procedures recurrent neural network resting state network resting-state
Online Access:	View Fulltext in Publisher

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View Fulltext in Publisher

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

Internet

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