| Full text | |
| Author(s): |
Vieira, Bruno Hebling
[1, 2]
;
Dubois, Julien
[3, 4]
;
Calhoun, Vince D.
[2, 5, 6]
;
Salmon, Carlos Ernesto Garrido
[1]
Total Authors: 4
|
| Affiliation: | [1] Univ Sao Paulo, InBrain Lab, Dept Fis, Ribeirao Preto - Brazil
[2] Emory Univ, Triinst Ctr Translat Res Neuroimaging & Data Sci, Georgia State Univ, Georgia Inst Technol, Atlanta, GA 30322 - USA
[3] CALTECH, Pasadena, CA 91125 - USA
[4] Cedars Sinai Med Ctr, Los Angeles, CA 90048 - USA
[5] Mind Res Network, Albuquerque, NM - USA
[6] Georgia Inst Technol, Sch Elect & Comp Engn, Atlanta, GA 30332 - USA
Total Affiliations: 6
|
| Document type: | Journal article |
| Source: | Human Brain Mapping; v. 42, n. 18 SEP 2021. |
| Web of Science Citations: | 0 |
| 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. (AU) | |
| FAPESP's process: | 18/11881-1 - Machine learning prediction of intellectual abilities from magnetic resonance neuroimaging |
| Grantee: | Bruno Hebling Vieira |
| Support Opportunities: | Scholarships in Brazil - Doctorate |
| FAPESP's process: | 17/02752-0 - Prediction of human intelligence through neuroimaging features |
| Grantee: | Carlos Ernesto Garrido Salmon |
| Support Opportunities: | Regular Research Grants |