ArticleHuman brain mapping2021
A deep learning based approach identifies regions more relevant than resting-state networks to the prediction of general intelligence from resting-state fMRI.
Article in Human brain mapping, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Brain regions beyond the visual cortex are relevant to subjective time prediction from fMRI salient events in a visual naturalistic context.Brain imaging and behavior · 2026Article
- Article
- Self-supervised graph contrastive learning with diffusion augmentation for functional MRI analysis and brain disorder detection.Medical image analysis · 2025Article
- Individual cognitive traits can be predicted from task-based dynamic functional connectivity with a deep convolutional-recurrent model.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Pre-acquired Functional Connectivity Predicts Choice Inconsistency.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Accounting for temporal variability in functional magnetic resonance imaging improves prediction of intelligence.Human brain mapping · 2023Article
- Deep learning in neuroimaging data analysis: Applications, challenges, and solutions.Frontiers in neuroimaging · 2022Review
- Mapping white matter structural covariance connectivity for single subject using wavelet transform with T1-weighted anatomical brain MRI.Frontiers in neuroscience · 2022Article
- A deep learning based approach identifies regions more relevant than resting-state networks to the prediction of general intelligence from resting-state fMRI.Human brain mapping · 2021Article
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Authors and funding
4 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.