ArticleNeuroImage2022
Predicting individual task contrasts from resting-state functional connectivity using a surface-based convolutional network.
Article in NeuroImage, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- From rest to focus: pharmacological modulation of the relationship between resting state dorsal attention network dynamics and task-based brain activation.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Trial
- Efficient Deep Learning Models for Predicting Individualized Task Activation From Resting-State Functional Connectivity.Human brain mapping · 2026Article
- TADM-CGAN: a resting-state to task activation map prediction framework using temporal attention-driven diffusion models and conditional generative adversarial networks.Scientific reports · 2026Article
- Generating synthetic task-based brain fingerprints for population neuroscience using deep learning.Communications biology · 2025Article
- Multimodal contrastive learning on rs-fMRI to quantify whole-brain network recovery after hypothalamic hamartoma surgery.Biomedical engineering online · 2025Article
- Inflexible Orbitofrontal Cortex Functional Connectivity From Rest to Acute Stress in Alcohol Use Disorder.Addiction biology · 2025Article
- Covariance-on-covariance regression.Biometrics · 2025Article
- Task-Rest Reconfiguration Efficiency of the Reward Network Across Adolescence and Its Association With Early Life Stress and Depressive Symptoms.Journal of the American Academy of Child and Adolescent Psychiatry · 2025Article
- GEMReg: a spatio-temporal grayordinate ensemble modelling framework for predicting task activation maps from resting-state fMRI.Frontiers in neuroscience · 2025Article
- Predicting task-related brain activity from resting-state brain dynamics with fMRI Transformer.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Resting-state functional connectivity changes following audio-tactile speech training.Frontiers in neuroscience · 2025Article
- Individualised prediction of longitudinal change in multimodal brain imaging.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- The Prediction of Brain Activity from Connectivity: Advances and Applications.The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry · 2024Review
- Predicting high-level visual areas in the absence of task fMRI.Scientific reports · 2024Article
- A comparison of fMRI presurgical mapping techniques with intraoperative brain mapping-based validation.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Temporal dysregulation of the somatomotor network in agitated depression.Brain communications · 2024Article
- Causally informed activity flow models provide mechanistic insight into network-generated cognitive activations.NeuroImage · 2023Article
- Localized activity alternations in periventricular nodular heterotopia-related epilepsy.CNS neuroscience & therapeutics · 2023Article
- Accurate predictions of individual differences in task-evoked brain activity from resting-state fMRI using a sparse ensemble learner.NeuroImage · 2022Article
- Deep learning in neuroimaging data analysis: Applications, challenges, and solutions.Frontiers in neuroimaging · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Task-based and resting-state represent the two most common experimental paradigms of functional neuroimaging. While resting-state offers a flexible and scalable approach for characterizing brain function, task-based techniques provide superior localization. In this paper, we build on recent deep learning methods to create a model that predicts task-based contrast maps from resting-state fMRI scans. Specifically, we propose BrainSurfCNN, a surface-based fully-convolutional neural network model that works with a representation of the brain's cortical sheet. BrainSurfCNN achieves exceptional predictive accuracy on independent test data from the Human Connectome Project, which is on par with the repeat reliability of the measured subject-level contrast maps. Conversely, our analyses reveal that a previously published benchmark is no better than group-average contrast maps. Finally, we demonstrate that BrainSurfCNN can generalize remarkably well to novel domains with limited training data.
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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.