ArticleNeuroImage2022
Accurate predictions of individual differences in task-evoked brain activity from resting-state fMRI using a sparse ensemble learner.
Article in NeuroImage, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Neurobiological and behavioral relevance of intrinsic functional connectome constraints on task-evoked neural activation.iScience · 2026Article
- Integration of resting-state and stimulus-fMRI uncovers reduced network flexibility in post-surgical pain.Scientific reports · 2026Article
- Generating synthetic task-based brain fingerprints for population neuroscience using deep learning.Communications biology · 2025Article
- The Beholder's Share: Bridging art and neuroscience to study individual differences in subjective experience.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Predicting task-related brain activity from resting-state brain dynamics with fMRI Transformer.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- GEMReg: a spatio-temporal grayordinate ensemble modelling framework for predicting task activation maps from resting-state fMRI.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
- Exploring the neural basis of non-invasive prehabilitation in brain tumour patients: An fMRI-based case report of language network plasticity.Frontiers in oncology · 2024Article
- Dynamic effective connectivity among large-scale brain networks mediates risk of anxiety.Human brain mapping · 2023Article
- An atlas of trait associations with resting-state and task-evoked human brain functional organizations in the UK Biobank.Imaging neuroscience (Cambridge, Mass.) · 2023Article
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Authors and funding
6 authors.
Funding
Abstract
Modelling and predicting individual differences in task-fMRI activity can have a wide range of applications from basic to clinical neuroscience. It has been shown that models based on resting-state activity can have high predictive accuracy. Here we propose several improvements to such models. Using a sparse ensemble learner, we show that (i) features extracted using Stochastic Probabilistic Functional Modes (sPROFUMO) outperform the previously proposed dual-regression approach, (ii) that the shape and overall intensity of individualised task activations can be modelled separately and explicitly, (iii) training the model on predicting residual differences in brain activity further boosts individualised predictions. These results hold for both surface-based analyses of the Human Connectome Project data as well as volumetric analyses of UK-biobank data. Overall, our model achieves state of the art prediction accuracy on par with the test-retest reliability of task-fMRI scans, suggesting that it has potential to supplement traditional task localisers.
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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.