ArticleNeuroImage2021
Hierarchical modelling of functional brain networks in population and individuals from big fMRI data.
Article in NeuroImage, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- The role of age in the relationship between brain structure and cognition: moderator or confound?Cerebral cortex (New York, N.Y. : 1991) · 2026Article
- A general framework for investigating neurodevelopment of brain functional networks using multisite and longitudinal neuroimaging.The annals of applied statistics · 2026Article
- A review of decomposition methods for brain states estimation.Biomedical engineering online · 2026Review
- A Functional Resting-State Network Atlas Based on 420 Older Adults with Hypertension.bioRxiv : the preprint server for biology · 2025Article
- Independent Filter Analysis for Group Discrimination in fMRI.bioRxiv : the preprint server for biology · 2025Article
- Bias in data-driven replicability analysis of univariate brain-wide association studies.Scientific reports · 2025Article
- Multiscale modes of functional brain connectivity.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Aberrant dynamic functional network connectivity in patients with diffuse axonal injury.Scientific reports · 2024Article
- Reducing individual differences in task fMRI with OGRE (One-step General Registration and Extraction) preprocessing.bioRxiv : the preprint server for biology · 2024Article
- Multilayer meta-matching: Translating phenotypic prediction models from multiple datasets to small data.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Sparse Bayesian modeling of hierarchical independent component analysis: Reliable estimation of individual differences in brain networks.Biometrics · 2023Article
- A primer on Variational Laplace (VL).NeuroImage · 2023Article
- Evaluating functional brain organization in individuals and identifying contributions to network overlap.bioRxiv : the preprint server for biology · 2023Article
- Comparison between gradients and parcellations for functional connectivity prediction of behavior.NeuroImage · 2023Article
- Identification of overlapping and interacting networks reveals intrinsic spatiotemporal organization of the human brain.NeuroImage · 2023Article
- Evaluating functional brain organization in individuals and identifying contributions to network overlap.Imaging neuroscience (Cambridge, Mass.) · 2023Article
- Accurate predictions of individual differences in task-evoked brain activity from resting-state fMRI using a sparse ensemble learner.NeuroImage · 2022Article
- Modelling variability in functional brain networks using embeddings.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
A major goal of large-scale brain imaging datasets is to provide resources for investigating heterogeneous populations. Characterisation of functional brain networks for individual subjects from these datasets will have an enormous potential for prediction of cognitive or clinical traits. We propose for the first time a technique, Stochastic Probabilistic Functional Modes (sPROFUMO), that is scalable to UK Biobank (UKB) with expected 100,000 participants, and hierarchically estimates functional brain networks in individuals and the population, while allowing for bidirectional flow of information between the two. Using simulations, we show the model's utility, especially in scenarios that involve significant cross-subject variability, or require delineation of fine-grained differences between the networks. Subsequently, by applying the model to resting-state fMRI from 4999 UKB subjects, we mapped resting state networks (RSNs) in single subjects with greater detail than has been possible previously in UKB (>100 RSNs), and demonstrate that these RSNs can predict a range of sensorimotor and higher-level cognitive functions. Furthermore, we demonstrate several advantages of the model over independent component analysis combined with dual-regression (ICA-DR), particularly with respect to the estimation of the spatial configuration of the RSNs and the predictive power for cognitive traits. The proposed model and results can open a new door for future investigations into individualised profiles of brain function from big data.
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