ArticleNeuroImage2020
Modelling subject variability in the spatial and temporal characteristics of functional modes.
Article in NeuroImage, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- White matter variability, cognition, and disorders: a systematic review.Brain structure & function · 2022Pooled it
- Untamed: Unconstrained Tensor Decomposition and Graph Node Embedding for Cortical Parcellation.Human brain mapping · 2026Article
- Functional fractionation of large-scale brain networks in the human subcortex.bioRxiv : the preprint server for biology · 2025Article
- A Functional Resting-State Network Atlas Based on 420 Older Adults with Hypertension.bioRxiv : the preprint server for biology · 2025Article
- Article
- Action-mode subnetworks for decision-making, action control, and feedback.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Mapping individualized multi-scale hierarchical brain functional networks from fMRI by self-supervised deep learning.bioRxiv : the preprint server for biology · 2025Article
- Opaque ontology: neuroimaging classification of ICD-10 diagnostic groups in the UK Biobank.GigaScience · 2025Article
- Multiscale modes of functional brain connectivity.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Higher amplitudes of visual networks are associated with trait- but not state-depression.Psychological medicine · 2024Article
- Review
- The Prediction of Brain Activity from Connectivity: Advances and Applications.The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry · 2024Review
- Opaque Ontology: Neuroimaging Classification of ICD-10 Diagnostic Groups in the UK Biobank.bioRxiv : the preprint server for biology · 2024Article
- Should one go for individual- or group-level brain parcellations? A deep-phenotyping benchmark.Brain structure & function · 2024Article
- Evaluating functional brain organization in individuals and identifying contributions to network overlap.bioRxiv : the preprint server for biology · 2023Article
- Identification of overlapping and interacting networks reveals intrinsic spatiotemporal organization of the human brain.NeuroImage · 2023Article
- Computing personalized brain functional networks from fMRI using self-supervised deep learning.Medical image analysis · 2023Article
- Evaluating functional brain organization in individuals and identifying contributions to network overlap.Imaging neuroscience (Cambridge, Mass.) · 2023Article
- Challenges in Identifying Individualized Brain Biomarkers of Late Life Depression.Advances in geriatric medicine and research · 2023Article
- Accurate predictions of individual differences in task-evoked brain activity from resting-state fMRI using a sparse ensemble learner.NeuroImage · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Recent work has highlighted the scale and ubiquity of subject variability in observations from functional MRI data (fMRI). Furthermore, it is highly likely that errors in the estimation of either the spatial presentation of, or the coupling between, functional regions can confound cross-subject analyses, making accurate and unbiased representations of functional data essential for interpreting any downstream analyses. Here, we extend the framework of probabilistic functional modes (PFMs) (Harrison et al., 2015) to capture cross-subject variability not only in the mode spatial maps, but also in the functional coupling between modes and in mode amplitudes. A new implementation of the inference now also allows for the analysis of modern, large-scale data sets, and the combined inference and analysis package, PROFUMO, is available from git.fmrib.ox.ac.uk/samh/profumo. A new implementation of the inference now also allows for the analysis of modern, large-scale data sets. Using simulated data, resting-state data from 1000 subjects collected as part of the Human Connectome Project (Van Essen et al., 2013), and an analysis of 14 subjects in a variety of continuous task-states (Kieliba et al., 2019), we demonstrate how PFMs are able to capture, within a single model, a rich description of how the spatio-temporal structure of resting-state fMRI activity varies across subjects. We also compare the new PFM model to the well established independent component analysis with dual regression (ICA-DR) pipeline. This reveals that, under PFM assumptions, much more of the (behaviorally relevant) cross-subject variability in fMRI activity should be attributed to the variability in spatial maps, and that, after accounting for this, functional coupling between modes primarily reflects current cognitive state. This has fundamental implications for the interpretation of cross-sectional studies of functional connectivity that do not capture cross-subject variability to the same extent as PFMs.
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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.