ReviewMagnetic resonance imaging2019
Machine learning in resting-state fMRI analysis.
Review in Magnetic resonance imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 94 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
94 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Somatosensory Deficits After Stroke: Insights From MRI Studies.Frontiers in neurology · 2022Pooled it
- Systematic Review of Functional MRI Applications for Psychiatric Disease Subtyping.Frontiers in psychiatry · 2021Pooled it
- On the value of radiomics in addition to clinical measures in emotional conflict fMRI for predicting sertraline response in major depressive disorder.Scientific reports · 2026Trial
- Predicting pediatric anxiety from the temporal pole using neural responses to emotional faces.Scientific reports · 2021Trial
- Brain Connectivity and Machine Learning Approaches to assess the underlying neurobiology and prediction accuracy of anorexia nervosa: A replication study.Psychiatry research. Neuroimaging · 2026Article
- A foundation-model approach to pediatric headache classification from resting-state fMRI.ArXiv · 2026Article
- Modeling differences in neurodevelopmental maturity of the reading network using support vector regression on functional connectivity data.Developmental cognitive neuroscience · 2026Article
- Benchmarking resting state fMRI connectivity pipelines for classification: robust accuracy despite processing variability in cross-site eye state prediction.Brain informatics · 2026Article
- Functional magnetic resonance imaging study on short-term memory changes in children after congenital heart surgery.Journal of cardiothoracic surgery · 2026Article
- Enhancing depression diagnosis with augmented brain signal driven decorrelated graph neural networks.Communications medicine · 2026Article
- Neuroinflammation and blood-brain barrier dysfunction in cerebral small vessel disease: mechanisms, biomarkers, and therapeutic implications.European journal of medical research · 2026Review
- Modelling learning dynamics in autism therapy through explainable multimodal representation learning.Frontiers in neuroscience · 2026Article
- Shifts in brain dynamics and drivers of consciousness state transitions.Frontiers in computational neuroscience · 2026Article
- Article
- Machine learning combined with resting-state functional MRI to characterize functional brain differences in post-stroke depression.Frontiers in psychiatry · 2026Article
- A foundation-model approach to pediatric headache classification from resting-state fMRI.Proceedings of machine learning research · 2026Article
- Identify MRI negative temporal lobe epilepsy with resting fMRI indicators and machine learning techniques.Scientific reports · 2025Article
- Limited generalizability of dynamic fMRI correlates of adolescent rumination.Nature. Mental health · 2025Article
- DAGMNet: Dual-Branch Attention-Pruned Graph Neural Network for Multimodal sMRI and fMRI Fusion in Autism Prediction.Biomedicines · 2025Article
- Limited generalizability of dynamic fMRI correlates of adolescent rumination.bioRxiv : the preprint server for biology · 2025Article
34 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Machine learning techniques have gained prominence for the analysis of resting-state functional Magnetic Resonance Imaging (rs-fMRI) data. Here, we present an overview of various unsupervised and supervised machine learning applications to rs-fMRI. We offer a methodical taxonomy of machine learning methods in resting-state fMRI. We identify three major divisions of unsupervised learning methods with regard to their applications to rs-fMRI, based on whether they discover principal modes of variation across space, time or population. Next, we survey the algorithms and rs-fMRI feature representations that have driven the success of supervised subject-level predictions. The goal is to provide a high-level overview of the burgeoning field of rs-fMRI from the perspective of machine learning applications.
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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.