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Machine learning in resting-state fMRI analysis

arXiv 1812.11477 ↗First posted 30 Dec 2018cs.LG cs.CV q-bio.QMcited by 25

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Abstract (arXiv)

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 present 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.

Key facts. Cited by 25 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Dec 2018.

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Citing papers per year: 2021: 4; 2022: 3; 2023: 10; 2024: 3; 2025: 3; 2026: 2.

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  1. 2023BrainNPT: Pre-training of Transformer networks for brain network classificationcited by 9
  2. 2022Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regressioncited by 4
  3. 2023Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric researchcited by 4
  4. 2023Leveraging Brain Modularity Prior for Interpretable Representation Learning of fMRIcited by 4
  5. 2023Preserving Specificity in Federated Graph Learning for fMRI-based Neurological Disorder Identificationcited by 4
  6. 2021Understanding Cognitive Fatigue from fMRI Scans with Self-supervised Learningcited by 2
  7. 2024STARFormer: A Novel Spatio-Temporal Aggregation Reorganization Transformer of FMRI for Brain Disorder Diagnosiscited by 2
  8. 2026Moving Beyond Functional Connectivity: Time-Series Modeling for fMRI-Based Brain Disorder Classificationcited by 2
  9. 2021Brain dynamics via Cumulative Auto-Regressive Self-Attentioncited by 1
  10. 2023mSPD-NN: A Geometrically Aware Neural Framework for Biomarker Discovery from Functional Connectomics Manifoldscited by 1
  11. 2025BrainMT: A Hybrid Mamba-Transformer Architecture for Modeling Long-Range Dependencies in Functional MRI Datacited by 1
  12. 2021Weighted Ensemble-model and Network Analysis: A method to predict fluid intelligence via naturalistic functional connectivitycited by 0
13 more
  1. 2021Using CNNs for AD classification based on spatial correlation of BOLD signals during the observationcited by 0
  2. 2022Highly Accurate FMRI ADHD Classification using time distributed multi modal 3D CNNscited by 0
  3. 2022Comparative study of machine learning and deep learning methods on ASD classificationcited by 0
  4. 2023Bayesian Models of Functional Connectomics and Behaviorcited by 0
  5. 2023TiBGL: Template-induced Brain Graph Learning for Functional Neuroimaging Analysiscited by 0
  6. 2023Zero-shot Learning of Individualized Task Contrast Prediction from Resting-state Functional Connectomescited by 0
  7. 2023Classification of developmental and brain disorders via graph convolutional aggregationcited by 0
  8. 2023Correlation-Distance Graph Learning for Treatment Response Prediction from rs-fMRIcited by 0
  9. 2024Emergent Language Symbolic Autoencoder (ELSA) with Weak Supervision to Model Hierarchical Brain Networkscited by 0
  10. 2024Functional Brain Network Identification in Opioid Use Disorder Using Machine Learning Analysis of Resting-State fMRI BOLD Signalscited by 0
  11. 2025Hybrid Deep Learning Model to Estimate Cognitive Effort from fNIRS Signalscited by 0
  12. 2025Graph Neural Networks with Transformer Fusion of Brain Connectivity Dynamics and Tabular Data for Forecasting Future Tobacco Usecited by 0
  13. 2026A foundation-model approach to pediatric headache classification from rs-fMRIcited by 0