# Machine learning in resting-state fMRI analysis

arXiv: https://arxiv.org/abs/1812.11477 · HTML: https://mlsanity.com/paper/1812.11477/

## Key facts

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

## Built on by (most cited first, 25 of 25)

- [BrainNPT: Pre-training of Transformer networks for brain network classification](https://mlsanity.com/paper/2305.01666/) (2023)
- [Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression](https://mlsanity.com/paper/2205.15858/) (2022)
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- [Leveraging Brain Modularity Prior for Interpretable Representation Learning of fMRI](https://mlsanity.com/paper/2306.14080/) (2023)
- [Preserving Specificity in Federated Graph Learning for fMRI-based Neurological Disorder Identification](https://mlsanity.com/paper/2308.10302/) (2023)
- [Understanding Cognitive Fatigue from fMRI Scans with Self-supervised Learning](https://mlsanity.com/paper/2106.15009/) (2021)
- [STARFormer: A Novel Spatio-Temporal Aggregation Reorganization Transformer of FMRI for Brain Disorder Diagnosis](https://mlsanity.com/paper/2501.00378/) (2024)
- [Moving Beyond Functional Connectivity: Time-Series Modeling for fMRI-Based Brain Disorder Classification](https://mlsanity.com/paper/2602.08262/) (2026)
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- [mSPD-NN: A Geometrically Aware Neural Framework for Biomarker Discovery from Functional Connectomics Manifolds](https://mlsanity.com/paper/2303.14986/) (2023)
- [BrainMT: A Hybrid Mamba-Transformer Architecture for Modeling Long-Range Dependencies in Functional MRI Data](https://mlsanity.com/paper/2506.22591/) (2025)
- [Weighted Ensemble-model and Network Analysis: A method to predict fluid intelligence via naturalistic functional connectivity](https://mlsanity.com/paper/2101.01973/) (2021)
- [Using CNNs for AD classification based on spatial correlation of BOLD signals during the observation](https://mlsanity.com/paper/2104.10596/) (2021)
- [Highly Accurate FMRI ADHD Classification using time distributed multi modal 3D CNNs](https://mlsanity.com/paper/2205.11993/) (2022)
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- [Bayesian Models of Functional Connectomics and Behavior](https://mlsanity.com/paper/2301.06182/) (2023)
- [TiBGL: Template-induced Brain Graph Learning for Functional Neuroimaging Analysis](https://mlsanity.com/paper/2309.07947/) (2023)
- [Zero-shot Learning of Individualized Task Contrast Prediction from Resting-state Functional Connectomes](https://mlsanity.com/paper/2310.14105/) (2023)
- [Classification of developmental and brain disorders via graph convolutional aggregation](https://mlsanity.com/paper/2311.07370/) (2023)
- [Correlation-Distance Graph Learning for Treatment Response Prediction from rs-fMRI](https://mlsanity.com/paper/2311.10463/) (2023)
- [Emergent Language Symbolic Autoencoder (ELSA) with Weak Supervision to Model Hierarchical Brain Networks](https://mlsanity.com/paper/2404.10031/) (2024)
- [Functional Brain Network Identification in Opioid Use Disorder Using Machine Learning Analysis of Resting-State fMRI BOLD Signals](https://mlsanity.com/paper/2410.19147/) (2024)
- [Hybrid Deep Learning Model to Estimate Cognitive Effort from fNIRS Signals](https://mlsanity.com/paper/2504.13883/) (2025)
- [Graph Neural Networks with Transformer Fusion of Brain Connectivity Dynamics and Tabular Data for Forecasting Future Tobacco Use](https://mlsanity.com/paper/2512.23137/) (2025)
- [A foundation-model approach to pediatric headache classification from rs-fMRI](https://mlsanity.com/paper/2608.07287/) (2026)

## 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.