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Self-Supervised Learning to Unveil Brain Dysfunctional Signatures in Brain Disorders: Methods and Applications
Ying Li1, Yanwu Yang2, Yuchu Chen1
1School of Electronic and Information Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, China.
Health Data Science
|August 6, 2025
Summary
Self-supervised learning (SSL) models decode brain dysfunction from functional neuroimaging, aiding in brain disorder diagnosis. These methods offer scalable detection and prediction for conditions like Alzheimer's and Parkinson's disease.
Area of Science:
- Neuroscience
- Machine Learning
- Medical Imaging
Background:
- Decoding brain dysfunction from functional neuroimaging is vital for understanding brain disorders.
- Self-supervised learning (SSL) offers a powerful approach for analyzing complex functional neuroimaging data.
- SSL addresses challenges like data heterogeneity and limited labeled data in clinical neuroscience.
Purpose of the Study:
- To provide a comprehensive overview of SSL techniques in functional neuroimaging.
- To highlight SSL applications in neuropsychiatric disorders.
- To discuss the potential of SSL for disease detection and prediction.
Main Methods:
- Overview of three SSL categories: contrastive, generative, and generative-contrastive learning.
- Discussion of basic principles and representative methods within each category.
- Focus on applications in functional magnetic resonance imaging (fMRI) and electroencephalography (EEG).
Main Results:
- SSL effectively extracts neurofunctional features, overcoming data limitations.
- Demonstrated applications in Alzheimer's disease, Parkinson's disease, and epilepsy.
- SSL shows potential for multimodal integration and dynamic network modeling.
Conclusions:
- SSL models provide scalable and effective methods for detecting and predicting brain disorders.
- Future applications include transdiagnostic psychosis subtyping and decoding task-based brain recordings.
- Addressing interpretability and data heterogeneity are key for clinical translation.
