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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

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From Big to Small: Emerging Methods for Enhancing Precision Psychiatry Through Transfer Learning.

Yu-Wei Wang¹, Avram J Holmes², B T Thomas Yeo³

¹Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut.

Biological Psychiatry

|October 31, 2025

Summary

Transfer learning enhances brain-behavior predictive models by leveraging large datasets. This approach improves the accuracy of predicting clinical outcomes from neuroimaging data in precision psychiatry.

Keywords:

Ensemble learning MRI Personalized medicine Precision imaging Treatment fMRI

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Area of Science:

Neuroscience
Machine Learning
Psychiatry

Background:

Precision psychiatry aims to link neurobiology to clinical outcomes.
Brain-behavior predictive modeling is key for understanding neural mechanisms.
Limited patient data hinders clinical adoption of these models.

Purpose of the Study:

To provide an overview of transfer learning for brain-behavior modeling.
To highlight its utility in predicting clinical outcomes.
To discuss practical considerations for clinical adoption.

Main Methods:

Leveraging large-scale neuroimaging datasets.
Applying transfer learning to adapt models for smaller, specific datasets.
Fine-tuning pre-trained models for accurate prediction.

Main Results:

Pre-trained models can predict clinical features from new neuroimaging data.
Transfer learning enhances model generalizability and interpretability.
Demonstrated utility in predicting clinical outcomes.

Conclusions:

Transfer learning offers a solution to data limitations in clinical neuroimaging.
It holds potential for advancing mechanistic understanding in psychiatry.
It can bolster the clinical utility of brain-behavior models.