Related Experiment Video
Updated: May 15, 2026
Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
Published on: November 8, 2012
Source-free collaborative domain adaptation via multi-perspective feature enrichment for functional MRI analysis
Yuqi Fang1, Jinjian Wu2, Qianqian Wang1
1Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Abstract:
Resting-state functional MRI (rs-fMRI) is increasingly employed in multi-site research to analyze neurological disorders, but there exists cross-site/domain data heterogeneity caused by site effects such as differences in scanners/protocols. Existing domain adaptation methods that reduce fMRI heterogeneity generally require accessing source domain data, which is challenging due to privacy concerns and/or data storage burdens. To this end, we propose a source-free collaborative domain adaptation (SCDA) framework using only a pretrained source model and unlabeled target data. Specifically, a multi-perspective feature enrichment method (MFE) is developed to dynamically exploit target fMRIs from multiple views. To facilitate efficient source-to-target knowledge transfer without accessing source data, we initialize MFE using parameters of a pretrained source model. We also introduce an unsupervised pretraining strategy using 3,806 unlabeled fMRIs from three large-scale auxiliary databases. Experimental results on three public and one private datasets show the efficacy of our method in cross-scanner and cross-study prediction.
Insights
This study introduces a novel source-free domain adaptation framework for resting-state functional MRI (rs-fMRI) data. The method effectively reduces data heterogeneity across sites without needing original source data, improving neurological disorder analysis.
Area of Science:
- Neuroimaging
- Machine Learning
- Medical Informatics
Background:
- Resting-state functional MRI (rs-fMRI) is vital for multi-site neurological disorder research.
- Cross-site data heterogeneity, stemming from scanner and protocol variations, poses a significant challenge.
- Current domain adaptation techniques often necessitate access to source domain data, raising privacy and storage concerns.
Purpose of the Study:
- To propose a novel source-free collaborative domain adaptation (SCDA) framework.
- To address the limitations of existing methods by eliminating the need for source data access.
- To enhance the analysis of neurological disorders using heterogeneous rs-fMRI data.
Main Methods:
- Developed a source-free collaborative domain adaptation (SCDA) framework.
- Introduced a multi-perspective feature enrichment (MFE) method to leverage target fMRI data from multiple views.
- Employed an unsupervised pretraining strategy on a large dataset (3,806 unlabeled fMRIs) and initialized MFE with a pretrained source model for efficient knowledge transfer.
Main Results:
- Demonstrated the efficacy of the SCDA framework in reducing rs-fMRI data heterogeneity.
- Achieved successful cross-scanner and cross-study prediction performance.
- Validated the method on three public and one private dataset.
Conclusions:
- The proposed SCDA framework offers a viable solution for domain adaptation in multi-site rs-fMRI studies.
- It effectively mitigates cross-site data heterogeneity without requiring access to source data.
- This approach facilitates more robust and generalizable analysis of neurological disorders.
Related Concept Videos
01:24Magnetic Resonance Imaging
01:27Imaging Studies IV: Magnetic Resonance Imaging