Meta-matching to translate phenotypic predictive models from big to small data on structural MRI
Motivation: Small sample size on structural MRI is evitable in reality and significantly limits phenotypic prediction performance. Goal(s): Our goal was to improve prediction performance on small datasets for structural MRI brain imaging. Approach: We adapted the meta-matching framework from functional to structural MRI, and compared it with baseline methods (Elastic net and direct transfer learning). Results: Our meta-matching-based approaches can greatly boost behavioral prediction performance for different small-scale structural MRI datasets. Impact: Our meta-matching-based methods should be able to make good predictions for a variety of neurological and psychiatric disorders even if the availability of structural MRI brain imaging is quite small.
Authors
- Lijun An (ORCID: https://orcid.org/0000-0003-1030-4625)
- Simon Bodo Eickhoff (ORCID: https://orcid.org/0000-0001-6363-2759)
- B.T. Thomas Yeo (ORCID: https://orcid.org/0000-0002-0119-3276)
- Pansheng Chen (ORCID: https://orcid.org/0009-0009-8881-8643)
- Danilo Bzdok (ORCID: https://orcid.org/0000-0003-3466-6620)
- Ru Kong (ORCID: https://orcid.org/0000-0001-7842-0329)
- Naren Wulan (ORCID: https://orcid.org/0009-0005-3974-7528)
- Chen Zhang (ORCID: https://orcid.org/0000-0002-1579-0047)
- Avram J. Holmes (ORCID: https://orcid.org/0000-0001-6583-803X)
Publication Details
- Journal
- Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
- Published
- 2024-11-26
- DOI
- https://doi.org/10.58530/2024/4521
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00