A multimodal ensemble stacking model improves brain age prediction and reveals associations with schizophrenia symptoms
Our findings demonstrate that integrating sMRI and FA features improves brain age prediction accuracy and generalization. Furthermore, the correlation between brainPAD and clinical symptoms highlights its potential as a biomarker for disease progression and treatment monitoring. These results underscore the value of multimodal neuroimaging and machine learning in advancing psychiatric neuroimaging research and paving the way for clinical applications in schizophrenia and related disorders. Further investigation with larger sample sizes is required to validate and extend these findings.
Authors
- Junhyeok Lee (ORCID: https://orcid.org/0009-0008-8089-1881)
- Sung Woo Joo (ORCID: https://orcid.org/0000-0001-6555-9110)
- Hye Won Park (ORCID: https://orcid.org/0000-0002-3322-1323)
- Juhyuk Han
- Seo Yeong Kim (ORCID: https://orcid.org/0009-0007-8537-5045)
- Jung Sun Lee (ORCID: https://orcid.org/0000-0003-2171-2720)
- Won Hee Lee (ORCID: https://orcid.org/0000-0002-3112-5354)
Institutions
- Kyung Hee University (KR)
- University of Ulsan (KR)
Publication Details
- Journal
- Frontiers in Psychiatry
- Published
- 2025-09-04
- DOI
- https://doi.org/10.3389/fpsyt.2025.1600479
- Primary Topic
- Functional Brain Connectivity Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Iran Telecommunication Research Center
- Ministry of Science and ICT, South Korea
- Institute for Information and Communications Technology Promotion