A multimodal ensemble stacking model improves brain age prediction and reveals associations with schizophrenia symptoms

Dados Bibliográficos

ID15519271
AutoresJunhyeok Lee (0009-0008-8089-1881, Kyung Hee University), Seo Yeong Kim (0009-0007-8537-5045, Kyung Hee University), Hye Won Park (0000-0002-2669-3621, Kyung Hee University), Juhyuk Han (Kyung Hee University), Sung Woo Joo (0000-0001-6555-9110, University of Ulsan), Jung Sun Lee (0000-0003-2171-2720, Kyung Hee University), Jungsun Lee, Won Hee Lee (0000-0002-3112-5354, Kyung Hee University, autor correspondente)
Ano2025
Volume16
Páginas1600479-1600479
Data de publicação2025-09-04
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoFrontiers in Psychiatry (JOURNAL)
Identificadores do periódicoISSN: 1664-0640 • E-ISSN: 1664-0640
EditoraFrontiers Media (PUBLISHER • CH)
DOI10.3389/fpsyt.2025.1600479
PMID40980054
OpenAlexW4413998644
IdiomaEN
Referências citadas65

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

Psychiatry · Schizophrenia (object-oriented programming · Functional Brain Connectivity Studies · Machine Learning in Healthcare · Medicine · Neuroscience · Psychology · Schizophrenia research and treatment