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

Datos 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 de correspondencia)
Año2025
Volumen16
Páginas1600479-1600479
Fecha de publicación2025-09-04
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaFrontiers in Psychiatry (JOURNAL)
Identificadores de la revistaISSN: 1664-0640 • E-ISSN: 1664-0640
EditorialFrontiers Media (PUBLISHER • CH)
DOI10.3389/fpsyt.2025.1600479
PMID40980054
OpenAlexW4413998644
IdiomaEN
Referencias 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