AUTHOR=Ju Yi , Zhao Tong , Gao Zaifen , Hu Wenguang , Luo Jiejian , Cheng Nian , Liu Chunli , Jiang Yuwu , Hong Bo , Ji Taoyun , Yan Yuxiang 

TITLE=Brain age prediction model based on electroencephalogram signal and its application in children with autism spectrum disorders

JOURNAL=Frontiers in Neurology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1605291

DOI=10.3389/fneur.2025.1605291

ISSN=1664-2295

ABSTRACT=BackgroundThere is a lack of objective biomarkers for brain developmental abnormalities of autism spectrum disorder (ASD). We used EEG and deep learning to conduct a brain aging study in ASD.Methods(1) A total of 659 healthy children and 98 ASD patients were retrospectively recruited. (2) An Auto-EEG-Brain AGE prediction model based on the Gate Recurrent Unit (GRU) neural network method was constructed. (3) Using the constructed model, we evaluated the difference between the brain age of ASD and that of healthy controls, and assessed the feasibility in the clinical assessment of ASD.Results(1) The correlation coefficient (r-value) of the model exceeded 0.8 at the whole-brain level, with the highest value reaching 0.91. (2) r-values of the ASD group amounted to 0.76 at the level of the whole brain and ranged from 0.66 to 0.7 at the level of the sub-brain regions. The mean value of the brain age gap estimate (Brain AGE) in the whole brain is 0.76 years; in the sub-brain model, was 0.64–1.18 years.ConclusionWe constructed the EEG-Brain AGE prediction model, which can identify an individual’s brain development and be used as a biomarker for the brain development assessment in ASD.