Replication and Refinement of Brain Age Model for adolescent development

• Type of Material: Article
• Authors: Bhaskar Ray, Emory University; Jiayu Cehn, Emory Unviersity; Zening Fu, Emory University; Pranav Suresh, Emory University; Bishal Thapaliya, Emory Unversity; Britny Farahdel, Emory University; Vince D. Calhoun, Emory University; Jingyu Liu, Emory University
• Language: English
• Date: 2023-08-18
• Publisher: NIH
• Publication Version: Preprint (Prior to Peer Review)
• Copyright Statement: The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): https://doi.org/10.1101%2F2023.08.16.553472
• Title of Journal or Parent Work: bioRxiv
• Grant/Funding Information: This work is supported by NIH R01DA049238 and NSF 2112455 to VC and JL.
• Keywords: multi-modal data; structural MRI; brain age estimation; modeling; replication
• Research Categories: Psychology, Cognitive

Abstract

The discrepancy between chronological age and estimated brain age, known as the brain age gap, may serve as a biomarker to reveal brain development and neuropsychiatric problems. This has motivated many studies focusing on the accurate estimation of brain age using different features and models, of which the generalizability is yet to be tested. Our recent study has demonstrated that conventional machine learning models can achieve high accuracy on brain age prediction during development using only a small set of selected features from multimodal brain imaging data. In the current study, we tested the replicability of various brain age models on the Adolescent Brain Cognitive Development (ABCD) cohort. We proposed a new refined model to improve the robustness of brain age prediction. The direct replication test for existing brain age models derived from the age range of 8-22 years onto the ABCD participants at baseline (9 to 10 years old) and year-two follow-up (11 to 12 years old) indicate that pre-trained models could capture the overall mean age failed precisely estimating brain age variation within a narrow range. The refined model, which combined broad prediction of the pre-trained model and granular information with the narrow age range, achieved the best performance with a mean absolute error of 0.49 and 0.48 years on the baseline and year-two data, respectively. The brain age gap yielded by the refined model showed significant associations with the participants’ information processing speed and verbal comprehension ability on baseline data.