Generalizability of 3D CNN models for age estimation in diverse youth populations using structural MRI.

Autor: Mendes SL; Center of Mathematics, Computing, and Cognition, Universidade Federal Do ABC, Rua Arcturus N. 03, São Bernardo Do Campo, SP, 09606-070, Brazil., Pinaya WHL; Department of Biomedical Engineering, King's College London, London, SE1 7EH, UK. walter.diaz_sanz@kcl.ac.uk., Pan PM; Escola Paulista de Medicina, Universidade Federal de São Paulo, R. Maj. Maragliano (UNIFESP), 241-Vila Mariana, São Paulo, SP, 04017-030, Brazil., Jackowski AP; Escola Paulista de Medicina, Universidade Federal de São Paulo, R. Maj. Maragliano (UNIFESP), 241-Vila Mariana, São Paulo, SP, 04017-030, Brazil.; Department of Education, ICT and Learning, Østfold University College, Halden, Norway., Bressan RA; Escola Paulista de Medicina, Universidade Federal de São Paulo, R. Maj. Maragliano (UNIFESP), 241-Vila Mariana, São Paulo, SP, 04017-030, Brazil., Sato JR; Center of Mathematics, Computing, and Cognition, Universidade Federal Do ABC, Rua Arcturus N. 03, São Bernardo Do Campo, SP, 09606-070, Brazil.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2023 Apr 27; Vol. 13 (1), pp. 6886. Date of Electronic Publication: 2023 Apr 27.
DOI: 10.1038/s41598-023-33920-7
Abstrakt: Recently, several studies have investigated the neurodevelopment of psychiatric disorders using brain data acquired via structural magnetic resonance imaging (sMRI). These analyses have shown the potential of sMRI data to provide a relatively precise characterization of brain structural biomarkers. Despite these advances, a relatively unexplored question is how reliable and consistent a model is when assessing subjects from other independent datasets. In this study, we investigate the performance and generalizability of the same model architecture trained from distinct datasets comprising youths in diverse stages of neurodevelopment and with different mental health conditions. We employed models with the same 3D convolutional neural network (CNN) architecture to assess autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), brain age, and a measure of dimensional psychopathology, the Child Behavior Checklist (CBCL) total score. The investigated datasets include the Autism Brain Imaging Data Exchange II (ABIDE-II, N = 580), Attention Deficit Hyperactivity Disorder (ADHD-200, N = 922), Brazilian High-Risk Cohort Study (BHRCS, N = 737), and Adolescent Brain Cognitive Development (ABCD, N = 11,031). Models' performance and interpretability were assessed within each dataset (for diagnosis tasks) and inter-datasets (for age estimation). Despite the demographic and phenotypic differences of the subjects, all models presented significant estimations for age (p value < 0.001) within and between datasets. In addition, most models showed a moderate to high correlation in age estimation. The results, including the models' brain regions of interest (ROI), were analyzed and discussed in light of the youth neurodevelopmental structural changes. Among other interesting discoveries, we found that less confounded training datasets produce models with higher generalization capacity.
(© 2023. The Author(s).)
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje