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

• Published in: Scientific reports Vol. 13; no. 1; p. 6886
• Main Authors: Mendes, Sergio Leonardo, Pinaya, Walter Hugo Lopez, Pan, Pedro Mario, Jackowski, Andrea Parolin, Bressan, Rodrigo Affonseca, Sato, João Ricardo
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 27-04-2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/378/116; 639/705/1042; 639/705/258; Adolescent; Age determination; Attention Deficit Disorder with Hyperactivity - diagnosis; Attention deficit hyperactivity disorder; Autism; Autism Spectrum Disorder - psychology; Brain; Brain - diagnostic imaging; Brain - pathology; Child; Cognitive ability; Cohort Studies; Datasets; Humanities and Social Sciences; Humans; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Mental disorders; multidisciplinary; Neural networks; Neural Networks, Computer; Neurodevelopment; Neurodevelopmental disorders; Neuroimaging; Psychopathology; Science; Science (multidisciplinary)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-33920-7

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.