Estimating Gender and Age from Brain Structural MRI of Children and Adolescents: A 3D Convolutional Neural Network Multitask Learning Model

Author(s)

Mendes, Sergio Leonardo ^[1] ; Lopez Pinaya, Walter Hugo ^[2] ; Pan, Pedro ^[3] ; Sato, Joao Ricardo ^[1]

Affiliation

^[1] Univ Fed ABC, Ctr Math Comp & Cognit, Rua Arcturus 03, BR-09606070 Sao Bernardo Do Campo, SP - Brazil

^[2] Kings Coll London, Dept Biomed Engn, London SE1 7EH - England

^[3] Univ Fed Sao Paulo, Escola Paulista Med, R Maj Maragliano 241, BR-04017030 Sao Paulo, SP - Brazil

• Document type: Journal article
• Source: COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE; v. 2021, MAY 26 2021.
• Web of Science Citations: 0

Abstract

Despite recent advances, assessing biological measurements for neuropsychiatric disorders is still a challenge, where confounding variables such as gender and age (as a proxy for neurodevelopment) play an important role. This study explores brain structural magnetic resonance imaging (sMRI) from two public data sets (ABIDE-II and ADHD-200) with healthy control (HC, N = 894), autism spectrum disorder (ASD, N = 251), and attention deficit hyperactivity disorder (ADHD, N = 357) individuals. We used gray and white matter preprocessed via voxel-based morphometry (VBM) to train a 3D convolutional neural network with a multitask learning strategy to estimate gender, age, and mental health status from structural brain differences. Gradient-based methods were employed to generate attention maps, providing clinically relevant identification of most representative brain regions for models' decision-making. This approach resulted in satisfactory predictions for gender and age. ADHD-200-trained models, evaluated in 10-fold cross-validation procedures on test set, obtained a mean absolute error (MAE) of 1.43 years (+/- 0.22 SD) for age prediction and an area under the curve (AUC) of 0.85 (+/- 0.04 SD) for gender classification. In out-of-sample validation, the best-performing ADHD-200 models satisfactorily predicted age (MAE = 1.57 years) and gender (AUC = 0.89) in the ABIDE-II data set. The models' accuracy was in line with the current state-of-the-art machine learning applications in neuroimaging. Key regions for models' accuracy were presented as a meaningful graphical output. New implementations, such as the use of VBM along with a 3D convolutional neural network multitask learning model and a brain imaging graphical output, reinforce the relevance of the proposed workflow. (AU)

• FAPESP's process: 18/21934-5 - Network statistics: theory, methods, and applications
• Grantee: André Fujita
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 18/04654-9 - Time series, wavelets and high dimensional data
• Grantee: Pedro Alberto Morettin
• Support Opportunities: Research Projects - Thematic Grants