Early adolescent brain markers of late adolescent academic functioning

• Published in: Brain imaging and behavior Vol. 13; no. 4; pp. 945 - 952
• Main Authors: Meruelo, Alejandro Daniel, Jacobus, Joanna, Idy, Erick, Nguyen-Louie, Tam, Brown, Gregory, Tapert, Susan Frances
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.08.2019; Springer Nature B.V
• Subjects: Academic Success; Adolescence; Adolescent; Adolescents; Analysis of covariance; Bayes Theorem; Bayesian analysis; Biomarkers; Biomedical and Life Sciences; Biomedicine; Brain; Brain - diagnostic imaging; Brain - growth & development; Brain Mapping - methods; Cerebral Cortex - diagnostic imaging; Child; Child development; Cognition - physiology; Cortex; Covariance; Demographics; Demography; Female; Forecasting - methods; Hemispheric laterality; Humans; Intelligence; Intelligence Tests; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Morphometry; Neuropsychology; Neuroradiology; Neurosciences; Original Research; Performance prediction; Psychiatry; Sensitivity and Specificity; Teenagers; Adolescence; Cortical thickness; naïve Bayesian classifier; Magnetic resonance imaging; Academic performance
• ISSN: 1931-7557; 1931-7565; 1931-7565
• DOI: 10.1007/s11682-018-9912-2

Academic performance in adolescence strongly influences adult prospects. Intelligence quotient (IQ) has historically been considered a strong predictor of academic performance. Less objectively explored have been morphometric features. We analyzed brain MRI morphometry metrics in early adolescence (age 12–14 years) as quantitative predictors of academic performance over high school using a naïve Bayesian classifier approach with n  = 170 subjects. Based on the mean GPA, subjects were divided into high (GPA ≥3.54; n  = 87) and low (GPA <3.54; n  = 83) academic performers. Covariance analysis was performed to look at the influence of subject demographics. We examined predictive features from the 343 available regions (surface areas, cortical thickness, and subcortical volumes) and applied 4 algorithms for selection and reduction of attributes using Weka. Cortical thickness measures performed better than surface areas or subcortical volumes as predictors of academic performance. We identified 15 cortical thickness regions most predictive of academic performance, three of which have not been described in the literature predictive of academic performance. These were in the left hemisphere fusiform, bilateral insula, and left hemisphere paracentral regions. Prediction had a sensitivity of 0.65 and specificity of 0.73 with independent validation. Follow-up independent t-test analyses between high and low academic achievers on 10 of 15 regions showed between-group significance at the p  < 0.05 level. High achievers demonstrated thicker cortices than low achievers. These newly identified regions may help pinpoint new targets for further study in understanding the developing adolescent brain in the classroom setting.