ArticleBrain imaging and behavior2019
Early adolescent brain markers of late adolescent academic functioning.
Article in Brain imaging and behavior, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Distinct associations between multimodal brain measures and psychopathology domains predict adolescent functioning.bioRxiv : the preprint server for biology · 2026Article
- The Impact of Smartphone Use on Brain Function in Adolescence: A Scoping Review.Pediatric reports · 2026Review
- Structural Brain Correlates of Multidomain Resilience Among Youth Exposed to Neighborhood Disadvantage.Biological psychiatry global open science · 2025Article
- Cortical Structure in Nodes of the Default Mode Network Estimates General Intelligence.Brain and behavior · 2025Article
- Structural brain correlates of non-verbal cognitive ability in 5-year-old children: Findings from the FinnBrain birth cohort study.Human brain mapping · 2023Article
- Assessing cross-lagged associations between depression, anxiety, and binge drinking in the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) study.Drug and alcohol dependence · 2023Article
- Brain structural parameters correlate with University Selection Test outcomes in Chilean high school graduates.Scientific reports · 2022Article
- Article
- Linked patterns of biological and environmental covariation with brain structure in adolescence: a population-based longitudinal study.Molecular psychiatry · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
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.
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