Early adolescent brain markers of late adolescent academic functioning

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.

Linked patterns of biological and environmental covariation with brain structure in adolescence: a population-based longitudinal study

“…IMAGEN participants underwent the same comprehensive evaluation twice, at age 14 years and at age 19 years thus enabling us to identify factors associated with brain structure at baseline and also with developmental brain changes over the inter-scan interval. We hypothesized that the patterns of covariation identified here would largely follow a “positive-negative” axis of covariation previously shown in studies of young adults [ 30 – 32 ], which have also emphasized that negative influences of social adversity and substance exposure amongst environmental factors. Our aim was to quantify, in the same integrative model, the contribution of biological programmed variables (i.e., age and sex) and variables relating to personal, social and environmental factors.…”

Associations between brain structure and sleep patterns across adolescent development

“…We selected four sleep outcomes corresponding to key dimensions of sleep health 85 : sleep duration (total sleep time in minutes), timing (midpoint between sleep onset and offset in minutes from midnight), continuity (minutes awake after sleep onset; WASO), and regularity (intra-individual standard deviation of midpoint in minutes). The first three outcomes were averaged over the [5][6][7] tracking days most proximal to their MRI scan; regularity was calculated from the available days of recording. Sleep variables were natural log transformed to normalize distributions.…”

“…These brain regions are implicated in salience detection (pars orbitalis), motor function (precentral), memory (entorhinal, middle temporal), and attention and visuospatial perception (superior parietal cortex, lateral occipital) 101 . Given that sleep is associated with diverse range of mental, cognitive and physical health outcomes in adolescence [1][2][3][4][5][6][7][8][9][10] , it is reasonable that naturalistic sleep is related to brain structure in regions that support multiple functions. Some of these relationships were modulated by self-reported sex, consistent with reported sex differences in sleep patterns and brain development 17,[49][50][51][52][53][54] .…”

“…The precise timing and amount of these changes in youths likely influences multiple adult outcomes. Optimal sleep and brain maturation are each known to influence adolescent health and functioning, including academic/vocational achievement, mental health, and/or risk behaviors [1][2][3][4][5][6][7][8][9][10] . However, relationships between gray matter structure and sleep patterns over adolescence are not fully understood; furthermore, it is unknown whether these relationships vary as a function of age.…”

Adaptación y estudio de las propiedades psicométricas del Cuestionario de consecuencias de consumo de alcohol en jóvenes, forma breve, en muestra española

“…Es notoria su influencia en el área pre-frontal encargada de las funciones ejecutivas, la atención, planificación, flexibilidad cognitiva, toma de decisiones y autocontrol, así como sobre el hipocampo encargado de la memoria y el aprendizaje. Ambas zonas son de las últimas en madurar y por tanto más vulnerables a sufrir cambios con el abuso de alcohol que se produce a partir de los 12 o 13 años, pudiendo incluso llegar a experimentar modificaciones irreversibles (Guerri y Pascual, 2019;Hagler et al, 2019;López-Caneda, Cadaveira y Campanella, 2018;Meruelo et al, 2019;Sullivan et al, 2019). Problemas de este tipo pueden ser las lagunas de memoria (Cox et al, 2019;McGovern, 2019;Wombacher, Matig, Sheff y Scott, 2019) y una mayor dificultad para pensar con claridad o para realizar correctamente tareas (Krieger, Young, Anthenien y Neighbors, 2018;McGovern, 2019;Naudé et al, 2019;Zamroziewicz et al, 2017), lo que puede dar lugar al fracaso y al absentismo escolar (Gakh, Coughenour, Assoumou y Vanderstelt, 2019;Niu, Jeong y Willoughby, 2020;Ribeiro, Fernandes y Macêdo, 2019).…”