Modeling the Contribution of Genetic Variation to Cognitive Gains Following Training with a Machine Learning Approach

Abstract

The objective of this research was to develop robust predictive models of the gains in working memory (WM) and fluid intelligence (Gf ) following executive attention training in children, using genetic markers, gender, and age variables. We explore the influence of genetic variables on individual differences in susceptibility to intervention. Sixty-six children (males: 54.2%) aged 50.9-75.9 months participated in a four-weeks computerized training program. Information on genes involved in the regulation of dopamine, serotonin, norepinephrine, and acetylcholine was collected. The standardized pre-to post-training gains of two dependent measures were considered: WM Span backwards condition (WISC-III) and the IQ-f factor from the Kaufman Brief Intelligence Test (K-BIT). A machine-learning methodology was implemented utilizing multilayer perceptron artificial neural networks (ANN) with a backpropagation algorithm. Both ANN models reached high overall accuracy in their predictive classification. Variations in genes involved in dopamine and norepinephrine neurotransmission affect children's susceptibility to benefit from executive attention training, a pattern that is consistent with previous studies.

Predicting Effortful Control at 3 Years of Age from Measures of Attention and Home Environment in Infancy: A Machine Learning Approach

“…The ANN methodology has the advantage of capturing complex and nonlinear relationships among these early variables which seem to be indicators of a lower level of self-regulated behavior at a later age, even when there were no significant differences in individual predictors between the children at risk and moderate/high EC. The evaluation measures of the ANN in this study are consistent with previous research indicating their robustness for modeling complex patterns among variables associated with self-regulation and educational outcomes [ 65 , 86 , 87 , 88 , 89 , 90 , 91 ].…”

“…However, the SES-executive functions relationship varies between low to medium in size depending on several moderators such as the SES variability in the sample, number, and methods used to measure EF, but it remains stable across childhood [ 103 ]. Although the sample in this study has a modest SES variability, the pattern of interaction effects between these early SES factors in the environment with cognitive markers of attentional functions resulted in a plausible model in the ANN analyses [ 91 , 104 ].…”

Predicting Effortful Control at 3 Years of Age From Measures of Attention and Home Environment in Infancy: A Machine Learning Approach

“…The ANN methodology has the advantage of capturing complex and nonlinear relationships among these early variables which seem to be indicators of a lower level of self-regulated behavior at a later age, even when there were no significant differences in individual predictors between the children at risk and the rest of the subjects. The evaluation measures of the ANN in this study are consistent with previous research indicating their robustness for modeling complex patterns among variables associated with self-regulation and educational outcomes [60,[83][84][85][86][87][88].…”

“…However, the SESexecutive functions relationship varies between low to medium in size depending on several moderators such as the SES variability in the sample, number and methods used to measure EF, but it remains stable across childhood [100]. Although the sample in this study has a modest SES variability, the pattern of interaction effects between these early SES factors in the environment with cognitive markers of attentional functions resulted in a plausible model in the ANN analyses [88,101].…”