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

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