A Computational Model for Cerebral Cortical Dysfunction in Autism Spectrum Disorders

Abstract

Perturbations to the microscopic level balance between synaptic excitation and inhibition and neuron organization in the cerebral cortex are suggested to underlie autism spectrum disorder (ASD) traits. The mechanism linking these perturbations to cognitive behaviors in ASD is unknown. This study strives to bridge this gap by generating clinically testable diagnostic and pharmacological predictions based on the effect of synaptic imbalance and neuron distribution on a computational local circuit model of the cerebral cortex. We use a computational microscopic model of the cerebral cortex that incorporates N-methyl-D-aspartate and gamma-aminobutyric acid synaptic kinetics. We employ the model circuit during model tasks similar to visually guided and gap oculomotor saccade tasks and interpret qualitative model predictions of saccade hypometria and dysmetria. We consider the effects of varying the excitatory to inhibitory synaptic balance, neuron density, and neuron clustering in this model. An increase of synaptic excitation over synaptic inhibition results in increased hypometria and dysmetria. Similar effects by either reduced inhibition or increased excitation suggest that a variety of pharmacological compounds can be used for both screening and medical management. On the other hand, any change to the microscopic neuron anatomy that increases the effective maximum distance between excitatory neurons decreases hypometria but has no affect on dysmetria. Perturbations to a computational model of a local cerebral cortical circuit can account for saccade hypometria and dysmetria reported in ASD studies. This approach may provide a direct link between cerebral cortical function and ASD behaviors.