Variability in the Severity of Developmental Disorders: a Neurocomputational Account of Developmental Regression in Autism

Developmental disorders show wide variations in severity even when, on genetic grounds, it is known that there is a common underlying cause. We use connectionist models of development combined with population modelling techniques to explore possible mechanistic causes of variations in disorder severity. Specifically, we investigate the plausibility of the hypothesis that disorder variability stems from the interaction of the common cause of the disorder with variations in neurocomputational parameters also present in the typically developing population. We base our simulations on a model of developmental regression in autism, which proposes that this phenomenon arises from over-aggressive synaptic pruning [1]. We simulated a population of 1000 networks in which 641 exhibited the behavioural marker of regression in their developmental trajectories in learning a notional cognitive domain. Aside from the known single cause of the disorder (an atypical connectivity pruning parameter), we then analysed which neurocomputational parameters contributed to variation in a number of characteristics of developmental regression. These included the timing of regression onset, its severity, its behavioural specificity, and the speed and extent of subsequent recovery. Results are related to existing causal frameworks that explain the origins of developmental deficits.