The over-pruning hypothesis of autism
Thomas, Michael S. C.
Knowland, Victoria C. P.
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Thomas, M., Davis, R., Karmiloff-Smith, A., Knowland, V. and Charman, T. (2016) 'The over-pruning hypothesis of autism', Developmental Science, 19(2), pp. 284-305.
This article outlines the over-pruning hypothesis of autism. The hypothesis originates in a neurocomputational model of the regressive sub-type (Thomas, Knowland & Karmiloff-Smith, 2011a, 2011b). Here we develop a more general version of the over-pruning hypothesis to address heterogeneity in the timing of manifestation of ASD, including new computer simulations which reconcile the different observed developmental trajectories (early onset, late onset, regression) via a single underlying atypical mechanism; and which show how unaffected siblings of individuals with ASD may differ from controls either by inheriting a milder version of the pathological mechanism or by co-inheriting the risk factors without the pathological mechanism. The proposed atypical mechanism involves overly aggressive synaptic pruning in infancy and early childhood, an exaggeration of a normal phase of brain development. We show how the hypothesis generates novel predictions that differ from existing theories of ASD including that (1) the first few months of development in ASD will be indistinguishable from typical, and (2) the earliest atypicalities in ASD will be sensory and motor rather than social. Both predictions gain cautious support from emerging longitudinal studies of infants at-risk of ASD. We review evidence consistent with the over-pruning hypothesis, its relation to other current theories (including C. Frith's under-pruning proposal; C. Frith, 2003, 2004), as well as inconsistent data and current limitations. The hypothesis situates causal accounts of ASD within a framework of protective and risk factors (Newschaffer et al., 2012); clarifies different versions of the broader autism phenotype (i.e. the implication of observed similarities between individuals with autism and their family members); and integrates data from multiple disciplines, including behavioural studies, neuroscience studies, genetics, and intervention studies.