Mechanisms of Developmental Regression in Autism and the Broader Phenotype: A Neural Network Modeling Approach

Autor/inn/en	Thomas, Michael S. C.; Knowland, Victoria C. P.; Karmiloff-Smith, Annette
Titel	Mechanisms of Developmental Regression in Autism and the Broader Phenotype: A Neural Network Modeling Approach
Quelle	In: Psychological Review, 118 (2011) 4, S.637-654 (18 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0033-295X
DOI	10.1037/a0025234
Schlagwörter	Autism; Language Impairments; Prediction; Developmental Stages; Child Development; Neurological Organization; Brain Hemisphere Functions; Neurological Impairments; Environmental Influences; Probability; Correlation; Behavior Problems; Genetics; Symptoms (Individual Disorders); Sensory Experience; Physical Disabilities; Children; Computation; At Risk Persons + Suchen Sie Ihr Suchwort? Autismus; Speech disorder; Speech disorders; Speech disabilities; Speech disability; Speech handicap; Speech handicaps; Speech impairment; Speech impairments; Language handicaps; Sprachbehinderung; Vorhersage; Kindesentwicklung; Neurodegenerative Erkrankung; Environmental influence; Umwelteinfluss; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Korrelation; Humangenetik; Psychiatrische Symptomatik; Sinnerfahrung; Physical handicap; Körperbehinderung; Child; Kind; Kinder; Risikogruppe
Abstract	Loss of previously established behaviors in early childhood constitutes a markedly atypical developmental trajectory. It is found almost uniquely in autism and its cause is currently unknown (Baird et al., 2008). We present an artificial neural network model of developmental regression, exploring the hypothesis that regression is caused by overaggressive synaptic pruning and identifying the mechanisms involved. We used a novel population-modeling technique to investigate developmental deficits, in which both neurocomputational parameters and the learning environment were varied across a large number of simulated individuals. Regression was generated by the atypical setting of a single pruning-related parameter. We observed a probabilistic relationship between the atypical pruning parameter and the presence of regression, as well as variability in the onset, severity, behavioral specificity, and recovery from regression. Other neurocomputational parameters that varied across the population modulated the risk that an individual would show regression. We considered a further hypothesis that behavioral regression may index an underlying anomaly characterizing the broader autism phenotype. If this is the case, we show how the model also accounts for several additional findings: shared gene variants between autism and language impairment (Vernes et al., 2008); larger brain size in autism but only in early development (Redcay & Courchesne, 2005); and the possibility of quasi-autism, caused by extreme environmental deprivation (Rutter et al., 1999). We make a novel prediction that the earliest developmental symptoms in the emergence of autism should be sensory and motor rather than social and review empirical data offering preliminary support for this prediction. (Contains 3 tables, 7 figures and 3 footnotes.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10