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Outcome predictors in autism spectrum disorders preschoolers undergoing treatment as usual: insights from an observational study using artificial neural networks

Authors Narzisi A, Muratori F, Buscema M, Calderoni S, Grossi E

Received 20 January 2015

Accepted for publication 19 March 2015

Published 30 June 2015 Volume 2015:11 Pages 1587—1599


Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 4

Editor who approved publication: Dr Roger Pinder

Antonio Narzisi,1 Filippo Muratori,1,2 Massimo Buscema,3,4 Sara Calderoni,1 Enzo Grossi3,5

1Department of Developmental Neuroscience, IRCCS Stella Maris Foundation, 2Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy; 3Semeion Research Centre of Sciences of Communication, Rome, Italy; 4Department of Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO, USA; 5Autism Research Unit, Villa Santa Maria Institute, Tavernerio, Italy

Background: Treatment as usual (TAU) for autism spectrum disorders (ASDs) includes eclectic treatments usually available in the community and school inclusion with an individual support teacher. Artificial neural networks (ANNs) have never been used to study the effects of treatment in ASDs. The Auto Contractive Map (Auto-CM) is a kind of ANN able to discover trends and associations among variables creating a semantic connectivity map. The matrix of connections, visualized through a minimum spanning tree filter, takes into account nonlinear associations among variables and captures connection schemes among clusters. Our aim is to use Auto-CM to recognize variables to discriminate between responders versus no responders at TAU.
Methods: A total of 56 preschoolers with ASDs were recruited at different sites in Italy. They were evaluated at T0 and after 6 months of treatment (T1). The children were referred to community providers for usual treatments.
Results: At T1, the severity of autism measured through the Autism Diagnostic Observation Schedule decreased in 62% of involved children (Response), whereas it was the same or worse in 37% of the children (No Response). The application of the Semeion ANNs overcomes the 85% of global accuracy (Sine Net almost reaching 90%). Consequently, some of the tested algorithms were able to find a good correlation between some variables and TAU outcome. The semantic connectivity map obtained with the application of the Auto-CM system showed results that clearly indicated that “Response” cases can be visually separated from the “No Response” cases. It was possible to visualize a response area characterized by “Parents Involvement high”. The resultant No Response area strongly connected with “Parents Involvement low”.
Conclusion: The ANN model used in this study seems to be a promising tool for the identification of the variables involved in the positive response to TAU in autism.

Keywords: autism spectrum disorders, treatment, intervention, artificial neural networks, outcome

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