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.

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

MURATORI, FILIPPO;CALDERONI, SARA;
2015

Abstract

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.
Narzisi, Antonio; Muratori, Filippo; Buscema, Massimo; Calderoni, Sara; Grossi, Enzo
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11568/785830
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