ArticleNeuropsychiatric disease and treatment2015
Outcome predictors in autism spectrum disorders preschoolers undergoing treatment as usual: insights from an observational study using artificial neural networks.
Article in Neuropsychiatric disease and treatment, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 18 citations in OpenAlex.
- Gut to brain interaction in Autism Spectrum Disorders: a randomized controlled trial on the role of probiotics on clinical, biochemical and neurophysiological parameters.BMC psychiatry · 2016Trial
- AI-based autism identification from hyperspectral imaging detection of oxidative stress in pediatric red blood cells.Communications medicine · 2026Article
- An Overview of Deep Learning Algorithms and Their Applications in Neuropsychiatry.Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2021Article
- Predictors of Treatment Outcome in Preschoolers with Autism Spectrum Disorder: An Observational Study in the Greater Geneva Area, Switzerland.Journal of autism and developmental disorders · 2020Observational
- Towards a Multivariate Biomarker-Based Diagnosis of Autism Spectrum Disorder: Review and Discussion of Recent Advancements.Seminars in pediatric neurology · 2020Review
- Effects of Probiotic Supplementation on Gastrointestinal, Sensory and Core Symptoms in Autism Spectrum Disorders: A Randomized Controlled Trial.Frontiers in psychiatry · 2020Article
- Artificial neural networks reveal individual differences in metacognitive monitoring of memory.PloS one · 2019Article
- Motor Skills as Moderators of Core Symptoms in Autism Spectrum Disorders: Preliminary Data From an Exploratory Analysis With Artificial Neural Networks.Frontiers in psychology · 2018Article
- Observational
- Applying machine learning to identify autistic adults using imitation: An exploratory study.PloS one · 2017Article
- Can biological components predict short-term evolution in Autism Spectrum Disorders? A proof-of-concept study.Italian journal of pediatrics · 2016Observational
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTreatment 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.
methodsA 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.
resultsAt 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".
conclusionThe 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.
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