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

Narzisi A, Muratori F, Buscema M, Calderoni S, Grossi E - Neuropsychiatr Dis Treat (2015)

Bottom Line: 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.The resultant No Response area strongly connected with "Parents Involvement low".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.

View Article: PubMed Central - PubMed

Affiliation: Department of Developmental Neuroscience, IRCCS Stella Maris Foundation, University of Pisa, Pisa, Italy.

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.

No MeSH data available.


Related in: MedlinePlus

Semantic connectivity map obtained with Auto-Cm System.Notes: The figures on the arches of the graph refer to the strength of the association between two adjacent nodes. The range of this value is from 0 to 1. Red arrow points to the no response group; green arrow points to the response group.Abbreviations: ADOS-CSS, Autism Diagnostic Observation Schedule-Calibrated Severity Score; CBCL, Child Behavior Checklist; int, internalizing; ext, externalizing; tot, total; Griffiths (locomotor, Locomotor development; personal, Personal–social development; speech, Hearing and speech; eye, Hand and eye coordination; general, General quotient); PSI, Parenting Stress Index; Vineland (Com, Communication; Daily Living, Daily Living Skills; Soc, Socialization).
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f1-ndt-11-1587: Semantic connectivity map obtained with Auto-Cm System.Notes: The figures on the arches of the graph refer to the strength of the association between two adjacent nodes. The range of this value is from 0 to 1. Red arrow points to the no response group; green arrow points to the response group.Abbreviations: ADOS-CSS, Autism Diagnostic Observation Schedule-Calibrated Severity Score; CBCL, Child Behavior Checklist; int, internalizing; ext, externalizing; tot, total; Griffiths (locomotor, Locomotor development; personal, Personal–social development; speech, Hearing and speech; eye, Hand and eye coordination; general, General quotient); PSI, Parenting Stress Index; Vineland (Com, Communication; Daily Living, Daily Living Skills; Soc, Socialization).

Mentions: Figure 1 reports the semantic connectivity map. As described by Coppedè,21 in order to better understand the meaning of the connections, a numerical value is applied to each edge of the graph. This value, deriving from the original weight developed by Auto-CM during the training phase scaled from 0 to 1, is proportional to the strength of the connections between two variables. Moreover, by means of Auto-CM, it is possible to obtain not only the direction of the association as provided by standard statistical analyses but also specifically the strength of this association (link strength [LS]).


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

Narzisi A, Muratori F, Buscema M, Calderoni S, Grossi E - Neuropsychiatr Dis Treat (2015)

Semantic connectivity map obtained with Auto-Cm System.Notes: The figures on the arches of the graph refer to the strength of the association between two adjacent nodes. The range of this value is from 0 to 1. Red arrow points to the no response group; green arrow points to the response group.Abbreviations: ADOS-CSS, Autism Diagnostic Observation Schedule-Calibrated Severity Score; CBCL, Child Behavior Checklist; int, internalizing; ext, externalizing; tot, total; Griffiths (locomotor, Locomotor development; personal, Personal–social development; speech, Hearing and speech; eye, Hand and eye coordination; general, General quotient); PSI, Parenting Stress Index; Vineland (Com, Communication; Daily Living, Daily Living Skills; Soc, Socialization).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4494609&req=5

f1-ndt-11-1587: Semantic connectivity map obtained with Auto-Cm System.Notes: The figures on the arches of the graph refer to the strength of the association between two adjacent nodes. The range of this value is from 0 to 1. Red arrow points to the no response group; green arrow points to the response group.Abbreviations: ADOS-CSS, Autism Diagnostic Observation Schedule-Calibrated Severity Score; CBCL, Child Behavior Checklist; int, internalizing; ext, externalizing; tot, total; Griffiths (locomotor, Locomotor development; personal, Personal–social development; speech, Hearing and speech; eye, Hand and eye coordination; general, General quotient); PSI, Parenting Stress Index; Vineland (Com, Communication; Daily Living, Daily Living Skills; Soc, Socialization).
Mentions: Figure 1 reports the semantic connectivity map. As described by Coppedè,21 in order to better understand the meaning of the connections, a numerical value is applied to each edge of the graph. This value, deriving from the original weight developed by Auto-CM during the training phase scaled from 0 to 1, is proportional to the strength of the connections between two variables. Moreover, by means of Auto-CM, it is possible to obtain not only the direction of the association as provided by standard statistical analyses but also specifically the strength of this association (link strength [LS]).

Bottom Line: 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.The resultant No Response area strongly connected with "Parents Involvement low".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.

View Article: PubMed Central - PubMed

Affiliation: Department of Developmental Neuroscience, IRCCS Stella Maris Foundation, University of Pisa, Pisa, Italy.

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.

No MeSH data available.


Related in: MedlinePlus