Display options
Cite
Narzisi A, Muratori F, Buscema M, et al. Outcome predictors in autism spectrum disorders preschoolers undergoing treatment as usual: insights from an observational study using artificial neural networks. Neuropsychiatr Dis Treat. 2015;11:1587-99doi: 10.2147/NDT.S81233.
Narzisi, A., Muratori, F., Buscema, M., Calderoni, S., & Grossi, E. (2015). Outcome predictors in autism spectrum disorders preschoolers undergoing treatment as usual: insights from an observational study using artificial neural networks. Neuropsychiatric disease and treatment, 111587-99. https://doi.org/10.2147/NDT.S81233
Narzisi, Antonio, et al. "Outcome predictors in autism spectrum disorders preschoolers undergoing treatment as usual: insights from an observational study using artificial neural networks." Neuropsychiatric disease and treatment vol. 11 (2015): 1587-99. doi: https://doi.org/10.2147/NDT.S81233
Narzisi A, Muratori F, Buscema M, Calderoni S, Grossi E. Outcome predictors in autism spectrum disorders preschoolers undergoing treatment as usual: insights from an observational study using artificial neural networks. Neuropsychiatr Dis Treat. 2015 Jun 30;11:1587-99. doi: 10.2147/NDT.S81233. eCollection 2015. PMID: 26170671; PMCID: PMC4494609.
Copy Download .nbib Format: NLM
Share it on

Neuropsychiatr Dis Treat. 2015 Jun 30;11:1587-99. doi: 10.2147/NDT.S81233. eCollection 2015.

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

Neuropsychiatric disease and treatment

Antonio Narzisi, Filippo Muratori, Massimo Buscema, Sara Calderoni, Enzo Grossi

Affiliations

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

PMID: 26170671 PMCID: PMC4494609 DOI: 10.2147/NDT.S81233

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.

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

References

  1. J Am Acad Child Adolesc Psychiatry. 2014 Feb;53(2):237-57 - PubMed
  2. Neural Comput. 1998 Sep 15;10(7):1895-1923 - PubMed
  3. Prog Brain Res. 2013;207:255-72 - PubMed
  4. Artif Intell Med. 2005 Jul;34(3):279-305 - PubMed
  5. BMC Med Genomics. 2010 Sep 24;3:42 - PubMed
  6. Res Dev Disabil. 2013 Sep;34(9):2967-85 - PubMed
  7. J Autism Dev Disord. 1987 Dec;17(4):565-76 - PubMed
  8. Am J Ment Retard. 2005 Nov;110(6):417-38 - PubMed
  9. J Am Geriatr Soc. 2002 Nov;50(11):1857-60 - PubMed
  10. Neuropsychiatr Dis Treat. 2014 Apr 08;10:577-86 - PubMed
  11. BMC Pediatr. 2008 Jun 17;8:24 - PubMed
  12. Eur J Gastroenterol Hepatol. 2010 Oct;22(10):1163-8 - PubMed
  13. Am J Med. 1990 Nov;89(5):630-8 - PubMed
  14. Curr Alzheimer Res. 2008 Oct;5(5):481-98 - PubMed
  15. J Am Acad Child Adolesc Psychiatry. 2014 Feb;53(2):133-4 - PubMed
  16. Curr Alzheimer Res. 2010 Mar;7(2):173-87 - PubMed
  17. J Intellect Disabil Res. 2006 Dec;50(Pt 12):874-82 - PubMed
  18. Behav Res Ther. 1972 Nov;10(4):297-317 - PubMed
  19. J Clin Child Psychol. 1998 Jun;27(2):168-79 - PubMed
  20. Dig Liver Dis. 2003 Apr;35(4):222-31 - PubMed
  21. J Autism Dev Disord. 2004 Apr;34(2):189-98 - PubMed
  22. Curr Clin Pharmacol. 2014 Feb;9(1):17-26 - PubMed
  23. World J Gastroenterol. 2008 Jan 28;14(4):563-8 - PubMed
  24. PLoS Med. 2013 Dec;10(12):e1001572; discussion e1001572 - PubMed
  25. J Consult Clin Psychol. 1987 Feb;55(1):3-9 - PubMed
  26. Pediatrics. 2010 Jan;125(1):e17-23 - PubMed
  27. Gastrointest Endosc. 2011 Feb;73(2):218-26, 226.e1-2 - PubMed
  28. Pediatr Ann. 2011 Nov;40(11):569-74 - PubMed
  29. Int J Dev Neurosci. 2008 Nov;26(7):699-704 - PubMed
  30. Lancet. 1997 Dec 13;350(9093):1761-6 - PubMed
  31. Autism. 2009 Jan;13(1):93-115 - PubMed
  32. J Autism Dev Disord. 2014 Dec;44(12):2981-95 - PubMed
  33. Med Phys. 2003 Sep;30(9):2350-9 - PubMed
  34. J Autism Dev Disord. 2008 Aug;38(7):1278-91 - PubMed
  35. Biol Psychiatry. 1994 Jul 1;36(1):5-20 - PubMed
  36. Conf Proc IEEE Eng Med Biol Soc. 2009;2009:2555-8 - PubMed
  37. Subst Use Misuse. 2007;42(2-3):267-304 - PubMed
  38. J Autism Dev Disord. 1998 Feb;28(1):15-23 - PubMed
  39. Ann Hum Genet. 2005 Nov;69(Pt 6):693-706 - PubMed
  40. Health Econ. 1994 Sep-Oct;3(5):333-45 - PubMed
  41. J Autism Dev Disord. 1992 Jun;22(2):141-53 - PubMed
  42. Dev Psychopathol. 2013 Nov;25(4 Pt 2):1455-72 - PubMed
  43. Subst Use Misuse. 1998 Jan;33(2):233-70 - PubMed
  44. Front Hum Neurosci. 2013 Jul 05;7:354 - PubMed
  45. IEEE Trans Pattern Anal Mach Intell. 2006 Oct;28(10):1619-30 - PubMed
  46. PLoS One. 2012;7(4):e33224 - PubMed
  47. J Autism Dev Disord. 2009 May;39(5):693-705 - PubMed
  48. J Am Acad Child Adolesc Psychiatry. 2013 Jun;52(6):572-81.e1 - PubMed
  49. Autism. 2001 Dec;5(4):407-29 - PubMed
  50. Cochrane Database Syst Rev. 2013 Apr 30;(4):CD009774 - PubMed
  51. Brain Dev. 2013 Feb;35(2):133-8 - PubMed
  52. Dig Liver Dis. 2007 Mar;39(3):278-85 - PubMed
  53. Immun Ageing. 2013 Jan 10;10(1):1 - PubMed
  54. Annu Rev Clin Psychol. 2010;6:447-68 - PubMed
  55. J Autism Dev Disord. 2000 Apr;30(2):137-42 - PubMed
  56. Lancet. 2010 Jun 19;375(9732):2152-60 - PubMed
  57. PLoS One. 2008;3(11):e3755 - PubMed
  58. J Intellect Dev Disabil. 2009 Jun;34(2):173-86 - PubMed
  59. Psychol Rev. 2011 Oct;118(4):637-54 - PubMed
  60. Am J Gastroenterol. 2010 Jun;105(6):1327-37 - PubMed
  61. Int J Data Min Bioinform. 2008;2(4):362-404 - PubMed

Publication Types