Autism in DSM-IV vs DSM-V: What if Machine Learning Could Help Us See Things More Clearly?
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1
Fonds National de la Recherche Scientifique, Belgium
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2
University of Mons, Belgium
Over the last years, the evolution of the Diagnostic and Statistical Manual of mental disorders (DSM) has been subject to a lively debate between people for and against the resulting changes in clinical practices. Autism Spectrum Disorder (ASD) figures among the syndromes that have seen the most significant changes between the fourth and fifth versions of DSM. The ASD category includes three distinct DSM-IV conditions: autistic disorder, Asperger’s and Pervasive Developmental Disorder Not Otherwise Specified (PDD-NOS). This revision is highly controversial, especially since it awakens fears of reduced access to healthcare. Indeed, the clinical criteria related to ASD are perceived as less stringent and may thus exclude a part of children previously diagnosed with Asperger’s or PDD-NOS. In this work, we shed light on the interest of mathematical approaches to investigate whether the changes involved by DSM-V as regards ASD are genuinely required. Moreover, we show how modern Machine Learning (ML) can be used to capture the complexity of the neuropathology through the detection of explanatory markers and the development of systems able to recommend a diagnosis. For such a purpose, we considered a data sample extracted from the publicly available ABIDE (Autism Brain Imaging Data Exchange) dataset, including neurotypical and ASD children aged between 6 and 12 years old (n = 177). The ASD children were also diagnosed as autistic, Asperger’s or PDD-NOS, based on DSM-IV criteria. Moreover, for each subject, the sample contains Blood Level Oxygen Dependent (BOLD) signals, at resting-state, given a brain parcellation in 90 brain regions. The results tend to show that the autism group is distinct from the Asperger’s and PDD-NOS conditions. It also appears that the assessment of the brain activity is relevant to make a diagnosis with interesting predictive performances. These results are promising and gives grounds of hope of completing the current formal assessment based on descriptive clinical criteria.
Acknowledgements
Sarah Itani is a research fellow of the Belgian Fund for Scientific Research (F.R.S.-FNRS).
Keywords:
Autism (ASD),
artificial intelligence,
DSM,
diagnosis,
Children,
machine learning
Conference:
13th National Congress of the Belgian Society for Neuroscience , Brussels, Belgium, 24 May - 24 May, 2019.
Presentation Type:
Poster presentation
Topic:
Behavioral/Systems Neuroscience
Citation:
Itani
S,
Rossignol
M,
Lecron
F and
Fortemps
P
(2019). Autism in DSM-IV vs DSM-V: What if Machine Learning Could Help Us See Things More Clearly?.
Front. Neurosci.
Conference Abstract:
13th National Congress of the Belgian Society for Neuroscience .
doi: 10.3389/conf.fnins.2019.96.00086
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Received:
26 Apr 2019;
Published Online:
27 Sep 2019.
*
Correspondence:
Mrs. Sarah Itani, Fonds National de la Recherche Scientifique, Brussels, Belgium, sarah.itani@umons.ac.be