Abstract
Autism spectrum disorder (ASD) is a combination of developmental anomalies that causes social and behavioral impairments, affecting around 2% of US children. Common symptoms include difficulties in communications, interactions, and behavioral disabilities. The onset of symptoms can start in early childhood, yet repeated visits to a pediatric specialist are needed before reaching a diagnosis. Still, this diagnosis is usually subjective, and scores can vary from one specialist to another. Previous literature suggests differences in brain development, environmental, and/or genetic factors play a role in developing autism, yet scientists still do not know exactly the pathology of this disorder. Currently, the gold standard diagnosis of ASD is a set of diagnostic evaluations, such as the Autism Diagnostic Observation Schedule (ADOS) or Autism Diagnostic Interview–Revised (ADI-R) report. These gold standard diagnostic instruments are an intensive, lengthy, and subjective process that involves a set of behavioral and communications tests and clinical history information conducted by a team of qualified clinicians. Emerging advancements in neuroimaging and machine learning techniques can provide a fast and objective alternative to conventional repetitive observational assessments. This paper provides a thorough study of implementing feature engineering tools to find discriminant insights from brain imaging of white matter connectivity and using a machine learning framework for an accurate classification of autistic individuals. This work highlights important findings of impacted brain areas that contribute to an autism diagnosis and presents promising accuracy results. We verified our proposed framework on a large publicly available DTI dataset of 225 subjects from the Autism Brain Imaging Data Exchange-II (ABIDE-II) initiative, achieving a high global balanced accuracy over the 5 sites of up to 99% with 5-fold cross validation. The data used was slightly unbalanced, including 125 autistic subjects and 100 typically developed (TD) ones. The achieved balanced accuracy of the proposed technique is the highest in the literature, which elucidates the importance of feature engineering steps involved in extracting useful knowledge and the promising potentials of adopting neuroimaging for the diagnosis of autism.
Highlights
Autism spectrum disorder (ASD), famously known as just autism, is a pervasive developmental disorder manifested as problems in social interactions and communications, both verbal and non-verbal [1,2,3]
Despite the numerous studies of autism-related changes in white matter integrity, the objective of this work is to implement a comprehensive ML-CAD system that, besides its ability to classify ASD vs. typically developed (TD) subjects, identifies brain areas correlated with autism, and was validated on a big, publicly available dataset using DTI data
Diffusion-weighted MRI scans for a total of 225 subjects were used: 125 ASDs and 100 TDs, with age ranges between 5.128 years and
Summary
Autism spectrum disorder (ASD), famously known as just autism, is a pervasive developmental disorder manifested as problems in social interactions and communications, both verbal and non-verbal [1,2,3]. Current approved diagnosis techniques require significant clinical experience, assessing different aspects via a standard testing/scoring system, such as the ADOS [5] or ADI-R [6]. Those tests are subjective and can be time consuming and challenging, with limited accuracy of around 80–85% [7]. Clinicians may not always agree with the results of those tests [8] This is our main motivation for developing a neuroimaging-based alternative that can provide a non-subjective evaluation that may help clinicians reach a faster, more reliable diagnosis. Previous neurobiological studies investigated connections between ASD and underlying structure, trying to describe brain abnormalities associated with autism traits. Since the emergence of MRI, plenty of studies appeared to investigate connections between ASD and underlying brain features, either shape and volume features using structural MRI [9], or white matter (WM) diffusivity [10] anomalies using DTI, while others performed correlations of ASD with either task-based or resting-state functionality [11]
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