Identifying Autism Spectrum Disorder in fMRI Brain Scans
Date
2023-04-11
Authors
Enns, Keanelek
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Abstract
Autism Spectrum Disorder (ASD) affects a large portion of the global population
both directly and indirectly. The biological etiology of the disorder is not sufficiently
understood, and current diagnoses rely on behavioural indicators which do not provide
a reliable basis for diagnosis until about 2 years of age. Identifying a biological marker
of ASD would aid in understanding the disorder and potentially allow for earlier,
more objective diagnoses and treatments to improve the quality of life of individuals
possessing ASD. The analysis of functional connectivity in the brain using functional
Magnetic Resonance Imaging (fMRI) has been identified as a promising method for
discovering such biological markers.
This study recreated the work of Lanciano et al. in their paper “Explainable
Classification of Brain Networks via Contrast Subgraphs”, but found inconsistent
results with what was claimed. The methods were modified in various ways to improve
accuracy and performance. A new, simpler method named Discriminative Edges
(DE) was developed which achieved similar accuracies with improved performance
and explainability. DE was also adapted to receive raw correlation matrices as well
as thresholded correlation matrices representing brain networks, and it was found
that raw correlation matrices provided more useful information for classification. A
replication package was provided to aid future researchers in validating and improving
upon these results. Suggestions for future work based on the findings of this study
were provided, the most important being to procure more datasets, discover data-
driven subcategories of ASD, and maintain replicability in studies.
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Keywords
Machine Learning, Neuroscience, Autism Spectrum Disorder, Explainability, Interpretability, Diagnosis, fMRI, Brain, Graph