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Autism spectrum disorder (ASD) affects the brain connectivity at differentlevels. Nonetheless, non-invasively distinguishing such effects using magneticresonance imaging (MRI) remains very challenging to machine learning diagnosticframeworks due to ASD heterogeneity. So far, existing network neuroscienceworks mainly focused on functional (derived from functional MRI) and structural(derived from diffusion MRI) brain connectivity, which might not capturerelational morphological changes between brain regions. Indeed, machinelearning (ML) studies for ASD diagnosis using morphological brain networksderived from conventional T1-weighted MRI are very scarce. To fill this gap, weleverage crowdsourcing by organizing a Kaggle competition to build a pool ofmachine learning pipelines for neurological disorder diagnosis with applicationto ASD diagnosis using cortical morphological networks derived from T1-weightedMRI. During the competition, participants were provided with a training datasetand only allowed to check their performance on a public test data. The finalevaluation was performed on both public and hidden test datasets based onaccuracy, sensitivity, and specificity metrics. Teams were ranked using eachperformance metric separately and the final ranking was determined based on themean of all rankings. The first-ranked team achieved 70% accuracy, 72.5%sensitivity, and 67.5% specificity, while the second-ranked team achieved63.8%, 62.5%, 65% respectively. Leveraging participants to design ML diagnosticmethods within a competitive machine learning setting has allowed theexploration and benchmarking of wide spectrum of ML methods for ASD diagnosisusing cortical morphological networks.

Machine Learning Methods for Brain Network Classification: Application to Autism Diagnosis using Cortical Morphological Networks