A novel pipeline leveraging surface-based features of small subcortical structures to classify individuals with autism spectrum disorder.

Progress in Neuro-psychopharmacology & Biological Psychiatry
Yu FuBin Hu

Abstract

Autism spectrum disorder (ASD) is accompanied with widespread impairment in social-emotional functioning. Classification of ASD using sensitive morphological features derived from structural magnetic resonance imaging (MRI) of the brain may help us to better understand ASD-related mechanisms and improve related automatic diagnosis. Previous studies using T1 MRI scans in large heterogeneous ABIDE dataset with typical development (TD) controls reported poor classification accuracies (around 60%). This may because they only considered surface-based morphometry (SBM) as scalar estimates (such as cortical thickness and surface area) and ignored the neighboring intrinsic geometry information among features. In recent years, the shape-related SBM achieves great success in discovering the disease burden and progression of other brain diseases. However, when focusing on local geometry information, its high dimensionality requires careful treatment in its application to machine learning. To address the above challenges, we propose a novel pipeline for ASD classification, which mainly includes the generation of surface-based features, patch-based surface sparse coding and dictionary learning, Max-pooling and ensemble classifiers based on ...Continue Reading

Related Concepts

Related Feeds

Autism

Autism spectrum disorder is associated with challenges with social skills, repetitive behaviors, and often accompanied by sensory sensitivities and medical issues. Here is the latest research on autism.