Brain-Shapelet: A Framework for Capturing Instantaneous Abnormalities in Brain Activity for Autism Spectrum Disorder Diagnosis

Abstract

Some symptoms of Autism Spectrum Disorder (ASD), such as anxiety and depression, often manifest intermittently rather than continuously, complicating the identification of reliable pathophysiological biomarkers. Meanwhile, functional connectivity networks (FCNs) generate high-dimensional connectomes, making it difficult to accurately capture instantaneous abnormal biomarkers of neurological disorders. To address this issue, we propose a framework, called Brain-Shapelet, to extract discriminative subsequences (Shapelets) from functional magnetic resonance imaging (fMRI) data for capturing instantaneous abnormalities in brain activity. It applies random walk algorithm on group-representative brain network to obtain brain region sets, and aggregates their blood oxygen level-dependent (BOLD) signals to extract Shapelets that reflect the associations between different brain regions at the same time point. Specially, we develop a feature selection strategy to reduce redundancy in Shapelets and optimize classification performance. Brain-Shapelet places greater emphasis on short-term brain activity alterations, allowing it to capture instantaneous abnormalities more effectively. It is evaluated on the ABIDE dataset and achieves a classification accuracy of 82.8%, significantly outperforming traditional brain network modeling methods. The proposed co-occurrence rate, occurrence frequency, and Gini coefficient metrics quantify the contributions of brain regions from the perspective of Shapelets, offering valuable insights for ASD diagnosis.