Individual Brain Morphological Connectome Indicator Based on Jensen–Shannon Divergence Similarity Estimation for Autism Spectrum Disorder Identification

Abstract

BackgroundStructural magnetic resonance imaging (sMRI) reveals abnormalities in patients with autism spectrum syndrome (ASD). Previous connectome studies of ASD have failed to identify the individual neuroanatomical details in preschool-age individuals. This paper aims to establish an individual morphological connectome method to characterize the connectivity patterns and topological alterations of the individual-level brain connectome and their diagnostic value in patients with ASD.MethodsBrain sMRI data from 24 patients with ASD and 17 normal controls (NCs) were collected; participants in both groups were aged 24–47 months. By using the Jensen–Shannon Divergence Similarity Estimation (JSSE) method, all participants’s morphological brain network were ascertained. Student’s t-tests were used to extract the most significant features in morphological connection values, global graph measurement, and node graph measurement.ResultsThe results of global metrics’ analysis showed no statistical significance in the difference between two groups. Brain regions with meaningful properties for consensus connections and nodal metric features are mostly distributed in are predominantly distributed in the basal ganglia, thalamus, and cortical regions spanning the frontal, temporal, and parietal lobes. Consensus connectivity results showed an increase in most of the consensus connections in the frontal, parietal, and thalamic regions of patients with ASD, while there was a decrease in consensus connectivity in the occipital, prefrontal lobe, temporal lobe, and pale regions. The model that combined morphological connectivity, global metrics, and node metric features had optimal performance in identifying patients with ASD, with an accuracy rate of 94.59%.ConclusionThe individual brain network indicator based on the JSSE method is an effective indicator for identifying individual-level brain network abnormalities in patients with ASD. The proposed classification method can contribute to the early clinical diagnosis of ASD.