The Altered Pattern of the Functional Connectome Related to Pathological Biomarkers in Individuals for Autism Spectrum Disorder Identification

ObjectiveAutism spectrum disorder (ASD) is a common neurodevelopmental disorder characterized by the development of multiple symptoms, with incidences rapidly increasing worldwide. An important step in the early diagnosis of ASD is to identify informative biomarkers. Currently, the use of functional brain network (FBN) is deemed important for extracting data on brain imaging biomarkers. Unfortunately, most existing studies have reported the utilization of the information from the connection to train the classifier; such an approach ignores the topological information and, in turn, limits its performance. Thus, effective utilization of the FBN provides insights for improving the diagnostic performance. MethodsWe propose the combination of the information derived from both FBN and its corresponding graph theory measurements to identify and distinguish ASD from normal controls (NCs). Specifically, a multi-kernel support vector machine (MK-SVM) was used to combine multiple types of information. ResultsThe experimental results illustrate that the combination of information from multiple connectome features (i.e., functional connections and graph measurements) can provide a superior identification performance with an area under the receiver operating characteristic curve (ROC) of 0.9191 and an accuracy of 82.60%. Furthermore, the graph theoretical analysis illustrates that the significant nodal graph measurements and consensus connections exists mostly in the salience network (SN), default mode network (DMN), attention network, frontoparietal network, and social network. ConclusionThis work provides insights into potential neuroimaging biomarkers that may be used for the diagnosis of ASD and offers a new perspective for the exploration of the brain pathophysiology of ASD through machine learning.

Automated summary

This study proposes a method using functional brain network (FBN) and graph theory measurements to identify and distinguish autism spectrum disorder (ASD) from normal controls (NCs). The experimental results demonstrate that this method achieves superior identification performance and reveals important brain networks associated with ASD. This provides a new perspective for the early diagnosis of ASD and the exploration of its brain pathophysiology through machine learning.