Identifying Boys With Autism Spectrum Disorder Based on Whole-Brain Resting-State Interregional Functional Connections Using a Boruta-Based Support Vector Machine Approach

Lei Zhao; Yun-Kai Sun; Shao-Wei Xue; Hong Luo; Xiao-Dong Lu; Lan-Hua Zhang

doi:10.3389/fninf.2022.761942

Identifying Boys With Autism Spectrum Disorder Based on Whole-Brain Resting-State Interregional Functional Connections Using a Boruta-Based Support Vector Machine Approach

Front Neuroinform. 2022 Feb 22:16:761942. doi: 10.3389/fninf.2022.761942. eCollection 2022.

Authors

Lei Zhao^{1

2

3}, Yun-Kai Sun⁴, Shao-Wei Xue^{1

2

3}, Hong Luo^{1

2

3}, Xiao-Dong Lu^{1

2

3}, Lan-Hua Zhang⁵

Affiliations

¹ Centre for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China.
² Institute of Psychological Science, Hangzhou Normal University, Hangzhou, China.
³ Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China.
⁴ Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
⁵ College of Medical Information and Engineering, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai'an, China.

Abstract

An increasing number of resting-state functional magnetic resonance neuroimaging (R-fMRI) studies have used functional connections as discriminative features for machine learning to identify patients with brain diseases. However, it remains unclear which functional connections could serve as highly discriminative features to realize the classification of autism spectrum disorder (ASD). The aim of this study was to find ASD-related functional connectivity patterns and examine whether these patterns had the potential to provide neuroimaging-based information to clinically assist with the diagnosis of ASD by means of machine learning. We investigated the whole-brain interregional functional connections derived from R-fMRI. Data were acquired from 48 boys with ASD and 50 typically developing age-matched controls at NYU Langone Medical Center from the publicly available Autism Brain Imaging Data Exchange I (ABIDE I) dataset; the ASD-related functional connections identified by the Boruta algorithm were used as the features of support vector machine (SVM) to distinguish patients with ASD from typically developing controls (TDC); a permutation test was performed to assess the classification performance. Approximately, 92.9% of participants were correctly classified by a combined SVM and leave-one-out cross-validation (LOOCV) approach, wherein 95.8% of patients with ASD were correctly identified. The default mode network (DMN) exhibited a relatively high network degree and discriminative power. Eight important brain regions showed a high discriminative power, including the posterior cingulate cortex (PCC) and the ventrolateral prefrontal cortex (vlPFC). Significant correlations were found between the classification scores of several functional connections and ASD symptoms (p < 0.05). This study highlights the important role of DMN in ASD identification. Interregional functional connections might provide useful information for the clinical diagnosis of ASD.

Keywords: Boruta; autism spectrum disorder; functional connection (FC); resting-state functional magnetic resonance neuroimaging (R-fMRI); support vector machine.