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.
Highlights
Autism spectrum disorder (ASD) is a neurodevelopmental disability characterized by persistent deficits in social communication associated with restricted and repetitive patterns of behavior, interests, or activities (Minshew and Williams, 2007)
The results indicated that 92.9% of subjects were correctly classified using an support vector machine (SVM) method by leave-one-out crossvalidation (LOOCV)
The default mode network (DMN) and occipital network (ON) showed relatively higher network degree (ND) in pattern 1, while relatively higher ND was found in the DMN and frontoparietal network (FPN) in pattern 2
Summary
Autism spectrum disorder (ASD) is a neurodevelopmental disability characterized by persistent deficits in social communication associated with restricted and repetitive patterns of behavior, interests, or activities (Minshew and Williams, 2007). In a previous study (Dosenbach et al, 2010), the 160 regions of interest (ROIs) covering the whole brain were labeled and grouped into six networks, namely, the default mode network (DMN), frontoparietal network (FPN), cingulo-opercular network (CON), sensorimotor network (SMN), occipital network (ON), and cerebellum network (CN). This brain atlas was defined based on activation patterns of the brain across different cognitive tasks and may support additional information for interpreting developmental changes in brain function
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