Abstract
BackgroundClinical and etiological varieties remain major obstacles to decompose heterogeneity in autism spectrum disorders (ASD). Recently, neuroimaging raised new hope to identify neurosubtypes of ASD for further understanding the biological mechanisms behind the disorder.MethodsIn this study, brain structural MRI data and clinical measures of 221 male subjects with ASD and 257 healthy controls were selected from 7 independent sites from the Autism Brain Image Data Exchange database (ABIDE). Heterogeneity through discriminative analysis (HYDRA), a recently-proposed semi-supervised clustering method was utilized to divide individuals with ASD into several neurosubtypes by regional volumetric measures of gray matter, white matter, and cerebrospinal fluid. Voxel-wise volume, clinical measures, dynamic resting-state functional magnetic resonance imaging (R-fMRI) measures among different neurosubtypes of ASD were explored. In addition, support vector machine (SVM) model was applied to test whether the neurosubtyping of ASD could improve diagnostic accuracy of ASD.ResultsTwo neurosubtypes of ASD with different voxel-wise volumetric patterns were revealed. The full-scale intelligence quotient (IQ), verbal IQ, Autism Diagnostic Observation Schedule (ADOS) total scores and ADOS severity scores were significantly different between the two neurosubtypes, the total intracranial volume was correlated with performance IQ in Subtype 1 and was correlated with ADOS communication score and ADOS social score in Subtype 2. Compared with Subtype 2, Subtype 1 showed lower dynamic R-fMRI measures, lower dynamic functional architecture stability, higher mean and lower standard deviation (SD) of concordance among dynamic R-fMRI measures in cerebellum. In addition, classification accuracies between ASD neurosubtypes and healthy controls were significantly improved compared with classification accuracy between entire ASD group and healthy controls.LimitationsThe present study excluded female subjects and left-handed subjects, which limited the ability to investigate the associations between these factors and the heterogeneity of ASD.ConclusionsThe two distinct neuroanatomical subtypes of ASD validated by other data modalities not only adds reliability of the result, but also bridges from brain phenomenology to clinical behavior. The current neurosubtypes of ASD could facilitate understanding the neuropathology of this disorder and could be potentially used to improve clinical decision-making process and optimize treatment.
Highlights
Clinical and etiological varieties remain major obstacles to decompose heterogeneity in autism spectrum disorders (ASD)
Two subtypes of ASD based on structural magnetic resonance imaging (MRI) In this study, we evaluated the consistency of clustering assignment by adjusting the number of clusters from 2 to 8 using adjusted rand index (ARI)
The maximum ARI value was found at K = 2 (ARI = 0.82), which indicated that the ASD samples were optimally partitioned by 2 subtypes based on the volumes of anatomical Region of interest (ROI)
Summary
Clinical and etiological varieties remain major obstacles to decompose heterogeneity in autism spectrum disorders (ASD). Neuroimaging raised new hope to identify neurosubtypes of ASD for further understanding the biological mechanisms behind the disorder. ASD is characterized by impairments in social cognition as well as restricted and repetitive behaviors (RRB) [3]. Different from other psychiatric disorders characterized by symptom severity, patients with ASD display a broad range of behavior types and severities [4]. The high biological and clinical heterogeneity of ASD patients have hindered attempts at understanding the neurobiological mechanisms of the disorder [9]. The analysis of ASD mainly depends on the spectrum of symptom severity [10], while the results of these efforts have been neither distinguishable nor fully reflecting the underlying biology [11]. Despite the cancellation of ASD subtypes, subtyping of ASD has several clinical benefits, such as early and accurate detection, developmental trajectories, and response to treatment
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