Common and unique multimodal covarying patterns in autism spectrum disorder subtypes

Jing Sui,Jessica A Turner,Robin Morris,Dongmei Zhi,Zening Fu,Vince D Calhoun,Rongtao Jiang,Thomas P Deramus,Shile Qi

doi:10.1186/s13229-020-00397-4

Abstract

BackgroundThe heterogeneity inherent in autism spectrum disorder (ASD) presents a substantial challenge to diagnosis and precision treatment. Heterogeneity across biological etiologies, genetics, neural systems, neurocognitive attributes and clinical subtypes or phenotypes has been observed across individuals with ASD.MethodsIn this study, we aim to investigate the heterogeneity in ASD from a multimodal brain imaging perspective. The Autism Diagnostic Observation Schedule (ADOS) was used as a reference to guide functional and structural MRI fusion. DSM-IV-TR diagnosed Asperger’s disorder (n = 79), pervasive developmental disorder-not otherwise specified [PDD-NOS] (n = 58) and Autistic disorder (n = 92) from ABIDE II were used as discovery cohort, and ABIDE I (n = 400) was used for replication.ResultsDorsolateral prefrontal cortex and superior/middle temporal cortex are the primary common functional–structural covarying cortical brain areas shared among Asperger’s, PDD-NOS and Autistic subgroups. Key differences among the three subtypes are negative functional features within subcortical brain areas, including negative putamen–parahippocampus fractional amplitude of low-frequency fluctuations (fALFF) unique to the Asperger’s subtype; negative fALFF in anterior cingulate cortex unique to PDD-NOS subtype; and negative thalamus–amygdala–caudate fALFF unique to the Autistic subtype. Furthermore, each subtype-specific brain pattern is correlated with different ADOS subdomains, with social interaction as the common subdomain. The identified subtype-specific patterns are only predictive for ASD symptoms manifested in the corresponding subtypes, but not the other subtypes.ConclusionsAlthough ASD has a common neural basis with core deficits linked to social interaction, each ASD subtype is strongly linked to unique brain systems and subdomain symptoms, which may help to better understand the underlying mechanisms of ASD heterogeneity from a multimodal neuroimaging perspective.LimitationsThis study is male based, which cannot be generalized to the female or the general ASD population.

Highlights

The heterogeneity inherent in autism spectrum disorder (ASD) presents a substantial challenge to diagnosis and precision treatment
We focused on four goals: (1) to identify multimodal brain networks associated with overall Autism Diagnostic Observation Schedule (ADOS) scores within ASD and among its subtypes (Asperger’s, pervasive developmental disorder-not otherwise specified [Pervasive developmental disorder-not otherwise specified (PDD-NOS)] and Autistic); (2) to assess group differences between ASD/ subtypes and typically developing controls (TDC) of these identified networks; (3) to identify the common and unique multimodal neurobehavioral attributes identified among the Asperger’s, PDD-NOS and Autistic subgroups; and (4) to evaluate the predictability of the identified brain networks in predicting ADOS and Social Responsiveness Scale (SRS) total scores in an independent sample (ABIDE I)
ASD‐related multimodal patterns ASD-related joint components were identified (Fig. 2), which correlated with total ADOS scores, which are false discovery rate correction (FDR) corrected

Summary

Introduction

The heterogeneity inherent in autism spectrum disorder (ASD) presents a substantial challenge to diagnosis and precision treatment. Multi-level heterogeneity [3] across biological and behavioral attributes including genetics [4, 5], neural systems [6, 7] and clinical phenotypes has been observed in individuals with ASD. Many clinicians and researchers still hypothesize that it is comprised of different subgroups or multidimensional nodes or subdivisions [7, 16, 17] that are thought to have potential etiological and treatment value, these have yet to be clearly identified and operationalized Such remarkable heterogeneity across multiple levels of dimensions reflects a core challenge underlying the neurobehavioral modeling of ASD, or its subtypes

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