Common and unique multimodal covarying patterns in autism spectrum disorder subtypes.

Autor: Qi S; Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, 30303, USA., Morris R; Department of Psychology and Neuroscience Institute, Georgia State University, Atlanta, GA, 30302, USA., Turner JA; Department of Psychology and Neuroscience Institute, Georgia State University, Atlanta, GA, 30302, USA., Fu Z; Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, 30303, USA., Jiang R; Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.; University of Chinese Academy of Sciences, Beijing, 100190, China., Deramus TP; Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, 30303, USA., Zhi D; Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.; University of Chinese Academy of Sciences, Beijing, 100190, China., Calhoun VD; Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, 30303, USA. vcalhoun@gsu.edu., Sui J; Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, 30303, USA. jing.sui@nlpr.ia.ac.cn.; Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. jing.sui@nlpr.ia.ac.cn.; University of Chinese Academy of Sciences, Beijing, 100190, China. jing.sui@nlpr.ia.ac.cn.; Institute of Automation, Chinese Academy of Sciences Center for Excellence in Brain Science, Beijing, 100190, China. jing.sui@nlpr.ia.ac.cn.
Jazyk: angličtina
Zdroj: Molecular autism [Mol Autism] 2020 Nov 18; Vol. 11 (1), pp. 90. Date of Electronic Publication: 2020 Nov 18.
DOI: 10.1186/s13229-020-00397-4
Abstrakt: Background: The 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.
Methods: In 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.
Results: Dorsolateral 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.
Conclusions: Although 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.
Limitations: This study is male based, which cannot be generalized to the female or the general ASD population.
Databáze: MEDLINE
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