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Biol Psychiatry. 2019 Mar 7. pii: S0006-3223(19)30147-7. doi: 10.1016/j.biopsych.2019.02.019. [Epub ahead of print]

The Functional Brain Organization of an Individual Allows Prediction of Measures of Social Abilities Transdiagnostically in Autism and Attention-Deficit/Hyperactivity Disorder.

Author information

1
Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut. Electronic address: evelyn.lake@yale.edu.
2
Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, Maryland.
3
Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut.
4
Yale Child Study Center, Yale University, New Haven, Connecticut.
5
Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut.
6
Department of Psychology, Yale University, New Haven, Connecticut; Department of Psychology, University of Chicago, Chicago, Illinois.
7
Department of Psychiatry (MNS), College of Physicians and Surgeons, Columbia University, New York, New York.
8
Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut; Department of Psychology, Yale University, New Haven, Connecticut; Department of Neurobiology, Yale University, New Haven, Connecticut.
9
Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut; Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut; Department of Neurosurgery, Yale School of Medicine, Yale University, New Haven, Connecticut.

Abstract

BACKGROUND:

Autism spectrum disorder and attention-deficit/hyperactivity disorder (ADHD) are associated with complex changes as revealed by functional magnetic resonance imaging. To date, neuroimaging-based models are not able to characterize individuals with sufficient sensitivity and specificity. Further, although evidence shows that ADHD traits occur in individuals with autism spectrum disorder, and autism spectrum disorder traits in individuals with ADHD, the neurofunctional basis of the overlap is undefined.

METHODS:

Using individuals from the Autism Brain Imaging Data Exchange and ADHD-200, we apply a data-driven, subject-level approach, connectome-based predictive modeling, to resting-state functional magnetic resonance imaging data to identify brain-behavior associations that are predictive of symptom severity. We examine cross-diagnostic commonalities and differences.

RESULTS:

Using leave-one-subject-out and split-half analyses, we define networks that predict Social Responsiveness Scale, Autism Diagnostic Observation Schedule, and ADHD Rating Scale scores and confirm that these networks generalize to novel subjects. Networks share minimal overlap of edges (<2%) but some common regions of high hubness (Brodmann areas 10, 11, and 21, cerebellum, and thalamus). Further, predicted Social Responsiveness Scale scores for individuals with ADHD are linked to ADHD symptoms, supporting the hypothesis that brain organization relevant to autism spectrum disorder severity shares a component associated with attention in ADHD. Predictive connections and high-hubness regions are found within a wide range of brain areas and across conventional networks.

CONCLUSIONS:

An individual's functional connectivity profile contains information that supports dimensional, nonbinary classification in autism spectrum disorder and ADHD. Furthermore, we can determine disorder-specific and shared neurofunctional pathology using our method.

KEYWORDS:

ADHD; Autism spectrum disorder; Functional MRI; Functional connectivity; Magnetic resonance imaging; Predictive modeling

PMID:
31010580
DOI:
10.1016/j.biopsych.2019.02.019
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