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• W2921949803 abstract "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 ( 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." @default.
• W2921949803 created "2019-03-22" @default.
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• W2921949803 date "2019-08-01" @default.
• W2921949803 modified "2023-10-12" @default.
• W2921949803 title "The Functional Brain Organization of an Individual Allows Prediction of Measures of Social Abilities Transdiagnostically in Autism and Attention-Deficit/Hyperactivity Disorder" @default.
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• W2921949803 doi "https://doi.org/10.1016/j.biopsych.2019.02.019" @default.
• W2921949803 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7311928" @default.
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