Heterogeneity of executive function revealed by a functional random forest approach across ADHD and ASD

Autor: Michaela Cordova, Anders Perrone, Beth Hoover Langhorst, Alice M. Graham, Kiryl Shada, Oscar Miranda-Dominguez, Emma Schifsky, Eric Feczko, Olivia Doyle, Joel T. Nigg, Eric Fombonne, Damion V. Demeter, Damien A. Fair
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Male
Resting State Functional Connectivity MRI
Adolescent
Autism Spectrum Disorder
Cognitive Neuroscience
lcsh:Computer applications to medicine. Medical informatics
behavioral disciplines and activities
ASD
lcsh:RC346-429
050105 experimental psychology
Machine Learning
03 medical and health sciences
Executive Function
0302 clinical medicine
mental disorders
medicine
ADHD
Humans
0501 psychology and cognitive sciences
Radiology
Nuclear Medicine and imaging
Adhd symptoms
Child
lcsh:Neurology. Diseases of the nervous system
Null model
Functional connectivity
05 social sciences
Brain
Cognition
Regular Article
medicine.disease
Magnetic Resonance Imaging
Random forest
Neurology
Autism spectrum disorder
Attention Deficit Disorder with Hyperactivity
lcsh:R858-859.7
Female
Neurology (clinical)
Psychology
Neurocognitive
030217 neurology & neurosurgery
rs-fMRI
Clinical psychology
Zdroj: NeuroImage : Clinical
NeuroImage: Clinical, Vol 26, Iss, Pp-(2020)
ISSN: 2213-1582
Popis: Highlights • Functional random forest identified transdiagnostic ASD and ADHD subtypes. • Subtypes are directly tied to ADHD symptoms, relevant to ASD and ADHD. • Neurocognitive subtypes do not map one to one with functional connectivity trends. • There may be multiple mechanistic “pathways” to observed phenotypes.
Background Those with autism spectrum disorder (ASD) and/or attention-deficit-hyperactivity disorder (ADHD) exhibit symptoms of hyperactivity and inattention, causing significant hardships for families and society. A potential mechanism involved in these conditions is atypical executive function (EF). Inconsistent findings highlight that EF features may be shared or distinct across ADHD and ASD. With ADHD and ASD each also being heterogeneous, we hypothesized that there may be nested subgroups across disorders with shared or unique underlying mechanisms. Methods Participants (N = 130) included adolescents aged 7–16 with ASD (n = 64) and ADHD (n = 66). Typically developing (TD) participants (n = 28) were included for a comparative secondary sub-group analysis. Parents completed the K-SADS and youth completed an extended battery of executive and other cognitive measures. A two stage hybrid machine learning tool called functional random forest (FRF) was applied as a classification approach and then subsequently to subgroup identification. We input 43 EF variables to the classification step, a supervised random forest procedure in which the features estimated either hyperactive or inattentive ADHD symptoms per model. The FRF then produced proximity matrices and identified optimal subgroups via the infomap algorithm (a type of community detection derived from graph theory). Resting state functional connectivity MRI (rs-fMRI) was used to evaluate the neurobiological validity of the resulting subgroups. Results Both hyperactive (Mean absolute error (MAE) = 0.72, Null model MAE = 0.8826, (t(58) = −4.9, p
Databáze: OpenAIRE
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