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

Michaela Cordova, Kiryl Shada, Damion V Demeter, Olivia Doyle, Oscar Miranda-Dominguez, Anders Perrone, Emma Schifsky, Alice Graham, Eric Fombonne, Beth Langhorst, Joel Nigg, Damien A Fair, Eric Feczko

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26 Scopus citations


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 < .001) and inattentive (MAE = 0.7, Null model MAE = 0.85, t(58) = −4.4, p < .001) symptoms were predicted better than chance by the EF features selected. Subgroup identification was robust (Hyperactive: Q = 0.2356, p < .001; Inattentive: Q = 0.2350, p < .001). Two subgroups representing severe and mild symptomology were identified for each symptom domain. Neuroimaging data revealed that the subgroups and TD participants significantly differed within and between multiple functional brain networks, but no consistent “severity” patterns of over or under connectivity were observed between subgroups and TD. Conclusion: The FRF estimated hyperactive/inattentive symptoms and identified 2 distinct subgroups per model, revealing distinct neurocognitive profiles of Severe and Mild EF performance per model. Differences in functional connectivity between subgroups did not appear to follow a severity pattern based on symptom expression, suggesting a more complex mechanistic interaction that cannot be attributed to symptom presentation alone.

Original languageEnglish (US)
Article number102245
JournalNeuroImage: Clinical
StatePublished - Mar 2020

Bibliographical note

Funding Information:
The authors of this paper would like to thank the members of the Developmental Cognition and Neuroimaging (DCAN) lab under the leadership of Dr. Damien Fair, Dr. Alice Graham, Dr. Oscar Miranda-Dominguez, Dr. Lisa Karstens, and Michaela Cordova, the mentorship of Dr. Eric Feczko, and Dr. Joel Nigg's ADHD Project lab for their contributions to the ASD and ADHD research studies. We would also like to express our appreciation for the families that gave their valuable time and effort to participate in these studies. This research was supported by the National Institutes of Health (grants R01 MH096773 and K99/R00 MH091238 , R01 MH115357 , R01 MH086654 , U24 DA04112 , U01 DA041148 ), the Oregon Clinical and Translational Research Institute , the Gates Foundation , and the Destefano Innovation Fund . Dr. Eric Feczko was supported by the National Library of Medicine ( T15LM007088 ).

Publisher Copyright:
© 2020


  • ADHD
  • ASD
  • Executive function
  • Machine learning
  • rs-fMRI

PubMed: MeSH publication types

  • Research Support, Non-U.S. Gov't
  • Journal Article
  • Research Support, N.I.H., Extramural


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