Attention-deficit/hyperactivity disorder (ADHD) is among the many syndromes in the psychiatric nosology for which etiological signal and clinical prediction are weak. Reducing phenotypic and mechanistic heterogeneity should be useful to arrive at stronger etiological and clinical prediction signals. We discuss key conceptual and methodological issues, highlighting the role of dimensional features aligned with Research Domain Criteria and cognitive, personality, and temperament theory as well as neurobiology. We describe several avenues of work in this area, utilizing different statistical, computational, and machine learning approaches to resolve heterogeneity in ADHD. We offer methodological and conceptual recommendations. Methodologically, we propose that an integrated approach utilizing theory and advanced computational logic to address targeted questions, with consideration of developmental context, can render the heterogeneity problem tractable for ADHD. Conceptually, we conclude that the field is on the cusp of justifying an emotionally dysregulated subprofile in ADHD that may be useful for clinical prediction and treatment testing. Cognitive profiles, while more nascent, may be useful for clinical prediction and treatment assignment in different ways depending on developmental stage. Targeting these psychological profiles for neurobiological and etiological study to capture different pathophysiological routes remains a near-term opportunity. Subtypes are likely to be multifactorial, cut across multiple dimensions, and depend on the research or clinical outcomes of interest for their ultimate selection. In this context parallel profiles based on cognition, emotion, and specific neural signatures appear to be on the horizon, each with somewhat different utilities. Efforts to integrate such cross-cutting profiles within a conceptual dysregulation framework are well underway.
|Original language||English (US)|
|Number of pages||12|
|Journal||Biological Psychiatry: Cognitive Neuroscience and Neuroimaging|
|State||Published - Aug 2020|
Bibliographical noteFunding Information:
This work was supported by National Institute of Health Grant Nos. R37-MH-59105 (principal investigator, JTN), R01-MH099064 (principal investigator, JTN), R01 MH115357 (multiple principal investigators, DAF, JTN), R01 MH096773 (principal investigator, DAF). SLK was supported by National Institutes of Health Grant No. K23-MH108656. DAF is a founder of Nous Imaging, Inc. but its activities are unrelated to the current study and any potential conflict of interest has been reviewed and managed by Oregon Health & Science University. The other authors report no biomedical financial interests or potential conflicts of interest.
This work was supported by National Institute of Health Grant Nos. R37-MH-59105 (principal investigator, JTN), R01-MH099064 (principal investigator, JTN), R01 MH115357 (multiple principal investigators, DAF, JTN), R01 MH096773 (principal investigator, DAF). SLK was supported by National Institutes of Health Grant No. K23-MH108656.
© 2020 Society of Biological Psychiatry
- Machine learning
PubMed: MeSH publication types
- Journal Article
- Research Support, N.I.H., Extramural