Literature DB >> 32305325

Toward a Revised Nosology for Attention-Deficit/Hyperactivity Disorder Heterogeneity.

Joel T Nigg1, Sarah L Karalunas2, Eric Feczko3, Damien A Fair3.   

Abstract

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.
Copyright © 2020 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  ADHD; Cognition; Emotion; Heterogeneity; Machine learning; Subtypes

Mesh:

Year:  2020        PMID: 32305325      PMCID: PMC7423612          DOI: 10.1016/j.bpsc.2020.02.005

Source DB:  PubMed          Journal:  Biol Psychiatry Cogn Neurosci Neuroimaging        ISSN: 2451-9022


  138 in total

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Review 2.  Heterogeneity and Subtyping in Attention-Deficit/Hyperactivity Disorder-Considerations for Emerging Research Using Person-Centered Computational Approaches.

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Authors:  Natalie V Miller; Amie A Hane; Kathryn A Degnan; Nathan A Fox; Andrea Chronis-Tuscano
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Authors:  Sarah L Karalunas; Cynthia L Huang-Pollock; Joel T Nigg
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Review 9.  Sketching the Power of Machine Learning to Decrypt a Neural Systems Model of Behavior.

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10.  Multiclass Classification for the Differential Diagnosis on the ADHD Subtypes Using Recursive Feature Elimination and Hierarchical Extreme Learning Machine: Structural MRI Study.

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Journal:  PLoS One       Date:  2016-08-08       Impact factor: 3.240

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4.  Intrinsic Functional Connectivity in the Default Mode Network Differentiates the Combined and Inattentive Attention Deficit Hyperactivity Disorder Types.

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5.  Longitudinal network model of the co-development of temperament, executive functioning, and psychopathology symptoms in youth with and without ADHD.

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Review 6.  Annual Research Review: The contributions of the RDoC research framework on understanding the neurodevelopmental origins, progression and treatment of mental illnesses.

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7.  Assessing sluggish cognitive tempo and ADHD inattention in elementary students: Empirical differentiation, invariance across sex and grade, and measurement precision.

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9.  Adult ADHD and emerging models of maladaptive personality: a meta-analytic review.

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10.  Facial emotion recognition impairment predicts social and emotional problems in children with (subthreshold) ADHD.

Authors:  Anouck I Staff; Marjolein Luman; Saskia van der Oord; Catharina E Bergwerff; Barbara J van den Hoofdakker; Jaap Oosterlaan
Journal:  Eur Child Adolesc Psychiatry       Date:  2021-01-07       Impact factor: 5.349

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