Commentary: Insights from across diagnostic boundaries: ADHD in the RDoC era--a commentary on Scerif and Baker (2015)

Cognitive, emotional, and social functioning of preschoolers with attention deficit hyperactivity problems

BACKGROUND

Attention Deficit Hyperactivity Disorder (ADHD) is associated with deficits in different functional domains. It remains unclear if deficits in different domains are equally strong in early childhood, and which deficits are specific to ADHD. Here, we describe functional domains in preschoolers and assess deficits in children with ADHD problems, by comparing them to preschoolers with other mental health problems or who develop typically.

METHODS

The ADHD Study assessed 1195 ca. 3.5 years old preschoolers through a semi-structured parent interview, parent questionnaires, and with neuropsychological tests. We determined functional domains by applying factor analytic methods to a broad set of questionnaire- and test-scales. Using resulting factor scores, we employed a Bayesian hierarchical regression to estimate functional deficits in children with ADHD.

RESULTS

We found that preschoolers' functioning could be described along the seven relatively independent dimensions activity level and regulation, executive function, cognition, language, emotion regulation, introversion, and sociability. Compared to typically developing preschoolers, those with ADHD had deficits in all domains except introversion and sociability. Only deficits in activity level regulation and executive functions were larger than 0.5 standardised mean deviations and larger than deficits of children with other mental health problems.

CONCLUSIONS

Preschoolers with ADHD have deficits in multiple functional domains, but only impairments in activity level and regulation and executive functions are specific for ADHD and large enough to be clinically significant. Research on functioning in these domains will be important for understanding the development of ADHD, and for improving treatment and prevention approaches.

Attention-Deficit/Hyperactivity Disorder Predominantly Inattentive Subtype/Presentation: Research Progress and Translational Studies

Research on the predominantly inattentive attention-deficit/hyperactivity disorder (ADHD-PI) subtype/presentation is important given its high prevalence, but paradoxically it is under-recognized and undertreated. The temporal stability of the inattention symptom could impact the high worldwide prevalence of ADHD-PI. Some evidence suggests differences in the nature of attentional deficit in ADHD-PI vs. that in other subtypes. Impairments in neuropsychological, neurocognitive, and social functioning are also evident in ADHD-PI, which could be specific to the subtype (e.g., processing speed, social perception, and skills), or differ from others in severity. Neuroimaging studies have also revealed ADHD-PI-specific neuropathological abnormalities and those that are shared with other subtypes. ADHD-PI is highly comorbid with learning and internalizing (e.g., anxiety and depression) disorders. There is no solid evidence for ADHD-PI-specific genetic etiologies and differential responses of subtypes to ADHD medications. Translational studies have used the Wistar Kyoto/NCrl substrain which requires further characterizations as an ADHD-PI model. Overall, ADHD-PI research has been conducted in the context of the Diagnostic and Statistical Manual, which arguably does not conform to the widely recognized "dimensional" view of ADHD. The Research Domain Criteria has been proposed to provide a novel framework for understanding the nature of neuropsychiatric illnesses and ultimately improve their diagnosis and treatment.

Machine learning and big data: Implications for disease modeling and therapeutic discovery in psychiatry

INTRODUCTION

Machine learning capability holds promise to inform disease models, the discovery and development of novel disease modifying therapeutics and prevention strategies in psychiatry. Herein, we provide an introduction on how machine learning/Artificial Intelligence (AI) may instantiate such capabilities, as well as provide rationale for its application to psychiatry in both research and clinical ecosystems.

METHODS

Databases PubMed and PsycINFO were searched from 1966 to June 2016 for keywords:Big Data, Machine Learning, Precision Medicine, Artificial Intelligence, Mental Health, Mental Disease, Psychiatry, Data Mining, RDoC, and Research Domain Criteria. Articles selected for review were those that were determined to be aligned with the objective of this particular paper.

RESULTS

Results indicate that AI is a viable option to build useful predictors of outcome while offering objective and comparable accuracy metrics, a unique opportunity, particularly in mental health research. The approach has also consistently brought notable insight into disease models through processing the vast amount of already available multi-domain, semi-structured medical data. The opportunity for AI in psychiatry, in addition to disease-model refinement, is in characterizing those at risk, and it is likely also relevant to personalizing and discovering therapeutics.

CONCLUSIONS

Machine learning currently provides an opportunity to parse disease models in complex, multi-factorial disease states (e.g. mental disorders) and could possibly inform treatment selection with existing therapies and provide bases for domain-based therapeutic discovery.

Evaluating the Psychometric Properties of the Conners Adult ADHD Rating Scales

Objective: Characterized by significant work-related impairments, difficulties with relationships, and higher rates of criminal behavior and substance abuse, ADHD is now understood as a disability that can persist throughout a lifetime. Differing opinions remain, however, regarding adult symptoms of ADHD, particularly with regard to gender differences. These issues add to the challenges of constructing a reliable and valid measure of ADHD for use with adults in research and practice. Method: We review the theoretical foundations of ADHD and psychometric properties of a major assessment device, the Conners' Adult ADHD Rating Scales (CAARS). Results: Results of the literature review found (a) a wide range of internal consistency estimates, with lowest values for the Diagnostic and Statistical Manual of Mental Disorders (DSM)-Hyperactive/Impulsive Symptoms subscale when completed by women, and (b) discriminant validity estimates in the same range as convergent validity estimates. Conclusion: These findings raise questions about the construct validity of the CAARS and suggest directions for future research.

Attention-deficit hyperactivity disorder (ADHD): progress and controversy in diagnosis and treatment

Knowledge about attention-deficit hyperactivity disorder (ADHD) is rapidly accumulating. Recent advances in diagnosis, genetics, neuroimaging, drug and non-drug treatments are considered, and the results are related to the critical attack on the ADHD diagnosis, which argues it a medicalising social construct, unhelpfully sustaining power relationships. The advances reviewed suggest that, while this attack can be conclusively dismissed as wrong and misleading, the phenomenological definition of ADHD is no longer sufficient for construct validity, though continues to be valuable as a guide for clinicians. The humanising and individualising concerns underlying the attack on the diagnosis could usefully be redirected to improving effective measurement of patient outcomes.