Distinct neuropsychological profiles within ADHD: a latent class analysis of cognitive control, reward sensitivity and timing

The known and unknown about attention deficit hyperactivity disorder (ADHD) genetics: a special emphasis on Arab population

Attention deficit hyperactivity disorder (ADHD) is a clinically and genetically heterogeneous neurodevelopmental syndrome characterized by behavioral appearances such as impulsivity, inattention, and hyperactivity. The prevalence of ADHD is high in childhood when compared to adults. ADHD has been significantly advanced by genetic research over the past 25 years. However, it is logically conceivable that both genetic and/or non-genetic factors, such as postnatal environmental and social influences, are associated with ADHD phenotype in Arab populations. While genetic influences are strongly linked with the etiology of ADHD, it remains obscure how consanguinity which is an underlying factor for many genetic diseases, contributes to ADHD subtypes. Arabian Gulf Nations have one the highest rates of consanguineous marriages, and consanguinity plays an important contributing factor in many genetic diseases that exist in higher percentages in Arabian Gulf Nations. Therefore, the current review aims to shed light on the genetic variants associated with ADHD subtypes in Arabian Gulf nations and Saudi Arabia in particular. It also focuses on the symptoms and the diagnosis of ADHD before turning to the neuropsychological pathways and subgroups of ADHD. The impact of a consanguinity-based understanding of the ADHD subtype will help to understand the genetic variability of the Arabian Gulf population in comparison with the other parts of the world and will provide novel information to develop new avenues for future research in ADHD.

Latent profile analysis reveals overlapping ARFID and shape/weight motivations for restriction in eating disorders

BACKGROUND

DSM-5 differentiates avoidant/restrictive food intake disorder (ARFID) from other eating disorders (EDs) by a lack of overvaluation of body weight/shape driving restrictive eating. However, clinical observations and research demonstrate ARFID and shape/weight motivations sometimes co-occur. To inform classification, we: (1) derived profiles underlying restriction motivation and examined their validity and (2) described diagnostic characterizations of individuals in each profile to explore whether findings support current diagnostic schemes. We expected, consistent with DSM-5, that profiles would comprise individuals endorsing solely ARFID or restraint (i.e. trying to eat less to control shape/weight) motivations.

METHODS

We applied latent profile analysis to 202 treatment-seeking individuals (ages 10-79 years [M = 26, s.d. = 14], 76% female) with ARFID or a non-ARFID ED, using the Nine-Item ARFID Screen (Picky, Appetite, and Fear subscales) and the Eating Disorder Examination-Questionnaire Restraint subscale as indicators.

RESULTS

A 5-profile solution emerged: Restraint/ARFID-Mixed (n = 24; 8% [n = 2] with ARFID diagnosis); ARFID-2 (with Picky/Appetite; n = 56; 82% ARFID); ARFID-3 (with Picky/Appetite/Fear; n = 40; 68% ARFID); Restraint (n = 45; 11% ARFID); and Non-Endorsers (n = 37; 2% ARFID). Two profiles comprised individuals endorsing solely ARFID motivations (ARFID-2, ARFID-3) and one comprising solely restraint motivations (Restraint), consistent with DSM-5. However, Restraint/ARFID-Mixed (92% non-ARFID ED diagnoses, comprising 18% of those with non-ARFID ED diagnoses in the full sample) endorsed ARFID and restraint motivations.

CONCLUSIONS

The heterogeneous profiles identified suggest ARFID and restraint motivations for dietary restriction may overlap somewhat and that individuals with non-ARFID EDs can also endorse high ARFID symptoms. Future research should clarify diagnostic boundaries between ARFID and non-ARFID EDs.

A critical review of hot executive functioning in youth attention-deficit/hyperactivity disorder: Methodological limitations, conceptual considerations, and future directions

Hot executive functioning (EF) - EF under emotionally or motivationally salient conditions - is a putative etiology of attention-deficit/hyperactivity disorder (ADHD), disruptive behavior problems (DBPs), and their related impairments. Despite two decades of research, the present study is the first review of the construct in youth ADHD, with a particular focus on the role of task design, age, and DBPs, as well as relevant conceptual and methodological considerations. While certain hot EF tasks have been investigated extensively (e.g., choice impulsivity), substantial inconsistency in measurement of the broader construct remains, severely limiting conclusions. Future research should a) consider the extent to which various hot EF tasks relate to one another, a higher order factor, and other related constructs; b) further investigate task design, particularly the elicitation of emotion or motivation and its anticipated effect on EF; and c) incorporate multiple levels of analysis to validate similarities and differences among tasks with regard to the affective experiences and cognitive demands they elicit. With improved measurement and conceptual clarity, hot EF has potential to advance the literature on etiological pathways to ADHD, DBPs and associated impairments and, more broadly, may represent a useful tool for understanding the influence of emotion and motivation on cognition.

Further validation of a new ADHD screening questionnaire measuring parents' explanations (time processing, cognition, and motivation) of inattention symptoms in their school-aged children

The triple pathway model suggests that different neuropsychological factors underlie symptoms of inattention (i.e., time, cognition and/or motivation problems). However, screening instruments asking individuals to judge the link between these neuropsychological factors and inattention are lacking. The recently developed screening questionnaire, PASSC, aims to examine these factors possibly causing inattention by asking parents to indicate to what extent their child experiences inattention symptoms and to what extent different neuropsychological factors explain this inattention. The present study extends prior validation research of the PASSC by examining associations between PASSC inattention explained by time, cognition, and/or motivation and children's performance on tests measuring these same three constructs. Results indicated positive correlations between PASSC inattention explained by time and less accurate performance on a time discrimination test, and between PASSC inattention explained by cognition and more working memory errors as well as higher attention switching costs. Furthermore, children whose parents indicated that their inattention was best explained by cognition showed higher switching costs than children whose inattention was best explained by motivation. This support for construct validity of the PASSC is limited to two PASSC explanations (i.e., time, cognition) and a subset of tests (i.e., time discrimination, attention switching, memory span). Future research should focus on integrating PASSC and performance test results to differentiate between children with attention problems but different underlying neuropsychological problems. Concluding, the PASSC can be a promising screening tool to identify inattention in children and the underlying explanation indicated by parents.

Data-driven profiles of attention-deficit/hyperactivity disorder using objective and ecological measures of attention, distractibility, and hyperactivity

In the past two decades, the traditional nosology of attention-deficit/hyperactivity disorder (ADHD) has been criticized for having insufficient discriminant validity. In line with current trends, in the present study, we combined a data-driven approach with the advantages of virtual reality aiming to identify novel behavioral profiles of ADHD based on ecological and performance-based measures of inattention, impulsivity, and hyperactivity. One hundred and ten Spanish-speaking participants (6-16 years) with ADHD (medication-naïve, n = 57) and typically developing participants (n = 53) completed AULA, a continuous performance test embedded in virtual reality. We performed hybrid hierarchical k-means clustering methods over the whole sample on the normalized t-scores of AULA main indices. A five-cluster structure was the most optimal solution. We did not replicate ADHD subtypes. Instead, we identified two clusters sharing clinical scores on attention indices, susceptibility to distraction, and head motor activity, but with opposing scores on mean reaction time and commission errors; two clusters with good performance; and one cluster with average scores but increased response variability and slow RT. DSM-5 subtypes cut across cluster profiles. Our results suggest that latency of response and response inhibition could serve to distinguish among ADHD subpopulations and guide neuropsychological interventions. Motor activity, in contrast, seems to be a common feature among ADHD subgroups. This study highlights the poor feasibility of categorical systems to parse ADHD heterogeneity and the added value of data-driven approaches and VR-based assessments to obtain an accurate characterization of cognitive functioning in individuals with and without ADHD.

Identifying subgroups of attention-deficit/hyperactivity disorder from the psychopathological and neuropsychological profiles

The Research Domain Criteria (RDoC) advocates the dimensional approach in characterizing mental disorders. We followed RDoC to characterize children with ADHD using profiling based on the cognitive and psychopathological domains. We aimed to identify and validate ADHD subtypes with different clinical characteristics and functional impairments. We recruited 362 drug-naïve children with ADHD and 103 typically developing controls. The cluster analysis was used to identify subgroups based on the Child Behaviour Checklist (CBCL) and the Behaviour Rating Inventory of Executive Function (BRIEF). The subgroups' clinical characteristics and functional impairments were assessed using the WEISS Functional Impairment Rating Scale-Parent Report (WFIRS-P) and the Conners Parent Symptom Questionnaire (PSQ). The cluster analysis yielded four subgroups: (1) ADHD with severe impairment in psychopathology and executive functions (EF), (2) ADHD with mild executive dysfunctions and normal-level psychopathology, (3) ADHD with severe externalizing problems and (4) ADHD with severe executive dysfunctions. These subgroups showed different clinical characteristics and degrees of functional impairment. The EF impairment group displayed more serious learning problems and worse life skills than the externalizing group. The two groups with externalizing problems (i.e. the severe impairment group and the externalizing group) both exhibited higher rates of the combined subtype of ADHD and higher rates of comorbid ODD. Different subtypes of ADHD displayed different profiles of internalizing and externalizing problems and levels of executive dysfunctions. In particular, the subtype with severe impairment in EF exhibited more learning problems and worse life skills, suggesting EF is a critical target for intervention in children with ADHD.

Mentalizing in Adolescents and Young Adults with Attention Deficit Hyperactivity Disorder: Associations with Age and Attention Problems

INTRODUCTION

Growing, albeit heterogenous evidence questions whether attention deficit/hyperactivity disorder (ADHD) is associated with socio-cognitive impairments, especially beyond childhood. This study focuses on mentalizing - the socio-cognitive ability to attribute and reason in terms of mental states. We aimed to characterize mentalizing performance in terms of correct scores and types of errors in adolescents and young adults with ADHD.

METHODS

Forty-nine adolescents and adults with ADHD and 49 healthy controls matched for age and gender completed a computerized naturalistic mentalizing task, the Movie for Assessment of Social Cognition (MASC). Repeated measures analyses of variance examined the effects of age group and ADHD diagnosis on MASC performance. Additionally, associations between mentalizing scores, the severity of attention problems, and the presence of comorbidity were explored in the ADHD group.

RESULTS

Results showed an increased prevalence of hypomentalizing errors in adolescents with ADHD. Lower mentalizing scores in adolescents with ADHD were correlated with indices of inattentiveness, impulsivity, and vigilance problems. Hypomentalizing errors in adolescents showed to be particularly associated with inattentiveness, after controlling for age and comorbidity. In contrast, adults with ADHD performed similarly to controls and their scores on the mentalizing task were not correlated to attention problems.

CONCLUSION

These findings highlight potential developmental differences in mentalizing abilities in ADHD youths and their association with attentional impairments.

Parsing altered gray matter morphology of depression using a framework integrating the normative model and non-negative matrix factorization

The high inter-individual heterogeneity in individuals with depression limits neuroimaging studies with case-control approaches to identify promising biomarkers for individualized clinical decision-making. We put forward a framework integrating the normative model and non-negative matrix factorization (NMF) to quantitatively assess altered gray matter morphology in depression from a dimensional perspective. The proposed framework parses altered gray matter morphology into overlapping latent disease factors, and assigns patients distinct factor compositions, thus preserving inter-individual variability. We identified four robust disease factors with distinct clinical symptoms and cognitive processes in depression. In addition, we showed the quantitative relationship between the group-level gray matter morphological differences and disease factors. Furthermore, this framework significantly predicted factor compositions of patients in an independent dataset. The framework provides an approach to resolve neuroanatomical heterogeneity in depression.

Mapping the neuroanatomical heterogeneity of OCD using a framework integrating normative model and non-negative matrix factorization

Obsessive-compulsive disorder (OCD) is a spectrum disorder with high interindividual heterogeneity. We propose a comprehensible framework integrating normative model and non-negative matrix factorization (NMF) to quantitatively estimate the neuroanatomical heterogeneity of OCD from a dimensional perspective. T1-weighted magnetic resonance images of 98 first-episode untreated patients with OCD and matched healthy controls (HCs, n = 130) were acquired. We derived individualized differences in gray matter morphometry using normative model and parsed them into latent disease factors using NMF. Four robust disease factors were identified. Each patient expressed multiple factors and exhibited a unique factor composition. Factor compositions of patients were significantly correlated with severity of symptom, age of onset, illness duration, and exhibited sex differences, capturing sources of clinical heterogeneity. In addition, the group-level morphological differences obtained with two-sample t test could be quantitatively derived from the identified disease factors, reconciling the group-level and subject-level findings in neuroimaging studies. Finally, we uncovered two distinct subtypes with opposite morphological differences compared with HCs from factor compositions. Our findings suggest that morphological differences of individuals with OCD are the unique combination of distinct neuroanatomical patterns. The proposed framework quantitatively estimating neuroanatomical heterogeneity paves the way for precision medicine in OCD.

Mechanism-based groups of children with ADHD are associated with distinct domains of impairment

Person-oriented analyses are commonly used to identify subgroups of children with mental health conditions in the hopes that they will meaningfully inform the taxonomy, assessment, and treatment of psychological disorder. However, whether these data-driven groups are demonstrably better at predicting important aspects of adaptive functioning than standard DSM taxonomy has not been established. Using Attention-Deficit-Hyperactivity-Disorder (ADHD) as a model condition, we utilized dimensions of personality and cognitive ability to identify person-centered profiles of school-aged children (N=246) and evaluated the association of these profiles with critical areas of adaptive functioning. A single profile ("Conscientious") represented non-ADHD controls and was characterized by faster drift rate and higher executive functioning scores. Three profiles ("Disagreeable," "Negative Emotionality," and "Extraverted") were identified for children with ADHD. Drift rate, but not executive functioning, distinguished among ADHD profiles, which were also distinctly associated with comorbid externalizing and internalizing psychopathology, social skills, and academic achievement. In contrast, the Diagnostic and Statistical Manual (DSM) presentations were not informative and showed similar patterns of impairment across domains. Person-centered profiles of children with ADHD are associated with distinct adaptive functioning deficits and may be useful in informing clinical practice.

Heritability and Clinical Characteristics of Neuropsychological Profiles in Youth With and Without Elevated ADHD Symptoms

OBJECTIVE

In the last decade, there has been an increase in research that aims to parse heterogeneity in attention deficit hyperactivity disorder (ADHD). The current study tests heritability of latent class neuropsychological subtypes.

METHOD

Latent class analysis was used to derive subtypes in a sample of school-age twins (N = 2,564) enriched for elevated ADHD symptoms.

RESULTS

Five neuropsychological profiles replicated across twin 1 and twin 2 datasets. Latent class membership was heritable overall, but heritability varied by profile and was lower than heritability of ADHD status. Variability in neuropsychological performance across domains was the strongest predictor of elevated ADHD symptoms. Neuropsychological profiles showed distinct associations with age, psychiatric symptoms and reading ability.

CONCLUSION

Neuropsychological profiles are associated with unique neurocognitive presentations, but are not strong candidate endophenotypes for ADHD diagnosis.

Classifying Young Children with Attention-Deficit/Hyperactivity Disorder Based on Child, Parent, and Family Characteristics: A Cross-Validation Study

We aimed to identify subgroups of young children with differential risks for ADHD, and cross-validate these subgroups with an independent sample of children. All children in Study 1 (N = 120) underwent psychological assessments and were diagnosed with ADHD before age 7. Latent class analysis (LCA) classified children into risk subgroups. Study 2 (N = 168) included an independent sample of children under age 7. A predictive model from Study 1 was applied to Study 2. The latent class analyses in Study 1 indicated preference of a 3-class solution (BIC = 3807.70, p < 0.001). Maternal education, income-to-needs ratio, and family history of psychopathology, defined class membership more strongly than child factors. An almost identical LCA structure from Study 1 was replicated in Study 2 (BIC = 5108.01, p < 0.001). Indices of sensitivity (0.913, 95% C.I. 0.814−0.964) and specificity (0.788, 95% C.I. 0.692−0.861) were high across studies. It is concluded that the classifications represent valid combinations of child, parent, and family characteristics that are predictive of ADHD in young children.

Toward Precision Medicine in ADHD

Attention-Deficit Hyperactivity Disorder (ADHD) is a complex and heterogeneous neurodevelopmental condition for which curative treatments are lacking. Whilst pharmacological treatments are generally effective and safe, there is considerable inter-individual variability among patients regarding treatment response, required dose, and tolerability. Many of the non-pharmacological treatments, which are preferred to drug-treatment by some patients, either lack efficacy for core symptoms or are associated with small effect sizes. No evidence-based decision tools are currently available to allocate pharmacological or psychosocial treatments based on the patient's clinical, environmental, cognitive, genetic, or biological characteristics. We systematically reviewed potential biomarkers that may help in diagnosing ADHD and/or stratifying ADHD into more homogeneous subgroups and/or predict clinical course, treatment response, and long-term outcome across the lifespan. Most work involved exploratory studies with cognitive, actigraphic and EEG diagnostic markers to predict ADHD, along with relatively few studies exploring markers to subtype ADHD and predict response to treatment. There is a critical need for multisite prospective carefully designed experimentally controlled or observational studies to identify biomarkers that index inter-individual variability and/or predict treatment response.

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.

Which Child Will Benefit From a Behavioral Intervention for ADHD? A Pilot Study to Predict Intervention Efficacy From Individual Reward Sensitivity

Objective: This article aims to assess whether individual differences in reward sensitivity can be used to predict which children with attention-deficit/hyperactivity disorder (ADHD) will benefit most from behavioral interventions that include reinforcement. Methods: A 12-week behavioral intervention was offered to 21 children with ADHD and their parents. Reward sensitivity was assessed prior to the intervention using a combination of psychological and physiological measures. ADHD symptoms were assessed pre- and posttreatment using the Strengths and Weaknesses of ADHD and Normal behavior (SWAN) rating scale. Results: Lower scores on one of the questionnaire scales were associated with greater pre/posttreatment differences in ADHD symptoms. Conclusion: We found that pre/posttreatment change was associated with one measure of parent-rated reward sensitivity. Children with low impulsive negative behavior toward gaining reward improved most during treatment. This result suggests that aspects of reward-related behaviors in ADHD may be useful to predict the effectiveness of treatment.

Presenting problem profiles for adolescents with ADHD: differences by sex, age, race, and family adversity

BACKGROUND

Adolescents with attention-deficit/hyperactivity disorder (ADHD) experience developmentally distinct challenges from children and adults with ADHD. Yet no work in this age group identifies treatment-related phenotypes that can inform treatment matching, development of tailored treatments, and screening efforts.

METHOD

This study uses Latent Profile Analysis to detect unique presenting problem profiles among adolescents with ADHD and to test whether these profiles differ by key individual characteristics (age, sex, race, family adversity level). Participants were 854 ethnically diverse adolescents (ages 10-17) from the ADHD Teen Integrative Data Analysis Longitudinal (TIDAL) dataset who were assessed at clinical referral. Parent, adolescent, and teacher ratings, educational testing, and school records measured eight key presenting problems at intake.

RESULTS

A three-profile solution emerged. ADHD simplex (63.7%) was characterized by a mix of the ADHD-Inattentive and ADHD-Combined subtypes, moderate impairment levels, and infrequent comorbidities. ADHD + internalizing (11.4%) was characterized by higher likelihood of comorbid anxiety and/or depression. The disruptive/disorganized ADHD (24.9%) profile was characterized by severe organization, time management, and planning (OTP) problems, the ADHD-Combined subtype, and frequent disruptive behavior at school. Age did not vary across these phenotypes. More females were present in the ADHD + internalizing phenotype; males were more likely to be found in the disruptive/disorganized ADHD phenotype. Higher family adversity and African American race were associated with the disruptive/disorganized ADHD phenotype.

CONCLUSIONS

Adolescents with ADHD demonstrate varying presenting problem phenotypes that vary by sex, family adversity, and race/ethnicity. Consideration of these phenotypes may inform treatment matching and efforts to improve screening among under-diagnosed groups.

Heterogeneity Within Youth With Childhood-Onset Conduct Disorder in the ABCD Study

The purpose of this study was to examine if personality traits can be used to characterize subgroups of youth diagnosed with childhood-onset conduct disorder (CD). Participants were 11,552 youth from the Adolescent Brain Cognitive Development study. Data used in this report came from doi: 10.15154/1504041 (M age 9.92; 45.3% female, 49.6% white, 19.0% Hispanic). A subset of this sample (n = 365) met criteria for CD. Latent profile analyses (LPA) were performed on this subgroup (n = 365) to define profiles of individuals with CD based on self-report measures of impulsivity, punishment sensitivity, reward response, and callous-unemotional traits. Follow up analyses determined if these groups differed on clinically relevant variables including psychopathology, environmental risk factors, social risk factors, and neurocognitive functioning. Participants with a CD diagnosis scored significantly higher on psychological, environmental, social, and neurocognitive risk factors. The LPA revealed three unique profiles, which differed significantly on liability for broad psychopathology and domain-specific liability for externalizing psychopathology but were largely matched on environmental and social risk factors. These unique configurations provide a useful way to further parse clinically relevant subgroups within youth who meet criteria for childhood-onset CD, setting the stage for prospective longitudinal research using these latent profiles to better understand the development of youth with childhood-onset CD.

Subtypes of inhibitory and reward activation associated with substance use variation in adolescence: A latent profile analysis of brain imaging data

The present study identified subgroups based on inhibitory and reward activation, two key neural functions involved in risk-taking behavior, and then tested the extent to which subgroup differences varied by age, sex, behavioral and familial risk, and substance use. Participants were 145 young adults (18-21 years old; 40.0% female) from the Michigan Longitudinal Study. Latent profile analysis (LPA) was used to establish subgroups using task-based brain activations. Demographic and substance use differences between subgroups were then examined in logistic regression analyses. Whole-brain task activations during a functional magnetic resonance imaging go/no-go task and monetary incentive delay task were used to identify beta weights as input for LPA modeling. A four-class model showed the best fit with the data. Subgroups were categorized as: (1) low inhibitory activation/moderate reward activation (39.7%), (2) moderate inhibitory activation/low reward activation (22.7%), (3) moderate inhibitory activation/high reward activation (25.2%), and (4) high inhibitory activation/high reward activation (12.4%). Compared with the other subgroups, Class 2 was older, less likely to have parental alcohol use disorder, and had less alcohol use. Class 4 was the youngest and had greater marijuana use. Classes 1 and 3 did not differ significantly from the other subgroups. These findings demonstrate that LPA applied to brain activations can be used to identify distinct neural profiles that may explain heterogeneity in substance use outcomes and may inform more targeted substance use prevention and intervention efforts.

Pre-appointment online assessment of patient complexity: Towards a personalized model of neuropsychological assessment

Recent events such as the global pandemic of COVID-19 have challenged neuropsychologists to scale up their capacity to conduct portions of their assessment remotely. While more complex patients will likely continue to require on-site, office-based interaction and assessment, the current emergency-based expansion of online and telehealth evaluation practices may ultimately lay the groundwork for more routine, online assessment of patients with less complex presentations in the future. To this end, the current study evaluated a pre-appointment, online methodology for differentiating referred pediatric patients based upon the scope and severity of their caregiver-reported adaptive, academic, attentional, behavioral, and emotional impairment. Prior to on-site assessment, parents/caregivers of 2197 children (Mean age = 10.0y, range = 4-19y, 62% male) completed an online developmental history form screening for symptoms of adaptive, attentional, learning, affective, and behavioral impairment; 71% of those children eventually underwent assessment. Using latent class analysis, the data supported a reproducible 4-class model consisting of groups of children at increased risk for: 1) severe multi-domain dysfunction; the "High Complexity" group, 30%, 2) behavioral-affective (but not academic) dysregulation; the "Behavioral Focus" group, 13%, 3) academic (but not behavioral-affective) problems; the "Academic and Inattention" group, 37%, and 4) patients with minimal clinical complexity; the "Low Complexity" group, 20%. Comparison of pre-visit classification with day-of-assessment standardized test scores supported the validity of patient subtypes. Moving forward, pre-appointment clarification of patient complexity may support efficient patient triage with regard to assessment modality (e.g., on-site or online) and length of appointment (e.g., comprehensive or targeted).

Treatment Efficacy and Clinical Effectiveness of EEG Neurofeedback as a Personalized and Multimodal Treatment in ADHD: A Critical Review

PURPOSE

Recent reviews have proposed that scientifically validated standard EEG neurofeedback (NF) protocols are an efficacious and specific treatment for attention-deficit hyperactivity disorder (ADHD). Here, we review the current evidence for the treatment efficacy and clinical effectiveness of NF in ADHD to investigate whether NF treatment personalization (standard protocols matched to the electrophysiological features of ADHD) and combination with other interventions (psychosocial, sleep hygiene and nutritional advice) might yield superior long-term treatment outcomes relative to non-personalized NF and medication monotreatments.

METHODS

The electronic databases PubMed and PsycINFO were systematically searched using our key terms. Of the 38 resulting studies, 11 randomized controlled trials (RCTs) and open-label studies were eligible for inclusion. Studies were analyzed for effect sizes and remission rates at the end of treatment and at follow-up. The effects of personalized and multimodal NF treatments were compared to non-personalized NF monotreatments and with two benchmark medication studies.

RESULTS

The analysis of RCTs indicated that the long-term effects of personalized NF interventions were superior to non-personalized NF and comparable to those of medication alone or in combination with behavioral intervention. The analysis of open-label trials further indicates that the interaction of NF with parental interventions, sleep and nutritional advice might yield superior clinical effectiveness relative to NF and medication monotreatments.

CONCLUSION

Personalized and multimodal NF interventions seem to yield superior treatment efficacy relative to NF alone and superior clinical effectiveness relative to medication. We propose that treatment outcomes may be further enhanced by adjusting NF non-specific factors (eg, reinforcement contingencies) to specific ADHD characteristics (eg, reward sensitivity). Future NF research should focus on the systematic evaluation of the treatment outcomes of personalized and multimodal treatments.

ADHD: Current Concepts and Treatments in Children and Adolescents

Attention deficit hyperactivity disorder (ADHD) is among the most frequent disorders within child and adolescent psychiatry, with a prevalence of over 5%. Nosological systems, such as the Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5) and the International Classification of Diseases, editions 10 and 11 (ICD-10/11) continue to define ADHD according to behavioral criteria, based on observation and on informant reports. Despite an overwhelming body of research on ADHD over the last 10 to 20 years, valid neurobiological markers or other objective criteria that may lead to unequivocal diagnostic classification are still lacking. On the contrary, the concept of ADHD seems to have become broader and more heterogeneous. Thus, the diagnosis and treatment of ADHD are still challenging for clinicians, necessitating increased reliance on their expertise and experience. The first part of this review presents an overview of the current definitions of the disorder (DSM-5, ICD-10/11). Furthermore, it discusses more controversial aspects of the construct of ADHD, including the dimensional versus categorical approach, alternative ADHD constructs, and aspects pertaining to epidemiology and prevalence. The second part focuses on comorbidities, on the difficulty of distinguishing between "primary" and "secondary" ADHD for purposes of differential diagnosis, and on clinical diagnostic procedures. In the third and most prominent part, an overview of current neurobiological concepts of ADHD is given, including neuropsychological and neurophysiological researches and summaries of current neuroimaging and genetic studies. Finally, treatment options are reviewed, including a discussion of multimodal, pharmacological, and nonpharmacological interventions and their evidence base.

The definition and measurement of heterogeneity

Heterogeneity is an important concept in psychiatric research and science more broadly. It negatively impacts effect size estimates under case-control paradigms, and it exposes important flaws in our existing categorical nosology. Yet, our field has no precise definition of heterogeneity proper. We tend to quantify heterogeneity by measuring associated correlates such as entropy or variance: practices which are akin to accepting the radius of a sphere as a measure of its volume. Under a definition of heterogeneity as the degree to which a system deviates from perfect conformity, this paper argues that its proper measure roughly corresponds to the size of a system's event/sample space, and has units known as numbers equivalent. We arrive at this conclusion through focused review of more than 100 years of (re)discoveries of indices by ecologists, economists, statistical physicists, and others. In parallel, we review psychiatric approaches for quantifying heterogeneity, including but not limited to studies of symptom heterogeneity, microbiome biodiversity, cluster-counting, and time-series analyses. We argue that using numbers equivalent heterogeneity measures could improve the interpretability and synthesis of psychiatric research on heterogeneity. However, significant limitations must be overcome for these measures-largely developed for economic and ecological research-to be useful in modern translational psychiatric science.

Towards a brain-based predictome of mental illness

Neuroimaging-based approaches have been extensively applied to study mental illness in recent years and have deepened our understanding of both cognitively healthy and disordered brain structure and function. Recent advancements in machine learning techniques have shown promising outcomes for individualized prediction and characterization of patients with psychiatric disorders. Studies have utilized features from a variety of neuroimaging modalities, including structural, functional, and diffusion magnetic resonance imaging data, as well as jointly estimated features from multiple modalities, to assess patients with heterogeneous mental disorders, such as schizophrenia and autism. We use the term "predictome" to describe the use of multivariate brain network features from one or more neuroimaging modalities to predict mental illness. In the predictome, multiple brain network-based features (either from the same modality or multiple modalities) are incorporated into a predictive model to jointly estimate features that are unique to a disorder and predict subjects accordingly. To date, more than 650 studies have been published on subject-level prediction focusing on psychiatric disorders. We have surveyed about 250 studies including schizophrenia, major depression, bipolar disorder, autism spectrum disorder, attention-deficit hyperactivity disorder, obsessive-compulsive disorder, social anxiety disorder, posttraumatic stress disorder, and substance dependence. In this review, we present a comprehensive review of recent neuroimaging-based predictomic approaches, current trends, and common shortcomings and share our vision for future directions.

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

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.

Supervised mixture of experts models for population health

We propose a machine learning driven approach to derive insights from observational healthcare data to improve public health outcomes. Our goal is to simultaneously identify patient subpopulations with differing health risks and to find those risk factors within each subpopulation. We develop two supervised mixture of experts models: a Supervised Gaussian Mixture model (SGMM) for general features and a Supervised Bernoulli Mixture model (SBMM) tailored to binary features. We demonstrate the two approaches on an analysis of high cost drivers of Medicaid expenditures for inpatient stays. We focus on the three diagnostic categories that accounted for the highest percentage of inpatient expenditures in New York State (NYS) in 2016. When compared with state-of-the-art learning methods (random forests, boosting, neural networks), our approaches provide comparable prediction performance while also extracting insightful subpopulation structure and risk factors. For problems with binary features the proposed SBMM provides as good or better performance than alternative methods while offering insightful explanations. Our results indicate the promise of such approaches for extracting population health insights from electronic health care records.

Approaches to Defining Common and Dissociable Neurobiological Deficits Associated With Psychopathology in Youth

Psychiatric disorders show high rates of comorbidity and nonspecificity of presenting clinical symptoms, while demonstrating substantial heterogeneity within diagnostic categories. Notably, many of these psychiatric disorders first manifest in youth. We review progress and next steps in efforts to parse heterogeneity in psychiatric symptoms in youths by identifying abnormalities within neural circuits. To address this fundamental challenge in psychiatry, a number of methods have been proposed. We provide an overview of these methods, broadly organized into dimensional versus categorical approaches and single-view versus multiview approaches. Dimensional approaches including factor analysis and canonical correlation analysis aim to capture dimensional associations between psychopathology and brain measures across a continuous spectrum from health to disease. In contrast, categorical approaches, such as clustering and community detection, aim to identify subtypes of individuals within a class of symptoms or brain features. We highlight several studies that apply these methods to samples of youths and discuss issues to consider when using these approaches. Finally, we end by highlighting avenues for future research.

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

Few if any experts believe that existing psychiatric diagnostic categories included in DSM and ICD are actually discrete disease entities. Attention-deficit/hyperactivity disorder (ADHD) is emblematic of the problems in the existing psychiatric classification system. ADHD symptoms reliably cluster into two correlated dimensions in factor analysis. However, children with ADHD vary considerably in their symptom profiles, symptom trajectories, clinical outcomes, and biological and psychological correlates. Thus, the field has sought alternative approaches that harness the dimensions of emotional, cognitive, and behavioral functioning that underlie ADHD and other existing psychiatric categories to create informative phenotypes that improve clinical prediction and clarify etiology. Within ADHD, cognitive (neuropsychological) and temperament/personality features have received considerable attention. In some cases, subphenotypes based on these features appear to improve on existing classifications and could eventually be translated into clinical practice. This review summarizes findings from subphenotyping efforts in ADHD that use cognitive, emotion-related, and other features to highlight major considerations for research applying person-oriented approaches to inform an improved psychiatric nosology. Considerations related to feature selection, validation of newly proposed divisions, defining populations of interest, and incorporating a developmental perspective are discussed.

Neurocognitive heterogeneity across the spectrum of psychopathology: need for improved approaches to deficit detection and intervention

Neurocognition is one of the strongest predictors of clinical and functional outcomes across the spectrum of psychopathology, yet there remains a dearth of unified neurocognitive nosology and available neurocognition-targeted interventions. Neurocognitive deficits manifest in a transdiagnostic manner, with no psychiatric disorder uniquely affiliated with one specific deficit. In fact, recent research has identified that essentially all investigated disorders are comprised of 3-4 neurocognitive profiles. This within-disorder neurocognitive heterogeneity has hampered the development of novel, neurocognition-targeted interventions, as only a portion of patients with any given disorder possess neurocognitive deficits that would warrant neurocognitive intervention. The development of criteria and terminology to characterize these neurocognitive deficit syndromes would provide clinicians with the opportunity to more systematically identify and treat their patients and provide researchers the opportunity to develop neurocognition-targeted interventions for patients. This perspective will summarize recent work and discuss possible approaches for neurocognition-focused diagnosis and treatment in psychiatry.

Neurocognitive Profiles in Children With ADHD and Their Predictive Value for Functional Outcomes

OBJECTIVE

We examined whether neurocognitive profiles could be distinguished in children with ADHD and typically developing (TD) children, and whether neurocognitive profiles predicted externalizing, social, and academic problems in children with ADHD.

METHOD

Neurocognitive data of 81 children with ADHD and 71 TD children were subjected to confirmatory factor analysis. The resulting factors were used for community detection in the ADHD and TD group.

RESULTS

Four subgroups were detected in the ADHD group, characterized by (a) poor emotion recognition, (b) poor interference control, (c) slow processing speed, or (d) increased attentional lapses and fast processing speed. In the TD group, three subgroups were detected, closely resembling Subgroups (a) to (c). Neurocognitive subgroups in the ADHD sample did not differ in externalizing, social, and academic problems.

CONCLUSION

We found a neurocognitive profile unique to ADHD. The clinical validity of neurocognitive profiling is questioned, given the lack of associations with functional outcomes.

Progress in psychoradiology, the clinical application of psychiatric neuroimaging

Psychoradiology is an emerging field that applies radiological imaging technologies to psychiatric conditions. In the past three decades, brain imaging techniques have rapidly advanced understanding of illness and treatment effects in psychiatry. Based on these advances, radiologists have become increasingly interested in applying these advances for differential diagnosis and individualized patient care selection for common psychiatric illnesses. This shift from research to clinical practice represents the beginning evolution of psychoradiology. In this review, we provide a summary of recent progress relevant to this field based on their clinical functions, namely the (1) classification and subtyping; (2) prediction and monitoring of treatment outcomes; and (3) treatment selection. In addition, we provide guidelines for the practice of psychoradiology in clinical settings and suggestions for future research to validate broader clinical applications. Given the high prevalence of psychiatric disorders and the importance of increased participation of radiologists in this field, a guide regarding advances in this field and a description of relevant clinical work flow patterns help radiologists contribute to this fast-evolving field.

Specific Effects of Individualized Cognitive Training in Children with Attention-Deficit/Hyperactivity Disorder (ADHD): The Role of Pre-Training Cognitive Impairment and Individual Training Performance

Objective: We investigated the impact of the pre-training neuropsychological (NP) impairment and of the training progress on the NP and behavioural outcome after computerized cognitive training (CogT) in children with ADHD. Method: Thirty-one participants underwent individualized CogT (focussing on one or two cognitive domains: working memory, inhibition, attention) over 12 weeks. NP tests and behaviour ratings served as outcome measures. Results: After CogT, significant improvements emerged according to parents' ratings, but only on very few NP test measures. Children with milder/no pre-training NP impairment showed larger improvements on behavioural ratings than more impaired children. A steeper training performance slope was related to better behavioural outcomes. Conclusion: We find partial support for specific effects of CogT, but the assumption that an individually tailored selection of training tasks would be particularly beneficial for children with ADHD with NP deficits was not confirmed. Trial registration number: NCT02358941.

Perceptual timing in children with attention-deficit/hyperactivity disorder (ADHD) as measured by computer-based experiments versus real-life tasks: protocol for a cross-sectional experimental study in an ambulatory setting

INTRODUCTION

The goal of this study is to get a better understanding of the fundamentals of perceptual timing deficits, that is, difficulties with estimating durations of explicitly attended temporal intervals, in children with attention-deficit/hyperactivity disorder (ADHD). Whereas these deficits were repeatedly demonstrated in laboratory studies using computer-based timing tasks, we will additionally implement a more practical task reflecting real-life activity. In doing so, the research questions of the planned study follow a hierarchically structured path 'from lab to life': Are the timing abilities of children with ADHD really disturbed both in the range of milliseconds and in the range of seconds? What causes these deficits? Do children with ADHD rather display a global perceptual timing deficit, or do different 'timing types' exist? Are timing deficits present during real-life activities as well, and are they based on the same mechanisms as in computerised tasks?

METHODS AND ANALYSES

A quasi-experimental study with two groups of male children aged 8-12 years (ADHD; controls) and with a cross-sectional design will be used to address our research questions. Statistical analyses of the dependent variables will comprise (repeated) measures analyses of variance, stepwise multiple regression analyses and latent class models. With an estimated dropout rate of 25%, power analysis indicated a sample size of 140 subjects (70 ADHD, 70 controls) to detect medium effect sizes.

ETHICS AND DISSEMINATION

Ethics approval was obtained from the ethics committee of the Faculty of Medicine, University of Rostock. Results will be disseminated to researcher, clinician and patient communities in peer-reviewed journals and at scientific conferences, at a meeting of the local ADHD competence network and on our web page which will summarise the study results in an easily comprehensible manner.

TRIAL REGISTRATION NUMBER

DRKS00015760.

Classification Accuracy of Neuroimaging Biomarkers in Attention-Deficit/Hyperactivity Disorder: Effects of Sample Size and Circular Analysis

BACKGROUND

Motivated by an inconsistency between reports of high diagnosis-classification accuracies and known heterogeneity in attention-deficit/hyperactivity disorder (ADHD), this study assessed classification accuracy in studies of ADHD as a function of methodological factors that can bias results. We hypothesized that high classification results in ADHD diagnosis are inflated by methodological factors.

METHODS

We reviewed 69 studies (of 95 studies identified) that used neuroimaging features to predict ADHD diagnosis. Based on reported methods, we assessed the prevalence of circular analysis, which inflates classification accuracy, and evaluated the relationship between sample size and accuracy to test if small-sample models tend to report higher classification accuracy, also an indicator of bias.

RESULTS

Circular analysis was detected in 15.9% of ADHD classification studies, lack of independent test set was noted in 13%, and insufficient methodological detail to establish its presence was noted in another 11.6%. Accuracy of classification ranged from 60% to 80% in the 59.4% of reviewed studies that met criteria for independence of feature selection, model construction, and test datasets. Moreover, there was a negative relationship between accuracy and sample size, implying additional bias contributing to reported accuracies at lower sample sizes.

CONCLUSIONS

High classification accuracies in neuroimaging studies of ADHD appear to be inflated by circular analysis and small sample size. Accuracies on independent datasets were consistent with known heterogeneity of the disorder. Steps to resolve these issues, and a shift toward accounting for sample heterogeneity and prediction of future outcomes, will be crucial in future classification studies in ADHD.

Can we use neuroimaging data to differentiate between subgroups of children with ADHD symptoms: A proof of concept study using latent class analysis of brain activity

BACKGROUND

Multiple pathway models of ADHD suggest that multiple, separable biological pathways may lead to symptoms of the disorder. If this is the case, it should be possible to identify subgroups of children with ADHD based on distinct patterns of brain activity. Previous studies have used latent class analysis (LCA) to define subgroups at the behavioral and cognitive level and to then test whether they differ at the neurobiological level. In this proof of concept study, we took a reverse approach. We applied LCA to functional imaging data from two previously published studies to explore whether we could identify subgroups of children with ADHD symptoms at the neurobiological level with a meaningful relation to behavior or neuropsychology.

METHODS

Fifty-six children with symptoms of ADHD (27 children with ADHD and 29 children with ASD and ADHD symptoms) and 31 typically developing children performed two neuropsychological tasks assessing reward sensitivity and temporal expectancy during functional magnetic resonance imaging. LCA was used to identify subgroups with similar patterns of brain activity separately for children with ADHD-symptoms and typically developing children. Behavioral and neuropsychological differences between subgroups were subsequently investigated.

RESULTS

For typically developing children, a one-subgroup model gave the most parsimonious fit, whereas for children with ADHD-symptoms a two-subgroup model best fits the data. The first ADHD subgroup (n = 49) showed attenuated brain activity compared to the second subgroup (n = 7) and to typically developing children (n = 31). Notably, the ADHD subgroup with attenuated brain activity showed less behavioral problems in everyday life.

CONCLUSIONS

In this proof of concept study, we showed that we could identify distinct subgroups of children with ADHD-symptoms based on their brain activity profiles. Generalizability was limited due to the small sample size, but ultimately such neurobiological profiles could improve insight in individual prognosis and treatment options.

An emotion recognition subtyping approach to studying the heterogeneity and comorbidity of autism spectrum disorders and attention-deficit/hyperactivity disorder

Background

Emotion recognition dysfunction has been reported in both autism spectrum disorders (ASD) and attention-deficit/hyperactivity disorder (ADHD). This suggests that emotion recognition is a cross-disorder trait that may be utilised to understand the heterogeneous psychopathology of ASD and ADHD. We aimed to identify emotion recognition subtypes and to examine their relation with quantitative and diagnostic measures of ASD and ADHD to gain further insight into disorder comorbidity and heterogeneity.

Methods

Factor mixture modelling was used on speed and accuracy measures of auditory and visual emotion recognition tasks. These were administered to children and adolescents with ASD (N = 89), comorbid ASD + ADHD (N = 64), their unaffected siblings (N = 122), ADHD (N = 111), their unaffected siblings (N = 69), and controls (N = 220). Identified classes were compared on diagnostic and quantitative symptom measures.

Results

A four-class solution was revealed, with the following emotion recognition abilities: (1) average visual, impulsive auditory; (2) average-strong visual and auditory; (3) impulsive/imprecise visual, average auditory; (4) weak visual and auditory. The weakest performing class (4) contained the highest percentage of patients (66.07%) and the lowest percentage controls (10.09%), scoring the highest on ASD/ADHD measures. The best performing class (2) demonstrated the opposite: 48.98% patients, 15.26% controls with relatively low scores on ASD/ADHD measures.

Conclusions

Subgroups of youths can be identified that differ both in quantitative and qualitative aspects of emotion recognition abilities. Weak emotion recognition abilities across sensory domains are linked to an increased risk for ASD as well as ADHD, although emotion recognition impairments alone are neither necessary nor sufficient parts of these disorders.

Electronic supplementary material

The online version of this article (10.1186/s11689-018-9249-6) contains supplementary material, which is available to authorized users.

Latent class analysis of attention and white matter correlation in children with attention-deficit/hyperactivity disorder

This study aimed to explore attentional patterns among children with inattentive attention-deficit/hyperactivity disorder (ADHD-I) and children with typical development (TD), using a latent class analysis (LCA). Patterns of brain connectivity were also explored. The sample comprised 29 ADHD-I and 29 TD matched children. An LCA was conducted to reclassify subjects according to their attentional performance, considering cognitive measures of attention and behavioral symptoms, regardless of group of origin. The new clusters were then compared in respect to brain white matter measurements (extracted from diffusion tensor imaging). Participants were rearranged in 2 new latent classes, according to their performance in an attention task and the results of behavioral scales, resulting in groups with more homogeneous attentional profiles. A comparison of the 2 new classes using the white matter measurements revealed increased fractional anisotropy in the left inferior fronto-occipital fasciculus and left inferior longitudinal fasciculus for the class composed by participants with a higher risk of attentional problems. The findings indicated that it was possible to observe variability regarding neuropsychological profile, accompanied by underpinning neurobiological differences, even among individuals with the same disorder subtype - inattentive ADHD. This specific data-driven clustering analysis may help to enhance understanding of the pathophysiology of the disorder's phenotypes.

A multicohort, longitudinal study of cerebellar development in attention deficit hyperactivity disorder

BACKGROUND

The cerebellum supports many cognitive functions disrupted in attention deficit hyperactivity disorder (ADHD). Prior neuroanatomic studies have been often limited by small sample sizes, inconsistent findings, and a reliance on cross-sectional data, limiting inferences about cerebellar development. Here, we conduct a multicohort study using longitudinal data, to characterize cerebellar development.

METHODS

Growth trajectories of the cerebellar vermis, hemispheres and white matter were estimated using piecewise linear regression from 1,656 youth; of whom 63% had longitudinal data, totaling 2,914 scans. Four cohorts participated, all contained childhood data (age 4-12 years); two had adolescent data (12-25 years). Growth parameters were combined using random-effects meta-analysis.

RESULTS

Diagnostic differences in growth were confined to the corpus medullare (cerebellar white matter). Here, the ADHD group showed slower growth in early childhood compared to the typically developing group (left corpus medullare z = 2.49, p = .01; right z = 2.03, p = .04). This reversed in late childhood, with faster growth in ADHD in the left corpus medullare (z = 2.06, p = .04). Findings held when gender, intelligence, comorbidity, and psychostimulant medication were considered.

DISCUSSION

Across four independent cohorts, containing predominately longitudinal data, we found diagnostic differences in the growth of cerebellar white matter. In ADHD, slower white matter growth in early childhood was followed by faster growth in late childhood. The findings are consistent with the concept of ADHD as a disorder of the brain's structural connections, formed partly by developing cortico-cerebellar white matter tracts.

Zur Bedeutung auffälliger Exekutivfunktionen in der Diagnostik einer Aufmerksamkeitsdefizit-/Hyperaktivitätsstörung

Zusammenfassung. Defizite in Exekutivfunktionen und insbesondere in der Inhibitionsfähigkeit gelten verschiedenen Modellen zufolge als Kerndefizite einer Aufmerksamkeitsdefizit-/Hyperaktivitätsstörung (ADHS). Die Defizite sind sowohl auf einer Verhaltensebene als auch auf neurobiologischer Ebene belegt, finden aber bislang kaum Einzug in die klinische Diagnostik. Verschiedene Erhebungsverfahren werden vorgestellt und die Probleme im Bereich der klinischen Diagnostik skizziert. Viele Aufgaben messen nicht eine spezifische Exekutivfunktion, sondern umfassen immer auch andere kognitive Prozesse wie zum Beispiel Aufmerksamkeit oder Test- und Leistungsmotivation. Die Sensitivität vieler Aufgaben ist aufgrund der Heterogenität von ADHS durch verschiedene Entwicklungspfade oft nicht gewährleistet und Defizite in Exekutivfunktionen und der Inhibitionsfähigkeit sind auch nicht spezifisch für ADHS. Dennoch ist eine Diagnostik auffälliger Exekutivfunktionen und insbesondere von Defiziten in der Inhibitionsfähigkeit angebracht, da nur so Aussagen über zugrunde liegende Prozesse und Ursachen einer ADHS getroffen werden können, welche die Voraussetzung für gezielte Interventionen darstellen, wie zum Beispiel Inhibitionstrainings oder Neurofeedback.

Deciding on the Starting Number of Classes of a Latent Class Tree

In recent studies, latent class tree (LCT) modeling has been proposed as a convenient alternative to standard latent class (LC) analysis. Instead of using an estimation method in which all classes are formed simultaneously given the specified number of classes, in LCT analysis a hierarchical structure of mutually linked classes is obtained by sequentially splitting classes into two subclasses. The resulting tree structure gives a clear insight into how the classes are formed and how solutions with different numbers of classes are substantively linked to one another. A limitation of the current LCT modeling approach is that it allows only for binary splits, which in certain situations may be too restrictive. Especially at the root node of the tree, where an initial set of classes is created based on the most dominant associations present in the data, it may make sense to use a model with more than two classes. In this article, we propose a modification of the LCT approach that allows for a nonbinary split at the root node, and we provide methods to determine the appropriate number of classes in this first split, based either on theoretical grounds or on a relative improvement of fit measure. This novel approach also can be seen as a hybrid of a standard LC model and a binary LCT model, in which an initial, oversimplified but interpretable model is refined using an LCT approach. Furthermore, we show how to apply an LCT model when a nonstandard LC model is required. These new approaches are illustrated using two empirical applications: one on social capital and the other on (post)materialism.

Response inhibition and emotional cognition improved by atomoxetine in children and adolescents with ADHD: The ACTION randomized controlled trial

Although the non-stimulant medication atomoxetine is effective for attention-deficit hyperactivity disorder (ADHD) in children and adolescents, there are still significant gaps in our knowledge about whether atomoxetine improves anxiety symptoms or cognition in children. Furthermore, while cognition has been proposed as an intermediate phenotype for ADHD dysfunction, the relationships between clinical and cognitive outcomes are not yet understood. We addressed these knowledge gaps in a controlled trial using objective assessments of both general and emotional cognitive functions implicated in ADHD and in anxiety, which commonly co-occurs with ADHD. A total of 136 children and adolescents with ADHD (ages 6-17years; 80% male; 31.6% with a comorbid anxiety disorder) were enrolled in a randomized double-blind, placebo-controlled, cross-over trial of 6-weeks treatment with atomoxetine. Of these, 109 completed the second cross-over phase. Selected cognitive domains associated with ADHD and anxiety disorders (Sustained attention, response inhibition and fearful face identification) were assessed using a normed, computerized test battery. Symptom outcomes were assessed by parent reports on the ADHD Rating Scale-IV and Conners' Anxious-Shy subscale. For completers, atomoxetine caused a greater improvement in the primary cognitive outcomes of response inhibition and fear identification compared to placebo, but not in sustained attention. Atomoxetine also improved ADHD and anxiety symptoms. Anxiety symptoms improved most for ADHD and anxiety disorder combined, but presence of an anxiety disorder did not moderate any other outcomes. Changes in cognitive and clinical outcomes were not correlated. These findings contribute to the foundations of measurement-based treatment planning and offer targets for probing the mechanisms of atomoxetine action.

Are there distinct cognitive and motivational sub-groups of children with ADHD?

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) is proposed to be a neuropsychologically heterogeneous disorder that encompasses two distinct sub-groups, one with executive function (EF) deficits and one with delay aversion (DA). However, such claims have often been based on studies that have operationalized neuropsychological deficits using a categorical approach - using intuitive but rather arbitrary, clinical cut-offs. The current study applied an alternative empirical approach to sub-grouping in ADHD, latent profile analysis (LPA), and attempted to validate emerging subgroups through clinically relevant correlates.

METHODS

One-hundred medication-naïve children with ADHD and 96 typically developing children (6-14 years) completed nine EF and three DA tasks as well as an odor identification test. Parents and teachers provided reports of the children's behavior (ADHD and EF). Models of the latent structure of scores on EF and DA tests were contrasted using confirmatory factor analysis (CFA). LPA was carried out based on factor scores from the CFA and sub-groups were compared in terms of odor identification and behavior.

RESULTS

A model with one DA and two EF factors best fit the data. LPA resulted in four sub-groups that differed in terms of general level of neuropsychological performance (ranging from high to very low), odor identification, and behavior. The sub-groups did not differ in terms of the relative EF and DA performance. Results in the ADHD group were replicated in the control group.

CONCLUSIONS

While EF and DA appear to be dissociable constructs; they do not yield distinct sub-groups when sub-grouping is based on a statistical approach such as LPA.

Transdiagnostic Symptom Clusters and Associations With Brain, Behavior, and Daily Function in Mood, Anxiety, and Trauma Disorders

IMPORTANCE

The symptoms that define mood, anxiety, and trauma disorders are highly overlapping across disorders and heterogeneous within disorders. It is unknown whether coherent subtypes exist that span multiple diagnoses and are expressed functionally (in underlying cognition and brain function) and clinically (in daily function). The identification of cohesive subtypes would help disentangle the symptom overlap in our current diagnoses and serve as a tool for tailoring treatment choices.

OBJECTIVE

To propose and demonstrate 1 approach for identifying subtypes within a transdiagnostic sample.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study analyzed data from the Brain Research and Integrative Neuroscience Network Foundation Database that had been collected at the University of Sydney and University of Adelaide between 2006 and 2010 and replicated at Stanford University between 2013 and 2017. The study included 420 individuals with a primary diagnosis of major depressive disorder (n = 100), panic disorder (n = 53), posttraumatic stress disorder (n = 47), or no disorder (healthy control participants) (n = 220). Data were analyzed between October 2016 and October 2017.

MAIN OUTCOMES AND MEASURES

We followed a data-driven approach to achieve the primary study outcome of identifying transdiagnostic subtypes. First, machine learning with a hierarchical clustering algorithm was implemented to classify participants based on self-reported negative mood, anxiety, and stress symptoms. Second, the robustness and generalizability of the subtypes were tested in an independent sample. Third, we assessed whether symptom subtypes were expressed at behavioral and physiological levels of functioning. Fourth, we evaluated the clinically meaningful differences in functional capacity of the subtypes. Findings were interpreted relative to a complementary diagnostic frame of reference.

RESULTS

Four hundred twenty participants with a mean (SD) age of 39.8 (14.1) years were included in the final analysis; 256 (61.0%) were female. We identified 6 distinct subtypes characterized by tension (n=81; 19%), anxious arousal (n=55; 13%), general anxiety (n=38; 9%), anhedonia (n=29; 7%), melancholia (n=37; 9%), and normative mood (n=180; 43%), and these subtypes were replicated in an independent sample. Subtypes were expressed through differences in cognitive control (F5,383 = 5.13, P < .001, ηp2 = 0.063), working memory (F5,401 = 3.29, P = .006, ηp2 = 0.039), electroencephalography-recorded β power in a resting paradigm (F5,357 = 3.84, P = .002, ηp2 = 0.051), electroencephalography-recorded β power in an emotional paradigm (F5,365 = 3.56, P = .004, ηp2 = 0.047), social functional capacity (F5,414 = 21.33, P < .001, ηp2 = 0.205), and emotional resilience (F5,376 = 15.10, P < .001, ηp2 = 0.171).

CONCLUSIONS AND RELEVANCE

These findings offer a data-driven framework for identifying robust subtypes that signify specific, coherent, meaningful associations between symptoms, behavior, brain function, and observable real-world function, and that cut across DSM-IV-defined diagnoses of major depressive disorder, panic disorder, and posttraumatic stress disorder.

Similar Subgroups Based on Cognitive Performance Parse Heterogeneity in Adults With ADHD and Healthy Controls

OBJECTIVE

To characterize heterogeneity in adults with ADHD we aimed to identify subgroups within the adult ADHD spectrum, which differ in their cognitive profile.

METHOD

Neuropsychological data from adults with ADHD ( n = 133) and healthy control participants ( n = 132) were used in a confirmatory factor analysis. The resulting six cognitive factors were correlated across participants to form networks. We used a community detection algorithm to cluster these networks into subgroups.

RESULTS

Both the ADHD and control group separated into three profiles that differed in cognitive performance. Profile 1 was characterized by aberrant attention and inhibition, profile 2 by increased delay discounting, and profile 3 by atypical working memory and verbal fluency.

CONCLUSION

Our findings suggest that qualitative differences in neuropsychological performance exist in both control and ADHD adult individuals. This extends prior findings in children with and without ADHD and provides a framework to parse participants into well-defined subgroups.

Five factor model personality traits relate to adult attention-deficit/hyperactivity disorder but not to their distinct neurocognitive profiles

Deficits in multiple neuropsychological domains and specific personality profiles have been observed in attention-deficit/hyperactivity disorder (ADHD). In this study we investigated whether personality traits are related to neurocognitive profiles in adults with ADHD. Neuropsychological performance and Five Factor Model (FFM) personality traits were measured in adults with ADHD (n = 133) and healthy controls (n = 132). Three neuropsychological profiles, derived from previous community detection analyses, were investigated for personality trait differences. Irrespective of cognitive profile, participants with ADHD showed significantly higher Neuroticism and lower Extraversion, Agreeableness, and Conscientiousness than healthy controls. Only the FFM personality factor Openness differed significantly between the three profiles. Higher Openness was more common in those with aberrant attention and inhibition than those with increased delay discounting and atypical working memory / verbal fluency. The results suggest that the personality trait Openness, but not any other FFM factor, is linked to neurocognitive profiles in ADHD. ADHD symptoms rather than profiles of cognitive impairment have associations with personality traits.

Identification of biotypes in Attention-Deficit/Hyperactivity Disorder, a report from a randomized, controlled trial

Attention-Deficit/Hyperactivity Disorder (ADHD) is a heterogeneous disorder. Current subtypes lack longitudinal stability or prognostic utility. We aimed to identify data-driven biotypes using multiple cognitive measures, then to validate these biotypes using EEG, ECG, and clinical response to atomoxetine as external validators. Study design was a double-blind, randomized, placebo-controlled crossover trial of atomoxetine including 116 subjects ages 6 through 17 with diagnosis of ADHD and 56 typically developing controls. Initial features for unsupervised machine learning included a cognitive battery with 20 measures affected in ADHD. External validators included baseline mechanistic validators (using electroencephalogram/EEG and electrocardiogram/ECG) and clinical response (ADHD Rating Scale and correlation with cognitive change). One biotype, labeled impulsive cognition, was characterized by increased errors of commission and shorter reaction time, had greater EEG slow wave (theta/delta) power and greater resting heart rate. The second biotype, labeled inattentive cognition, was characterized by longer/more variable reaction time and errors of omission, had lower EEG fast wave (beta) power, resting heart rate that did not differ from controls, and a strong correlation (r = -0.447, p < 0.001) between clinical response to atomoxetine and improvement in verbal memory immediate recall. ADHD comprises at least two biotypes that cut across current subtype criteria and that may reflect distinct arousal mechanisms. The findings provide evidence that further investigation of cognitive subtypes may be at least as fruitful as symptom checklist-based subtypes for development of biologically-based diagnostics and interventions for ADHD.

Children with ADHD symptoms show decreased activity in ventral striatum during the anticipation of reward, irrespective of ADHD diagnosis

BACKGROUND

Changes in reward processing are thought to be involved in the etiology of attention-deficit/hyperactivity disorder (ADHD), as well as other developmental disorders. In addition, different forms of therapy for ADHD rely on reinforcement principles. As such, improved understanding of reward processing in ADHD could eventually lead to more effective treatment options. However, differences in reward processing may not be specific to ADHD, but may be a trans-diagnostic feature of disorders that involve ADHD-like symptoms.

METHODS

In this event-related fMRI study, we used a child-friendly version of the monetary incentive delay task to assess performance and brain activity during reward anticipation. Also, we collected questionnaire data to assess reward sensitivity in daily life. For final analyses, data were available for 27 typically developing children, 24 children with ADHD, and 25 children with an autism spectrum disorder (ASD) and ADHD symptoms.

RESULTS

We found decreased activity in ventral striatum during anticipation of reward in children with ADHD symptoms, both for children with ADHD as their primary diagnosis and in children with autism spectrum disorder and ADHD symptoms. We found that higher parent-rated sensitivity to reward was associated with greater anticipatory activity in ventral striatum for children with ADHD symptoms. In contrast, there was no relationship between the degree of ADHD symptoms and activity in ventral striatum.

CONCLUSIONS

We provide evidence of biological and behavioral differences in reward sensitivity in children with ADHD symptoms, regardless of their primary diagnosis. Ultimately, a dimensional brain-behavior model of reward sensitivity in children with symptoms of ADHD may be useful to refine treatment options dependent on reward processing.

What to expect and when to expect it: an fMRI study of expectancy in children with ADHD symptoms

Changes in cognitive control and timing have both been implicated in ADHD. Both are involved in building and monitoring expectations about the environment, and altering behavior if those expectations are violated. In ADHD, problems with expectations about future events have high face validity, as this would be associated with behavior that is inappropriate only given a certain context, similar to symptoms of the disorder. In this fMRI study, we used a timing manipulated go/nogo task to assess brain activity related to expectations about what (cognitive control) and when (timing) events would occur. We hypothesized that problems in building expectations about the environment are a more general, trans-diagnostic characteristic of children with hyperactive, impulsive and inattentive symptoms. To address this, we included children with ASD and symptoms of ADHD, in addition to children with ADHD and typically developing children. We found between-group differences in brain activity related to expectations about when (timing), but not what events will occur (cognitive control). Specifically, we found timing-related hypo-activity that was in part unique to children with a primary diagnosis of ADHD (left pallidum) and in part shared by children with similar levels of ADHD symptoms and a primary diagnosis of ASD (left subthalamic nucleus). Moreover, we found poorer task performance related to timing, but only in children with ASD and symptoms of ADHD. Ultimately, such neurobiological changes in children with ADHD symptoms may relate to a failure to build or monitor expectations and thereby hinder the efficiency of their interaction with the environment.

Investigating the Impact of Cognitive Load and Motivation on Response Control in Relation to Delay Discounting in Children with ADHD

Attention-deficit/hyperactivity disorder (ADHD) is characterized by deficits in impulse control across a range of behaviors, from simple actions to those involving complex decision-making (e.g., preference for smaller-sooner versus larger later rewards). This study investigated whether changes in motor response control with increased cognitive load and motivational contingencies are associated with decision-making in the form of delay discounting among 8-12 year old children with and without ADHD. Children with ADHD (n = 26; 8 girls) and typically developing controls (n = 40; 11 girls) completed a standard go/no-go (GNG) task, a GNG task with motivational contingencies, a GNG task with increased cognitive load, and two measures of delay discounting: a real-time task in which the delays and immediately consumable rewards are experienced in real-time, and a classic task involving choices about money at longer delays. Children with ADHD, particularly girls, exhibited greater delay discounting than controls during the real-time discounting task, whereas diagnostic groups did not significantly differ on the classic discounting task. The effect of cognitive load on response control was uniquely associated with greater discounting on the real-time task for children with ADHD, but not for control children. The effect of motivational contingencies on response control was not significantly associated with delay discounting for either diagnostic group. The findings from this study help to inform our understanding of the factors that influence deficient self-control in ADHD, suggesting that impairments in cognitive control may contribute to greater delay discounting in ADHD.

Understanding Heterogeneity in Clinical Cohorts Using Normative Models: Beyond Case-Control Studies

BACKGROUND

Despite many successes, the case-control approach is problematic in biomedical science. It introduces an artificial symmetry whereby all clinical groups (e.g., patients and control subjects) are assumed to be well defined, when biologically they are often highly heterogeneous. By definition, it also precludes inference over the validity of the diagnostic labels. In response, the National Institute of Mental Health Research Domain Criteria proposes to map relationships between symptom dimensions and broad behavioral and biological domains, cutting across diagnostic categories. However, to date, Research Domain Criteria have prompted few methods to meaningfully stratify clinical cohorts.

METHODS

We introduce normative modeling for parsing heterogeneity in clinical cohorts, while allowing predictions at an individual subject level. This approach aims to map variation within the cohort and is distinct from, and complementary to, existing approaches that address heterogeneity by employing clustering techniques to fractionate cohorts. To demonstrate this approach, we mapped the relationship between trait impulsivity and reward-related brain activity in a large healthy cohort (N = 491).

RESULTS

We identify participants who are outliers within this distribution and show that the degree of deviation (outlier magnitude) relates to specific attention-deficit/hyperactivity disorder symptoms (hyperactivity, but not inattention) on the basis of individualized patterns of abnormality.

CONCLUSIONS

Normative modeling provides a natural framework to study disorders at the individual participant level without dichotomizing the cohort. Instead, disease can be considered as an extreme of the normal range or as-possibly idiosyncratic-deviation from normal functioning. It also enables inferences over the degree to which behavioral variables, including diagnostic labels, map onto biology.

Beyond Lumping and Splitting: A Review of Computational Approaches for Stratifying Psychiatric Disorders

Heterogeneity is a key feature of all psychiatric disorders that manifests on many levels, including symptoms, disease course, and biological underpinnings. These form a substantial barrier to understanding disease mechanisms and developing effective, personalized treatments. In response, many studies have aimed to stratify psychiatric disorders, aiming to find more consistent subgroups on the basis of many types of data. Such approaches have received renewed interest after recent research initiatives, such as the National Institute of Mental Health Research Domain Criteria and the European Roadmap for Mental Health Research, both of which emphasize finding stratifications that are based on biological systems and that cut across current classifications. We first introduce the basic concepts for stratifying psychiatric disorders and then provide a methodologically oriented and critical review of the existing literature. This shows that the predominant clustering approach that aims to subdivide clinical populations into more coherent subgroups has made a useful contribution but is heavily dependent on the type of data used; it has produced many different ways to subgroup the disorders we review, but for most disorders it has not converged on a consistent set of subgroups. We highlight problems with current approaches that are not widely recognized and discuss the importance of validation to ensure that the derived subgroups index clinically relevant variation. Finally, we review emerging techniques-such as those that estimate normative models for mappings between biology and behavior-that provide new ways to parse the heterogeneity underlying psychiatric disorders and evaluate all methods to meeting the objectives of such as the National Institute of Mental Health Research Domain Criteria and Roadmap for Mental Health Research.