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

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.

LATENT SUBGROUP IDENTIFICATION IN IMAGE-ON-SCALAR REGRESSION

Image-on-scalar regression has been a popular approach to modeling the association between brain activities and scalar characteristics in neuroimaging research. The associations could be heterogeneous across individuals in the population, as indicated by recent large-scale neuroimaging studies, for example, the Adolescent Brain Cognitive Development (ABCD) Study. The ABCD data can inform our understanding of heterogeneous associations and how to leverage the heterogeneity and tailor interventions to increase the number of youths who benefit. It is of great interest to identify subgroups of individuals from the population such that: (1) within each subgroup the brain activities have homogeneous associations with the clinical measures; (2) across subgroups the associations are heterogeneous, and (3) the group allocation depends on individual characteristics. Existing image-on-scalar regression methods and clustering methods cannot directly achieve this goal. We propose a latent subgroup image-on-scalar regression model (LASIR) to analyze large-scale, multisite neuroimaging data with diverse sociode-mographics. LASIR introduces the latent subgroup for each individual and group-specific, spatially varying effects, with an efficient stochastic expectation maximization algorithm for inferences. We demonstrate that LASIR outperforms existing alternatives for subgroup identification of brain activation patterns with functional magnetic resonance imaging data via comprehensive simulations and applications to the ABCD study. We have released our reproducible codes for public use with the software package available on Github.

Attention-deficit hyperactivity disorder and youth's emotion dysregulation: A systematic review of fMRI studies

A number of adverse outcomes are associated with emotion dysregulation, and ADHD secondary to emotion dysregulation is a frequent comorbidity that may result in poorer performance and quality of life among youth. Recent neuroimaging research has observed significant functional differences in youth with ADHD compared to healthy controls. Therefore, the aims of this systematic review were to summarize the literature on functional connectivity of emotion dysregulation in youth with ADHD, identify methodological challenges and limitations, and provide directions for future research. Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines were followed to conduct a systematic review across three databases, and 13 studies were identified for inclusion. Across the studies, 14 primary regions were examined; 12 of the 13 studies identified a significant association between functional connectivity of emotion dysregulation and youth with ADHD. Although studies varied in the structures investigated, the most notable differences appeared in the prefrontal cortex, orbitofrontal cortex, amygdala, and ventral striatum in ADHD participants. Future research concerning this topic may help contribute to the understanding of this association by utilizing longitudinal research designs, controlling for psychiatric comorbidities and emotion dysregulation severity, inclusion of more diverse samples and ensuring that studies are adequately powered.

Modern views of machine learning for precision psychiatry

In light of the National Institute of Mental Health (NIMH)'s Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. We further review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We also discuss explainable AI (XAI) and neuromodulation in a closed human-in-the-loop manner and highlight the ML potential in multi-media information extraction and multi-modal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.

A systematic review of randomized controlled trials on efficacy and safety of transcranial direct current stimulation in major neurodevelopmental disorders: ADHD, autism, and dyslexia

OBJECTIVE

Among the target groups in child and adolescent psychiatry, transcranial direct current stimulation (tDCS) has been more applied in neurodevelopmental disorders specifically, attention-deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and dyslexia. This systematic review aims to provide the latest update on published randomized-controlled trials applying tDCS in these disorders for evaluating its efficacy and safety.

METHODS

Based on a pre-registered protocol (PROSPERO: CRD42022321430) and using the PRISMA approach, a literature search identified 35 randomized controlled trials investigating the effects of tDCS on children and adolescents with ADHD (n = 17), ASD (n = 11), and dyslexia (n = 7).

RESULTS

In ADHD, prefrontal anodal tDCS is reported more effective compared to stimulation of the right inferior frontal gyrus. Similarly in ASD, prefrontal anodal tDCS was found effective for improving behavioral problems. In dyslexia, stimulating temporoparietal regions was the most common and effective protocol. In ASD and dyslexia, all tDCS studies found an improvement in at least one of the outcome variables while 64.7% of studies (11 of 17) in ADHD found a similar effect. About 88% of all tDCS studies with a multi-session design in 3 disorders (16 of 18) reported a significant improvement in one or all outcome variables after the intervention. Randomized, double-blind, controlled trials consisted of around 70.5%, 36.3%, and 57.1% of tDCS studies in ADHD, ASD, and dyslexia, respectively. tDCS was found safe with no reported serious side effects in 6587 sessions conducted on 745 children and adolescents across 35 studies.

CONCLUSION

tDCS was found safe and partially effective. For evaluation of clinical utility, larger randomized controlled trials with a double-blind design and follow-up measurements are required. Titration studies that systematically evaluate different stimulation intensities, duration, and electrode placement are lacking.

Who benefits from diagnostic labels for developmental disorders?

The number of diagnoses of developmental disorders is on the rise and the use of labels for developmental disorders, such as attention-deficit/hyperactivity disorder and autism spectrum disorder, is widening. Diagnostic labels can play an important role in helping those who display atypical behaviour and their caregivers to cope with associated challenges and, possibly, to get treatment. But these labels are increasingly contested and associated with a variety of harmful effects. In this paper, we analyze the role diagnostic labels can play in four different contexts (scientific, therapeutic, social, and administrative) and identify what various stakeholders stand to gain or lose with continued, expanded, or abolished use of those labels. Our analysis reveals labels serve different purposes in each of these contexts, benefitting different stakeholders. Any overall evaluation, critique, or defence of labels needs to consider the interests of all stakeholders in these contexts.

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.

The Role of Quantitative Electroencephalogram in the Diagnosis and Subgrouping of Attention-Deficit/Hyperactivity Disorder

Attention-deficit/hyperactivity disorder (ADHD) leads to functional decline in academic performance, interpersonal relationships, and development in school-aged children. Early diagnosis and appropriate intervention can significantly reduce the functional decline caused by ADHD. Currently, there is no established biological marker for ADHD. Some studies have suggested that various indicators from the quantitative electroencephalogram (QEEG) may be useful biological markers for the diagnosis of ADHD. Until the 2010s, theta/beta ratio (TBR) was a biomarker candidate for ADHD that consistently showed high diagnostic value. However, limitations of TBR have recently been reported. Studies have demonstrated that phase-amplitude coupling, especially theta phase-gamma amplitude coupling, are related to cognitive dysfunction and may assist in the diagnosis of ADHD. As yet, the underlying mechanism is not clearly established, and the clinical efficacy of these biomarkers needs to be proven through well-controlled studies. Based on the heterogeneous characteristics of ADHD, subgrouping through QEEG plays a key role in diagnosis and treatment planning. Sophisticated, well-designed studies and meta-analyses are necessary to confirm these findings.

Psychometric properties of a new ADHD screening questionnaire: Parent report on the (potential) underlying explanation of inattention in their school-aged children

The present study examined psychometric properties of a recently developed parent report screening questionnaire, i.e., Parent ADHD Screening questionnaire: Signaling the Core explanation underlying behavioral symptoms (PASSC). The PASSC aims to measure (1) ADHD symptoms and (2) what parents view to be the main underlying explanation(s) of these symptoms. The PASSC questions 3 (potential) underlying explanations based on the triple pathway model (TPM): i.e., time, cognition and/or motivation problems. Parents of 1166 Dutch children aged 4-12 filled in the PASSC, as well as 2 questionnaires measuring time, cognition and motivation (i.e., the FTF and the SPSRQ-C). Reliability of the PASSC is good, indicated by high internal consistency of the sumscores. Principal component analyses supported the distinction between inattention and hyperactivity-impulsivity symptoms as defined in the DSM-5, and the distinction between the 3 TPM explanations given by parents for inattention, but not for hyperactivity-impulsivity symptoms. The majority of parents selected one and the same explanation for inattention problems of their child, most often being cognition (31.2%) and motivation (28.2%). PASSC validity was further supported by positive associations between the explanation sumscores for inattention symptoms and other parent questionnaires measuring the same constructs (i.e., time, cognition and motivation; convergent validity), although we found no evidence for discriminant validity. Groups (based on age group, sex and ADHD diagnosis) differed on the PASSC sumscores in the expected directions. Concluding, the PASSC is a promising tool to assess a child's ADHD symptoms as well as the parent view on (potential) explanation(s) of inattention.

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.

Resolving heterogeneity in transcranial electrical stimulation efficacy for attention deficit hyperactivity disorder

While the treatment of Attention Deficit Hyperactivity Disorder (ADHD) is dominated by pharmacological agents, transcranial electrical stimulation (tES) is gaining attention as an alternative method for treatment. Most current meta-analyses have suggested that tES can improve cognitive functions that are otherwise impaired in ADHD, such as inhibition and working memory, as well as alleviated clinical symptoms. Here we review some of the promising findings in the field of tES. At the same time, we highlight two factors, which hinder the effective application of tES in treating ADHD: 1) the heterogeneity of tES protocols used in different studies; 2) patient profiles influencing responses to tES. We highlight potential solutions for overcoming such limitations, including the use of active machine learning, and provide simulated data to demonstrate how these solutions could also improve the understanding, diagnosis, and treatment of ADHD.

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.

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.

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

BACKGROUND

Those with autism spectrum disorder (ASD) and/or attention-deficit-hyperactivity disorder (ADHD) exhibit symptoms of hyperactivity and inattention, causing significant hardships for families and society. A potential mechanism involved in these conditions is atypical executive function (EF). Inconsistent findings highlight that EF features may be shared or distinct across ADHD and ASD. With ADHD and ASD each also being heterogeneous, we hypothesized that there may be nested subgroups across disorders with shared or unique underlying mechanisms.

METHODS

Participants (N = 130) included adolescents aged 7-16 with ASD (n = 64) and ADHD (n = 66). Typically developing (TD) participants (n = 28) were included for a comparative secondary sub-group analysis. Parents completed the K-SADS and youth completed an extended battery of executive and other cognitive measures. A two stage hybrid machine learning tool called functional random forest (FRF) was applied as a classification approach and then subsequently to subgroup identification. We input 43 EF variables to the classification step, a supervised random forest procedure in which the features estimated either hyperactive or inattentive ADHD symptoms per model. The FRF then produced proximity matrices and identified optimal subgroups via the infomap algorithm (a type of community detection derived from graph theory). Resting state functional connectivity MRI (rs-fMRI) was used to evaluate the neurobiological validity of the resulting subgroups.

RESULTS

Both hyperactive (Mean absolute error (MAE) = 0.72, Null model MAE = 0.8826, (t(58) = -4.9, p < .001) and inattentive (MAE = 0.7, Null model MAE = 0.85, t(58) = -4.4, p < .001) symptoms were predicted better than chance by the EF features selected. Subgroup identification was robust (Hyperactive: Q = 0.2356, p < .001; Inattentive: Q = 0.2350, p < .001). Two subgroups representing severe and mild symptomology were identified for each symptom domain. Neuroimaging data revealed that the subgroups and TD participants significantly differed within and between multiple functional brain networks, but no consistent "severity" patterns of over or under connectivity were observed between subgroups and TD.

CONCLUSION

The FRF estimated hyperactive/inattentive symptoms and identified 2 distinct subgroups per model, revealing distinct neurocognitive profiles of Severe and Mild EF performance per model. Differences in functional connectivity between subgroups did not appear to follow a severity pattern based on symptom expression, suggesting a more complex mechanistic interaction that cannot be attributed to symptom presentation alone.

Functional Neuroimaging in Psychiatry-Aiding in Diagnosis and Guiding Treatment. What the American Psychiatric Association Does Not Know

While early efforts in psychiatry were focused on uncovering the neurobiological basis of psychiatric symptoms, they made little progress due to limited ability to observe the living brain. Today, we know a great deal about the workings of the brain; yet, none of this neurobiological awareness has translated into the practice of psychiatry. The categorical system which dominates psychiatric diagnosis and thinking fails to match up to the real world of genetics, sophisticated psychological testing, and neuroimaging. Nevertheless, the American Psychiatric Association (APA) recently published a position paper stating that neuroimaging provided no benefit to the diagnosis and treatment of psychiatric disorders. Using the diagnosis of depression as a model, we illustrate how setting aside the unrealistic expectation of a pathognomonic "fingerprint" for categorical diagnoses, we can avoid missing the biological and, therefore, treatable contributors to psychopathology which can and are visualized using functional neuroimaging. Infection, toxicity, inflammation, gut-brain dysregulation, and traumatic brain injury can all induce psychiatric manifestations which masquerade as depression and other psychiatric disorders. We review these and provide illustrative clinical examples. We further describe situations for which single photon emission computed tomography (SPECT) and positron emission tomography (PET) functional neuroimaging already meet or exceed the criteria set forth by the APA to define a neuroimaging biomarker, including the differential diagnosis of Alzheimer's disease and other dementias, the differential diagnosis of ADHD, and the evaluation of traumatic brain injury. The limitations, both real and perceived, of SPECT and PET functional neuroimaging in the field of psychiatry are also elaborated. An important overarching concept for diagnostic imaging in all its forms, including functional neuroimaging, is that imaging allows a clinician to eliminate possibilities, narrow the differential diagnosis, and tailor the treatment plan. This progression is central to any medical diagnostic process.