Biomarkers for attention-deficit/hyperactivity disorder (ADHD). A consensus report of the WFSBP task force on biological markers and the World Federation of ADHD

Leveraging ANFIS with Adam and PSO optimizers for Parkinson's disease

Parkinson's disease (PD) is an age-related neurodegenerative disorder characterized by motor deficits, including tremor, rigidity, bradykinesia, and postural instability. According to the World Health Organization, about 1 % of the global population has been diagnosed with PD, and this figure is expected to double by 2040. Early and accurate diagnosis of PD is critical to slowing down the progression of the disease and reducing long-term disability. Due to the complexity of the disease, it is difficult to accurately diagnose it using traditional clinical tests. Therefore, it has become necessary to develop intelligent diagnostic models that can accurately detect PD. This article introduces a novel hybrid approach for accurate prediction of PD using an ANFIS with two optimizers, namely Adam and PSO. ANFIS is a type of fuzzy logic system used for nonlinear function approximation and classification, while Adam optimizer has the ability to adaptively adjust the learning rate of each individual parameter in an ANFIS at each training step, which helps the model find a better solution more quickly. PSO is a metaheuristic approach inspired by the behavior of social animals such as birds. Combining these two methods has potential to provide improved accuracy and robustness in PD diagnosis compared to existing methods. The proposed method utilized the advantages of both optimization techniques and applied them on the developed ANFIS model to maximize its prediction accuracy. This system was developed by using an open access clinical and demographic data. The chosen parameters for the ANFIS were selected through a comparative experimental analysis to optimize the model considering the number of fuzzy membership functions, number of epochs of ANFIS, and number of particles of PSO. The performance of the two ANFIS models: ANFIS (Adam) and ANFIS (PSO) focusing at ANFIS parameters and various evaluation metrics are further analyzed in detail and presented, The experimental results showed that the proposed ANFIS (PSO) shows better results in terms of loss and precision, whereas, the ANFIS (Adam) showed the better results in terms of accuracy, f1-score and recall. Thus, this adaptive neural-fuzzy algorithm provides a promising strategy for the diagnosis of PD, and show that the proposed models show their suitability for many other practical applications.

The Australian Traumatic Brain Injury Initiative: Statement of Working Principles and Rapid Review of Methods to Define Data Dictionaries for Neurological Conditions

The Australian Traumatic Brain Injury Initiative (AUS-TBI) aims to develop a health informatics approach to collect data predictive of outcomes for persons with moderate-severe TBI across Australia. Central to this approach is a data dictionary; however, no systematic reviews of methods to define and develop data dictionaries exist to-date. This rapid systematic review aimed to identify and characterize methods for designing data dictionaries to collect outcomes or variables in persons with neurological conditions. Database searches were conducted from inception through October 2021. Records were screened in two stages against set criteria to identify methods to define data dictionaries for neurological conditions (International Classification of Diseases, 11th Revision: 08, 22, and 23). Standardized data were extracted. Processes were checked at each stage by independent review of a random 25% of records. Consensus was reached through discussion where necessary. Thirty-nine initiatives were identified across 29 neurological conditions. No single established or recommended method for defining a data dictionary was identified. Nine initiatives conducted systematic reviews to collate information before implementing a consensus process. Thirty-seven initiatives consulted with end-users. Methods of consultation were "roundtable" discussion (n = 30); with facilitation (n = 16); that was iterative (n = 27); and frequently conducted in-person (n = 27). Researcher stakeholders were involved in all initiatives and clinicians in 25. Importantly, only six initiatives involved persons with lived experience of TBI and four involved carers. Methods for defining data dictionaries were variable and reporting is sparse. Our findings are instructive for AUS-TBI and can be used to further development of methods for defining data dictionaries.

A state-of-the-art overview of candidate diagnostic biomarkers for Attention-deficit/hyperactivity disorder (ADHD)

INTRODUCTION

Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental conditions and is highly heterogeneous in terms of symptom profile, associated cognitive deficits, comorbidities, and outcomes. Heterogeneity may also affect the ability to recognize and diagnose this condition. The diagnosis of ADHD is primarily clinical but there are increasing research efforts aiming at identifying biomarkers that can aid the diagnosis.

AREAS COVERED

We first discuss the definition of biomarkers and the necessary research steps from discovery to implementation. We then provide a broad overview of research studies on candidate diagnostic biomarkers in ADHD encompassing genetic/epigenetic, biochemical, neuroimaging, neurophysiological and neuropsychological techniques. Finally, we critically appraise current limitations in the field and suggest possible ways forward.

EXPERT OPINION

Despite the large number of studies and variety of techniques used, no promising biomarkers have been identified so far. Clinical and biological heterogeneity as well as methodological limitations, including small sample size, lack of standardization, confounding factors, and poor replicability, have hampered progress in the field. Going forward, increased international collaborative efforts are warranted to support larger and more robustly designed studies, develop multimodal datasets to combine biomarkers and improve diagnostic accuracy, and ensure reproducibility and meaningful clinical translation.

Human transcriptome array analysis and diffusion tensor imaging in attention-deficit/hyperactivity disorder

The mRNA markers identified using microarray assay and diffusion tensor magnetic resonance imaging (DTI) were applied to elucidate the pathophysiology of attention-deficit hyperactivity disorder (ADHD). First, we obtained total RNA from leukocytes from three children with ADHD and three healthy controls for analysis with microarray assays. Subsequently, we applied real-time quantitative polymerase chain reaction (qRT‒PCR) assays to validate the differential expression of 7 genes (COX7B, CYCS, TFAM, UTP14A, ZNF280C, IFT57 and NDUFB5) between 130 ADHD patients and 70 controls, and we built an ADHD prediction model based on the ΔCt values of aforementioned seven genes (AUROC = 0.98). Finally, in a validation group (28 patients with ADHD and 27 healthy controls), mRNA expression of the above seven genes also significantly differentiated ADHD patients from controls (AUROC value = 0.91). The DTI analysis showed increased fractional anisotropy (FA) of the forceps minor, superior corona radiata, posterior corona radiata and anterior corona radiata in ADHD patients. Moreover, the FA of the right superior corona radiata tract was positively correlated with ΔCt levels of the COX7B gene and the IFT57 gene. The results shed a new light on a genetic profile of ADHD that may help in deciphering the white matter microstructural features in disease pathogenesis.

Measuring attention of ADHD patients by means of a computer game featuring biometrical data gathering

ADHD is a neurodevelopmental disorder diagnosed mainly in children, marked by inattention and hyperactivity-impulsivity. The symptoms are highly variable, such as different ages of onset and potential comorbidities, contributing to frequent misdiagnoses. Professionals note a gap in modern diagnostic tools, making accurate identification challenging. To address this, recent studies recommend gamification for better ADHD diagnosis and treatment, though further research is essential to confirm its efficacy. This work aims to create a serious game, namely "Attention Slackline", to assess attention levels. The game, designed with expert input, requires players to concentrate on a specific point to recognize specific patterns while managing distractions. A controlled experiment tested its precision, and results were compared with established attention tests by a correlation analysis. Statistical analysis confirmed the game's validity, especially in tracking attention through correct responses and errors. Preliminary evidence suggests that "Attention Slackline" may serve as a credible instrument for the assessment of attentional capacities in individuals with ADHD, given that its outcomes have been empirically shown to correlate with those derived from a well-established attention assessment methodology.

Inclusion of a computerized test in ADHD diagnosis process can improve trust in the specialists' decision and elevate adherence levels

Attention deficit and hyperactivity disorder (ADHD) affects many life aspects of children and adults. Accurate identification, diagnosis and treatment of ADHD can facilitate better care. However, ADHD diagnosis and treatment methods are subject of controversy. Objective measures can elevate trust in specialist's decision and treatment adherence. In this observational study we asked whether knowing that a computerized test was included in ADHD diagnosis process results in more trust and intention to adhere with treatment recommendations. Questionnaires were administered to 459 people, 196 men, average age = 40.57 (8.90). Questions regarding expected trust and adherence, trust trait, trust in physician and health-care-institutions, and ADHD scales followed a scenario about parents referred to a neurologist for sons' ADHD diagnosis. The scenario presented to the test group (n = 185) mentioned that a computerized test was part of the diagnostic process. The control group scenario didn't mention any computerized test in the diagnostic process. Test group participants expressed more trust in the diagnosis and greater levels of intention for treatment adherence. Group differences in intention for treatment adherence were mediated by trust in decision. Inclusion of a computerized test in ADHD diagnosis process can improve trust in the specialists' decision and elevate adherence levels.

Longitudinal Stability of Neural Correlates of Pediatric Attention Deficit Hyperactivity Disorder: A Pilot Study of Event Related Potentials and Electroencephalography

OBJECTIVE

Stability and developmental effects of electroencephalography (EEG) and event related potential (ERP) correlates of ADHD are understudied. This pilot study examined stability and developmental changes in ERP and EEG metrics of interest.

METHODS

Thirty-seven 7 to 11-year-old children with ADHD and 15 typically developing (TD) children completed EEG twice, 11 to 36 months apart. A series of mixed effects linear models were run to examine stability and developmental effects of EEG and ERP metrics.

RESULTS

Stability and developmental effects of EEG and ERP correlates of ADHD varied considerably across metrics. P3 amplitude was stable over time and showed diverging developmental trajectories across groups. Developmental differences were apparent in error related ERPs and resting aperiodic exponent. Theta-beta ratio was stable over time among all children.

CONCLUSIONS

Developmental trajectories of EEG and ERP correlates of ADHD are candidate diagnostic markers. Replication with larger samples is needed.

Poor response to methylphenidate is associated with a smaller dorsal attentive network in adult Attention-Deficit/Hyperactivity Disorder (ADHD)

Stimulants, such as methylphenidate (MPH), are effective in treating attention-deficit/hyperactivity disorder (ADHD), but there is individual variability in response, especially in adults. To improve outcomes, we need to understand the factors associated with adult treatment response. This longitudinal study investigated whether pre-treatment anatomy of the fronto-striatal and fronto-parietal attentional networks was associated with MPH treatment response. 60 adults with ADHD underwent diffusion brain imaging before starting MPH treatment, and response was measured at two months. We tested the association between brain anatomy and treatment response by using regression-based approaches; and compared the identified anatomical characteristics with those of 20 matched neurotypical controls in secondary analyses. Finally, we explored whether combining anatomical with clinical and neuropsychological data through machine learning provided a more comprehensive profile of factors associated with treatment response. At a group level, a smaller left dorsal superior longitudinal fasciculus (SLF I), a tract responsible for the voluntary control of attention, was associated with a significantly lower probability of being responders to two-month MPH-treatment. The association between the volume of the left SLF I and treatment response was driven by improvement on both inattentive and hyperactive/impulsive symptoms. Only non-responders significantly differed from controls in this tract metric. Finally, our machine learning approach identified clinico-neuropsychological factors associated with treatment response, such as higher cognitive performance and symptom severity at baseline. These novel findings add to our understanding of the pathophysiological mechanisms underlying response to MPH, pointing to the dorsal attentive network as playing a key role.

Sleep Spindle-Related EEG Connectivity in Children with Attention-Deficit/Hyperactivity Disorder: An Exploratory Study

Attention-deficit/hyperactivity disorder (ADHD) is a neurobehavioral disorder with known brain abnormalities but no biomarkers to support clinical diagnosis. Recently, EEG analysis methods such as functional connectivity have rekindled interest in using EEG for ADHD diagnosis. Most studies have focused on resting-state EEG, while connectivity during sleep and spindle activity has been underexplored. Here we present the results of a preliminary study exploring spindle-related connectivity as a possible biomarker for ADHD. We compared sensor-space connectivity parameters in eight children with ADHD and nine age/sex-matched healthy controls during sleep, before, during, and after spindle activity in various frequency bands. All connectivity parameters were significantly different between the two groups in the delta and gamma bands, and Principal Component Analysis (PCA) in the gamma band distinguished ADHD from healthy subjects. Cluster coefficient and path length values in the sigma band were also significantly different between epochs, indicating different spindle-related brain activity in ADHD.

Differences between DSM-5-TR and ICD-11 revisions of attention deficit/hyperactivity disorder: A commentary on implications and opportunities

Current ICD-11 descriptions for attention deficit hyperactivity disorder (ADHD) were recently published online, in the same year as the DSM-5-TR (text revised edition) was released. In this commentary, we compare and contrast the DSM-5/DSM-5-TR and ICD-11 diagnostic criteria, summarize important differences, and underscore their clinical and research implications. Overall, three major differences emerge: (1) The number of diagnostic criteria for inattention (IA), hyperactivity (HY) and impulsivity (IM) symptoms (i.e., DSM-5-TR has nine IA and nine HY/IM symptoms, whereas ICD-11 has 11 IA and 11 HY/IM sym-ptoms); (2) the clarity and standardization of diagnostic thresholds (i.e., the diagnostic thresholds for symptom count in IA and HY/IM domains are explicitly specified in DSM-5-TR, whereas in ICD-11 they are not); and (3) the partitioning of HY and IM symptoms into sub-dimensions (i.e., difference in partitioning HY and IM symptom domains relates to the differences between the current and previous editions of DSM and ICD, and this has important research implications). Currently, no ICD-11 based ADHD rating scales exist and while this absence represents an obstacle for respective research and clinical practice, it also presents opportunities for research development. This article highlights these challenges, possible remedies and novel research opportunities.

Machine Learning and MRI-based Diagnostic Models for ADHD: Are We There Yet?

OBJECTIVE

Machine learning (ML) has been applied to develop magnetic resonance imaging (MRI)-based diagnostic classifiers for attention-deficit/hyperactivity disorder (ADHD). This systematic review examines this literature to clarify its clinical significance and to assess the implications of the various analytic methods applied.

METHODS

A comprehensive literature search on MRI-based diagnostic classifiers for ADHD was performed and data regarding the utilized models and samples were gathered.

RESULTS

We found that, although most studies reported the classification accuracies, they varied in choice of MRI modalities, ML models, cross-validation and testing methods, and sample sizes. We found that the accuracies of cross-validation methods inflated the performance estimation compared with those of a held-out test, compromising the model generalizability. Test accuracies have increased with publication year but were not associated with training sample sizes. Improved test accuracy over time was likely due to the use of better ML methods along with strategies to deal with data imbalances.

CONCLUSION

Ultimately, large multi-modal imaging datasets, and potentially the combination with other types of data, like cognitive data and/or genetics, will be essential to achieve the goal of developing clinically useful imaging classification tools for ADHD in the future.

Eye movement behavior in a real-world virtual reality task reveals ADHD in children

Eye movements and other rich data obtained in virtual reality (VR) environments resembling situations where symptoms are manifested could help in the objective detection of various symptoms in clinical conditions. In the present study, 37 children with attention deficit hyperactivity disorder and 36 typically developing controls (9-13 y.o) played a lifelike prospective memory game using head-mounted display with inbuilt 90 Hz eye tracker. Eye movement patterns had prominent group differences, but they were dispersed across the full performance time rather than associated with specific events or stimulus features. A support vector machine classifier trained on eye movement data showed excellent discrimination ability with 0.92 area under curve, which was significantly higher than for task performance measures or for eye movements obtained in a visual search task. We demonstrated that a naturalistic VR task combined with eye tracking allows accurate prediction of attention deficits, paving the way for precision diagnostics.

A Developmental Perspective on Facets of Impulsivity and Brain Activity Correlates From Adolescence to Adulthood

BACKGROUND

On a theoretical level, impulsivity represents a multidimensional construct associated with acting without foresight, inefficient inhibitory response control, and alterations in reward processing. On an empirical level, relationships and changes in associations between different measures of impulsivity from adolescence into young adulthood and their relation to neural activity during inhibitory control and reward anticipation have not been fully understood.

METHODS

We used data from IMAGEN, a longitudinal multicenter, population-based cohort study in which 2034 healthy adolescents were investigated at age 14, and 1383 were reassessed as young adults at age 19. We measured the construct of trait impulsivity using self-report questionnaires and neurocognitive indices of decisional impulsivity. With functional magnetic resonance imaging, we assessed brain activity during inhibition error processing using the stop signal task and during reward anticipation in the monetary incentive delay task. Correlations were analyzed, and mixed-effect models were fitted to explore developmental and predictive effects.

RESULTS

All self-report and neurocognitive measures of impulsivity proved to be correlated during adolescence and young adulthood. Further, pre-supplementary motor area and inferior frontal gyrus activity during inhibition error processing was associated with trait impulsivity in adolescence, whereas in young adulthood, a trend-level association with reward anticipation activity in the ventral striatum was found. For adult delay discounting, a trend-level predictive effect of adolescent neural activity during inhibition error processing emerged.

CONCLUSIONS

Our findings help to inform theories of impulsivity about the development of its multidimensional nature and associated brain activity patterns and highlight the need for taking functional brain development into account when evaluating neuromarker candidates.

Neuroprotection in late life attention-deficit/hyperactivity disorder: A review of pharmacotherapy and phenotype across the lifespan

For decades, psychostimulants have been the gold standard pharmaceutical treatment for attention-deficit/hyperactivity disorder (ADHD). In the United States, an astounding 9% of all boys and 4% of girls will be prescribed stimulant drugs at some point during their childhood. Recent meta-analyses have revealed that individuals with ADHD have reduced brain volume loss later in life (>60 y.o.) compared to the normal aging brain, which suggests that either ADHD or its treatment may be neuroprotective. Crucially, these neuroprotective effects were significant in brain regions (e.g., hippocampus, amygdala) where severe volume loss is linked to cognitive impairment and Alzheimer's disease. Historically, the ADHD diagnosis and its pharmacotherapy came about nearly simultaneously, making it difficult to evaluate their effects in isolation. Certain evidence suggests that psychostimulants may normalize structural brain changes typically observed in the ADHD brain. If ADHD itself is neuroprotective, perhaps exercising the brain, then psychostimulants may not be recommended across the lifespan. Alternatively, if stimulant drugs are neuroprotective, then this class of medications may warrant further investigation for their therapeutic effects. Here, we take a bottom-up holistic approach to review the psychopharmacology of ADHD in the context of recent models of attention. We suggest that future studies are greatly needed to better appreciate the interactions amongst an ADHD diagnosis, stimulant treatment across the lifespan, and structure-function alterations in the aging brain.

Neuromorphological and Neurofunctional Correlates of ADHD and ADD in the Auditory Cortex of Adults

Attention deficit (hyperactivity) disorder (AD(H)D) is one of the most common neurodevelopmental disorders in children with up to 60% probability of prevailing into adulthood. AD(H)D has far-fetching negative impacts on various areas of life. Until today, no observer-independent diagnostic biomarker is available for AD(H)D, however recent research found evidence that AD(H)D is reflected in auditory dysfunctions. Furthermore, the official diagnostic classification systems, being mainly the ICD-10 in Europe and the DSM-5 in the United States, are not entirely consistent. The neuro-auditory profiles of 82 adults (27 ADHD, 30 ADD, 25 controls) were measured via structural magnetic resonance imaging (MRI) and magnetoencephalography (MEG) to determine gray matter volumes and activity of auditory subareas [Heschl’s gyrus (HG) and planum temporale (PT)]. All three groups (ADHD, ADD, and controls) revealed distinct neuro-auditory profiles. In the left hemisphere, both ADHD and ADD showed reduced gray matter volumes of the left HG, resulting in diminished left HG/PT ratios. In the right hemisphere, subjects with ADHD were characterized by lower right HG/PT ratios and ADD by a similar right HG/PT ratio compared to controls. Controls and ADD had well-balanced hemispheric response patterns, ADHD a left-right asynchrony. With this study, we present the structural and functional differences in the auditory cortex of adult patients with AD(H)D.

Integration of electroencephalogram (EEG) and motion tracking sensors for objective measure of attention-deficit hyperactivity disorder (MAHD) in pre-schoolers

We developed an integrated device composed of a single-probe Electroencephalogram (EEG) and Charge Coupled Device (CCD) based motion sensors for objective measurement of Attention-deficit Hyperactivity Disorder (ADHD). While the measurement of attention-deficit hyperactivity disorder (MAHD) relies on the EEG signal for the assessment of attention during a given structured task, the CCD sensor depicts the movement pattern of the subjects engaged in a continuous performance task. A statistical analysis of attention and movement patterns was performed, and the accuracy of completed tasks was analyzed using indigenously developed software. The device with the embedded software is intended to improve certainty with criterion E. We used the EEG signal from a single-channel dry sensor placed on the frontal lobe of the head of the subjects (3-5 year old pre-schoolers). During the performance of the task power for delta and beta, EEG waves from the subjects are found to be correlated with relaxation and attention/cognitive load conditions. While the relaxation condition of the subject hints at hyperactivity, a more direct CCD-based motion sensor is used to track the physical movement of the subject engaged in a continuous performance task. We used our indigenously developed software for statistical analysis to derive a scale for the objective assessment of ADHD. We also compared our scale with clinical ADHD evaluations and found a significant correlation between the objective assessment of the ADHD subjects and the clinician's conventional evaluation. MAHD, the integrated device, is supposed to be an auxiliary tool to improve the accuracy of ADHD diagnosis by supporting greater criterion E certainty.

Machine learning models effectively distinguish attention-deficit/hyperactivity disorder using event-related potentials

Accurate diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) is a significant challenge. Misdiagnosis has significant negative medical side effects. Due to the complex nature of this disorder, there is no computational expert system for diagnosis. Recently, automatic diagnosis of ADHD by machine learning analysis of brain signals has received an increased attention. This paper aimed to achieve an accurate model to discriminate between ADHD patients and healthy controls by pattern discovery. Event-Related Potentials (ERP) data were collected from ADHD patients and healthy controls. After pre-processing, ERP signals were decomposed and features were calculated for different frequency bands. The classification was carried out based on each feature using seven machine learning algorithms. Important features were then selected and combined. To find specific patterns for each model, the classification was repeated using the proposed patterns. Results indicated that the combination of complementary features can significantly improve the performance of the predictive models. The newly developed features, defined based on band power, were able to provide the best classification using the Generalized Linear Model, Logistic Regression, and Deep Learning with the average accuracy and Receiver operating characteristic curve > %99.85 and > 0.999, respectively. High and low frequencies (Beta, Delta) performed better than the mid, frequencies in the discrimination of ADHD from control. Altogether, this study developed a machine learning expert system that minimises misdiagnosis of ADHD and is beneficial for the evaluation of treatment efficacy.

Eye Tracking During a Continuous Performance Test: Utility for Assessing ADHD Patients

OBJECTIVE

The use of continuous performance tests (CPTs) for assessing ADHD related cognitive impairment is ubiquitous. Novel psychophysiological measures may enhance the data that is derived from CPTs and thereby improve clinical decision-making regarding diagnosis and treatment. As part of the current study, we integrated an eye tracker with the MOXO-dCPT and assessed the utility of eye movement measures to differentiate ADHD patients and healthy controls.

METHOD

Adult ADHD patients and gender/age-matched healthy controls performed the MOXO-dCPT while their eye movements were monitored (n = 33 per group).

RESULTS

ADHD patients spent significantly more time gazing at irrelevant regions, both on the screen and outside of it, than healthy controls. The eye movement measures showed adequate ability to classify ADHD patients. Moreover, a scale that combined eye movement measures enhanced group prediction, compared to the sole use of conventional MOXO-dCPT indices.

CONCLUSIONS

Integrating an eye tracker with CPTs is a feasible way of enhancing diagnostic precision and shows initial promise for clarifying the cognitive profile of ADHD patients. Pending replication, these findings point toward a promising path for the evolution of existing CPTs.

Promising Developments in the Use of Induced Pluripotent Stem Cells in Research of ADHD

Although research using animal models, peripheral and clinical biomarkers, multimodal neuroimaging techniques and (epi)genetic information has advanced our understanding of Attention-Deficit Hyperactivity Disorder (ADHD), the aetiopathology of this neurodevelopmental disorder has still not been elucidated. Moreover, as the primary affected tissue is the brain, access to samples is problematic. Alternative models are therefore required, facilitating cellular and molecular analysis. Recent developments in stem cell research have introduced the possibility to reprogram somatic cells from patients, in this case ADHD, and healthy controls back into their pluripotent state, meaning that they can then be differentiated into any cell or tissue type. The potential to translate patients' somatic cells into stem cells, and thereafter to use 2- and 3-dimensional (2D and 3D) neuronal cells to model neurodevelopmental disorders and/or test novel drug therapeutics, is discussed in this chapter.

The scientific integrity of ADHD: A critical examination of the underpinning theoretical constructs

Prior to the establishment and promotion of ADHD as a psychiatric disorder, the labels "minimal brain dysfunction" (MBD), "hyperactivity" (HA), and "learning disability" (LD) were diagnostic terms for children with hard-to-manage behaviors. At the time, these labels and the treatment interventions, especially the heavy reliance on stimulant medications, were subject to criticism. Nearly half a century later, these criticisms apply equally to ADHD, suggesting a disturbing lack of progress in this area of child psychiatry. Therefore, the aim of this article is to examine the scientific integrity of ADHD, to establish why this is the case. I use a philosophy of science framework to track the initial thinking, the plausibility, and the acceptance of ADHD. I establish that ADHD, along with the evolving biomedical model for psychiatry, was accepted in the third edition of the American Psychiatric Association's (APA) Diagnostic and Statistical Manual (DSM-III) as the result of bias and compromise between theorists' of different persuasions. Although initial ideas are expected to be subjective, they also need to demonstrate plausibility prior to empirical investigation. Research from the disciplines of biological psychiatry and cognitive psychology influenced the creation of ADHD, so I critically examine specific ideas that underpinned these disciplines at that time. I find these to be implausible and not congruent with current scientific knowledge, this extends to more recent theory. I conclude there is little good reason to consider DSM-III's concept of ADHD as empirically confirmed, nor do I find good reason to expect such confirmation will be forthcoming.

Behavioral and Neurophysiological Markers of ADHD in Children, Adolescents, and Adults: A Large-Scale Clinical Study

This study aimed to re-evaluate the possible differences between attention-deficit/hyperactivity disorder (ADHD) subjects and healthy controls in the context of a standard Go/NoGo task (visual continuous performance test [VCPT]), frequently used to measure executive functions. In contrast to many previous studies, our sample comprises children, adolescents, and adults. We analyzed data from 447 ADHD patients and 227 healthy controls. By applying multivariate linear regression analyses, we controlled the group differences between ADHD patients and controls for age and sex. As dependent variables we used behavioral (number of omission and commission errors, reaction time, and reaction time variability) and neurophysiological measures (event-related potentials [ERPs]). In summary, we successfully replicated the deviations of ADHD subjects from healthy controls. The differences are small to moderate when expressed as effect size measures (number of omission errors: d = 0.60, reaction time variability: d = 0.56, contingent negative variation (CNV) and P3 amplitudes: -0.35 < d < -0.47, ERP latencies: 0.21 < d < 0.29). Further analyses revealed no substantial differences between ADHD subtypes (combined, inattentive, and hyperactive/impulsive presentation), subgroups according to high- and low-symptomatic burden or methylphenidate intake for their daily routine. We successfully replicated known differences between ADHD subjects and controls for the behavioral and neurophysiological variables. However, the small-to-moderate effect sizes limit their utility as biomarkers in the diagnostic procedure. The incongruence of self-reported symptomatic burden and clinical diagnosis emphasizes the challenges of the present clinical diagnosis with low reliability, which partially accounts for the low degree of discrimination between ADHD subjects and controls.

The World Federation of ADHD International Consensus Statement: 208 Evidence-based conclusions about the disorder

BACKGROUND

Misconceptions about ADHD stigmatize affected people, reduce credibility of providers, and prevent/delay treatment. To challenge misconceptions, we curated findings with strong evidence base.

METHODS

We reviewed studies with more than 2000 participants or meta-analyses from five or more studies or 2000 or more participants. We excluded meta-analyses that did not assess publication bias, except for meta-analyses of prevalence. For network meta-analyses we required comparison adjusted funnel plots. We excluded treatment studies with waiting-list or treatment as usual controls. From this literature, we extracted evidence-based assertions about the disorder.

RESULTS

We generated 208 empirically supported statements about ADHD. The status of the included statements as empirically supported is approved by 80 authors from 27 countries and 6 continents. The contents of the manuscript are endorsed by 366 people who have read this document and agree with its contents.

CONCLUSIONS

Many findings in ADHD are supported by meta-analysis. These allow for firm statements about the nature, course, outcome causes, and treatments for disorders that are useful for reducing misconceptions and stigma.

What can we learn about performance validity from TOVA response profiles?

Given the functional impairments associated with Attention Deficit/Hyperactivity Disorder (AD/HD), a valid diagnosis is important. However, particularly when carried out in adulthood, the diagnostic process can be challenging and is complicated by conclusive evidence that a proportion of individuals referred for evaluation of AD/HD exaggerate or feign their symptoms. Relatively few methods, however, exist to identify such feigning. While continuous performance tests (CPTs) may provide useful information regarding performance validity, the question remains as to whether there are consistent patterns of exaggeration demonstrated by those feigning AD/HD. Thus, this study used cluster analysis to determine whether valid and reliable performance clusters would emerge based on CPT performance. Using archival data from a university-based AD/HD screening clinic, we investigated the performance of 305 adults on the Test of Variables of Attention (TOVA). Three profiles emerged, including one cluster who demonstrated exceptionally low performance on the TOVA, exceptionally high reports of AD/HD symptomology, and higher rates of failure on symptom and performance validity tests. The implication from our analysis is that this group most likely represents individuals who were exaggerating or magnifying their difficulties. The results reaffirm previous research showing that performance profiles on a continuous performance test can be used as an indicator of credible performance.

Biomarker support for ADHD diagnosis based on Event Related Potentials and scores from an attention test

ADHD is a heterogeneous neurodevelopmental disorder associated with dysfunctions in several brain systems. Objective markers of brain dysfunction for clinical assessment are lacking. Many studies applying electroencephalography (EEG) and neuropsychological tests find significant differences between ADHD and controls, but the effect sizes (ES) are often too small for diagnostic purposes. This study aimed to compute a diagnostic index for ADHD by combining behavioral test scores from a cued visual go/no-go task and Event Related Potentials (ERPs). Sixty-one children (age 9-12 years) diagnosed with ADHD and 69 age- and gender-matched typically developing children (TDC) underwent EEG-recording while tested on a go/no-go task. Based on comparisons of ERP group-means and task-performance, variables that differed significantly between the groups with at least moderate ES were converted to a five points percentile scale and multiplied by the ES of the variable. The sum-scores of the variables constituted the diagnostic index. The index discriminated significantly between patients and TDC with a large ES. This index was applied to an independent sample (20 ADHD, 21 TDC), distinguishing the groups with an even larger ES. The diagnostic index described has the potential to support assessment. Further research establishing diagnostic indexes for differential diagnoses is needed.

Metacognition in adult ADHD: subjective and objective perspectives on self-awareness of cognitive functioning

Although attention deficit hyperactivity disorder (ADHD) in adulthood is associated with marked cognitive impairment, research on metacognition in adult ADHD is scarce. Deficits in metacognition may have a negative impact on treatment adherence, functional outcomes, and everyday life. This study explores metacognition, specifically self-awareness of cognitive performance, in adults with ADHD by combining objective and subjective assessments. Forty-seven patients with ADHD and 47 control individuals completed a neuropsychological assessment battery including tests for attention, executive functions and memory (objective assessment), as well as questionnaires for cognitive functioning and symptom severity (subjective assessment; self- and informant-report). Participants evaluated their test performance of the objective assessment after test completion by selecting a percentile rank which was subtracted from their normed test result, yielding a discrepancy score. Compared to controls, adults with ADHD showed impairments in attention (medium effects) and memory (small and medium effects), but not in executive functions. The discrepancy scores between self-evaluation and cognitive performance revealed deficits in self-awareness of attentional functions (small effects), but not in executive functions and memory in patients with ADHD compared to controls. Discrepancy scores between self- and informant-reports of cognitive functioning revealed no significant differences. Adults with ADHD show impairments in metacognition in attentional functions, but may have intact metacognitive abilities in other domains. Patients with ADHD tend to overestimate their abilities, especially in attentional functions. Subjective and objective measures of metacognition may not correspond, highlighting the need for clinicians to not solely rely on patients' self-report in their assessment.

Attention-Deficit/Hyperactivity Disorder (ADHD): Integrating the MOXO-dCPT with an Eye Tracker Enhances Diagnostic Precision

Clinical decision-making may be enhanced when combining psychophysiological sensors with computerized neuropsychological tests. The current study explored the utility of integrating an eye tracker with a commercially available continuous performance test (CPT), the MOXO-dCPT. As part of the study, the performance of adult attention-deficit/hyperactivity disorder (ADHD) patients and healthy controls (n = 43, n = 42, respectively) was compared in the integrated system. More specifically, the MOXO-dCPT has four stages, which differ in their combinations of ecological visual and auditory dynamic distractors. By exploring the participants' performance in each of the stages, we were able to show that: (a) ADHD patients spend significantly more time gazing at irrelevant areas of interest (AOIs) compared to healthy controls; (b) visual distractors are particularly effective in impacting ADHD patients' eye movements, suggesting their enhanced utility in diagnostic procedures; (c) combining gaze direction data and conventional CPT indices enhances group prediction, compared to the sole use of conventional indices. Overall, the findings indicate the utility of eye tracker-integrated CPTs and their enhanced diagnostic precision. They also suggest that the use of attention-grabbing visual distractors may be a promising path for the evolution of existing CPTs by shortening their duration and enhancing diagnostic precision.

Environmental risk factors, protective factors, and peripheral biomarkers for ADHD: an umbrella review

BACKGROUND

Many potential environmental risk factors, environmental protective factors, and peripheral biomarkers for ADHD have been investigated, but the consistency and magnitude of their effects are unclear. We aimed to systematically appraise the published evidence of association between potential risk factors, protective factors, or peripheral biomarkers, and ADHD.

METHODS

In this umbrella review of meta-analyses, we searched PubMed including MEDLINE, Embase, and the Cochrane Database of Systematic Reviews, from database inception to Oct 31, 2019, and screened the references of relevant articles. We included systematic reviews that provided meta-analyses of observational studies that examined associations of potential environmental risk factors, environmental protective factors, or peripheral biomarkers with diagnosis of ADHD. We included meta-analyses that used categorical ADHD diagnosis criteria according to DSM, hyperkinetic disorder according to ICD, or criteria that were less rigorous than DSM or ICD, such as self-report. We excluded articles that did not examine environmental risk factors, environmental protective factors, or peripheral biomarkers of ADHD; articles that did not include a meta-analysis; and articles that did not present enough data for re-analysis. We excluded non-human studies, primary studies, genetic studies, and conference abstracts. We calculated summary effect estimates (odds ratio [OR], relative risk [RR], weighted mean difference [WMD], Cohen's d, and Hedges' g), 95% CI, heterogeneity I² statistic, 95% prediction interval, small study effects, and excess significance biases. We did analyses under credibility ceilings, and assessed the quality of the meta-analyses with AMSTAR 2 (A Measurement Tool to Assess Systematic Reviews 2). This study is registered with PROSPERO, number CRD42019145032.

FINDINGS

We identified 1839 articles, of which 35 were eligible for inclusion. These 35 articles yielded 63 meta-analyses encompassing 40 environmental risk factors and environmental protective factors (median cases 16 850, median population 91 954) and 23 peripheral biomarkers (median cases 175, median controls 187). Evidence of association was convincing (class I) for maternal pre-pregnancy obesity (OR 1·63, 95% CI 1·49 to 1·77), childhood eczema (1·31, 1·20 to 1·44), hypertensive disorders during pregnancy (1·29, 1·22 to 1·36), pre-eclampsia (1·28, 1·21 to 1·35), and maternal acetaminophen exposure during pregnancy (RR 1·25, 95% CI 1·17 to 1·34). Evidence of association was highly suggestive (class II) for maternal smoking during pregnancy (OR 1·6, 95% CI 1·45 to 1·76), childhood asthma (1·51, 1·4 to 1·63), maternal pre-pregnancy overweight (1·28, 1·21 to 1·35), and serum vitamin D (WMD -6·93, 95% CI -9·34 to -4·51).

INTERPRETATION

Maternal pre-pregnancy obesity and overweight; pre-eclampsia, hypertension, acetaminophen exposure, and smoking during pregnancy; and childhood atopic diseases were strongly associated with ADHD. Previous familial studies suggest that maternal pre-pregnancy obesity, overweight, and smoking during pregnancy are confounded by familial or genetic factors, and further high-quality studies are therefore required to establish causality.

FUNDING

None.

Magnesium, Iron, Zinc, Copper and Selenium Status in Attention-Deficit/Hyperactivity Disorder (ADHD)

In this study, we critically review the literature concerning the relation of Mg, Fe, Zn, Cu and Se and attention-deficit/hyperactivity disorder (ADHD). Elemental status is estimated using peripheral blood parameters, hair, urine, daily intake and response to supplementation. The observed associations between concentration levels of the elements Mg, Fe, Zn, Cu and Se and ADHD symptoms are contradictory. This is partly due to the heterogeneity and complexity of the disorder. As a trend, lower ferritin and zinc levels can be observed. However, this correlation is not causative, as illustrated by placebo-controlled trials reporting conflicting evidence on the efficacy of supplementation. Well-defined studies on changes in concentration levels of the elements in relation to ADHD symptoms before and after treatment with therapeutics it will be possible to shed more light on the significance of these elements in this behavioral disorder. The discussion on whether a change in concentration of an element is cause or consequence of ADHD is not within the scope of this article.

Possible links between gut-microbiota and attention-deficit/hyperactivity disorders in children and adolescents

An association between gut-microbiota and several neuropsychiatric conditions including autism, depression, anxiety, schizophrenia, and attention-deficit/hyperactivity disorder (ADHD) has been observed. Despite being the most prevalent neurodevelopmental disorders in children and adolescents worldwide, the etiology and curative approaches to treatment of ADHD remain unclear. There is a probability that gut-microbiota may contribute to ADHD via bidirectional communication between the gut and brain, a system known as the "gut-brain axis". Although a mechanistic link in the gut-brain axis in ADHD has been proposed, there is still a lack of information about the correlation of the microbiome profile with the mechanisms involved. The objective of this review was to summarize the diversity of the gut-microbiota and taxonomic profiles in children and adolescents with ADHD. In this review, we have provided an overview of the association between ADHD and gut-microbiota. The evidence pertinent to potentially distinctive gut-microbiota in children and adolescents with ADHD is also discussed and compared to that of their non-ADHD peers. Finally, the implications and future directions for investigation into the gut microbiome in ADHD patients are proposed.

Event-Related Potentials for Diagnosing Children and Adults With ADHD

Objective: The diagnosis of ADHD is based on behavioral criteria, which allow for subjective variability and invite criticism regarding the reality of the disorder. In this situation, more objective criteria would be desirable. We review the scientific literature for diagnostic tests based on event-related potentials (ERPs). Method: Seven studies met the inclusion criteria of reporting the sensitivity and specificity of an ERP-based classifier discriminating participants with ADHD from healthy controls. Study quality was rated using the second version of the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) system. Results: Overall, study quality was acceptable. The largest biases were lack of representativeness and overfitting. Sensitivities and specificities ranged from 57% to 96%, and 63% to 92%, respectively. However, no two studies used the same diagnostic test. Conclusion: There is a serious lack of coordination in worldwide efforts to find more objective ERP-based criteria for the diagnosis of ADHD. Concerted action is needed.

Machine learning classification of ADHD and HC by multimodal serotonergic data

Serotonin neurotransmission may impact the etiology and pathology of attention-deficit and hyperactivity disorder (ADHD), partly mediated through single nucleotide polymorphisms (SNPs). We propose a multivariate, genetic and positron emission tomography (PET) imaging classification model for ADHD and healthy controls (HC). Sixteen patients with ADHD and 22 HC were scanned by PET to measure serotonin transporter (SERT') binding potential with [¹¹C]DASB. All subjects were genotyped for thirty SNPs within the HTR1A, HTR1B, HTR2A and TPH2 genes. Cortical and subcortical regions of interest (ROI) were defined and random forest (RF) machine learning was used for feature selection and classification in a five-fold cross-validation model with ten repeats. Variable selection highlighted the ROI posterior cingulate gyrus, cuneus, precuneus, pre-, para- and postcentral gyri as well as the SNPs HTR2A rs1328684 and rs6311 and HTR1B rs130058 as most discriminative between ADHD and HC status. The mean accuracy for the validation sets across repeats was 0.82 (±0.09) with balanced sensitivity and specificity of 0.75 and 0.86, respectively. With a prediction accuracy above 0.8, the findings underlying the proposed model advocate the relevance of the SERT as well as the HTR1B and HTR2A genes in ADHD and hint towards disease-specific effects. Regarding the high rates of comorbidities and difficult differential diagnosis especially for ADHD, a reliable computer-aided diagnostic tool for disorders anchored in the serotonergic system will support clinical decisions.

Frontal brain activity in individuals at risk for schizophrenic psychosis and bipolar disorder during the emotional Stroop task - an fNIRS study

OBJECTIVES

The emotional Stroop effect is defined as increased reaction times to emotional stimuli compared to neutral ones. It has been often reported in the literature, on both behavioral and neurophysiological level. The goal of this study was to investigate the frontal brain activation in individuals at risk for schizophrenic psychosis and bipolar disorder during an emotional Stroop task. We expected to observe decreased activation in the at-risk individuals compared to the healthy controls.

METHODS

Individuals at high risk for psychosis (HR), at ultra-high risk for psychosis (UHR), at risk for bipolar disorder (BIP) and healthy controls (HC) performed an emotional Stroop task, which included positively, negatively and neutrally valenced words. Functional near-infrared spectroscopy (fNIRS) was used to measure levels of oxygenated hemoglobin (O2Hb) representing brain activity in the dorsolateral prefrontal and frontotemporal cortex.

RESULTS

Results showed significantly decreased levels of O2Hb in the right dorsolateral prefrontal cortex (DLPFC) in the HR and UHR groups compared to the HC, indicating lower activity. Even though the decrease was independent from the valence of the words, it was the most visible for the negative ones. Moreover, significantly lower O2Hb levels in the frontotemporal cortex (FTC) were observed in all at risk groups compared to the HC.

CONCLUSIONS

Lower activity in the FTC in groups at risk for psychosis and bipolar disorder reflects unspecific dysfunctions. Decreased activity in the DLPFC in the HR and UHR groups indicates that hypofrontality can be found already in individuals at risk for schizophrenic psychosis.

EEG/ERP-based biomarker/neuroalgorithms in adults with ADHD: Development, reliability, and application in clinical practice

Objectives: The electrophysiological characteristics of attention-deficit/hyperactivity disorder (ADHD) and recent machine-learning methods promise easy-to-use approaches that can complement existing diagnostic tools when sufficiently large samples are used. Neuroalgorithms are models of multidimensional brain networks by means of which ADHD patient data can be separated from healthy control data.Methods: Spontaneous electroencephalographic and event-related potential (ERP) data were collected three times over the course of 2 years from a multicentre sample of adults comprising 181 patients with ADHD and 147 healthy controls. Spectral power and ERP amplitude and latency measures were used as input data for a semi-automatic machine-learning framework.Results: ADHD patients and healthy controls could be classified with a sensitivity ranging from 75% to 83% and specificity values of 71% to 77%. In the analysis of the repeated measurements, sensitivity values of the selected logistic regression model remained high (72% and 76%), while specificity values slightly decreased over time (64% and 67%).Conclusions: Implementation of the system in clinical practice requires facilities to track affected networks, as well as expertise in neuropathophysiology. Therefore, the use of neuroalgorithms can enhance the diagnostic process by making it less subjective and more reliable and linking it to the underlying pathology.

Is it possible to recognize children diagnosed with ADHD from their facial anthropometric measures? A case-control study

The recent main focus of the researches on Attention-deficit Hyperactivity Disorder is on identifying behavioral phenotypes. For this purpose, neuroanatomical factors have recently become a focus. This study aimed to investigate whether the individuals diagnosed with Attention-deficit Hyperactivity Disorder differ from healthy individuals in terms of facial anthropometric measurements. Forty children, diagnosed with Attention-deficit Hyperactivity Disorder, were included in the study as the case group, and forty healthy children were included in the study as the control group. Two photographs were taken from the facial region, and anthropometric measurements were performed using the computer program "Image J" in the computer environment. It was found that a strong relationship between Attention-deficit Hyperactivity Disorder and nasal width, ear length and upper face debt length. The results obtained from the research support the knowledge that there is a close relationship between the forebrain development process and the facial development process during the embryonic development process.

Reliability and correlates of intraindividual variability in the oculomotor system

Even if all external circumstances are kept equal, the oculomotor system shows intraindividual variability over time, affecting measures such as microsaccade rate, blink rate, pupil size, and gaze position. Recently, some of these measures have been associated with ADHD on a between-subject level. However, it remains unclear to what extent these measures constitute stable individual traits. In the current study, we investigate the intraindividual reliability of these oculomotor features. Combining results over three experiments (> 100 healthy participants), we find that most measures show good intra-individual reliability over different time points (repeatability) as well as over different conditions (generalisation). However, we find evidence against any correlation with self-assessed ADHD tendencies, mind wandering, and impulsivity. As such, the oculomotor system shows reliable intra-individual reliability, but its benefit for investigating self-assessed individual differences in healthy subjects remains unclear. With our results, we highlight the importance of reliability and statistical power when studying between-subject differences.

Perinatal antibiotics alter preterm infant EEG and neurobehavior in the Family Nurture Intervention trial

Early exposure to antibiotics has been shown to increase risk for poor neurobehavioral development, particularly with regard to attention deficit disorders. Clinically, electroencephalography (EEG) is increasingly used as a biomarker of these deficits. Less is known about the effects of antibiotics on neurobehavioral and neurophysiological outcomes in preterm infants, a population at particularly high risk for attention deficits and perinatal antibiotic exposure. This study examines the effects of perinatal antibiotic exposure on neonatal EEG and attention deficits as measured by the Child Behavior Checklist in 4- to 5-year-old children who were enrolled in an NICU-based randomized controlled trial comparing Family Nurture Intervention (FNI) to standard care. Antibiotic-exposed infants had increased attention problems and there was a main effect of antibiotic exposure such that exposed infants had higher EEG power. This effect was fourfold greater in infants who received standard NICU care compared to those who received the intervention, suggesting a buffering effect of the intervention. We hypothesize that the relationship between antibiotic exposure and altered neurodevelopment may be due to effects of antibiotics on the microbiome, and that FNI may buffer these adverse consequences.

Dehydroepiandrosterone sulfate, free testosterone, and sex hormone-binding globulin on susceptibility to attention-deficit/hyperactivity disorder

The neuroendocrine system may affect the pathophysiology of gender differences in attention deficit/hyperactivity disorder (ADHD). This study examines whether the relationships among dehydroepiandrosterone sulfate (DHEA-S), free testosterone, or sex hormone-binding globulin (SHBG) and ADHD presentations exhibit gender differences. A total of 113 boys and 35 girls with ADHD (all drug naïve) and 46 and 26 healthy control boys and girls, respectively, were recruited. Blood samples were obtained to measure the serum levels of DHEA-S, free testosterone, and SHBG in each child. The Swanson, Nolan, and Pelham Scale for ADHD Version IV (SNAP-IV) was used to evaluate behavioral symptoms and the Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV) and the Conners' Continuous Performance Test (CPT) were utilized to assess neurocognitive functions. Patients with ADHD had lower DHEA-S levels than male and female healthy control subjects, and no significant differences were observed in free testosterone and SHBG levels between the patients and the controls. DHEA-S levels were negatively correlated with children's impulsivity performance in the CPT. SHBG levels were negatively correlated with ADHD behavior symptoms among boys. Free testosterone levels were not significantly correlated with either ADHD clinical symptoms or neuropsychological functions. We propose that DHEA-S serves as a potential biomarker of ADHD and is consistently involved in the pathogenesis of ADHD in both boys and girls. SHBG may be involved in behaviors associated with ADHD in boys. Additional studies with basic scientific measures are warranted to elucidate the relationship between androgen hormones and clinical presentations of ADHD.

Clinical Validation of Eye Vergence as an Objective Marker for Diagnosis of ADHD in Children

OBJECTIVE

ADHD youth show poor oculomotor control. Recent research shows that attention-related eye vergence is weak in ADHD children.

METHOD

To validate vergence as a marker to classify ADHD, we assessed the modulation in the angle of vergence of children ( n = 43) previously diagnosed with ADHD while performing an attention task and compared the results with age-matched clinical controls ( n = 19) and healthy peers ( n = 30).

RESULTS

We observed strong vergence responses in healthy participants and weak vergence in the clinical controls. ADHD children showed no significant vergence responses. Machine-learning models classified ADHD patients ( n = 21) from healthy controls ( n = 21) with an accuracy of 96.3% (false positive [FP]: 5.12%; false negative [FN]: 0%; area under the curve [AUC]: 0.99) and ADHD children ( n = 11) from clinical controls ( n = 14) with an accuracy of 85.7% (FP: 4.5%; FN: 19.2%, AUC: 0.90).

CONCLUSION

In combination with an attention task, vergence responses can be used as an objective marker to detect ADHD in children.

Peripheral Mitochondrial DNA Copy Number is Increased in Korean Attention-Deficit Hyperactivity Disorder Patients

The involvement of mitochondrial dysfunction in the pathophysiology of attention-deficit hyperactivity disorder (ADHD) has been suggested in several reports. Mitochondrial DNA (mtDNA) copy number as well as methylation of the D-loop region and peroxisome-proliferator-activated receptor γ co-activator-1α (PPARGC1A) are considered biomarkers for mitochondrial dysfunction. We compared the mtDNA copy number and methylation ratio of the D-loop region and PPARGC1A between ADHD patients and controls and also among ADHD subtypes. The present study included 70 subjects with ADHD and 70 age- and gender-matched healthy controls (HCs). We measured the relative mtDNA copy number in peripheral blood cells using quantitative polymerase chain reaction (qPCR), and the methylation ratio was measured using methylation-specific PCR (MSP) after bisulfite conversion. The relative mtDNA copy number was significantly higher in ADHD patients than in HCs (p = 0.028). The mtDNA methylation ratio of PPARGC1A was decreased in ADHD patients compared with HCs (p = 0.008). After adjusting for IQ level, only the mtDNA copy number differed between the ADHD and HCs (p = 0.01). There was a significant difference in the methylation ratio of PPARGC1A among ADHD subtypes. These results suggest the possible involvement of mitochondrial dysfunction in the pathophysiology of ADHD. Further large cohort studies investigating the correlation between clinical markers and biomarkers of mitochondrial dysfunction are warranted.

A Low-Level Perceptual Correlate of Behavioral and Clinical Deficits in ADHD

In many studies of attention-deficit hyperactivity disorder (ADHD), stimulus encoding and processing (perceptual function) and response selection (executive function) have been intertwined. To dissociate deficits in these functions, we introduced a task that parametrically varied low-level stimulus features (orientation and color) for fine-grained analysis of perceptual function. It also required participants to switch their attention between feature dimensions on a trial-by-trial basis, thus taxing executive processes. Furthermore, we used a response paradigm that captured task-irrelevant motor output (TIMO), reflecting failures to use the correct stimulus-response rule. ADHD participants had substantially higher perceptual variability than controls, especially for orientation, as well as higher TIMO. In both ADHD and controls, TIMO was strongly affected by the switch manipulation. Across participants, the perceptual variability parameter was correlated with TIMO, suggesting that perceptual deficits are associated with executive function deficits. Based on perceptual variability alone, we were able to classify participants into ADHD and controls with a mean accuracy of about 77%. Participants' self-reported General Executive Composite score correlated not only with TIMO but also with the perceptual variability parameter. Our results highlight the role of perceptual deficits in ADHD and the usefulness of computational modeling of behavior in dissociating perceptual from executive processes.

Is the Theta/Beta EEG Marker for ADHD Inherently Flawed?

OBJECTIVE

Despite advances in our understanding of ADHD as a neurodevelopmental disorder, robust biomarkers are yet to be established in clinical practice. More than 40 years of electroencephalography (EEG)-based research has culminated in the recent Food and Drug Administration (FDA) approval of the theta/beta (EEG power) ratio (TBR) as a diagnostic marker of ADHD.

METHOD

This review article focuses on resting-state EEG power research in ADHD.

RESULTS

Inconsistent findings in the literature and suggestions of reduced specificity threaten the utility of TBR as a biomarker of ADHD. The use of fixed EEG bands may be a significant limitation, particularly in youth, and alternative approaches are needed.

CONCLUSION

We propose that a personalized theta-to-alpha cut point or "transition frequency" is a better frame of reference for the measurement of TBR. Such an approach is better placed to test maturational lag and cortical hypoarousal models of ADHD and may in turn have greater utility in supporting diagnosis.

Advances, challenges, and promises in pediatric neuroimaging of neurodevelopmental disorders

Recent years have witnessed the proliferation of neuroimaging studies of neurodevelopmental disorders (NDDs), particularly of children with autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and Tourette's syndrome (TS). Neuroimaging offers immense potential in understanding the biology of these disorders, and how it relates to clinical symptoms. Neuroimaging techniques, in the long run, may help identify neurobiological markers to assist clinical diagnosis and treatment. However, methodological challenges have affected the progress of clinical neuroimaging. This paper reviews the methodological challenges involved in imaging children with NDDs. Specific topics include correcting for head motion, normalization using pediatric brain templates, accounting for psychotropic medication use, delineating complex developmental trajectories, and overcoming smaller sample sizes. The potential of neuroimaging-based biomarkers and the utility of implementing neuroimaging in a clinical setting are also discussed. Data-sharing approaches, technological advances, and an increase in the number of longitudinal, prospective studies are recommended as future directions. Significant advances have been made already, and future decades will continue to see innovative progress in neuroimaging research endeavors of NDDs.

Blood-Bourne MicroRNA Biomarker Evaluation in Attention-Deficit/Hyperactivity Disorder of Han Chinese Individuals: An Exploratory Study

Background: Attention-deficit/hyperactivity disorder (ADHD) is a highly genetic neurodevelopmental disorder, and its dysregulation of gene expression involves microRNAs (miRNAs). The purpose of this study was to identify potential miRNAs biomarkers and then use these biomarkers to establish a diagnostic panel for ADHD. Design and methods: RNA samples from white blood cells (WBCs) of five ADHD patients and five healthy controls were combined to create one pooled patient library and one control library. We identified 20 candidate miRNAs with the next-generation sequencing (NGS) technique (Illumina). Blood samples were then collected from a Training Set (68 patients and 54 controls) and a Testing Set (20 patients and 20 controls) to identify the expression profiles of these miRNAs with real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR). We used receiver operating characteristic (ROC) curves and the area under the curve (AUC) to evaluate both the specificity and sensitivity of the probability score yielded by the support vector machine (SVM) model. Results: We identified 13 miRNAs as potential ADHD biomarkers. The ΔCt values of these miRNAs in the Training Set were integrated to create a biomarker model using the SVM algorithm, which demonstrated good validity in differentiating ADHD patients from control subjects (sensitivity: 86.8%, specificity: 88.9%, AUC: 0.94, p < 0.001). The results of the blind testing showed that 85% of the subjects in the Testing Set were correctly classified using the SVM model alignment (AUC: 0.91, p < 0.001). The discriminative validity is not influenced by patients' age or gender, indicating both the robustness and the reliability of the SVM classification model. Conclusion: As measured in peripheral blood, miRNA-based biomarkers can aid in the differentiation of ADHD in clinical settings. Additional studies are needed in the future to clarify the ADHD-associated gene functions and biological mechanisms modulated by miRNAs.