Rethinking Intelligence Quotient Exclusion Criteria Practices in the Study of Attention Deficit Hyperactivity Disorder

Sex and intelligence quotient differences in age of diagnosis among youth with attention-deficit hyperactivity disorder

OBJECTIVES

Attention-deficit/hyperactivity disorder (ADHD) is the most common neurodevelopmental condition and is characterized by inattention, hyperactivity, and impulsivity. Research suggests that some populations, such as females and individuals with high intelligence quotients may be a risk for late ADHD diagnosis and subsequent treatment. Our goal is to advance our understanding of ADHD diagnosis, by examining (1) how child sex and cognitive abilities together are related to the age of diagnosis and (2) whether symptom presentation, current internalizing and externalizing symptoms, and demographic factors are related to age of diagnosis.

METHODS

Our analyses contained children who completed the required tests (N = 568) from a pre-existing dataset of 1380 children with ADHD from the Province of Ontario Neurodevelopmental Disorders (POND) Network (pond-network.ca). First, we conducted a moderation analysis with sex as the predictor, cognitive abilities as the moderator, and age of diagnosis as the outcome. Second, we conducted correlation analyses examining how symptom presentation, current internalizing and externalizing symptoms, and demographic factors are related to age of diagnosis.

RESULTS

Higher IQ was related to a later age of diagnosis. Higher hyperactive-impulsive symptoms and externalizing symptoms were related to an earlier age of diagnosis. Internalizing symptoms were trend associated with a later age of diagnosis in girls. Higher socioeconomic status and non-White maternal ethnicity were related to later age of diagnosis.

CONCLUSIONS

IQ, sex, ADHD symptomology, internalizing symptoms, externalizing symptoms, and socio-demographic factors affect the age of diagnosis.

The diagnosis of ADHD in children and adolescents with epilepsy: a scoping review

Attention-deficit hyperactivity disorder (ADHD) is often diagnosed in children and adolescents with epilepsy, but clear clinical guidelines on how to make this diagnosis are still lacking. Without these guidelines, there is no consensus between specialists on how to proceed when assessing children with epilepsy for ADHD, which can negatively impact the quality of care being offered to this population. As a first step toward gaining more specific clinical guidelines, this scoping review was aimed at documenting the tools and procedures used to diagnose ADHD in children and adolescents with epilepsy over time and at determining whether the diagnoses were made in accordance with clinical guidelines and recommendations. The literature search was conducted using PsycINFO, PubMed, and CINAHL. Studies were included if conducted with children and adolescents aged between 4 and 18 years with epilepsy being evaluated for ADHD. Studies were clustered according to their publication date and the reported diagnostic procedures were identified. Forty-nine out of 3854 records were included. Results highlight discrepancies between how ADHD was diagnosed in reviewed studies and clinical guidelines or recommendations. Indeed, most studies did not use a multi-method and multi-informant approach when diagnosing ADHD in children with epilepsy, with no improvement over time. Future studies aimed at diagnosing ADHD in children and adolescents should ensure that they are following clinical guidelines and recommendations, in addition to adapting their diagnostic procedures to the presence of any neurological comorbidities, such as epilepsy.

Onset of Inattention and Hyperactivity in Children and Adolescents With Epilepsy 6 months After the Diagnosis

OBJECTIVE

Complete or major symptoms of ADHD are often present in epilepsy. This study evaluated inattention and hyperactivity symptoms over the first 6 months in newly diagnosed pediatric epilepsy without comorbid ADHD.

METHOD

Children and adolescents with newly diagnosed epilepsy were followed for 6 months after starting antiseizure medications. The Nisonger Child Behavior Rating Form (NCBRF), Adverse Event Profile (AEP), and the Revised Wechsler Intelligence Scale for Children were used.

RESULTS

There was a marked increase in attention difficulties while a moderate increase in hyperactivity levels. AEP scores, changes in non-verbal aspects of intelligence, levels of hyperactivity at the follow-up, and attention at baseline were significant predictors for inattention. In contrast, only levels of hyperactivity at the baseline and inattention at the follow-up were significant predictors for hyperactivity.

CONCLUSION

Significant inattention and hyperactivity levels originated 6 months after the diagnosis of epilepsy and starting antiseizure medication.

Brain-phenotype models fail for individuals who defy sample stereotypes

Individual differences in brain functional organization track a range of traits, symptoms and behaviours1-12. So far, work modelling linear brain-phenotype relationships has assumed that a single such relationship generalizes across all individuals, but models do not work equally well in all participants13,14. A better understanding of in whom models fail and why is crucial to revealing robust, useful and unbiased brain-phenotype relationships. To this end, here we related brain activity to phenotype using predictive models-trained and tested on independent data to ensure generalizability15-and examined model failure. We applied this data-driven approach to a range of neurocognitive measures in a new, clinically and demographically heterogeneous dataset, with the results replicated in two independent, publicly available datasets16,17. Across all three datasets, we find that models reflect not unitary cognitive constructs, but rather neurocognitive scores intertwined with sociodemographic and clinical covariates; that is, models reflect stereotypical profiles, and fail when applied to individuals who defy them. Model failure is reliable, phenotype specific and generalizable across datasets. Together, these results highlight the pitfalls of a one-size-fits-all modelling approach and the effect of biased phenotypic measures18-20 on the interpretation and utility of resulting brain-phenotype models. We present a framework to address these issues so that such models may reveal the neural circuits that underlie specific phenotypes and ultimately identify individualized neural targets for clinical intervention.

Resting-State Brain Variability in Youth With Attention-Deficit/Hyperactivity Disorder

In this study, we sought to determine the nature of the abnormality in resting-state default mode network (DMN) activation and explore its correlation with functional connectivity in attention-deficit/hyperactivity disorder (ADHD). We obtained resting-state functional magnetic resonance images of youth with ADHD and typically developing counterparts from the publicly available ADHD-200 database. We used data from Peking University (232 scans) and New York University (172 scans); the scan repetition time was 2 s for both data collection sites. We applied generalized estimating equations to estimate the variability of the averaged blood-oxygen-level-dependent (BOLD) time series extracted from the DMN at rest. We performed network-based statistics to determine the association between the observed differences in BOLD signal variability and altered functional connectivity. We analyzed data from 105 youth with ADHD (age: mean 12.17, standard deviation 2.31, median 12.25; 15.2% female, 84.8% male) and 140 typically developing youth (age: mean 11.99, standard deviation 2.28, median 11.85; 47.1% female, 52.9% male), who aged 7-17 years. The imaging data were cross-sectionally collected for each participant at one time point. We observed a greater number of significant BOLD signal changes and higher-order polynomial significant associations in youth with ADHD. Moreover, there were significant between-group differences in BOLD signal change after the first 140 s, which coincided with decreased resting-state functional connectivity within the DMN in youth with ADHD. Increased variability of neural signaling was intermittently observed in the brains of youth with ADHD at rest, thereby indicating their default mode state was more unstable than that of typically developing youth.

A longitudinal investigation of cognitive functioning and its relationship to symptom severity and academic functioning in treatment seeking youth with AHDH

Background:

Children with ADHD tend to present with poorer cognitive functioning leaving them more vulnerable to a range of negative outcomes. To date, only a handful of longitudinal studies have examined the stability of Wechsler composite scores in children and adolescents with ADHD, and none of them used a more recent version of the Wechsler Intelligence Scales for Children (WISC), than the WISC-III.

Objective:

The present study investigates the cognitive stability and its longitudinal relationship with the severity of the child’s ADHD symptoms and school grades.

Method:

Cognitive functioning was measured with the fourth editions of the WISC-IV or the Wechsler Adult Intelligence Scales (WAIS-IV) at baseline and at a 3-4-year follow-up in children with ADHD (n = 125, mean age = 11.40 years, SD = 3.27) and a Control group of schoolchildren (n = 59, mean age = 11.97 years, SD = 2.15). The stability of cognitive functioning and the relationship between cognitive functioning, ADHD and grades were evaluated using linear mixed models and logistic regression.

Results:

Standardized composite scores of Full scale IQ, Verbal Comprehension, and Processing Speed declined between baseline and follow-up in the ADHD group. ADHD symptom scores were associated with Working Memory scores. Together, the severity of concurrent ADHD symptoms and lower scores for verbal comprehension at baseline and follow-up were associated with an increased risk of not achieving grades at follow-up in youth with ADHD.

Conclusions:

Youth with ADHD often present with cognitive impairments, not improved over time. Together these increase the risk of poorer academic outcomes. Concurrent evaluation of symptom severity and cognitive functions can add potentially useful information in terms of treatment planning, and school supports to prevent school failure.

Computerized Eye-Tracking Training Improves the Saccadic Eye Movements of Children with Attention-Deficit/Hyperactivity Disorder

Abnormal saccadic eye movements, such as longer anti-saccade latency and lower pro-saccade accuracy, are common in children with attention-deficit/hyperactivity disorder (ADHD). The present study aimed to investigate the effectiveness of computerized eye-tracking training on improving saccadic eye movements in children with ADHD. Eighteen children with ADHD (mean age = 8.8 years, 10 males) were recruited and assigned to either the experimental (n = 9) or control group (n = 9). The experimental group underwent an accumulated 240 min of eye-tracking training within two weeks, whereas the control group engaged in web game playing for the same amount of time. Saccadic performances were assessed using the anti- and pro-saccade tasks before and after training. Compared to the baseline, only the children who underwent the eye-tracking training showed significant improvements in saccade latency and accuracy in the anti- and pro-saccade tasks, respectively. In contrast, the control group exhibited no significant changes. These preliminary findings support the use of eye-tracking training as a safe non-pharmacological intervention for improving the saccadic eye movements of children with ADHD.

Investigation of Brain Functional Networks in Children Suffering from Attention Deficit Hyperactivity Disorder

ADHD defects the recognition of facial emotions. This study assesses the neurophysiological differences between children with ADHD and matched healthy controls during a face emotional recognition task. The study also explores how brain connectivity is affected by ADHD. Electroencephalogram (EEG) signals were recorded from 64 scalp electrodes. Event-related phase coherence (ERPCOH) method was applied to pre-processed signals, and functional connectivity between any pair of electrodes was computed in different frequency bands. A logistic regression (LR) classifier with elastic net regularization (ENR) was trained to classify ADHD and HC participants using the functional connectivity of frequency bands as a potential biomarker. Subsequently, the brain network is constructed using graph-theoretic techniques, and graph indices such as clustering coefficient (C) and shortest path length (L) were calculated. Significant intra-hemispheric and the inter-hemispheric discrepancy between ADHD and healthy control (HC) groups in the beta band was observed. The graph features indicate that the clustering coefficient is significantly higher in the ADHD group than that in the HC group. At the same time, the shortest path length is significantly lower in the beta band. ADHD's brain networks have a problem in transferring information among various neural regions, which can cause a deficiency in the processing of facial emotions. The beta band seems better to reflect the differences between ADHD and HC. The observed functional connectivity and graph differences could also be helpful in ADHD investigations.

Attention-deficit/hyperactivity disorder in children and adolescents: An event-related potential study of working memory

Working memory (WM) impairments have been frequently observed as an important feature of attention-deficit/hyperactivity disorder (ADHD). Event-related potential (ERP) differences between ADHD and healthy controls (HC) would be expected during WM task performance. Especially, the so-called slow wave (SW), which is related to the retention process, might present amplitude differences in ADHD. In this ERP study participated twenty-nine ADHD children and adolescents and thirty-four HC. WM performance was assessed using the Working Memory Test Battery for Children (WMTB-C), and ERPs were analyzed with a Delayed Match-To-Sample (DMTS) task. ADHD sample showed worse behavioral performance in both WMTB-C and DMTS task, and higher SW amplitude during the retention phase of the DMTS task. Additionally, the principal component analysis indicated that the scores on the component explaining the centro-parietal SW were significantly different between ADHD subjects and HC. The observed impaired neurophysiological activity during the encoding and retention periods in ADHD, which would be the origin of the behavioral deficits in WM task performance, might be reflecting a delayed maturation of the neural processes underlying the centro-parietal SW.

Frequency coupling of low and high frequencies in the EEG of ADHD children and adolescents in closed and open eyes conditions

The present report examines the possible differences in absolute Power Spectral Density (PSD), the topography of brain rhythms, and low frequency (delta and theta) vs. beta PSD when attention deficit disorder (ADHD) children and controls are compared. These results would potentially be useful to test the validity of the developmental lag and differential developmental models for ADHD. The EEG resting state under the experimental conditions of open and closed eyes were recorded in samples of control subjects and children with ADHD (6-17 years old). The PSD from 0 to 46 Hz was calculated and ANOVAs were performed to compare the groups of subjects in the two experimental conditions. To observe differences in the co-maturation of the brain rhythms between the groups of subjects, correlations of the PSD of all frequency ranges were computed. These results showed an increase in delta power in children with ADHD compared to control subjects. The topographies of the different brain rhythms were similar in children with ADHD and controls. The maturational power-to-power frequency-coupling between low frequencies and beta rhythms was lower in children with ADHD. The increased delta PSD in ADHD and the similar brain rhythms topographies in children with ADHD and controls support the developmental lag model, whereas the decreased co-maturation of low frequencies vs. beta PSD in children with ADHD suggests a differential maturation rate for low and beta frequencies in children with ADHD compared to controls.

Young Adult Outcomes for Children With 22q11 Deletion Syndrome and Comorbid ADHD

Background

22q11.2 deletion syndrome (22q11DS) is a common microdeletion syndrome associated with a variety of negative health, cognitive, emotional, and behavioral outcomes. 22q11DS is comorbid with many psychiatric disorders including attention-deficit/hyperactivity disorder (ADHD). The current study aimed to investigate the cognitive, behavioral, and functional outcomes that a childhood ADHD diagnosis predicts to in adulthood.

Methods

This longitudinal study followed 52 individuals with 22q11DS over 9 years. Childhood ADHD was operationalized both categorically (Diagnostic and statistical manual - 4th edition (DSM-IV) ADHD diagnoses) and dimensionally (inattentive and hyperactive-impulsive symptoms) and was tested as predictors of young adult outcomes.

Results

As young adults, children with 22q11DS + baseline ADHD had more parent-reported executive dysfunction and lower levels of clinician-rated overall functioning than those with 22q11DS yet without ADHD. Dimensional symptoms of ADHD in childhood did not predict young adult outcomes. No self-report differences emerged between those with and without baseline ADHD. The majority (82.4%) of individuals with 22q11DS + baseline ADHD were never treated with an ADHD medication.

Conclusions

A categorical diagnosis of ADHD in childhood predicted a greater variety of worse outcomes than dimensional levels of ADHD symptoms. Despite the significant impact of comorbid ADHD in 22q11DS, evidence-based treatment rates were low.

Neurophysiological and behavioral correlates of alertness impairment and compensatory processes in ADHD evidenced by the Attention Network Test

In Attention Deficit Hyperactivity disorder (ADHD), fMRI studies show asymmetric alterations: widespread hypoactivation in anterior cortical areas and hyperactivation in some posterior regions, and the latter is considered to be related to compensatory processes. In Posner’s attentional networks, an important role is attributed to functional interhemispheric asymmetries. The psychophysiological Attention Network Test (ANT), which measures the efficiency of the alerting, orienting, and executive networks, seems particularly informative for ADHD. Potentials related to ANT stimuli (ANT-RPs) have revealed reduced cognitive potential P3 in ADHD. However, there are no studies associated with asymmetry of ANT-RPs. In the present study, conducted with 20 typically developing boys and 19 boys with ADHD, aged 11–13 years, the efficiency of the three Posner’s networks regarding performance and amplitude asymmetries in ANT-RPs was evaluated according to the arithmetic difference of these parameters between different cue and target presentation conditions. The results were correlated to Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR) scores. Regarding accuracy and intraindividual variation in reaction time, ADHD subjects showed lower efficiency of executive and alerting network, and this effect was correlated with DSM. Regarding alerting network, ANT-RPs in ADHD did not have the right-side amplitude prevalence in the temporal regions, which was observed in controls. In all ANT conditions, significantly higher asymmetries were observed in ADHD than in controls in the occipital regions 40–200 ms after target onset. Their amplitude in ADHD subjects was inversely proportional to DSM scores of inattentiveness and directly proportional to accuracy and efficiency of the executive network. The results suggest impaired alerting and executive networks in ADHD and compensatory occipital mechanisms.

Estimating biological accuracy of DSM for attention deficit/hyperactivity disorder based on multivariate analysis for small samples

Objective

To estimate whether the “Diagnostic and Statistical Manual of Mental Disorders” (DSM) is biologically accurate for the diagnosis of Attention Deficit/ Hyperactivity Disorder (ADHD) using a biological-based classifier built by a special method of multivariate analysis of a large dataset of a small sample (much more variables than subjects), holding neurophysiological, behavioral, and psychological variables.

Methods

Twenty typically developing boys and 19 boys diagnosed with ADHD, aged 10–13 years, were examined using the Attentional Network Test (ANT) with recordings of event-related potentials (ERPs). From 774 variables, a reduced number of latent variables (LVs) were extracted with a clustering of variables method (CLV), for further reclassification of subjects using the k-means method. This approach allowed a multivariate analysis to be applied to a significantly larger number of variables than the number of cases.

Results

From datasets including ERPs from the mid-frontal, mid-parietal, right frontal, and central scalp areas, we found 82% of agreement between DSM and biological-based classifications. The kappa index between DSM and behavioral/psychological/neurophysiological data was 0.75, which is regarded as a “substantial level of agreement”.

Discussion

The CLV is a useful method for multivariate analysis of datasets with much less subjects than variables. In this study, a correlation is found between the biological-based classifier and the DSM outputs for the classification of subjects as either ADHD or not. This result suggests that DSM clinically describes a biological condition, supporting its validity for ADHD diagnostics.

Prenatal Maternal Stress and Child IQ

The evidence for negative influences of maternal stress during pregnancy on child cognition remains inconclusive. This study tested the association between maternal prenatal stress and child intelligence in 4,251 mother-child dyads from a multiethnic population-based cohort in the Netherlands. A latent factor of prenatal stress was constructed, and child IQ was tested at age 6 years. In Dutch and Caribbean participants, prenatal stress was not associated with child IQ after adjustment for maternal IQ and socioeconomic status. In other ethnicities no association was found; only in the Moroccan/Turkish group a small negative association between prenatal stress and child IQ was observed. These results suggest that prenatal stress does not predict child IQ, except in children from less acculturated minority groups.

Changing relations among cognitive abilities across development: implications for measurement and research

OBJECTIVE

The constructs of intelligence and executive function (EF) are commonly used in neuropsychological, cognitive, and developmental research, and in the context of clinical assessment. Yet, we have a limited understanding of the changing age-related associations among these cognitive constructs and the implications for measurement and research. The objectives of this study were to compare hypothetical models using intellectual abilities (non-age corrected scores of intelligence or IQ) and experimental measures of EF and to better understand the role of age in determining the associations between these cognitive abilities at two different periods of development. We also incorporated prediction of ADHD-related difficulties.

METHOD

We examined intellectual abilities and EF in a typically developing child sample (N = 250) and young-adult sample (N = 329). We used confirmatory factor analysis to estimate models for each developmental period: a one-factor model of general cognitive ability and a two-factor model of intelligence and EF. ADHD-related difficulties were regressed on the factors from each model.

RESULTS

Age was more strongly related to all cognitive abilities in the child sample than in the young-adult sample. In the factor analytic models, higher amounts of cognitive test score variance were explained by both models in the child sample than in the young-adult sample. Further, in the child sample, the general cognitive ability factor (combining intellectual abilities and EF) was a significant predictor of ADHD-related difficulties, but the separate intellectual ability and EF factors were not.

CONCLUSIONS

Variables highly associated with age (such as intellectual ability and EF) should not be statistically controlled when assessing cognitive constructs especially in child samples when there is rapid change in cognitive abilities.