Reliability and validity of a DSM-IV based ADHD screener

Identifying individuals with attention‐deficit/hyperactivity disorder based on multisite resting‐state functional magnetic resonance imaging: A radiomics analysis

Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders, characterized by symptoms of age-inappropriate inattention, hyperactivity, and impulsivity. Apart from behavioral symptoms investigated by psychiatric methods, there is no standard biological test to diagnose ADHD. This study aimed to explore whether the radiomics features based on resting-state functional magnetic resonance (rs-fMRI) have more discriminative power for the diagnosis of ADHD. The rs-fMRI of 187 subjects with ADHD and 187 healthy controls were collected from 5 sites of ADHD-200 Consortium. A total of four preprocessed rs-fMRI images including regional homogeneity (ReHo), amplitude of low-frequency fluctuation (ALFF), voxel-mirrored homotopic connectivity (VMHC) and network degree centrality (DC) were used in this study. From each of the four images, we extracted 93 radiomics features within each of 116 automated anatomical labeling brain areas, resulting in a total of 43,152 features for each subject. After dimension reduction and feature selection, 19 radiomics features were retained (5 from ALFF, 9 from ReHo, 3 from VMHC and 2 from DC). By training and optimizing a support vector machine model using the retained features of training dataset, we achieved the accuracy of 76.3% and 77.0% (areas under curve = 0.811 and 0.797) in the training and testing datasets, respectively. Our findings demonstrate that radiomics can be a novel strategy for fully utilizing rs-fMRI information to distinguish ADHD from healthy controls. The rs-fMRI-based radiomics features have the potential to be neuroimaging biomarkers for ADHD.

Cognitive Neuroscience of Attention Deficit Hyperactivity Disorder (ADHD) and Its Clinical Translation

This review focuses on the cognitive neuroscience of Attention Deficit Hyperactivity Disorder (ADHD) based on functional magnetic resonance imaging (fMRI) studies and on recent clinically relevant applications such as fMRI-based diagnostic classification or neuromodulation therapies targeting fMRI deficits with neurofeedback (NF) or brain stimulation. Meta-analyses of fMRI studies of executive functions (EFs) show that ADHD patients have cognitive-domain dissociated complex multisystem impairments in several right and left hemispheric dorsal, ventral and medial fronto-cingulo-striato-thalamic and fronto-parieto-cerebellar networks that mediate cognitive control, attention, timing and working memory (WM). There is furthermore emerging evidence for abnormalities in orbital and ventromedial prefrontal and limbic areas that mediate motivation and emotion control. In addition, poor deactivation of the default mode network (DMN) suggests an abnormal interrelationship between hypo-engaged task-positive and poorly "switched off" hyper-engaged task-negative networks, both of which are related to impaired cognition. Translational cognitive neuroscience in ADHD is still in its infancy. Pattern recognition analyses have attempted to provide diagnostic classification of ADHD using fMRI data with respectable classification accuracies of over 80%. Necessary replication studies, however, are still outstanding. Brain stimulation has been tested in heterogeneously designed, small numbered proof of concept studies targeting key frontal functional impairments in ADHD. Transcranial direct current stimulation (tDCS) appears to be promising to improve ADHD symptoms and cognitive functions based on some studies, but larger clinical trials of repeated stimulation with and without cognitive training are needed to test clinical efficacy and potential costs on non-targeted functions. Only three studies have piloted NF of fMRI-based frontal dysfunctions in ADHD using fMRI or near-infrared spectroscopy, with the two larger ones finding some improvements in cognition and symptoms, which, however, were not superior to the active control conditions, suggesting potential placebo effects. Neurotherapeutics seems attractive for ADHD due to their safety and potential longer-term neuroplastic effects, which drugs cannot offer. However, they need to be thoroughly tested for short- and longer-term clinical and cognitive efficacy and their potential for individualized treatment.

Aberrant Cross-Brain Network Interaction in Children With Attention-Deficit/Hyperactivity Disorder and Its Relation to Attention Deficits: A Multisite and Cross-Site Replication Study

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) is increasingly viewed as a disorder stemming from disturbances in large-scale brain networks, yet the exact nature of these impairments in affected children is poorly understood. We investigated a saliency-based triple-network model and tested the hypothesis that cross-network interactions between the salience network (SN), central executive network, and default mode network are dysregulated in children with ADHD. We also determined whether network dysregulation measures can differentiate children with ADHD from control subjects across multisite datasets and predict clinical symptoms.

METHODS

Functional magnetic resonance imaging data from 180 children with ADHD and control subjects from three sites in the ADHD-200 database were selected using case-control design. We investigated between-group differences in resource allocation index (RAI) (a measure of SN-centered triple network interactions), relation between RAI and ADHD symptoms, and performance of multivariate classifiers built to differentiate children with ADHD from control subjects.

RESULTS

RAI was significantly lower in children with ADHD than in control subjects. Severity of inattention symptoms was correlated with RAI. Remarkably, these findings were replicated in three independent datasets. Multivariate classifiers based on cross-network coupling measures differentiated children with ADHD from control subjects with high classification rates (72% to 83%) for each dataset. A novel cross-site classifier based on training data from one site accurately (62% to 82%) differentiated children with ADHD on test data from the two other sites.

CONCLUSIONS

Aberrant cross-network interactions between SN, central executive network, and default mode network are a reproducible feature of childhood ADHD. The triple-network model provides a novel, replicable, and parsimonious systems neuroscience framework for characterizing childhood ADHD and predicting clinical symptoms in affected children.

Classifying adolescent attention-deficit/hyperactivity disorder (ADHD) based on functional and structural imaging

Attention-deficit/hyperactivity disorder (ADHD) is a common disabling psychiatric disorder associated with consistent deficits in error processing, inhibition and regionally decreased grey matter volumes. The diagnosis is based on clinical presentation, interviews and questionnaires, which are to some degree subjective and would benefit from verification through biomarkers. Here, pattern recognition of multiple discriminative functional and structural brain patterns was applied to classify adolescents with ADHD and controls. Functional activation features in a Flanker/NoGo task probing error processing and inhibition along with structural magnetic resonance imaging data served to predict group membership using support vector machines (SVMs). The SVM pattern recognition algorithm correctly classified 77.78% of the subjects with a sensitivity and specificity of 77.78% based on error processing. Predictive regions for controls were mainly detected in core areas for error processing and attention such as the medial and dorsolateral frontal areas reflecting deficient processing in ADHD (Hart et al., in Hum Brain Mapp 35:3083-3094, 2014), and overlapped with decreased activations in patients in conventional group comparisons. Regions more predictive for ADHD patients were identified in the posterior cingulate, temporal and occipital cortex. Interestingly despite pronounced univariate group differences in inhibition-related activation and grey matter volumes the corresponding classifiers failed or only yielded a poor discrimination. The present study corroborates the potential of task-related brain activation for classification shown in previous studies. It remains to be clarified whether error processing, which performed best here, also contributes to the discrimination of useful dimensions and subtypes, different psychiatric disorders, and prediction of treatment success across studies and sites.

A New Approach to Investigate the Association between Brain Functional Connectivity and Disease Characteristics of Attention-Deficit/Hyperactivity Disorder: Topological Neuroimaging Data Analysis

Background

Attention-deficit/hyperactivity disorder (ADHD) is currently diagnosed by a diagnostic interview, mainly based on subjective reports from parents or teachers. It is necessary to develop methods that rely on objectively measureable neurobiological data to assess brain-behavior relationship in patients with ADHD. We investigated the application of a topological data analysis tool, Mapper, to analyze the brain functional connectivity data from ADHD patients.

Methods

To quantify the disease severity using the neuroimaging data, the decomposition of individual functional networks into normal and disease components by the healthy state model (HSM) was performed, and the magnitude of the disease component (MDC) was computed. Topological data analysis using Mapper was performed to distinguish children with ADHD (n = 196) from typically developing controls (TDC) (n = 214).

Results

In the topological data analysis, the partial clustering results of patients with ADHD and normal subjects were shown in a chain-like graph. In the correlation analysis, the MDC showed a significant increase with lower intelligence scores in TDC. We also found that the rates of comorbidity in ADHD significantly increased when the deviation of the functional connectivity from HSM was large. In addition, a significant correlation between ADHD symptom severity and MDC was found in part of the dataset.

Conclusions

The application of HSM and topological data analysis methods in assessing the brain functional connectivity seem to be promising tools to quantify ADHD symptom severity and to reveal the hidden relationship between clinical phenotypic variables and brain connectivity.

Predictive neurofunctional markers of attention-deficit/hyperactivity disorder based on pattern classification of temporal processing

OBJECTIVE

Attention-deficit/hyperactivity disorder (ADHD) is currently diagnosed on the basis of subjective measures, despite evidence for multi-systemic structural and neurofunctional deficits. A consistently observed neurofunctional deficit is in fine-temporal discrimination (TD). The aim of this proof-of-concept study was to examine the feasibility of distinguishing patients with ADHD from controls using multivariate pattern recognition analyses of functional magnetic resonance imaging (fMRI) data of TD.

METHOD

A total of 20 medication-naive adolescent male patients with ADHD and 20 age-matched healthy controls underwent fMRI while performing a TD task. The fMRI data were analyzed with Gaussian process classifiers to predict individual ADHD diagnosis based on brain activation patterns.

RESULTS

The pattern of brain activation correctly classified up to 80% of patients and 70% of controls, achieving an overall classification accuracy of 75%. The distributed activation networks with the highest delineation between patients and controls corresponded to a distributed network of brain regions involved in TD and typically compromised in ADHD, including inferior and dorsolateral prefrontal, insula, and parietal cortices, and the basal ganglia, anterior cingulate, and cerebellum. These regions overlapped with areas of reduced activation in patients with ADHD relative to controls in a univariate analysis, suggesting that these are dysfunctional regions.

CONCLUSIONS

We show evidence that pattern recognition analyses combined with fMRI using a disorder-sensitive task such as timing have potential in providing objective diagnostic neuroimaging biomarkers of ADHD.

Imaging the ADHD brain: disorder-specificity, medication effects and clinical translation

A plethora of magnetic resonance imaging studies have shown that ADHD is characterized by multiple functional and structural neural network abnormalities beyond the classical fronto-striatal model, including fronto-parieto-temporal, fronto-cerebellar and even fronto-limbic networks. There is evidence for a maturational delay in brain structure development which likely extends to brain function and structural and functional connectivity, but this needs corroboration by longitudinal imaging studies. Dysfunction of the ventrolateral prefrontal cortex seems to be more pronounced relative to other pediatric disorders and is also the most consistent target of acute psychostimulant medication. Future studies are likely to focus on using neuroimaging for clinical translation such as for individual diagnostic and prognostic classification and as a neurotherapy to reverse brain function abnormalities.

Pattern classification of response inhibition in ADHD: toward the development of neurobiological markers for ADHD

The diagnosis of Attention Deficit Hyperactivity Disorder (ADHD) is based on subjective measures despite evidence for multisystemic structural and functional deficits. ADHD patients have consistent neurofunctional deficits in motor response inhibition. The aim of this study was to apply pattern classification to task‐based functional magnetic resonance imaging (fMRI) of inhibition, to accurately predict the diagnostic status of ADHD. Thirty adolescent ADHD and thirty age‐matched healthy boys underwent fMRI while performing a Stop task. fMRI data were analyzed with Gaussian process classifiers (GPC), a machine learning approach, to predict individual ADHD diagnosis based on task‐based activation patterns. Traditional univariate case‐control analyses were also performed to replicate previous findings in a relatively large dataset. The pattern of brain activation correctly classified up to 90% of patients and 63% of controls, achieving an overall classification accuracy of 77%. The regions of the discriminative network most predictive of controls included later developing lateral prefrontal, striatal, and temporo‐parietal areas that mediate inhibition, while regions most predictive of ADHD were in earlier developing ventromedial fronto‐limbic regions, which furthermore correlated with symptom severity. Univariate analysis showed reduced activation in ADHD in bilateral ventrolateral prefrontal, striatal, and temporo‐parietal regions that overlapped with areas predictive of controls, suggesting the latter are dysfunctional areas in ADHD. We show that significant individual classification of ADHD patients of 77% can be achieved using whole brain pattern analysis of task‐based fMRI inhibition data, suggesting that multivariate pattern recognition analyses of inhibition networks can provide objective diagnostic neuroimaging biomarkers of ADHD. Hum Brain Mapp 35:3083–3094, 2014. © 2013 Wiley Periodicals, Inc.

Tourette syndrome-associated psychopathology: roles of comorbid attention-deficit hyperactivity disorder and obsessive-compulsive disorder

OBJECTIVE

Individuals with Tourette syndrome (TS) often display comorbid symptoms of attention-deficit hyperactivity disorder (ADHD) and obsessive-compulsive disorder (OCD), as well as externalizing and internalizing behaviors. This study was aimed to examine the impacts of tic severity, ADHD symptoms, and OCD on internalizing (e.g., anxiety) and externalizing (e.g., aggression) psychopathology.

METHODS

Using linear regressions, we examined how tics, ADHD, and OCD symptoms predicted the externalization and internalization behaviors measured by the Child Behavior Checklist in a clinical sample of children and adolescents with TS. In addition, Child Behavior Checklist scales were compared among children with TS without ADHD, TS and ADHD, ADHD without TS, and unaffected control group.

RESULTS

In the TS group, externalizing behaviors were predicted by tic severity, inattention, and hyperactivity/impulsivity but not by OCD symptoms, whereas internalizing behaviors were predicted by inattention and OCD symptoms but not by tic severity or hyperactivity/impulsivity. Comparison among different clinical groups revealed main effects of TS and ADHD on both externalizing and internalizing behaviors.

CONCLUSION

These findings suggest that tics, ADHD, and OCD symptoms differentially explain the variance in externalizing and internalizing behavioral problems in individuals with TS. In addition, the data support the notion that TS is itself a risk factor for behavioral problems, mandating that children with TS even without ADHD and OCD still need to be assessed and treated for psychopathology.

Using the Conners' Teacher Rating Scale-Revised in school children referred for assessment

OBJECTIVE

Predictive validity of the Conners' Teacher Rating Scale-Revised (CTRS-R) was evaluated against a semi-structured clinical teacher interview in school children referred for diagnostic assessment of attention-deficit hyperactivity disorder (ADHD). We hypothesized that extreme scale values would increase diagnostic certainty and that classification errors would be associated with comorbid conditions.

METHOD

Children (n = 1038), aged 6 to 12 years, were screened using the CTRS-R and their teachers were interviewed. Three levels of T scores on the 3 Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) subscales of the CTRS-R were compared with DSM-IV symptom thresholds by interview. Where subscale scores and interviews showed highest agreement, presence of comorbid disruptive behavioural disorders, reading disability, language impairment, and low IQ were investigated for children classified correctly, compared with incorrectly.

RESULTS

T scores of 60 and above on all CTRS-R DSM-IV subscales offered high sensitivity, from 91% to 94%. Only on subscales M (hyperactive-impulsive) and N (total) did T scores of less than 60 offer posttest probabilities of less than 10%, confirming that a child does not reach diagnostic threshold by interview. T scores of 80 and more offered high specificity, from 88% to 93%, but did not provide high posttest probabilities that children reach diagnostic criteria. Classification errors were associated with more language impairment among false positives than true positives on the M (18.9%, compared with 11.3%, P = 0.04) and N (19.0%, compared with 9.5%, P = 0.023) subscales, and more reading disabilities among false positives than among true positives on the N subscale (35.2%, compared with 21.6%, P = 0.009).

CONCLUSIONS

The ability of the CTRS-R to predict whether clinically referred children reach DSM-IV criteria for ADHD at school is limited.

The utility of a continuous performance test embedded in virtual reality in measuring ADHD-related deficits

OBJECTIVE

Continuous performance tasks (CPT) are popular in the diagnostic process of Attention Deficit/Hyperactivity Disorder (ADHD), providing an objective measure of attention for a disorder with otherwise subjective criteria. Aims of the study were to: (1) compare the performance of children with ADHD on a CPT embedded within a virtual reality classroom (VR-CPT) to the currently used Test of Variables of Attention (TOVA) CPT, and (2) assess how the VR environment is experienced.

METHODS

Thirty-seven boys, 9 to 17 years, with (n = 20) and without ADHD (n = 17) underwent 3 CPT's: VR-CPT, the same CPT without VR (No VR-CPT) and the TOVA. Immediately following CPT, subjects described their subjective experiences on the Short Feedback Questionnaire. Results were analyzed using analysis of variance with repeated measures.

RESULTS

Children with ADHD performed poorer on all CPT's. The VR-CPT showed similar effect sizes to the TOVA. Subjective feelings of enjoyment were most positive for VR-CPT.

CONCLUSION

The VR-CPT is a sensitive and user-friendly assessment tool to aid diagnosis in ADHD.

The shifting subtypes of ADHD: classification depends on how symptom reports are combined

Research on the correlates of ADHD subtypes has yielded inconsistent findings, perhaps because the procedures used to define subtypes vary across studies. We examined this possibility by investigating whether the ADHD subtype distribution in a community sample was sensitive to different methods for combining informant data. We conducted a study to screen all children in grades 1-5 (N = 7847) in a North Carolina County for ADHD. Teachers completed a DSM-IV behavior rating scale and parents completed a structured telephone interview. We found substantial differences in the distribution of ADHD subtypes depending on whether one or both sources were used to define the subtypes. When parent and teacher data were combined, the procedures used substantially influenced subtype distribution. We conclude the ADHD subtype distribution is sensitive to how symptom information is combined and that standardization of the subtyping process is required to advance our understanding of the correlates of different ADHD subtypes.

Neurodevelopmental characteristics of children with learning impairments classified according to the double-deficit hypothesis

The double-deficit model has been examined primarily in relation to reading. We investigated whether children classified according to the double-deficit model would exhibit differences in other neuropsychological domains. Children referred for learning problems (N = 188), ages 7 to 11, were classified by double-deficit subtype. Only three of the four groups predicted by the model could be identified. There were no group differences in IQ or attention problems. The three groups showed different neuropsychological profiles, involving functional domains other than reading and language. Differences also emerged in nonverbal low-level information processing. The double-deficit group was generally most severely affected. The double-deficit groupings identify children with different neuropsychological profiles and variation in the efficiency of basic online information processing, extending beyond the oral and written language domain.

Neurobehavioral factors associated with referral for learning problems in a community sample: evidence for an adaptational model for learning disorders

We evaluated community general education (CGE; n = 178), community special education (CSE; n = 30) and hospital-referred (HR, n = 145) children (ages 7-6 to 11-11) prospectively over a 2-year period. During this period, 17 CGE children were referred for evaluation (community referred; CR). Prior to referral, CR children performed more poorly than community-nonreferred (CNR) children on cognitive ability, academic achievement, attention problems, and information processing. CR group performance was equivalent to that of CSE and HR groups, but HR children showed poorer academic achievement. Referred children performed more poorly on all measures than nonreferred, whether they met formal diagnostic criteria for a learning disorder or not. Learning disorders may be better conceptualized as a context-dependent problem of functional adaptation than as a disability analogous to physical disabilities, raising questions about the validity of using psychometric test scores as the criterion for identification.

The developmental course of processing speed in children with and without learning disabilities

This study contrasted the development of processing speed in children with and without learning disabilities. We examined whether the same global mechanism presumed to be responsible for the normal developmental improvement in processing speed might also be associated with the processing speed deficiencies observed in children with learning impairments. One hundred and twenty-two children with learning disabilities in reading and/or math and 206 non-disabled community controls participated. There were no differences in relation of age to the development of processing speed for children with and without learning disabilities. We interpreted these results as suggesting that the underlying etiologies for the normal developmental change in processing speed and for the relative deficiencies in processing speed seen among children with learning disabilities were different.

Motor sequence learning and reading ability: is poor reading associated with sequencing deficits?

Although it is widely assumed that children with learning disabilities have "sequencing problems," these have not been well specified. A non-verbal serial reaction time (SRT) paradigm was used to evaluate motor sequence learning in 422 children between ages 7 and 11 in relation to reading, cognitive ability level, and attention problems. The children demonstrated the response profile typically associated with motor sequence learning, but the component of the profile indicative of implicit sequence learning was not reliably associated with any of the predictors. Cognitive ability predicted overall response time; cognitive ability, reading, and attention problems each predicted overall accuracy. Explicit learning was predicted by cognitive ability, but not by reading or attention problems. Thus, we found no evidence that poor reading is preferentially associated with a domain general deficit in sequential learning.

Preadolescent girls with attention-deficit/hyperactivity disorder: I. Background characteristics, comorbidity, cognitive and social functioning, and parenting practices

This study investigated a diverse sample of girls (6-12 years of age) with attention-deficit/hyperactivity disorder (ADHD), combined type (n = 93) and inattentive type (n = 47), plus age- and ethnicity-matched comparison girls (n = 88), who participated in research summer programs. Speech and language problems, grade retention, and adoption characterized the ADHD sample; documented abuse characterized the combined type. Girls with ADHD showed dysfunction in terms of externalizing and internalizing behaviors and comorbidities, cognitive and academic performance, authoritarian parenting, and peer status. The inattentive type was more socially isolated but less rejected by peers than the combined type. ADHD-related impairment was independent of age and disruptive comorbidity. Further examination of processes related to psychopathology and competencies in girls with ADHD is needed.

Prevalence of attention deficit/hyperactivity disorder among adults in obesity treatment

BACKGROUND

Bariatric patients showing poor "focus" during treatment more often failed to lose weight or maintain reduced weight. Evaluation of these patients identified a number having attention deficit/hyperactivity disorder (ADHD), evidently a potent factor limiting successful weight control. After searches found no published reports describing comorbid ADHD and obesity, this report was conceived to begin exploring the prevalence and characteristics of these patients.

METHOD

Clinical records of 215 patients receiving obesity treatment during 2000 were reviewed. Data collected and analyzed included age, sex, beginning and ending body mass index (BMI), number of clinic visits, months of treatment, and diagnostic category (ADHD, some ADHD symptoms, non-ADHD). DSM-IV criteria were used, except age of onset was modified to <= 12 years.

RESULTS

Whole sample ADHD prevalence was 27.4% (CI:21.1,32.9), but 42.6% (CI: 36.3% to 48.9%) for BMI >= 40. Mean weight loss among obese patients with ADHD (OB+ADHD) was 2.6 BMI (kg/m2) vs. 4.0 for non-ADHD (NAD) (p < 0.002). For BMI >= 40, OB+ADHD had BMI loss 2.9 vs. 7.0 (NAD) (p < 0.004). OB+ADHD had more clinic visits, with a trend toward longer treatment duration.

CONCLUSIONS

ADHD was highly prevalent among obese patients and highest in those with extreme obesity. Comorbid obesity and ADHD symptoms rendered treatment less successful compared to NAD counterparts. Reasons for the comorbidity are unknown, but may involve brain dopamine or insulin receptor activity. If replicated in further studies, these findings have important implications for treatment of severe and extreme obesity.

Information processing deficits in children with attention-deficit/hyperactivity disorder, inattentive type, and children with reading disability

We examined the information processing capabilities of children diagnosed with the inattentive subtype of attention-deficit/hyperactivity disorder (ADHD) who had been characterized as having a sluggish cognitive tempo. Children referred for school-related problems (n = 81) and nonreferred community controls (n = 149) participated. Of the referred children, 24 met criteria for ADHD, 42 met criteria for reading disability (RD), and 9 of these were comorbid for RD and ADHD. Children with ADHD differed from those without ADHD on a visual search task but not on an auditory processing task; the reverse was true for children with RD. Decomposition of the visual search task into component operations demonstrated that children in the ADHD group had a slow processing rate that was not attributable to inattention. The children with ADHD were not globally poor at information processing or inattentive, but they demonstrated diminished speed of visual processing.