•Fusing spatial and temporal features is effective in fMRI classification.•Models considering sequence achieve better results than timing invariant models.•fMRI scans longer than one minute contain ...enough information for ADHD diagnosis.•deep-learned features outperform hand-crafted features in fMRI analysis.
Attention Deficit/Hyperactivity Disorder (ADHD) is one kind of neurodevelopmental disorders common in children. Due to the complexity of the pathological mechanism, there is a lack of objective diagnostic methods up to now. This paper aimed to propose automatic ADHD diagnostic method using resting state functional magnetic resonance imaging (rs-fMRI) data with the spatio-temporal deep learning models. Unlike traditional methods, this paper constructed a deep learning method called 4-D CNN based on granular computing which were trained based on derivative changes in entropy, and can calculate granularity at a coarse level by stacking layers. Considering the structure of rs-fMRI as time-series 3-D frames, several models of spatial and temporal granular computing and fusion were proposed, including feature pooling, long short-term memory (LSTM) and spatio-temporal convolution. This paper introduced an approach to augment dataset which can sample one subject's rs-fMRI frames into several relatively short term pieces with a fixed stride. The public dataset of ADHD-200 Consortium was used to train and validate our method. And the results of evaluations showed that our method outperformed traditional methods on the dataset (accuracy: 71.3%, AUC: 0.80). Therefore, our 4-D CNN method can be used to build more accurate automatic assistant diagnosis tool of ADHD.
To provide an overview of treatments in the pipeline for adults with attention-deficit/hyperactivity disorder (ADHD), we searched https://clinicaltrials.gov/and and ...https://www.clinicaltrialsregister.eu/ from 01/01/2010–10/18/2023 for ongoing or completed phase 2 or 3 randomised controlled trials (RCTs), assessing pharmacological or non-pharmacological interventions for adults with ADHD with no current regulatory approval. We found 90 eligible RCTs. Of these, 24 (27 %) reported results with statistical analysis for primary efficacy endpoints. While several pharmacological and non-pharmacological interventions had evidence of superiority compared to the control condition from a single RCT, centanafadine (norepinephrine, dopamine, and serotonin re-uptake inhibitor) was the only treatment with evidence of efficacy on ADHD core symptoms (small effect size=0.28–0.40) replicated in at least one additional RCT, alongside reasonable tolerability. Overall, the body of ongoing RCTs in adults with ADHD is insufficient, without any intervention on the horizon to match the efficacy of stimulant treatment or atomoxetine and with better tolerability profile. Additional effective and well tolerated treatments for adults with ADHD require development and testing.
•There is a need for additional treatments for ADHD in adults.•We found 90 relevant RCTs registered in the past decade.•Centanafadine was the only treatment with replicated evidence of efficacy (small).•Additional treatments for adults with ADHD should be developed.
We comprehensively reviewed research assessing differences in attention-deficit hyperactivity disorder (ADHD) subtypes to examine the possibility that ADHD/combined type (ADHD/Q and ...ADHD/predominantly inattentive type (ADHD/I) are distinct and unrelated disorders. Differences among subtypes were examined along dimensions identified as being important in documenting the distinctiveness of two disorders. These include essential and associated features, demographics, measures of cognitive and neuropsychological functioning, family history, treatment response, and prognosis. Important differences among subtypes were found in several areas of study, supporting the conclusion that ADHD/C and ADHD/I may best be characterized as distinct disorders. We identify major limitations of the available research and present future directions for research.
We conducted a systematic review and meta-analysis to quantify the effect of attention-deficit/hyperactivity disorder (ADHD) medication on quality of life (QoL), and to understand whether this effect ...differs between stimulants and non-stimulants.
From the dataset of a published network meta-analysis (Cortese et al., 20181), updated on 27th February 2023 (https://med-adhd.org/), we identified randomized controlled trials (RCTs) of ADHD medications for individuals aged 6 years or more with a diagnosis of ADHD based on the DSM (from third to fifth editions) or the International Classification of Diseases (ICD; ninth or tenth revision), reporting data on QoL (measured with a validated scale). The risk of bias for each RCTs was assessed using the Cochrane Risk of Bias tool 2. Multi-level meta-analytic models were conducted with R 4.3.1.
We included 17 RCTs (5,388 participants in total; 56% randomized to active medication) in the meta-analyses. We found that amphetamines (Hedges g = 0.51, 95% CI = 0.08, 0.94), methylphenidate (0.38; 0.23, 0.54), and atomoxetine (0.30; 0.19, 0.40) were significantly more efficacious than placebo in improving QoL in people with ADHD, with moderate effect size. For atomoxetine, these effects were not moderated by the length of intervention, and did not differ between children/adolescents and adults.
In addition to being efficacious in reducing ADHD core symptom severity, both stimulant and non-stimulant medications are efficacious in improving QoL in people with ADHD, albeit with lower effect sizes. Future research should explore whether, and to what degree, combining pharmacological and non-pharmacological interventions is likely to further improve QoL in people with ADHD.
Effects of pharmacological treatment for ADHD on quality of life: a systematic review and meta-analysis; https://osf.io/;qvgps.
•A new method is proposed to convert EEG data to an image-like data for deep learning models.•What has been learned by CNN can be revealed, and the learning capacity of DL models is amazing.•The ...proposed framework can accurately identify ASD using EEG data and some practical suggestions are given based on validation results.
The convolutional neural network (CNN) is a mainstream deep learning (DL) algorithm. However, the application of DL techniques in attention-deficit/hyperactivity disorder (ADHD) studies is still limited. Electroencephalography (EEG) is an informative neuroimaging tool. In this study, we propose a DL framework for the ADHD identification problem by combining an EEG-based brain network with the CNN. By reorganizing the order of the channels, we proposed a new form of the connectivity matrix to adapt the concept of the convolution operation of the CNN. The correlations between the deep features derived from the CNN models and 13 hand-crafted measures of the brain network were also analyzed. We collected EEG data from 50 children with ADHD (9 girls, mean age: 10.44 ± 0.75) and 51 handedness- and age-matched controls, and we used mutual information (MI) to quantify the synchronization between channels. We demonstrated the feasibility of the framework and discussed some critical concerns in the application of the framework. Some of the practical suggestions were also given based on the validation results. The proposed framework achieved a convincing performance with an accuracy of 94.67% on the test data. We also validated the validity of the form of the connectivity matrix, which enabled the models to achieve better performance. This finding suggests that the data representation in the DL framework is important. Seventeen deep features showed significant between-group differences, and had significant correlations with hand-crafted measures, thereby reflecting the amazing learning ability of the method for finding the deviations in the brain network of children with ADHD. The proposed framework is broadly applicable to the ADHD identification problem. Nevertheless, the validation of this methodology with a large and well-matched sample of children is needed in the future.
Controversy abounds regarding the symptom dimensions of attention problems, impulsivity, and hyperactivity, developmentally extreme and impairing levels of which compose the diagnostic category of ...attention deficit hyperactivity disorder (ADHD). I highlight causal factors, underlying mechanisms, developmental trajectories, and female manifestations of ADHD, integrating the psychobiological underpinnings of this syndrome with contextual factors related to its clinical presentation, impairments, and soaring increases in diagnosed prevalence. Indeed, despite strong heritability, ADHD is expressed via transactional patterns of influence linked to family-, school-, peer-, neighborhood-, and policy-related factors. Moreover, intervention strategies must take into account both pharmacologic and behavioral modalities if the goal is to enhance competencies, rather than symptom reduction per se. A comprehensive understanding of ADHD mandates multiple levels of analysis-spanning genes, neurotransmission, brain pathways, individual skill levels, family socialization, peer relationships, and educational and cultural forces-which must be integrated and synthesized to surpass reductionist accounts, reduce stigma, and maximize the impact of prevention- and intervention-related efforts.
Objective: To examine how ADHD evaluations are documented for postsecondary students requesting disability eligibility. Method: A total of 100 psychological reports submitted for eligibility ...determination were coded for documentation of Diagnostic and Statistical Manual of Mental Disorders (DSM) criteria, methods and instruments used in the evaluations, and recommended academic accommodations. Results: Results showed that a minimal number of reports (≤1%) documented that students met all DSM criteria for ADHD. Psychologists rarely documented childhood impairment, symptoms across settings, or the use of rule-outs. Symptom severity was emphasized over current impairment. The majority of psychologists utilized a multi-informant, multi-method evaluation approach, but certain methods (e.g., symptom validity tests, record reviews) were limited in use. Most reports included recommendations for academic accommodations, with extended time being the most common (72%). Conclusion: This study raises awareness to the aspects of adequate ADHD evaluation and subsequent documentation that can be improved by psychologists. Recommendations are made regarding valid documentation of ADHD for disability determination purposes.
•High-resolution pollution estimates were successfully combined with cohort data.•Age-12 pollution exposure was not associated with age-12 mental health problems.•But age-12 pollution exposure was ...significantly associated with age-18 depression.•Associations with depression held even after controlling for common risk factors.•Elevated odds of age-18 conduct disorder among children exposed to air pollution.
Air pollution is a worldwide environmental health issue. Increasingly, reports suggest that poor air quality may be associated with mental health problems, but these studies often use global measures and rarely focus on early development when psychopathology commonly emerges. To address this, we combined high-resolution air pollution exposure estimates and prospectively-collected phenotypic data to explore concurrent and longitudinal associations between air pollutants of major concern in urban areas and mental health problems in childhood and adolescence. Exploratory analyses were conducted on 284 London-based children from the Environmental Risk (E-Risk) Longitudinal Twin Study. Exposure to annualized PM2.5 and NO2 concentrations was estimated at address-level when children were aged 12. Symptoms of anxiety, depression, conduct disorder, and attention-deficit hyperactivity disorder were assessed at ages 12 and 18. Psychiatric diagnoses were ascertained from interviews with the participants at age 18. We found no associations between age-12 pollution exposure and concurrent mental health problems. However, age-12 pollution estimates were significantly associated with increased odds of major depressive disorder at age 18, even after controlling for common risk factors. This study demonstrates the potential utility of incorporating high-resolution pollution estimates into large epidemiological cohorts to robustly investigate associations between air pollution and youth mental health.
Objectives:
To examine how the concept of prevention is applicable to adolescent ADHD, which preventive interventions may be feasible, and which methods can be used to evaluate effectiveness.
Method:
...Following a literature search for prevention clinical trials relevant to adolescent ADHD, selected studies are critically reviewed to identify suitable targets and promising interventions.
Results:
There is some evidence from controlled studies that interventions delivered to prepubertal children at high risk for ADHD or diagnosed with ADHD may decrease the incidence or persistence of ADHD in adolescence. Uncontrolled follow-up of clinical samples and population studies suggest that treatment of adolescents with ADHD can decrease the risk for several negative functional outcomes in youth. A controlled trial found a specific cognitive training intervention to decrease risky driving.
Conclusions:
Prevention of ADHD and associated negative outcomes is possible and of high clinical relevance. Assessing prevention effects is methodologically challenging, but feasible.
Adult attention-deficit/hyperactivity disorder (ADHD) is an early-onset disorder with many functional impairments and psychiatric comorbidities. Although no treatment fully mitigates impairments ...associated with ADHD, effective management is possible with pharmacologic and nonpharmacologic treatments. The etiology and pathophysiology of ADHD are remarkably complex and the disorder is continuously distributed in the population. While these findings have been well documented in studies with predominantly white samples, ADHD may affect racial and ethnic minorities differentially, given diagnostic and treatment disparities. This review provides an updated overview of the epidemiology, etiology, neurobiology, and neuropharmacology of ADHD, addressing racial and ethnic disparities whereby data are available.
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