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Effective and accurate diagnosis of attention-deficit/hyperactivity disorder (ADHD) is currently of significant interest. ADHD has been associated with multiple cortical features from structural MRI data. However, most existing learning algorithms for ADHD identification contain obvious defects, such as time-consuming training, parameters selection, etc. The aims of this study were as follows: (1) Propose an ADHD classification model using the extreme learning machine (ELM) algorithm for automatic, efficient and objective clinical ADHD diagnosis. (2) Assess the computational efficiency and the effect of sample size on both ELM and support vector machine (SVM) methods and analyze which brain segments are involved in ADHD. High-resolution three-dimensional MR images were acquired from 55 ADHD subjects and 55 healthy controls. Multiple brain measures (cortical thickness, etc.) were calculated using a fully automated procedure in the FreeSurfer software package. In total, 340 cortical features were automatically extracted from 68 brain segments with 5 basic cortical features. F-score and SFS methods were adopted to select the optimal features for ADHD classification. Both ELM and SVM were evaluated for classification accuracy using leave-one-out cross-validation. We achieved ADHD prediction accuracies of 90.18% for ELM using eleven combined features, 84.73% for SVM-Linear and 86.55% for SVM-RBF. Our results show that ELM has better computational efficiency and is more robust as sample size changes than is SVM for ADHD classification. The most pronounced differences between ADHD and healthy subjects were observed in the frontal lobe, temporal lobe, occipital lobe and insular. Our ELM-based algorithm for ADHD diagnosis performs considerably better than the traditional SVM algorithm. This result suggests that ELM may be used for the clinical diagnosis of ADHD and the investigation of different brain diseases.

Numerous small clinical trials have been carried out to study the behaviourally defined efficacy and safety of short-acting methylphenidate compared with placebo for attention-deficit disorder (ADD) in individuals aged 18 years and less. However, no meta-analyses that carefully examined these questions have been done. We reviewed the behavioural evidence from all the randomized controlled trials that compared methylphenidate and placebo, and completed a meta-analysis. We searched several electronic sources for articles published between 1981 and 1999: MEDLINE, EMBASE, PsychINFO, ERIC, CINAHL, HEALTHSTAR, Biological Abstracts, Current Contents and Dissertation Abstracts. The Cochrane Library Trials Registry and Current Controlled Trials were also consulted. A study was considered eligible for inclusion if it entailed the following: a placebo-controlled randomized trial that involved short-acting methylphenidate and participants aged 18 years or less at the start of the trial who had received any primary diagnosis of ADD that was made in a systematic and reproducible way. We included 62 randomized trials that involved a total of 2897 participants with a primary diagnosis of ADD (e.g., with or without hyperactivity). The median age of trial participants was 8.7 years, and the median \"percent male\" composition of trials was 88.1%. Most studies used a crossover design. Using the scores from 2 separate indices, this collection of trials exhibited low quality. Interventions lasted, on average, 3 weeks, with no trial lasting longer than 28 weeks. Each primary outcome (hyperactivity index) demonstrated a significant effect of methylphenidate (effect size reported by teacher 0.78, 95% confidence interval [CI] 0.64-0.91; effect size reported by parent 0.54, 95% CI 0.40-0.67). However, these apparent beneficial effects are tempered by a strong indication of publication bias and the lack of robustness of the findings, especially those involving core ADD features. Methylphenidate also has an adverse event profile that requires consideration. For example, clinicians only need to treat 4 children to identify an episode of decreased appetite. Short-acting methylphenidate has a statistically significant clinical effect in the short-term treatment of individuals with a diagnosis of ADD aged 18 years and less. However, the extension of this placebo-controlled effect beyond 4 weeks of treatment has not been demonstrated. Exact knowledge of the extent and definition of the short-term behavioural usefulness of methylphenidate is questioned.

Neurodevelopmental disorders, specifically autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) have undergone considerable diagnostic evolution in the past decade. In the United States, the current system in place is the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), whereas worldwide, the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) serves as a general medical system. This review will examine the differences in neurodevelopmental disorders between these two systems. First, we will review the important revisions made from the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) to the DSM-5, with respect to ASD and ADHD. Next, we will cover the similarities and differences between ASD and ADHD classification in the DSM-5 and the ICD-10, and how these differences may have an effect on neurodevelopmental disorder diagnostics and classification. By examining the changes made for the DSM-5 in 2013, and critiquing the current ICD-10 system, we can help to anticipate and advise on the upcoming ICD-11, due to come online in 2017. Overall, this review serves to highlight the importance of progress towards complementary diagnostic classification systems, keeping in mind the difference in tradition and purpose of the DSM and the ICD, and that these systems are dynamic and changing as more is learned about neurodevelopmental disorders and their underlying etiology. Finally this review will discuss alternative diagnostic approaches, such as the Research Domain Criteria (RDoC) initiative, which links symptom domains to underlying biological and neurological mechanisms. The incorporation of new diagnostic directions could have a great effect on treatment development and insurance coverage for neurodevelopmental disorders worldwide.

Objective : To compare the prevalence of Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) mania symptoms in a prepubertal and early adolescent bipolar disorder phenotype(PEA-BP) to those with attention deficit hyperactivity disorder (ADHD) and normal community controls (CC). Methods : To optimize generalizeability, subjects with PEA-BP and ADHD were consecutively ascertained from outpatient pediatric and psychiatric sites, and CC subjects were obtained from a random survey. All 268 subjects (93 with PEA-BP, 81 with ADHD, and 94 CC) received comprehensive, blind, baseline research assessments of mothers about their children and of children about themselves. PEA-BP was defined by DSM-IV mania with elation and/or grandiosity as one criterion to ensure that subjects had one of the two cardinal symptoms of mania and to avoid diagnosing mania only by criteria that overlapped with those for ADHD. Results : Five symptoms (i.e., elation, grandiosity, flight of ideas/racing thoughts, decreased need for sleep, and hypersexuality) provided the best discrimination of PEA-BP subjects from ADHD and CC controls. These five symptoms are also mania-specific in DSM-IV (i.e., they do not overlap with DSM-IV symptoms for ADHD). Irritability, hyperactivity, accelerated speech, and distractibility were very frequent in both PEA-BP and ADHD groups and therefore were not useful for differential diagnosis. Concurrent elation and irritability occurred in 87.1% of subjects with PEA-BP. Data on suicidality, psychosis, mixed mania, and continuous rapid cycling were also provided. Conclusion : Unlike late teenage/adult onset bipolar disorder, even subjects with PEA-BP selected for DSM-IV mania with cardinal symptoms have high rates of comorbid DSM-IV ADHD. High rates of concurrent elation and irritability were similar to those in adult mania.

Although DSM-5 stipulates that symptoms of attention-deficit hyperactivity disorder (ADHD) are the same for adults as children, clinical observations suggest that adults have more diverse deficits than children in higher-level executive functioning and emotional control. Previous psychometric analyses to evaluate these observations have been limited in ways addressed in the current study, which analyzes the structure of an expanded set of adult ADHD symptoms in three pooled US samples: a national household sample, a sample of health plan members, and a sample of adults referred for evaluation at an adult ADHD clinic. Exploratory factor analysis found four factors representing executive dysfunction/inattention (including, but not limited to, all the DSM-5 inattentive symptoms, with non-DSM symptoms having factor loadings comparable to those of DSM symptoms), hyperactivity, impulsivity, and emotional dyscontrol. Empirically-derived multivariate symptom profiles were broadly consistent with the DSM-5 inattentive-only, hyperactive/impulsive-only, and combined presentations, but with inattention including executive dysfunction/inattention and hyperactivity-only limited to hyperactivity without high symptoms of impulsivity. These results show that executive dysfunction is as central as DSM-5 symptoms to adult ADHD, while emotional dyscontrol is more distinct but nonetheless part of the combined presentation of adult ADHD.

Attention Deficit Hyperactivity Disorder (ADHD) is characterized by deficits in attention, hyperactivity, and/or impulsivity. Resting-state functional connectivity analysis has emerged as a promising approach for ADHD classification using resting-state functional magnetic resonance imaging (rs-fMRI), although with limited accuracy. Recent studies have highlighted dynamic changes in functional connectivity patterns among ADHD children. In this study, we introduce Skip-Vote-Net, a novel deep learning-based network designed for classifying ADHD from typically developing children (TDC) by leveraging dynamic connectivity analysis on rs-fMRI data collected from 222 participants included in the NYU dataset within the ADHD-200 database. Initially, for each subject, functional connectivity matrices were constructed from overlapping segments using Pearson’s correlation between mean time series of 116 regions of interest defined by the Automated Anatomical Labeling (AAL) 116 atlas. Skip-Vote-Net was then developed, employing a majority voting mechanism to classify ADHD/TDC children, as well as distinguishing between the two main subtypes: the inattentive subtype (ADHD I ) and the predominantly combined subtype (ADHD C ). The proposed method was evaluated across four classification scenarios: (1) two-class classification of ADHD from TD children using balanced data, (2) two-class classification between ADHD and TD children using unbalanced data, (3) two-class classification between ADHD I and ADHD C , and (4) three-class classification among ADHD I , ADHD C , and TD children. Using Skip-Vote-Net, we achieved mean classification accuracies of 97% ± 1.87 and 97.7% ± 2.2 for the balanced and unbalanced classification cases, respectively. Furthermore, the mean classification accuracy for discriminating between ADHD I and ADHD C reached 99.4% ± 1.21. Finally, the proposed method demonstrated an average accuracy of 98.86% ± 1.03 in classifying ADHD I , ADHD C , and TD children collectively. Our findings highlight the superior performance of Skip-Vote-Net over existing methods in the classification of ADHD, showcasing its potential as an effective diagnostic tool for identifying ADHD subtypes and distinguishing ADHD from typically developing children.

Aim Previously, we reported on the efficacy and safety of guanfacine extended‐release (GXR) in Japanese adults with attention‐deficit/hyperactivity disorder (ADHD) from a phase 3, double‐blind, placebo‐controlled, randomized trial. In this exploratory post hoc analysis, we assessed the efficacy and/or safety of GXR in the following subgroups: ADHD‐combined (ADHD‐C) and ADHD‐predominantly inattentive (ADHD‐I) subtypes, age (≥31, <31 years), sex (male, female), and body weight (≥50, <50 kg). Methods The primary efficacy endpoint was change from baseline in the Japanese version of the investigator‐rated ADHD‐Rating Scale‐IV (ADHD‐RS‐IV) with adult prompts (total scores) at week 10. Results The efficacy analysis population included 200 patients (GXR, 100; placebo, 100). ADHD‐RS‐IV total score effect sizes (GXR vs placebo) were similar across all subgroups (total population: 0.52, ADHD‐C: 0.51, ADHD‐I: 0.52, ≥31 years: 0.61, <31 years: 0.47, male: 0.50, female: 0.57). There were no major differences in the incidence/types of treatment‐emergent adverse events (TEAEs) across the subgroups. The incidence of significant TEAEs (34.3%, 10.6%) and TEAEs leading to discontinuation (34.3%, 12.1%) were approximately three times higher in females than males, respectively. The incidence of TEAEs in patients weighing <50 kg and ≥50 kg was 100% and 73.6% during dose optimization and 40% and 24.4% during the maintenance period, respectively. Conclusion Findings from this post hoc analysis in adults with ADHD support the efficacy and safety of GXR regardless of ADHD subtype, age, or sex and suggest that careful monitoring for TEAEs and GXR dose optimization is considered for all patients, as needed. In this exploratory post hoc analysis, we assessed the efficacy and/or safety of guanfacine extended release (GXR) in the following subgroups: attention‐deficit/hyperactivity disorder (ADHD) subtypes (ADHD‐combined, ADHD‐predominantly inattentive), age (≥31 years, <31 years), sex (male, female), and body weight (<50 kg, ≥50 kg). ADHD‐Rating Scale‐IV with adult prompts total score effect sizes (GXR vs placebo at 10 weeks) were similar across all subgroups. The findings support the efficacy and safety of GXR in adults regardless of ADHD subtype, age, or sex and suggest that careful monitoring for TEAEs and GXR dose optimization is considered for all patients, as needed.

This work focuses on the efficiency of the knowledge distillation approach in generating a lightweight yet powerful BERT-based model for natural language processing (NLP) applications. After the model creation, we applied the resulting model, LastBERT, to a real-world task—classifying severity levels of Attention Deficit Hyperactivity Disorder (ADHD)-related concerns from social media text data. Referring to LastBERT, a customized student BERT model, we significantly lowered model parameters from 110 million BERT base to 29 million-resulting in a model approximately 73.64% smaller. On the General Language Understanding Evaluation (GLUE) benchmark, comprising paraphrase identification, sentiment analysis, and text classification, the student model maintained strong performance across many tasks despite this reduction. The model was also used on a real-world ADHD dataset with an accuracy of 85%, F1 score of 85%, precision of 85%, and recall of 85%. When compared to DistilBERT (66 million parameters) and ClinicalBERT (110 million parameters), LastBERT demonstrated comparable performance, with DistilBERT slightly outperforming it at 87%, and ClinicalBERT achieving 86% across the same metrics. These findings highlight the LastBERT model’s capacity to classify degrees of ADHD severity properly, so it offers a useful tool for mental health professionals to assess and comprehend material produced by users on social networking platforms. The study emphasizes the possibilities of knowledge distillation to produce effective models fit for use in resource-limited conditions, hence advancing NLP and mental health diagnosis. Furthermore underlined by the considerable decrease in model size without appreciable performance loss is the lower computational resources needed for training and deployment, hence facilitating greater applicability. Especially using readily available computational tools like Google Colab and Kaggle Notebooks. This study shows the accessibility and usefulness of advanced NLP methods in pragmatic world applications.

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that often persists into adulthood, significantly impacting daily functioning and quality of life. Sex differences influence ADHD presentation, with females experiencing delayed diagnosis and distinct patterns of severity and comorbidities. Exploring these differences is essential for improving diagnostic accuracy and developing tailored interventions. This study examines ADHD severity, psychiatric comorbidities, and functional impairment by ADHD subtype and sex. This population-based study included 900 adults diagnosed with ADHD. ADHD severity, comorbidities, and functional outcomes were assessed using validated tools. Bivariate analyses and General Linear Models (GLMs) were applied to examine sex- and subtype-specific effects and their interactions. Females exhibited greater ADHD severity (  < 0.001), higher levels of depression (  = 0.003) and anxiety (  < 0.001), lower substance use (  < 0.001), poorer functioning (  = 0.039), and greater disability (  = 0.001) than males. No significant sex differences were found in ADHD subtype distribution or age of symptom onset; however, females were diagnosed with ADHD later than males (  < 0.001). The combined ADHD subtype was associated with greater clinical severity, higher levels of depression, anxiety, and impulsive symptoms, increased substance use, and greater disability. A significant interaction between sex and subtype was observed only for disability, with females in the combined subtype exhibiting the most pronounced impairment. ADHD presents differently across sexes and subtypes, with specific interactions influencing disability. These findings emphasize the importance of considering sex and ADHD subtype independently to enhance diagnostic accuracy and develop targeted treatment strategies.

The classification of neuroimaging data for the diagnosis of certain brain diseases is one of the main research goals of the neuroscience and clinical communities. In this study, we performed multiclass classification using a hierarchical extreme learning machine (H-ELM) classifier. We compared the performance of this classifier with that of a support vector machine (SVM) and basic extreme learning machine (ELM) for cortical MRI data from attention deficit/hyperactivity disorder (ADHD) patients. We used 159 structural MRI images of children from the publicly available ADHD-200 MRI dataset. The data consisted of three types, namely, typically developing (TDC), ADHD-inattentive (ADHD-I), and ADHD-combined (ADHD-C). We carried out feature selection by using standard SVM-based recursive feature elimination (RFE-SVM) that enabled us to achieve good classification accuracy (60.78%). In this study, we found the RFE-SVM feature selection approach in combination with H-ELM to effectively enable the acquisition of high multiclass classification accuracy rates for structural neuroimaging data. In addition, we found that the most important features for classification were the surface area of the superior frontal lobe, and the cortical thickness, volume, and mean surface area of the whole cortex.