Diagnosis Of Attention Deficit Hyperactivity Disorder Research Articles

Are the concepts of "chronic mania" in German-speaking psychiatry around 1900 early contributions to the disorder of the adult form of attention deficit hyperactivity disorder (ADHD)?

The adult form of attention deficit hyperactivity disorder (ADHD) has increasingly become afocus of adult psychiatry. Despite long-established diagnostic criteria and specific therapeutic approaches for the disorder, the common misconception that ADHD is a"fad" has persisted. Examining the history of psychiatry can make an educational contribution by showing that the adult form of ADHD is a continuously existing illness phenomenon. The present study examines the discussion of sometimes prominent authors about "chronic mania" in German-speaking psychiatry around 1900. The individual concepts were analyzed for their content and compared with each other and with modern diagnostic manuals for adult ADHD. The aim of this work is to question and discuss whether these "chronic-manic concepts" are part of the conceptual history of adult ADHD and whether agap in the history of this disorder can be filled with their help. It is concluded that in the early twentieth century neurologists reported and discussed about patients who nowadays would almost certainly receive the diagnosis of ADHD. The psychiatrists had difficulty in classifying this disorder into their nosological schemes but their "chronic-manic concepts" show clear parallels to the current diagnostic criteria for adult ADHD and its symptoms.

Risk of traumatic brain injury among patients with ADHD and their unaffected siblings.

As the relationship between attention deficit hyperactivity disorder (ADHD) and traumatic brain injury (TBI) is gaining increasing attention, the TBI risk in patients with ADHD, unaffected siblings of ADHD probands, and non-ADHD controls remains unclear. Overall, 18,645 patients with ADHD, 18,880 unaffected siblings of ADHD probands, and 188,800 age-/sex-matched controls were followed up from enrollment to the end of 2011. The cases of TBI and TBI requiring hospitalization were identified during follow-up. Patients with ADHD (hazard ratio [HR]: 1.57) and unaffected siblings (HR: 1.20) had an increased risk of any TBI compared with non-ADHD controls. Surprisingly, the likelihood of developing TBI requiring hospitalization during follow-up was higher in the unaffected siblings group (HR: 1.21) than in the control group, whereas it was lower in the ADHD probands group (HR: 0.86). Patients with ADHD and unaffected siblings of ADHD probands were more likely to develop any TBI during follow-up than controls. Unaffected siblings of patients with ADHD exhibited the highest risk of subsequent TBI requiring hospitalization compared with patients with ADHD and healthy controls. Therefore, TBI risk in patients with ADHD and their unaffected siblings would require further investigation. ADHD diagnosis and ADHD trait are associated with risk of traumatic brain injury (TBI). Both patients with ADHD and their unaffected siblings were more likely to develop TBI during the follow-up compared with the control group. TBI requiring hospitalization occurred more in the sibling group than in the proband group. TBI risk should be closely monitored among unaffected siblings of patients with ADHD.

Electroencephalogram-Based ConvMixer Architecture for Recognizing Attention Deficit Hyperactivity Disorder in Children

Attention deficit hyperactivity disorder (ADHD) is a neuro-developmental disorder that affects approximately 5–10% of school-aged children worldwide. Early diagnosis and intervention are essential to improve the quality of life of patients and their families. In this study, we propose ConvMixer-ECA, a novel deep learning architecture that combines ConvMixer with efficient channel attention (ECA) blocks for the accurate diagnosis of ADHD using electroencephalogram (EEG) signals. The model was trained and evaluated using EEG recordings from 60 healthy children and 61 children with ADHD. A series of experiments were conducted to evaluate the performance of the ConvMixer-ECA. The results showed that the ConvMixer-ECA performed well in ADHD recognition with 94.52% accuracy. The incorporation of attentional mechanisms, in particular ECA, improved the performance of ConvMixer; it outperformed other attention-based variants. In addition, ConvMixer-ECA outperformed state-of-the-art deep learning models including EEGNet, CNN, RNN, LSTM, and GRU. t-SNE visualization of the output of this model layer validated the effectiveness of ConvMixer-ECA in capturing the underlying patterns and features that separate ADHD from typically developing individuals through hierarchical feature learning. These outcomes demonstrate the potential of ConvMixer-ECA as a valuable tool to assist clinicians in the early diagnosis and intervention of ADHD in children.

Access to early diagnosis for attention-deficit/hyperactivity disorder among children and adolescents in Mexico City at specialized mental health services

BackgroundIn Mexico, this pioneering research was undertaken to assess the accessibility of timely diagnosis of Dyads [Children and adolescents with Attention Deficit Hyperactivity Disorder (ADHD) and their primary caregivers] at specialized mental health services. The study was conducted in two phases. The first phase involved designing an “Access Pathway” aimed to identify barriers and facilitators for ADHD diagnosis; several barriers, with only the teacher being identified as a facilitator. In the second phase, the study aimed to determine the time taken for dyads, to obtain a timely diagnosis at each stage of the Access Pathway. As well as identify any disparities based on gender and socioeconomic factors that might affect the age at which children can access a timely diagnosis.MethodIn a retrospective cohort study, 177 dyads participated. To collect data, the Acceda Survey was used, based on the robust Conceptual Model Levesque, 2013. The survey consisted of 48 questions that were both dichotomous and polytomous allowing the creation of an Access Pathway that included five stages: the age of perception, the age of search, the age of first contact with a mental health professional, the age of arrival at the host hospital, and the age of diagnosis. The data was meticulously analyzed using a comprehensive descriptive approach and a nonparametric multivariate approach by sex, followed by post-hoc Mann-Whitney’s U tests. Demographic factors were evaluated using univariable and multivariable Cox regression analyses.Results71% of dyads experienced a late, significantly late, or highly late diagnosis of ADHD. Girls were detected one year later than boys. Both boys and girls took a year to seek specialized mental health care and an additional year to receive a formal specialized diagnosis. Children with more siblings had longer delays in diagnosis, while caregivers with formal employment were found to help obtain timely diagnoses.ConclusionsOur findings suggest starting the Access Pathway where signs and symptoms of ADHD are detected, particularly at school, to prevent children from suffering consequences. Mental health school-based service models have been successfully tested in other latitudes, making them a viable option to shorten the time to obtain a timely diagnosis.

Dispensing of attention-deficit hyperactivity disorder medications for adults in Aotearoa New Zealand

aim: To report dispensing trends for attention-deficit hyperactivity disorder (ADHD) in Aotearoa New Zealand, focussing on adults in order to highlight increasing demand for ADHD treatment by adults and to prompt discussion. method: Demographic and dispensing data for ADHD were obtained from the Pharmaceutical Collection between the years 2006 and 2022. This was stratified according to child (<18 years) and adult (≥18 years) populations. Population dispensing rates for methylphenidate and atomoxetine were calculated. Key findings are reported to reveal demographic and dispensing trends for medication treated ADHD in Aotearoa New Zealand. results: More males are dispensed ADHD medication than females, although this is less evident for adults (54.8% male). Māori adults are dispensed ADHD medication at a lower rate (10.1%) than Māori children (22.9%). There was a 10-fold increase in dispensing of ADHD medication for adults compared to a three-fold increase for children over the study period. New dispensing for adults doubled between 2011 and 2022. conclusion: Medication treatment for adult ADHD is increasing in Aotearoa New Zealand and includes treatment for persisting childhood ADHD and new diagnoses made in adulthood. Despite increases, dispensing rates for ADHD remain lower than prevalence estimates, suggesting a significant treatment gap. Addressing the treatment gap for ADHD may reduce negative effects of ADHD, but wider social influences should also be considered.

ADHD diagnosis guided by functional brain networks combined with domain knowledge

Utilizing functional magnetic resonance imaging (fMRI) to model functional brain networks (FBNs) is increasingly prominent in attention-deficit/hyperactivity disorder (ADHD) research, revealing neural impact and mechanisms through the exploration of activated brain regions. However, current FBNs-based methods face two major challenges. The primary challenge stems from the limitations of existing modeling methods in accurately capturing both regional correlations and long-distance dependencies (LDDs) within the dynamic brain, thereby affecting the diagnostic accuracy of FBNs as biomarkers. Additionally, limited sample size and class imbalance also pose a challenge to the learning performance of the model. To address the issues, we propose an automated diagnostic framework, which integrates modeling, multimodal fusion, and classification into a unified process. It aims to extract representative FBNs and efficiently incorporate domain knowledge to guide ADHD classification. Our work mainly includes three-fold: 1) A multi-head attention-based region-enhancement module (MAREM) is designed to simultaneously capture regional correlations and LDDs across the entire sequence of brain activity, which facilitates the construction of representative FBNs. 2) The multimodal supplementary learning module (MSLM) is proposed to integrate domain knowledge from phenotype data with FBNs from neuroimaging data, achieving information complementarity and alleviating the problems of insufficient medical data and unbalanced sample categories. 3) An ADHD automatic diagnosis framework guided by FBNs and domain knowledge (ADF-FAD) is proposed to help doctors make more accurate decisions, which is applied to the ADHD-200 dataset to confirm its effectiveness. The results indicate that the FBNs extracted by MAREM perform well in modeling and classification. After with MSLM, the model achieves accuracy of 92.4%, 74.4%, and 80% at NYU, PU, and KKI, respectively, demonstrating its ability to effectively capture crucial information related to ADHD diagnosis. Codes are available at https://github.com/zhuimengxuebao/ADF-FAD.

Association of Childhood Attention Deficit/Hyperactivity Disorder With Adolescent Traditional and Cyberbullying Victimization in the Digital Generation: A Population-Based Cohort Study

PurposeThis population-based cohort study aimed to examine the association with childhood attention deficit/hyperactivity disorder (ADHD) and bullying experiences during adolescence among Digital Generation individuals, exploring both traditional and cyberbullying. MethodsThis study included data from 15,240 participants, collected from the Taiwan Adolescent to Adult Longitudinal Study project. Participants, initially in seventh and 10th grade in 2015, were selected through a multistage stratified sampling approach. Self-report questionnaires assessed traditional and cyberbullying victimization experiences during adolescence, with 5-year longitudinal follow-up. Childhood ADHD diagnoses were identified by linking data to Taiwan's National Health Insurance Research Database from 2000 to 2015. Logistic regression models were employed to examine the relationship between childhood ADHD and bullying victimization while controlling for relevant covariates. ResultsIndividuals diagnosed with childhood ADHD exhibited a significantly higher likelihood of experiencing bullying during adolescence (adjusted odds ratio (aOR) = 1.52, 95% confidence interval (CI): 1.28–1.80). This association extended to various forms of bullying, including physical (aOR = 1.42, 95% CI: 1.20–1.68), verbal (aOR = 1.42, 95% CI: 1.20–1.67), relational (aOR = 1.45, 95% CI: 1.22–1.71), and cyber (aOR = 1.35, 95% CI: 1.14–1.61). Additional factors positively associated with bullying victimization included male, binge drinking, and depression, while a positive campus atmosphere was protective against bullying. However, there is no evidence for interactions between these factors and ADHD in their associations with bullying. DiscussionChildhood ADHD increases the risk of both traditional and cyberbullying during adolescence. Recognizing this risk is essential for targeted interventions and further research on underlying mechanisms.

The WHO Adult ADHD self-report Scale used in a clinical sample of patients with overlapping symptoms - psychometric properties of and scoring methods for the Swedish translation

Background The WHO Adult ADHD Self-report Scale (ASRSv1.1 and ASRS-S) is used for screening for attention-deficit/hyperactivity disorder (ADHD). The capacity of the Swedish version of the scale to discriminate ADHD from borderline personality disorder (BPD) and bipolar disorder (BP) has not been tested. Aim Evaluate scoring methods, psychometric properties, and diagnostic accuracy of the Swedish versions of ASRSv1.1/ASRS-S in a group of patients with ADHD and/or BPD and/or BP. Method A total of 151 young adult psychiatric patients diagnosed with ADHD, BPD and/or BD completed ASRSv1.1 and the Wender Utah Rating Scale (WURS) for ADHD symptoms, and the Sheehan Disability Scale (SDS) for functional impairment. ADHD diagnoses were assessed with the Schedule for Affective Disorders and Schizophrenia (K-SADS) interview. Both versions of the scale were analysed through dichotomised and non-dichotomised scoring for diagnostic accuracy analysis. Results The internal consistency for ASRSv1.1/ASRS-S was satisfactory with α 0.913 and 0.743, respectively. The two-factor structure of the ASRSv1.1 and the one factor structure of ASRS-S were supported by the confirmatory factor analyses. A strong positive correlation was found between ASRSv1.1 and WURS and a moderate level of correlation was found between ASRSv1.1 and SDS. The area under the curve for both scoring methods were excellent with an area under the curve (AUC) of 0.808 and 0.817, respectively. Optimal cut-off scores were in line with the original recommendations. Conclusion The Swedish translation of ASRSv1.1/ASRS-S has psychometric properties comparable to other populations and the capacity to screen for ADHD in patients with overlapping symptoms.

Assessment of anxiety in children with neurodevelopment disorders: Rasch analysis of the Spence Children's Anxiety Scale.

Anxiety is common in neurodevelopmental disorders (NDD). The parent version of the Spence Children's Anxiety Scale (SCAS-P) is a widely used measure to assess anxiety across a broad range of childhood populations. However, assessment of the measurement properties of the SCAS-P in NDDs have been limited. The present study aimed to assess the psychometric properties of the SCAS-P in children with attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) using Rasch Measurement Theory. Data from the Province of Ontario Neurodevelopmental Disorders Network Registry were used in the analysis. Children (ages 6-13 years old) with a primary diagnosis of ADHD (n=146) or ASD (n=104) were administered the SCAS-P. Rasch Measurement Theory was used to assess measurement properties of the SCAS-P, including unidimensionality and item-level fit, category ordering, item targeting, person separation index and reliability and differential item functioning. The SCAS-P fit well to the Rasch model in both ADHD and ASD, including unidimensionality, satisfactory category ordering and goodness-of-fit. However, item-person measures showed poor precision at lower levels of anxiety. Some items showed differential item functioning, including items within the obsessive-compulsive, panic/agoraphobia and physical injury fears domains, suggesting that the presentation of anxiety may differ between ADHD and ASD. Overall, the results generally support the use of the SCAS-P to screen and monitor anxiety symptoms in children with ADHD and ASD. Future studies would benefit from examination of more severely anxious NDD cohort, including those with clinically diagnosed anxiety.

ADHD Patients with Suicidal Behaviour: Risk Factors, Comorbidities, and Clinical Profile: A Naturalistic Exploratory Study

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder that disrupts brain functioning and is characterized by hyperactivity, impulsivity, and inattention. With varying presentations and complex etiological factors contributing to the development of ADHD, along with its persistence into adulthood, ADHD must be studied. Researchers have taken an interest in the relationship between ADHD and suicide, which is a serious public health concern with increasing prevalence rates in the Americas. The current literature reveals conflicting views on the importance of psychiatric comorbidities in the development of suicidal behaviours in ADHD patients. Therefore, this study aimed to determine whether there were significant differences between adult ADHD patients with suicide risk and adult ADHD patients without suicide risk. This study was a naturalistic retrospective chart review pilot study that used a sample of adults with a confirmed diagnosis of ADHD from January 2023 to August 2023. Using convenience sampling and sets of inclusion and exclusion criteria, patient data were sequentially collected from Med Access electronic medical records. The control and experimental groups each consisted of 50 patients (100) ranging from 19 to 58 years old. Our quantitative data were analyzed using non-parametric statistical tests, including the Chi-Square test and the Mann–Whitney U test. The results showed significant associations between ADHD patients with suicide risk and (1) borderline personality disorder; (2) binge eating disorder; (3) seven specific psychosocial risk factors; and (4) a higher number of antidepressant medication trials. No significant associations were found with other psychiatric disorders; however, there are important sex differences in terms of the risk factors. Our pilot study reveals several significant differences between adult ADHD patients with suicide risk and those without suicide risk. However, given our limited sample size and limitations, we hope our study encourages larger-scale studies to further investigate this relationship to improve its generalizability.

Autism and attention-deficit/hyperactivity disorder in children with Dravet syndrome: A population-based study.

To identify on a population basis the prevalence of autism and attention-deficit/hyperactivity disorder (ADHD) in children with Dravet syndrome and factors associated with symptoms of autism and ADHD. Forty-one of 48 children with Dravet syndrome living in Sweden, born between 1st January 2000 and 31st December 2018 underwent assessment including measures of autism and ADHD. Diagnoses of autism and ADHD were made with respect to DSM-5 criteria. Factors associated with features of autism and ADHD were analysed via regression. Twenty-five of the 41 children fulfilled DSM-5 criteria for autism spectrum disorder and 12 of 37 children considered for an ADHD diagnosis fulfilled DSM-5 criteria for ADHD. Severe intellectual disability was significantly associated with a greater degree of autistic features (p < 0.001) and a DSM-5 diagnosis of autism spectrum disorder (p = 0.029). Younger children had significantly more features of ADHD (p = 0.004) and features of inattention were significantly more common than features of hyperactivity/impulsivity (p < 0.001). Children with Dravet syndrome often have significant features of autism and ADHD, primarily inattentive type. Screening for autism and ADHD should be routine in children with Dravet syndrome.

Prevalence and clinical phenotypes of adult patients with attention deficit hyperactivity disorder and comorbid behavioral addictions.

Although the correlation between substance use disorder and attention deficit hyperactivity disorder (ADHD) has been largely studied, less is known about the correlation between behavioral addictions and ADHD. Thus, the aim of the present study was to investigate the prevalence of behavioral addictions in a large sample of adult patients with a primary diagnosis of ADHD and to compare the clinical profile of ADHD patients with and without behavioral addictions comorbidity. 248 consecutive adults newly diagnosed as ADHD patients were assessed through a series of validated scales for gambling disorder, internet, sex, shopping and food addictions. ADHD patients with at least one comorbid behavioral addiction were compared to non-comorbid patients on ADHD symptoms, impulsivity, mood and anxiety symptoms and functional impairment. 58.9% of patients had at least one behavioral addiction comorbidity. Of the whole sample, 31.9% of the patients had a comorbidity with one behavioral addiction while the 27% showed a comorbidity with two or more behavioral addictions. Internet addiction was the most common comorbidity (33.9%) followed by food addiction (28.6%), shopping addiction (19%), sex addiction (12.9%) and gambling disorder (3.6%). ADHD patients with comorbid behavioral addictions showed higher ADHD current and childhood symptoms, higher cognitive and motor impulsivity, higher mood and anxiety symptoms and higher functional impairment. Behavioral addictions are highly frequent in adult ADHD patients. Comorbid patients seem to have a more complex phenotype characterized by more severe ADHD, mood and anxiety symptoms, higher impulsivity levels and greater functional impairment.

The underlying neuropsychological and neural correlates of the impaired Chinese reading skills in children with attention deficit hyperactivity disorder.

Impaired basic academic skills (e.g., word recognition) are common in children with Attention Deficit Hyperactivity Disorder (ADHD). The underlying neuropsychological and neural correlates of impaired Chinese reading skills in children with ADHD have not been substantially explored. Three hundred and two children with ADHD (all medication-naïve) and 105 healthy controls underwent the Chinese language skill assessment, and 175 also underwent fMRI scans (84 ADHD and 91 controls). Between-group and mediation analyses were applied to explore the interrelationships of the diagnosis of ADHD, cognitive dysfunction, and impaired reading skills. Five ADHD-related brain functional networks, including the default mode network (DMN) and the dorsal attention network (DAN), were built using predefined regions of interest. Voxel-based group-wise comparisons were performed. The ADHD group performed worse than the control group in word-level reading ability tests, with lower scores in Chinese character recognition (CR) and word chains (WS) (all P < 0.05). With full-scale IQ and sustained attention in the mediation model, the direct effect of ADHD status on the CR score became insignificant (P = 0.066). The underlying neural correlates for the orthographic knowledge (OT) and CR differed between the ADHD and the control group. The ADHD group tended to recruit more DMN regions to maintain their reading performance, while the control group seemed to utilize more DAN regions. Children with ADHD generally presented impaired word-level reading skills, which might be caused by impaired sustained attention and lower IQ. According to the brain functional results, we infer that ADHD children might utilize a different strategy to maintain their orthographic knowledge and character recognition performance.

Using artificial intelligence methods to study the effectiveness of exercise in patients with ADHD.

Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder that significantly affects children and adults worldwide, characterized by persistent inattention, hyperactivity, and impulsivity. Current research in this field faces challenges, particularly in accurate diagnosis and effective treatment strategies. The analysis of motor information, enriched by artificial intelligence methodologies, plays a vital role in deepening our understanding and improving the management of ADHD. The integration of AI techniques, such as machine learning and data analysis, into the study of ADHD-related motor behaviors, allows for a more nuanced understanding of the disorder. This approach facilitates the identification of patterns and anomalies in motor activity that are often characteristic of ADHD, thereby contributing to more precise diagnostics and tailored treatment strategies. Our approach focuses on utilizing AI techniques to deeply analyze patients' motor information and cognitive processes, aiming to improve ADHD diagnosis and treatment strategies. On the ADHD dataset, the model significantly improved accuracy to 98.21% and recall to 93.86%, especially excelling in EEG data processing with accuracy and recall rates of 96.62 and 95.21%, respectively, demonstrating precise capturing of ADHD characteristic behaviors and physiological responses. These results not only reveal the great potential of our model in improving ADHD diagnostic accuracy and developing personalized treatment plans, but also open up new research perspectives for understanding the complex neurological logic of ADHD. In addition, our study not only suggests innovative perspectives and approaches for ADHD treatment, but also provides a solid foundation for future research exploring similar complex neurological disorders, providing valuable data and insights. This is scientifically important for improving treatment outcomes and patients' quality of life, and points the way for future-oriented medical research and clinical practice.

Empirically derived symptom profiles in adults with attention-Deficit/hyperactivity disorder: An unsupervised machine learning approach

Background Attention-deficit/hyperactivity disorder (ADHD) is associated with various cognitive, behavioral, and mood symptoms that complicate diagnosis and treatment. The heterogeneity of these symptoms may also vary depending on certain sociodemographic factors. It is therefore important to establish more homogenous symptom profiles in patients with ADHD and determine their association with the patient’s sociodemographic makeup. The current study used unsupervised machine learning to identify symptom profiles across various cognitive, behavioral, and mood symptoms in adults with ADHD. It was then examined whether symptom profiles differed based on relevant sociodemographic factors. Methods Participants were 382 adult outpatients (62% female; 51% non-Hispanic White) referred for neuropsychological evaluation for ADHD. Results Employing Gaussian Mixture Modeling, we identified two distinct symptom profiles in adults with ADHD: “ADHD-Plus Symptom Profile” and “ADHD-Predominate Symptom Profile.” These profiles were primarily differentiated by internalizing psychopathology (Cohen’s d = 1.94-2.05), rather than by subjective behavioral and cognitive symptoms of ADHD or neurocognitive test performance. In a subset of 126 adults without ADHD who were referred for the same evaluation, the unsupervised machine learning algorithm only identified one symptom profile. Group comparison analyses indicated that female patients were most likely to present with an ADHD-Plus Symptom Profile (χ2 = 5.43, p < .001). Conclusion The machine learning technique used in this study appears to be an effective way to elucidate symptom profiles emerging from comprehensive ADHD evaluations. These findings further underscore the importance of considering internalizing symptoms and patients’ sex when contextualizing adult ADHD diagnosis and treatment.

Serum Biomarker Analysis in Pediatric ADHD: Implications of Homocysteine, Vitamin B12, Vitamin D, Ferritin, and Iron Levels.

The current diagnosis of attention deficit hyperactivity disorder (ADHD) is based on history, clinical observation, and behavioral tests. There is a high demand to find biomarkers for the diagnosis of ADHD. The aim of this study is to analyze the serum profiles of several biomarkers, including homocysteine (Hcy), vitamin B12, vitamin D, ferritin, and iron, in a cohort of 133 male subjects (6.5-12.5 years), including 67 individuals with an ADHD diagnosis based on DSM-V criteria and 66 age-matched healthy boys (healthy controls, HC). Assessments for ADHD included the Iowa Conners' Teacher Rating Scale (CPRS) and the ADHDT test, as well as cognitive assessments using the Wechsler Intelligence Scale for Children-Revised (WISC-R) and the TROG-2 language comprehension test. Hcy and iron were quantified using spectrophotometry, while vitamin B12 and total 25-hydroxy vitamin D levels were determined using an electrochemiluminescence immunoassay (ECLIA) and ferritin was measured using a particle-enhanced immunoturbidimetric assay. The results showed significantly increased Hcy levels and decreased vitamin B12 levels in ADHD patients compared to HCs. Multiple logistic regression analysis indicated that Hcy is a potential prognostic indicator for ADHD. These results suggest that elevated homocysteine and decreased vitamin B12 may serve as markers for the diagnosis and prognosis of ADHD.

Cluster analysis of teachers report for identifying symptoms of autism spectrum and/or attention deficit hyperactivity in school population: EPINED study.

An early detection of Neurodevelopmental Disorders (NDDs) is crucial for their prognosis; however, the clinical heterogeneity of some disorders, such as autism spectrum disorder (ASD) or attention-deficit hyperactivity disorder (ADHD) is an obstacle to accurate diagnoses in children. In order to facilitate the screening process, the current study aimed to identify symptom-based clusters among a community-based sample of preschool and school-aged children, using behavioral characteristics reported by teachers. A total of 6894 children were assessed on four key variables: social communication differences, restricted behavior patterns, restless-impulsiveness, and emotional lability (pre-schoolers) or inattention and hyperactivity-impulsivity (school-aged). From these behavioral profiles, four clusters were identified for each age group. A cluster of ASD + ADHD and others including children with no pathology was clearly identified, whereas two other clusters were characterized by subthreshold ASD and/or ADHD symptoms. In the school-age children, the presence of ADHD was consistently observed with ASD patterns. In pre-schoolers, teachers were more proficient at identifying children who received a diagnosis for either ASD and/or ADHD from an early stage. Considering the significance of early detection and intervention of NDDs, teachers' insights are important. Therefore, promptly identifying subthreshold symptoms in children can help to minimize consequences in social and academic functioning.

Prevalence of co-occurring conditions among youths receiving treatment with primary anxiety, ADHD, or depressive disorder diagnoses

IntroductionAnxiety disorders, depressive disorders, and attention-deficit/hyperactivity disorder (ADHD) are some of the most common conditions that youths (&amp;lt;18 years old) receive mental health treatment for. These conditions are associated with high-risk substance use or substance use disorders (SUDs). This study sought to identify the proportion of youths (&amp;lt;18 years old) with anxiety disorders, depressive disorders, or ADHD as a primary diagnosis in community mental health centers (CMHCs) having co-occurring high-risk substance use or a SUD.MethodsAnalysis included binary logistic regression models using the Mental Health Client-Level Data 2017–2019 datasets which contains annual cross-sectional administrative data from mental health treatment facilities. The final sample included n = 458,888 youths with an anxiety disorder as a primary diagnosis, n = 570,388 youths with a depressive disorder as a primary diagnosis, and n = 945,277 youths with ADHD as a primary diagnosis.ResultsIn the subsample with anxiety as a primary diagnosis, approximately 5% of youth had high-risk substance use or a SUD. Approximately 10% of youth with depression as a primary diagnosis had high-risk substance use or a SUD. Among youth with ADHD as a primary diagnosis, 5% had high-risk substance use or a SUD. Odds of having a co-occurring high-risk substance use or SUD differed based on the youth’s age, race and ethnicity, gender, and other mental health diagnoses.ConclusionsEffective care for this high-need youth population at CMHCs will require mental health clinicians to possess knowledge and skills related to substance use treatment.

The relationship between female orgasmic disorder, attention-deficit/hyperactivity disorder, and depression in Dominican women

Abstract Background Female orgasmic disorder is listed in the DSM-5 and is defined as the persistent or recurrent inability to have an orgasm. Many depressed women may experience sexual dysfunction, including female orgasmic disorder. Aim The study sought to analyze the relationship between depressive disorders and attention-deficit/hyperactivity disorder (ADHD) and their influence on the development of female orgasmic disorder. Methods A total of 221 Dominican women participated in this case-control study. The case group consisted of 107 women diagnosed with female orgasmic disorder, while the control group consisted of 114 women without any sexual dysfunction. Outcomes The diagnosis of ADHD was obtained from the participants’ medical records, previously conducted using the DSM-5-TR criteria. The Beck Depression Inventory II was used to assess the severity of depressive symptoms in both groups. Results There was a significant relationship between female orgasmic disorder and ADHD and depression. The results of multiple logistic regression indicated that the highest risk of female orgasmic disorder was observed in women with ADHD (odds ratio [OR], 4.91; 95% confidence interval [CI], 2.46-9.20; P &amp;lt; .001), women with severe depression (OR, 2.50; 95% CI, 1.08-6.96; P = .04), and women who had sexual intercourse that focused on penetration (OR, 2.02; 95% CI, 1.03-3.98; P = .04). Clinical Implications These findings may have important implications for the prevention and treatment of sexual disorders in women. Strengths and Limitations This design selected all diagnosed cases of female orgasmic disorder and did not select a specific subgroup. However, some limitations must be considered. This study was conducted in a single clinic, although it should be noted that it is the main clinic for the treatment of sexual dysfunction in the country. A further limitation could be that this type of study design does not allow for statements about causality to be made. Conclusion There is an increased risk of female orgasmic disorder in women with ADHD, with severe depression, and who engage in penetrative sex.

Gabor filter-based statistical features for ADHD detection.

Attention deficit/hyperactivity disorder (ADHD) is a neuropsychological disorder that occurs in children and is characterized by inattention, impulsivity, and hyperactivity. Early and accurate diagnosis of ADHD is very important for effective intervention. The aim of this study is to develop a computer-aided approach to detecting ADHD using electroencephalogram (EEG) signals. Specifically, we explore a Gabor filter-based statistical features approach for the classification of EEG signals into ADHD and healthy control (HC). The EEG signal is processed by a bank of Gabor filters to obtain narrow-band signals. Subsequently, a set of statistical features is extracted. The computed features are then subjected to feature selection. Finally, the obtained feature vector is given to a classifier to detect ADHD and HC. Our approach achieves the highest classification accuracy of 96.4% on a publicly available dataset. Furthermore, our approach demonstrates better classification accuracy than the existing methods.