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Attention-deficit/hyperactivity disorder (ADHD) is a heterogeneous disorder with a high degree of psychiatric and physical comorbidity, which complicates its diagnosis in childhood and adolescence. We analyzed registry data from 238,696 persons born and living in Sweden between 1995 and 1999. Several machine learning techniques were used to assess the ability of registry data to inform the diagnosis of ADHD in childhood and adolescence: logistic regression, random Forest, gradient boosting, XGBoost, penalized logistic regression, deep neural network (DNN), and ensemble models. The best fitting model was the DNN, achieving an area under the receiver operating characteristic curve of 0.75, 95% CI (0.74–0.76) and balanced accuracy of 0.69. At the 0.45 probability threshold, sensitivity was 71.66% and specificity was 65.0%. There was an overall agreement in the feature importance among all models (τ > .5). The top 5 features contributing to classification were having a parent with criminal convictions, male sex, having a relative with ADHD, number of academic subjects failed, and speech/learning disabilities. A DNN model predicting childhood and adolescent ADHD trained exclusively on Swedish register data achieved good discrimination. If replicated and validated in an external sample, and proven to be cost-effective, this model could be used to alert clinicians to individuals who ought to be screened for ADHD and to aid clinicians’ decision-making with the goal of decreasing misdiagnoses. Further research is needed to validate results in different populations and to incorporate new predictors.

Human brain mapping 43(1), 37-55 (2022). doi:10.1002/hbm.25029 special issue: "Special Issue: The ENIGMA Consortium: the first 10 years / Issue Edited by: P.M. Thompson, N. Jahanshad, L. Schmaal, J.A. Turner, A. Winkler, S.I. Thomopoulos, G.F. Egan, P. Kochunov", Published by Wiley-Liss, New York, NY

Despite a growing literature on the complex bidirectional relationship of ADHD and substance use, reviews specifically focusing on alcohol are scarce. ADHD and AUD show a significant genetic overlap, including genes involved in gluatamatergic and catecholaminergic neurotransmission. ADHD drives risky behavior and negative experiences throughout the lifespan that subsequently enhance a genetically increased risk for Alcohol Use Disorders (AUD). Impulsive decisions and a maladaptive reward system make individuals with ADHD vulnerable for alcohol use and up to 43 % develop an AUD; in adults with AUD, ADHD occurs in about 20 %, but is vastly under-recognized and under-treated. Thus, routine screening and treatment procedures need to be implemented in AUD treatment. Long-acting stimulants or non-stimulants can be used to treat ADHD in individuals with AUD. However, it is crucial to combine medical treatment for ADHD with pharmacotherapy and psychotherapy for AUD, and other comorbid disorders. Identification of individuals at risk for AUD, especially those with ADHD and conduct disorder or oppositional defiant disorder, is a key factor to prevent negative outcomes.

Applications of machine learning (ML) in biomedical science are growing rapidly, spurred by interdisciplinary collaborations, aggregation of large datasets, accessibility of analytic routines, and availability of powerful computers. With this increased usage comes a responsibility for education, borne equally by data scientists plying their wares in medical research and biomedical scientists harnessing such methods to glean knowledge from data. This article provides a critical review of ML, covering common ML methods and historical trends of their use in psychiatry, and identifying areas of opportunity for future applications of ML in biological psychiatry. We also establish the ML in Psychiatry (MLPsych) Consortium, enumerate its objectives, and provide a set of standards (Guidelines for REporting ML Investigations in Neuropsychiatry [GREMLIN]) for designing and reporting studies that use ML. This review serves as a cautiously optimistic primer on ML for those on the precipice as they prepare to dive into the field, either as dedicated methodological practitioners or, at the very least, well-informed consumers.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub ( https://covid19forecasthub.org/ ) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.

Background: Polygenic risk scores (PRSs), which sum the effects of SNPs throughout the genome to measure risk afforded by common genetic variants, have improved our ability to estimate disorder risk for Attention-Deficit/Hyperactivity Disorder (ADHD) but the accuracy of risk prediction is rarely investigated. Methods: With the goal of improving risk prediction, we performed gene set analysis of GWAS data to select gene sets associated with ADHD within a training subset. For each selected gene set, we generated gene set polygenic risk scores (gsPRSs), which sum the effects of SNPs for each selected gene set. We created gsPRS for ADHD and for phenotypes having a high genetic correlation with ADHD. These gsPRS were added to the standard PRS as input to machine learning models predicting ADHD. We used feature importance scores to select gsPRS for a final model and to generate a ranking of the most consistently predictive gsPRS. Results: For a test subset that had not been used for training or validation, a random forest (RF) model using PRSs from ADHD and genetically correlated phenotypes and an optimized group of 20 gsPRS had an area under the receiving operating characteristic curve (AUC) of 0.72 (95% CI: 0.70 to 0.74). This AUC was a statistically significant improvement over logistic regression models and RF models using only PRS from ADHD and genetically correlated phenotypes. Conclusions: Summing risk at the gene set level and incorporating genetic risk from disorders with high genetic correlations with ADHD improved the accuracy of predicting ADHD. Learning curves suggest that additional improvements would be expected with larger study sizes. Our study suggests that better accounting of genetic risk and the genetic context of allelic differences results in more predictive models.

Variations in SLC9A9 gene expression and protein function are associated with multiple human diseases, which range from Attention-deficit/hyperactivity disorder (ADHD) to glioblastoma multiforme. In an effort to determine the full spectrum of human disease associations with SLC9A9, we performed a systematic review of the literature. We also review SLC9A9's biochemistry, protein structure, and function, as well as its interacting partners with the goal of identifying mechanisms of disease and druggable targets. We report gaps in the literature regarding the genes function along with consistent trends in disease associations that can be used to further research into treating the respective diseases. We report that SLC9A9 has strong associations with neuropsychiatric diseases and various cancers. Interestingly, we find strong overlap in SLC9A9 disease associations and propose a novel role for SLC9A9 in neuropsychiatric comorbidity. In conclusion, SLC9A9 is a multifunctional protein that, through both its endosome regulatory function and its protein-protein interaction network, has the ability to modulate signaling axes, such as the PI3K pathway, among others.

Background Children with attention‐deficit/hyperactivity disorder (ADHD) have a high risk for substance use disorders (SUDs). Early identification of at‐risk youth would help allocate scarce resources for prevention programs. Methods Psychiatric and somatic diagnoses, family history of these disorders, measures of socioeconomic distress, and information about birth complications were obtained from the national registers in Sweden for 19,787 children with ADHD born between 1989 and 1993. We trained (a) a cross‐sectional random forest (RF) model using data available by age 17 to predict SUD diagnosis between ages 18 and 19; and (b) a longitudinal recurrent neural network (RNN) model with the Long Short‐Term Memory (LSTM) architecture to predict new diagnoses at each age. Results The area under the receiver operating characteristic curve (AUC) was 0.73(95%CI 0.70–0.76) for the random forest model (RF). Removing prior diagnosis from the predictors, the RF model was still able to achieve significant AUCs when predicting all SUD diagnoses (0.69, 95%CI 0.66–0.72) or new diagnoses (0.67, 95%CI: 0.64, 0.71) during age 18–19. For the model predicting new diagnoses, model calibration was good with a low Brier score of 0.086. Longitudinal LSTM model was able to predict later SUD risks at as early as 2 years age, 10 years before the earliest diagnosis. The average AUC from longitudinal models predicting new diagnoses 1, 2, 5 and 10 years in the future was 0.63. Conclusions Population registry data can be used to predict at‐risk comorbid SUDs in individuals with ADHD. Such predictions can be made many years prior to age of the onset, and their SUD risks can be monitored using longitudinal models over years during child development. Nevertheless, more work is needed to create prediction models based on electronic health records or linked population registers that are sufficiently accurate for use in the clinic.

BackgroundReward dysfunction has been implicated in many psychiatric disorders such as attention-deficit/hyperactivity disorders, depression, and substance use disorders. However, psychiatric comorbidities are common, and the specificity of reward dysfunction to individual psychopathologies is unknown. The objective of this study was to evaluate the association between reward functioning and specific or general psychopathologies.Methods1044 adults and their 1215 children (ages 6-12) completed various measures of the Positive Valence System domain from the Research Domain Criteria (RDoC), which included the Delayed and Probability Discounting Tasks, Energy Expenditure for Reward Task, and Iowa Gambling Task. Children also completed the Experienced Pleasure Scale for children, while adults completed the Temporal Experience of Pleasure Scale and the Behavioral Activation System tasks. Psychopathology was assessed using the Child Behavior Checklist (CBCL) for children and the Adult Self Report (ASR) for parents.ResultsOne general factor identified via principal factors factor analysis explained the majority of variance in psychopathology in both groups. Reward measures in both adults and children were significantly associated with general psychopathology as well as most specific psychopathologies. Some associations between reward and psychopathology did not hold following removal of general psychopathology; nonetheless, certain reward constructs were uniquely associated with specific disorder problems but not general psychopathology.ConclusionDisorder problem specific associations with reward functioning can be identified after removal of comorbidity. General propensity toward psychopathology is significantly but not uniquely correlated with reward dysfunction. Altogether, this may have broader implications for future study of the role of reward in disease pathogenesis.

The global pandemic of coronavirus disease 2019 (COVID-19) has killed almost two million people worldwide and over 400 thousand in the United States (US). As the pandemic evolves, informed policy-making and strategic resource allocation relies on accurate forecasts. To predict the spread of the virus within US counties, we curated an array of county-level demographic and COVID-19-relevant health risk factors. In combination with the county-level case and death numbers curated by John Hopkins university, we developed a forecasting model using deep learning (DL). We implemented an autoencoder-based Seq2Seq model with gated recurrent units (GRUs) in the deep recurrent layers. We trained the model to predict future incident cases, deaths and the reproductive number

The global pandemic of coronavirus disease 2019 (COVID-19) has killed almost two million people worldwide and over 400 thousand in the United States (US). As the pandemic evolves, informed policy-making and strategic resource allocation relies on accurate forecasts. To predict the spread of the virus within US counties, we curated an array of county-level demographic and COVID-19-relevant health risk factors. In combination with the county-level case and death numbers curated by John Hopkins university, we developed a forecasting model using deep learning (DL). We implemented an autoencoder-based Seq2Seq model with gated recurrent units (GRUs) in the deep recurrent layers. We trained the model to predict future incident cases, deaths and the reproductive number, R. For most counties, it makes accurate predictions of new incident cases, deaths and R values, up to 30 days in the future. Our framework can also be used to predict other targets that are useful indices for policymaking, for example hospitalization or the occupancy of intensive care units. Our DL framework is publicly available on GitHub and can be adapted for other indices of the COVID-19 spread. We hope that our forecasts and model can help local governments in the continued fight against COVID-19.

Na+/H+ Exchanger 9 (NHE9) is an endosomal membrane protein encoded by the Solute Carrier 9A, member 9 gene (SLC9A9). SLC9A9 has been implicated in attention deficit hyperactivity disorder (ADHD), autism spectrum disorders (ASDs), epilepsy, multiple sclerosis and cancers. To better understand the function of NHE9 and the effects of disease-associated variants on protein–protein interactions, we conducted a quantitative analysis of the NHE9 interactome using co-immunoprecipitation and isobaric labeling-based quantitative mass spectrometry. We identified 100 proteins that interact with NHE9. These proteins were enriched in known functional pathways for NHE9: the endocytosis, protein ubiquitination and phagosome pathways, as well as some novel pathways including oxidative stress, mitochondrial dysfunction, mTOR signaling, cell death and RNA processing pathways. An ADHD-associated mutation (A409P) significantly altered NHE9’s interactions with a subset of proteins involved in caveolae-mediated endocytosis and MAP2K2-mediated downstream signaling. An ASD nonsense mutation in SLC9A9, R423X, produced no-detectable amount of NHE9, suggesting the overall loss of NHE9 functional networks. In addition, seven of the NHE9 interactors are products of known autism candidate genes (Simons Foundation Autism Research Initiative, SFARI Gene) and 90% of the NHE9 interactome overlap with SFARI protein interaction network PIN (p

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multi-model ensemble forecast that combined predictions from dozens of different research groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-week horizon 3-5 times larger than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks. Significance Statement This paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the US. Results show high variation in accuracy between and within stand-alone models, and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public health action.

Objectives:While opioids temporarily alleviate pain, the overshoot of balancing pain drivers may increase pain, leading to opioid induced hyperalgesia (OIH). Our goal was to find out what chronic opioid treatment does to pain tolerance as measured by the cold pressor test (CPT), an objective measure of pain tolerance, and to find an alternative effective treatment for chronic pain and FM.Materials and Methods:The setting was an academic addiction medicine service that has an embedded pain service. Patients had routine clinical care starting with an evaluation that included assessment of medical and psychiatric conditions. Participants were 55 patients with OIH and 21 patients with fibromyalgia; all had at least two CPTs. Treatment included a single dose of buprenorphine for detoxification. In this open-label case series, patients were treated with low dose naltrexone (LDN), a pure opioid receptor antagonist that, we hypothesize, treats OIH and FM by restoring endogenous opioid tone.Results:Comparing initial and last CPT times, those with OIH more than quadrupled their pain tolerance, and those with FM doubled theirs. This improved pain tolerance for OIH and FM was statistically significant (p< 0.0001 andp= 0.003, respectively) and had a large effect size (r= 0.82 andr= 0.63, respectively).Discussion:Results suggest that patients on chronic opioid therapy should have pain tolerance measured by CPT with detoxification and LDN provided to correct opioid induced hyperalgesia if found. FM may also be treated with LDN. The main limitation of the findings was lack of a randomized control group treated with placebo.

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of “big data” (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA’s activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material.

Background Suicide is a major public health concern globally. Accurately predicting suicidal behavior remains challenging. This study aimed to use machine learning approaches to examine the potential of the Swedish national registry data for prediction of suicidal behavior. Methods and findings The study sample consisted of 541,300 inpatient and outpatient visits by 126,205 Sweden-born patients (54% female and 46% male) aged 18 to 39 (mean age at the visit: 27.3) years to psychiatric specialty care in Sweden between January 1, 2011 and December 31, 2012. The most common psychiatric diagnoses at the visit were anxiety disorders (20.0%), major depressive disorder (16.9%), and substance use disorders (13.6%). A total of 425 candidate predictors covering demographic characteristics, socioeconomic status (SES), electronic medical records, criminality, as well as family history of disease and crime were extracted from the Swedish registry data. The sample was randomly split into an 80% training set containing 433,024 visits and a 20% test set containing 108,276 visits. Models were trained separately for suicide attempt/death within 90 and 30 days following a visit using multiple machine learning algorithms. Model discrimination and calibration were both evaluated. Among all eligible visits, 3.5% (18,682) were followed by a suicide attempt/death within 90 days and 1.7% (9,099) within 30 days. The final models were based on ensemble learning that combined predictions from elastic net penalized logistic regression, random forest, gradient boosting, and a neural network. The area under the receiver operating characteristic (ROC) curves (AUCs) on the test set were 0.88 (95% confidence interval [CI] = 0.87–0.89) and 0.89 (95% CI = 0.88–0.90) for the outcome within 90 days and 30 days, respectively, both being significantly better than chance (i.e., AUC = 0.50) (p < 0.01). Sensitivity, specificity, and predictive values were reported at different risk thresholds. A limitation of our study is that our models have not yet been externally validated, and thus, the generalizability of the models to other populations remains unknown. Conclusions By combining the ensemble method of multiple machine learning algorithms and high-quality data solely from the Swedish registers, we developed prognostic models to predict short-term suicide attempt/death with good discrimination and calibration. Whether novel predictors can improve predictive performance requires further investigation., In a study of national registry data, Qi Chen and colleagues use machine learning models to predict suicidal behavior following a visit to psychiatric specialty care among adults in Sweden., Author summary Why was this study done? Suicidal behavior is overrepresented in people with mental illness and contributes to the substantial public health burden of psychiatric conditions. Accurately predicting suicidal behavior has long been challenging. The potential of applying machine learning to linked national datasets to predict suicidal behavior remains unknown. What did the researchers do and find? We identified a sample of 541,300 inpatient and outpatient visits to psychiatric specialty care in Sweden during 2011 and 2012. The sample was then divided into a training dataset and a test dataset. We first trained prediction models separately for suicide attempt/death within 90 days and 30 days following a visit to psychiatric specialty care, using 4 different machine learning algorithms. We then used an ensemble method to combine the performance of the trained models with the intention to achieve an overall performance superior than each individual model. The final model based on the ensemble method achieved the best predictive performance. This model was applied to test dataset and showed good model discrimination and calibration for both the 90-day and 30-day outcomes. What do these findings mean? Our findings suggest that combining machine learning with registry data has the potential to accurately predict short-term suicidal behavior. An approach combining 4 machine learning methods showed an overall predictive performance slightly better than each individual model.

Objective: Machine learning (ML) has been applied to develop magnetic resonance imaging (MRI)-based diagnostic classifiers for attention-deficit/hyperactivity disorder (ADHD). This systematic review examines this literature to clarify its clinical significance and to assess the implications of the various analytic methods applied. Methods: A comprehensive literature search on MRI-based diagnostic classifiers for ADHD was performed and data regarding the utilized models and samples were gathered. Results: We found that, although most studies reported the classification accuracies, they varied in choice of MRI modalities, ML models, cross-validation and testing methods, and sample sizes. We found that the accuracies of cross-validation methods inflated the performance estimation compared with those of a held-out test, compromising the model generalizability. Test accuracies have increased with publication year but were not associated with training sample sizes. Improved test accuracy over time was likely due to the use of better ML methods along with strategies to deal with data imbalances. Conclusion: Ultimately, large multi-modal imaging datasets, and potentially the combination with other types of data, like cognitive data and/or genetics, will be essential to achieve the goal of developing clinically useful imaging classification tools for ADHD in the future.

Alzheimer's disease is one of the diseases that mostly affects older people without being a part of aging. The most common symptoms include problems with communicating and abstract thinking, as well as disorientation. It is important to detect Alzheimer's disease in early stages so that cognitive functioning would be improved by medication and training. In this paper, we propose two attention model networks for detecting Alzheimer's disease from MRI images to help early detection efforts at the preclinical stage. We also compare the performance of these two attention network models with a baseline model. Recently available OASIS-3 Longitudinal Neuroimaging, Clinical, and Cognitive Dataset is used to train, evaluate and compare our models. The novelty of this research resides in the fact that we aim to detect Alzheimer's disease when all the parameters, physical assessments, and clinical data state that the patient is healthy and showing no symptoms, Comment: 10 pages, 5 figures

Blood-based microarray studies comparing individuals affected with autism spectrum disorder (ASD) and typically developing individuals help characterize differences in circulating immune cell functions and offer potential biomarker signal. We sought to combine the subject-level data from previously published studies by mega-analysis to increase the statistical power. We identified studies that compared ex vivo blood or lymphocytes from ASD-affected individuals and unrelated comparison subjects using Affymetrix or Illumina array platforms. Raw microarray data and clinical meta-data were obtained from seven studies, totaling 626 affected and 447 comparison subjects. Microarray data were processed using uniform methods. Covariate-controlled mixed-effect linear models were used to identify gene transcripts and co-expression network modules that were significantly associated with diagnostic status. Permutation-based gene-set analysis was used to identify functionally related sets of genes that were over- and under-expressed among ASD samples. Our results were consistent with diminished interferon-, EGF-, PDGF-, PI3K-AKT-mTOR-, and RAS-MAPK-signaling cascades, and increased ribosomal translation and NK-cell related activity in ASD. We explored evidence for sex-differences in the ASD-related transcriptomic signature. We also demonstrated that machine-learning classifiers using blood transcriptome data perform with moderate accuracy when data are combined across studies. Comparing our results with those from blood-based studies of protein biomarkers (e.g., cytokines and trophic factors), we propose that ASD may feature decoupling between certain circulating signaling proteins (higher in ASD samples) and the transcriptional cascades which they typically elicit within circulating immune cells (lower in ASD samples). These findings provide insight into ASD-related transcriptional differences in circulating immune cells. © 2016 Wiley Periodicals, Inc.

This study tested the causal link between Attention Deficit/Hyperactivity Disorder (ADHD) and Internet addiction (IA) and investigated motivational and executive dysfunction as explanatory mechanisms in this association. A sample of 682 young adults completed self-report measures both at Time1 and Time2, six-months apart, including 54 ADHD participants diagnosed by the Conners’ Adult ADHD Rating Scale and the Continuous Performance Test. According to the performance in four cognitive tasks, ADHD participants were classified into three groups based on the dual pathway model of ADHD: executive dysfunction (ED), motivational dysfunction (MD) and combined dysfunction (CD). Participants’ severity of IA symptoms was assessed using the self-report Chen IA Scale. Results indicated that ADHD scores at Time1 predicted IA scores at Time2 but not vice versa. ADHD participants were easier to be IA than controls, while the severity of IA among the three ADHD groups changed differently. The MD and CD groups became more excessively engaged in Internet use over the course of the six-months while the ED group was unchanged. These findings identify ADHD as a potential risk factor for IA and suggest that motivational dysfunction, characterized by an excessive preference for immediate reward over delayed rewards, is a better predictor of IA than executive dysfunction.

The stimulant, methylphenidate (MPH), is commonly used to treat attention deficit hyperactivity disorder (ADHD) and has been increasingly prescribed for school age children and adolescents. Concerns regarding its long-term effects on later substance use disorders (SUDs) have been raised. Previous animal studies have produced contradictory results regarding whether early exposure to MPH increases or protects against SUD in adulthood. The goal of our study was to determine if clinically relevant doses of MPH during adolescence alter cocaine responsiveness in adulthood in a rat model of ADHD, the spontaneous hypertensive rat (SHR). We pretreated SHRs with saline or MPH (2.5 mg/kg once or twice day) via oral gavage during their dark cycle from postnatal day 35 (p35) to p44. Adult rats (p80) were assessed in an eight-session cocaine-conditioned place preference test (CPP). Four doses of cocaine were administered via intraperitoneal injection (i.p.) during the conditioning sessions: 1, 5, 10 and 20 mg/kg. Once per day MPH treatment had a small sensitizing effect on baseline general locomotor activity in a novel environment at p80 as well as a limited suppressive effect on reward-specific locomotor activity as measured by the decreased preference to enter the cocaine-paired chamber. This treatment did not have any effect on the amount of time that rats chose to spend in the cocaine-paired chamber. Twice per day MPH treatment had no effect on locomotion or drug-preference. Our results suggest that MPH treatment of ADHD rats during adolescence does not alter preference for cocaine in adulthood.

Autism spectrum disorders (ASDs) are a group of debilitating neurodevelopmental disorders thought to have genetic etiology, due to their high heritability. The endosomal system has become increasingly implicated in ASD pathophysiology. In an attempt to summarize the association between endosomal system genes and ASDs we performed a systematic review of the literature. We searched PubMed for relevant articles. Simons Foundation Autism Research Initiative (SFARI) gene database was used to exclude articles regarding genes with less than minimal evidence for association with ASDs. Our search retained 55 articles reviewed in two categories: genes that regulate and genes that are regulated by the endosomal system. Our review shows that the endosomal system is a novel pathway implicated in ASDs as well as other neuropsychiatric disorders. It plays a central role in aspects of cellular physiology on which neurons and glial cells are particularly reliant, due to their unique metabolic and functional demands. The system shows potential for biomarkers and pharmacological intervention and thus more research into this pathway is warranted.

Attention deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) commonly co-occur. With the DSM-5, clinicians are permitted to make an ASD diagnosis in the context of ADHD. In earlier versions of the DSM, this was not acceptable. Both ASD and ADHD are reported to have had substantial increases in prevalence within the past 10 years. As a function of both the increased prevalence of both disorders as well as the ability to make an ASD diagnosis in ADHD, there has been a significant amount of research focusing on the comorbidity between ADHD and ASD in the past few years. Here, we provide an update on the biological, cognitive and behavioral overlap/distinctiveness between the two neurodevelopmental disorders with a focus on data published in the last four years. Treatment strategies for the comorbid condition as well as future areas of research and clinical need are discussed.

The Research Domain Criteria (RDoC) address three types of aggression: frustrative non-reward, defensive aggression and offensive/proactive aggression. This review sought to present the evidence for genetic underpinnings of aggression and to determine to what degree prior studies have examined phenotypes that fit into the RDoC framework. Although the constructs of defensive and offensive aggression have been widely used in the animal genetics literature, the human literature is mostly agnostic with regard to all the RDoC constructs. We know from twin studies that about half the variance in behavior may be explained by genetic risk factors. This is true for both dimensional, trait-like, measures of aggression and categorical definitions of psychopathology. The non-shared environment seems to have a moderate influence with the effects of shared environment being unclear. Human molecular genetic studies of aggression are in an early stage. The most promising candidates are in the dopaminergic and serotonergic systems along with hormonal regulators. Genome-wide association studies have not yet achieved genome-wide significance, but current samples are too small to detect variants having the small effects one would expect for a complex disorder. The strongest molecular evidence for a genetic basis for aggression comes from animal models comparing aggressive and non-aggressive strains or documenting the effects of gene knockouts. Although we have learned much from these prior studies, future studies should improve the measurement of aggression by using a systematic method of measurement such as that proposed by the RDoC initiative.

Human genome-wide association studies (GWAS), transcriptome analyses of animal models, and candidate gene studies have advanced our understanding of the genetic architecture of aggressive behaviors. However, each of these methods presents unique limitations. To generate a more confident and comprehensive view of the complex genetics underlying aggression, we undertook an integrated, cross-species approach. We focused on human and rodent models to derive eight gene lists from three main categories of genetic evidence: two sets of genes identified in GWAS studies, four sets implicated by transcriptome-wide studies of rodent models, and two sets of genes with causal evidence from online Mendelian inheritance in man (OMIM) and knockout (KO) mice reports. These gene sets were evaluated for overlap and pathway enrichment to extract their similarities and differences. We identified enriched common pathways such as the G-protein coupled receptor (GPCR) signaling pathway, axon guidance, reelin signaling in neurons, and ERK/MAPK signaling. Also, individual genes were ranked based on their cumulative weights to quantify their importance as risk factors for aggressive behavior, which resulted in 40 top-ranked and highly interconnected genes. The results of our cross-species and integrated approach provide insights into the genetic etiology of aggression.

Background Na+/H+ exchanger 9 (SLC9A9) is an autism-risk gene localized in the endosomal system, which is a novel biological pathway that over-represents autism-risk genes. SLC9A9 regulates the endosome-recycling compartment through alkalinizing the endosomal lumen; although, it's broader biological functions have not been elucidated. Objectives Achieve a broader biological understanding of SLC9A9 through co-expression analysis and provide in vitro data confirming the endocytic regulation of autophagy and cell survival. Methods We use bioinformatics techniques to determine gene co-regulations, protein-protein interactions and pathway enrichments and confirm those results in an in vitro system. We perform WGCNA on human RNA-sequencing data from Allen Brain Atlas focusing on SLC9A9 co-expression modules in the pre- and postnatal hippocampus, cortex and amygdala. We use N2a cells as a neuronal model system to provide evidence that SLC9A9 gene-dosage affects autophagy induction and cell survival signals through lipofectamine 2000 transfections of SLC9A9 cDNA constructs. We assess immunoblots of N2a protein lysate for critical autophagy protein LC3-I/II, and cell survival protein BCL2. In addition, we challenge N2a cells with chloroquine to assess their viability at increasing levels of dosage. Results Our bioinformatics study shows SLC9A9 co-expression modules are enriched with mTOR and autophagic intermediates, as well as Bcl-2 pathway constituents. Our in vitro work demonstrates that SLC9A9 overexpression in N2a cells significantly decreases LC3-I to LC3-II conversion and significantly increases Bcl-2 levels. In addition, it increases phospho-AKT and phospho-mTOR levels, decreases LC3-II puncta formation and autophagic rate and provides resistance to drug treatments that typically result in cell death. Discussion Our results confirm the co-regulation of SLC9A9 and autophagic molecules. The in vitro data better defines the role of SLC9A9 in autophagic regulation, in that it inhibits the conversion of LC3-I to LC3-II and reduces the rate of autophagy. Although in need of replication in a true neuronal model, our data suggests that SLC9A9 expression could be a signaling and ion-transport brake, effectively inhibiting autophagic processes. When dysregulated, this has the potential to impact pruning and thus neuronal morphology and function. Conclusions: these results provide further insight into how SLC9A9 is impacting neuronal function that eventually leads to autistic phenotypes. We provide preliminary evidence as to how SLC9A9 could potentially be associated with autism spectrum disorders via autophagy and cell survival pathways.

This review covers an introduction of traditional Chinese medicine (TCM) in treating attention-deficit/hyperactivity disorder (ADHD), focusing on the traditional theoretic basis from the perspective of TCM regarding ADHD's cause, pathogenesis, methods of syndrome differentiation, and rationale for treatment. The authors present commonly accepted and successfully practiced clinical procedures used in China for diagnosis and treatment of ADHD by TCM clinicians along with the supportive clinical evidence. The authors hope to inspire more research to better understand the mechanisms underlying the therapies and to promote appropriate incorporation of TCM therapies with Western pharmacologic treatment to better help patients with ADHD.

Behavioral and genetic differences among Wistar-Kyoto (WKY) rats from different vendors and different breeders have long been observed, but generally overlooked. In our prior work, we found that two closely related WKY substrains, the WKY/NCrl and WKY/NHsd rats, differ in a small percentage of their genome which appeared to be highly enriched for autism risk genes. Although both substrains have been used widely in studies of hypertension, attention deficit/hyperactivity disorder (ADHD) and depression, they have not been tested for any autism-related behavioral phenotypes. Furthermore, these two substrains have often been used interchangeably in previous studies; no study has systematically examined the phenotypic differences that could be attributed by their small yet potentially meaningful genetic differences. In this paper we compared these two substrains on a battery of neurobehavioral tests. Although two substrains were similar in locomotor activity, WKY/NCrl rats were significantly different from WKY/NHsd rats in the elevated plus maze test, as well as measures of social interaction and ultrasonic vocalization. These strains were also compared with Sprague Dawley (SD) rats, a common outbred strain, and spontaneous hypertensive rats (SHR), an inbred rat model for ADHD and hypertension, which were derived from the same ancestor strain as the WKY strains. Our behavioral findings suggest that WKY/NCrl rats may be useful as a model autism spectrum disorders due to their lower social interest, lower ultrasonic vocalization and higher anxiety levels when WKY/NHsd rats are used as the control strain. Given the small genetic difference between the two inbred substrains, future studies to identify the exact gene and sequence variants that differ between the two may be useful for identifying the genetic mechanisms underlying these behaviors.

For most species, aggressive behavior is an evolutionally conserved and genetically specified trait that is essential for survival and fitness. Like many other heritable traits, aggressive behavior has complex genetic bases. Human GWAS studies, genetic mapping and transcriptome analysis of animal models and candidate gene studies in the past decades have provided many useful information for our understanding of genetic architecture underlying aggressive behaviors. However, no genome-wide association has been achieved due to underpowered samples. We present a cross-species and network-based approach to integrate the genetic findings from different types of studies in human and rodent models. We investigated various types of networks formed by these candidate genes, including gene co-expression, protein-protein interaction, canonical pathways and evolutionary profile similarity networks. This analysis helped us to identify top-ranked candidate genes and additional candidates based on their topological importance and the relationship with known candidate genes.

Much psychiatric genetic research has focused on a 40-base pair variable number of tandem repeats (VNTR) polymorphism located in the 3′-untranslated region (3′UTR) of the dopamine active transporter (DAT) gene (SLC6A3). This variant produces two common alleles with 9- and 10-repeats (9R and 10R). Studies associating this variant with in vivo DAT activity in humans have had mixed results. We searched for studies using positron emission tomography (PET) or single-photon emission computed tomography (SPECT) to evaluate this association. Random effects meta-analyses assessed the association of the 3′UTR variant with DAT activity. We also evaluated heterogeneity among studies and evidence for publication bias. We found twelve studies comprising 511 subjects, 125 from PET studies and 386 from SPECT studies. The PET studies provided highly significant evidence that the 9R allele was associated with increased DAT activity in human adults. The SPECT studies were highly heterogeneous. As a group, they suggested no association between the 3′UTR polymorphism and DAT activity. When the analysis was limited to the most commonly used ligand, [123I]β-CIT, stratification by affection status dramatically reduced heterogeneity and revealed a significant association of the 9R allele with increased DAT activity for healthy subjects. In humans, the 9R allele of the 3′UTR polymorphism of SLC6A3 regulates dopamine activity in the striatal brain regions independent of the presence of neuropsychiatric illness. Differences in study methodology account for the heterogeneous results across individual studies.

The diverse spatial and temporal expression of alternatively spliced transcript isoforms shapes neurodevelopment and plays a major role in neuronal adaptability. Although alternative splicing is extremely common in the brain, its role in mental illnesses such as schizophrenia has received little attention. To examine this relationship, postmortem brain tissue was obtained from 20 individuals with schizophrenia (SZ) and 20 neuropsychiatrically normal comparison subjects. Gray matter samples were extracted from two brain regions implicated in the disorder: Brodmann Area 10 and caudate. Affymetrix Human Gene 1.0 ST arrays were used on four subjects per group to attain an initial profile of differential expression of transcribed elements within and across brain regions in SZ. Numerous genes of interest with altered mRNA transcripts were identified by microarray through the differential expression of particular exons and 3′ untranslated regions (UTRs) between diagnostic groups. Select microarray results—including dysregulation of ENAH exon 11a and CPNE3 3′UTR—were verified by qRTPCR and replicated in the remaining independent sample of 16 SZ patients and 16 normal comparison subjects. These results, if further replicated, clearly illustrate the importance of Identifying transcriptomic variants in expression studies, and implicate novel candidate genes in the disorder.

SLC9A9 is a sodium hydrogen exchanger present in the recycling endosome and highly expressed in the brain. It is implicated in neuropsychiatric disorders, including autism spectrum disorders (ASDs). Little research concerning its gene expression patterns and biological pathways has been conducted. We sought to investigate its possible biological roles in autism-associated brain regions throughout development. We conducted a weighted gene co-expression network analysis on RNA-seq data downloaded from Brainspan. We compared prenatal and postnatal gene expression networks for three ASD-associated brain regions known to have high SLC9A9 gene expression. We also performed an ASD-associated single nucleotide polymorphism enrichment analysis and a cell signature enrichment analysis. The modules showed differences in gene constituents (membership), gene number, and connectivity throughout time. SLC9A9 was highly associated with immune system functions, metabolism, apoptosis, endocytosis, and signaling cascades. Gene list comparison with co-immunoprecipitation data was significant for multiple modules. We found a disproportionately high autism risk signal among genes constituting the prenatal hippocampal module. The modules were enriched with astrocyte and oligodendrocyte markers. SLC9A9 is potentially involved in the pathophysiology of ASDs. Our investigation confirmed proposed functions for SLC9A9, such as endocytosis and immune regulation, while also revealing potential roles in mTOR signaling and cell survival.. By providing a concise molecular map and interactions, evidence of cell type and implicated brain regions we hope this will guide future research on SLC9A9. Autism Res 2017, 10: 414-429. © 2016 International Society for Autism Research, Wiley Periodicals, Inc.

SLC9A9 [solute carrier family 9, member 9, also known as Na+/H+ exchanger member 9 (NHE9)], has been implicated in human attention deficit/hyperactivity disorder (ADHD), autism, and rat studies of hyperactivity and inattentiveness. SLC9A9 is a membrane protein that regulates the luminal pH of the recycling endosome. We recently reported the interactions of SLC9A9 with two molecules: calcineurin homologous protein (CHP) and receptor for activated C-kinase 1 (RACK1). We also reported two novel SLC9A9 mutations and abnormal gene expression profiles in the brains of an inattentive type rat model of ADHD (WKY/NCrl rat). In this study, we further examined the expression and relationship of SLC9A9 and 9 additional genes (CHP, RACK1, CaM, PPP3R1, PPP1R10, PKCm, CaMKI, NR2B, PLCb1) that may directly or indirectly interact with SLC9A9 in the hippocampus of the WKY/NCrl rat and the spontaneously hypertensive rat (SHR) model of the combined type of ADHD. We found that the expression levels of these genes were significantly correlated, suggesting that they may be coregulated. Principal component analysis identified two main factors that accounted for 94% of the expression variance of the 10 genes. Significant differences were found for both factors across the 3 different rat strains. The two ADHD rat models (WKY/NCrl and SHR), although different from each other in adulthood, showed similar profiles in adolescence. Both models were significantly different from WKY/NHsd control rats at both ages. The expression abnormalities of each gene were evaluated and their roles in cell signaling processes such as calcium signaling and protein phosphorylation are discussed. Our results suggest that abnormalities in SLC9A9-mediated signaling pathways could contribute to the ADHD phenotype of two rat models (WKY/NCrl and SHR/NCrl), and that the perturbation of the SLC9A9 network is age-dependent.

Genetic studies of human aggression have mainly focused on known candidate genes and pathways regulating serotonin and dopamine signaling and hormonal functions. These studies have taught us much about the genetics of human aggression, but no genetic locus has yet achieved genome-significance. We here present a review based on a paradoxical hypothesis that studies of rare, functional genetic variations can lead to a better understanding of the molecular mechanisms underlying complex multifactorial disorders such as aggression. We examined all aggression phenotypes catalogued in Online Mendelian Inheritance in Man (OMIM), an Online Catalog of Human Genes and Genetic Disorders. We identified 95 human disorders that have documented aggressive symptoms in at least one individual with a well-defined genetic variant. Altogether, we retrieved 86 causal genes. Although most of these genes had not been implicated in human aggression by previous studies, the most significantly enriched canonical pathways had been previously implicated in aggression (e.g., serotonin and dopamine signaling). Our findings provide strong evidence to support the causal role of these pathways in the pathogenesis of aggression. In addition, the novel genes and pathways we identified suggest additional mechanisms underlying the origins of human aggression. Genome-wide association studies with very large samples will be needed to determine if common variants in these genes are risk factors for aggression. © 2015 Wiley Periodicals, Inc.

Autism spectrum disorders (ASDs) are a group of neurodevelopmental disorders which begin in childhood and persist into adulthood. They cause lifelong impairments and are associated with substantial burdens to patients, families, and society. Genetic studies have implicated the sodium/proton exchanger (NHE) nine gene, Slc9a9, to ASDs and attention-deficit/hyperactivity disorder(ADHD). Slc9a9 encodes, NHE9, a membrane protein of the late recycling endosomes. The recycling endosome plays an important role in synapse development and plasticity by regulating the trafficking of membrane neurotransmitter receptors and transporters. Here we tested the hypothesis that Slc9a9 knock-out (KO) mice would show ADHD-like and ASD-like traits. Ultrasonic vocalization (USV) recording showed that Slc9a9 KO mice emitted fewer calls and had shorter call durations, which suggest communication impairment. Slc9a9 KO mice lacked a preference for social novelty, but did not show deficits in social approach; Slc9a9 KO mice spent more time self-grooming, an indicator for restricted and repetitive behavior. We did not observe hyperactivity or other behavior impairments which are commonly comorbid with ASDs in human, such as anxiety-like behavior. Our study is the first animal behavior study that links Slc9a9 to ASDs. By eliminatingNHE9 activity, it provides strong evidence that lack of Slc9a9leads to ASD-like behaviors in mice and provides the field with a new mouse model of ASDs.

This review covers an introduction of traditional Chinese medicine (TCM) in treating attention-deficit/hyperactivity disorder (ADHD), focusing on the traditional theoretic basis from the perspective of TCM regarding ADHD's cause, pathogenesis, methods of syndrome differentiation, and rationale for treatment. The authors present commonly accepted and successfully practiced clinical procedures used in China for diagnosis and treatment of ADHD by TCM clinicians along with the supportive clinical evidence. The authors hope to inspire more research to better understand the mechanisms underlying the therapies and to promote appropriate incorporation of TCM therapies with Western pharmacologic treatment to better help patients with ADHD.

Objective: To clarify the role of oxidative stress and antioxidant activity in ADHD. Method: We examined the association of ADHD and oxidative stress by applying random effects meta-analysis to studies of oxidative stress and antioxidant status in medication naive patients with ADHD and controls. Results: Six studies of a total of 231 ADHD patients and 207 controls met our selection criteria. The association between ADHD and antioxidant status was not significant. We found a significant association between ADHD and oxidative stress that could not be accounted for by publication bias. The significant association lost significance after correcting for intrastudy clustering. No one observation accounted for the positive result. Conclusion: These results are preliminary given the small number of studies. They suggest that patients with ADHD have normal levels of antioxidant production, but that their response to oxidative stress is insufficient, leading to oxidative damage.

ADHD and autism spectrum disorder are common psychiatric comorbidities to each another. In addition, there is behavioral, biological and neuropsychological overlap between the two disorders. There are also several important differences between autism spectrum disorder and ADHD. Treatment strategies for the comorbid condition will also be reviewed. Future areas of research and clinical need will be discussed.