Psychiatric, Neurological, and Somatic Comorbidities in Intermittent Explosive Disorder: a retrospective cohort study of electronic health records.

Importance: Intermittent Explosive Disorder (IED) is an understudied psychiatric condition that presents with repeated episodes of impulsive aggression and poorly regulated emotional control, often resulting in interpersonal and societal consequences. Better understanding of comorbidities will allow for enhanced screening, diagnosis, and treatment of patients.

Objective: To investigate prevalence and associations of IED with psychiatric, neurological, and somatic disorders using real-world data.

A Systematic Review of the Etiology and Neurobiology of Intermittent Explosive Disorder.

Intermittent Explosive Disorder (IED) is characterized by repeated inability to control aggressive impulses. Although the etiology and neurobiology of impulsive anger and impulse control disorders have been reviewed, no systematic review on these aspects has been published for IED specifically. We conducted a systematic search in seven electronic databases for publications about IED, screened by two authors, and retained twenty-four studies for the review.

Recovery from opioid use on a neuropsychoanalytic service.

Background: Little is known about recovery from opioid use disorder (OUD) or outcomes of detoxification and drug-free treatment of chronic opioid therapy (COT). Harm reduction with medications for opioid use disorder (MOUD) is regarded as the only legitimate treatment.

Methods: The Institutional Review Board (IRB) approved reporting deidentified outcomes.

Post-COVID-19 Mental Health Distress in 13 Million Youth: A Retrospective Cohort Study of Electronic Health Records.

Objective: To investigate the impact of the SARS-CoV-2 infection on the rates of mental disorders in youth.

Method: The study involved 7,519,465 children and 5,338,496 adolescents from the TriNetX Research Network, all without prior mental disorder histories. Among them, 290,145 children and 223,667 adolescents had SARS-CoV-2-positive tests or confirmed COVID-19 diagnoses.

Effects of ADHD and ADHD treatment on glycemic management in type 1 diabetes: A systematic review and meta-analysis of observational studies.

Aims: Studies suggested a higher prevalence of Attention-deficit/hyperactivity disorder (ADHD) in individuals with Type 1 Diabetes Mellitus (T1D). However, it is unclear how ADHD impacts glycemia and diabetes-related complications. This systematic review and meta-analysis aimed to investigate the effect of ADHD and ADHD medications on HbA1c and acute complications in T1D.

A primer on the use of machine learning to distil knowledge from data in biological psychiatry.

Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and interdisciplinary collaborations, public availability of large datasets, an increase in the accessibility of analytic routines, and the availability of powerful computing resources. With this increased access and exposure to machine learning comes a responsibility for education and a deeper understanding of its bases and bounds, borne equally by data scientists seeking to ply their analytic wares in medical research and by biomedical scientists seeking to harness such methods to glean knowledge from data.

ADHD medication discontinuation and persistence across the lifespan: a retrospective observational study using population-based databases.

Background: Although often intended for long-term treatment, discontinuation of medication for ADHD is common. However, cross-national estimates of discontinuation are missing due to the absence of standardised measures. The aim of this study was to determine the rate of ADHD treatment discontinuation across the lifespan and to describe similarities and differences across countries to guide clinical practice.

Reward Functioning in General and Specific Psychopathology in Children and Adults.

Objective: Problems with reward processing have been implicated in multiple psychiatric disorders, but psychiatric comorbidities are common and their specificity to individual psychopathologies is unknown. Here, we evaluate the association between reward functioning and general or specific psychopathologies.

Method: 1,213 adults and their1,531 children (ages 6-12) completed various measures of the Positive Valence System domain from the Research Domain Criteria (RDoC).

Daily cannabis use may cause cannabis-induced hyperalgesia.

Background And Objectives: Public opinion about cannabis as a medical treatment is generally favorable. As many as 35% of primary care patients report medical use of cannabis, most commonly for pain treatment. We designed a way to test whether cannabis helps chronic pain.

Association of ADHD symptoms with type 2 diabetes and cardiovascular comorbidities in adults receiving outpatient diabetes care.

Background: The relationship between attention-deficit/hyperactivity disorder (ADHD) symptoms and type 2 diabetes mellitus (T2D) and its cardiovascular outcomes have not been sufficiently studied.

Methods: 2,986 adults with T2D from the Joslin Diabetes Center at Upstate Medical University were assessed for ADHD-like symptoms, executive dysfunction, and emotional control using the Adult Self-Report Scale V1.1 (ASRS) expanded version.

Machine Learning and MRI-based Diagnostic Models for ADHD: Are We There Yet?

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.

Predicting childhood and adolescent attention-deficit/hyperactivity disorder onset: a nationwide deep learning approach.

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.

Association Between ADHD and COVID-19 Infection and Clinical Outcomes: A Retrospective Cohort Study From Electronic Medical Records.

Objective: Though psychiatric illnesses have been associated with increased COVID-19 infection risk, limited information exists about the relationship between ADHD and COVID-19.

Methods: Using the TriNetX COVID-19 Research Network, we examined the impact of ADHD diagnosis and treatment on COVID-19 infection rates and outcomes.

Results: ADHD patients had greater risk of COVID-19 (risk ratio (RR) 1.

Structural brain morphometry as classifier and predictor of ADHD and reward-related comorbidities.

Attention deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders, and around two-thirds of affected children report persisting problems in adulthood. This negative trajectory is associated with high comorbidity with disorders like obesity, depression, or substance use disorder (SUD). Decreases in cortical volume and thickness have also been reported in depression, SUD, and obesity, but it is unclear whether structural brain alterations represent unique disorder-specific profiles.

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States.

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.

Ensemble classification of autism spectrum disorder using structural magnetic resonance imaging features.

Background: Autism spectrum disorder (ASD) is characterized by a spectrum of social and communication impairments and rigid and stereotyped behaviors that have a neurodevelopmental origin. Although many imaging studies have reported structural and functional alterations in multiple brain regions, clinically useful diagnostic imaging biomarkers for ASD remain unavailable.

Methods: In this study, we applied machine learning (ML) models to regional volumetric and cortical thickness data from the largest structural magnetic resonance imaging (sMRI) dataset available from the Enhancing Neuro Imaging Genetics Through Meta-Analysis (ENIGMA) consortium (1833 subjects with ASD and 1838 without ASD; age range: 1.

Alcohol use disorders and ADHD.

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).

A seq2seq model to forecast the COVID-19 cases, deaths and reproductive R numbers in US counties.

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.

A seq2seq model to forecast the COVID-19 cases, deaths and reproductive numbers in US counties.

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.

The Effects of Low Dose Naltrexone on Opioid Induced Hyperalgesia and Fibromyalgia.

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. The setting was an academic addiction medicine service that has an embedded pain service.

Evidence for similar structural brain anomalies in youth and adult attention-deficit/hyperactivity disorder: a machine learning analysis.

Attention-deficit/hyperactivity disorder (ADHD) affects 5% of children world-wide. Of these, two-thirds continue to have impairing symptoms of ADHD into adulthood. Although a large literature implicates structural brain differences of the disorder, it is not clear if adults with ADHD have similar neuroanatomical differences as those seen in children with recent reports from the large ENIGMA-ADHD consortium finding structural differences for children but not for adults.