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.

SLC9A9 (solute carrier family 9, member 9, also known as Na+/H+ exchanger member (NHE9)) is a membrane protein that regulates the luminal pH of the recycling endosome, an essential organelle for synaptic transmission and plasticity. SLC9A9 has been implicated in human attention deficit hyperactivity disorder (ADHD) and in rat studies of hyperactivity. We examined the SLC9A9 gene sequence and expression profile in prefrontal cortex, dorsal striatum and hippocampus in two genetic rat models of ADHD. We report two mutations in a rat model of inattentive ADHD, the WKY/NCrl rat, which affect the interaction of SLC9A9 with calcineurin homologous protein (CHP). We observed an age-dependent abnormal expression of SLC9A9 in brains of this inattentive model and in the Spontaneous Hypertensive Rat (SHR) model of ADHD. Our data suggest a novel mechanism whereby SLC9A9 sequence variants and abnormalities in gene expression could contribute to the ADHD-like symptoms of rat models and possibly the pathophysiology of ADHD in humans.

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.

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. This paper uses deep learning neural network classification models to determine if there are neuroanatomical changes in the brains of children with ADHD that are also observed for adult ADHD, and vice versa. We found that structural MRI data can significantly separate ADHD from control participants for both children and adults. Consistent with the prior reports from ENIGMA-ADHD, prediction performance and effect sizes were better for the child than the adult samples. The model trained on adult samples significantly predicted ADHD in the child sample, suggesting that our model learned anatomical features that are common to ADHD in childhood and adulthood. These results support the continuity of ADHD’s brain differences from childhood to adulthood. In addition, our work demonstrates a novel use of neural network classification models to test hypotheses about developmental continuity.