The Effect of COVID-19 Public Health Measures On Mental Health in California.

California was the first state to implement statewide public health measures, including lockdown and curfews, to mitigate transmission of SARS-CoV-2. The implementation of these public health measures may have had unintended consequences related to mental health for persons in California. This study is a retrospective review of electronic health records of patients who sought care in the University of California Health System to examine changes in mental health status during the pandemic. Data were extracted prior to the pandemic (March-October 2019) and during the pandemic (March-October 2020). Weekly values of new mental health disorders were extracted and further classified based on age. Paired t-tests were performed to test for differences in the occurrence of each mental health disorder for each age group. A two-way ANOVA was performed to assess for between group differences. When compared with pre-pandemic diagnoses, persons aged 26-35 had the greatest increase in mental health diagnoses overall during the pandemic, specifically for anxiety, bipolar disorder, depression, mood disturbance, and psychosis. The mental health of persons age 25-35 were more affected than any other age group.

The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium.

Current knowledge about functional connectivity in obsessive-compulsive disorder (OCD) is based on small-scale studies, limiting the generalizability of results. Moreover, the majority of studies have focused only on predefined regions or functional networks rather than connectivity throughout the entire brain. Here, we investigated differences in resting-state functional connectivity between OCD patients and healthy controls (HC) using mega-analysis of data from 1024 OCD patients and 1028 HC from 28 independent samples of the ENIGMA-OCD consortium. We assessed group differences in whole-brain functional connectivity at both the regional and network level, and investigated whether functional connectivity could serve as biomarker to identify patient status at the individual level using machine learning analysis. The mega-analyses revealed widespread abnormalities in functional connectivity in OCD, with global hypo-connectivity (Cohen's d: -0.27 to -0.13) and few hyper-connections, mainly with the thalamus (Cohen's d: 0.19 to 0.22). Most hypo-connections were located within the sensorimotor network and no fronto-striatal abnormalities were found. Overall, classification performances were poor, with area-under-the-receiver-operating-characteristic curve (AUC) scores ranging between 0.567 and 0.673, with better classification for medicated (AUC = 0.702) than unmedicated (AUC = 0.608) patients versus healthy controls. These findings provide partial support for existing pathophysiological models of OCD and highlight the important role of the sensorimotor network in OCD. However, resting-state connectivity does not so far provide an accurate biomarker for identifying patients at the individual level.

Right Prefrontal Cortical Thickness Is Associated With Response to Cognitive-Behavioral Therapy in Children With Obsessive-Compulsive Disorder.

OBJECTIVE: Cognitive-behavioral therapy (CBT) is considered a first-line treatment for obsessive-compulsive disorder (OCD) in pediatric and adult populations. Nevertheless, some patients show partial or null response. The identification of predictors of CBT response may improve clinical management of patients with OCD. Here, we aimed to identify structural magnetic resonance imaging (MRI) predictors of CBT response in 2 large series of children and adults with OCD from the worldwide ENIGMA-OCD consortium. METHOD: Data from 16 datasets from 13 international sites were included in the study. We assessed which variations in baseline cortical thickness, cortical surface area, and subcortical volume predicted response to CBT (percentage of baseline to post-treatment symptom reduction) in 2 samples totaling 168 children and adolescents (age range 5-17.5 years) and 318 adult patients (age range 18-63 years) with OCD. Mixed linear models with random intercept were used to account for potential cross-site differences in imaging values. RESULTS: Significant results were observed exclusively in the pediatric sample. Right prefrontal cortex thickness was positively associated with the percentage of CBT response. In a post hoc analysis, we observed that the specific changes accounting for this relationship were a higher thickness of the frontal pole and the rostral middle frontal gyrus. We observed no significant effects of age, sex, or medication on our findings. CONCLUSION: Higher cortical thickness in specific right prefrontal cortex regions may be important for CBT response in children with OCD. Our findings suggest that the right prefrontal cortex plays a relevant role in the mechanisms of action of CBT in children.

COVID Symptoms, Symptom Clusters, and Predictors for Becoming a Long-Hauler Looking for Clarity in the Haze of the Pandemic.

Post-acute sequelae of SARS-CoV-2 (PASC) is defined as persistent symptoms after apparent recovery from acute COVID-19 infection, also known as COVID-19 long-haul. We performed a retrospective review of electronic health records (EHR) from the University of California COvid Research Data Set (UC CORDS), a de-identified EHR of PCR-confirmed SARS-CoV-2-positive patients in California. The purposes were to (1) describe the prevalence of PASC, (2) describe COVID-19 symptoms and symptom clusters, and (3) identify risk factors for PASC. Data were subjected to non-negative matrix factorization to identify symptom clusters, and a predictive model of PASC was developed. PASC prevalence was 11% (277/2,153), and of these patients, 66% (183/277) were considered asymptomatic at days 0-30. Five PASC symptom clusters emerged and specific symptoms at days 0-30 were associated with PASC. Women were more likely than men to develop PASC, with all age groups and ethnicities represented. PASC is a public health priority.

The Burden of Antipsychotic-Induced Weight Gain and Metabolic Syndrome in Children.

Antipsychotic medications are critical to child and adolescent psychiatry, from the stabilization of psychotic disorders like schizophrenia, bipolar disorder, and psychotic depression to behavioral treatment of autism spectrum disorder, tic disorders, and pediatric aggression. While effective, these medications carry serious risk of adverse events-most commonly, weight gain and cardiometabolic abnormalities. Negative metabolic consequences affect up to 60% of patients and present a major obstacle to long-term treatment. Since antipsychotics are often chronically prescribed beginning in childhood, cardiometabolic risk accumulates. An increased susceptibility to antipsychotic-induced weight gain (AIWG) has been repeatedly documented in children, particularly rapid weight gain. Associated cardiometabolic abnormalities include central obesity, insulin resistance, dyslipidemia, and systemic inflammation. Lifestyle interventions and medications such as metformin have been proposed to reduce risk but remain limited in efficacy. Furthermore, antipsychotic medications touted to be weight-neutral in adults can cause substantial weight gain in children. A better understanding of the biological underpinnings of AIWG could inform targeted and potentially more fruitful treatments; however, little is known about the underlying mechanism. As yet, modest genetic studies have nominated a few risk genes that explain only a small percentage of the risk. Recent investigations have begun to explore novel potential mechanisms of AIWG, including a role for gut microbiota and microbial metabolites. This article reviews the problem of AIWG and AP metabolic side effects in pediatric populations, proposed mechanisms underlying this serious side effect, and strategies to mitigate adverse impact. We suggest future directions for research efforts that may advance the field and lead to improved clinical interventions.

COVID Symptoms, Symptom Clusters, and Predictors for Becoming a Long-Hauler: Looking for Clarity in the Haze of the Pandemic.

Emerging data suggest that the effects of infection with SARS-CoV-2 are far reaching extending beyond those with severe acute disease. Specifically, the presence of persistent symptoms after apparent resolution from COVID-19 have frequently been reported throughout the pandemic by individuals labeled as "long-haulers". The purpose of this study was to assess for symptoms at days 0-10 and 61+ among subjects with PCR-confirmed SARS-CoV-2 infection. The University of California COvid Research Data Set (UC CORDS) was used to identify 1407 records that met inclusion criteria. Symptoms attributable to COVID-19 were extracted from the electronic health record. Symptoms reported over the previous year prior to COVID-19 were excluded, using nonnegative matrix factorization (NMF) followed by graph lasso to assess relationships between symptoms. A model was developed predictive for becoming a long-hauler based on symptoms. 27% reported persistent symptoms after 60 days. Women were more likely to become long-haulers, and all age groups were represented with those aged 50 ± 20 years comprising 72% of cases. Presenting symptoms included palpitations, chronic rhinitis, dysgeusia, chills, insomnia, hyperhidrosis, anxiety, sore throat, and headache among others. We identified 5 symptom clusters at day 61+: chest pain-cough, dyspnea-cough, anxiety-tachycardia, abdominal pain-nausea, and low back pain-joint pain. Long-haulers represent a very significant public health concern, and there are no guidelines to address their diagnosis and management. Additional studies are urgently needed that focus on the physical, mental, and emotional impact of long-term COVID-19 survivors who become long-haulers.

CYP2D6 genotype may moderate measures of brain structure in methamphetamine users.

Chronic methamphetamine use is linked to abnormalities in brain structure, which may reflect neurotoxicity related to metabolism of the drug. As the cytochrome P450 2D6 (CYP2D6) enzyme is central to the metabolism of methamphetamine, genotypic variation in its activity may moderate effects of methamphetamine on brain structure and function. This study explored the relationship between CYP2D6 genotype and measures of brain structure and cognition in methamphetamine users. Based on the function of genetic variants, a CYP2D6 activity score was determined in 82 methamphetamine-dependent (Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition [DSM-IV] criteria) and 79 healthy-control participants who completed tests of cognitive function (i.e., attention, memory, and executive function); most were also evaluated with structural magnetic resonance imaging (MRI) (66 methamphetamine-dependent and 52 controls). The relationship between CYP2D6 activity score and whole brain cortical thickness differed by group (interaction p = 0.024), as increasing CYP2D6 activity was associated with thinner cortical thickness in the methamphetamine users (ß = -0.254; p = 0.035), but not in control subjects (ß = 0.095; p = 0.52). Interactions between CYP2D6 activity and group were nonsignificant for hippocampal volume (ps > 0.05), but both hippocampi showed trends similar to those observed for cortical thickness (negative relationships in methamphetamine users [ps < 0.05], and no relationships in controls [ps > 0.50]). Methamphetamine users had lower cognitive scores than control subjects (p = 0.007), but there was no interaction between CYP2D6 activity score and group on cognition (p > 0.05). Results suggest that CYP2D6 genotypes linked to higher enzymatic activity may confer risk for methamphetamine-induced deficits in brain structure. The behavioral consequences of these effects are unclear and warrant additional investigation.

No significant elevation of translocator protein binding in the brains of recently abstinent methamphetamine users.

BACKGROUND: Radioligands for the translocator protein (TSPO) 18 kDa have been used with positron emission tomography (PET) to assess neuroinflammation and microglial activation in psychiatric disorders. One study using this approach showed substantial TSPO elevation throughout the brain in chronic methamphetamine users following long-term abstinence (0.5-4 years), but clients typically present for treatment earlier in abstinence. METHODS: We used PET with [11C]DAA1106 to compare standardized uptake values (SUVs) as an index of TSPO binding in the brains of methamphetamine-dependent participants who were abstinent for < 6 months (n = 11) and healthy controls (n = 12). We also assayed other typical correlates of Methamphetamine Dependence (e.g., striatal D2-type dopamine receptor deficits, depressed mood, anxiety and impaired emotion regulation). RESULTS: Methamphetamine users exhibited depression (p < 0.0001), anxiety (p = 0.002), difficulties in emotional regulation (p = 0.01), and lower striatal dopamine D2-type receptor availability vs. controls (p = 0.02). SUVs for [11C]DAA1106 were larger in all brain regions of methamphetamine-dependent participants vs. controls, but the effect size was small to medium and not statistically significant. CONCLUSIONS: The discrepancy between the lack of significant difference in TSPO binding in early-abstinent methamphetamine users vs. controls in this study and a previous report of elevated binding in longer-abstinent methamphetamine users may reflect methodological differences or limitations of TSPO binding as an index of neuroinflammation. It also seems possible that gliosis increases over time during the first 6 months of abstinence; longitudinal studies could clarify this possibility.

Brain structural covariance networks in obsessive-compulsive disorder: a graph analysis from the ENIGMA Consortium.

Brain structural covariance networks reflect covariation in morphology of different brain areas and are thought to reflect common trajectories in brain development and maturation. Large-scale investigation of structural covariance networks in obsessive-compulsive disorder (OCD) may provide clues to the pathophysiology of this neurodevelopmental disorder. Using T1-weighted MRI scans acquired from 1616 individuals with OCD and 1463 healthy controls across 37 datasets participating in the ENIGMA-OCD Working Group, we calculated intra-individual brain structural covariance networks (using the bilaterally-averaged values of 33 cortical surface areas, 33 cortical thickness values, and six subcortical volumes), in which edge weights were proportional to the similarity between two brain morphological features in terms of deviation from healthy controls (i.e. z-score transformed). Global networks were characterized using measures of network segregation (clustering and modularity), network integration (global efficiency), and their balance (small-worldness), and their community membership was assessed. Hub profiling of regional networks was undertaken using measures of betweenness, closeness, and eigenvector centrality. Individually calculated network measures were integrated across the 37 datasets using a meta-analytical approach. These network measures were summated across the network density range of K = 0.10-0.25 per participant, and were integrated across the 37 datasets using a meta-analytical approach. Compared with healthy controls, at a global level, the structural covariance networks of OCD showed lower clustering (P < 0.0001), lower modularity (P < 0.0001), and lower small-worldness (P = 0.017). Detection of community membership emphasized lower network segregation in OCD compared to healthy controls. At the regional level, there were lower (rank-transformed) centrality values in OCD for volume of caudate nucleus and thalamus, and surface area of paracentral cortex, indicative of altered distribution of brain hubs. Centrality of cingulate and orbito-frontal as well as other brain areas was associated with OCD illness duration, suggesting greater involvement of these brain areas with illness chronicity. In summary, the findings of this study, the largest brain structural covariance study of OCD to date, point to a less segregated organization of structural covariance networks in OCD, and reorganization of brain hubs. The segregation findings suggest a possible signature of altered brain morphometry in OCD, while the hub findings point to OCD-related alterations in trajectories of brain development and maturation, particularly in cingulate and orbitofrontal regions.

Genome-wide association study on antipsychotic-induced weight gain in Europeans and African-Americans.

BACKGROUND: Antipsychotic (AP) medications are the first line of treatment for schizophrenia. However, most conferr a risk of antipsychotic-induced weight gain (AIWG). The objective of this investigation was to conduct a genome-wide association study (GWAS) of AIWG, followed by comprehensive, post-GWAS approaches. METHODS: We investigated n = 201 schizophrenia or schizoaffective disorder patients of European and African American ancestry who were treated primarily with clozapine or olanzapine. We conducted a genome-wide association analysis for AIWG, defined primarily as a percentage of weight change from baseline. RESULTS: When examining Europeans (n = 147), we noticed an association between rs62097526 (ß = 0.39, p = 3.59 × 10-6, CADD = 2.213) variant, located downstream of the CIDEA gene, which is considered a risk factor for AIWG. In the entire sample, we observed a significant association between rs1525085 (ß = 0.411, p = 3.15 × 10-9) variant of the DGKB gene and AIWG. The association was nominally significant in Europeans (ß = 0.271, p = 0.002) and African Americans (ß = 0.579, p = 5.73 × 10-5) with the same risk allele. Our top genes (p < 5 × 10-5) were enriched in the GWAS catalog for the risk of obesity and interacted with the known risk factors for obesity (G6PD) and diabetes (IRS1). In addition, these genes are targeted by miRNAs related to schizophrenia (mir-34a) and obesity (mir-19b). However, our polygenic risk score analyses did not provide support for major genetic overlap between obesity and the risk of AIWG. CONCLUSIONS: In summary, we propose that the CIDEA and DGKB genes are risk factors for AIWG in transethnic populations. Additionally, our evidence suggests that the G6PD and IRS1 gene-related pathways might be involved in AIWG.

Clinical Pharmacogenetics Implementation Consortium Guideline for Cytochrome P450 (CYP)2D6 Genotype and Atomoxetine Therapy.

Atomoxetine is a nonstimulant medication used to treat attention-deficit/hyperactivity disorder (ADHD). Cytochrome P450 (CYP)2D6 polymorphisms influence the metabolism of atomoxetine thereby affecting drug efficacy and safety. We summarize evidence from the published literature supporting these associations and provide therapeutic recommendations for atomoxetine based on CYP2D6 genotype (updates at www.cpicpgx.org).

No effect of attentional bias modification training in methamphetamine users receiving residential treatment.

RATIONALE: Attentional bias toward drug-related stimuli is a feature of drug addiction that is linked to craving and drug-seeking behavior. OBJECTIVES/METHOD: An attentional bias modification (ABM) program was tested in 42 methamphetamine-dependent clients (DSM-IV criteria) receiving residential treatment for their drug use. Participants were randomly assigned to one of two groups (N = 21 each), receiving 12 sessions of either computerized ABM training (designed to train attention away from methamphetamine stimuli 100% of the time) or an attentional control condition (designed to train attention away from methamphetamine stimuli 50% of the time). Outcome measures included attentional bias to methamphetamine-related stimuli on a probe detection task, self-reported craving, and preferences to view methamphetamine-related images on a Simulated Drug Choice Task. A subset of participants (N = 17) also underwent fMRI in a cue-induced craving paradigm. RESULTS: Poor split-half reliability was observed for the probe detection task. Using this task, attentional bias toward methamphetamine-related stimuli was greater after training than at baseline, irrespective of group (p = 0.037). Spontaneous and cue-induced methamphetamine craving diminished with time (ps < 0.01), but ABM training did not influence these effects (group by time interactions, ps > 0.05). ABM training did not influence selection of methamphetamine-related pictures in the Simulated Drug Choice task (p > 0.05). In the fMRI assessment, cue-induced activation in the ventromedial prefrontal cortex was reduced over time, without an effect of ABM training. CONCLUSIONS: ABM training did not improve several clinically relevant variables in treatment-seeking methamphetamine users. Additional research is needed to improve the measurement of attentional bias.

Effect of overnight smoking abstinence on a marker for microglial activation: a [11C]DAA1106 positron emission tomography study.

RATIONALE: Microglia are the main immune cells in the central nervous system and participate in neuroinflammation. When activated, microglia express increased levels of the translocator protein 18 kDa (TSPO), thereby making TSPO availability a marker for neuroinflammation. Using positron emission tomography (PET) scanning, our group recently demonstrated that smokers in the satiated state had 16.8% less binding of the radiotracer [11C]DAA1106 (a radioligand for TSPO) in the brain than nonsmokers. OBJECTIVES: We sought to determine the effect of overnight smoking abstinence on [11C]DAA1106 binding in the brain. METHODS: Forty participants (22 smokers and 18 nonsmokers) completed the study (at one of two sites) and had usable data, which included images from a dynamic [11C]DAA1106 PET scanning session (with smokers having been abstinent for 17.9 ± 2.3 h) and a blood sample for TSPO genotyping. Whole brain standardized uptake values (SUVs) were determined, and analysis of variance was performed, with group (overnight abstinent smoker vs. nonsmoker), site, and TSPO genotype as factors, thereby controlling for site and genotype. RESULTS: Overnight abstinent smokers had lower whole brain SUVs (by 15.5 and 17.0% for the two study sites) than nonsmokers (ANCOVA, P = 0.004). The groups did not significantly differ in injected radiotracer dose or body weight, which were used to calculate SUV. CONCLUSIONS: These results in overnight abstinent smokers are similar to those in satiated smokers, indicating that chronic cigarette smoking leads to global impairment of microglial activation which persists into early abstinence. Other explanations for study results, such as smoking leading to reduced numbers of microglia or smokers having more rapid metabolism of the radiotracer than nonsmokers, are also possible.

Parental overcontrol x OPRM1 genotype interaction predicts school-aged children's sympathetic nervous system activation in response to performance challenge.

Parental overcontrol (OC), the excessive regulation of a child's behavior, cognition, and emotion, is associated with the development of child anxiety. While studies have shown that genetic factors may increase sensitivity to stress, genetic vulnerability to parental OC has not been examined in anxiety etiology. A functional polymorphism in the mu opioid receptor OPRM1 (A118G, rs1799971) has been shown to impact stress reactivity. Using a community sample of children (N = 85, 9-12 years old), we examined the main and interactive effects of maternal OC and child OPRM1 genotype in predicting children's sympathetic nervous system reactivity during a performance stressor. Neither OC nor genotype predicted children's electrodermal activity (EDA); however, the interaction between OC and child genotype significantly predicted stress reactivity, as indexed by EDA, during the challenging task. Among children with the minor G-allele, higher maternal OC was associated with higher reactivity. In A homozygotes, maternal OC was not associated with EDA, suggesting a diathesis-stress pattern of gene x environment interaction. We discuss implications for anxiety etiology and intervention.

Interaction between the Opioid Receptor OPRM1 Gene and Mother-Child Language Style Matching Prospectively Predicts Children's Separation Anxiety Disorder Symptoms.

Recent research suggests that lower mother-child language style matching (LSM) is associated with greater physiological reactivity and insecure attachment in school-aged children, but to date no studies have explored this measure of parent-child behavioral matching for its association with children's anxiety symptoms, a well-known correlate of attachment insecurity and heightened physiological reactivity. There is also considerable evidence of genetic risk for anxiety, including possession of the OPRM1 minor allele, 118G. In the current study (N = 44), we expand upon what is known about children's genetic and environmental risk for anxiety by examining the unique and interactive effects of mother-child LSM and the OPRM1 polymorphism A118G on school-aged children's separation anxiety disorder (SAD) symptoms. SAD symptoms were measured both concurrently with LSM and OPRM1 genotype and two years later through self-report. No significant associations emerged between LSM or OPRM1 and concurrent Time 1 SAD symptoms. However, lower LSM and 118G minor allele possession were both associated with greater SAD symptoms at Time 2; further, the interaction between LSM and OPRM1 genotype significantly predicted SAD symptoms beyond the main effects of the two variables. Possession of the minor allele was only associated with greater SAD symptoms among children in low LSM dyads, whereas children with the minor allele in high LSM dyads showed non-significantly lower SAD symptoms. These findings and a proportion affected analysis provide support for a differential susceptibility model of gene by environment interactions for the OPRM1 gene. We discuss the implications for predicting children's separation anxiety across development.

Effect of Cigarette Smoking on a Marker for Neuroinflammation: A [11C]DAA1106 Positron Emission Tomography Study.

This corrects the article DOI: 10.1038/npp.2017.48.

An Empirical Comparison of Meta- and Mega-Analysis With Data From the ENIGMA Obsessive-Compulsive Disorder Working Group.

Objective: Brain imaging communities focusing on different diseases have increasingly started to collaborate and to pool data to perform well-powered meta- and mega-analyses. Some methodologists claim that a one-stage individual-participant data (IPD) mega-analysis can be superior to a two-stage aggregated data meta-analysis, since more detailed computations can be performed in a mega-analysis. Before definitive conclusions regarding the performance of either method can be drawn, it is necessary to critically evaluate the methodology of, and results obtained by, meta- and mega-analyses. Methods: Here, we compare the inverse variance weighted random-effect meta-analysis model with a multiple linear regression mega-analysis model, as well as with a linear mixed-effects random-intercept mega-analysis model, using data from 38 cohorts including 3,665 participants of the ENIGMA-OCD consortium. We assessed the effect sizes and standard errors, and the fit of the models, to evaluate the performance of the different methods. Results: The mega-analytical models showed lower standard errors and narrower confidence intervals than the meta-analysis. Similar standard errors and confidence intervals were found for the linear regression and linear mixed-effects random-intercept models. Moreover, the linear mixed-effects random-intercept models showed better fit indices compared to linear regression mega-analytical models. Conclusions: Our findings indicate that results obtained by meta- and mega-analysis differ, in favor of the latter. In multi-center studies with a moderate amount of variation between cohorts, a linear mixed-effects random-intercept mega-analytical framework appears to be the better approach to investigate structural neuroimaging data.