Faculty Bibliography

Hippocampal volume loss is prominent in first episode schizophrenia (FES) and has been associated with poor clinical outcomes and with BDNF genotype; antidepressants are believed to reverse hippocampal volume loss via release of BDNF. In a 12-month, placebo-controlled add-on trial of the antidepressant, citalopram, during the maintenance phase of FES, negative symptoms were improved with citalopram. We now report results of structural brain imaging at baseline and 6 months in 63 FES patients (34 in citalopram group) from the trial to assess whether protection against hippocampal volume loss contributed to improved negative symptoms with citalopram. Hippocampal volumetric integrity (HVI) did not change significantly in the citalopram or placebo group and did not differ between treatment groups, whereas citalopram was associated with greater volume loss of the right CA1 subfield. Change in cortical thickness was associated with SANS change in 4 regions (left rostral anterior cingulate, right frontal pole, right cuneus, and right transverse temporal) but none differed between treatment groups. Our findings suggest that minimal hippocampal volume loss occurs after stabilization on antipsychotic treatment and that citalopram's potential benefit for negative symptoms is unlikely to result from protection against hippocampal volume loss or cortical thinning.

Importance/UNASSIGNED:To date, the association of psychiatric diagnoses with mortality in patients infected with coronavirus disease 2019 (COVID-19) has not been evaluated. Objective/UNASSIGNED:To assess whether a diagnosis of a schizophrenia spectrum disorder, mood disorder, or anxiety disorder is associated with mortality in patients with COVID-19. Design, Setting, and Participants/UNASSIGNED:This retrospective cohort study assessed 7348 consecutive adult patients for 45 days following laboratory-confirmed COVID-19 between March 3 and May 31, 2020, in a large academic medical system in New York. The final date of follow-up was July 15, 2020. Patients without available medical records before testing were excluded. Exposures/UNASSIGNED:Patients were categorized based on the following International Statistical Classification of Diseases, Tenth Revision, Clinical Modification diagnoses before their testing date: (1) schizophrenia spectrum disorders, (2) mood disorders, and (3) anxiety disorders. Patients with these diagnoses were compared with a reference group without psychiatric disorders. Main Outcomes and Measures/UNASSIGNED:Mortality, defined as death or discharge to hospice within 45 days following a positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) test result. Results/UNASSIGNED:Of the 26 540 patients tested, 7348 tested positive for SARS-CoV-2 (mean [SD] age, 54 [18.6] years; 3891 [53.0%] women). Of eligible patients with positive test results, 75 patients (1.0%) had a history of a schizophrenia spectrum illness, 564 (7.7%) had a history of a mood disorder, and 360 (4.9%) had a history of an anxiety disorder. After adjusting for demographic and medical risk factors, a premorbid diagnosis of a schizophrenia spectrum disorder was significantly associated with mortality (odds ratio [OR], 2.67; 95% CI, 1.48-4.80). Diagnoses of mood disorders (OR, 1.14; 95% CI, 0.87-1.49) and anxiety disorders (OR, 0.96; 95% CI, 0.65-1.41) were not associated with mortality after adjustment. In comparison with other risk factors, a diagnosis of schizophrenia ranked behind only age in strength of an association with mortality. Conclusions and Relevance/UNASSIGNED:In this cohort study of adults with SARS-CoV-2-positive test results in a large New York medical system, adults with a schizophrenia spectrum disorder diagnosis were associated with an increased risk for mortality, but those with mood and anxiety disorders were not associated with a risk of mortality. These results suggest that schizophrenia spectrum disorders may be a risk factor for mortality in patients with COVID-19.

Active-duty Army personnel can be exposed to traumatic warzone events and are at increased risk for developing post-traumatic stress disorder (PTSD) compared with the general population. PTSD is associated with high individual and societal costs, but identification of predictive markers to determine deployment readiness and risk mitigation strategies is not well understood. This prospective longitudinal naturalistic cohort study-the Fort Campbell Cohort study-examined the value of using a large multidimensional dataset collected from soldiers prior to deployment to Afghanistan for predicting post-deployment PTSD status. The dataset consisted of polygenic, epigenetic, metabolomic, endocrine, inflammatory and routine clinical lab markers, computerized neurocognitive testing, and symptom self-reports. The analysis was computed on active-duty Army personnel (N = 473) of the 101st Airborne at Fort Campbell, Kentucky. Machine-learning models predicted provisional PTSD diagnosis 90-180 days post deployment (random forest: AUC = 0.78, 95% CI = 0.67-0.89, sensitivity = 0.78, specificity = 0.71; SVM: AUC = 0.88, 95% CI = 0.78-0.98, sensitivity = 0.89, specificity = 0.79) and longitudinal PTSD symptom trajectories identified with latent growth mixture modeling (random forest: AUC = 0.85, 95% CI = 0.75-0.96, sensitivity = 0.88, specificity = 0.69; SVM: AUC = 0.87, 95% CI = 0.79-0.96, sensitivity = 0.80, specificity = 0.85). Among the highest-ranked predictive features were pre-deployment sleep quality, anxiety, depression, sustained attention, and cognitive flexibility. Blood-based biomarkers including metabolites, epigenomic, immune, inflammatory, and liver function markers complemented the most important predictors. The clinical prediction of post-deployment symptom trajectories and provisional PTSD diagnosis based on pre-deployment data achieved high discriminatory power. The predictive models may be used to determine deployment readiness and to determine novel pre-deployment interventions to mitigate the risk for deployment-related PTSD.

This chapter describes recent clinical trials for opioid use disorder (OUD), an area that has rapidly accelerated in response to the opioid overdose crisis in the USA and newly appropriated funding. Trials involve a wide range of compounds including cannabinoids and psychedelics, new and existing compounds targeting domains emerging from addiction neuroscience, agents repurposed from other indications, and novel strategies including vaccines, enzymes, and other biologicals. In parallel, new formulations of existing compounds offer immediate promise, as do a variety of web-based interventions and smartphone-delivered apps. Trials focused on implementing existing effective interventions in mainstream healthcare settings, and others focused on special populations, e.g., adolescents, criminal justice, pregnant women, native Americans, etc., have the potential to vastly expand treatment in the near term. Given the range of ongoing and recent trials, this chapter is not intended to be an exhaustive review but rather to present an overview of approaches within the framework of the opioid treatment cascade and the context of current OUD pharmacotherapies.

BACKGROUND:Converging evidence implicates the anterior hippocampus in the proximal pathophysiology of schizophrenia. Although resting state functional connectivity (FC) holds promise for characterizing anterior hippocampal circuit abnormalities and their relationship to treatment response, this technique has not yet been used in first-episode psychosis (FEP) patients in a manner that distinguishes the anterior from posterior hippocampus. METHODS:We used masked-hippocampal-group-independent component analysis with dual regression to contrast subregional hippocampal-whole brain FC between healthy controls (HCs) and antipsychotic naïve FEP patients (N = 61, 36 female). In a subsample of FEP patients (N = 27, 15 female), we repeated this analysis following 8 weeks of second-generation antipsychotic treatment and explored whether baseline FC predicted treatment response using random forest. RESULTS:Relative to HC, untreated FEP subjects displayed reproducibly lower FC between the left anteromedial hippocampus and cortical regions including the anterior cingulate and insular cortex (P < .05, corrected). Anteromedial hippocampal FC increased in FEP patients following treatment (P < .005), and no longer differed from HC. Random forest analysis showed baseline anteromedial hippocampal FC with four brain regions, namely the insular-opercular cortex, superior frontal gyrus, precentral gyrus, and postcentral gyrus predicted treatment response (area under the curve = 0.95). CONCLUSIONS:Antipsychotic naïve FEP is associated with lower FC between the anterior hippocampus and cortical regions previously implicated in schizophrenia. Preliminary analysis suggests that random forest models based on hippocampal FC may predict treatment response in FEP patients, and hence could be a useful biomarker for treatment development.