The MATRICS consensus cognitive battery for the assessment of cognitive impairment in schizotypal personality disorder.

Cognitive deficits are a core impairment across the range of schizophrenia (SZ) spectrum disorders, including schizotypal personality disorder (SPD). The MATRICS Consensus Cognitive Battery (MCCB) was developed to be a robust, specific, and valid cognitive assessment battery to assess cognition in clinical trials for treating cognitive impairments in SZ. Despite the similarity of cognitive impairments shown in SPD and SZ and the clear relevance of uniform assessment across a diagnostic spectrum, the MCCB has yet to be validated in SPD. As such, this is the first study to evaluate the sensitivity of the MCCB for the assessment of cognitive function in individuals with SPD. Participants were 30 individuals with SPD and 54 healthy controls (HC) assessed with the MCCB and supplemental neurocognitive assessments (Trails B, DOT test, Paced Auditory Serial Addition Test (PASAT), AX Continuous Performance Task (AX-CPT), and N-back). Individuals with SPD performed worse than HC participants on all MCCB subtests, as well as on converging supplemental tasks including Trails B, DOT test, PASAT, AX-CPT, and N-back. These results indicate that the MCCB was sensitive to cognitive impairment in SPD compared to controls. SPD participants demonstrate impairments similar to data of SZ participants within the literature, although to a slightly lesser degree of severity. Taken together, these results highlight the generalizability of using the MCCB across SZ spectrum diagnostic groups to assess cognition. Such findings allow for further comparison across disorders, greater understanding of the cognitive characteristics in the spectrum, and use of uniform assessment within cognitive intervention research.

The effect of SARS-CoV-2 infection and vaccination on Th17 and regulatory T cells in a pregnancy cohort in NYC.

Disturbances in T-cells, specifically the Th17/Treg balance, have been implicated in adverse pregnancy outcomes. We investigated these two T-cell populations following pre-pregnancy and pregnancy SARS-CoV-2 infection and COVID-19 vaccination in 351 participants from a pregnancy cohort in New York City (Generation C; 2020-2022). SARS-CoV-2 infection status was determined via laboratory or medical diagnosis and COVID-19 vaccination status via survey and electronic medical records data. Peripheral blood mononuclear cells (PBMCs) were collected at routine prenatal visits throughout gestation (median 108 days; IQR 67-191 days) with repeated measures for 104 participants (29.6%). T-cell populations CD4+/CD3+, Th17/CD4+, Treg/CD4+ and the Th17/Treg ratio were quantified using flow cytometry. Results showed that inter-individual differences are a main influencing factor in Th17 and Treg variance, however total variance explained remained small (R2 = 15-39%). Overall, Th17 and Treg populations were not significantly affected by SARS-CoV-2 infection during pregnancy in adjusted linear mixed models (p>0.05), however comparison of repeated measures among SARS-CoV-2 infected participants and non-infected controls suggests a relative increase of the Th17/Treg ratio following infection. In addition, the Th17/Treg ratio was significantly higher after SARS-CoV-2 infection prior to pregnancy (10-138 weeks) compared to controls (ß=0.48, p=0.003). COVID-19 vaccination was not associated with Th17 and Treg cells. Our findings suggest an impact of SARS-CoV-2 infection on the Th17/Treg ratio, likely depending on severity of infection, yet the observed trends and their potential consequences for pregnancy outcomes require further investigation. Our study contributes to growing evidence that COVID-19 vaccination during pregnancy does not lead to an exacerbated immune response.

Increased postpartum anxiety symptoms after perinatal SARS-CoV-2 infection in a large, prospective pregnancy cohort in New York City.

Numerous studies reported an increase of postpartum mood symptoms during the COVID-19 pandemic. Yet, the link between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and perinatal mental health is less well understood. We investigated the associations between prenatal SARS-CoV-2 infection and postpartum depressive and anxiety symptoms, including examinations of infection timing and pandemic timeline. We included 595 participants from Generation C, a prospective pregnancy cohort in New York City (2020-2022). Prenatal SARS-CoV-2 infection was determined via laboratory or medical diagnosis. Depression and anxiety symptoms were measured 4-12 weeks postpartum using the Edinburgh Postnatal Depression Scale (EPDS) and Generalized Anxiety Disorder questionnaire (GAD), respectively. Quantile regressions were conducted with prenatal SARS-CoV-2 infection as exposure and continuously measured EPDS and GAD scores as outcomes. We reran the analyses in those with COVID-19-like symptoms in the trimester during which infection occurred. 120 (20.1%) participants had prenatal SARS-CoV-2 infection. After adjusting for socio-demographic, obstetric and other maternal health factors, prenatal SARS-CoV-2 infection was associated with higher median postpartum anxiety scores (b = 0.55, 95% CI = 0.15; 0.96). Late gestation infection (b = 1.15, 95% CI = 0.22; 2.09) and symptomatic infection (b = 1.15, 95% CI = 0.12; 2.18) were also associated with higher median postpartum anxiety scores. No associations were found with depressive symptoms. The associations were not moderated by time since the start of the pandemic. This study suggests that prenatal SARS-CoV-2 infection increases the risk of postpartum anxiety symptoms among participants reporting median anxiety symptoms. Given that this association was not affected by pandemic timing and that SARS-CoV-2 transmission continues, individuals infected with SARS-CoV-2 during pregnancy should be monitored for postpartum anxiety symptoms.

Exploring real-world symptom impact and improvement in well-being domains for tardive dyskinesia in VMAT2 inhibitor-treated patients via clinician survey and chart review.

Introduction: Two vesicular monoamine transporter 2 (VMAT2) inhibitors are approved in the United States (US) for the treatment of tardive dyskinesia (TD). There is a paucity of information on the impact of VMAT2 inhibitor treatment on patient social and physical well-being. The study objective was to elucidate clinician-reported improvement in symptoms and any noticeable changes in social or physical well-being in patients receiving VMAT2 inhibitors. Methods: A web-based survey was offered to physicians, nurse practitioners, and physician assistants based in the US who prescribed valbenazine for TD within the past 24 months. Clinicians reported data from the charts of patients who met the inclusion criteria and were allowed to recall missing information. Results: Respondents included 163 clinicians who reviewed charts of 601 VMAT2-treated patients with TD: 47% had TD symptoms in ≥2 body regions, with the most common being in the head or face and upper extremities. Prior to treatment, 93% of patients showed impairment in ≥1 social domain, and 88% were impaired in ≥1 physical domain. Following treatment, among those with improvement in TD symptoms (n = 540), 80% to 95% showed improvement in social domains, 90% to 95% showed improvement in physical domains, and 73% showed improvement in their primary psychiatric condition. Discussion: In VMAT2-treated patients with TD symptom improvement, clinicians reported concomitant improvement in psychiatric disorder symptoms and in social and physical well-being. Regular assessment of TD impact on these types of domains should occur simultaneously with movement disorder ratings when evaluating the value of VMAT2 inhibitor therapy.

Functional neuroimaging of resilience to trauma: convergent evidence and challenges for future research.

Resilience is broadly defined as the ability to adapt successfully following stressful life events. Here, we review functional MRI studies that investigated key psychological factors that have been consistently linked to resilience to severe adversity and trauma exposure. These domains include emotion regulation (including cognitive reappraisal), reward responsivity, and cognitive control. Further, we briefly review functional imaging evidence related to emerging areas of study that may potentially facilitate resilience: namely social cognition, active coping, and successful fear extinction. Finally, we also touch upon ongoing issues in neuroimaging study design that will need to be addressed to enable us to harness insight from such studies to improve treatments for - or, ideally, guard against the development of - debilitating post-traumatic stress syndromes.

Clinical features of individuals with schizotypal personality disorder with and without suicidal ideation.

This study compared demographic and clinical features in a sample of 384 participants: healthy controls (HC; n = 166) and individuals with schizotypal personality disorder (SPD) with (n = 50) and without (n = 168) suicidal ideation (SI) to examine specific risk factors for suicidality in SPD. Compared to the non-SI group, the SI group showed significantly greater severity of depression, aggression, impulsivity, affective lability, schizotypal features, poorer social adjustment, and had fewer social contacts. Individuals in the SI group were also more likely to have a history of a suicide attempt and comorbid borderline personality disorder in comparison to the non-SI group. Logistic regression analysis indicated that severity of depression and the number of social contacts drove the difference between the SI and non-SI groups. Compared with both SPD subgroups, the HC group was significantly less depressed, aggressive, impulsive, affectively labile, had fewer schizotypal features, was better socially adjusted, and had more social contacts. This study indicates that overall, the SI group is a more severely impaired group of individuals with SPD compared to the non-SI group. Better educating medical professionals about the diagnosis and management of SPD and its associations with suicidality is warranted.

Serotonin transporter availability in physically aggressive personality disordered patients: associations with trait and state aggression, and response to fluoxetine.

RATIONALE: Characterizing the neuroanatomical basis of serotonergic abnormalities in severe, chronic, impulsive aggression will allow for rational treatment selection, development of novel therapeutics, and biomarkers to identify at-risk individuals. OBJECTIVES: The aim of this study is to identify associations between regional serotonin transporter (5-HTT) availability and trait and state aggression, as well as response to the anti-aggressive effects of fluoxetine. METHODS: We examined 5-HTT availability using positron emission tomography (PET) imaging with [11C]DASB in personality disordered patients with current physical intermittent explosive disorder (IED; n = 18), and healthy comparison participants (HC; n = 11), in the anterior cingulate cortex (ACC), amygdala (AMY), ventral striatum (VST), and midbrain (MID). After PET imaging, IED patients were treated with fluoxetine 20 mg daily (n = 9) or placebo (n = 6) for 12 weeks. Trait and state aggression, trait callousness, and childhood trauma were assessed. RESULTS: In IED patients, trait aggression was positively associated with [11C]DASB binding in the ACC and VST; covarying for trait callousness and childhood trauma enhanced these correlations. Baseline state aggression was positively correlated with ACC [11C]DASB in IED patients. Greater baseline VST [11C]DASB binding predicted greater decreases in state aggression with fluoxetine treatment. CONCLUSIONS: Consistent with prior reports, ACC 5-HTT is related to trait aggression, and adjusting for factors related to proactive (callousness) and reactive (childhood trauma) aggression subtypes further resolves this relationship. Novel findings of the study include a better understanding of the association between regional 5-HTT availability and state aggression, and the involvement of VST 5-HTT with trait aggression, and with the anti-aggressive effects of fluoxetine.

Hyperreactivity and Impaired Habituation of Startle Amplitude During Unpleasant Pictures in Borderline but Not Schizotypal Personality Disorder: Quantifying Emotion Dysregulation.

BACKGROUND: Borderline personality disorder (BPD) is characterized by greater intensity of reactions to unpleasant emotional cues and a slower-than-normal return of these responses to baseline. Habituation is defined as decreased response to repeated stimulation. Affect-modulated startle (AMS), a translational psychophysiological approach, is mediated by the amygdala and used to study emotion processing in both humans and animals. This is the first study to examine the specificity of habituation anomalies in BPD during passive emotional and neutral picture processing. METHODS: A total of 90 participants were studied: patients with BPD (n = 35), patients with schizotypal personality disorder (n = 26; included as a psychopathological comparison group), and healthy control subjects (n = 29). Participants received rigorous clinical assessments, and patients were unmedicated. AMS was examined during a series of intermixed unpleasant, neutral, and pleasant pictures. RESULTS: Compared with the other groups, patients with BPD showed greater overall AMS during unpleasant pictures and prolonged habituation of startle amplitude during unpleasant pictures from early to later trials. The groups did not differ in AMS during neutral or pleasant pictures or self-reported picture valence. Among the patients with BPD, prolonged habituation to unpleasant pictures was associated with greater symptom severity and suicidal/self-harming behavior. CONCLUSIONS: These findings 1) indicate that abnormal processing of and habituation to unpleasant pictures is observed in BPD but not schizotypal personality disorder, suggesting that these deficits are not simply characteristics of personality disorders in general; 2) are consistent with studies showing deficient amygdala habituation to unpleasant pictures in BPD; and 3) have significant implications for clinical assessment and treatment of BPD, e.g., alternative therapies for BPD such as gradual exposure to unpleasant emotional stimuli or amygdala neurofeedback may aid habituation deficits.

Frontotemporal thalamic connectivity in schizophrenia and schizotypal personality disorder.

Schizotypal personality disorder (SPD) resembles schizophrenia, but with attenuated brain abnormalities and the absence of psychosis. The thalamus is integral for processing and transmitting information across cortical regions and widely implicated in the neurobiology of schizophrenia. Comparing thalamic connectivity in SPD and schizophrenia could reveal an intermediate schizophrenia-spectrum phenotype to elucidate neurobiological risk and protective factors in psychosis. We used rsfMRI to investigate functional connectivity between the mediodorsal nucleus (MDN) and pulvinar, and their connectivity with frontal and temporal cortical regions, respectively in 43 healthy controls (HCs), and individuals in the schizophrenia-spectrum including 45 psychotropic drug-free individuals with SPD, and 20 individuals with schizophrenia-related disorders [(schizophrenia (n = 10), schizoaffective disorder (n = 8), schizophreniform disorder (n = 1) and psychosis NOS (n = 1)]. Individuals with SPD had greater functional connectivity between the MDN and pulvinar compared to individuals with schizophrenia. Thalamo-frontal (i.e., between the MDN and rostral middle frontal cortex) connectivity was comparable in SPD and HCs; in SPD greater connectivity was associated with less symptom severity. Individuals with schizophrenia had less thalamo-frontal connectivity and thalamo-temporal (i.e., pulvinar to the transverse temporal cortex) connectivity compared with HCs. Thalamo-frontal functional connectivity may be comparable in SPD and HCs, but abnormal in schizophrenia, and that this may be protective against psychosis in SPD.

Intensive Dialectical Behavior Treatment for Individuals With Borderline Personality Disorder With and Without Substance Use Disorders.

Treatment of borderline personality disorder (BPD) with comorbid substance use disorder can be challenging due to symptom overlap and limited assessment methods. Preliminary evidence has shown promising effectiveness of dialectical behavioral therapy (DBT) for BPD with comorbid substance use disorders. The current study compared the benefits of a 28-day transitional DBT treatment program for individuals with BPD with and without substance use disorders through evaluating the changes in coping skills, generalized anxiety, and depression symptom scales at admission and discharge. A total of 76 patients were split into two groups: Group 1 consisted of individuals with BPD without substance use disorders (n = 41), and Group 2 involved individuals with BPD and a substance use disorder (SUD) (n = 35). A univariate general linear model showed significant differences between the two groups in improvement of coping skills and depressive symptoms. After a 28-day transitional DBT treatment program there were significant decreases from severe to moderate depression scores in both groups. Our findings support the effectiveness of DBT treatment in patients with comorbid BPD and SUD.

Shift in Social Media App Usage During COVID-19 Lockdown and Clinical Anxiety Symptoms: Machine Learning-Based Ecological Momentary Assessment Study.

BACKGROUND: Anxiety symptoms during public health crises are associated with adverse psychiatric outcomes and impaired health decision-making. The interaction between real-time social media use patterns and clinical anxiety during infectious disease outbreaks is underexplored. OBJECTIVE: We aimed to evaluate the usage pattern of 2 types of social media apps (communication and social networking) among patients in outpatient psychiatric treatment during the COVID-19 surge and lockdown in Madrid, Spain and their short-term anxiety symptoms (7-item General Anxiety Disorder scale) at clinical follow-up. METHODS: The individual-level shifts in median social media usage behavior from February 1 through May 3, 2020 were summarized using repeated measures analysis of variance that accounted for the fixed effects of the lockdown (prelockdown versus postlockdown), group (clinical anxiety group versus nonclinical anxiety group), the interaction of lockdown and group, and random effects of users. A machine learning-based approach that combined a hidden Markov model and logistic regression was applied to predict clinical anxiety (n=44) and nonclinical anxiety (n=51), based on longitudinal time-series data that comprised communication and social networking app usage (in seconds) as well as anxiety-associated clinical survey variables, including the presence of an essential worker in the household, worries about life instability, changes in social interaction frequency during the lockdown, cohabitation status, and health status. RESULTS: Individual-level analysis of daily social media usage showed that the increase in communication app usage from prelockdown to lockdown period was significantly smaller in the clinical anxiety group than that in the nonclinical anxiety group (F1,72=3.84, P=.05). The machine learning model achieved a mean accuracy of 62.30% (SD 16%) and area under the receiver operating curve 0.70 (SD 0.19) in 10-fold cross-validation in identifying the clinical anxiety group. CONCLUSIONS: Patients who reported severe anxiety symptoms were less active in communication apps after the mandated lockdown and more engaged in social networking apps in the overall period, which suggested that there was a different pattern of digital social behavior for adapting to the crisis. Predictive modeling using digital biomarkers-passive-sensing of shifts in category-based social media app usage during the lockdown-can identify individuals at risk for psychiatric sequelae.

Pramipexole to Improve Cognition in Bipolar Disorder: A Randomized Controlled Trial.

BACKGROUND: Adults with bipolar disorder (BD) often experience neurocognitive impairment that negatively impacts functioning and quality of life. Previous trials have found that dopamine agonist agents improve cognition in healthy volunteers and that adults with BD who have stable mood and mild cognitive deficits may also benefit. We hypothesized that pramipexole, a dopamine agonist, would improve neurocognitive function in patients with BD. METHODS: We recruited 60 adults (aged 18-65 years) with a diagnosis of BD I or II for an 8-week, double-blind, placebo-controlled trial (NCT02397837). All had stable mood and clinically significant neurocognitive impairment at baseline. Participants were randomized to receive pramipexole (n = 31) or a placebo (n = 29), dose was initiated at 0.125 mg 2 times a day and increased to a target of 4.5 mg/d. RESULTS: At trial end, the primary outcome, MATRICS Consensus Cognitive Battery composite score, had not improved more in the pramipexole group (mean [SD] = 1.15 [5.4]) than in the placebo group (mean [SD] = 4.12 [5.2], Cohen's d = 0.56, P = 0.049), and mixed models, controlling for symptoms, showed no association between treatment group and MATRICS Consensus Cognitive Battery scores. No serious adverse events were reported. CONCLUSIONS: These results suggest that pramipexole is not an efficacious cognitive enhancement agent in BD, even in a sample enriched for characteristics that were associated with a beneficial response in prior work. There are distinct cognitive subgroups among adults with BD and may be related differences in neurobiology that affect response to pramipexole. Additional research to better understand the onset and nature of the cognitive deficits in people with BD will be an important step toward a more personalized approach to treatment.

Alexithymia, Affective Lability, Impulsivity, and Childhood Adversity in Borderline Personality Disorder.

Long-standing theories of borderline personality disorder (BPD) suggest that symptoms develop at least in part from childhood adversity. Emotion dysregulation may meaningfully mediate these effects. The current study examined three factors related to emotion dysregulation-alexithymia, affective lability, and impulsivity-as potential mediators of the relation between childhood adversity and BPD diagnosis in 101 individuals with BPD and 95 healthy controls. Path analysis compared three distinct models informed by the literature. Results supported a complex mediation model wherein (a) alexithymia partially mediated the relation of childhood adversity to affective lability and impulsivity; (b) affective lability mediated the relation of childhood adversity to BPD diagnosis; and (c) affective lability and impulsivity mediated the relation of alexithymia to BPD diagnosis. Findings suggest that affective lability and alexithymia are key to understanding the relationship between childhood adversity and BPD. Interventions specifically targeting affective lability, impulsivity, and alexithymia may be particularly useful for this population.

Predicting Emotional States Using Behavioral Markers Derived From Passively Sensed Data: Data-Driven Machine Learning Approach.

BACKGROUND: Mental health disorders affect multiple aspects of patients' lives, including mood, cognition, and behavior. eHealth and mobile health (mHealth) technologies enable rich sets of information to be collected noninvasively, representing a promising opportunity to construct behavioral markers of mental health. Combining such data with self-reported information about psychological symptoms may provide a more comprehensive and contextualized view of a patient's mental state than questionnaire data alone. However, mobile sensed data are usually noisy and incomplete, with significant amounts of missing observations. Therefore, recognizing the clinical potential of mHealth tools depends critically on developing methods to cope with such data issues. OBJECTIVE: This study aims to present a machine learning-based approach for emotional state prediction that uses passively collected data from mobile phones and wearable devices and self-reported emotions. The proposed methods must cope with high-dimensional and heterogeneous time-series data with a large percentage of missing observations. METHODS: Passively sensed behavior and self-reported emotional state data from a cohort of 943 individuals (outpatients recruited from community clinics) were available for analysis. All patients had at least 30 days' worth of naturally occurring behavior observations, including information about physical activity, geolocation, sleep, and smartphone app use. These regularly sampled but frequently missing and heterogeneous time series were analyzed with the following probabilistic latent variable models for data averaging and feature extraction: mixture model (MM) and hidden Markov model (HMM). The extracted features were then combined with a classifier to predict emotional state. A variety of classical machine learning methods and recurrent neural networks were compared. Finally, a personalized Bayesian model was proposed to improve performance by considering the individual differences in the data and applying a different classifier bias term for each patient. RESULTS: Probabilistic generative models proved to be good preprocessing and feature extractor tools for data with large percentages of missing observations. Models that took into account the posterior probabilities of the MM and HMM latent states outperformed those that did not by more than 20%, suggesting that the underlying behavioral patterns identified were meaningful for individuals' overall emotional state. The best performing generalized models achieved a 0.81 area under the curve of the receiver operating characteristic and 0.71 area under the precision-recall curve when predicting self-reported emotional valence from behavior in held-out test data. Moreover, the proposed personalized models demonstrated that accounting for individual differences through a simple hierarchical model can substantially improve emotional state prediction performance without relying on previous days' data. CONCLUSIONS: These findings demonstrate the feasibility of designing machine learning models for predicting emotional states from mobile sensing data capable of dealing with heterogeneous data with large numbers of missing observations. Such models may represent valuable tools for clinicians to monitor patients' mood states.

Social media and smartphone app use predicts maintenance of physical activity during Covid-19 enforced isolation in psychiatric outpatients.

There is growing concern that the social and physical distancing measures implemented in response to the Covid-19 pandemic may negatively impact health in other areas, via both decreased physical activity and increased social isolation. Here, we investigated whether increased engagement with digital social tools may help mitigate effects of enforced isolation on physical activity and mood, in a naturalistic study of at-risk individuals. Passively sensed smartphone app use and actigraphy data were collected from a group of psychiatric outpatients before and during imposition of strict Covid-19 lockdown measures. Data were analysed using Gaussian graphical models: a form of network analysis which gives insight into the predictive relationships between measures across timepoints. Within-individuals, we found evidence of a positive predictive path between digital social engagement, general smartphone use, and physical activity-selectively under lockdown conditions (N = 127 individual users, M = 6201 daily observations). Further, we observed a positive relationship between social media use and total daily steps across individuals during (but not prior to) lockdown. Although there are important limitations on the validity of drawing causal conclusions from observational data, a plausible explanation for our findings is that, during lockdown, individuals use their smartphones to access social support, which may help guard against negative effects of in-person social deprivation and other pandemic-related stress. Importantly, passive monitoring of smartphone app usage is low burden and non-intrusive. Given appropriate consent, this could help identify people who are failing to engage in usual patterns of digital social interaction, providing a route to early intervention.

Actigraphic recording of motor activity in depressed inpatients: a novel computational approach to prediction of clinical course and hospital discharge.

Depressed patients present with motor activity abnormalities, which can be easily recorded using actigraphy. The extent to which actigraphically recorded motor activity may predict inpatient clinical course and hospital discharge remains unknown. Participants were recruited from the acute psychiatric inpatient ward at Hospital Rey Juan Carlos (Madrid, Spain). They wore miniature wrist wireless inertial sensors (actigraphs) throughout the admission. We modeled activity levels against the normalized length of admission-'Progress Towards Discharge' (PTD)-using a Hierarchical Generalized Linear Regression Model. The estimated date of hospital discharge based on early measures of motor activity and the actual hospital discharge date were compared by a Hierarchical Gaussian Process model. Twenty-three depressed patients (14 females, age: 50.17 ± 12.72 years) were recruited. Activity levels increased during the admission (mean slope of the linear function: 0.12 ± 0.13). For n = 18 inpatients (78.26%) hospitalised for at least 7 days, the mean error of Prediction of Hospital Discharge Date at day 7 was 0.231 ± 22.98 days (95% CI 14.222-14.684). These n = 18 patients were predicted to need, on average, 7 more days in hospital (for a total length of stay of 14 days) (PTD = 0.53). Motor activity increased during the admission in this sample of depressed patients and early patterns of actigraphically recorded activity allowed for accurate prediction of hospital discharge date.

Amphetamine-induced striatal dopamine release in schizotypal personality disorder.

RATIONALE: Previous research has suggested that schizotypal personality disorder (SPD), a condition that shares clinical and cognitive features with schizophrenia, may be associated with elevated striatal dopamine functioning; however, there are no published studies of dopamine release within subregions of the striatum in SPD. OBJECTIVES: To characterize dopamine release capacity in striatal subregions and its relation to clinical and cognitive features in SPD. METHODS: We used positron emission tomography with [11C]raclopride and an amphetamine challenge to measure dopamine D2-receptor availability (binding potential, BPND), and its percent change post-amphetamine (∆BPND) to index amphetamine-induced dopamine release, in subregions of the striatum in 16 SPD and 16 healthy control participants. SPD participants were evaluated with measures of schizotypal symptom severity and working memory. RESULTS: There were no significant group differences in BPND or ∆BPND in any striatal subregion or whole striatum. Among SPD participants, cognitive-perceptual symptoms were associated at trend level with ∆BPND in the ventral striatum, and disorganized symptoms were significantly negatively related to ∆BPND in several striatal subregions. CONCLUSIONS: In contrast to previous findings, SPD was not associated with elevated striatal dopamine release. However, in SPD, there was a moderate positive association between ventral striatal dopamine release and severity of cognitive-perceptual symptoms, and negative associations between striatal dopamine release and severity of disorganized symptoms. Future larger scale investigations that allow for the separate examination of subgroups of participants based on clinical presentation will be valuable in further elucidating striatal DA functioning in SPD.

Neuroimaging predictors of response to cognitive remediation and social skills training: A pilot study in veterans with schizophrenia.

Neuroimaging may predict response to cognitive remediation therapy and social skills training (CRT + SST) in schizophrenia. Identifying biological predictors of response is crucial for treatment decision making given not all patients respond to such interventions. Nineteen veterans with schizophrenia enrolled in an 8-week trial of CRT + SST. Ten participants completed diffusion tensor imaging (DTI) at baseline. Baseline fractional anisotropy (FA) in the superior longitudinal fasciculus (SLF) and overall average FA predicted improvements in visual-spatial working memory, and social cognition, respectively. Neuroimaging may be useful in identifying therapeutic targets in schizophrenia.

Pharmacological risk factors associated with hospital readmission rates in a psychiatric cohort identified using prescriptome data mining.

BACKGROUND: Worldwide, over 14% of individuals hospitalized for psychiatric reasons have readmissions to hospitals within 30 days after discharge. Predicting patients at risk and leveraging accelerated interventions can reduce the rates of early readmission, a negative clinical outcome (i.e., a treatment failure) that affects the quality of life of patient. To implement individualized interventions, it is necessary to predict those individuals at highest risk for 30-day readmission. In this study, our aim was to conduct a data-driven investigation to find the pharmacological factors influencing 30-day all-cause, intra- and interdepartmental readmissions after an index psychiatric admission, using the compendium of prescription data (prescriptome) from electronic medical records (EMR). METHODS: The data scientists in the project received a deidentified database from the Mount Sinai Data Warehouse, which was used to perform all analyses. Data was stored in a secured MySQL database, normalized and indexed using a unique hexadecimal identifier associated with the data for psychiatric illness visits. We used Bayesian logistic regression models to evaluate the association of prescription data with 30-day readmission risk. We constructed individual models and compiled results after adjusting for covariates, including drug exposure, age, and gender. We also performed digital comorbidity survey using EMR data combined with the estimation of shared genetic architecture using genomic annotations to disease phenotypes. RESULTS: Using an automated, data-driven approach, we identified prescription medications, side effects (primary side effects), and drug-drug interaction-induced side effects (secondary side effects) associated with readmission risk in a cohort of 1275 patients using prescriptome analytics. In our study, we identified 28 drugs associated with risk for readmission among psychiatric patients. Based on prescription data, Pravastatin had the highest risk of readmission (OR = 13.10; 95% CI (2.82, 60.8)). We also identified enrichment of primary side effects (n = 4006) and secondary side effects (n = 36) induced by prescription drugs in the subset of readmitted patients (n = 89) compared to the non-readmitted subgroup (n = 1186). Digital comorbidity analyses and shared genetic analyses further reveals that cardiovascular disease and psychiatric conditions are comorbid and share functional gene modules (cardiomyopathy and anxiety disorder: shared genes (n = 37; P = 1.06815E-06)). CONCLUSIONS: Large scale prescriptome data is now available from EMRs and accessible for analytics that could improve healthcare outcomes. Such analyses could also drive hypothesis and data-driven research. In this study, we explored the utility of prescriptome data to identify factors driving readmission in a psychiatric cohort. Converging digital health data from EMRs and systems biology investigations reveal a subset of patient populations that have significant comorbidities with cardiovascular diseases are more likely to be readmitted. Further, the genetic architecture of psychiatric illness also suggests overlap with cardiovascular diseases. In summary, assessment of medications, side effects, and drug-drug interactions in a clinical setting as well as genomic information using a data mining approach could help to find factors that could help to lower readmission rates in patients with mental illness.

Current Knowledge on Gene-Environment Interactions in Personality Disorders: an Update.

PURPOSE OF REVIEW: We review the existing literature on gene-environment interactions (G×E) and epigenetic changes primarily in borderline personality disorder (BPD) but also in antisocial, schizotypal, and avoidant personality disorders. RECENT FINDINGS: Research supports that susceptibility genes to BPD or its underlying traits may be expressed under certain environmental conditions such as physical or childhood sexual abuse. Epigenetic modifications of neurodevelopment- and stress-related genes are suggested to underlie the relationship between early life adversary and borderline personality disorder. Only limited studies have investigated the role of gene-environment interactions and epigenetic changes in the genesis of antisocial, schizotypal, and avoidant personality disorders. Considering the lack of pharmacological treatment for most personality disorders, the emerging evidence on the critical role of G×E and epigenetic changes in the genesis of personality disorders could help develop more biologically oriented therapeutic approaches. Future studies should explore the potential of this new therapeutic dimension.