Transdiagnostic dimensions of psychosis in the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP)

The validity of the classification of non-affective and affective psychoses as distinct entities has been disputed, but, despite calls for alternative approaches to defining psychosis syndromes, there is a dearth of empirical efforts to identify transdiagnostic phenotypes of psychosis. We aimed to investigate the validity and utility of general and specific symptom dimensions of psychosis cutting across schizophrenia, schizoaffective disorder and bipolar I disorder with psychosis. Multidimensional item-response modeling was conducted on symptom ratings of the Positive and Negative Syndrome Scale, Young Mania Rating Scale, and Montgomery-angstrom sberg Depression Rating Scale in the multicentre Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) consortium, which included 933 patients with a diagnosis of schizophrenia (N=397), schizoaffective disorder (N=224), or bipolar I disorder with psychosis (N=312). A bifactor model with one general symptom dimension, two distinct dimensions of non-affective and affective psychosis, and five specific symptom dimensions of positive, negative, disorganized, manic and depressive symptoms provided the best model fit. There was further evidence on the utility of symptom dimensions for predicting B-SNIP psychosis biotypes with greater accuracy than categorical DSM diagnoses. General, positive, negative and disorganized symptom dimension scores were higher in African American vs. Caucasian patients. Symptom dimensions accurately classified patients into categorical DSM diagnoses. This study provides evidence on the validity and utility of transdiagnostic symptom dimensions of psychosis that transcend traditional diagnostic boundaries of psychotic disorders. Findings further show promising avenues for research at the interface of dimensional psychopathological phenotypes and basic neurobiological dimensions of psychopathology

Local gyrification index in probands with psychotic disorders and their first-degree relatives

BACKGROUND: Psychotic disorders are characterized by aberrant neural connectivity. Alterations in gyrification, the pattern and degree of cortical folding, may be related to the early development of connectivity. Past gyrification studies have relatively small sample sizes, yield mixed results for schizophrenia (SZ), and are scant for psychotic bipolar (BP) and schizoaffective (SZA) disorders and for relatives of these conditions. Here we examine gyrification in psychotic disorder patients and their first-degree relatives as a possible endophenotype. METHODS: Regional Local Gyrification Index (LGI) values, as measured by FreeSurfer software, were compared between 243 controls, 388 psychotic disorder probands, and 300 of their first-degree relatives. For patients, LGI values were examined grouped across psychotic diagnoses and then separately for SZ, SZA, and BP. Familiality (heritability) values and correlations with clinical measures were also calculated for regional LGI values. RESULTS: Probands exhibited significant hypogyria compared to controls in three brain regions and relatives with axis II cluster A disorders showed nearly significant hypogyria in these same regions. LGI values in these locations were significantly heritable and uncorrelated with any clinical measure. Observations of significant CONCLUSIONS: Psychotic disorders appear to be characterized by significant regionally localized hypogyria, particularly in cingulate cortex. This abnormality may be a structural endophenotype marking risk for psychotic illness and it may help elucidate etiological underpinnings of psychotic disorders

An opportunity for primary prevention research in psychotic disorders

An opportunity has opened for research into primary prevention of psychotic disorders, based on progress in endophenotypes, genetics, and genomics. Primary prevention requires reliable prediction of susceptibility before any symptoms are present. We studied a battery of measures where published data supports abnormalities of these measurements prior to appearance of initial psychosis symptoms. These neurobiological and behavioral measurements included cognition, eye movement tracking, Event Related Potentials, and polygenic risk scores. They generated an acceptably precise separation of healthy controls from outpatients with a psychotic disorder. Methods The Bipolar and Schizophrenia Network on Intermediate Phenotypes (B-SNIP) measured this battery in an ancestry-diverse series of consecutively recruited adult outpatients with a psychotic disorder and healthy controls. Participants include all genders, 16 to 50 years of age, 261 with psychotic disorders (Schizophrenia (SZ) 109, Bipolar with psychosis (BPP) 92, Schizoaffective disorder (SAD) 60), 110 healthy controls. Logistic Regression, and an extension of the Linear Mixed Model to include analysis of pairwise interactions between measures (Environmental kernel Relationship Matrices (ERM)) with multiple iterations, were performed to predict case-control status. Each regression analysis was validated with four-fold cross-validation. Results and conclusions Sensitivity, specificity, and Area Under the Curve of Receiver Operating Characteristic of 85%, 62%, and 86%, respectively, were obtained for both analytic methods. These prediction metrics demonstrate a promising diagnostic distinction based on premorbid risk variables. There were also statistically significant pairwise interactions between measures in the ERM model. The strong prediction metrics of both types of analytic model provide proof-of-principle for biologically-based laboratory tests as a first step toward primary prevention studies. Prospective studies of adolescents at elevated risk, vs. healthy adolescent controls, would be a next step toward development of primary prevention strategies

Functional Connectivity of Brain Structures Correlates with Treatment Outcome in Major Depressive Disorder

Identifying biosignatures to assess the probability of response to an antidepressant for patients with major depressive disorder (MDD) is critically needed. Functional connectivity MRI (fcMRI) offers the promise to provide such a measure. Previous work with fcMRI demonstrated that the correlation in signal from one region to another is a measure of functional connectivity. In this pilot work, a baseline non-task fcMRI was acquired in 14 adults with MDD who were free of all medications. Participants were then treated for 8 weeks with an antidepressant and then clinically re-evaluated. Probabilistic anatomic regions of interest (ROI) were defined for 16 brain regions (eight for each hemisphere) previously identified as being important in mood disorders. These ROIs were used to determine mean time courses for each individual's baseline non-task fcMRI. The correlations in time courses between 16 brain regions were calculated. These calculated correlations were considered to signify measures of functional connectivity. The degree of connectivity for each participant was correlated with treatment outcome. Among 13 participants with 8 weeks follow-up data, connectivity measures in several regions, especially the subcallosal cortex, were highly correlated with treatment outcome. These connectivity measures could provide a means to evaluate how likely a patient is to respond to an antidepressant treatment. Further work using larger samples is required to confirm these findings and to assess if measures of functional connectivity can be used to predict differential outcomes between antidepressant treatments

Molecular adaptations of the blood–brain barrier promote stress resilience vs. depression

Preclinical and clinical studies suggest that inflammation and vascular dysfunction contribute to the pathogenesis of major depressive disorder (MDD). Chronic social stress alters blood–brain barrier (BBB) integrity through loss of tight junction protein claudin-5 (cldn5) in male mice, promoting passage of circulating proinflammatory cytokines and depression-like behaviors. This effect is prominent within the nucleus accumbens, a brain region associated with mood regulation; however, the mechanisms involved are unclear. Moreover, compensatory responses leading to proper behavioral strategies and active resilience are unknown. Here we identify active molecular changes within the BBB associated with stress resilience that might serve a protective role for the neurovasculature. We also confirm the relevance of such changes to human depression and antidepressant treatment. We show that permissive epigenetic regulation of cldn5 expression and low endothelium expression of repressive cldn5-related transcription factor foxo1 are associated with stress resilience. Regionand endothelial cell-specific whole transcriptomic analyses revealed molecular signatures associated with stress vulnerability vs. resilience. We identified proinflammatory TNFα/NFκB signaling and hdac1 as mediators of stress susceptibility. Pharmacological inhibition of stress-induced increase in hdac1 activity rescued cldn5 expression in the NAc and promoted resilience. Importantly, we confirmed changes in HDAC1 expression in the NAc of depressed patients without antidepressant treatment in line with CLDN5 loss. Conversely, many of these deleterious CLDN5-related molecular changes were reduced in postmortem NAc from antidepressanttreated subjects. These findings reinforce the importance of considering stress-induced neurovascular pathology in depression and provide therapeutic targets to treat this mood disorder and promote resilience