Sex differences in autonomic adverse effects related to antipsychotic treatment and associated hormone profiles.

Autonomic adverse effects of antipsychotic drugs (APs) cause clinical challenges, but few studies have investigated sex differences and their underlying biological pathways. Sex-specific regulation of relevant hormones could be involved. We investigated sex differences in autonomic adverse effects related to olanzapine, quetiapine, risperidone, and aripiprazole, and the role of hormones related to APs. Patients with severe mental disorders (N = 1318) were included and grouped based on AP monotherapy: olanzapine (N = 364), quetiapine (N = 211), risperidone (N = 102), aripiprazole (N = 138), and no AP (N = 503). Autonomic symptoms from the Udvalg for Kliniske Undersøgelser (UKU) side effect scale was analyzed with logistic regression, adjusting for age, diagnosis, and polypharmacy. Further, we analyzed associations between autonomic symptoms and hormones related to APs. We found associations between autonomic adverse effects and APs, with sex-specific risk for palpitations/tachycardia associated with hormonal changes related to APs. Results showed increased salivation associated with aripiprazole, reduced salivation with quetiapine, and nausea/vomiting and palpitations/tachycardia with olanzapine, and higher risk of nausea/vomiting, diarrhea, constipation, polyuria/polydipsia, and palpitations/tachycardia in females. Significant sex x AP interaction was found for palpitations/tachycardia, with higher risk in risperidone-treated males, which was associated with different hormone profiles of prolactin, cortisol, and insulin. Our findings implicate a role of several hormones in the sex-specific autonomic adverse effects related to APs.

Telomere length and verbal learning in bipolar disorders.

INTRODUCTION: Recent studies indicate accelerated ageing processes, shorter telomere length and poorer cognitive functioning in patients with bipolar disorder. The neurobiology underlying cognitive function in bipolar disorder is yet to be established. We anticipated that accelerated ageing as indicated by shortened telomere length, would be associated with reduced cognitive performance in bipolar disorder, particularly for ageing sensitive functions such as memory and learning. METHODS: The study consisted of 647 participants (bipolar disorder [n = 246] and healthy controls [n = 401]). All participants underwent a standardized neuropsychological test battery, including working memory, executive functioning, processing speed, verbal learning, and verbal memory. Leucocyte telomere length was measured via blood and determined by quantitative real-time Polymerase Chain Reaction (qPCR) providing a telomere to single copy ratio (T/S ratio). The T/S ratio was used as an estimate of the mean telomere length of each participant. All analyses were adjusted for medication, Daily Defined Dose (DDD), chronological age, sex, and ethnicity. RESULTS: Patients had shorter telomere lengths than healthy controls (Cohen's d = 0.11, p = 0.01). Within patients', a positive association was observed for verbal learning and telomere length (ß = 0.14, p = 0.025), along with a trend for verbal memory and telomere length (ß = 0.11, p = 0.07). No other associations were observed for telomere length and cognitive functioning in the patient or the control group (p > 0.1). CONCLUSION: Our study may suggest poorer brain health in bipolar disorder as indexed by shorter telomere length and reduced learning correlates. However, the role of telomere length on cognitive functioning in bipolar disorder seems limited.

Visual processing deficits in patients with schizophrenia spectrum and bipolar disorders and associations with psychotic symptoms, and intellectual abilities.

Objective: Low-level sensory disruption is hypothesized as a precursor to clinical and cognitive symptoms in severe mental disorders. We compared visual discrimination performance in patients with schizophrenia spectrum disorder or bipolar disorder with healthy controls, and investigated associations with clinical symptoms and IQ. Methods: Patients with schizophrenia spectrum disorder (n = 32), bipolar disorder (n = 55) and healthy controls (n = 152) completed a computerized visual discrimination task. Participants responded whether the latter of two consecutive grids had higher or lower spatial frequency, and discrimination thresholds were estimated using an adaptive maximum likelihood procedure. Case-control differences in threshold were assessed using linear regression, F-test and post-hoc pair-wise comparisons. Linear models were used to test for associations between visual discrimination threshold and psychotic symptoms derived from the PANSS and IQ assessed using the Matrix Reasoning and Vocabulary subtests from the Wechsler Abbreviated Scale of Intelligence (WASI). Results: Robust regression revealed a significant main effect of diagnosis on discrimination threshold (robust F = 6.76, p = .001). Post-hoc comparisons revealed that patients with a schizophrenia spectrum disorder (mean = 14%, SD = 0.08) had higher thresholds compared to healthy controls (mean = 10.8%, SD = 0.07, ß = 0.35, t = 3.4, p = .002), as did patients with bipolar disorder (12.23%, SD = 0.07, ß = 0.21, t = 2.42, p = .04). There was no significant difference between bipolar disorder and schizophrenia (ß = -0.14, t = -1.2, p = .45). Linear models revealed negative associations between IQ and threshold across all participants when controlling for diagnostic group (ß = -0.3, t = -3.43, p = .0007). This association was found within healthy controls (t = -3.72, p = .0003) and patients with bipolar disorder (t = -2.53, p = .015), and no significant group by IQ interaction on threshold (F = 0.044, p = .97). There were no significant associations between PANSS domain scores and discrimination threshold. Conclusion: Patients with schizophrenia spectrum or bipolar disorders exhibited higher visual discrimination thresholds than healthy controls, supporting early visual deficits among patients with severe mental illness. Discrimination threshold was negatively associated with IQ among healthy controls and bipolar disorder patients. These findings elucidate perception-related disease mechanisms in severe mental illness, which warrants replication in independent samples.

Retrospectively assessed childhood trauma experiences are associated with illness severity in mental disorders adjusted for symptom state.

Converging evidence suggests that childhood trauma is a causal factor in schizophrenia (SZ) and in bipolar disorders (BD). Here, we investigated whether retrospective reports are associated with severity of illness, independent of current symptom state in a large sample of participants with SZ or BD. We included 1260 individuals (SZ [n = 461], BD [n = 352]), and healthy controls; HC [n = 447]) recruited from the same catchment area. A history of childhood trauma was obtained with the Childhood Trauma Questionnaire (CTQ). Diagnosis and episodes were obtained with the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I). Clinical symptoms (state) were assessed with the Positive and Negative Syndrome scale (PANSS), the Calgary Depression Scale (CDSS). Trait related illness characteristics were assessed with age at illness onset, number of episodes, and lifetime suicide attempts. Patients who reported multiple types of childhood trauma experiences had significantly more severe illness course including an earlier illness onset, more mood episodes, and increased risk of at least one suicide attempt, also after adjusting for current symptom state. Retrospective assessed childhood trauma experiences are associated with illness severity in mental disorders adjusted for symptom state. Our results strengthen the role of childhood trauma in development of psychopathology.

Sex-specific associations between metabolic hormones, severe mental disorders and antipsychotic treatment.

BACKGROUND: Metabolic dysregulation has been associated with severe mental disorders (SMD) and with antipsychotic (AP) treatment, but the role of sex is unknown. To identify possible sex-related processes linked to SMD and AP treatment, we investigated sex differences in associations between hormones involved in metabolic regulation in patients with SMD compared to healthy controls (HC) and AP treatment. METHODS: We included patients with SMD (N = 1753) and HC (N = 1194) and measured hormones involved in metabolic regulation (insulin, cortisol, thyroid-stimulating hormone (TSH), thyroxine, leptin, adiponectin, testosterone, sex hormone-binding globulin (SHBG), prolactin). Patients were grouped according to use of first-generation AP (N = 163), second-generation AP (N = 1087) or no use of AP (N = 503). Hormones were used one by one as dependent variables in multiple regression analyses with interactions between sex and SMD patients versus HC, and between sex and AP treatment, followed by analyses in males and females separately. RESULTS: We found significant interactions between sex and SMD patients versus HC for testosterone, SHBG and adiponectin, with significantly higher testosterone and lower adiponectin levels in females. Furthermore, we found significant interaction between sex and AP groups for TSH, testosterone and insulin, with significantly lower TSH levels in AP-treated females, and lower testosterone and higher insulin levels in AP-treated males. CONCLUSIONS: Our findings suggest sex differences in metabolic hormones related to both SMD and AP treatment, indicating sex-dependent mechanisms. Clinicians should be aware of potential sex-specific metabolic changes during AP treatment and experimental studies are warranted to clarify the underlying mechanisms.

The shared genetic basis of mood instability and psychiatric disorders: A cross-trait genome-wide association analysis.

Recent genome-wide association studies of mood instability (MOOD) have found significant positive genetic correlation with major depression (DEP) and weak correlations with other psychiatric disorders. We investigated the polygenic overlap between MOOD and psychiatric disorders beyond genetic correlation to better characterize putative shared genetic determinants. GWAS summary statistics for schizophrenia (SCZ, n = 105,318), bipolar disorder (BIP, n = 413,466), DEP (n = 450,619), attention-deficit hyperactivity disorder (ADHD, n = 53,293), and MOOD (n = 363,705) were analyzed using the bivariate causal mixture model and conjunctional false discovery rate methods. MOOD correlated positively with all psychiatric disorders, but with wide variation in strength (rg = 0.10-0.62). Of 10.4 K genomic variants influencing MOOD, 4 K-9.4 K influenced psychiatric disorders. Furthermore, MOOD was jointly associated with DEP at 163 loci, SCZ at 110, BIP at 60 and ADHD at 25. Fifty-three jointly associated loci were overlapping across two or more disorders, seven of which had discordant effect directions on psychiatric disorders. Genes mapped to loci associated with MOOD and all four disorders were enriched in a single gene-set, "synapse organization." The extensive polygenic overlap indicates shared molecular underpinnings across MOOD and psychiatric disorders. However, distinct patterns of genetic correlation and effect directions may relate to differences in the core clinical features of each disorder.

Sex differences in antipsychotic-related triglyceride levels are associated with metabolic hormone differences in patients with severe mental disorders.

BACKGROUND: Adverse effects of antipsychotics (AP) contribute to cardiovascular disease (CVD) risk in patients with severe mental disorders (SMD). We investigated sex differences in AP-related CVD risk factors and the role of metabolic hormones. METHODS: Patients with SMD (N = 1791) receiving AP with different CVD risk were recruited and grouped into olanzapine and/or clozapine (N = 532), other APs (N = 744) or no use of APs (N = 515). Associations between CVD risk factor (total cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), body mass index (BMI), glucose, blood pressure), sex and AP groups were tested in multiple linear regression with interactions, controlling for diagnostic group, lifestyle factors, polypharmacy, age and ethnicity. Next, we tested associations between sex differences in AP-related CVD risk factors and metabolic regulatory hormones. RESULTS: AP groups and male sex were significantly associated with higher levels of LDL-C, TG and BMI, and lower levels of HDL-C. Significant interaction between AP groups and sex were found for TG (p = 0.017), with larger increase in males. Serum adiponectin, insulin, cortisol, leptin, testosterone, free thyroxine and thyroid-stimulating hormone (TSH) were associated with TG levels (all p ≤ 0.001), and a significant interaction with sex for insulin (p = 0.005), cortisol (p = 0.016), leptin (p < 0.001) and TSH (p = 0.001). CONCLUSIONS: We found more severe AP-related CVD risk factors in male patients with SMD. The male-dependent increase in TG levels was associated with leptin, insulin, cortisol and TSH levels. Clinicians treating patients with SMD should be aware of increased vulnerability for AP-related lipid abnormalities in males.

In vivo hippocampal subfield volumes in bipolar disorder-A mega-analysis from The Enhancing Neuro Imaging Genetics through Meta-Analysis Bipolar Disorder Working Group.

The hippocampus consists of anatomically and functionally distinct subfields that may be differentially involved in the pathophysiology of bipolar disorder (BD). Here we, the Enhancing NeuroImaging Genetics through Meta-Analysis Bipolar Disorder workinggroup, study hippocampal subfield volumetry in BD. T1-weighted magnetic resonance imaging scans from 4,698 individuals (BD = 1,472, healthy controls [HC] = 3,226) from 23 sites worldwide were processed with FreeSurfer. We used linear mixed-effects models and mega-analysis to investigate differences in hippocampal subfield volumes between BD and HC, followed by analyses of clinical characteristics and medication use. BD showed significantly smaller volumes of the whole hippocampus (Cohen's d = -0.20), cornu ammonis (CA)1 (d = -0.18), CA2/3 (d = -0.11), CA4 (d = -0.19), molecular layer (d = -0.21), granule cell layer of dentate gyrus (d = -0.21), hippocampal tail (d = -0.10), subiculum (d = -0.15), presubiculum (d = -0.18), and hippocampal amygdala transition area (d = -0.17) compared to HC. Lithium users did not show volume differences compared to HC, while non-users did. Antipsychotics or antiepileptic use was associated with smaller volumes. In this largest study of hippocampal subfields in BD to date, we show widespread reductions in nine of 12 subfields studied. The associations were modulated by medication use and specifically the lack of differences between lithium users and HC supports a possible protective role of lithium in BD.

Outcomes associated with different vaccines in individuals with bipolar disorder and impact on the current COVID-19 pandemic- a systematic review.

Bipolar disorder (BD) might be associated with higher infection rates of coronavirus disease (COVID-19) which in turn could result in worsening the clinical course and outcome. This may be due to a high prevalence of somatic comorbidities and an increased risk of delays in and poorer treatment of somatic disease in patients with severe mental illness in general. Vaccination is the most important public health intervention to tackle the ongoing pandemic. We undertook a systematic review regarding the data on vaccinations in individuals with BD. Proportion of prevalence rates, efficacy and specific side effects of vaccinations and in individuals with BD were searched. Results show that only five studies have investigated vaccinations in individuals with BD, which substantially limits the interpretation of overall findings. Studies on antibody production after vaccinations in BD are very limited and results are inconsistent. Also, the evidence-based science on side effects of vaccinations in individuals with BD so far is poor.

Sex-Specific Effect of Serum Lipids and Body Mass Index on Psychotic Symptoms, a Cross-Sectional Study of First-Episode Psychosis Patients.

Background: Schizophrenia is a disorder with considerable heterogeneity in course and outcomes, which is in part related to the patients' sex. Studies report a link between serum lipids, body mass index (BMI), and therapeutic response. However, the role of sex in these relationships is poorly understood. In a cross-sectional sample of first-episode psychosis (FEP) patients, we investigated if the relationship between serum lipid levels (total cholesterol, HDL-C, LDL-C, and triglycerides), BMI, and symptoms differs between the sexes. Methods: We included 435 FEP patients (males: N = 283, 65%) from the ongoing Thematically Organized Psychosis (TOP) study. Data on clinical status, antipsychotics, lifestyle, serum lipid levels, and BMI were obtained. The Positive and Negative Syndrome Scale (PANSS) and the Calgary Depression Scale for Schizophrenia (CDSS) were used to assess psychotic and depressive symptoms. General linear models were employed to examine the relationship between metabolic variables and symptomatology. Results: We observed a female-specific association between serum HDL-C levels and negative symptoms (B = -2.24, p = 0.03) and between triglycerides levels (B = 1.48, p = 0.04) and BMI (B = 0.27, p = 0.001) with depressive symptoms. When controlling for BMI, only the association between serum HDL-C levels and negative symptoms remained significant. Moreover, the HDL-C and BMI associations remained significant after controlling for demography, lifestyle, and illness-related factors. Conclusion: We found a relationship between metabolic factors and psychiatric symptoms in FEP patients that was sex-dependent.

Extensive bidirectional genetic overlap between bipolar disorder and cardiovascular disease phenotypes.

Patients with bipolar disorder (BIP) have a high risk of cardiovascular disease (CVD), despite considerable individual variation. The mechanisms underlying comorbid CVD in BIP remain largely unknown. We investigated polygenic overlap between BIP and CVD phenotypes, including CVD risk factors and coronary artery disease (CAD). We analyzed large genome-wide association studies of BIP (n = 51,710) and CVD phenotypes (n = 159,208-795,640), using bivariate causal mixture model (MiXeR), which estimates the total amount of shared genetic variants, and conjunctional false discovery rate (FDR), which identifies specific overlapping loci. MiXeR revealed polygenic overlap between BIP and body mass index (BMI) (82%), diastolic and systolic blood pressure (20-22%) and CAD (11%) despite insignificant genetic correlations. Using conjunctional FDR < 0.05, we identified 129 shared loci between BIP and CVD phenotypes, mainly BMI (n = 69), systolic (n = 53), and diastolic (n = 53) blood pressure, of which 22 are novel BIP loci. There was a pattern of mixed effect directions of the shared loci between BIP and CVD phenotypes. Functional analyses indicated that the shared loci are linked to brain-expressed genes and involved in neurodevelopment, lipid metabolism, chromatin assembly/disassembly and intracellular processes. Altogether, the study revealed extensive polygenic overlap between BIP and comorbid CVD, implicating shared molecular genetic mechanisms. The mixed effect directions of the shared loci suggest variation in genetic susceptibility to CVD across BIP subgroups, which may underlie the heterogeneity of CVD comorbidity in BIP patients. The findings suggest more focus on targeted lifestyle interventions and personalized pharmacological treatment to reduce CVD comorbidity in BIP.

Evidence for Reduced Long-Term Potentiation-Like Visual Cortical Plasticity in Schizophrenia and Bipolar Disorder.

Several lines of research suggest that impairments in long-term potentiation (LTP)-like synaptic plasticity might be a key pathophysiological mechanism in schizophrenia (SZ) and bipolar disorder type I (BDI) and II (BDII). Using modulations of visually evoked potentials (VEP) of the electroencephalogram, impaired LTP-like visual cortical plasticity has been implicated in patients with BDII, while there has been conflicting evidence in SZ, a lack of research in BDI, and mixed results regarding associations with symptom severity, mood states, and medication. We measured the VEP of patients with SZ spectrum disorders (n = 31), BDI (n = 34), BDII (n = 33), and other BD spectrum disorders (n = 2), and age-matched healthy control (HC) participants (n = 200) before and after prolonged visual stimulation. Compared to HCs, modulation of VEP component N1b, but not C1 or P1, was impaired both in patients within the SZ spectrum (χ 2 = 35.1, P = 3.1 × 10-9) and BD spectrum (χ 2 = 7.0, P = 8.2 × 10-3), including BDI (χ 2 = 6.4, P = .012), but not BDII (χ 2 = 2.2, P = .14). N1b modulation was also more severely impaired in SZ spectrum than BD spectrum patients (χ 2 = 14.2, P = 1.7 × 10-4). N1b modulation was not significantly associated with Positive and Negative Syndrome Scale (PANSS) negative or positive symptoms scores, number of psychotic episodes, Montgomery and Åsberg Depression Rating Scale (MADRS) scores, or Young Mania Rating Scale (YMRS) scores after multiple comparison correction, although a nominal association was observed between N1b modulation and PANSS negative symptoms scores among SZ spectrum patients. These results suggest that LTP-like plasticity is impaired in SZ and BD. Adding to previous genetic, pharmacological, and electrophysiological evidence, these results implicate aberrant synaptic plasticity as a mechanism underlying SZ and BD.

Polygenic overlap and shared genetic loci between loneliness, severe mental disorders, and cardiovascular disease risk factors suggest shared molecular mechanisms.

Clinical and epidemiological evidence suggest that loneliness is associated with severe mental disorders (SMDs) and increases the risk of cardiovascular disease (CVD). However, the mechanisms underlying the relationship between loneliness, SMDs, and CVD risk factors remain unknown. Here we explored overlapping genetic architecture and genetic loci shared between SMDs, loneliness, and CVD risk factors. We analyzed large independent genome-wide association study data on schizophrenia (SCZ), bipolar disorder (BD), major depression (MD), loneliness and CVD risk factors using bivariate causal mixture mode (MiXeR), which estimates the total amount of shared variants, and conditional false discovery rate to evaluate overlap in specific loci. We observed substantial genetic overlap between SMDs, loneliness and CVD risk factors, beyond genetic correlation. We identified 149 loci jointly associated with loneliness and SMDs (MD n = 67, SCZ n = 54, and BD n = 28), and 55 distinct loci jointly associated with loneliness and CVD risk factors. A total of 153 novel loneliness loci were found. Most of the shared loci possessed concordant effect directions, suggesting that genetic risk for loneliness may increase the risk of both SMDs and CVD. Functional analyses of the shared loci implicated biological processes related to the brain, metabolic processes, chromatin and immune system. Altogether, the study revealed polygenic overlap between loneliness, SMDs and CVD risk factors, providing new insights into their shared genetic architecture and common genetic mechanisms.

Heart rate variability is associated with disease severity in psychosis spectrum disorders.

While a growing literature links cardiac autonomic dysregulation to a variety of psychiatric disorders, the relationship between cardiac autonomic functioning and specific symptoms in schizophrenia (SZ) and bipolar disorder (BD) remains elusive. Thus, we investigated heart rate variability (HRV), a proxy for vagal activity, as a biological marker for symptom severity in patients with SZ and BD. HRV was calculated in 35 patients with SZ and 52 patients with BD, as well as in 149 healthy controls. In the patient groups, symptom severity and function were measured by the Positive and Negative Syndrome Scale (PANSS) and the Global Assessment of Functioning (GAF) scale. Results showed that HRV was significantly lower in both clinical groups compared to the healthy controls, with no significant HRV differences between patient groups. PANSS general psychopathology scores, GAF symptom scores, and GAF function scores showed statistically significant associations with HRV across groups. These results suggest that disease severity is associated with autonomic dysfunction and that HRV may provide a potential biomarker of disease severity in SZ and BD.

Brain Age Prediction Reveals Aberrant Brain White Matter in Schizophrenia and Bipolar Disorder: A Multisample Diffusion Tensor Imaging Study.

BACKGROUND: Schizophrenia (SZ) and bipolar disorder (BD) share substantial neurodevelopmental components affecting brain maturation and architecture. This necessitates a dynamic lifespan perspective in which brain aberrations are inferred from deviations from expected lifespan trajectories. We applied machine learning to diffusion tensor imaging (DTI) indices of white matter structure and organization to estimate and compare brain age between patients with SZ, patients with BD, and healthy control (HC) subjects across 10 cohorts. METHODS: We trained 6 cross-validated models using different combinations of DTI data from 927 HC subjects (18-94 years of age) and applied the models to the test sets including 648 patients with SZ (18-66 years of age), 185 patients with BD (18-64 years of age), and 990 HC subjects (17-68 years of age), estimating the brain age for each participant. Group differences were assessed using linear models, accounting for age, sex, and scanner. A meta-analytic framework was applied to assess the heterogeneity and generalizability of the results. RESULTS: Tenfold cross-validation revealed high accuracy for all models. Compared with HC subjects, the model including all feature sets significantly overestimated the age of patients with SZ (Cohen's d = -0.29) and patients with BD (Cohen's d = 0.18), with similar effects for the other models. The meta-analysis converged on the same findings. Fractional anisotropy-based models showed larger group differences than the models based on other DTI-derived metrics. CONCLUSIONS: Brain age prediction based on DTI provides informative and robust proxies for brain white matter integrity. Our results further suggest that white matter aberrations in SZ and BD primarily consist of anatomically distributed deviations from expected lifespan trajectories that generalize across cohorts and scanners.

Translating big data to better treatment in bipolar disorder - a manifesto for coordinated action.

Bipolar disorder (BD) is a major healthcare and socio-economic challenge. Despite its substantial burden on society, the research activity in BD is much smaller than its economic impact appears to demand. There is a consensus that the accurate identification of the underlying pathophysiology for BD is fundamental to realize major health benefits through better treatment and preventive regimens. However, to achieve these goals requires coordinated action and innovative approaches to boost the discovery of the neurobiological underpinnings of BD, and rapid translation of research findings into development and testing of better and more specific treatments. To this end, we here propose that only a large-scale coordinated action can be successful in integrating international big-data approaches with real-world clinical interventions. This could be achieved through the creation of a Global Bipolar Disorder Foundation, which could bring government, industry and philanthropy together in common cause. A global initiative for BD research would come at a highly opportune time given the seminal advances promised for our understanding of the genetic and brain basis of the disease and the obvious areas of unmet clinical need. Such an endeavour would embrace the principles of open science and see the strong involvement of user groups and integration of dissemination and public involvement with the research programs. We believe the time is right for a step change in our approach to understanding, treating and even preventing BD effectively.

Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group.

Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47-67.00, ROC-AUC = 71.49%, 95% CI = 69.39-73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70-60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen's Kappa = 0.83, 95% CI = 0.829-0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data.

Distinct structural brain circuits indicate mood and apathy profiles in bipolar disorder.

Bipolar disorder (BD) is a severe manic-depressive illness. Patients with BD have been shown to have gray matter (GM) deficits in prefrontal, frontal, parietal, and temporal regions; however, the relationship between structural effects and clinical profiles has proved elusive when considered on a region by region or voxel by voxel basis. In this study, we applied parallel independent component analysis (pICA) to structural neuroimaging measures and the positive and negative syndrome scale (PANSS) in 110 patients (mean age 34.9 ±â€¯11.65) with bipolar disorder, to examine networks of brain regions that relate to symptom profiles. The pICA revealed two distinct symptom profiles and associated GM concentration alteration circuits. The first PANSS pICA profile mainly involved anxiety, depression and guilty feelings, reflecting mood symptoms. Reduced GM concentration in right temporal regions predicted worse mood symptoms in this profile. The second PANSS pICA profile generally covered blunted affect, emotional withdrawal, passive/apathetic social withdrawal, depression and active social avoidance, exhibiting a withdrawal or apathy dominating component. Lower GM concentration in bilateral parietal and frontal regions showed worse symptom severity in this profile. In summary, a pICA decomposition suggested BD patients showed distinct mood and apathy profiles differing from the original PANSS subscales, relating to distinct brain structural networks.

Improvement in verbal learning over the first year of antipsychotic treatment is associated with serum HDL levels in a cohort of first episode psychosis patients.

To investigate whether changes in serum lipids are associated with cognitive performance in first episode psychosis (FEP) patients during their first year of antipsychotic drug treatment. One hundred and thirty-two antipsychotic-treated FEP patients were included through the TOP study along with 83 age- and gender-matched healthy controls (HC). Information regarding cognitive performance, psychotic symptoms, lifestyle, body mass index, serum lipids [total cholesterol, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein cholesterol, and triglycerides] and antipsychotic treatment was obtained at baseline and after 1 year. The cognitive test battery is comprised of assessments for verbal learning, processing speed, working memory, verbal fluency, and inhibition. Mixed-effects models were used to study the relationship between changes over time in serum lipids and cognitive domains, controlling for potential confounders. There was a significant group by HDL interaction effect for verbal learning (F = 11.12, p = 0.001), where an increase in HDL levels was associated with improvement in verbal learning in FEP patients but not in HC. Practice effects, lifestyle, and psychotic symptoms did not significantly affect this relationship. Antipsychotic-treated FEP patients who increased in HDL levels during the first year of follow-up exhibited better verbal learning capacity. Further investigations are needed to clarify the underlying mechanisms.