Schizophrenia Spectrum Disorders Research Articles

Copper excess in psychiatric disorders: a focus on mood spectrum disorders and sex

BackgroundMeta-analyses show increased copper (Cu) levels in major depression disorder. However, the association of Cu biomarkers with clinical classification in other mental health disorders has not been fully explored. MethodsTo this aim, we compared an extensive panel of Cu biomarkers, composed of Cu, ceruloplasmin (Cp) Cp activity, Cp specific activity, Cu not bound to ceruloplasmin (non-Cp Cu, also known as ‘free’ copper) in 171 consecutive patients affected by psychiatric disorders and in 61 healthy controls (HC) using MANOVA adjusting for the effect of sex and age, and studied their association with the clinical scale outcomes at psychiatric examination, namely Global Assessment of Functioning, Clinical Global Impression, and Brief Psychiatric Rating Scale. Resultsindividuals with psychiatric disorders were classified as 109 patients affected by mood spectrum disorders (MSD), 20 patients with schizophrenia spectrum disorders (SSD), and 42 with personality disorders (PD). Cu and non-Cp Cu were increased in psychiatric individuals than in HC, which also differed among the patients stratified per the clinical classification, being higher in the MSD individuals. The analysis stratified for sex revealed that women from the patient group, and specifically from the MSD group, had increased levels of Cu and non-Cp Cu than healthy women, while no difference was revealed in men. A logistic regression model considering the effect of sex and age revealed that non-Cp Cu could explain 26 % increased odds of having MSD per µmol/L unit increase (OR = 1.26; p = 0.0008; 95 % CI 1.099–1.436), that reached 40 % when considering only women. This result was driven by non-Cp Cu that correctly classified 64.1 % MSD (70 % in women) individuals vs. HC in a decision tree model, with values higher than 2.1 µmol/L which could distinguish the majority of MSD patients (86.3 % MSD vs. 13.7 % HC in women). None of the biological variables under study correlated with outcomes of the clinical scales, substances, or alcohol abuse. ConclusionCurrent results suggest mild Cu toxicity in women with MSD, as revealed by a value of non-Cp Cu higher than 2.1 µmol/L, which can be further investigated to assess its potential diagnostic accuracy in bigger and longitudinal cohorts.

B - 7 Factor Structure of the Beck Depression Inventory-II in Epilepsy

Abstract Objective This study aimed to explore the factor structure of the Beck Depression Inventory, Second Edition (BDI-II) in persons with epilepsy (PWE) and assess its correspondence with validated BDI-II cognitive and somatic-affective factors associated with depression in other populations. Method Retrospective data from 69 PWE (ages 18–72) who completed the BDI-II at a National Association of Epilepsy Centers level 4 epilepsy center were analyzed. Patients with diagnoses of bipolar disorder and schizophrenia spectrum disorders were excluded. An Exploratory Factor Analysis (EFA) using the psych package in R Studio was conducted, allowing factors to correlate with oblique factor rotation. The number of factors was determined using eigenvalues >1 and scree plots. Results Statistical support is strongest for a one-factor solution with eigenvalue 7.91; however, a two-factor solution (Factor two eigenvalue = 1.16) also met criteria. These factors accounted for 30% and 10% of the variance, respectively. Due to the low eigenvalue, few item loadings (3), and low variance explained by the second factor, item-level considerations were implemented to determine whether a two-factor solution added clinically relevant information. Conclusion Although a one-factor solution was best supported by the data, a second factor including sadness, feeling of punishment, and suicidal thoughts may be clinically relevant given elevated suicide risk and qualitative research reporting punishment feelings among PWE.

Optimizing graph neural network architectures for schizophrenia spectrum disorder prediction using evolutionary algorithms

Background and Objective:The accurate diagnosis of schizophrenia spectrum disorder plays an important role in improving patient outcomes, enabling timely interventions, and optimizing treatment plans. Functional connectivity analysis, utilizing functional magnetic resonance imaging data, has been demonstrated to offer invaluable biomarkers conducive to clinical diagnosis. However, previous studies mainly focus on traditional machine learning methods or hand-crafted neural networks, which may not fully capture the spatial topological relationship between brain regions. Methods:This paper proposes an evolutionary algorithm (EA) based graph neural architecture search (GNAS) method. EA-GNAS has the ability to search for high-performance graph neural networks for schizophrenia spectrum disorder prediction. Moreover, we adopt GNNExplainer to investigate the explainability of the acquired architectures, ensuring that the model’s predictions are both accurate and comprehensible. Results:The results suggest that the graph neural network model, derived using genetic algorithm search, outperforms under five-fold cross-validation, achieving a fitness of 0.1850. Relative to conventional machine learning and other deep learning approaches, the proposed method yields superior accuracy, F1 score, and AUC values of 0.8246, 0.8438, and 0.8258, respectively. Conclusion:Based on a multi-site dataset from schizophrenia spectrum disorder patients, the findings reveal an enhancement over prior methods, advancing our comprehension of brain function and potentially offering a biomarker for diagnosing schizophrenia spectrum disorder.

Discussing the concept of substance-induced psychosis (SIP).

Substance-induced psychosis (SIP) is characterized by both substance use and a psychotic state, and it is assumed that the first causes the latter. In ICD-10 the diagnosis is categorized as and grouped together with substance use disorders, and to a large extent also treated as such in the health care system. Though criticism of the diagnostic construct of SIP dates back several decades, numerous large and high-quality studies have been published during the past 5-10 years that substantiate and amplify this critique. The way we understand SIP and even how we name it is of major importance for treatment and it has judicial consequences. It has been demonstrated that substance use alone is not sufficient to cause psychosis, and that other risk factors besides substance use are at play. These are risk factors that are also known to be associated with schizophrenia spectrum disorders. Furthermore, register-based studies from several different countries find that a large proportion, around one in four, of those who are initially diagnosed with an SIP over time are subsequently diagnosed with a schizophrenia spectrum disorder. This scoping review discusses the construct validity of SIP considering recent evidence. We challenge the immanent causal assumption in SIP, and advocate that the condition shares many features with the schizophrenia spectrum disorders. In conclusion, we argue that SIP just as well could be considered a first-episode psychotic disorder in patients with substance use.

Demographic and clinical characteristics of individuals with psychosis symptoms who died by suicide: Findings of a psychological autopsy study

Suicide is a critical public health concern among individuals with schizophrenia spectrum disorders (SSDs). Still, significant gaps remain in understanding relationships between suicide outcomes and both demographic and clinical characteristics. Data were examined from 57 adults of a psychological autopsy study who had psychosis symptoms and died between 1989 and 2017 in the Midwestern United States. This study compared demographic and clinical characteristics of those who died by suicide (n = 26) to those who died by natural (n = 26) or accidental (n = 5) causes. Those who died by suicide were more often younger, white/Caucasian, more educated, and more often employed than those who died by natural or accidental causes (p < .05). Furthermore, symptoms of depression, recurrent suicidal ideation, history of suicide attempt, and being in a first episode of psychosis were experienced significantly more by those who died by suicide in comparison to natural or accidental causes (p < .05). Findings highlight the need to consider depression in suicide risk for psychosis populations, intervene in early stages of psychosis illness, and implement suicide prevention strategies tailored to individuals with psychosis and SSDs. Implications point towards the need for tailored interventions to mitigate risk for suicide death in this vulnerable population.

Applying neural network algorithms to ascertain reported experiences of violence in routine mental healthcare records and distributions of reports by diagnosis.

Experiences of violence are important risk factors for worse outcome in people with mental health conditions; however, they are not routinely collected be mental health services, so their ascertainment depends on extraction from text fields with natural language processing (NLP) algorithms. Applying previously developed neural network algorithms to routine mental healthcare records, we sought to describe the distribution of recorded violence victimisation by demographic and diagnostic characteristics. We ascertained recorded violence victimisation from the records of 60,021 patients receiving care from a large south London NHS mental healthcare provider during 2019. Descriptive and regression analyses were conducted to investigate variation by age, sex, ethnic group, and diagnostic category (ICD-10 F chapter sub-headings plus post-traumatic stress disorder (PTSD) as a specific condition). Patients with a mood disorder (adjusted odds ratio 1.63, 1.55-1.72), personality disorder (4.03, 3.65-4.45), schizophrenia spectrum disorder (1.84, 1.74-1.95) or PTSD (2.36, 2.08-2.69) had a significantly increased likelihood of victimisation compared to those with other mental health diagnoses. Additionally, patients from minority ethnic groups (1.10 (1.02-1.20) for Black, 1.40 (1.31-1.49) for Asian compared to White groups) had significantly higher likelihood of recorded violence victimisation. Males were significantly less likely to have reported recorded violence victimisation (0.44, 0.42-0.45) than females. We thus demonstrate the successful deployment of machine learning based NLP algorithms to ascertain important entities for outcome prediction in mental healthcare. The observed distributions highlight which sex, ethnicity and diagnostic groups had more records of violence victimisation. Further development of these algorithms could usefully capture broader experiences, such as differentiating more efficiently between witnessed, perpetrated and experienced violence and broader violence experiences like emotional abuse.

Impact of Biological Sex on Emotional Perception Among Adults With Schizophrenia Spectrum Disorders: Protocol for a Systematic Review.

It is well established that individuals with schizophrenia experience deficits in emotional perception that can impact long-term social and occupational functioning. Understanding the factors that impact these impairments is important for targeting interventions to improve recovery. In the general population, compared with males, females tend to show greater perception of emotions. Whether this sex difference persists in schizophrenia is less clear. In contrast to males, females diagnosed with schizophrenia tend to have a higher age of disease onset and better premorbid functioning but do not necessarily have better outcomes. Effective treatments for social cognitive impairments are highly relevant to long-term functional rehabilitation. A greater understanding of the cognitive deficits in emotional perception within females and males living with schizophrenia may assist interventions to be better tailored to individuals. This systematic review aims to collate, synthesize, and critically appraise evidence considering the influence of biological sex (female and male) on the emotional perception of individuals with schizophrenia. This is a systematic review protocol based on the PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols) guidelines. The electronic databases MEDLINE, Embase, CENTRAL, CINAHL, and PsycINFO will be systematically searched. To be included in this review, studies must compare the emotional perceptions of male and female participants older than 18 years who have a primary diagnosis of a schizophrenia spectrum disorder. Qualitative studies, case reports, case series, unpublished manuscripts, and studies not reported in English will be excluded. Key search strategies will include combinations of the following terms: "men," "male," "man," "female," "women," "woman," "sex," "gender," "emotional perception," "emotional processing," "schizophrenia," "schizophren," "psychotic disorders," "psychosis," "psychoses," "psychotic," "schizoaffective," "schizotypal personality disorder," and "schizotyp." Identified studies will be uploaded to the web-based Covidence systematic review management software. The risk of bias for individual studies will be assessed using the relevant Joanna Briggs Institute checklist tools. The GRADE (Grading of Recommendations Assessment, Development, and Evaluation) system will also be used to evaluate the strength of the evidence base. Findings will be synthesized to provide a systematic summary of the existing literature. If sufficiently comparable data to permit meta-analysis emerges, a random-effects meta-analysis will be performed. This systematic review was registered with the PROSPERO (International Prospective Register of Systematic Reviews) in October 2023. The search and screening of study titles and abstracts are currently underway. Data are expected to be extracted and analyzed in July 2024. Results will contribute to an improved understanding of the social cognitive profiles of males and females with schizophrenia. This knowledge is expected to inform the adaptation of interventions to improve functional outcomes. PROSPERO CRD42023463561; https://tinyurl.com/34sr3rnf. DERR1-10.2196/56977.

Task-based functional neural correlates of social cognition across autism and schizophrenia spectrum disorders

BackgroundAutism and schizophrenia spectrum disorders (SSDs) both feature atypical social cognition. Despite evidence for comparable group-level performance in lower-level emotion processing and higher-level mentalizing, limited research has examined the neural basis of social cognition across these conditions. Our goal was to compare the neural correlates of social cognition in autism, SSDs, and typically developing controls (TDCs).MethodsData came from two harmonized studies in individuals diagnosed with autism or SSDs and TDCs (aged 16–35 years), including behavioral social cognitive metrics and two functional magnetic resonance imaging (fMRI) tasks: a social mirroring Imitate/Observe (ImObs) task and the Empathic Accuracy (EA) task. Group-level comparisons, and transdiagnostic analyses incorporating social cognitive performance, were run using FSL’s PALM for each task, covarying for age and sex (1000 permutations, thresholded at p < 0.05 FWE-corrected). Exploratory region of interest (ROI)-based analyses were also conducted.ResultsImObs and EA analyses included 164 and 174 participants, respectively (autism N = 56/59, SSD N = 50/56, TDC N = 58/59). EA and both lower- and higher-level social cognition scores differed across groups. While canonical social cognitive networks were activated, no significant whole-brain or ROI-based group-level differences in neural correlates for either task were detected. Transdiagnostically, neural activity during the EA task, but not the ImObs task, was associated with lower- and higher-level social cognitive performance.LimitationsDespite attempting to match our groups on age, sex, and race, significant group differences remained. Power to detect regional brain differences is also influenced by sample size and multiple comparisons in whole-brain analyses. Our findings may not generalize to autism and SSD individuals with co-occurring intellectual disabilities.ConclusionsThe lack of whole-brain and ROI-based group-level differences identified and the dimensional EA brain-behavior relationship observed across our sample suggest that the EA task may be well-suited to target engagement in novel intervention testing. Our results also emphasize the potential utility of cross-condition approaches to better understand social cognition across autism and SSDs.

Association of Weight Status and Waist Circumference with Physical Activity in people with Schizophrenia Spectrum Disorders and healthy controls

BackgroundIndividuals with Schizophrenia Spectrum Disorders (SSD) often suffer from obesity and do limited Physical Activity (PA). PA has many beneficial effects on a variety of somatic and mental variables and it should be strengthened among people with mental disorders. The relationship between Body Mass Index (BMI), Waist Circumference (WC), and PA in this population is poorly understood, with a lack of precise PA assessment. This study investigates the association between BMI, WC, weight, and PA in individuals with SSD and controls using accelerometers. MethodsOne hundred twenty-six patients with SSD (residents and outpatients) and 110 sex- and age-matched controls were enrolled. Clinical, sociodemographic, and quality-of-life data were collected. PA was measured with a tri-axial ActiGraph GT9X and quantified by Vector Magnitude (VM). Relationships between PA and BMI, WC, and weight changes were analysed using linear regression models. ResultsPatients were more likely to be unmarried, unemployed, and less educated compared to controls (p < 0.001). Residents had more medical comorbidities (p = 0.001), while outpatients had higher BMI, weight, and WC (p < 0.001). Residents reported more severe psychopathology, lower functioning, and greater use of psychopharmacological medications (p < 0.001). Higher PA levels were not significantly associated with lower BMI, WC, or weight. Although not statistically significant, increased PA showed a trend towards lower obesity risk. ConclusionsSociodemographic, medical, and clinical characteristics of individuals with SSD define vulnerability factors that can inform tailored interventions to improve PA.

Detecting schizophrenia, bipolar disorder, psychosis vulnerability and major depressive disorder from 5 minutes of online-collected speech.

Psychosis poses substantial social and healthcare burdens. The analysis of speech is a promising approach for the diagnosis and monitoring of psychosis, capturing symptoms like thought disorder and flattened affect. Recent advancements in Natural Language Processing (NLP) methodologies enable the automated extraction of informative speech features, which has been leveraged for early psychosis detection and assessment of symptomology. However, critical gaps persist, including the absence of standardized sample collection protocols, small sample sizes, and a lack of multi-illness classification, limiting clinical applicability. Our study aimed to (1) identify an optimal assessment approach for the online and remote collection of speech, in the context of assessing the psychosis spectrum and evaluate whether a fully automated, speech-based machine learning (ML) pipeline can discriminate among different conditions on the schizophrenia-bipolar spectrum (SSD-BD-SPE), help-seeking comparison subjects (MDD), and healthy controls (HC) at varying layers of analysis and diagnostic complexity. We adopted online data collection methods to collect 20 minutes of speech and demographic information from individuals. Participants were categorized as "healthy" help-seekers (HC), having a schizophrenia-spectrum disorder (SSD), bipolar disorder (BD), major depressive disorder (MDD), or being on the psychosis spectrum with sub-clinical psychotic experiences (SPE). SPE status was determined based on self-reported clinical diagnosis and responses to the PHQ-8 and PQ-16 screening questionnaires, while other diagnoses were determined based on self-report from participants. Linguistic and paralinguistic features were extracted and ensemble learning algorithms (e.g., XGBoost) were used to train models. A 70%-30% train-test split and 30-fold cross-validation was used to validate the model performance. The final analysis sample included 1140 individuals and 22,650 minutes of speech. Using 5-minutes of speech, our model could discriminate between HC and those with a serious mental illness (SSD or BD) with 86% accuracy (AUC = 0.91, Recall = 0.7, Precision = 0.98). Furthermore, our model could discern among HC, SPE, BD and SSD groups with 86% accuracy (F1 macro = 0.855, Recall Macro = 0.86, Precision Macro = 0.86). Finally, in a 5-class discrimination task including individuals with MDD, our model had 76% accuracy (F1 macro = 0.757, Recall Macro = 0.758, Precision Macro = 0.766). Our ML pipeline demonstrated disorder-specific learning, achieving excellent or good accuracy across several classification tasks. We demonstrated that the screening of mental disorders is possible via a fully automated, remote speech assessment pipeline. We tested our model on relatively high number conditions (5 classes) in the literature and in a stratified sample of psychosis spectrum, including HC, SPE, SSD and BD (4 classes). We tested our model on a large sample (N = 1150) and demonstrated best-in-class accuracy with remotely collected speech data in the psychosis spectrum, however, further clinical validation is needed to test the reliability of model performance.

Antipsychotic exposure and infection risk in people with schizophrenia spectrum disorders during the COVID-19 pandemic: a Danish nationwide registry study

Infection risk and mortality are increased in schizophrenia spectrum disorders, which was corroborated during the COVID-19 pandemic. However, evidence is lacking regarding the additional impact of antipsychotic drugs, and the highly debated safety of clozapine treatment during large-scale infection outbreaks. Therefore, we aimed to investigate risk of COVID-19 and non-COVID respiratory infections during exposure to antipsychotics. We used several nationwide Danish registers (National Prescription Registry, National Hospital Registry, Psychiatric Research Register, Microbiology Database, Vaccination Registry, Cause of Death Registry, and Database for Labour market Research) to investigate all individuals aged 18 years or older with a schizophrenia spectrum disorder (ICD-10: F20-F29) living in Denmark between Jan 1 and March 1, 2020. Antipsychotic exposure groups were defined as prevalent-users and incident-users. The full observation period was March 1, 2020 to Dec 31, 2021. Antipsychotic exposure was defined in a time-varying manner and compared with non-exposure. Risk was calculated for mild infection outcomes (positive SARS-CoV-2 PCR and anti-infective drug prescriptions) and severe infection outcomes (hospitalisation and death) related to COVID-19 and non-COVID-19 respiratory infections. Outcomes were adjusted for demographics, socio-economic factors, and comorbidity. Of 85 083 individuals (44 293 men [52·1%] and 40 790 women [47·9%], median age 45·8 years [IQR 31·1-60·2]) with pre-existing schizophrenia spectrum disorders, 30 984 had antipsychotic exposure periods. Ethnicity data were not available. During antipsychotic exposure compared with non-exposed periods, assessing mild infection outcomes, risk of a positive SARS-CoV-2 test was decreased (hazard ratio 0·91 [95% CI 0·85-0·97]) and risk of redeeming an anti-infective drug was not statistically significantly different (1·01 [0·97-1·06]). For severe infection outcomes, COVID-19-related hospitalisation risk was increased (1·28 [1·07-1·52]) although COVID-19-related death was not statistically significantly increased (1·24 [0·82-1·86]). For non-COVID-19 respiratory infections, risk was increased both for hospitalisation (1·61 [1·44-1·79]) and death (1·61 [1·18-2·21]). Specifically, COVID-19 hospitalisation risk was increased in individuals older than 70 years, and non-COVID-19 hospitalisation risk increased in individuals older than 40 years and death risk in age groups of 50-59 years and 70-79 years. Based on homogeneity testing, no apparent excess risk of any outcome was observed with clozapine exposure compared with other antipsychotics. During antipsychotic exposure compared with unexposed periods, risk of severe infection outcomes increases. It seems reasonable to initiate infection countermeasures, such as pneumococcal vaccination, in people older than 40 years with schizophrenia spectrum disorders, who commence or are treated with antipsychotics. We do not suggest the avoidance of specific antipsychotics but rather adherence to treatment guidelines and a call for increased vigilance regarding this at-risk group. Mental Health Services of the Capital Region of Denmark.

DNA methylation and histone modifications associated with antipsychotic treatment: a systematic review.

Antipsychotic medications are essential when treating schizophrenia spectrum and other psychotic disorders, but the efficacy and tolerability of these medications vary from person to person. This interindividual variation is likely mediated, at least in part, by epigenomic processes that have yet to be fully elucidated. Herein, we systematically identified and evaluated 65 studies that examine the influence of antipsychotic drugs on epigenomic changes, including global methylation (9 studies), genome-wide methylation (22 studies), candidate gene methylation (16 studies), and histone modification (18 studies). Our evaluation revealed that haloperidol was consistently associated with increased global hypermethylation, which corroborates with genome-wide analyses, mostly performed by methylation arrays. In contrast, clozapine seems to promote hypomethylation across the epigenome. Candidate-gene methylation studies reveal varying effects post-antipsychotic therapy. Some genes like Glra1 and Drd2 are frequently found to undergo hypermethylation, whereas other genes such as SLC6A4, DUSP6, and DTNBP1 are more likely to exhibit hypomethylation in promoter regions. In examining histone modifications, the literature suggests that clozapine changes histone methylation patterns in the prefrontal cortex, particularly elevating H3K4me3 at the Gad1 gene and affecting the transcription of genes like mGlu2 by modifying histone acetylation and interacting with HDAC2 enzymes. Risperidone and quetiapine, however, exhibit distinct impacts on histone marks across different brain regions and cell types, with risperidone reducing H3K27ac in the striatum and quetiapine modifying global H3K9me2 levels in the prefrontal cortex, suggesting antipsychotics demonstrate selective influence on histone modifications, which demonstrates a complex and targeted mode of action. While this review summarizes current knowledge, the intricate dynamics between antipsychotics and epigenetics clearly warrant more exhaustive exploration with the potential to redefine our understanding and treatment of psychiatric conditions. By deciphering the epigenetic changes associated with drug treatment and therapeutic outcomes, we can move closer to personalized medicine in psychiatry.

Adverse obstetric and neonatal outcomes associated with maternal schizophrenia-spectrum disorders and prenatal antipsychotic use: a meta-analysis of 37,214,330 pregnancy deliveries and propensity-score weighted population-based cohort study assessing confounder dependency of risk estimates.

Studies demonstrated increased obstetric and neonatal complications in women with schizophrenia-spectrum disorder (SSD), but most inadequately addressed confounders and rarely considered antipsychotic effects. We conducted a meta-analysis and a population-based cohort study evaluating associations of adverse obstetric/neonatal outcomes with SSD and prenatal antipsychotic use. In the meta-analysis, we searched four databases from inception to October-31-2023 and generated pooled risk estimates using random-effect models. In the cohort study, we identified women aged 15-50 years with SSD-diagnosis from electronic-heath-record database of public healthcare-services who delivered first/singleton children between 2003 and 2018 in Hong Kong. Propensity-score weighted regression-analyses incorporating important confounders including maternal pre-existing and gestational morbidities, substance/alcohol abuse, and psychotropic use, were performed to assess risk of adverse obstetric/neonatal outcomes in SSD-women versus non-SSD-women, and subsequently treated-SSD and untreated-SSD subgroups to disentangle effects of SSD from antipsychotic exposure. The meta-analysis (studies = 18, women = 37,214,330, including 42,926 SSD-women) found significant associations of SSD with 12 of 17 analyzed negative obstetric/neonatal outcomes (with pooled relative risk ranged:1.12-2.10), including placental complications, induced labor, Caesarean delivery, fetal distress, stillbirth, preterm birth, small-for-gestational-age, low birth weight, low APGAR scores, neonatal and post-neonatal deaths. However, the cohort study (466,358 women, including 804 SSD-women) revealed that elevated risk of most study outcomes in unadjusted-models were markedly-attenuated or became non-significant in propensity-score weighted adjusted-models, except index-delivery hospitalization ≥7 days (odds ratio [OR] = 1.76 [95% CI = 1.33-2.34]), preterm birth (OR = 1.48 [95% CI = 1.09-2.00]) and neonatal special-care admission (OR = 1.65 [95% CI = 1.35-2.01]). Apart from higher neonatal special-care admission in treated-SSD than untreated-SSD women (OR = 1.75 [95% CI = 1.23-2.52]), no significant between-group differences emerged in other outcomes. In sum, elevated risk of most obstetric/neonatal complications reported in SSD-women might largely be explained by maternal physical comorbidities, substance/alcohol use disorders and other confounders. Interventions targeting modifiable maternal risk factors should be incorporated in prenatal care for SSD-women to minimize avoidable adverse outcomes.

Treatment Outcomes for Asian Americans Diagnosed with Schizophrenia Spectrum Disorder.

We implemented a pilot study to investigate symptoms and functional outcomes of Asian Americans treated in urban community mental health centers for a diagnosis of schizophrenia spectrum disorder. Furthermore, we investigated whether these outcomes differed between East and Southeast Asians. We collected quantitative data from 75 participants recruited using a nonprobability sampling strategy from six urban community mental health centers. We used the Positive and Negative Syndrome Scale (Kay et al. in Schizophrenia Bulletin 13(2):261-276, 1987) and the Strauss and Carpenter Outcome Scale (Strauss and Carpenter in Archives of General Psychiatry 27(6):739-746, 1972) to measure their symptoms and functional outcomes. To compare the outcomes between East and Southeast Asians, we used a multivariable logistic regression model, which adjusted for the estimated effects of age, sex assigned at birth, and age at onset of illness for each outcome examined. The data shows that the treatment outcomes for this group are poor. Only a small number of participants experienced symptomatic remission (30.67%), role restoration (34.67%), and clinical recovery (21.33%). The majority of those who did not experience clinical recovery had difficulties sustaining symptomatic remission and restoring role functioning (54.67%). However, more participants achieved social restoration (68.00%). The results did not vary by national origin groups and sex assigned at birth. However, the participant's age, the age at which the illness began, or both determined whether the treatment outcomes were favorable. Findings underscore the need for interventions that improve symptom control to increase the likelihood of other favorable outcomes.

Exposure to green spaces and schizophrenia: a systematic review.

The mental health benefits of exposure to green spaces are well known. This systematic review summarizes the evidence of green space exposure for people with schizophrenia spectrum disorders (SSDs), focusing on incidence and mental health outcomes, including mental health symptoms and health service use. The study was pre-registered (PROSPERO ID: CRD42023431954), and conducted according to PRISMA guidelines. Seven databases, reference lists, and gray literature sources were searched. Methodological quality was assessed using The Quality Assessment Tool for Quantitative Studies. 126 studies were screened, and 12 studies were eligible for inclusion. Seven studies found that exposure to green space was associated with a reduced risk of schizophrenia (lowest to highest green space exposure: HRs = 0.62-0.37; IRRs = 1.52-1.18), with five studies reporting a dose-response relationship. Of these studies, four examined childhood exposure and the remainder examined adult exposure. Regarding health service use, proximity to green space was not significantly associated with length of hospital admission, though greater green space exposure was associated with reduced hospital admission rates. Three studies found reduced symptoms of anxiety (d = -0.70-2.42), depression (d = -0.97-1.70) and psychosis (d = -0.94) with greater green space exposure. Exposure to green space reduces the risk of schizophrenia, and there is emerging evidence of the potential benefits of green space for reducing symptoms and health service use among people with SSDs. Future research using experimental and longitudinal designs will provide more robust evidence of the benefits of green space for people with SSDs.

The role of human involvement and support in digital mental health interventions for people with schizophrenia spectrum disorders: a critical review.

Schizophrenia spectrum disorders (SDD) are characterized by a complex array of psychosis symptoms, and typically require ongoing and long-term support, including pharmacological and nonpharmacological management. Digital mental health interventions (DMHIs) have been suggested as a novel therapeutic approach to enable low-cost, scalable improvements in quality of care for adults living with SSD. However, the types and role of human involvement and support within DMHIs is currently unknown. Several recent systematic reviews and meta-analyses have investigated the potential efficacy of DMHIs for people with SSD, with scant yet emerging systematic evidence on the effects of human support within DMHIs on mental health outcomes. Further, several recent individual studies examined the efficacy of DMHIs with human support among people with SSD and provided valuable insights into the potential key elements of such support on outcomes relevant to this population. The current critical review provides the first narrative synthesis of available evidence to guide clinicians and intervention develops in designing DMHIs with adequate human support that may enhance long-term outcomes of people living with SSD.

Gray matter matters: Cognitive stability and flexibility in schizophrenia spectrum disorder.

Cognitive dysfunction constitutes a core characteristic of schizophrenia spectrum disorders (SZ). Specifically, deficits in updating generative models (i.e., cognitive flexibility) and shielding against distractions (i.e., cognitive stability) are considered critical contributors to cognitive impairment in these patients. Here, we examined the structural integrity of frontostriatal networks and their associations with reduced cognitive stability and flexibility in SZ patients. In a sample of 21 patients diagnosed with SZ and 22 healthy controls, we measured gray matter volume (GMV) using structural MRI. Further, cognitive stability and flexibility were assessed using a switch-drift paradigm, quantifying the successful ignoring of distracters and detection of rule switches. Compared to controls, patients showed significantly smaller GMV in the whole brain and three predefined regions of interest: the medial prefrontal cortex (mPFC), inferior frontal gyrus (IFG), and caudate nucleus (CN). Notably, GMV in these areas positively correlated with correct rule-switch detection but not with ignoring rule-compatible drifts. Further, the volumetric differences between SZ patients and controls were statistically explainable by considering the behavioral performance in the switch-drift task. Our results indicate that morphological abnormalities in frontostriatal networks are associated with deficient flexibility in SZ patients and highlight the necessity of minimizing neurodevelopmental and progressive brain atrophy in this population.

Multivariate Association Between Functional Connectivity Gradients and Cognition in Schizophrenia Spectrum Disorders

BackgroundSchizophrenia spectrum disorders (SSDs), which are characterized by social cognitive deficits, have been associated with dysconnectivity in unimodal (e.g., visual, auditory) and multimodal (e.g., default mode and frontoparietal) cortical networks. However, little is known about how such dysconnectivity is related to social and nonsocial cognition and how such brain-behavior relationships associate with clinical outcomes of SSDs. MethodsWe analyzed cognitive (nonsocial and social) measures and resting-state functional magnetic resonance imaging data from the SPINS [Social Processes Initiative in Neurobiology of the Schizophrenia(s)] study (247 stable participants with SSDs and 172 healthy control participants, ages 18–55 years). We extracted gradients from parcellated connectomes and examined the association between the first 3 gradients and the cognitive measures using partial least squares correlation (PLSC). We then correlated the PLSC dimensions with functioning and symptoms in the SSD group. ResultsThe SSD group showed significantly lower differentiation on all 3 gradients. The first PLSC dimension explained 68.53% (p < .001) of the covariance and showed a significant difference between the SSD and the control group (bootstrap p < .05). PLSC showed that all cognitive measures were associated with gradient scores of unimodal and multimodal networks (gradient 1); auditory, sensorimotor, and visual networks (gradient 2); and perceptual networks and the striatum (gradient 3), which were less differentiated in SSDs. Furthermore, the first dimension was positively correlated with negative symptoms and functioning in the SSD group. ConclusionsThese results suggest a potential role of lower differentiation of brain networks in cognitive and functional impairments in SSDs.

Disability and Adverse Effects of Oral Versus Long-Acting Injectable Antipsychotics in Schizophrenia-Spectrum and Bipolar Disorder: A Comparison Based on Data-Driven Taxonomy.

Patients undergoing antipsychotic treatment for psychiatric disorders may experience challenges in functioning, either stemming from the severity of the illness or from the tolerability issues of prescribed medications. The aims of this cross-sectional study are to investigate the impact of adverse effects of antipsychotic drugs on patients' daily life functioning, comparing oral and long-acting injectable (LAI) antipsychotics, and further dividing antipsychotics by receptor-binding profiles based on recently defined data-driven taxonomy. This study involved patients with schizophrenia and bipolar spectrum disorders taking oral or LAI antipsychotics. Disability and functioning levels were assessed using the World Health Organization Disability Assessment Schedule 2.0 (WHODAS), and the adverse effects of medications were evaluated using the Udvalg for Kliniske Undersogelser (UKU) Side Effect Rating Scale and its subscales. The total sample consisted of 126 participants with a diagnosis of schizophrenia-spectrum or bipolar disorder, and included 54 males and 72 females ranging from 18 to 78 years of age (mean45.1, standard deviation 14); 78 patients were taking oral antipsychotics and 48 were taking LAI antipsychotics, with subcategories of muscarinic (31), adrenergic/low dopamine (25), serotonergic/dopaminergic (23), dopaminergic (1), LAI muscarinic (15), LAI adrenergic (6), and LAI serotonergic/dopaminergic (25). The UKU total score for adverse effects showed significant correlations with WHODAS total score (ρ=0.475; p<0.001). Compared with oral antipsychotics, LAIs showed significantly lower scores in psychological (p=0.014), autonomic (p=0.008), other (p=0.004), and sexual adverse effects (p=0.008), as well as the UKU total score (p=0.002). The Kruskal-Wallis test showed a significant difference in adverse effects between LAI and oral muscarinic subgroups, with LAIs having lower scores compared with antipsychotics binding to muscarinic receptors (p=0.043). These findings indicate clinically relevant differences in adverse effects among formulations, warranting further investigation for future observational studies.

Longitudinal course of inflammatory-cognitive subgroups across first treatment severe mental illness and healthy controls.

While inflammation is associated with cognitive impairment in severe mental illnesses (SMI), there is substantial heterogeneity and evidence of transdiagnostic subgroups across schizophrenia (SZ) and bipolar (BD) spectrum disorders. There is however, limited knowledge about the longitudinal course of this relationship. Systemic inflammation (C-Reactive Protein, CRP) and cognition (nine cognitive domains) was measured from baseline to 1 year follow-up in first treatment SZ and BD (n = 221), and healthy controls (HC, n = 220). Linear mixed models were used to evaluate longitudinal changes separately in CRP and cognitive domains specific to diagnostic status (SZ, BD, HC). Hierarchical clustering was applied on the entire sample to investigate the longitudinal course of transdiagnostic inflammatory-cognitive subgroups. There were no case-control differences or change in CRP from baseline to follow-up. We confirm previous observations of case-control differences in cognition at both time-points and domain specific stability/improvement over time regardless of diagnostic status. We identified transdiagnostic inflammatory-cognitive subgroups at baseline with differing demographics and clinical severity. Despite improvement in cognition, symptoms and functioning, the higher inflammation - lower cognition subgroup (75% SZ; 48% BD; 38% HC) had sustained inflammation and lower cognition, more symptoms, and lower functioning (SMI only) at follow-up. This was in comparison to a lower inflammation - higher cognition subgroup (25% SZ, 52% BD, 62% HC), where SMI participants showed cognitive functioning at HC level with a positive clinical course. Our findings support heterogenous and transdiagnostic inflammatory-cognitive subgroups that are stable over time, and may benefit from targeted interventions.