Diagnosis Of Schizophrenia Research Articles

Suicidality and use of psychotropic medications in patients with schizophrenia: a prospective cohort study.

The lifetime prevalence of suicide is around 5% in patients with schizophrenia. Non-adherence to antipsychotic medication is an important risk factor, but prospective studies investigating joint effects of antipsychotic drugs, antidepressants, and benzodiazepines on suicidality are scarce. We aimed to investigate how use and non-use of psychotropic medications are associated with suicidality in schizophrenia. An open cohort study followed all patients consecutively admitted to a psychiatric acute unit during a 10-year period with a diagnosis of schizophrenia (n = 696). Cox multiple regression analyses were conducted with use of antipsychotics, antidepressants, and benzodiazepines as time-dependent variables. Adjustments were made for age, gender, depressive mood, agitated behavior, and use of alcohol and illicit substances. A total of 32 (4.6%) suicide events were registered during follow-up. Of these, 9 (28%) were completed suicides and 23 (72%) were attempted suicides. A total of 59 (8.5%) patients were readmitted with suicidal plans during the follow-up. Compared to non-use, use of antipsychotics was associated with 70% lower risk of attempted or completed suicide (adjusted hazard ratio [AHR] = 0.30, p < 0.01, CI 0.14-0.65) and 69% reduced risk of readmission with suicidal plans (AHR = 0.31, p < 0.01, CI 0.18-0.55). Use of prescribed benzodiazepines was associated with 126% increased risk of readmission with suicidal plans (AHR = 2.26, p = 0.01, CI 1.24-4.13). Adherence to antipsychotic medication is strongly associated with reduced suicidal risk in schizophrenia. The use of prescribed benzodiazepines was identified as a significant risk factor for being readmitted with suicidal plans.

Research on schizophrenia based on dementia, working memory and episodic memory deficits

Schizophrenia is associated with severe cognitive dysfunctions, including memory deficits that affect both working memory (WM) and episodic memory (EM). Memory deficits are also a typical symptom experienced by dementia patients that causes crucial functional problems in daily life. Meanwhile, past studies yielded evidence for the relationship between dementia and psychiatric disorders, indicating the emergence of psychotic symptoms during prodromal dementia. This article aims to investigate the relationship between schizophrenia and dementia plus the WM and EM deficits among schizophrenia patients. In the first part of this paper, the association between schizophrenia and dementia is examined based on a psychosocial study conducted among the Danish population, comparing the likelihood of developing dementia in patients with schizophrenia to that of individuals without schizophrenia. The review of previous research findings indicates a positive correlation between a diagnosis of schizophrenia and the later development of dementia. This phenomenon may be due to the reason that schizophrenia is positively associated with several established risk factors of dementia. Then, the topic of the potential deficits of WM and EM is discussed using self-ordered task results and neurobiological evidence. Studies have showed that schizophrenia is likely to lead significant WM and EM limitations that are closely related to prefrontal cortex impairment.

Knowledge, attitudes, and self-perceived skills regarding ultrahigh risk (UHR) for psychosis and schizophrenia in primary care physicians: new insights from Tunisia

BackgroundThere is evidence indicating that an important proportion of young people at ultrahigh risk (UHR) for psychosis do consult in primary care, but their identification may be hindered by multiple factors, such as primary care physicians (PCPs)’ limited skills and perceived stigma toward the condition. This study aimed to investigate attitudes/beliefs toward both UHR and schizophrenia and perceived self-efficacy in screening for, diagnosing, and managing these conditions among PCPs. The secondary objective was to determine the potential effects of psychosis level (UHR vs. schizophrenia) on PCP’s views.MethodsA cross-sectional survey was carried out among Tunisian PCPs working in primary care setting in Tunisia (N = 232, 66.4% females, mean age of 47.5 ± 9.9 years). An anonymous paper-and-pencil self-administered questionnaire was adopted to collect data. PCPs were asked to read vignettes describing individuals with UHR and schizophrenia and answer a series of questions that followed.ResultsThe schizophrenia vignette aroused significantly more feelings of “need to help the person” than the UHR vignette (87.8% vs. 88%, p = .022). The perceptions that the person depicted in the vignette is “strange” and “not intelligent” were more frequently endorsed for UHR than schizophrenia (18.7% vs. 16%, p = 0.028, and 8.4% vs. 5.6%, p = 0.043, respectively), whereas the person with schizophrenia was perceived are more dangerous than the UHR person (38.4% vs. 25.2%, p = 0.033). Only 28.9% and 14% of participants agreed that they have the necessary skills to make the diagnosis and manage the condition depicted in the UHR vignette, respectively.ConclusionFindings indicated that a high proportion of our Tunisian PCPs show a deficit in identifying both UHR and schizophrenia diagnoses. Given that PCPs can be key sources of referral of at-risk young help-seekers to specialized early intervention services, urgent interventions are needed to help PCPs enhance their knowledge and reduce stigma associated with psychosis.

Real-World Treatment of Schizophrenia in Adults With a 22q11.2 Microdeletion: Traitement dans le monde réel de la schizophrénie chez des adultes atteints du syndrome de microdélétion 22q11.2.

One in every 4 individuals born with a 22q11.2 microdeletion will develop schizophrenia. Thirty years of clinical genetic testing capability have enabled detection of this major molecular susceptibility for psychotic illness. However, there is limited literature on the treatment of schizophrenia in individuals with a 22q11.2 microdeletion, particularly regarding the issue of treatment resistance. From a large, well-characterized adult cohort with a typical 22q11.2 microdeletion followed for up to 25 years at a specialty clinic, we studied all 107 adults (49 females, 45.8%) meeting the criteria for schizophrenia or schizoaffective disorder. We performed a comprehensive review of lifetime (1,801 patient-years) psychiatric records to determine treatments used and the prevalence of treatment-resistant schizophrenia (TRS). We used Clinical Global Impression-Improvement (CGI-I) scores to compare within-individual responses to clozapine and nonclozapine antipsychotics. For a subgroup with contemporary data (n = 88, 82.2%), we examined antipsychotics and dosage at the last follow-up. Lifetime treatments involved on average 4 different antipsychotic medications per individual. Sixty-three (58.9%) individuals met the study criteria for TRS, a significantly greater proportion than for a community-based comparison (42.9%; χ2 = 10.38, df = 1, p < 0.01). The non-TRS group was enriched for individuals with genetic diagnosis before schizophrenia diagnosis. Within-person treatment response in TRS was significantly better for clozapine than for nonclozapine antipsychotics (p < 0.0001). At the last follow-up, clozapine was the most common antipsychotic prescribed, followed by olanzapine, risperidone, and paliperidone. Total antipsychotic chlorpromazine equivalent dosages were in typical clinical ranges (median: 450 mg; interquartile range: 300, 750 mg). The results for this large sample indicate that patients with 22q11.2 microdeletion have an increased propensity to treatment resistance. The findings provide evidence about how genetic diagnosis can inform clinical psychiatric management and could help reduce treatment delays. Further research is needed to shed light on the pathophysiology of antipsychotic response and on strategies to optimize outcomes.

Advances in MRI Research for First-Episode Schizophrenia: A Selective Review and NSFC-Funded Analysis.

The causes of schizophrenia remain unclear, and research has been hindered by the lack of quantifiable standards. However, magnetic resonance imaging (MRI) is addressing these challenges, revealing critical neurobiological details and emphasizing its importance in both evaluation and treatment. First, we reviewed the progress of research on structural MRI (sMRI), functional MRI (fMRI), multimodal/multiomics analysis, artificial intelligence, and neuromodulation in first-episode schizophrenia (FES) over the past 5 years. Second, we summarize the current state of schizophrenia research funded by the National Natural Science Foundation of China (NSFC) to facilitate academic exchange and cooperation both domestically and internationally. sMRI has identified early neurodevelopmental biomarkers in FES patients, and fMRI has highlighted functional abnormalities across disease stages. Multimodal/multiomics analysis has revealed complex brain-neurobiology interactions. Neuromodulation techniques, which directly modulate neural activity in specific brain regions, offer promising long-term benefits for stabilizing conditions and enhancing patients' quality of life. NSFC-funded analysis shows China is increasing its funding for schizophrenia research, though funding distribution remains uneven. The research focus has shifted from a single perspective on brain structure and function to multichannel, multimodal comprehensive analysis methods. This progress has driven the integration of machine learning-driven multiomics research, aiming to construct disease classification models, explore disease mechanisms, and guide treatment from multidimensional and interdisciplinary perspectives. MRI technology has provided new perspectives for the diagnosis and treatment of schizophrenia, especially the neurobiological foundations of the disease. Support from the NSFC provides a scientific and financial basis for future research and treatment, heralding scientific discoveries and technological innovations in this field and bringing hope to schizophrenia patients.

A multimodal vision transformer for interpretable fusion of functional and structural neuroimaging data.

Multimodal neuroimaging is an emerging field that leverages multiple sources of information to diagnose specific brain disorders, especially when deep learning-based AI algorithms are applied. The successful combination of different brain imaging modalities using deep learning remains a challenging yet crucial research topic. The integration of structural and functional modalities is particularly important for the diagnosis of various brain disorders, where structural information plays a crucial role in diseases such as Alzheimer's, while functional imaging is more critical for disorders such as schizophrenia. However, the combination of functional and structural imaging modalities can provide a more comprehensive diagnosis. In this work, we present MultiViT, a novel diagnostic deep learning model that utilizes vision transformers and cross-attention mechanisms to effectively fuse information from 3D gray matter maps derived from structural MRI with functional network connectivity matrices obtained from functional MRI using the ICA algorithm. MultiViT achieves an AUC of 0.833, outperforming both our unimodal and multimodal baselines, enabling more accurate classification and diagnosis of schizophrenia. In addition, using vision transformer's unique attentional maps in combination with cross-attentional mechanisms and brain function information, we identify critical brain regions in 3D gray matter space associated with the characteristics of schizophrenia. Our research not only significantly improves the accuracy of AI-based automated imaging diagnostics for schizophrenia, but also pioneers a rational and advanced data fusion approach by replacing complex, high-dimensional fMRI information with functional network connectivity, integrating it with representative structural data from 3D gray matter images, and further providing interpretative biomarker localization in a 3D structural space.

Rare but elevated incidence of hematological malignancy after clozapine use in schizophrenia: A population cohort study.

Clozapine is widely regarded as a highly efficacious psychotropic drug that is largely underused worldwide. Recent disproportionality analyses and nationwide case-control studies suggested a potential association between clozapine use and hematological malignancy (HM). Nevertheless, the absolute rate difference is not well-established due to the absence of analytic cohort studies. The clinical significance of such a potential risk remains unclear. We extracted data from a territory-wide public healthcare database from January 2001 to August 2022 in Hong Kong to conduct a retrospective cohort study of anonymized patients aged 18+ years with a diagnosis of schizophrenia who used clozapine or olanzapine (drug comparator with highly similar chemical structure and pharmacological mechanisms) for 90+ days, with at least 2 prior other antipsychotic use records within both groups. Weighted by inverse probability of treatment (IPTW) based on propensity scores, Poisson regression was used to estimate the incidence rate ratio (IRR) of HM between clozapine and olanzapine users. The absolute rate difference was also estimated. In total, 9,965 patients with a median follow-up period of 6.99 years (25th to 75th percentile: 4.45 to 10.32 years) were included, among which 834 were clozapine users. After IPTW, the demographic and clinical characteristics of clozapine users were comparable to those of olanzapine users. Clozapine users had a significant weighted IRR of 2.22 (95% confidence interval (CI) [1.52, 3.34]; p < 0.001) for HM compared to olanzapine users. The absolute rate difference was estimated at 57.40 (95% CI [33.24, 81.55]) per 100,000 person-years. Findings were consistent across subgroups by age and sex. Sensitivity analyses all supported the robustness of the results and showed good specificity to HM but no other cancers. The main limitation of this observational study is the potential residual confounding effects that could have arisen from the lack of randomization in clozapine or olanzapine use. Absolute rate difference in HM incidence associated with clozapine is small despite a 2-fold elevated rate. Given the rarity of HM and existing blood monitoring requirements, more restrictive indication for clozapine or special warnings may not be necessary.

Negative Association Between Schizophrenia and Subsequent Cancer Diagnoses—A Retrospective Cohort Study from Germany

Background: Since previous studies have reported contradictory findings regarding the relationship between schizophrenia and cancer, we evaluated the association between schizophrenia and cancer diagnoses. Methods: In this retrospective cohort study, the IQVIA Disease Analyzer database was utilized to examine the incidence of cancer among patients aged over 18 years diagnosed with schizophrenia in German general practices from 2005 to 2022. Patients with schizophrenia were compared with those without the condition, with adjustments made for age, sex, index year of diagnosis, average annual practitioners visit frequency, and comorbidity. Kaplan–Meier curves were used to analyze the 10-year cumulative incidence of schizophrenia and cancer in total amongst patients with and without schizophrenia. Univariate Cox regression analysis was performed to calculate Hazard Ratios (HR) of cancer risk and their 95% confidence intervals (CI) of cancer in total and of specific cancer types. Results: Patients with schizophrenia (N = 13.711) had a lower incidence of cancer diagnosis compared to those without (N = 68.555). Specifically, 10.4% of patients with schizophrenia and 12.5% of patients without the condition were diagnosed with cancer (p &lt; 0.001). Cox regression analysis showed a significant association between schizophrenia and subsequent cancer in the total population (HR: 0.82; 95% CI: 0.76–0.90), and among men (HR: 0.70; 95% CI: 0.61–0.80), but not among women (HR: 0.94, 95% CI: 0.84–1.04). Analyses stratified by cancer type and sex revealed a strong and significant association between schizophrenia and a decreased risk of prostate cancer in men (HR: 0.38; 95% CI: 0.24–0.61). Furthermore, there was also a negative association between schizophrenia and colorectal cancer risk in men, but statistical significance was not reached (HR: 0.58; 95% CI: 0.37–0.93). Conclusions: This study demonstrates negative associations between schizophrenia and subsequent cancer, and more specifically in men for prostate and colorectal cancer. However, further research is required to explore the underlying reasons for these associations.

Empathic disequilibrium in schizophrenia: An individual participant data meta-analysis

Schizophrenia involves substantial social difficulties, yet their nature remains unclear. Although empathy has been considered a promising social cognition construct, inconsistent findings have undermined its usefulness as a stable index for schizophrenia. This may be because previous studies overlooked the interdependency between the emotional and cognitive components of empathy. In this study, we investigated whether empathic disequilibrium, the intrapersonal imbalance between emotional and cognitive empathy, could be a meaningful schizophrenia marker. We conducted an individual-participant data meta-analysis, systematically searching the literature for studies involving participants with schizophrenia who completed the Interpersonal Reactivity Index, a validated empathy measure. Using emotional and cognitive empathy to capture empathic disequilibrium and the joint effect of cognitive and emotional empathy, we employed polynomial regression with response surface analysis to predict schizophrenia diagnosis and symptoms. Our analysis comprising ten studies (N = 1,080), revealed a non-linear association with the joint effect of cognitive and emotional empathy, as well as an association with empathic disequilibrium, suggesting emotional empathy overabundance, strongly and consistently predicted schizophrenia diagnosis. Additionally, empathic disequilibrium towards cognitive empathy overabundance was related to greater positive symptoms. The results suggest that empathic disequilibrium provides a stable behavioral marker related to schizophrenia, surpassing the utility of empathy alone. The findings deepen our understanding of schizophrenia phenomenology and can advance clinical and research practices.

Networks extracted from nonlinear fMRI connectivity exhibit unique spatial variation and enhanced sensitivity to differences between individuals with schizophrenia and controls

Schizophrenia is a chronic brain disorder associated with widespread alterations in functional brain connectivity. Although data-driven approaches such as independent component analysis are often used to study how schizophrenia impacts linearly connected networks, alterations within the underlying nonlinear functional connectivity structure remain largely unknown. Here we report the analysis of networks from explicitly nonlinear functional magnetic resonance imaging connectivity in a case–control dataset. We found systematic spatial variation, with higher nonlinear weight within core regions, suggesting that linear analyses underestimate functional connectivity within network centers. We also found that a unique nonlinear network incorporating default-mode, cingulo-opercular and central executive regions exhibits hypoconnectivity in schizophrenia, indicating that typically hidden connectivity patterns may reflect inefficient network integration in psychosis. Moreover, nonlinear networks including those previously implicated in auditory, linguistic and self-referential cognition exhibit heightened statistical sensitivity to schizophrenia diagnosis, collectively underscoring the potential of our methodology to resolve complex brain phenomena and transform clinical connectivity analysis.

Suicidal behavior among forensic psychiatric patients in Japan: Risk factors and implications for treatment.

In Japan, the Medical Treatment and Supervision Act (MTSA) was enacted in 2005 to provide intensive interventions aimed at reintegrating forensic psychiatric patients into society. However, these individuals face challenges with social reintegration and have an elevated risk of suicide. Therefore, identifying the risk factors for suicide among forensic psychiatric patients is essential. We compiled an extensive database spanning 12 years of data on Japanese forensic outpatients. Using a generalized linear model (GLM), we explored the relationship between suicidal behaviors-both attempted and completed suicides-and several variables, including outpatient pathways defined by law, histories of psychiatric outpatient/inpatient treatment, diagnoses of schizophrenia or mood disorders, types of crimes committed, and the types of victims involved. Our analysis included data from 2,263 Japanese forensic outpatients. Suicidal behaviors were observed in 6.9% of patients, with 1.5% completing suicide. Risk factors for suicidal behavior included being a direct outpatient (odds ratio [OR] = 1.63, 95% confidence interval [CI]: 1.14-2.34), having a history of outpatient treatment (OR = 1.92, 95% CI: 1.07-3.48), and being arrested for arson (OR = 2.24, 95% CI: 1.33-3.76). Conversely, a diagnosis of schizophrenia was associated with a reduced risk of suicidal behavior (OR = 0.61, 95% CI: 0.39-0.95). Forensic psychiatric patients are at a higher risk of suicide compared to the general population. It is essential to account for both risk and protective factors when planning interventions for these individuals.

The Academic Community Early Psychosis Intervention Network: Toward building a novel learning health system across six US states

AbstractIntroductionCompared to usual care, specialty services for first‐episode psychosis (FES) have superior patient outcomes. The Early Psychosis Intervention Network (EPINET), comprised of eight U.S. regional clinical networks, aims to advance the quality of FES care within the ethos of learning healthcare systems (LHS). Among these, the Academic Community (AC) EPINET was established to provide FES care, collect common data elements, leverage informatics, foster a culture of continuous learning and quality improvement, and engage in practice‐based research.MethodsWe designed and implemented a novel LHS of university‐affiliated FES programs within a hub (academic leadership team) and spoke (FES clinics) model. A series of site implementation meetings engaged stakeholders, setting the stage for a culture that values data collection and shared learning. We built clinical workflows to collect common data elements at enrollment and at consecutive 6‐month intervals in parallel to an informatics workflow to deliver outcome visualizations and drive quality improvement efforts.ResultsAll six clinical sites successfully implemented data capture workflows and engaged in the process of designing the informatics platform. Upon developing the structure, processes, and initial culture of the LHS, a total of 614 patients enrolled in AC‐EPINET, with the most common primary diagnoses of schizophrenia (32.1%) and unspecified psychotic disorders (23.6%). Visualized outcomes were delivered to clinical teams who began to consider locally relevant quality improvement projects.ConclusionsAC‐EPINET is a novel LHS, with a simultaneous focus on science, informatics, incentives, and culture. The work of developing AC‐EPINET thus far has highlighted the need for future LHS’ to be mindful of the complexities of data security issues, develop more automated informatic workflows, resource quality assurance efforts, and attend to building the cultural infrastructure with the input of all stakeholders.

Machine learning diagnosis of schizophrenia using structural and functional images of the brain

Abstract. This study aims to provide earlier, and faster schizophrenia diagnosis based on neurocognitive, structural, and behavioral measures using machine learning. This is because current ways of diagnosis, while accurate, delay patients ability to seek medical care before observable, lasting symptoms develop and are prone to error and discrimination. To provide diagnosis, we used Linear Support Vector Machine (linear SVM), Random Forest (RF), Multilayer Perceptron (MLP), and k-Nearest Neighbor (kNN), all trained with neurocognitive and behavioral measures combined with either structural or functional MRI data or both of 99 subjects from the OpenNeuro public dataset. 100 iterations of classification were run, and results showed a higher-than-average accuracy for all classifiers using all combinations of parameters, with a highest accuracy of 0.75 using linear SVM trained with behavioral and neurocognitive measures and fMRI data. We found correlations between structural changes in AAL3 brain regions and n-back working memory task performance, noting that the inferior parietal gyrus, right precuneus, supplementary motor area, and the central lateral thalamic nucleus have the highest feature importance. This means that future studies can select these features for further clinical examination or for machine learning diagnosis. We conclude that linear SVM provides the highest average diagnostic accuracy, and that fMRI data often leads to more accurate algorithmic decisions than sMRI data and thus should be weighed more in future studies.

Paternalizm i dyrektywność. Pomiędzy psychoterapią a poradnictwem – próba problematyzacji pojęć i podejść na przykładzie Soterii

The main purpose of the article is to problematize paternalism and directivity in the context of the differences between psychotherapy and counselling. It is often assumed that psychotherapy deals with mental disorders, while counselling deals with developmental crises and other problems of healthy people. Accepting such a perspective and psychotherapeutic ethics is equivalent to adopting a medical intervention discourse and involves treating the patient in a paternalistic and directive manner. This is problematic because the category of "mental disorders" itself, as well as the diagnostic criteria for particular diagnoses, are criticized from many different standpoints. The example of Soteria, a therapeutic approach for people with a diagnosis of psychosis or schizophrenia, shows that even in the case of such a "severe mental illness," a model of support closer to counselling, which allows greater autonomy and subjectivity of the patient, can be equally, if not more, effective than conventional medical and psychotherapeutic approaches operating in the axiology of medicine. The article can thus serve as a starting point for considering the formulation of the proper area of interest for counselling and psychotherapy, the related goals of these interventions and the ethical implications of such decisions.

On most informative regions for binary classification of schizophrenia based on resting state fMRI data done by selection of functionally homogeneous regions method

In this work we solve the problem of automatic binary classification of subjects with a diagnosis of schizophrenia and control groups on a data set obtained on a Siemens 3T tomograph. The data set included 36 subjects undergoing treatment at Psychiatric Hospital no. 1 Named after N.A. Alexeev of the Department of Health of Moscow (GBUZ PKB No. 1 DZM) and 36 subjects from the control group. Machine learning methods were used to solve this problem. As a result, an accuracy of 76% was achieved, which corresponds to the results obtained in other scientific studies. The highest accuracy was obtained for the local homogeneity parameter (regional homogeneity – ReHo), already known in the literature. At the same time, the set of features developed by the authors based on the method for identifying functionally homogeneous regions (FHR) gave a classification accuracy of 74%. But at the same time, the set of FHR features provides higher classification accuracy when using a small number of brain regions. For example, already in 8 regions, the FHR set provided an almost maximum classification accuracy of 72.5% (versus 65% for the ReHo set), which suggests that it is the selected 8 regions that give the highest level of separation.

A new EEG-based schizophrenia diagnosis method through a fuzzy DL model

A new EEG-based schizophrenia diagnosis method through a fuzzy DL model

Machine learning-based EEG analysis for enhanced schizophrenia classification and diagnosis

Abstract. Schizophrenia can have a significant impact on patients' lives, studies, and work. Patients may experience delusions, hallucinations, disorganized thinking, and abnormal behavior, among other symptoms. Therefore, research related to schizophrenia is of great importance. This paper proposes the design of a classifier based on machine learning for diagnosing schizophrenia. The classifier extracts N100 features, P300 features, and power features across different frequency bands from both time-domain and frequency-domain characteristics from Electroencephalography (EEG) signals. Given the small dataset, a Support Vector Machine (SVM) machine learning algorithm was chosen to process and classify the selected features. To address the limitations of SVM in handling high-dimensional data and nonlinear problems, this study introduces a comprehensive improvement method based on Bayesian optimization, Recursive Feature Elimination (RFE), and data augmentation. Bayesian optimization was used to find the best combination of hyperparameters for the model, thereby improving its performance; RFE was employed to assess feature importance and remove the least important features, enhancing the model's training efficiency and generalization capability. Additionally, data augmentation was included to increase the sample size and introduce diversity, thereby improving the model's robustness. The study found that these methods effectively improved classification accuracy and generalization ability.

Treatment satisfaction and effectiveness of Lurasidone on quality of life and functioning in adult patients with schizophrenia in the real-world Italian clinical practice: a prospective 3-month observational study.

Although second-generation antipsychotics (SGAs) have proven to be effective therapeutic options for patients with schizophrenia, there is a notable lack of evidence on patients' subjective perspectives regarding their well-being, quality of life, and satisfaction with these medications. This study aimed to evaluate the treatment satisfaction and effectiveness of lurasidone on quality of life and functioning in adult patients with schizophrenia in real-world Italian clinical practice. This was a multicentre, national, non-interventional, single-arm, 3-month prospective study. Patients who were naive to lurasidone treatment and whose treating physician had decided to start them on this medication were enrolled and evaluated over a 3-month period. Eligible patients were adults (≥ 18 years of age) with a primary diagnosis of schizophrenia who were being treated with lurasidone (for the first time [i.e., they were lurasidone naive]) as part of routine clinical practice. Efficacy endpoints were changes in patient/caregiver treatment satisfaction (seven-point Likert scale from the Treatment Satisfaction Questionnaire for Medication), patient quality of life and functioning (QLS), investigator-rated global assessment of functioning (CGI-S, IAQ) after 6 weeks and 3 months of lurasidone, and number of relapses and hospitalizations. Sixty-one patients were enrolled and 59 completed the study. The median dosage of lurasidone at baseline was 37.00mg/day. The median duration of titration was 86.0 days (Min 28; Max 115 days); the median number of dosage changes was 1.0. At the end of 3-month observation period, the median dose of lurasidone was 74.00mg/day. QoL and Functioning Score showed a trend of improvement over time, reaching a mean change from baseline of 9.8 at the end of the study. According to the CGI-S, the percentage of patients who were "markedly or severely ill" showed a continuous decrease from baseline to 3 months, from 62.29% to 8.20%. Patient satisfaction increased over time, with 80.32% of patients reporting that they were somewhat, fairly, or very satisfied (including 63.93% who were completely or very satisfied) at the end of the study. No relapses/hospitalizations for psychiatric reasons were reported. Lurasidone was well tolerated with no safety concerns or discontinuations due to AEs. Lurasidone represents a valid option for the treatment of schizophrenia and positively affects subjective well-being, quality of life and satisfaction. NCT06527885 retrospectively registered (01/08/2024).

Altered resting-state functional connectivity in a thalamo-cortico-cerebellar network in patients with schizophrenia

The diagnosis of schizophrenia is associated with a complex psychopathology related to disrupted brain circuitry causing a failure in coordinating information across brain sites with no consensus regarding the mechanisms. Although schizophrenia is well-studied, the great majority of studies investigated pre-selected ROIs or Seed-based connectivity. Whole brain ROI-wise studies that consider all ROIs available simultaneously are lacking. This technique helps understand large- and local-scale dynamics of information exchange across the whole brain. We investigated ROI-wise whole brain networks in 35 participants diagnosed with schizophrenia and 41 control participants. To unveil dysfunctions in brain subnetworks and to characterize network topology, we applied a statistical tool specially developed for network comparison called network-based statistic and graph theory. We observed a hyperconnected thalamo-cortico-cerebellar subnetwork in participants with schizophrenia; nodal analysis revealed higher number of thalamic connections. Our results suggest disruptions at the local level of the subnetwork rather than globally spread across the brain and driven by hyperconnectivity. Importantly, this subnetwork emerged from an exploratory analysis directly comparing ROI-wise whole brain network. This fact makes it an important contribution to the field as additional evidence, demonstrating the high reliability of malfunction in the local thalamo-cortico-cerebellar network.

Assessing the validity of a self-reported clinical diagnosis of schizophrenia.

The increasing availability of biobanks is changing the way individuals are identified for genomic research. This study assesses the validity of a self-reported clinical diagnosis of schizophrenia. The study included 1744 clinically-ascertained participants with schizophrenia or schizoaffective disorder depressed-type (SA-D) diagnosed by self-report and/or research interview and 1453 UK Biobank participants with self-reported and/or medical record diagnosis of schizophrenia or SA-D. Unaffected controls included a total of 501,837 participants. We assessed the positive predictive values (PPV) of self-reported clinical diagnoses against research interview and medical record diagnoses. Polygenic risk scores (PRS) and phenotypes relating to demographics, education and employment were compared across diagnostic groups. The variance explained (r2) in schizophrenia PRS for each diagnostic group was compared to samples in the Psychiatric Genomics Consortium (PGC). In the clinically-ascertained participants, the PPV of self-reported schizophrenia for a research diagnosis of schizophrenia was 0.70, which increased to 0.81 after expanding the research diagnosis to schizophrenia or SA-D. In UK Biobank, the PPV of self-reported schizophrenia for a medical record diagnosis was 0.74. Compared to participants who self-reported, participants with a clinically-ascertained research diagnosis were younger and more likely to have a high school qualification. Participants with a medical record diagnosis in UK Biobank were less likely to be employed or have a high school qualification than those who self-reported. Schizophrenia PRS did not differ between participants that had a diagnosis from self-report, research diagnosis or medical records. Polygenic liability r2, for all diagnosis definitions, fell within the distribution of PGC schizophrenia cohorts. Self-reported measures of schizophrenia are justified in genomic research to maximise sample size and reduce the burden of in-depth interviews on participants, although within sample validation of diagnoses is recommended.