The latest medical research on Schizophrenia

The research magnet gathers the latest research from around the web, based on your specialty area. Below you will find a sample of some of the most recent articles from reputable medical journals about schizophrenia gathered by our medical AI research bot.

The selection below is filtered by medical specialty. Registered users get access to the Plexa Intelligent Filtering System that personalises your dashboard to display only content that is relevant to you.

Want more personalised results?

Request Access

Outcomes During and After Early Intervention Services for First-Episode Psychosis: Results Over 5 Years From the RAISE-ETP Site-Randomized Trial.

Schizophrenia Bulletin

To examine long-term effects of early intervention services (EIS) for first-episode psychosis, we compared Heinrichs-Carpenter Quality of Life (QLS...

The Association Between Neighborhood Poverty and Hippocampal Volume Among Individuals at Clinical High-Risk for Psychosis: The Moderating Role of Social Engagement.

Schizophrenia Bulletin

Reductions in hippocampal volume (HV) have been associated with both prolonged exposure to stress and psychotic illness. This study sought to deter...

Dynamic Patterns of Symptoms and Functioning in Predicting Deliberate Self-harm in Patients with First-Episode Schizophrenia-Spectrum Disorders Over 3 Years.

Schizophrenia Bulletin

Patients with schizophrenia have a significant risk of self-harm. We aimed to explore the dynamic relationship between symptomatology, functioning and deliberate self-harm (DSH) and evaluate the feasibility of developing a self-harm risk prediction tool for patients with first-episode schizophrenia (FES).

Patients with FES (n = 1234) were followed up for 36 months. Symptomatology, functioning, treatment adherence and self-harm information were obtained monthly over the follow-up period. A time-varying vector autoregressive (VAR) model was used to study the contribution of clinical variables to self-harm over the 36th month. Random forest models for self-harm were established to classify the individuals with self-harm and predict future self-harm events.

Over a 36-month period, 187 patients with FES had one or more self-harm events. The depressive symptoms contributed the most to self-harm prediction during the first year, while the importance of positive psychotic symptoms increased from the second year onwards. The random forest model with all static information and symptom instability achieved a good area under the receiver operating characteristic curve (AUROC = 0.77 ± 0.023) for identifying patients with DSH. With a sliding window analysis, the averaged AUROC of predicting a self-event was 0.65 ± 0.102 (ranging from 0.54 to 0.78) with the best model being 6-month predicted future 6-month self-harm for month 11-23 (AUROC = 0.7).

Results highlight the importance of the dynamic relationship of depressive and positive psychotic symptoms with self-harm and the possibility of self-harm prediction in FES with longitudinal clinical data.

Schizophrenia Polygenic Risk and Experiences of Childhood Adversity: A Systematic Review and Meta-analysis.

Schizophrenia Bulletin

Schizophrenia has been robustly associated with multiple genetic and environmental risk factors. Childhood adversity is one of the most widely replicated environmental risk factors for schizophrenia, but it is unclear if schizophrenia genetic risk alleles contribute to this association.

In this systematic review and meta-analysis, we assessed the evidence for gene-environment correlation (genes influence likelihood of environmental exposure) between schizophrenia polygenic risk score (PRS) and reported childhood adversity. We also assessed the evidence for a gene-environment interaction (genes influence sensitivity to environmental exposure) in relation to the outcome of schizophrenia and/or psychosis. This study was registered on PROSPERO (CRD42020182812). Following PRISMA guidelines, a search for relevant literature was conducted using Cochrane, MEDLINE, PsycINFO, Web of Science, and Scopus databases until February 2022. All studies that examined the association between schizophrenia PRS and childhood adversity were included.

Seventeen of 650 identified studies met the inclusion criteria and were assessed against the Newcastle-Ottawa Scale for quality. The meta-analysis found evidence for gene-environment correlation between schizophrenia PRS and childhood adversity (r = .02; 95% CI = 0.01, 0.03; P = .001), but the effect was small and therefore likely to explain only a small proportion of the association between childhood adversity and psychosis. The 4 studies that investigated a gene-environment interaction between schizophrenia PRS and childhood adversity in increasing risk of psychosis reported inconsistent results.

These findings suggest that a gene-environment correlation could explain a small proportion of the relationship between reported childhood adversity and psychosis.

Neural Correlates of Variation in Personal Space and Social Functioning in Schizophrenia and Healthy Individuals.

Schizophrenia Bulletin

Changes in the regulation of interpersonal distance, or "personal space" (PS), have been repeatedly observed in schizophrenia and, in some studies, linked to negative symptoms. However, the neurobiological basis of these impairments is poorly understood.

Personal space measurements, functional connectivity of a brain network sensitive to intrusions into PS, and symptoms of social withdrawal and anhedonia were assessed, and associations among these outcomes measured, in 33 individuals with a psychotic disorder (primarily schizophrenia [SCZ]) and 36 control subjects (CON).

Personal space size was significantly higher (P = .002) and PS permeability (reflecting the capacity to tolerate intrusions into PS) was significantly lower (P = .021) in the SCZ relative to the CON group, and both measures were significantly correlated with social anhedonia and withdrawal in the full sample (all P < .007). Moreover, functional connectivity between the PS and default mode (DM) networks was significantly correlated with the permeability, but not the size, of PS in the full sample and in the SCZ and CON groups separately, and with social withdrawal in the SCZ group. Lastly, the association between PS-DM network connectivity and social withdrawal in the SCZ group was fully mediated by PS permeability.

Neural and behavioral aspects of PS regulation are linked to social motivation in both healthy individuals and those with psychotic disorders, suggesting that measurements of PS could serve as transdiagnostic markers of social functioning.

Increased Belief Instability in Psychotic Disorders Predicts Treatment Response to Metacognitive Training.

Schizophrenia Bulletin

In a complex world, gathering information and adjusting our beliefs about the world is of paramount importance. The literature suggests that patients with psychotic disorders display a tendency to draw early conclusions based on limited evidence, referred to as the jumping-to-conclusions bias, but few studies have examined the computational mechanisms underlying this and related belief-updating biases. Here, we employ a computational approach to understand the relationship between jumping-to-conclusions, psychotic disorders, and delusions.

We modeled probabilistic reasoning of 261 patients with psychotic disorders and 56 healthy controls during an information sampling task-the fish task-with the Hierarchical Gaussian Filter. Subsequently, we examined the clinical utility of this computational approach by testing whether computational parameters, obtained from fitting the model to each individual's behavior, could predict treatment response to Metacognitive Training using machine learning.

We observed differences in probabilistic reasoning between patients with psychotic disorders and healthy controls, participants with and without jumping-to-conclusions bias, but not between patients with low and high current delusions. The computational analysis suggested that belief instability was increased in patients with psychotic disorders. Jumping-to-conclusions was associated with both increased belief instability and greater prior uncertainty. Lastly, belief instability predicted treatment response to Metacognitive Training at the individual level.

Our results point towards increased belief instability as a key computational mechanism underlying probabilistic reasoning in psychotic disorders. We provide a proof-of-concept that this computational approach may be useful to help identify suitable treatments for individual patients with psychotic disorders.

Improving the Applicability of AI for Psychiatric Applications through Human-in-the-loop Methodologies.

Schizophrenia Bulletin

Machine learning (ML) and natural language processing have great potential to improve efficiency and accuracy in diagnosis, treatment recommendations, predictive interventions, and scarce resource allocation within psychiatry. Researchers often conceptualize such an approach as operating in isolation without much need for human involvement, yet it remains crucial to harness human-in-the-loop practices when developing and implementing such techniques as their absence may be catastrophic. We advocate for building ML-based technologies that collaborate with experts within psychiatry in all stages of implementation and use to increase model performance while simultaneously increasing the practicality, robustness, and reliability of the process.

We showcase pitfalls of the traditional ML framework and explain how it can be improved with human-in-the-loop techniques. Specifically, we applied active learning strategies to the automatic scoring of a story recall task and compared the results to a traditional approach.

Human-in-the-loop methodologies supplied a greater understanding of where the model was least confident or had knowledge gaps during training. As compared to the traditional framework, less than half of the training data were needed to reach a given accuracy.

Human-in-the-loop ML is an approach to data collection and model creation that harnesses active learning to select the most critical data needed to increase a model's accuracy and generalizability more efficiently than classic random sampling would otherwise allow. Such techniques may additionally operate as safeguards from spurious predictions and can aid in decreasing disparities that artificial intelligence systems otherwise propagate.

Hierarchical Symptom Components in Early Psychosis.

Schizophrenia Bulletin

Quantitative models of psychopathology can empirically guide subclassification of heterogeneous clinical presentations such as psychosis; they are particularly well-equipped to capture the nuanced symptomatology observed in first-episode psychosis. As well, components may be better aligned with biological variables. The current study sought to confirm and extend knowledge of the hierarchical structure of psychosis symptoms in first-episode psychosis. Based on past hierarchical work, we hypothesized that a 4 component level would be most closely associated with longitudinal disability.

Participants with early-stage psychosis (N = 370) underwent clinical assessment with the scale for the assessment of positive symptoms (SAPS), scale for assessment of negative symptoms (SANS), and global assessment scale(GAS). A subset was assessed at 6 months (N = 221) and 1 year (N = 207). Hierarchical symptom components were extracted at 12 levels. The predictive utility of the components for global functioning was tested.

As predicted, the 4-component model (reality distortion, thought disorder, inexpressivity, apathy/asociality) provided a superior prediction of functioning over other levels of the hierarchy. Baseline apathy/asociality longitudinally predicted functioning beyond the shared variance of the components at 6 months (b = -4.83, t(216) = -5.37, p < .001, R2adj = 0.12) and 1-year (b = -4.49, t(202) = -4.38, p < .001, R2adj = 0.09).

The hierarchical structure of psychotic symptomatology and its external validity have been robustly established in independent, longitudinal first-episode psychosis samples. The established model incorporates multiple levels of granularity that can be flexibly applied based on the level that offers the greatest predictive utility for external validators.

Schizophrenia in Translation: Why the Eye?

Schizophrenia Bulletin

Schizophrenia is increasingly recognized as a systemic disease, characterized by dysregulation in multiple physiological systems (eg, neural, cardi...

Neural Activation in the Ventromedial Prefrontal Cortex Precedes Conscious Experience of Being in or Out of a Transient Hallucinatory State.

Schizophrenia Bulletin

Auditory verbal hallucinations (AVHs) is not only a common symptom in schizophrenia but also observed in individuals in the general population. Despite extensive research, AVHs are poorly understood, especially their underlying neuronal architecture. Neuroimaging methods have been used to identify brain areas and networks that are activated during hallucinations. A characteristic feature of AVHs is, however, that they fluctuate over time, with varying frequencies of starts and stops. An unanswered question is, therefore, what neuronal events co-occur with the initiation and inhibition of an AVH episode.

We investigated brain activation with fMRI in 66 individuals who experienced multiple AVH-episodes while in the scanner. We extracted time-series fMRI-data and monitored changes second-by-second from 10 s before to 15 s after participants indicated the start and stop of an episode, respectively, by pressing a hand-held response-button.

We found a region in the ventromedial prefrontal cortex (VMPFC) which showed a significant increase in activation initiated a few seconds before participants indicated the start of an episode, and a corresponding decrease in activation initiated a few seconds before the end of an episode.

The consistent increase and decrease in activation in this area in advance of the consciously experienced presence or absence of the "voice" imply that this region may act as a switch in turning episodes on and off. The activation is unlikely to be confounded by motor responses. The findings could have clinical implications for brain stimulation treatments, like transcranial magnetic stimulation.

Probiotics Plus Dietary Fiber Supplements Attenuate Olanzapine-Induced Weight Gain in Drug-Naïve First-Episode Schizophrenia Patients: Two Randomized Clinical Trials.

Schizophrenia Bulletin

Antipsychotic-induced weight gain is associated with alterations to the composition of the gut microbiota. The purpose of this study was to determine the effect of probiotics plus dietary fiber on antipsychotic-induced weight gain.

Two sequential, randomized clinical trials were conducted. In Study 1, 90 drug-naïve, first-episode schizophrenia patients were randomized to receive either olanzapine plus probiotics or olanzapine monotherapy for 12 weeks. In Study 2, 60 drug-naïve, first-episode schizophrenia patients were randomly assigned to receive either olanzapine plus probiotics and dietary fiber or olanzapine monotherapy for 12 weeks.

In Study 1, no significant differences in weight gain were observed between the two groups. The insulin resistance index (IRI) was lower in the olanzapine plus probiotics group compared with the olanzapine monotherapy group at week 12 (estimated mean difference, -0.65, [95% confidence interval (CI), -1.10 to -0.20]; p = .005). In Study 2, weight gain was lower in the probiotics plus dietary fiber group than in the olanzapine monotherapy group at week 12 (estimated mean difference -3.45 kg, [95% CI, -5.91 to -1.00]; p = .007). At week 12, IRI increased significantly in the olanzapine monotherapy group (mean, 1.74; standard deviation (SD) = 1.11, p < .001), but not in the olanzapine plus probiotics and dietary fiber group (mean 0.47, SD = 2.16, p = .35) with an estimated mean difference of -0.95 between the two groups [95% CI, -1.77 to -0.14]; p = .022).

These results provide support for the efficacy and safety of probiotics plus dietary fiber in attenuating antipsychotic-induced weight gain in drug-naïve, first-episode schizophrenia patients.

Comparative Effectiveness of Antipsychotics in Preventing Readmission for First-Admission Schizophrenia Patients in National Cohorts From 2001 to 2017 in Taiwan.

Schizophrenia Bulletin

Antipsychotics remain the main treatment for schizophrenia, but their effectiveness is challenging to compare. We aimed to assess the comparative real-world effectiveness of antipsychotics in preventing readmission among patients in Asia with early-stage schizophrenia to inform clinical decision making.

We did a retrospective cohort study of first-admission schizophrenia patients (ICD-9-CM: 295; ICD-10-CM: F20 and F25) from January 1, 2001, to December 31, 2017. The cohort was identified from the National Health Insurance Research Database NHIRD for Psychiatric Inpatients. The exposure was any antipsychotics prescribed post-discharge. The primary outcome was the readmission risk due to psychotic disorders, which was measured by adjusted hazard ratios (aHRs). Within-individual extended Cox models were applied for analyses, where the periods of oral risperidone use served as his or her own control.

We selected 75 986 patients (men, 53.4%; mean [SD] age, 37.6 [12.0] years; mean [SD] duration of follow-up, 8.9 [5.0]) who were first admitted to psychiatric wards with schizophrenia in Taiwan. Among them, 47 150 patients (62.05%) had at least one readmission within 4 years. Compared to the period under treatment with oral risperidone, that under monotherapy with long-acting injectable antipsychotics (LAIs) had the lowest risk for psychotic readmission, with a risk reduction of 15-20%. However, the prevalence of person-prescription prevalence of LAIs remained low (< 10%) during the follow-up period.

The use of LAIs after the first admission for schizophrenia has notable advantages in preventing readmission. Such formulations should be offered earlier in the course of illness.