NPJ Schizophrenia最新文献

Quantification of treatment response is crucial to optimize outcomes for patients with schizophrenia. In this study, we evaluated the relationship between quantitative measures of clinician-rated symptom severity and self-rated side effects, well-being, and functioning among inpatients with schizophrenia using the six-item version of the Positive and Negative Syndrome Scale (PANSS-6), the Glasgow Antipsychotic Side-effect Scale (GASS), the WHO-Five Well-being Index (WHO-5), and the Sheehan Disability Scale (SDS). All measurements were conducted as close to admission and discharge as possible. Well-being and functioning were found to be most strongly associated with the additive effect of symptoms and side effects, while changes in side effects, well-being, and functioning appeared to be relatively independent from changes in symptom severity. The use of both symptom and side effect measures should inform clinical decision-making in the treatment of schizophrenia, as it has the potential to optimize functioning and well-being.

Neuroimaging studies have revealed that patients with schizophrenia exhibit disrupted resting-state functional connectivity. However, the inconsistent findings across these studies have hindered our comprehensive understanding of the functional connectivity changes associated with schizophrenia, and the molecular mechanisms associated with these alterations remain largely unclear. A quantitative meta-analysis was first conducted on 21 datasets, involving 1057 patients and 1186 healthy controls, to examine disrupted resting-state functional connectivity in schizophrenia, as measured by whole-brain voxel-wise functional network centrality (FNC). Subsequently, partial least squares regression analysis was employed to investigate the relationship between FNC changes and gene expression profiles obtained from the Allen Human Brain Atlas database. Finally, gene enrichment analysis was performed to unveil the biological significance of the altered FNC-related genes. Compared with healthy controls, patients with schizophrenia show consistently increased FNC in the right inferior parietal cortex extending to the supramarginal gyrus, angular gyrus, bilateral medial prefrontal cortex, and right dorsolateral prefrontal cortex, while decreased FNC in the bilateral insula, bilateral postcentral gyrus, and right inferior temporal gyrus. Meta-regression analysis revealed that increased FNC in the right inferior parietal cortex was positively correlated with clinical score. In addition, these observed functional connectivity changes were found to be spatially associated with the brain-wide expression of specific genes, which were enriched in diverse biological pathways and cell types. These findings highlight the aberrant functional connectivity observed in schizophrenia and its potential molecular underpinnings, providing valuable insights into the neuropathology of dysconnectivity associated with this disorder.

Psychotic symptoms typically emerge in adolescence. Age-associated thalamocortical connectivity differences in psychosis remain unclear. We analyzed diffusion-weighted imaging data from 1254 participants 8–23 years old (typically developing (TD):N = 626, psychosis-spectrum (PS): N = 329, other psychopathology (OP): N = 299) from the Philadelphia Neurodevelopmental Cohort. We modeled thalamocortical tracts using deterministic fiber tractography, extracted Q-Space Diffeomorphic Reconstruction (QSDR) and diffusion tensor imaging (DTI) measures, and then used generalized additive models to determine group and age-associated thalamocortical connectivity differences. Compared to other groups, PS exhibited thalamocortical reductions in QSDR global fractional anisotropy (GFA, p-values range = 3.0 × 10^–6–0.05) and DTI fractional anisotropy (FA, p-values range = 4.2 × 10^–4–0.03). Compared to TD, PS exhibited shallower thalamus-prefrontal age-associated increases in GFA and FA during mid-childhood, but steeper age-associated increases during adolescence. TD and OP exhibited decreases in thalamus-frontal mean and radial diffusivities during adolescence; PS did not. Altered developmental trajectories of thalamocortical connectivity may contribute to the disruptions observed in adults with psychosis.

Response to antipsychotic medications (AP) is subjected to a wide and unpredictable variability and efforts were directed to discover predictive biomarkers to personalize treatment. Electroencephalography abnormalities in subjects with schizophrenia are well established, as well as a pattern of EEG changes induced by APs. The aim of this review is to provide a synthesis of the EEG features that are related to AP efficacy, including both pre-treatment signatures and changes induced by APs during treatment. A systematic review of English articles using PubMed, PsychINFO and the Cochrane database of systematic reviews was undertaken until july 2023. Additional studies were added by hand search. Studies having as an endpoint the relationship between AP-related clinical improvement and electroencephalographic features were included. Heterogeneity prevented a quantitative synthesis. Out of 1232 records screened, 22 studies were included in a final qualitative synthesis. Included studies evaluated resting-state and task-related power spectra, functional connectivity, microstates and epileptic abnormalities. At pre-treatment resting-state EEG, the most relevant predictors of a poor response were a change in theta power compared to healthy control, a high alpha power and connectivity, and diminished beta power. Considering EEG during treatment, an increased theta power, a reduced beta-band activity, an increased alpha activity, a decreased coherence in theta, alpha and beta-band were related to a favorable outcome. EEG is promising as a method to create a predictive biomarker for response to APs; further investigations are warranted to harmonize and generalize the contradictory results of reviewed studies.

We evaluated two models to link stressful life events (SLEs) with the psychopathology of schizophrenia spectrum disorders (SSD). We separated SLEs into independent (iSLEs, unlikely influenced by one’s behavior) and dependent (dSLEs, likely influenced by one’s behavior). Stress-diathesis and stress generation models were evaluated for the relationship between total, i- and d- SLEs and the severity of positive, negative, and depressive symptoms in participants with SSD. Participants with SSD (n = 286; 196 males; age = 37.5 ± 13.5 years) and community controls (n = 121; 83 males; 35.4 ± 13.9 years) completed self-report of lifetime negative total, i- and d- SLEs. Participants with SSD reported a significantly higher number of total SLEs compared to controls (B = 1.11, p = 6.4 × 10^–6). Positive symptom severity was positively associated with the total number of SLEs (β = 0.20, p = 0.001). iSLEs (β = 0.11, p = 0.09) and dSLEs (β = 0.21, p = 0.0006) showed similar association with positive symptoms (p = 0.16) suggesting stress-diathesis effects. Negative symptom severity was negatively associated with the number of SLEs (β = –0.19, p = 0.003) and dSLEs (β = −0.20, p = 0.001) but not iSLEs (β = –0.04, p = 0.52), suggesting stress generation effects. Depressive symptom severity was positively associated with SLEs (β = 0.34, p = 1.0 × 10^–8), and the association was not statistically stronger for dSLEs (β = 0.33, p = 2.7 × 10^–8) than iSLEs (β = 0.21, p = 0.0006), p = 0.085, suggesting stress-diathesis effects. The SLE – symptom relationships in SSD may be attributed to stress generation or stress-diathesis, depending on symptom domain. Findings call for a domain-specific approach to clinical intervention for SLEs in SSD.

Cognitive impairments are a core feature of schizophrenia that have negative impacts on functional outcomes. However, it remains challenging to assess these impairments in clinical settings. Smartphone apps provide the opportunity to measure cognitive impairments in an accessible way; however, more research is needed to validate these cognitive assessments in schizophrenia. We assessed the initial accessibility, validity, and reliability of a smartphone-based cognitive test to measure cognition in schizophrenia. A total of 29 individuals with schizophrenia and 34 controls were included in the analyses. Participants completed the standard pen-and-paper Trail Making Tests (TMT) A and B, and smartphone-based versions, Jewels Trail Tests (JTT) A and B, at the single in-lab visit. Participants were asked to complete the JTT remotely once per week for three months. We also investigated how subjective sleep quality and mood may affect cognitive performance longitudinally. In-lab and remote JTT scores moderately and positively correlated with in-lab TMT scores. Moderate test-retest reliability was observed across the in-lab, first remote, and last remote completion times of the JTT. Additionally, individuals with schizophrenia had significantly lower performance compared to controls on both the in-lab JTT and TMT. Self-reported mood had a significant effect on JTT A performance over time but no other significant relationships were found remotely. Our results support the initial accessibility, validity and reliability of using the JTT to measure cognition in schizophrenia. Future research to develop additional smartphone-based cognitive tests as well as with larger samples and in other psychiatric populations are warranted.

Ulotaront, a trace amine-associated receptor 1 (TAAR1) and serotonin 5-HT1A receptors agonist, has demonstrated efficacy in the treatment of patients with an acute exacerbation of schizophrenia in a 4-week, double-blind, placebo-controlled study. The aim of this 26-week open-label extension study was to evaluate the safety and effectiveness of ulotaront (25/50/75 mg/d) in patients who completed the initial 4-week study. Of the 193 4-week completers, 157 patients (81.3%) continued into the open-label extension study; 66.9% were completers. Among all extension phase patients, treatment with ulotaront was associated with minimal changes in body weight (mean [SD] change from double-blind baseline: -0.3 [3.7] kg), cholesterol (median change, -2.0 mg/dL), triglycerides (median, -5.0 mg/dL), and prolactin (female, median, -3.4 ng/mL; male, median, -2.7 ng/mL). Movement disorder scales showed no extrapyramidal effects. Twenty-six weeks of extension phase treatment was associated with a mean (95% CI) observed change from open-label baseline in the PANSS total score of -22.6 (-25.6, -19.6; effect size, 1.46), and a mean (95% CI) change in the CGI-Severity score of -1.0 (-1.2, -0.8; effect size, 1.07). Long-term treatment with the TAAR1 agonist ulotaront, in the daily dose range of 25-75 mg, was characterized by a relatively high completion rate, an adverse event profile notable for the absence of extrapyramidal-related adverse effects, a low liability for adverse weight and metabolic effects, and no effect on prolactin levels. Additional studies are needed to further confirm the long-term efficacy and safety of ulotaront.

Subjects with first-episode psychosis experience substantial deficits in social cognition and metacognition. Although previous studies have investigated the role of profiles of individuals in social cognition and metacognition in chronic schizophrenia, profiling subjects with first-episode psychosis in both domains remains to be investigated. We used latent profile analysis to derive profiles of the abilities in 174 persons with first-episode psychosis using the Beck's Cognitive Insight Scale, the Faces Test, the Hinting Task, the Internal, Personal and Situational Attributions Questionnaire, and the Beads Task. Participants received a clinical assessment and a neuropsychological assessment. The best-fitting model was selected according to the Bayesian information criterion (BIC). We assessed the importance of the variables via a classification tree (CART). We derived three clusters with distinct profiles. The first profile (33.3%) comprised individuals with low social cognition. The second profile (60.9%) comprised individuals that had more proneness to present jumping to conclusions. The third profile (5.7%) presented a heterogeneous profile of metacognitive deficits. Persons with lower social cognition presented worse clinical and neuropsychological features than cluster 2 and cluster 3. Cluster 3 presented significantly worst functioning. Our results suggest that individuals with FEP present distinct profiles that concur with specific clinical, neuropsychological, and functional challenges. Each subgroup may benefit from different interventions.

Contextual influences on social behavior and affective dynamics are not well understood in schizophrenia. We examined the role of social context on emotions, and the motivation to interact in the future, using dynamic network analysis of ecological momentary assessment (EMA) data. Participants included 105 outpatients with schizophrenia or schizoaffective disorder (SZ) and 76 healthy comparators (HC) who completed 7 days, 7 times a day of EMA. Dynamic networks were constructed using EMA data to visualize causal interactions between emotional states, motivation, and context (e.g., location, social interactions). Models were extended to include the type and frequency of interactions and the motivation to interact in the near future. Results indicated SZ networks were generally similar to HC but that contextual influences on emotion and social motivation were more evident in SZ. Further, feedback loops in HC were likely adaptive (e.g., positive emotions leading to social motivation), but most were likely maladaptive in SZ (e.g., sadness leading to reduced happiness leading to increased sadness). Overall, these findings indicate that network analyses may be useful in specifying emotion regulation problems in SZ and that instability related to contextual influences may be a central aspect of aberrant regulation.

Machine learning (ML), one aspect of artificial intelligence (AI), involves computer algorithms that train themselves. They have been widely applied in the healthcare domain. However, many trained ML algorithms operate as 'black boxes', producing a prediction from input data without a clear explanation of their workings. Non-transparent predictions are of limited utility in many clinical domains, where decisions must be justifiable. Here, we apply class-contrastive counterfactual reasoning to ML to demonstrate how specific changes in inputs lead to different predictions of mortality in people with severe mental illness (SMI), a major public health challenge. We produce predictions accompanied by visual and textual explanations as to how the prediction would have differed given specific changes to the input. We apply it to routinely collected data from a mental health secondary care provider in patients with schizophrenia. Using a data structuring framework informed by clinical knowledge, we captured information on physical health, mental health, and social predisposing factors. We then trained an ML algorithm and other statistical learning techniques to predict the risk of death. The ML algorithm predicted mortality with an area under receiver operating characteristic curve (AUROC) of 0.80 (95% confidence intervals [0.78, 0.82]). We used class-contrastive analysis to produce explanations for the model predictions. We outline the scenarios in which class-contrastive analysis is likely to be successful in producing explanations for model predictions. Our aim is not to advocate for a particular model but show an application of the class-contrastive analysis technique to electronic healthcare record data for a disease of public health significance. In patients with schizophrenia, our work suggests that use or prescription of medications like antidepressants was associated with lower risk of death. Abuse of alcohol/drugs and a diagnosis of delirium were associated with higher risk of death. Our ML models highlight the role of co-morbidities in determining mortality in patients with schizophrenia and the need to manage co-morbidities in these patients. We hope that some of these bio-social factors can be targeted therapeutically by either patient-level or service-level interventions. Our approach combines clinical knowledge, health data, and statistical learning, to make predictions interpretable to clinicians using class-contrastive reasoning. This is a step towards interpretable AI in the management of patients with schizophrenia and potentially other diseases.