Schizophrenia Research

BackgroundNegative symptoms are core features of schizophrenia that relate strongly to functional impairment, yet interventions targeting these symptoms remain largely ineffective. Emerging theoretical work highlights how environmental factors may shape and maintain negative symptoms. Although racial disparities in schizophrenia diagnosis among Black Americans are well documented and linked to racial stress and psychosis, the impact of racial stress on negative symptoms has not been examined. This study provides an initial test of a novel theory proposing that racial stress - here measured by racial discrimination - influences negative symptom severity through exacerbation of negative cognitions about the self, particularly defeatist performance beliefs (DPB). Study DesignParticipants diagnosed with schizophrenia-spectrum disorder (SSD) (N = 208; 80 Black, 128 White) completed the Positive and Negative Syndrome Scale (PANSS), the Defeatist Beliefs Scale, and self-report measures of subjective racial and ethnic discrimination (Racial and Ethnic Minority Scale and General Ethnic Discrimination Scale). Relationships among variables were tested using linear regression and mediation analysis. Study ResultsBlack participants exhibited significantly greater total and experiential negative symptoms than White participants with no group difference in DPB. Racial discrimination explained 46% of the relationship between race and negative symptoms. Among Black participants, higher DPB were associated with greater negative symptom severity. Discrimination was positively related to both DPB and negative symptoms. DPB partially mediated the relationship between discrimination and negative symptoms. ConclusionsFindings suggest that racial stress contributes to negative symptom severity via defeatist beliefs among Black individuals, highlighting potential targets for culturally informed interventions.

Background: Diminished Expression (DE) and Amotivation/Apathy (AA) are widely recognized as two main factors of negative symptoms. This study aimed to 1) examine the longitudinal stability of the DE-AA structure and its variation throughout a 5-year follow-up in people with first-episode psychosis (FEP), and 2) investigate whether DE and AA have distinct predictive value compared with the unitary construct of negative symptoms. Study Design: 227 participants from the EUropean Network of National Schizophrenia Networks Studying Gene-Environment Interactions (EU-GEI) and Genetics and Psychosis (GAP) studies were included at FEP and were followed up 5 years later. One-factor (global negative symptoms), uncorrelated two-factor (DE-AA), and correlated two-factor structures were modelled using confirmatory factor analysis. Regression analyses were applied to examine the associations between these factors and negative symptom trajectories, functioning, and quality-of-life outcomes. Study Results: The correlated two-factor model composed of DE and AA best fitted the data and exhibited 5-year stability. The regression model adjusted for AA accounted for more variance (59.2%) than global negative symptoms (52.8%) in explaining the enduring course of negative symptoms. Baseline AA was the only negative symptom factor that significantly predicted individuals' functional outcome at follow-up (B=-1.76, p=0.037). All negative symptom dimensions negatively predicted employment status, whereas lower educational attainment was primarily related to AA severity at baseline. Conclusions: Our findings support the validity and longitudinal stability of the two-dimensional (DE-AA) approach to negative symptoms in individuals with FEP. AA in particular exhibited distinctive predictive value, underscoring its potential clinical utility for early identification and the development of targeted interventions.

BackgroundPsychosis has been conceptualised as a continuum extending from healthy individuals with psychotic-like experiences to clinical populations with schizophrenia. It is unclear which biological mechanisms found in chronic schizophrenia extend across the psychosis continuum to healthy individuals with high positive schizotypy (HS). In this study, we used computational modeling to test whether changes in effective connectivity and excitation/inhibition (E/I) balance reported in schizophrenia are also found in HS. MethodsA total of 2425 individuals from the general population were screened for HS. A subset (N=141) was invited for in-depth phenotyping. Resting-state functional magnetic resonance imaging (rsfMRI) and proton magnetic resonance spectroscopy (1H-MRS) were recorded in n=69 HS individuals and n=72 group-matched controls with low schizotypy (LS). We used dynamic causal modeling to estimate effective connectivity between bilateral primary auditory cortex (A1), superior temporal gyrus (STG), and inferior frontal gyrus (IFG). ResultsBilateral backward connectivity from IFG to STG was significantly reduced in HS compared to LS. Widespread cortical disinhibition in the auditory cortex-IFG network correlated with more severe positive schizotypy scores and impulsive nonconformity. Reduced excitability in the same network was correlated with stronger cognitive disorganisation. ConclusionsOur results favour a psychosis-continuum hypothesis, suggesting that reduced top-down drive from frontal cortex and compensatory allostatic upregulation of cortical excitability, as observed in chronic schizophrenia, also extend to groups with sub-clinical psychotic symptoms. Frontal cortex dysfunction may serve as a biologically interpretable biomarker of psychosis risk and a target for preventative interventions.

Background: The highly influential predictive processing theory of psychosis posits that symptoms arise from imbalances in the weighting of predictions (priors) and sensory evidence. Despite this theory's increasing prominence, studies often present conflicting results. This is particularly problematic as findings from single tasks with modest sample sizes are frequently used to advance a theory for a generalised altered reliance on priors in psychosis. Methods: This study presents a random-effects, multi-level meta-analysis (PROSPERO CRD42024574379) evaluating evidence for aberrant reliance on priors in psychosis across perceptual tasks. The search identified articles in Embase, MEDLINE, APA PsycINFO, and APA PsycArticles published between 1st January 2005 and 31st October 2024, with risk of bias assessed using the Newcastle-Ottawa Scale. Included articles (34 results from 27 studies) compared adults with schizophrenia-spectrum psychosis (SZ; n = 904) to healthy controls (n = 1,039) on behavioural measures representing reliance on priors. Results: Results provided no evidence for atypical reliance on priors in psychosis (g = .03, 95% CI [-0.27, 0.34]; p = .818) or associations with delusions (6 results; SZ = 183; r = -.16, 95% CI [-0.51, 0.19]; p = .293) or hallucinations (10 results; SZ = 370; r = .04, 95% CI [-0.28, 0.36]; p = .780). In contrast with the theory that psychosis may differentially affect priors at different levels of the cognitive hierarchy, a sub-group analysis indicated that a two-level hierarchical model of priors did not account for conflicting results (F(1,32) = 0.1, p = .758). Conclusion: These findings do not suggest that psychosis is associated with a generalised predictive processing deficit spanning multiple aspects of perception. Key words: psychosis, schizophrenia, predictive processing, prior expectations, perception

Schizophrenia is associated with profound working memory deficits, for which there are no approved treatments. Rare heterozygous null mutations in SETD1A, a gene encoding a key epigenetic regulatory protein, have been definitively linked to increased risk for schizophrenia and neurodevelopmental disorders. To investigate how SETD1A haploinsufficiency impacts the function of circuits supporting working memory, this study examined neural oscillatory synchrony across a network of brain regions critical for spatial working memory (SWM) in mice carrying a loss-of-function allele in the orthologous SETD1A gene. Local field potential recordings were performed in the prefrontal cortex, dorsal and ventral hippocampus, and thalamic nucleus reuniens in male and female wildtype and Setd1a+/- mice performing a delayed non-match to sample task of SWM. Setd1a+/- mice exhibited unaltered prefrontal-hippocampal neural oscillatory synchrony across frequencies and task epochs. In contrast, Setd1a+/- mice displayed reduced beta-frequency synchrony between the prefrontal cortex and nucleus reuniens during SWM maintenance and blunted bidirectional modulation of prefrontal-reuniens beta- and gamma-frequency synchrony across SWM task epochs. Collectively, this work expands our understanding of how genetic risk for schizophrenia alters functional connectivity within distributed circuits supporting SWM. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=104 SRC="FIGDIR/small/702577v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@8107acorg.highwire.dtl.DTLVardef@11ec407org.highwire.dtl.DTLVardef@d7ce18org.highwire.dtl.DTLVardef@1bb31f_HPS_FORMAT_FIGEXP M_FIG C_FIG

Background and HypothesisSchizophrenia is a disease characterized by various symptoms and has severe lifelong impacts on patients and their families. Despite various hypotheses and associated studies, the key mechanism in schizophrenia is not fully elucidated. In the present study, we focused on adropin, a peptide regulating energy metabolism, antioxidation, and neuroprotection. Study DesignIn both the group of healthy volunteers (HV) and the group of patients with some schizophrenia spectrum and other psychotic disorders (SZ), we evaluated adropin along with other variables such as anthropological factors, psychological well-being indicators, and laboratory test results. Study ResultsThe adropin levels in SZ were not significantly different from those in HV. Correlation analysis indicated five significant correlations beyond various natural correlations arising from fundamental proportional relationships and multifaceted psychological well-being indicators: (1) adropin versus right handgrip strength in the SZ group ({tau} = -0.82, P = 0.066); (2) adropin versus selenium in the total group ({tau} = 0.44, P = 0.053); (3) ferritin versus perceived stress in the total group ({tau} = -0.44, P = 0.053); (4) right versus left handgrip strength in the total group ({tau} = 0.70, P = 0.001) and in the SZ group ({tau} = 0.82, P = 0.075); and (5) selenium versus state anxiety in the total group ({tau} = 0.44, P = 0.053) and the SZ group ({tau} = 0.84, P = 0.066). ConclusionsThe present study provides a foundation for future studies and sheds light on the role of adropin in schizophrenia.

Schizophrenia is a serious mental disorder that changes the way people think, perceive, and manage daily life. Getting the diagnosis right is critical for proper treatment, but in practice it is often difficult. Current evaluations depend mostly on a clinicians judgment, and the overlap of symptoms with bipolar disorder or major depression makes the task even harder. EEG offers a safe and noninvasive way to study brain activity, yet no single EEG feature has been reliable enough to stand on its own. This makes it important to look at integrative approaches that bring together different aspects of brain dynamics. In this study, we analyzed EEG features to distinguish patients with schizophrenia from healthy controls. Spectral power was measured across {delta}, {theta}, , {beta}, and {gamma} bands. Temporal irregularity was quantified with Multiscale Permutation Entropy (MPE), which to our knowledge represents the first application of MPE to EEG in schizophrenia. Functional connectivity was estimated with the weighted Phase Lag Index in {theta}, , and {beta} bands, followed by extraction of graph measures including global efficiency, clustering coefficient, characteristic path length, and mean strength. These features were used to train Random Forest, Multi-Layer Perceptron, and Support Vector Machine classifiers. Among the models, Random Forest achieved the most reliable performance, reaching 99.7% accuracy under stratified 5-fold validation and 99.6% under leave-one-subject-out validation. Feature analysis showed that connectivity in {theta} and bands contributed most strongly to classification. Topographic maps of {theta}, , and {beta} activity also revealed regional group differences. Overall, the results suggest that combining spectral, entropy, and connectivity measures offers a promising framework for EEG-based detection of schizophrenia. Nevertheless, these findings are preliminary given the limited sample size (N=28), and replication in larger and more diverse cohorts is required before clinical translation.

Cognitive deficits are a core feature of schizophrenia, yet their neural mechanisms remain poorly understood. Network switching, a measure of how frequently brain networks change their interactions over time, has been linked to cognitive performance in healthy individuals and has been reported to be altered in schizophrenia. Recent evidence further suggests that the relationship between network switching and cognition depends on arousal, which is itself disrupted in schizophrenia. However, whether arousal-related alterations in network switching contribute to cognitive impairment in schizophrenia remains unclear. Here, we used concurrent resting-state functional MRI (fMRI) and pulse oximetry data from 39 healthy controls (HC), 27 psychiatric controls (PC), and 39 individuals with schizophrenia spectrum disorders (SSD) to examine whether network switching relates to indices of autonomic arousal. Additionally, in HC and SSD participants, we tested whether arousal moderated the association between network switching and performance on an attention task. We observed no group differences in autonomic arousal. However, PC exhibited higher dorsal default mode and anterior salience network switching rates compared to SSD participants. Additionally, autonomic arousal significantly moderated the relationship between network switching and cognitive performance in HC, an effect that was absent in SSD. Notably, these findings implicate network switching as a potential neural biomarker that differentiates PC from SSD. They also suggest that disrupted coupling between arousal state and network switching, rather than switching alone, may underlie cognitive dysfunction in SSD.

Backgroundspeech carries cues to variation in mental state in schizophrenia spectrum disorders/psychotic disorders, typically indexed with clinician-rated scales such as the PANSS. Progress in the automation of speech-based symptom modelling has been constrained by data scale and the underrepresentation of low-resource languages. In this study, we aggregate multi-center recordings to assemble a large corpus and assess symptom-prediction models at scale, to enable more objective and efficient assessments and the early detection of relapse-related signals from speech. MethodsWe compiled data from 453 patients with schizophrenia spectrum disorders, recruited from ten global sites, and clipped their speech recordings into 6,664 segments. Across three feature sets, acoustic-prosodic profile, pretrained multilingual embeddings, and their concatenation, we compared 16 algorithms to predict eight relapse-related PANSS items, including three positive (P1, P2, P3), three negative (N1, N4, N6), and two general (G5, G9) items, on speaker-disjoint splits (80% train, 10% test, and 10% validation). Performance was assessed by root-mean-squared-error (RMSE) at both segment and participant (median aggregation) levels. Best model per item underwent bias checks for age, sex, education, and symptom severity. OutcomesBest-performing models predicted symptoms with prediction errors of 1{middle dot}5 PANSS points or lower: P1 1{middle dot}494/1{middle dot}527, P2 1{middle dot}318/1{middle dot}107, P3 1{middle dot}407/1{middle dot}542, N1 1{middle dot}029/1{middle dot}030, N4 1{middle dot}452/1{middle dot}430, N6 0{middle dot}860/0{middle dot}855, G5 0{middle dot}850/0{middle dot}882, G9 1{middle dot}213/1{middle dot}282 (segment/participant). Performance of the pretrained multilingual embeddings surpassed acoustic-prosodic features and their concatenation. Results were comparable in low-resource languages (e.g., Czech). We found no bias by age, sex, or education, aside from reduced N4 accuracy in males; but performance degraded with higher symptom severity. InterpretationSpeech can support automatic assessment of schizophrenia symptoms using pretrained multilingual embeddings, even without the use of transcripts. Such models show promise as clinically meaningful, efficient, and low-burden tools for real-time monitoring of symptom trajectories. FundingEU Horizon research and innovation programme. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSAutomatic assessment of disease severity is a key issue in schizophrenia research, for which spontaneous speech offers a cost-effective, automatable solution. To evaluate existing evidence for speech-based symptom assessment, two reviewers (RHe, MK) searched PubMed, IEEE Xplore, arXiv, bioRxiv, and medRxiv for publications from inception to Aug 25, 2025, using the terms: ("symptom" OR "PANSS" OR "Positive and Negative Syndrome Scale") AND ("psychosis" OR "schizophrenia") AND ("language" OR "speech" OR "spontaneous speech") AND ("prediction" OR "machine learning" OR "deep learning" OR "algorithm" OR "neural network" OR "AI" OR "artificial intelligence"). Fourteen studies on symptom-level modelling were identified. Ten studies dichotomized clinical scores (e.g., PANSS) into low vs high for classification: five used conventional ML (e.g., random forests) and five used neural networks, with F1 scores ranging from 0{middle dot}60-0{middle dot}85. The remaining four studies, and two of the ten studies as mentioned above, modelled raw scores directly as regression tasks. Two relied solely on conventional regressors and the rest used neural networks, with errors from 0{middle dot}487 for single items (scale 1-7) to 8{middle dot}04 for summed scores (scale 18-126). All studies used free speech for elicitation, except one study, which used a reading task. Three studies incorporated additional tasks, such as picture description and immediate recall. None were multilingual: nine were in English, three in Chinese, one in Swiss German, and one in Brazilian Portuguese. Features spanned a wide range, including acoustic-prosodic profiles, morpho-syntactic structure, semantic organization, pragmatics (including sentiments), and even visual features capturing movement during talking. Representations from pretrained language models were also widely employed. Sample sizes (counting patients with schizophrenia) were generally small: eleven studies enrolled <50 patients, one had 65, and only two exceeded 100 patients. Some increased their effective sample size via multiple recordings per patient or by adding healthy controls and/or patients with other psychiatric disorders (e.g., depression). Added value of this studyTo our knowledge, this is the first multilingual, speech-based study modelling schizophrenia symptom severity with machine learning approach, and it includes the largest cohort of patients with schizophrenia to date. We further increased effective sample size by using diverse elicitation tasks and segmenting recordings into clips. This multilingual corpus empowers the usage of complex models and supports transfer learning from high-resource languages (e.g., English) to low-resource ones (e.g., Czech). For each of eight selected relapse-related PANSS items, the best audio-only models achieved RMSE < 1{middle dot}5, underscoring clinical relevance. We assessed potential biases: no effects were found for age, sex, or education (except poorer N4 performance in males), though performance declined at higher symptom severity. Trained models are released for use. Implications of all the available evidenceWe show that speech is a powerful signal for automatic assessment of schizophrenia symptom severity and holds promise for relapse prediction, even without transcripts. The approach readily extends to incorporate textual features (from manual or automatic transcripts) and more advanced models. Prospective studies with repeated recordings across relapse episodes are needed to validate the utility of our models on relapse prediction, for the sake of supporting precision psychiatry while reducing clinician burden.

BackgroundPrediction of response to antipsychotic medications remains elusive, and a biomarker assisting in treatment selection would drastically improve prognosis. The 40 Hz auditory steady state response (ASSR) is an EEG biomarker, mirroring the GABA-glutamate signaling and the excitation/inhibition balance, consistently been reported to be impaired in schizophrenia, on, with inconsistent evidence of an association with specific symptoms. MethodsN=69 schizophrenia inpatients with an acute psychotic episode underwent an EEG recording to assess event related spectral perturbation (ERSP), intertrial phase coherence (ITC) and phase amplitude coupling (PAC) during the ASSR task, aimed to assess their relationship with response to antipsychotics and with positive, negative, disorganized, excited and depressive symptoms. Moreover, patients were compared with controls (N=36), to delineate schizophrenia acute phase ASSR dynamics. ResultsResponders to treatment showed a decreased 40 Hz ERSP in both the early (0-0.2s post-stimulus; P=0.0013; d=-0.936) and late (0-2-1.2s post-stimulus; P=0.0022; d=-0.932) time windows compared to non-responders. Using logistic regression and bootstrap optimism correction, ERSP classified the two groups with 70% accuracy. Responders but not non-responders showed a reduced ERSP compared to controls (P=0.0211; d=-0.558). Patients had reduced early ITPC (P=0.0001; d=-1.015) vs controls. responders compared to non-responders had increased PAC in the early (P=0.0215; d00.65) and in patients vs controls, in both the early (P=0.0002; d=0.57) and the late (P=0.0006; d=0.74) windows. No association emerged between ASSR metrics and symptoms severity. ConclusionsASSR is a candidate biomarker for antipsychotic treatment personalization. Only responders to treatment presented a significant gamma-band impairment, in line with previous literature on stabilized outpatients, but not non-responders, indicating that a distinct neurobiology could exist.

Schizophrenia frequently follows a chronic relapsing-remitting course, comprising alternating episodes with and without psychotic symptoms (hereafter: psychosis and psychotic remission). One potential neurobiological correlate of this course is aberrant dopamine synthesis and storage (DSS) in the striatum, which can be estimated by 18F-DOPA positron emission tomography (PET). We hypothesised that striatal DSS in patients with schizophrenia decreases from psychosis to psychotic remission, with lower striatal DSS in patients during psychotic remission compared to healthy subjects. Additionally, we explored whether striatal DSS is associated with psychotic relapse after remission. 18F-DOPA PET scans and clinical assessments were conducted in 28 patients with schizophrenia at two timepoints, first during psychosis and second during early psychotic remission 6 weeks to 12 months after the first timepoint, as well as in 21 healthy controls, assessed twice in a comparable time interval. The averaged influx constant kicer as proxy for DSS was calculated for striatal subregions (i.e., nucleus accumbens, caudate, and putamen) using voxel-wise Patlak modelling with a cerebellar reference region. Mixed-effects models and post hoc analyses were used to test for longitudinal changes in kicer and cross-sectional group differences. An exploratory clinical follow-up 12 months after the second scan was conducted to assess psychotic relapse, and post hoc ANCOVAs were used to test for differences in kicer at each session between relapsing and non-relapsing patients. Kicer in both caudate and nucleus accumbens significantly changed from psychosis to psychotic remission compared to healthy controls, with a significant longitudinal decrease of caudate kicer in patients. Furthermore, kicer in both caudate and accumbens was significantly lower in patients during early psychotic remission compared to controls. At the exploratory clinical follow-up, 32% of patients had experienced a psychotic relapse; they showed higher caudate kicer compared to non-relapsing patients during psychosis, with no difference during psychotic remission. These findings provide evidence for the link between striatal, particularly caudate, DSS and the relapsing-remitting course of psychotic symptoms in schizophrenia, with lower caudate DSS during early psychotic remission. Data suggest altered striatal dopamine synthesis together with impaired DSS dynamics along the course of psychotic symptoms in schizophrenia.

BackgroundSchizophrenia is a severe neuropsychiatric disorder. Efforts to describe the underlying biology and establish diagnostic markers through non-invasive neuroimaging methods are ongoing, resulting in a range of theoretical brain-based frameworks. Prominent frameworks for aberrant schizophrenia-associated functional connectivity in resting-state functional magnetic resonance imaging (rsfMRI) include the dysconnectivity hypothesis, theory of cognitive dysmetria, and triple network theory. Although informative, prior work can be improved by increasing sample size, avoiding confirmation bias, and accounting for individual variability and the effects of medication and chronicity. MethodsWith these recommendations in mind, we employed a data-driven, whole-brain approach using a large multi-site rsfMRI dataset (N = 2,656; schizophrenia = 1,248). We used reference-guided independent component analysis (ICA) to generate subject-specific whole-brain functional network connectivity (FNC) and extract imaging markers of similarity to schizophrenia patterns. We modeled the relationship between medication dosage, age of onset, chronicity, symptom severity, and cognitive performance and FNC. ResultsOur analysis identified a reliable schizophrenia-FNC signature characterized by aberrantly stronger negative cerebellothalamic and positive thalamocortical connectivity, implicating sensory, motor, and associative cortical circuits. While medication and chronicity were significantly associated with these signatures, the core cerebellothalamic disruptions remained a robust marker of schizophrenia. ConclusionsThis work represents the largest schizophrenia-specific rsfMRI study to date, refines existing theoretical frameworks with a more nuanced map of how clinical variables interact with brain connectivity, and provides a high-fidelity template of schizophrenia-related connectivity. We have released this template as an open-source resource to facilitate reproducibility and accelerate the development of reliable rsfMRI-based schizophrenia biomarkers.

BackgroundSchizophrenia is marked by impairments in emotional processing and social cognition, yet traditional neuroimaging paradigms often lack the ecological validity to capture these deficits in real-world contexts. MethodsIn this study, we used intersubject correlation (ISC) analysis of functional MRI data to examine shared neural representations of naturalistic visual narratives in individuals with schizophrenia and healthy controls. Participants viewed short films designed to evoke happy, sad, and emotionally neutral responses, allowing us to compare how synchronized brain activity varied with emotional content across and within groups. ResultsHealthy controls showed greater ISC in regions associated with affective salience, emotion recognition, and social understanding, including the amygdala, insula, and temporal cortices. In contrast, participants with schizophrenia displayed higher synchrony in visual, subcortical, and frontal areas, suggesting a reliance on perceptual and executive systems. To isolate the effects of emotion from general visual processing, we compared ISC during emotional clips relative to neutral videos. This revealed significantly reduced synchrony in the bilateral amygdala in patients, highlighting a core dysfunction in affective engagement. Interestingly, neutral stimuli elicited unexpectedly strong synchronization in frontal and limbic regions in the schizophrenia group, possibly reflecting altered salience attribution to ambiguous or emotionally ambiguous content. ConclusionsThese results point to a functional reorganization of affective processing in schizophrenia, where impaired limbic recruitment is accompanied by compensatory engagement of perceptual and cognitive control networks. ISC during naturalistic stimulation emerges as a powerful tool for capturing subtle disruptions in shared emotional experience in psychiatric populations.

ObjectiveCognitive deficits are a leading cause of disability in schizophrenia and are linked to poor functional outcomes. There are no first line treatments for these deficits, and their neural basis is poorly understood. While schizophrenia is associated with widespread cognitive deficits, information processing speed is most profoundly impaired. Processing speed deficits have been associated with hyperconnectivity in the Default Mode Network (DMN). We therefore tested if modulating DMN connectivity with single or multiple sessions of transcranial magnetic stimulation (TMS) applied to an individualized DMN target would affect processing speed. MethodsIn the first study, 10 individuals with schizophrenia received single TMS sessions and underwent resting-state neuroimaging and processing speed assessment (Brief Assessment of Cognition in Schizophrenia digit symbol coding) acutely before and after each session. These sessions included excitatory (intermittent theta burst stimulation, iTBS); inhibitory (continuous theta burst stimulation, cTBS); and sham stimulation sessions. In the second study, 29 individuals (17 schizophrenia, 12 non-psychosis controls) received 5 accelerated sessions of cTBS with resting-state neuroimaging and processing speed assessment before and after the course of TMS sessions. ResultsIn the accelerated, multi-session DMN-targeted TMS trial, cTBS improved processing speed in the schizophrenia group (p=0.0124). In individuals with schizophrenia, reduction in DMN connectivity was linked to improvement in processing speed (p=0.021). These changes were dependent on age, where younger participants experienced greater processing speed improvements than older participants (p=0.006). ConclusionsIn sum, personalized network targeted TMS is a novel method for reducing cognitive impairment associated with schizophrenia.

Schizophrenia (SCZ) and type 2 diabetes mellitus (T2DM) are common comorbid disorders that severely impair patient prognosis and quality of life. This study aimed to explore the association between the methylenetetrahydrofolate reductase (MTHFR) C677T gene polymorphism and MTHFR promoter methylation in patients with comorbid SCZ and T2DM. A total of 120 participants were enrolled from Liaocheng Fourth Peoples Hospital between January 2025 and June 2025, comprising 30 subjects in each of the four groups: SCZ group, T2DM group, SCZ-T2DM comorbid (SCZ+T2DM) group, and healthy control (CTL) group. Corresponding primers were designed for genetic analysis, and methylation-specific PCR (MSP) was performed to detect the methylation level of the MTHFR promoter. Genotype distribution of the MTHFR C677T polymorphism was consistent with Hardy-Weinberg equilibrium (HWE) (p>0.05). The C677T polymorphism was significantly associated with an elevated risk of SCZ and T2DM comorbidity (p<0.05). Notably, the methylation rate of the MTHFR promoter in the SCZ+T2DM group (95.00%) was not significantly higher than that in the CTL group (90.00%) (p>0.05). In conclusion, the MTHFR gene may serve as a susceptibility gene for SCZ-T2DM comorbidity, whereas MTHFR promoter methylation is not associated with the pathogenesis of this comorbid condition. These results indicate that genetic variation in MTHFR, rather than promoter methylation, contributes critically to the comorbidity of SCZ and T2DM in the Han Chinese population. Our findings may provide novel molecular insights into their shared pathophysiology and inform future clinical strategies for patients with this complex phenotype.

ObjectiveTobacco use is a leading cause of mortality in schizophrenia, but treatments are partially effective. Default mode network (DMN) pathology is linked to tobacco use in schizophrenia, and transcranial magnetic stimulation (TMS) applied to the DMN affects craving in schizophrenia. To advance TMS therapeutics for tobacco use in schizophrenia, we used TMS experiments to 1) determine optimal stimulation parameters then 2) compare our optimal parameters against a well-established, effective TMS intervention for craving. MethodsIn Protocol Optimization TMS, nicotine-using individuals with schizophrenia (n=10) received single sessions of DMN-targeted TMS with pre/post neuroimaging and craving assessment. Neuroimaging analysis revealed bilateral parietal DMN connectivity was associated with craving change. In Comparative Effectiveness TMS (n=62), nicotine-using individuals with schizophrenia and non-psychosis controls participated in a crossover study comparing DMN-targeted and left dorsolateral prefrontal cortex (DLFPC)-targeted TMS with pre/post neuroimaging and craving assessment. Mixed effects models were used to determine effects of target, group, and relationship between craving change and connectivity change. ResultsIn Protocol Optimization TMS, increased craving was associated with increased bilateral parietal DMN connectivity (mean pFDR<0.012, r=0.60). In Comparative Effectiveness TMS, both interventions reduced craving (DLPFC: p=0.0015; DMN: p=0.0054) and bilateral parietal DMN connectivity (DLPFC: p=0.024; DMN: p=0.022). There was an interaction of bilateral parietal DMN connectivity change, group, and age (p=0.001) where connectivity change was associated with craving change in older individuals with schizophrenia (p=0.041) but not other groups. ConclusionsBilateral parietal DMN connectivity is a novel mechanism underlying craving in schizophrenia that can be engaged for therapeutic benefit.

BackgroundPeripheral inflammation is implicated in the pathophysiology of schizophrenia, but how inflammatory signals map onto the large-scale brain organization remains incompletely understood. MethodsWe applied a supervised multimodal fusion approach guided by interleukin-6 (IL-6) to gray matter volume (GMV) and resting-state regional homogeneity (ReHo) from a population-based discovery cohort in the UK Biobank. Brain components related to IL-6 were identified and then projected onto an independent schizophrenia cohort to examine their relevance to the disease. Imaging-transcriptomic analyses using the Allen Human Brain Atlas characterize the molecular substrates underlying the disease-relevant pattern. ResultsTwo ReHo components were significantly associated with plasma IL-6, while no GMV components showed robust IL-6 correlations. One of the components (ReHo IC4) exhibited a conserved functional pattern characterized by enhanced visual synchrony and reduced synchrony in the medial prefrontal cortex. This pattern remained unchanged in both the healthy controls and patients. In contrast, another component (ReHo IC8) showed increased synchrony in the default mode network and reduced synchrony in sensorimotor networks, and its loadings were significantly elevated in patients with schizophrenia. Imaging-transcriptomic analysis revealed the molecular architecture of this disease-amplified pattern. The default mode region was enriched in synaptic signaling pathways, while the sensorimotor region was linked to mitochondrial bioenergetic processes; both patterns significantly enriched with gene sets related to schizophrenia. ConclusionsThis study identified an IL-6-associated functional brain pattern that is amplified in schizophrenia, linking peripheral inflammation to disease-specific network dysregulation. The findings provide a systems-level framework for understanding how peripheral inflammation interacts with large-scale brain network activities in schizophrenia.

Background Cognitive impairment is a core feature of schizophrenia and a major determinant of functional disability. Executive deficits affect approximately 85% of patients and are associated with reduced activity in the prefrontal cortex (hypofrontality). Current pharmacological treatments show limited efficacy in improving cognition, highlighting the need for alternative therapeutic approaches. Combining non-invasive brain stimulation with cognitive remediation may enhance neuroplasticity and improve cognitive outcomes. Methods This prospective, randomized, double-blind, sham-controlled, parallel-group superiority clinical trial. A total of 120 adults aged 18-65 years with clinically stable schizophrenia diagnosed according to DSM-5 criteria will be enrolled at a single clinical center. Participants will be randomly assigned in a 1:1 ratio to receive either active transcranial direct current stimulation (tDCS) targeting the dorsolateral prefrontal cortex followed by cognitive remediation therapy (CRT) using the RehaCom system, or sham stimulation followed by the same cognitive training. Assessments will be conducted at three time points: prior to the intervention (V1), immediately after the intervention (V2), and during the follow-up visit 8 weeks after the intervention (V3). The primary outcome is change in cognitive performance measured with the CANTAB battery. Secondary outcomes include symptom severity assessed with the PANSS, global clinical status (CGI-S), and neurophysiological changes measured by EEG. Written informed consent will be obtained from all participants, and the study has received ethics committee approval. Discussion This trial will evaluate whether tDCS administered prior to cognitive training enhances cognitive improvement compared with cognitive training alone. The findings may inform the development of more effective interventions targeting cognitive deficits in schizophrenia. Trial registration ClinicalTrials.gov Identifier: NCT07273175. Registered on 25 November 2025.

BackgroundSchizophrenia is a neurodevelopmental disorder shaped by immune-related mechanisms, particularly dysregulated complement-mediated synaptic pruning. Genome-wide association studies have identified CSMD1 as a major schizophrenia risk gene, an association robustly replicated across populations of diverse ancestries. As a complement regulator, CSMD1 further links genetic vulnerability to synaptic refinement processes. However, the transcriptional status of CSMD1 and its homolog CSMD2 in individuals with schizophrenia (SZ individuals) remains poorly characterized. We conducted a meta-analysis of gene-expression datasets to determine whether CSMD1 and CSMD2 are differentially expressed in brain and peripheral tissues, and to assess the concordance between central and peripheral transcriptional signals. MethodsTranscriptional data were obtained from gene expression omnibus. Random-effects meta-analyses were performed on CSMD1 and CSMD2 expression data from 854 postmortem brain samples derived from 348 SZ individuals and 346 healthy controls (HC), and 295 peripheral blood samples from 162 SZ individuals and 133 HC. Sex-stratified analyses and meta-regressions evaluated potential moderators. ResultsIn brain tissues, CSMD2 expression was significantly increased in SZ individuals vs. HC (SMD: 0.22 [0.05; 0.39], adj-p=0.026), whereas CSMD1 showed no differential expression. The female-only meta-analysis revealed nominal CSMD2 overexpression (p=0.037) in brain tissues, not surviving correction. No significant transcriptional differences were detected in peripheral blood. ConclusionIn schizophrenia, our findings point to a dissociation between genetic vulnerability and transcriptional activity within the CSMD gene family. Schizophrenia is associated with selective brain CSMD2 overexpression, contrasting with unchanged CSMD1 transcription and absent peripheral blood alterations. These findings support complement-related dysregulation as a central pathway in schizophrenia.

BackgroundSchizophrenia is a highly heritable mental disorder associated with widespread anatomical alterations during neurodevelopment. Converging evidence suggests transcriptomic architecture underlying brain abnormalities in schizophrenia, while how individualized brain morphological deviations relate to gene expression levels remains unclear. MethodsTo investigate individual-level brain deviations and its transcriptomic signatures in schizophrenia, this study collected T1-weighted MRI data from 95 early-onset schizophrenia (EOS) patients and 99 typically developing (TD) controls. Normative modeling was used to measure individual deviations in cortical thickness and subcortical volume. Partial least squares regression was calculated to capture covarying patterns between structural deviations and whole-brain gene expression levels. Clustering analysis was performed on latent brain-gene covarying components, and the results were further functionally decoded through gene enrichment analyses. ResultsGroup-level comparisons suggested patients with EOS showed consistently decreased z-scores of cortical thickness in the frontal and temporal lobe regions, while increased inter-individual variability in the lingual gyrus. Clustering analysis of z-scores with transcriptomic signatures identified two distinct brain-gene covarying subtypes. Subtype 1 showed thickening cingulate gyrus, thinning occipital pole, and atrophic subcortical nuclei. Subtype 2 exhibited widespread cortical thinning across the frontal, parietal, temporal, and limbic regions, but enlarged subcortical nuclei. Genes underlying two subtypes were both enriched for neurodevelopmental diseases. However, subtype 1 was associated with synaptic transmission, and subtype 2 was related to cytoskeletal and neuronal connectivity. ConclusionThis study reveals individual-level anatomical deviations and transcriptomic heterogeneity in early-onset schizophrenia. The findings provide an individualized brain-gene coupling framework for understanding pathophysiology of schizophrenia during brain development.