Schizophrenia

Metabolic disorders (MDs) are a group of medical conditions that impact the metabolism. These complex processes may have common characteristics among various diseases, thereby suggesting the potential of drug repurposing. Employing Mendelian Randomization (MR), we constructed a causal network between 2,478 targetable drug-gene expressions (eQTLs) and 30 broadly reported metabolic disorders. Our study identified 499 drug target genes significantly associated with 27 metabolic disorders (|MR coefficient| > 0.2). Pathway enrichment analysis of drug target genes indicates that regulation of response stimulus may serve as a common pathway across 14 diseases. Based on 53 commonly used clinical drugs for 18 diseases, we elucidated novel therapeutic mechanisms of some drugs, such as the potential of Valproic Acid to treat Schizophrenia by affecting key genes in Alcoholism, SLC29A1, and HDAC4. Furthermore, we identified potential for drug repurposing in four diseases, with Manic Episode and Type 1 Diabetes sharing four novel drugs: Cannabidiol, Doxorubicin, Genistein, and Propylthiouracil. Additionally, we predicted 189 potential therapeutic drugs affecting the causal genes of diseases. Overall, we established a causal network between metabolic disorders and drug target genes, explored possible pathways for drug action on disease treatment, and proposed drug repurposing strategies for four diseases. HighlightsO_LITo the best of our understanding, the manuscript we have submitted delineates the inaugural drug-gene causal network tailored for Metabolic Disorders (MDs), covering an array of 30 diseases. C_LIO_LIThis investigation unveils potential novel drug mechanisms aimed at treating MDs, marking a pioneering exploration in this domain. C_LIO_LIWe introduce strategies for the repurposing of drugs targeting four specific MDs, suggesting innovative approaches to treatment. C_LIO_LIThe findings are presented through an easily navigable web interface, enabling thorough examination by users (https://quantlifebits.shinyapps.io/mrmetabopharm/). C_LI

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.

Pharmacogenomic studies on psychiatric drugs have slowly identified genetic variations that influence drug metabolism and treatment effectiveness in patients with mental illness. However, most of these studies have predominantly centered on people of European descent, leaving a substantial knowledge gap on the clinical implications of current pharmacogenomic evidence in multi-ancestry populations such as Africans. Thus, whether pharmacogenomic (PGx) genetic testing implemented in European populations would be valid for a population of African origin is unknown. The objective of this review was to appraise previous psychiatric pharmacogenomic studies in Africa and highlight challenges and opportunities to initiate PGx testing in the region. A systematic literature search was conducted on PubMed, Scopus, and Web of Science to identify studies published in the English language up to January 26, 2024. The primary outcomes were treatment response, remission, side effects, and drug metabolism in African psychiatric patients. The review included 42 pharmacogenomic studies that explored the genetic profiles of psychiatric patients in Africa. Despite the limited number of studies, our review found strong evidence of pharmacogenomic diversity within the African populations, emphasizing the importance of pharmacogenomic research in this population. A high degree of variability and differences in the frequencies of cytochrome P450 (CYPs) genotypes have been reported within the African population. It is estimated that 28% of North Africans and Ethiopians are ultrarapid metabolizers of several medications, mainly attributed to the increased activity of the CYP2D6 enzyme. This prevalence is significantly higher than that among Caucasians (10%), or Hispanics, Chinese, or Japanese populations (1%). Due to the defective CYP2C19*2 allele (at a frequency of 14%) and CYP2C19*3 allele (2% frequency), 5.2% of Ethiopians were identified as poor metabolizers of S-mephenytoin, a probe substrate used to assess the activity of the cytochrome P450 enzyme. In Tunisian patients with schizophrenia, genotyping the CYP1A2 gene and using therapeutic drug monitoring (TDM) improved the effectiveness and safety of clozapine. Among South African patients with schizophrenia, antipsychotic treatment response was associated with two gene variants (rs13025959 in the MYO7B gene with the C allele and rs10380 in the MTRR gene with the T allele). Overall, the review has identified evidence of pharmacogenomic diversity in African populations and recommended expanding pharmacogenomic studies while introducing PGx testing in this population. For the few characterized genes, Africans showed qualitative and quantitative differences in the profile of pharmacogenetic variants when compared to other ethnic groups. Limited research funding, inadequate infrastructure, and a shortage of skilled human resources might be a challenge, but by building upon local successes and through collaborations with international partners, it is possible to establish pharmacogenomic biobanks and leverage global genetic resources to initiate personalized treatment approaches in Africa.

Background and ObjectivesEmerging preclinical evidence suggests that vagal signals contribute to the development of schizophrenia-related abnormalities in brain and behavior. Whether vagal communication in general, and its impairment in particular, is a risk factor for schizophrenia in humans remains, however, unclear. Vagotomy, the surgical lesion of the vagus nerve, was routinely performed as a treatment for peptic ulcer before modern treatment options were available. Hence, the primary aim of this study was to investigate whether vagotomy modulates the subsequent risk of developing schizophrenia. Moreover, given the existence of diverse vagotomy techniques (i.e., "truncal" or "selective"), our secondary goal was to test whether the extent of denervation modulates the risk of schizophrenia. MethodsUsing a nationwide retrospective matched cohort design, we identified 8,315 vagotomized individuals from the Swedish National Patient Register during the period 1970-2020 and 40,855 non-vagotomized individuals matching for age, sex and type of peptic ulcer. The risk of being diagnosed with schizophrenia and associated psychoses (ICD10 codes F20-29) was analyzed using Cox proportional hazards regression models, including death as competing risk. ResultsWhen considering all types of vagotomy together, vagotomy was not significantly associated with schizophrenia (HR: 0.91 [0.72; 1.16]). However, truncal vagotomy (which denervates all subdiaphragmatic organs) significantly increased the risk of developing schizophrenia by 69% (HR: 1.69 [1.08; 2.64]), whereas selective vagotomy (which only denervates the stomach) showed no significant association (HR: 0.80 [0.61; 1.04]). DiscussionOur results provide epidemiological support for the hypothesis that impairments in vagal functions could increase the risk of schizophrenia. Notably, the finding that truncal but not selective vagotomy is associated with an increased risk of schizophrenia raises the possibility that the activity of subdiaphragmatic non-gastric vagal branches may be of particular relevance for the development of schizophrenia.

BackgroundMismatch negativity (MMN) is a component of the auditory event-related potential (ERP) that is elicited during a passive oddball paradigm where task-irrelevant infrequent deviants are presented in a stream of more frequent standard stimuli. MMN is believed to index a pre-attentive stage of auditory information processing closely linked to N-methyl-D-aspartate receptors (NMDAR). Ketamine is thought to act primarily as an NMDAR antagonist, has been used in clinical trials to model the symptoms of schizophrenia and is increasingly used in the clinic to treat depression. Various studies have reported that ketamine reduces MMN amplitude which, in turn, might reflect reduced function of NMDAR-mediated neurotransmission. Nonetheless, there is growing evidence showing MMN amplitude either having high variability or, paradoxically, moving in the opposite direction after ketamine in different individuals. MethodsIn here, we analyzed results from three independent ERP studies to test the hypothesis of a cross-over interaction ("disordinal" drug effect) between the duration-deviant MMN at baseline (without ketamine) and the direction and magnitude of the ketamine effect. To rule out regression to the mean (RTM), a statistical phenomenon that may also partially explain this cross-over interaction, we separately estimated RTM using a drug-free test-retest study. ResultsOur results are the first to statistically demonstrate the existence of a disordinal drug response to ketamine, where the direction and magnitude of ketamine-induced changes in MMN amplitude can be predicted by baseline MMN amplitude. ConclusionsThese new insights may contribute to novel precision medicine approaches to treatment of CNS disorders.

Auditory-verbal hallucinations (AVHs) are among the most disabling symptoms of schizophrenia and often persist despite the use of adequate antipsychotic treatment. Conventional low-frequency repetitive transcranial magnetic stimulation (rTMS) targeting the T3P3 scalp site has demonstrated limited efficacy, likely due to interindividual variability in AVH-related brain networks. In this multicenter, randomized, double-blind phase 3 trial, 70 patients with drug-resistant AVHs received active 1-Hz rTMS targeted either with an individualized fMRI-based symptom-capture procedure or by using conventional T3P3 localization. fMRI-guided rTMS yielded a greater reduction in Auditory Hallucination Rating Scale (AHRS) scores at one month (mean difference, -5.43; 95% CI, -8.92 to -1.94), and the effects were sustained at three and six months. The number-needed-to-treat for neuroguided rTMS was 3.5. Clinical response was associated with greater E-field overlap with AVH-related networks. These findings demonstrate that fMRI-guided neuronavigation increases rTMS efficacy, thus supporting its use to optimize the treatment of drug-resistant AVHs in schizophrenia.

The mechanisms underlying circuit dysfunctions in schizophrenia (SCZ) remain poorly understood. Auditory steady-state responses (ASSRs), especially in the gamma and beta band, have been suggested as a potential biomarker for SCZ. While the reduction of 40Hz power for 40Hz drive has been well established and replicated in SCZ patients, studies are inconclusive when it comes to an increase in 20Hz power during 40Hz drive. There might be several factors explaining the inconsistencies, including differences in the sensitivity of the recording modality (EEG vs MEG), differences in stimuli (click-trains vs amplitude-modulated tones) and large differences in the amplitude of the stimuli. Here, we used a computational model of ASSR deficits in SCZ and explored the effect of three SCZ-associated microcircuit alterations: reduced GABA activity, increased GABA decay times and NMDA receptor hypofunction. We investigated the effect of input strength on gamma (40 Hz) and beta (20 Hz) band power during gamma ASSR stimulation and saw that the pronounced increase in beta power during gamma stimulation seen experimentally could only be reproduced in the model when GABA decay times were increased and only for a specific range of input strengths. More specifically, when the input was in this specific range, the rhythmic drive at 40Hz produced a strong 40Hz rhythm in the control network; however, in the SCZ-like network, the prolonged inhibition led to a so-called beat-skipping, where the network would only strongly respond to every other input. This mechanism was responsible for the emergence of the pronounced 20Hz beta peak in the power spectrum. The other two microcircuit alterations were not able to produce a substantial 20 Hz component but they further narrowed the input strength range for which the network produced a beta component when combined with increased GABAergic decay times. Our finding that the beta component only existed for a specific range of input strengths might explain the seemingly inconsistent reporting in experimental studies and suggests that future ASSR studies should systematically explore different amplitudes of their stimuli. Furthermore, we provide a mechanistic link between a microcircuit alterations and an electrophysiological marker in schizophrenia and argue that more complex ASSR stimuli are needed to disentangle the nonlinear interactions of microcircuit alterations. The computational modelling approach put forward here is ideally suited to facilitate the development of such stimuli in a theory-based fashion.

Cognitive impairment is a central characteristic of schizophrenia. Executive functioning (EF) impairments are often seen in mental disorders, particularly schizophrenia, where they relate to adverse outcomes. As a heterogeneous construct, how specifically each dimension of EF to characterize the diagnostic and prognostic aspects of schizophrenia remains opaque. We used classification models with a stacking approach on systematically measured EFs to discriminate 195 patients with schizophrenia from healthy individuals. Baseline EF measurements were moreover employed to predict symptomatically remitted or non-remitted prognostic subgroups. EF feature importance was determined at the group-level and the ensuing individual importance scores were associated with four symptom dimensions. EF assessments of inhibitory control (interference and response inhibitions), followed by working memory, evidently predicted schizophrenia diagnosis (area under the curve [AUC]=0.87) and remission status (AUC=0.81). The models highlighted the importance of interference inhibition or working memory updating in accurately identifying individuals with schizophrenia or those in remission. These identified patients had high-level negative symptoms at baseline and those who remitted showed milder cognitive symptoms at follow-up, without differences in baseline EF or symptom severity compared to non-remitted patients. Our work indicates that impairments in specific EF dimensions in schizophrenia are differentially linked to individual symptom-load and prognostic outcomes. Thus, assessments and models based on EF may be a promising tool that can aid in the clinical evaluation of this disorder.

Auditory verbal hallucinations (AVH) in treatment-resistant schizophrenia (TR SZ) are often unresponsive to pharmacologic interventions, necessitating novel therapeutic approaches. We report a case of a patient with persistent AVH who underwent deep brain stimulation (DBS) of the substantia nigra pars reticulata (SNr). To investigate the neurophysiological effects of SNr stimulation on cortical activity during AVH, we performed intraoperative electrocorticography (ECoG) over the left inferior parietal cortex using a 63-channel grid. Real-time recordings captured episodes of AVH and revealed elevated theta-gamma phase-amplitude coupling (PAC), a marker of aberrant cortical synchronization. DBS intervention resulted in normalization of PAC dynamics, with both average PAC and spatial distribution returning to non-AVH levels. Clinically, SNr DBS was associated with a 64% reduction in AVH frequency. These findings provide the first real-time functional evidence linking SNr activity to cortical oscillatory changes underlying AVH. This case supports the potential of SNr as a novel neuromodulatory target in TR SZ and highlights theta-gamma PAC as a candidate biomarker for mechanistic tracking of AVH symptomatology.

An effective way to quantify metacognitive abilities is to ask participants to estimate their confidence in the accuracy of their response during a cognitive task. A recent meta-analysis1 raised the issue that most assessments of metacognitive abilities in schizophrenia spectrum disorders may be confounded with cognitive deficits, which are known to be present in this population. Therefore, it remains unclear whether the reported metacognitive deficits are metacognitive in nature, or rather inherited from cognitive deficits. Arbitrating between these two possibilities requires equating task performance between experimental groups. Here, we aimed to characterize metacognitive performance among individuals with schizophrenia across three tasks (visual detection, familiarity, recollection) using a within-subject design, while controlling experimentally for intra-individual task performance and statistically for between-subject task performance. In line with our hypotheses, we found no metacognitive deficit for visual detection and familiarity judgements. While we expected metacognition for recollection to be specifically impaired among individuals with schizophrenia, we found evidence in favor of an absence of a deficit in that domain also. The clinical relevance of our findings is discussed in light of a hierarchical framework of metacognition.

BackgroundAuditory hallucinations are among the most disabling symptoms in individuals with schizophrenia (SZ) and are linked to aberrant signaling within deep-striatal circuits, such as the nucleus accumbens (NAc) and caudate head (CH). However, causal tests of striatal involvement have been limited by the inaccessibility of these structures using noninvasive neuromodulatory techniques. Low-intensity focused ultrasound (LIFU) provides millimeter-scale precision capable of modulating deep-brain circuits, but its feasibility and impact on hallucinations in SZ remain unknown. MethodsSZ participated in a within-subject cross-over feasibility trial including two active LIFU sessions (NAc, CH) and one sham control (unfocused sonication), spaced one-week apart. Resting-state fMRI and hallucination symptoms were acquired at baseline and immediately post-sonication. ResultsLIFU was delivered safely and well-tolerated in all patients. Acoustic simulations show consistent engagement of both striatal targets across subjects. Clinically, SZ demonstrated significant reductions in hallucination severity following active LIFU to NAc and CH, relative to baseline. Mechanistically, SZ exhibited abnormally high striatal-superior temporal cortex (STC) connectivity at baseline. Immediately after sonication, active LIFU to NAc and CH produced robust reductions in striatal-STC coupling in SZ. ConclusionsThis first-in-human study demonstrates that deep striatal LIFU is safe, feasible, and produces functional-connectivity changes accompanied by hallucination severity reductions in SZ. The convergence of symptom improvement with reduced striatal-STC coupling provides mechanistic proof-of-concept evidence that this circuit provides a promising biomarker and therapeutic LIFU target in psychosis and motivates larger sham-controlled trials to test the causal role of striatal circuitry in hallucination generation in SZ.

STRUCTURED ABSTRACTO_ST_ABSBackground and HypothesisC_ST_ABSCannabis use has been linked to psychotic-like experiences (PLEs). Amid increasing legalization, we examined the extent to which cannabis use is associated with PLEs after adjusting for other risk factors in a contemporary United States sample. Study DesignWe performed a cross-sectional analysis of self-reported cannabis use and four types of self-reported PLEs (auditory and visual perceptual distortions, referential ideation, and persecutory ideation) in the population-based biobank, the All of Us Research Program release 8 (maximum analytic N = 62,153). Study ResultsCannabis ever-use (ORs = 1.21 - 1.44, p-values < 2.7e-6) and more frequent past 3-month cannabis use (within lifetime ever-users) were associated with all four PLEs ({chi}2(4) = 21.06 - 70.09, p-values = 3.08e-4 to 2.17e-14), and these associations remained when adjusting for personal and family history of schizophrenia and polygenic liability for schizophrenia. The schizophrenia polygenic score, but not cannabis use frequency, was correlated with greater likelihood of being prescribed medication for the PLEs. When adjusting for lifetime ever-use of other substances, cannabis ever-use was no longer associated with PLEs, while methamphetamine use, cigarette use, and opioid use were associated with PLEs (ORs = 1.22 to 1.65, p-values < 1.68e-05). ConclusionsPrior associations between cannabis use and PLEs may have been confounded by comorbid use of other substances. Future studies that distinguish cannabis use from other substance use in the etiology of PLEs could provide insight into this transdiagnostic construct.

Deep brain stimulation (DBS) of the substantia nigra pars reticulata (SNpr) is under investigation for managing auditory-verbal hallucinations (AVH) in treatment-resistant schizophrenia (TR-SZ). We assessed acute AVH suppression during initial SNpr-DBS programming in three TR-SZ patients and mapped associated brain network engagement using normative connectomes. One-month post-implantation, monopolar stimulation at each electrode contact was evaluated for its effect on AVH severity. Volumes of tissue activation (VTA) were integrated with normative structural and functional connectivity data to generate individualized network maps. Among 86 VTAs, stimulation sites associated with greatest AVH relief localized to left anterior-dorsal and right posterior-ventral SNpr. Greater AVH suppression correlated with structural connectivity to sensorimotor cortex, precuneus, angular and supramarginal gyri, and functional connectivity to the mediodorsal thalamus, orbitofrontal cortex, anterior cingulate, and dorsolateral prefrontal cortex. These preliminary results highlight specific SNpr subregions and circuits linked to acute symptom reduction, supporting the potential of network-targeted DBS for TR-SZ.

BackgroundEEG microstates provide a window into rapid, large-scale brain network dynamics. Despite showing alterations in schizophrenia, evidence in first-episode schizophrenia spectrum psychosis (FESSP) is limited. We assessed whether microstate temporal and transition features could identify a multivariate signature of FESSP, and whether these dynamics can track symptom severity. MethodsResting-state EEG was analysed in 69 participants (FESSP n=41, mean age: 22.49 years; healthy controls n=28, mean age: 21.33 years). Twenty-eight microstate temporal and transition features were extracted across microstate classes (A-D). Group classification accuracy was assessed using a linear support vector machine with stratified cross-validation and permutation testing. Within the FESSP group, we further assessed associations between microstate features and clinical scores using the Brief Psychiatric Rating Scale (BPRS), Scale for the Assessment of Positive Symptoms (SAPS), and Scale for the Assessment of Negative Symptoms (SANS). ResultsMultivariate microstate features provided above-chance discrimination of FESSP from controls (balanced accuracy=0.644; AUC=0.688; p=0.030). However, when comparing individual features between groups, no feature survived multiple-comparison correction consistent with characterisation of FESSP via a distributed multivariate pattern across correlated features. Within the FESSP group, microstate dynamics were most strongly linked to negative symptoms, with higher SANS scores associated with shorter microstate D durations ({rho}=-0.507, pFDR=0.020) and higher occurrence of microstates A and B ({rho}=0.434-0.443, pFDR=0.042). BPRS-18 and SAPS showed no associations with any features. ConclusionsUsing EEG microstate temporal and transition features with multivariate classification, we identified a pattern that differentiated FESSP from controls and showed selective associations with negative symptom severity.

How genetic risk variants may relate to brain abnormalities is crucial for understanding cross-scale pathophysiological mechanisms underlying schizophrenia. The present study identifies brain structural correlates of variation in gene expression in schizophrenia and its clinical significance. Of 43 patients with schizophrenia, RNA-seq data from blood samples, MRI, and clinical assessments were collected, together with data from 60 healthy controls. Gene expression differentiation between schizophrenia and health controls was assessed and cross-referenced to schizophrenia-related genomic variations (GWAS on 76,755 patients and 243,649 controls and GWAS on 22,778 East Asian patients) and brain gene expressions (samples from 559 patients and 175 individuals). Multivariate correlation analysis was employed to examine associations across gene expression, brain volume, and clinical assessments. Differentially expressed genes in blood samples from patients with schizophrenia were significantly enriched for genes previously reported in genome-wide association studies on schizophrenia (P = 0.002, false discovery rate corrected) and were associated with gene expression differentiation in the brain (P = 0.016, 5,000 permutations). Transcriptional levels of differentially expressed genes were found to significantly correlate with gray matter volume in the frontal and temporal regions of cognitive brain networks in schizophrenia (q < 0.05, false discovery rate corrected). A significant correlation was further observed between gene expression, gray matter volume, and performance in the Wechsler Adult Intelligence Scale test (P = 0.031). Our findings suggest that genomic variations in schizophrenia are associated with differentiation in the blood transcriptome, which further plays a role in individual variations in macroscale brain structure and cognition.

Reduced cortical inhibition by parvalbumin-expressing (PV) interneurons in schizophrenia is thought to be associated with impaired cortical processing in the prefrontal cortex and altered EEG signals such as oddball mismatch negativity (MMN). Recent studies also suggest loss of somatostatin (SST) interneuron inhibition. However, establishing the link between reduced interneuron inhibition and reduced MMN experimentally in humans is currently not possible. To overcome these challenges, we simulated spiking activity and EEG during baseline and oddball response in detailed models of human prefrontal microcircuits in health and schizophrenia, with reduced PV and SST interneuron inhibition as constrained by postmortem patient data. We showed that reduced PV interneuron inhibition can account for the decreased MMN amplitude seen in schizophrenia, with a threshold below which the amplitude effect was low as seen in at-risk patients. In contrast, reduced SST interneuron inhibition did not affect the MMN amplitude. We further showed that both types of inhibition loss were necessary to account for changes in resting EEG in schizophrenia, with reduced SST interneuron inhibition increasing theta power, and reduced PV interneuron inhibition leading to a right shift from alpha to beta frequencies. Our study thus links reduced PV and SST interneuron inhibition in schizophrenia to distinct EEG biomarkers that can serve to improve stratification and early detection using non-invasive brain signals.

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.

The present study aims to explain how to use the precision nomothetic approach to analyze the interconnections between the negative symptoms, cognitive dysfunctions and biomarkers of schizophrenia. We review our data obtained in different study groups of patients with (deficit) schizophrenia and show, using examples extracted from these studies, how Partial Least Squares (PLS) path analysis should be used to examine these complex associations. PLS path analysis combines factor and multiple regression analysis in mediated models. We show that a single latent trait can be extracted from negative symptom domains and psychosis, hostility, excitation, mannerism, formal thought disorders and psychomotor retardation (PHEMFP). Both the negative and PHEMFP concepts miss discriminant validity whilst a common latent construct may be extracted from the 6 negative and 6 PHEMFP subdomains, dubbed overall severity of schizophrenia (OSOS). A common latent factor may be extracted from neurocognitive test scores including executive functions, and semantic and episodic memory dubbed the general cognitive decline (G-CoDe) index. PLS analysis shows that the effects of neuroimmunotoxic pathways on OSOS are partly mediated by the G-CoDe and indicate that those pathways have also direct effects on OSOS. We explain that the intercorrelations between those features should be assessed in an unrestricted study group combining patients and controls. Moreover, further bifactorial factor analysis with the restricted schizophrenia group may disclose illness-specific covariations among the features. Machine learning discovered a new schizophrenia phenotype characterized by increased severity of AOPs, G-CoDe, and OSOS, dubbed "major neurocognitive psychosis".

Negative symptoms in schizophrenia, particularly motivational deficits, pose significant challenges to treatment and recovery. Despite their profound impact on functional outcomes, these symptoms remain poorly understood and inadequately addressed by current interventions. The CHANSS (Characterising Negative Symptoms in Schizophrenia) study aims to dissect the cognitive mechanisms underlying motivational impairments by focusing on three interconnected domains: executive cognition, motivational cognition, and meta-cognition. This large, international, cross-sectional study recruits a heterogeneous sample of patients across illness stages (from first-episode psychosis to treatment-resistant schizophrenia) and uses a comprehensive cognitive battery, clinical scales, self-report measures, and computerised cognitive tasks. Four novel tasks assess key processes in motivated behaviour: option generation, reward-based decision-making, risk sensitivity, and performance self-evaluation. By incorporating control for secondary influences like depression, psychosis, sedation, and illness chronicity, the study seeks to identify distinct cognitive and behavioural subtypes within motivational dysfunction. CHANSS tests the hypothesis that specific patient profiles exhibit predominant impairments in one or more cognitive domains, which may differentially affect goal-directed behaviour. The study design allows exploration of hierarchical relationships between cognitive processes, such as how executive deficits may cascade to impair motivation and self-evaluation. Ultimately, CHANSS aims to advance mechanistic understanding of motivational deficits in schizophrenia and pave the way for personalised, targeted interventions. Its findings may inform future clinical trials and contribute to a shift away from one-size-fits-all approaches toward more effective, stratified treatment strategies in schizophrenia.

Core symptoms in patients with schizophrenia spectrum disorder (SSD), such as hallucinations or ego-disturbances, have been associated with a failure of the forward model to adequately predict the sensory outcomes of self-generated actions. Importantly, depending on the requirements of the environment, forward model predictions must also be able to recalibrate flexibly, for example to account for additional delays between action and outcome. In this study, we aimed to investigate whether non-invasive brain stimulation via transcranial direct current stimulation (tDCS) can be used to improve these sensorimotor temporal recalibration mechanisms in patients and in healthy subjects. While receiving tDCS on the cerebellum, temporo-parietal junction (TPJ), supplementary motor area (SMA), or sham stimulation, patients with SSD and healthy control subjects were repeatedly exposed to delays between actively elicited or passively performed button press movements and auditory sensory outcomes. Effects of this procedure on temporal perception were assessed with a delay detection task. We found similar sensorimotor temporal recalibration effects in both SSD and healthy subjects. Furthermore, cerebellar tDCS facilitated recalibration effects in both groups. Our findings indicate that sensorimotor recalibration mechanisms may be preserved in SSD and highlight the importance of the cerebellum in both patients and healthy subjects for this process. Our results suggest that cerebellar tDCS could be a promising tool for addressing deficits in action-outcome monitoring and related adaptive sensorimotor processes in SSD, and potentially alleviating associated symptoms.