Gamma-band oscillations, generated by excitatory-inhibitory circuit interactions, are strongly implicated in schizophrenia, yet evidence on resting-state abnormalities remains inconsistent. We conducted a systematic review and meta-analysis of EEG and MEG studies comparing resting-state gamma activity in patients with schizophrenia and healthy controls, following PRISMA guidelines and assessing study quality with the Newcastle-Ottawa Scale. Twenty studies (n = 998 patients; n = 952 controls) were included. Standardized mean differences (Hedges’ g) were calculated and pooled using random-effects models. Results demonstrated a significant elevation of whole-brain gamma power in schizophrenia (g=0.371; 95% CI = 0.119–0.622; P < 0.001; I² = 78.2%). Region-specific analyses showed increases in frontal and temporal cortices, with smaller or inconsistent effects in parietal, occipital, and default mode network (DMN) regions. Meta-regression revealed illness duration (β=1.13) and medication status (β=0.43) as positive predictors, while eyes-open resting conditions attenuated effects (β=−0.70), indicating that both clinical chronicity and methodological factors contribute to heterogeneity. Publication bias was not evident by Egger’s test, although trim-and-fill suggested five potentially missing small-effect studies, reducing the pooled estimate to g=0.130. Sensitivity analyses confirmed that findings were not driven by outliers, and GRADE assessments rated the certainty of evidence as moderate for whole-brain gamma and low for regional outcomes. Taken together, these findings suggest that resting-state gamma power differences in schizophrenia represent a small and heterogeneous group-level effect, shaped by illness duration, medication status, and recording conditions. Rather than indicating a uniform abnormality, the results underscore substantial variability across studies and highlight the need for cautious interpretation. Future large-scale, longitudinal, and multimodal investigations-particularly in unmedicated and first-episode patients-are warranted to clarify the temporal dynamics, causal mechanisms, and potential translational relevance of resting-state gamma activity in schizophrenia.

Obesity and psychiatric disorders frequently co-occur, yet the heterogeneous genetic mechanisms underlying this comorbidity remain elusive. Here, we stratify the polygenic architecture of body mass index (BMI) into five biologically distinct clusters based on metabolic signatures and construct partitioned polygenic scores (pPGSs) for each. Using individual-level data from the UK Biobank (N = 310,708), we identify cluster-specific psychiatric risk profiles, with amino acid-related clusters showing the strongest associations. These effects are markedly modulated by sex and BMI: underweight individuals and females show amplified psychiatric vulnerability, while obesity attenuates certain associations. Integration of proteomic and metabolomic data reveals key molecular mediators, including TNN, LEP, and ANGPT2, that influence psychiatric outcomes in a BMI- and sex-dependent manner. Mediation analyses demonstrate that pPGSs primarily exert indirect effects on psychiatric disorders through immune and metabolic molecular networks. These findings highlight the functional heterogeneity of BMI genetics, providing a framework for precision psychiatry. Metabolic clustering of BMI-associated genetic variants reveals sex- and BMI-dependent psychiatric risk heterogeneity, mediated by coordinated immune and metabolic molecular networks.

Major depressive disorder (MDD) is a leading cause of global disability, yet systematic evaluations of quality of care disparities across regions are sparse. Leveraging data from the Global Burden of Disease (GBD) Study 2021, this study quantified the quality of care for MDD from 1990 to 2021 and examined socio-demographic inequities by age and sex. Data on MDD were extracted from the GBD 2021 study for the globe, 5 socio-demographic index (SDI) regions and 21 GBD regions. The quality of care index (QCI) is a composite, dimensionless index scaling from 0 to 100, with higher values indicating better quality of care. The age-standardized QCI was calculated using the Principal Component Analysis (PCA) method and further stratified by sex, age, and region. The gender disparity ratio (GDR) was used to characterize the sex disparities. The temporal trend of QCI and GDR by sex and age across SDI regions was further calculated. Globally, the QCI of MDD increased from 56.26 (1990) to 62.95 (2021), with low SDI regions consistently exhibiting the highest QCI (71.90 in 1990; 71.19 in 2021) and high SDI regions the lowest (40.28 to 51.55). Sex disparities widened as female QCI rose by 14.0

Gamma-band neural oscillations are critically involved in working memory and are disrupted in schizophrenia. Transcranial alternating current stimulation (tACS) at gamma frequency is a promising noninvasive approach to restore oscillatory synchrony and enhance cognition. This randomized, double-blind trial tested whether 40 Hz tACS targeting frontoparietal networks modulates gamma-band activity and connectivity during working memory, and whether these electrophysiological changes relate to cognition in schizophrenia. Patients with schizophrenia (n = 33) were randomized to 10 sessions of active or sham tACS over the left dorsolateral prefrontal cortex (F3) and right parietal cortex (P4), with cognition assessed using standardized neurocognitive measures (MATRICS Consensus Cognitive Battery, MCCB) and an n-back working-memory task. EEG during an n-back task was recorded pre- and post-intervention to assess gamma power, phase-locking value (PLV), and phase-amplitude coupling (PAC). A significant Group × Time interaction indicated that 1-back minus 0-back PLV increased in the active group but not in sham (P = 0.048, Cohen's d = 1.08). For PAC, a significant interaction showed that delta-high gamma coupling at F3 remained stable in the active group but declined in sham (P = 0.036, Cohen's d = 1.00). There was no significant correlation with n-back measures of working memory, but an exploratory significant finding linking this modulation to visual learning at 4-week follow-up. No significant group differences were found for MCCB total scores; however, a significant Group × Time interaction emerged for 0-back accuracy during EEG recording (P = 0.029, Cohen's d = 1.19). These findings demonstrate that 40 Hz tACS can enhance and preserve gamma synchrony in frontoparietal circuits during working memory. The maintained delta-gamma coupling in our exploratory findings on visual learning may suggest a relationship to sustained improvements in cognition over time, but needs additional confirmation.

The safety of antidepressants in bipolar disorder (BD) remains controversial, particularly regarding the risk of behavioral activation and worsening Non-Suicidal Self-Injury (NSSI). This multi-center retrospective cohort study included 575 patients with BD from 15 medical centers in China to evaluate the association between antidepressant use and NSSI frequency, suicidal ideation (SI), and suicidal behavior (SB) over a one-year period. Multivariable logistic regression revealed that antidepressant use was associated with higher odds of the worsening of NSSI (OR=1.90, p = 0.030), as well as the presence of SI (OR=1.70, p = 0.016) and SB (OR=1.80, p = 0.010). In exploratory subgroup analyses, point estimates were larger in patients with low household income (OR=2.74, p = 0.038) and non-depressive dominant polarity (OR=7.05, p < 0.001), and the association reached significance only in patients not receiving concurrent lithium (OR=2.29, p = 0.032) but not in lithium-treated patients (OR=1.38, p = 0.462); however, none of the formal interaction tests was statistically significant (all interaction p > 0.05). These findings indicate that antidepressant use in BD is associated with higher odds of worsening NSSI and suicidality. The observed subgroup differences did not reach statistical significance on interaction testing and should be regarded as hypothesis-generating.

The central resident immune system, commonly known as the glial system, comprises various glial cells that play a critical role in neuropsychiatric disorders. However, a systematic review exploring the relationships between the life cycles and daily rhythms of these immune cells and the pathological features of neuropsychiatric disorders is lacking. These immune cells exhibit unique developmental origins and circadian characteristics, resulting in rhythmic variations in functions such as phagocytosis, immune clearance, neurogenesis, and neurotransmitter recycling. These properties are crucial for understanding the pathological mechanisms underlying developmental disorders like major depressive disorder, autism spectrum disorder, and schizophrenia, as well as age-related conditions such as Alzheimer’s and Parkinson’s diseases. The daily rhythms of these immune cells correlate with diurnal variations in emotion, cognition, and motor function, involving shared processes like oxidative stress and neuroinflammation. This article systematically reviews the composition, life cycle changes, and circadian characteristics of central immune cells, highlighting their roles in neuropsychiatric diseases.

Bipolar disorder (BD) manifests both genetic predispositions and brain imaging abnormalities. Genetic analyses provide a powerful approach to disentangling the associations between BD and brain imaging-derived phenotype (IDP). However, in East Asian (EAS) populations, the genetic basis of these neuroimaging changes and their clinical relevance remains underexplored due to limited integrative evidence. We investigated the genetic architecture and clinical relevance of BD and brain IDPs in East Asian populations using individual-level data from the Chinese Bipolar Disorder (CN-BD) cohort (N = 3,920), summary statistics of BD from the Psychiatric Genomics Consortium (PGC) (N = 16,915) and of IDPs from the CHIMGEN Consortium (N = 7,058), and genotype and clinical data from the Chinese Longitudinal and Systematic Study of Bipolar Disorder cohort (CLASS-BD). We employed genetic correlation (GNOVA), multi-trait meta-analysis (MTAG, CPASSOC), and ancestry-specific fine-mapping (MESuSiE) to identify shared genetic loci. Functional annotation, protein association (SMR, BLISS), and spatiotemporal expression trajectory analyses were conducted. Bidirectional Mendelian Randomization (MR) assessed causal relationships, and polygenic risk score (PRS)-PheWAS explored phenome-level associations with clinical traits. Our multi-trait meta-analysis identified 11 pleiotropic single nucleotide variants (SNVs) implicated in BD-IDP pairs, showing convergent enrichment in lipid metabolism and neurotransmitter biosynthesis pathways. Fine-mapping revealed an EAS-specific causal signal (rs7941324 near RPS27P20), which demonstrated high posterior inclusion probability in EAS but not European ancestries, and colocalized with BD and specific brain alterations. MR revealed 27 IDP-to-BD and 35 BD-to-IDP causal relationships, distinguishing structural changes of the parietal and temporal gyri as markers preceding BD onset and the right precuneus area as a state marker that followed BD onset. Clinically, BD-IDP polygenic risk scores were significantly associated with depressive symptoms, anxiety, and suicide risk. Mediation analysis further indicated that depressive and anxiety symptoms statistically mediated the relationship between BD-IDP PRS and suicide risk. Moreover, PRS-PheWAS identified 135 significant associations with metabolic phenotypes, highlighting a psycho-metabolic nexus in BD. Our findings reveal associations between polygenicity and distinct BD brain patterns and clinical profiles. We identified ancestry-specific causal variants and bidirectional BD-IDP relationships. PRS associations with depressive symptoms, anxiety, suicide risk, and metabolic phenotypes highlight precision medicine potential for BD.

Inflammatory bowel disease (IBD) is frequently complicated by comorbid depression and anxiety, creating a therapeutic vicious cycle that is currently managed with fragmented, non-integrated treatments. Here, we introduce a colon-targeted, pH-responsive hydrogel microalgal system (CV@PA-gel) designed for synergistic treatment of IBD and its psychiatric comorbidities. This engineered platform co-encapsulates the natural neuroprotective agent paeoniflorin (PA) and the gut-microbiota modulator Chlorella vulgaris (CV) within a genipin-crosslinked carboxymethyl chitosan/sodium alginate matrix. The CV@PA-gel exhibits minimal drug release in the stomach but provides sustained, targeted release in the colon, significantly enhancing the oral bioavailability and intestinal retention of its cargo. In a murine model of chronic colitis, CV@PA-gel outperforms free PA by more effectively restoring gut barrier integrity, ameliorating systemic and hippocampal inflammation, and rescuing anxiety-, depressive-like, and cognitive behaviors. Mechanistically, our findings suggest that gut-derived systemic inflammation is associated with complement C3 activation and subsequent microglia-mediated polarization of neurotoxic A1 astrocytes in the hippocampus, leading to synaptic loss. PA, delivered precisely by the hydrogel, directly suppresses this cascade by inhibiting microglial release of key A1-inducing factors. Our work establishes a versatile biomaterials strategy for disrupting the gut-brain axis pathology, offering a powerful platform for the simultaneous management of intestinal and neuropsychiatric disorders.

BACKGROUND:Cardiovascular-Kidney-Metabolic (CKM) syndrome assesses the interconnections among metabolic, kidney, and cardiovascular diseases, rendering significant prognostic value for age-related chronic diseases and mortality. We aimed to investigate the effects of CKM syndrome on transitions between healthy status, mental disorders, and dementia and evaluate the potential mediating role of a CKM-related metabolomic signature in these associations. METHODS:This prospective longitudinal study used UK Biobank data from 375,203 midlife and older adults at baseline and 188,018 with metabolomic information. CKM was staged from 0 to 4. Mental disorders and dementia were identified via ICD-10. Multi-state models analyzed the impact of CKM on transitions from healthy status to mental disorders and dementia. Competing risk (death) models assessed the associations of CKM with specific mental disorders and dementia. Mediation role of CKM-related metabolomic signature was evaluated. RESULTS:We show that per-stage CKM increase elevates hazards of transitioning from healthy to mental disorders (HR = 1.24[1.22-1.26]) and subsequently to dementia (HR = 1.38[1.21-1.58]), or directly to dementia (HR = 1.27[1.21-1.33]). Worsening CKM stages are associated with bipolar, depressive, and anxiety disorders; whilst only advanced stages (3/4) associated with all dementia types. The CKM metabolomic signature mediates 34.9% and 8.1% of associations of CKM with pre-dementia mental disorders and dementia, respectively. CONCLUSIONS:CKM syndrome is associated with pre-dementia mental disorders and dementia, emphasizing the need for regular monitoring and early intervention to manage CKM progression and reduce geriatric neuropsychiatric disturbances.

Bipolar depression (BD-D) in adolescents and young adults is associated with disrupted neural circuits underlying affective regulation, particularly those involving the orbitofrontal cortex (OFC). Despite the promise of repetitive transcranial magnetic stimulation (rTMS) as a non-invasive intervention, effective targeting strategies that engage these dysfunctional circuits remain insufficiently explored. This study investigates the clinical efficacy of a novel rTMS protocol targeting the primary visual cortex (V1) node of the V1-OFC functional circuit in adolescents and young adults with BD-D. We conducted a double-blind randomized controlled trial. Fifty-two adolescents and young adults BD-D participants were randomized to active rTMS group (10 Hz, 100

Bipolar disorder (BD) research confronts challenges: blood-based biomarkers offer limited insights into neurobiology, while cerebrospinal fluid (CSF) collection is clinically unusual. Linking genetic susceptibility to pathophysiology remains crucial for biologically informed risk stratification. We integrated cohort data and genome-wide association study (GWAS) summary statistics: the largest BD meta-analysis, CSF multi-omics profiles including 3107 proteomic and 2602 metabolomic participants, and a validation cohort of 247,834 UK Biobank participants. Unsupervised clustering revealed four single-nucleotide variant (SNV) clusters: metabolic-imbalance, metabolic-active, human leukocyte antigen (HLA)+immune, and HLA-immune. These clusters exhibited distinct clinical features, with the metabolic-imbalance cluster showing multi-directional associations with 21 psychiatric traits, while the HLA-immune cluster was associated with emotional instability in BD patients (odds ratio [OR] = 1.14, p = 0.027). The optimized multimodal cluster-specific polygenic risk scores (PRS) model significantly outperformed clinical-only prediction factors (C-index = 0.77), with the metabolic-imbalance PRS contributing a 22.6% incremental predictive value (hazard ratio [HR] = 1.23, 95% CI: 1.04-1.45, p = 0.016). Risk reclassification showed an 84% reduction in false-negative rates in the low-risk subgroup, identifying a high-risk layer with a 17.6-fold increased BD incidence. Altogether, genetically informed substitutes for CSF biomarkers emerged as a scalable tool for risk prediction, overcoming the barriers of CSF collection while capturing neurobiological heterogeneity.

Emerging evidence underscores bidirectional communication along the microbiota-gut-brain axis in neuropsychiatric disorders. However, the field lacks dedicated metagenomic resources with standardized phenotyping for these conditions. Existing single-cohort studies face inherent limitations due to restricted sample sizes, confounding heterogeneity, and methodological fragmentation, compromising reproducibility and mechanistic insights. To overcome these challenges, we constructed the Gut Microbiome in Multinational Integrated Neuropsychiatric Disorders (GutMIND) database, a comprehensive resource integrating shotgun metagenomic data with harmonized metadata. Adhering to a standardized preprocessing protocol and rigorous quality control workflow, this dataset represents the largest gut-brain microbiome repository to date, encompassing 31 studies across 12 countries (n = 3,492) spanning 14 neuropsychiatric conditions. Utilizing this dataset, we characterized microbial community heterogeneity, which was significantly elevated in patients compared to healthy controls. Subsequently, we developed a computational framework, MetaClassifier, enabling the diagnosis of neuropsychiatric disorders and the identification of microbial biomarkers. Employing a comprehensive two-stage validation strategy, we first assessed the model utilizing taxonomic abundance profiles via nested cross-validation in the high-quality discovery cohort (n = 2,734), achieving a mean AUROC of 0.69 (range: 0.55-0.78) across 8 disorders. Its robustness was further confirmed in an independent platform-extended validation cohort (n = 400), yielding a mean AUROC of 0.71 (range: 0.60-0.76). We also developed the Microbial Gut-Brain Axis Health Index (MGBA-HI), which effectively distinguished neuropsychiatric status in both the high-quality cohort and the platform-extended cohort. Furthermore, integrative analysis of health-abundant species, index-derived biomarkers, and ecological prevalence, we identified 9 core neuropsychiatric-protective microbiota. These species predominantly exhibited metabolic capacities linked to glutamate synthesis and acetate production. Building upon this, the GutMIND framework ensures robust cross-cohort comparability while minimizing technical heterogeneity, thereby enhancing inferential rigor in gut microbiome-neuropsychiatry research. Notably, the MetaClassifier, MGBA-HI, and core microbiota hold translational potential for developing microbiome-based prognostic tools and personalized therapeutic strategies in neuropsychiatric disorders. The source code and usage instructions for MetaClassifier are accessible at https://github.com/juyanmei/MetaClassifier.

BACKGROUND:Bipolar disorder is a severe mental disorder affecting millions worldwide, necessitating comprehensive policies and interventions. AIMS:To provide assessment of global inequalities in the burden of bipolar disorder and their projected trajectories to 2050. METHODS:Global Burden of Disease 2021 data from 204 countries and territories were analyzed, stratified by age, gender, and Socio-demographic Index (SDI) quintiles. Age-standardized prevalence (ASPR), incidence (ASIR), and years lived with disability (ASR YLD) per 100,000 population were calculated. Inequalities were assessed using the slope index of inequality (SII) and concentration index (CI), and ARIMA models were applied to project trends to 2050. RESULTS:From 1990 to 2021, global incidence of BD increased, while prevalence and years lived with disability (YLDs) remained relatively stable (ASPR: 453.7 [95% UI: 381.6-540.8] to 454.6 [95% UI: 377.9-545.8]). Females consistently had higher prevalence than males (474.2 vs. 435.0 per 100,000 in 2021). High-SDI regions reported the highest rates, with Australasia reaching 1110.8 (95% UI: 940.3-1305.9). The SII for incidence rose slightly (10.87-11.38), while the CI declined (0.096-0.012), indicating increasing absolute but decreasing relative inequalities. Projections suggest a rising global burden, with female prevalence remaining higher and incidence rates converging between genders (global ASIR: 33.8 per 100,000). CONCLUSION:Global inequalities in bipolar disorder persist, disproportionately affecting females and high-SDI regions. Projected trends indicate an increasing burden with a narrowing gender gap in incidence, emphasizing the need for targeted interventions and further research on long-term impacts, including the effects of COVID-19.

In recent years, the promotion of multidisciplinary care and the heightened focus on patients' physical and mental well-being have sparked increased research interest in the mental health burden associated with ophthalmic diseases. In response, we assembled a multidisciplinary team of ophthalmologists, psychiatrists, neurobiologists, and computer scientists to create a systematic and forward-looking overview aimed at guiding future research in both fundamentals of life sciences and brain-computer interface as well as clinical practice. This overview centers on mood disorders, the most prevalent psychiatric conditions among this population. We integrate evidence on the neural, humoral, and inflammatory mechanisms that connect eye disease to mood dysregulation, while also detailing the ocular manifestations typical of mood-disordered patients, including their unique features and underlying mechanisms. Furthermore, we catalog current and emerging ophthalmic and psychiatric diagnostic tools and therapeutic strategies. Finally, we propose a comprehensive multidisciplinary framework for screening, treatment, patient education, and long-term follow-up, providing researchers and clinicians with an evidence-based resource for integrated care.

Objective The literature on large-scale studies of Chinese patients with adolescent-onset bipolar disorder (adolescent-onset BD) was limited. Based on the analysis of the National Bipolar Mania Pathway Survey (BIPAS) Phase II data, we examined the demographic and clinical characteristics of adults with adolescent-onset BD. Methods Among 899 participants diagnosed with BD from 20 mental health services, demographics and clinical data were collected at screening. Comparisons were made using chi-square (or Fisher's exact) tests and ANOVA. Multivariate logistic regression identified independent factors for adolescent-onset BD, and a CHAID decision tree analysis (SPSS) was constructed to detect risk factors. Results In the sample, 360 (40%) had adolescent-onset BD and 539 (60%) adult-onset BD. Significant differences between the two groups were observed in current age, number of episodes, years of education, gender, age of onset, education level, marital status, occupation, comorbid chronic physical illness, and first episode type. Stratified analysis also revealed significant differences between adolescent-onset BD I and BD II. Multivariate logistic regression identified younger onset age, more frequent episodes, lower education level, marital status, occupation, first episode type, and prior hospitalization as independent factors for adolescent-onset BD. The decision tree model selected current age as the first splitting variable, followed by occupation and marital status as the second, and years of education and prior hospitalization as the third. Conclusions Adolescent-onset BD exhibits distinct demographic and clinical features compared to adult-onset BD. Early recognition and tailored treatment strategies may improve prognosis and outcomes in this population.

Background Disordered sleep constitutes a global public health challenge and is closely associated with metabolic dysfunction, demanding a comprehensive quantitative assessment across the full spectrum of disorders and metabolic profiles. Methods A combined meta-analysis was performed including 45 eligible studies published before October 15th, 2025, identified through systematic searches of six publication database. Subgroup analysis additionally examined the influence of four parameters. The PROSPERO registration number is CRD420251168186. Results Compared to controlled individuals, participants with sleep apnea showed significantly elevated body mass index (SMD = 0.58, 95% CI 0.35–0.82), fasting glucose (SMD = 0.40, 95% CI 0.26–0.55), LDL (SMD = 0.25, 95% CI 0.08–0.41), and total cholesterol (SMD = 0.18, 95% CI 0.02–0.35), with particularly pronounced effects in elder individuals in Asian and North American populations. Sleep extension demonstrated protective effects on fasting glucose (RR = -1.16, 95%CI -1.84–0.48) and LDL (RR = 0.25, 95% CI 0.08–0.41). Little difference was observed between states before and after sleep intervention, while leptin levels increased significantly among male-dominant participants with sleep intervention (RR = 0.90, 95% CI 0.05–1.76). Effects of narcolepsy and idiopathic hypersomnia on metabolism remained uncertain. Significant heterogeneity, publication bias, and high bias of quality were determined in several studies, yet few changes after elimination and re-analyses were observed, possibly due to the general robustness and limited number of included studies. Conclusion Disordered sleep could adversely affect multiple metabolic indicators, and standardized, multi-ethnic, and multi-center cohorts are in need to further address such effects and detailed mechanism.

Alzheimer's disease (AD) is among the main causes of cognitive impairment,memory loss,and de-mentia,particularly in old adults. It has been listed as one of the most expensive,lethal,and burdening dis-eases of the 21st century and develops with the pro-cess of aging worldwide (Scheltens et al.,2021). Cur-rently,it is widely acknowledged that the typical patho-genesis of AD involves the deposition of amyloid-β(Aβ) and Tau proteins in the cerebral parenchyma and vasculature,intraneuronal neurofibrillary tangles,and the gradual degeneration of synapses (Scheltens et al.,2016;Rostagno,2022). According to several hypothe-ses,abnormalities and dysfunctions in vascular struc-ture,mitochondrial metabolism,oxidative stress,glu-cose utilization,and neuroinflammation are con-sidered fundamental for AD pathology (Scheltens et al.,2016).

Neuroinflammation may disrupt neurotransmitter signaling. This study investigated whether gut microbiota-induced neuroinflammation can regulate glutamate pathways in bipolar disorder (BD). Fecal microbiota transplantation (FMT) was performed to observe behavioral changes in the antibiotic-treated C57BL/6J male mouse model of bipolar depression. Gut microbial structure, circulating, and prefrontal levels of inflammatory factors, microglial activation, and transcription levels of N-methyl-d-aspartate receptor (NMDAR) and α-amino-3-hydroxy-5-methyl-4 isoxazole receptor (AMPAR) genes were measured in the “BD” and control mice. Furthermore, the effects of interleukin-1 (IL-1) receptor antagonist (IL-1RA) on the glutamate pathways were assessed. Compared with the control mice, “BD” mice displayed depression-like behaviors, with a lower diversity of gut bacteria and a decreased abundance of certain species. In addition, “BD” mice showed increased levels of inflammatory factors (e.g., IL-1β) in the serum and prefrontal cortex, microglial activation, and changes in the messenger RNA (mRNA) levels of NMDAR and AMPAR. Treatment with IL-1RA partially reversed the behavioral patterns, neuroinflammation, and transcription levels of glutamate receptors. The findings suggest that gut microbiota may influence glutamate receptor gene expression via an IL-1β-dependent pathway in a mouse model of BD, potentially contributing to neuroinflammatory mechanisms relevant to this disorder.