Schizophrenia (SCZ) is a neurodevelopmental disorder characterized by heterogeneous symptoms and multifactorial etiologies. Medial ganglionic eminence (MGE) spheroids generated from first-episode schizophrenia (FES) patients revealed accelerated neurodevelopmental trajectories and enhanced hypoxia responses via single-cell transcriptomics. Notably, FES patient-derived MGE spheroids exhibited defective interneuron migration, disrupted synaptic ultrastructure, and diminished network synchronization. To establish causal links, the gestational hypoxia mouse model recapitulated key pathologies, including reduced progenitor proliferation, abbreviated cell cycles, mismatched interneuron subtypes, and schizophrenia-like behavioral deficits in offspring. Critically, maternal administration of N-acetylcysteine (NAC) restored redox homeostasis and rescued both cellular and behavioral phenotypes. Collectively, these results demonstrate that developmental redox disruption directly impairs GABAergic circuit assembly, while supporting targeted antioxidant pharmacotherapy during gestation as a translatable strategy to mitigate neurodevelopmental risk.

Fixel-based analysis (FBA) is an advanced diffusion imaging method that enables the direct estimation of white matter microstructural properties beyond the limitations of traditional diffusion tensor imaging (DTI). Despite its potential, FBA has been rarely applied in schizophrenia research, and its value in providing complementary information to conventional tensor-based approaches remains to be fully established. In this study, we investigated white matter abnormalities of treatment-naïve, first-episode schizophrenia (FES) patients using both FBA and tensor-based method, and examined the concordance between the two approaches to better characterize the nature of white matter pathology in early SZ. MRI data were acquired from 94 treatment-naïve FES patients and 114 healthy controls (HCs). Fractional anisotropy (FA) and mean diffusivity (MD) were calculated using a conventional tensor-based method. In parallel, fibre density (FD), fibre-bundle cross-section (FC), and their combined metric (FDC) were estimate with FBA. White matter was segmented into 72 anatomically defined tracts based on fibre tracking. Between-group comparisons were conducted using a multivariate general linear model (GLM) to assess differences across diffusion metrics. Using the tensor-based method, six white matter tracts exhibited significantly altered FA, while 34 tracts showed significantly increased MD in FES patients compared to HCs (all t-values > 2.34 or t-values < -2.36, all FDR-p < 0.05). In contrast, FBA revealed more widespread abnormalities: 46 tracts showed significantly reduced FD, 29 tracts showed significantly reduced FC, and 52 tracts showed significantly reduced FDC (all t-values < -2.29, all FDR-p < 0.05). Notably, all tracts with significantly reduced FC metrics also demonstrated corresponding FDC reductions. No significant correlation was observed between any diffusion metrics and clinical characteristics (all FDR-p ˃ 0.05). This study highlights the remarkable advantages of the FBA in detecting WM microstructural abnormalities in individuals with FES.

BACKGROUND:Biological aging may contribute to the pathogenesis of major depressive disorder (MDD). However, whether and how peripheral transcriptomic aging increases the risk of MDD onset remains unclear. METHODS:Transcriptomic age was estimated using peripheral blood RNA sequencing data from 141 individuals with MDD and 134 healthy controls. The residuals of transcriptomic age regressed on chronological age were calculated to indicate transcriptomic aging acceleration. Enrichment analysis was performed to explore potential biological mechanisms underlying aging- and MDD-associated transcriptomic alterations. Associations between transcriptomic aging and clinical, neurocognitive, environmental, genetic, and neuroimaging phenotypes were examined. RESULTS:Participants with MDD exhibited significantly accelerated transcriptomic aging both before (t = 2.06, P = 0.040) and after adjusting for chronological age and sex (t = 3.72, P < 0.001). Enrichment analysis revealed shared terms in innate immune-related inflammation, ribosome biogenesis, and mitochondrial energy metabolism, while telomere length maintenance was specifically enriched in aging but not in MDD. No significant associations were found between transcriptomic aging and clinical symptoms, neurocognitive functions, childhood trauma exposure, or polygenic risk score. Neuroimaging analyses demonstrated that transcriptomic aging was associated with structural (t = -3.30, P = 0.001) and functional (t = 2.64, P = 0.009) alterations in the right insular cortex. Further analyses indicated that insular abnormalities partially mediated the impact of transcriptomic aging on MDD vulnerability. CONCLUSIONS:Transcriptomic aging may represent a novel risk factor for MDD. Disruption in the insular cortex may serve as a critical neural substrate through which accelerated transcriptomic aging increases vulnerability to MDD.

Background: The glymphatic system, essential for brain waste clearance and neuroimmune regulation, remains underexplored in the context of bipolar disorder (BD) among young populations. Methods: Using diffusion tensor image analysis along the perivascular space (DTI-ALPS), we compared ALPS indices derived from the conventional FSL-based (cFSL) pipeline with those from the individualized ALPS (iALPS) pipeline. A cohort of young adults comprising 77 individuals with BD and 289 healthy controls was analyzed to evaluate methodological consistency and to identify disorder-specific alterations in glymphatic function. Results: The two pipelines showed only moderate agreement (Lin's concordance correlation coefficient = 0.52-0.60), suggesting that differences in ROI placement strategies significantly affect ALPS estimation. While the cFSL pipeline detected no group differences, the iALPS pipeline identified a trend-level reduction in ALPS index in patients with BD during depressive episodes, particularly in the right hemisphere (p = 0.036, uncorrected, FDR-adjusted p = 0.071). No significant glymphatic alterations were observed in individuals with early-stage BD. Conclusions: These findings suggest that glymphatic dysfunction in psychiatric disorders may be phase-specific on illness. The use of individualized and automated analytical strategies, such as the iALPS pipeline, appears to enhance sensitivity to subtle, state-related brain changes that conventional methods may overlook. This methodological advancement provides a more biologically informed framework for future large-scale and longitudinal studies aimed at elucidating the role of glymphatic function in the pathophysiology of psychiatric disorders.

Background:Although morphological abnormalities of the corpus callosum (CC) have been reported in schizophrenia, findings across studies have been inconsistent. We systematically examined whether these morphological alterations are influenced by age. Methods:A total of 151 individuals with treatment-naïve first-episode schizophrenia (FES) and 278 healthy controls were included. T1-weighted structural MRI scans were used to segment the CC on the midsagittal plane into 100 equidistant points, and CC thickness was estimated at each point. To determine whether CC thickness abnormalities associated with schizophrenia were moderated by age, we applied the Johnson-Neyman technique. Additionally, we investigated the relationship between age-dependent CC thickness abnormalities and clinical symptoms using partial least-squares correlation analysis. Results:Abnormal CC thickness was observed in individuals with treatment-naïve FES, specifically within the rostral body, anterior midbody, isthmus, and splenium. These regions were thinner in younger patients compared with healthy controls but appeared thicker in older patients. Furthermore, increased CC thickness in older patients was associated with greater clinical symptom severity, whereas this association was not observed in younger patients. Conclusions:Our findings demonstrate that CC thickness abnormalities in treatment-naïve FES are age-dependent. The relationship between CC thickness and symptom severity also varies as a function of age. These results suggest that the CC may represent a critical biological target for age-sensitive, individualized therapeutic interventions in schizophrenia.

AIMS:Perivascular water diffusivity, as assessed by diffusion tensor imaging along the perivascular space (DTI-ALPS) index, serves as an indirect imaging marker potentially reflecting glymphatic function. This study investigated its associations with childhood trauma and major depressive disorder (MDD) and explored the related biological mechanisms. METHODS:We enrolled 86 patients with MDD and 74 matched healthy controls (HCs). Childhood trauma, cognitive performance, and peripheral blood transcriptomic profiles were evaluated. Regression and exploratory mediation analyses were performed. RESULTS:MDD patients exhibited lower DTI-ALPS index (F = 15.716, p < 0.001), and poor attention, processing speed, visual memory and executive function performance (q < 0.05) than HCs. In the MDD group, the DTI-ALPS index was negatively correlated with CTQ score (r = -0.251, p = 0.02), spatial working memory strategy (SWM_Stra; r = -0.272, p = 0.013) and rapid visual information processing mean latency (RVP_ML; r = -0.232, p = 0.036). The yellow, turquoise, and blue transcriptomic modules were associated with MDD and enriched in immune-inflammatory, intracellular trafficking, and mitochondrial metabolic pathways, whereas the magenta module was associated with the DTI-ALPS index (r = -0.25, p = 0.022) and enriched in hemostasis, platelet activation, and vascular integrity pathway. Mediation analyses revealed that the DTI-ALPS index partially mediated the relationships between childhood trauma and the diagnosis of MDD (indirect effect: coefficient = 0.0001, p = 0.0372). CONCLUSIONS:Altered DTI-ALPS may reflect links between childhood trauma, cognitive dysfunction, and vascular integrity, but as an indirect glymphatic marker, these findings require cautious interpretation.

BACKGROUND AND HYPOTHESIS:Shared clinical features and genetic factors in schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD) have led to the hypothesis of common pathophysiological mechanisms. This study aims to elucidate aberrant transdiagnostic structural covariance patterns across these disorders employing a multivariate analytical approach. STUDY DESIGN:Structural magnetic resonance imaging data were acquired from a sample of 704 subjects, comprising 244 healthy controls, 119 first-episode treatment-naïve SCZ individuals, 159 BD individuals, and 182 treatment-naïve MDD individuals. Seed-based partial least squares correlation analysis was applied to construct structural covariance networks (SCNs) across 6 predefined functional networks: the default mode network (DMN), dorsal attention network (DAN), frontoparietal control network (FPCN), somatomotor network (SMN), ventral attention network (VAN), and visual network. Network seeds were selected based on functional network definitions. Spatial distributions of SCNs were calculated, and individual network integrity indices were derived as measures of SCN strength. Group comparisons of network integrity were performed using multiple t-tests to identify network-specific alterations across the diagnostic groups. STUDY RESULTS:Structural covariance patterns exhibited spatial distributions akin to those of functional networks. Network integrity showed common reductions across all 3 disorders in DMN, DAN, and FPCN, while BD showed specific reductions in the SMN, and both BD and MDD showed reductions in the VAN. Furthermore, there was a significant correlation between individualized network integrity and clinical and cognitive manifestations. CONCLUSIONS:Our results highlight the potential of the integrity of SCNs as transdiagnostic biomarkers.

Oxylipins are bioactive lipid metabolites that may bridge immune dysregulation and depressive symptomatology. However, systematic alterations of oxylipin networks in MDD and their therapeutic implications remain unclear. In this study, a plasma oxylipidomics analysis was performed in 154 patients with MDD and 134 healthy controls (HC). Findings were validated in a chronic restraint stress (CRS) mouse model of depression, followed by pharmacological inhibition of lipoxygenase (LOX) pathways using ML-355 and zileuton. Behavioral tests and immunofluorescence analysis of microglial activation were conducted to assess therapeutic effects. MDD patients exhibited significantly elevated plasma levels of 22 oxylipins compared to HCs, including oxylipins derived from lipoxygenase (LOX), cytochrome P450 (CYP450), cyclooxygenase (COX), and non-enzymatic pathways. Notably, non-responders to antidepressant treatment displayed higher baseline levels of 20 oxylipins than both responders and HCs, and baseline oxylipin levels negatively correlated with Hamilton Depression Rating Scale (HAMD) score reduction rates. In CRS mice, the LOX pathway was activated, as evidenced by increased LOX levels in the blood and brain, as well as elevated plasma levels of LOX-derived oxylipins. Pharmacological inhibition of 12-lipoxygenase (12-LOX) with ML-355 significantly alleviated depressive-like and anxiety-like behaviors and reversed stress-induced microglial activation in the hippocampus and medial prefrontal cortex. The 5-LOX inhibitor zileuton reduced microglial activation in a region-dependent manner but did not significantly improve behavioral outcomes. These findings reveal elevated LOX-derived oxylipins as potential biomarkers predicting poor antidepressant response in MDD. Targeting the LOX pathway, particularly 12-LOX, represents a promising therapeutic strategy for depression by ameliorating neuroinflammatory processes.

Suicidal ideation (SI) in major depressive disorder (MDD) presents a serious clinical concern, yet its underlying biological mechanisms remain poorly understood. Using an exploratory, multimodal design, this study included 98 MDD patients with SI (MDD_SI), 61 without SI (MDD_nSI), and 233 healthy controls (HC), and collected functional MRI and peripheral blood transcriptomic data. Network-based statistics of resting-state functional connectivity (FC) identified brain network differences among the three groups, followed by graph-theoretical analysis of derived hub regions. Differential module connectivity (MDC) analyses of blood transcriptomic modules were performed to identify group-level differences, and exploratory correlation analyses were performed to examine associations between brain network topology and transcriptomic alterations. A functional network centered on the caudal temporal thalamus (cTtha) showed significant group differences (MDD_SI > MDD_nSI >HC; p < 0.001), with increased nodal efficiency of the left cTtha in both the MDD_SI (p = 0.043) and MDD_nSI (p < 0.001) groups compared with the HC group. Modules differentiating MDD_SI from MDD_nSI were enriched in antiviral immune responses and epigenetic regulation (darkred, MDC = 1.508, p = 0.04; yellow, MDC = 0.93, p = 0.01), while SI-specific alterations involved mitochondrial energy dysregulation and nSI-specific alterations involved vascular and translational pathways. Notably, one immune-related gene module was significantly correlated with left cTtha betweenness centrality (r = 0.274, p = 0.021), degree centrality (r = 0.262, p = 0.027), and nodal efficiency (r = 0.235, p = 0.048) in the MDD_SI group. This study indicates that MDD and SI are associated with cTtha-centered functional network alterations and peripheral transcriptomic dysregulation, and that, within MDD, SI may show a potential link between cTtha-centered brain network disruption and immune transcriptomic dysregulation. These results should be interpreted cautiously given the exploratory design, binary SI assessment, and modest effect sizes.

Sleep disturbances are prevalent in bipolar disorder (BD) patients, and the circadian locomotor output cycles kaput (Clock) gene plays a significant role in this process. The role of microglia (the brain-resident immune cells) in mediating this process remains uncertain. In this study, our findings showed that sleep loss induces mania-like behavior, microglial loss, and time-dependent gene expression changes. Moreover, diurnal oscillations in circadian rhythm-associated and inflammation-related gene expression in the mouse prefrontal cortex (PFC) were altered following sleep deprivation (SD). Further correlative analysis revealed correlations in gene expression between marker genes for microglia and Clock genes. In addition, the Clock mutation induces mania-like behavior, inhibition of neural activity, and microglial loss. Transcriptomic analysis revealed significant alterations in inflammatory pathways, circadian rhythm-related pathways, and the major histocompatibility protein complex in ClockΔ19 mice. Subsequent correlative analysis demonstrated significant correlations in gene expression among inflammation-, circadian rhythm-, and synapse-related genes within the PFC and hypothalamus of both male and female ClockΔ19 mice. In conclusion, our findings demonstrated behavioral, cellular, and molecular changes in SD-induced mice and Clock-mutant mice models. Microglia and CLOCK were associated with mania-like behaviors. Future research will likely focus on microglia-targeted approaches for the diagnosis and treatment of BD.

Background: Childhood maltreatment (CM) is a significant risk factor for major depressive disorder (MDD), yet the underlying biological mechanisms remain unclear. This study aimed to investigate brain functional networks and peripheral transcriptomics in patients with MDD who have a history of CM. Methods: Functional imaging data were collected and network-based statistics were used to identify differences in functional networks among MDD patients with CM (MDD_CM, n = 78), MDD patients without CM (MDD_nCM, n = 61), and healthy controls (HC, n = 126). Additionally, blood transcriptional data were clustered into co-expression modules, and module differential connectivity analysis was utilized to assess variations in gene co-expression network modules among the groups. Results: The results revealed a significant difference in an inferior occipital gyrus-centered functional network among the three groups. Furthermore, eight gene co-expression modules differed among the groups and were enriched in multiple branches related to immune responses or metabolic processes. Notably, a module enriched in type I interferon-related signaling pathways demonstrated a significant correlation with the disrupted network in the MDD_nCM group. Moreover, multiple immune-related gene modules were found to be significantly correlated with sleep disturbances in MDD_CM patients. Conclusions: Dysregulation of an inferior occipital gyrus-centered functional network and immune-related transcriptomic alterations significantly associate with the pathophysiology of MDD_CM.

The risk of suicide in patients with major depressive disorder (MDD) poses a major concern, with studies suggesting that genetics may be a contributing factor. Although there are many transcriptomic studies on postmortem brain tissue related to suicidal behavior, the blood transcriptional mechanisms of suicidal ideation (SI) remain unknown. This study utilized a weighted gene coexpression network analysis (WGCNA) approach to investigate the associations between gene coexpression modules and SI in individuals with MDD using peripheral blood RNA-seq data from 75 MDD patients with SI (MDD_SI), 82 MDD patients without SI (MDD_nSI), and 149 healthy controls (HC). An ANCOVA was conducted to assess differences in gene coexpression modules among groups, with age and sex included as covariates. The gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) databases were used to annotate module functions. Results indicated that the magenta module (associated with RNA splicing processes) differentiated MDD_SI from MDD_nSI (p = 0.021), while the green module (related to immune and inflammatory responses) distinguished MDD_SI from HC (p = 0.004). Additionally, three modules showed differences between MDD_nSI and HC: magenta (p = 0.009), brown (related to innate immunity and mitochondrial metabolism; p = 0.001), and turquoise (associated with energy metabolism and neurodegeneration; p = 0.005). Our findings highlight that gene expression regulation, immune response, and inflammation may be linked to SI in patients with MDD, while pathways associated with innate immunity, energy metabolism, mitochondrial function, and neurodegeneration appear to be more broadly related to MDD.

BACKGROUND:ClockΔ19 mice demonstrate behavioral characteristics and neurobiological changes that closely resemble those observed in bipolar disorder (BD). Notably, abnormalities in the hippocampus have been observed in patients with BD, yet direct molecular investigation of human hippocampal tissue remains challenging due to its limited accessibility. METHODS:To model BD, ClockΔ19 mice were employed. Weighted gene co-expression network analysis (WGCNA) was utilized to identify mutation-related modules, and changes in cell populations were determined using the computational deconvolution CIBERSORTx. Furthermore, GeneMANIA and protein-protein interactions (PPIs) were leveraged to construct a comprehensive interaction network. RESULTS:174 differentially expressed genes (DEGs) were identified, revealing abnormalities in rhythmic processes, mitochondrial metabolism, and various cell functions including morphology, differentiation, and receptor activity. Analysis identified 5 modules correlated with the mutation, with functional enrichment highlighting disturbances in rhythmic processes and neural cell differentiation due to the mutation. Furthermore, a decrease in neural stem cells (NSC), and an increase in astrocyte-restricted precursors (ARP), ependymocytes (EPC), and hemoglobin-expressing vascular cells (Hb-VC) in the mutant mice were observed. A network comprising 12 genes that link rhythmic processes to neural cell differentiation in the hippocampus was also identified. LIMITATIONS:This study focused on the hippocampus of mice, hence the applicability of these findings to human patients warrants further exploration. CONCLUSION:The ClockΔ19 mutation may disrupt circadian rhythm, myelination, and the differentiation of neural stem cells (NSCs) into glial cells. These abnormalities are linked to altered expression of key genes, including DPB, CIART, NR1D1, GFAP, SLC20A2, and KL. Furthermore, interactions between SLC20A2 and KL might provide a connection between circadian rhythm regulation and cell type transitions.

Sleep loss is a key trigger for a manic episode of bipolar disorder (BD), but the underlying microglial and molecular mechanisms remain unclear. Sleep loss induces microglial and inflammatory responses. Microglia, resident macrophages in the central nervous system, regulate synaptic pruning by engulfing dendritic spines. Here, we introduce a modified paradoxical sleep deprivation (SD) paradigm as a BD mouse model. After intermittent 16-h daily SD for 4 days, the mice showed mania-like behavior, reduced cytokine/chemokine production, mitochondrial damage, microglial loss, decreased synaptic engulfment by microglia, and synaptic gain. Single-nucleus RNA sequencing (snRNA-seq) revealed cell-type-specific inflammation- and synapse-related gene expression profiles in the prefrontal cortex (PFC) and hippocampus of SD-treated male mice. Interestingly, much more differentially expressed genes were observed in SD-treated female versus male mouse brain, especially in the PFC. Pharmacological depletion of microglia by colony stimulating factor-1 receptor (CSF1R) inhibitor PLX3397 blocked SD-induced inflammation-related and senescence-associated abnormalities in a sex-specific manner. Microglial elimination reversed SD-induced synapse gain and mania-like behavior in males but not in females. However, microglial inhibition by minocycline had no effect on SD-induced behaviors in a sex-independent manner. These findings demonstrate that microglia-mediated neuroinflammation and synaptic pruning contribute to SD-induced mania-like behavior in a mouse model of BD in a sex-specific manner.

BACKGROUND:This study aims to apply a semi-supervised machine learning approach for classifying major depressive disorder (MDD) patients into more homogeneous cognitive subtypes based on multidimensional cognitive profiles, and to perform multimodal neuroimaging to identify subtype-specific neural signatures. METHODS:A total of 147 MDD patients and 222 healthy controls (HCs) completed the Cambridge Neuropsychological Test Automated Battery (CANTAB) and magnetic resonance imaging (MRI) scans. Cognitive subtypes were derived based on neurocognitive profiles using heterogeneity through discriminative analysis (HYDRA). General linear models (GLMs) were employed to assess differences across groups in neurocognitive indexes and neuroimaging data followed by Tukey's post-hoc test for pairwise comparisons between the groups. RESULTS:Based on cognitive profiles, MDD patients were classified into cognitive deficit (CD, N = 75) and cognitive preservation (CP, N = 72) subtypes. Voxel-based morphometry (VBM) revealed reduced grey matter volume (GMV) in the left fusiform gyrus and left cerebellum in MDD patients when compared to HCs, with CD patients showing greater atrophy than patients in CP subtype. Meanwhile, the amplitude of low-frequency fluctuations (ALFF) in the temporal lobe of both MDD subtypes was decreased when compared to that of HCs, showing no inter-subtype differences. CONCLUSIONS:A subtype of MDD characterized by comprehensive cognitive deficits is associated with structural atrophy in the left fusiform gyrus and cerebellum, suggesting these regions as potential biomarkers for the cognitive deficit subtype of MDD. However, no significant differences in ALFF were observed between the two cognitive subgroups.

BACKGROUND:Childhood trauma is strongly linked to anxiety and depression, significantly increasing the risk of negative outcomes in adulthood. This study employed network analysis to investigate the complex interplay of anxiety and depression symptoms among Chinese college students, focusing on identifying the core symptoms most directly affected by childhood trauma and those exerting the greatest influence on others. METHODS:Data were collected from December 2020 to January 2021 from 2,266 college students at 16 institutions in southwestern and eastern coastal China. Depression, anxiety, and childhood trauma were assessed using the Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, and Childhood Trauma Questionnaire-28, respectively. Separate symptom networks were constructed for participants with and without childhood trauma experiences. Central indices were employed to identify the central symptom within each network. The accuracy and stability of the networks were then evaluated. Finally, a network comparison test was used to analyze differences in network properties between the trauma and non-trauma groups. RESULTS:Loss of Energy and Worry too much were the central symptoms in the non-trauma group, while anhedonia and nervousness were the central symptoms in the trauma group. There was a significant difference in the global strength of the network between the trauma group and the non-trauma group (pFDR< 0.01), but no significant difference in the distribution of edge weights between the two networks (pFDR =0.14). Anhedonia, Suicide ideation and Feeling afraid in the trauma group showed increased network centrality compared with the non-trauma group. CONCLUSIONS:This study demonstrates the profound impact of childhood trauma on the central symptoms of anxiety and depression in college students. Further research is warranted to investigate the specific pathways through which these symptoms develop, with the goal of developing targeted interventions for this vulnerable population.

Background Although accumulating studies have explored the neural underpinnings of intelligence quotient (IQ) in patients with bipolar disorder (BD), these studies utilized a classification/comparison scheme that emphasized differences between BD and healthy controls at a group level. The present study aimed to infer BD patients’ IQ scores at the individual level using a prediction model. Methods We applied a cross-validated Connectome-based Predictive Modeling (CPM) framework using resting-state fMRI functional connectivity (FCs) to predict BD patients’ IQ scores, including Verbal IQ (VIQ), Performance IQ (PIQ), and Full-Scale IQ (FSIQ). For each IQ domain, we selected the FCs that contributed to the predictions and described their distribution across eight widely-recognized functional networks. Moreover, we further explored the overlapping patterns of the contributed FCs for different IQ domains. Results The CPM achieved statistically significant prediction performance for three IQ domains in BD patients. Regarding the contributed FCs, we observed a widespread distribution of internetwork FCs across somatomotor visual, dorsal attention, and ventral attention networks, demonstrating their correspondence with aberrant FCs correlated to cognition deficits in BD patients. A convergent pattern in terms of contributed FCs for different IQ domains was observed, as evidenced by the shared-FCs with a leftward hemispheric dominance. Conclusions The present study preliminarily explored the feasibility of inferring individual IQ scores in BD patients using the FCs-based CPM framework. It is a step toward the development of applicable techniques for quantitative and objective cognitive assessment in BD patients and contributes novel insights into understanding the complex neural mechanisms underlying different IQ domains.

Major depressive disorder frequently leads to cognitive impairments, significantly affecting patients' quality of life. However, the neurobiological mechanisms underlying cognitive deficits remain unclear. This study aimed to explore multimodal imaging biomarkers associated with cognitive function in major depressive disorder. Five cognitive scores (sustained attention, visual recognition memory, pattern recognition memory, executive function, and working memory) were used as references to guide the fusion of gray matter volume and amplitude of the low frequency fluctuation. Social function was assessed after 2 yr. Linear regression analysis was performed to identify brain features that were associated with social function of patients with major depressive disorder. Finally, we included 131 major depressive disorder and 145 healthy controls. A multimodal frontal-insula-occipital network associated with sustained attention was found to be associated with social functioning in major depressive disorders. Analysis across different cognitive domains revealed that gray matter volume exhibited greater sensitivity to differences, while amplitude of the low frequency fluctuation consistently decreased in the right temporal-occipital-hippocampus circuit. The consistent functional changes across the 5 cognitive domains were related to symptom severity. Overall, these findings provide insights into biomarkers associated with multiple cognitive domains in major depressive disorder. These results may contribute to the development of effective treatment targeting cognitive deficits and social function.

AIMS:We aimed to evaluate the potential of a novel selective α-amino-3-hydroxy-5-methyl-4-isoxazole-propionic acid receptor (AMPAR) potentiator, LT-102, in treating cognitive impairments associated with schizophrenia (CIAS) and elucidating its mechanism of action.METHODS:The activity of LT-102 was examined by Ca2+ influx assays and patch-clamp in rat primary hippocampal neurons. The structure of the complex was determined by X-ray crystallography. The selectivity of LT-102 was evaluated by hERG tail current recording and kinase-inhibition assays. The electrophysiological characterization of LT-102 was characterized by patch-clamp recording in mouse hippocampal slices. The expression and phosphorylation levels of proteins were examined by Western blotting. Cognitive function was assessed using the Morris water maze and novel object recognition tests.RESULTS:LT-102 is a novel and selective AMPAR potentiator with little agonistic effect, which binds to the allosteric site formed by the intradimer interface of AMPAR's GluA2 subunit. Treatment with LT-102 facilitated long-term potentiation in mouse hippocampal slices and reversed cognitive deficits in a phencyclidine-induced mouse model. Additionally, LT-102 treatment increased the protein level of brain-derived neurotrophic factor and the phosphorylation of GluA1 in primary neurons and hippocampal tissues.CONCLUSION:We conclude that LT-102 ameliorates cognitive impairments in a phencyclidine-induced model of schizophrenia by enhancing synaptic function, which could make it a potential therapeutic candidate for CIAS.

BackgroundNeurocognitive impairment is one of the prominent manifestations of major depressive disorder (MDD). Childhood trauma enhances vulnerability to developing MDD and contributes to neurocognitive dysfunctions. However, the distinct impacts of different types of childhood trauma on neurocognitive processes in MDD remain unclear.MethodsThis study comprised 186 individuals diagnosed with MDD and 268 healthy controls. Childhood trauma was evaluated using the 28-item Childhood Trauma Questionnaire-Short Form. Neurocognitive abilities, encompassing sustained attention, vigilance, visual memory, and executive functioning, were measured by the Cambridge Neuropsychological Testing Automated Battery.ResultsMultivariable linear regressions revealed that childhood trauma and MDD diagnosis were independently associated with neurocognitive impairment. Physical neglect was associated with impaired visual memory and working memory. MDD diagnosis is associated with working memory and planning. Interactive analysis revealed that physical/sexual abuse was associated with a high level of vigilance and that emotional neglect was linked with better performance on cognitive flexibility in MDD patients. Furthermore, childhood emotional abuse, physical abuse, and emotional neglect were revealed to be risk factors for developing early-onset, chronic depressive episodes.ConclusionThus, specific associations between various childhood traumas and cognitive development in depression are complex phenomena that need further study.