Background The N-methyl-d-aspartate (NMDA) receptor hypofunction hypothesis is central to schizophrenia, yet pharmacological MK-801 models exhibit variability across strains and conditions. We systematically compared behavioral and synaptic phenotypes induced by subchronic MK-801 administration across commonly used mouse strains and characterized associated transcriptomic alterations in C57BL/6J mice. Methods Subchronic MK-801 administration during early adulthood was compared across C57BL/6J, ddY, and ICR strains under matched conditions. Recognition memory and medial prefrontal cortical dendritic spine density were assessed. Bulk and single-cell RNA sequencing were performed in C57BL/6 J mice following a drug-free washout period. Results MK-801 induced a directionally consistent reduction in dendritic spine density across all strains, reaching statistical significance in C57BL/6J and ddY mice. In contrast, a clear recognition memory impairment in the novel object recognition test was reliably detected only in C57BL/6J mice. Based on these combined phenotypes, C57BL/6J mice were selected for molecular profiling. Bulk RNA sequencing revealed persistent downregulation of synapse-related pathways alongside altered metabolic and translational programs. Single-cell RNA sequencing suggested exploratory transcriptional modulation within microglial states without major changes in overall cell-type composition. Conclusions Subchronic MK-801 administration produces directionally consistent synaptic alterations across strains and a reliable cognitive phenotype in C57BL/6J mice. These findings provide a practical and tractable framework for selecting and interpreting pharmacological MK-801 models in preclinical schizophrenia research.
BACKGROUND:Acute stress plays a key role in mental health, and there is a pressing need for accessible, brain-based interventions that strengthen stress regulation by directly targeting the underlying brain mechanisms. We therefore developed a real-time functional near-infrared spectroscopy (fNIRS)-informed neurofeedback training targeting a prefrontal region involved in cognitive emotion regulation (left lateral prefrontal cortex, PFC) and evaluated its potential to enhance stress regulation using the socially evaluated cold pressor test, an acute stress paradigm that combines physical and social stressors. METHODS:In a randomized double-blind, sham yoked feedback-controlled parallel-group trial, 60 young healthy adults underwent pretraining of cognitive emotion regulation with reappraisal strategies, then completed four neurofeedback runs applying reappraisal strategies to drive regulation, preceded and followed by baseline and maintenance runs without feedback. The training combined continuous neurofeedback from individualized lateral PFC channels to guide neurofunctional control. The primary outcome assessed activity at trained channels, while secondary outcomes assessed stress and pain regulation during the socially evaluated cold pressor test. RESULTS:Neurofeedback significantly promoted upregulation of lateral PFC activity throughout the training runs. Participants in the training group exhibited lower subjective stress - yet not pain - experience during the cold pressor test, as well as reduced post-training anxiety, reflecting a successful and domain-specific transfer of regulatory control. CONCLUSIONS:This study demonstrates the efficacy of real-time fNIRS lateral PFC neurofeedback in facilitating adaptive learning of regulatory skills, which transferred robustly to regulation of subjective stress under acute stressors. These findings point to neurofeedback training as a promising translational strategy for enhancing stress regulation.
Major depressive disorder is characterised by clinical and biological heterogeneity, yet evidence concerning melancholic, atypical, anxious, and psychotic depression is fragmented across biological systems and further complicated by heterogeneous phenotype definitions. This critical narrative synthesis integrated findings from studies using DSM criteria, the CORE measure, latent class analysis, and dimensional instruments across five domains: HPA-axis regulation, inflammation, neural circuitry and neuromodulatory systems, genetic liability, and circadian regulation. Across studies, biological associations varied by phenotype and domain; no phenotype was defined by a single abnormality. The most consistently supported components were marked HPA-axis hyperfunction in psychotic depression; hypercortisolaemia or impaired feedback in melancholic depression; immunometabolic activation in reversed-neurovegetative atypical profiles; and lower fractional anisotropy across fronto-limbic and association tracts in anxious depression. Support for other phenotype-associated components was narrower or less consistent, with many findings remaining preliminary, indirect, or operationalisation-dependent. Together, these domain-specific findings provide the evidence base for an integrative, hypothesis-generating model. Because the underlying data were derived largely from separate cohorts, the model synthesises phenotype-level evidence rather than demonstrating within-person configurations. Within this framework, the phenotypes are conceptualised as evidence-informed prototypes in a continuous multidimensional biological space; their positions, boundaries, and degrees of overlap remain to be determined in multimodal cohorts. The model predicts that mixed and intermediate presentations will show systematic combinations of alterations across these domains. Heuristic rather than taxonomic, the model organises heterogeneous evidence and frames questions for longitudinal and biomarker-stratified research without requiring a new clinical classification.
Peripheral inflammation has been implicated in Alzheimer's disease (AD), yet its links to hippocampal neurodegeneration and clinical progression remain incompletely defined. This study investigated associations of peripheral inflammatory markers with hippocampal volume fraction (HVF) and cognitive outcomes, alongside potential intermediary pathways. We included 320 participants (mean age 74.9 ± 6.9 years; 58.8% male) spanning cognitively unimpaired, mild cognitive impairment and AD individuals from the Alzheimer's Disease Neuroimaging Initiative. Peripheral inflammation was quantified using the neutrophils to lymphocytes ratio (NLR), platelet to lymphocytes ratio (PLR), and systemic immune-inflammation index (SII). We evaluated their cross-sectional associations with HVF, Aβ, tau and cerebrospinal fluid central inflammatory markers. Linear mixed-effects models characterized longitudinal HVF and cognitive trajectories, and Cox regression modelled clinical progression. Consequently, higher peripheral inflammatory markers correlated with reduced HVF (β = -0.12 to -0.17, q < 0.05). Mediation analyses indicated peripheral inflammatory burden may serve as an intermediary pathway linking APOE ε4-related genetic susceptibility to HVF. Longitudinally, higher baseline PLR and SII were associated with faster HVF decline, whereas no significant associations were observed with longitudinal cognitive trajectories. In an exploratory subgroup restricted to cognitively normal participants, the median SII tertile was tentatively associated with subsequent progression to MCI or AD (HR = 3.09, p = 0.02). Peripheral inflammatory markers showed no associations with Aβ, tau, or central inflammatory markers. These results support a link between systemic inflammatory burden and hippocampal vulnerability, while relationships with AD pathology and clinical progression warrant further investigation.
PURPOSE OF REVIEW:Targeting serotonergic receptors, as well as the serotonin uptake, is a relevant treatment approach for the treatment of depression. The 5-hydroxytryptamine 7 receptor (5-HT7R) is a G-protein coupled receptor belonging to the serotonergic receptor family, which has received a lot of attention due to its association with mood, circadian rhythm, and cognition. This review discusses the potential of 5-HT7R antagonists to treat depression, especially focusing on the pharmacological and bioengineering approaches. RECENT FINDINGS:The 5-HT7R is rather common in the central and peripheral tissues, such as in the brain, gastric tract, and vascular system. It primarily signals through the Gαs/cAMP pathway, although alternative Gα12-mediated signaling has also been reported. Preclinical studies show that selective 5-HT7R antagonists cause a reduction in the duration of immobility in rodent models, including the forced swim test (FST) and tail suspension test (TST), and have antidepressant-like action in the olfactory bulbectomy model of depression. Recent advances highlight the relevance of bioengineered experimental platforms and targeted drug delivery strategies to improve mechanistic understanding and optimize central nervous system (CNS)-specific modulation of 5-HT7R. SUMMARY:Accumulating evidence supports the therapeutic relevance of 5-HT7R antagonists in depression. Transforming receptor-delivering drug-targeted therapy together with bioengineering, human-relevant experimental models, and CNS-centred delivery systems could enhance translational understanding and therapeutic optimization. Collectively, these developments make 5-HT7R antagonism a promising direction of the future antidepressant research.
BACKGROUND:Major depression (MD) is increasingly understood as a disorder characterized by widespread abnormalities in intrinsic brain functional network organization. Although both MD and brain functional connectome architecture are highly heritable, the genetic architecture underlying their relationship remains poorly characterized. METHODS:We integrated genome-wide association studies of MD with 191 ICA-based resting-state functional connectome traits to investigate their shared genetic architecture. These traits captured intrinsic connectome organization across amplitude, functional connectivity, and global connectivity domains. Cross-trait genetic analyses were used to assess pleiotropic overlap between traits. Locus-level and gene-based analyses integrating multi-omics evidence were performed to characterize the biological relevance of shared genetic signals. RESULTS:We identified significant genetic overlap between MD and 148 of 191 brain functional connectome traits. Cross-trait analyses revealed widespread shared genetic signals organized into 627 genomic loci across amplitude, functional connectivity, and global connectivity measures. Among these, 193 loci showed evidence consistent with shared causal variants based on colocalization analyses. Gene-level integration mapped these loci to 1459 protein-coding genes (390 unique genes). Multi-layer prioritization identified 17 high-confidence genes supported by convergent genomic, transcriptomic, and proteomic evidence, with enrichment in neurodevelopmental and lipid-related metabolism pathways. CONCLUSIONS:This study provides a multi-scale characterization of the shared genetic architecture between MD and intrinsic brain functional connectome organization, revealing that shared genetic signals between MD and brain functional systems are distributed across multiple functional levels and converge at the molecular level.
Opioid use disorder (OUD) remains a chronic, relapsing condition; in the United States in 2024, approximately 79,000 drug-overdose deaths occurred, including about 54,000 involving opioids (NCHS Data Brief, 2026 (Centers for Disease Control and Prevention, National Center for Health Statistics, 2026)). Although current pharmacological treatments stabilize patients, residual anhedonia and relapse vulnerability may reflect persistent mesocorticolimbic dysfunction. This short review proposes a multi-timescale framework in which transcutaneous auricular vagus nerve stimulation (taVNS) serves as a non-invasive adjunct to medication-assisted treatment and psychotherapy. The proposed NTS-LC-VTA pathway may produce acute noradrenergic modulation of VTA dopamine neurons, intermediate receptor- and transcription-dependent adaptations, and longer-term BDNF/TrkB-associated synaptic remodeling. These mechanistic links remain hypotheses, and increased firing may not restore dopamine output after chronic opioid-related presynaptic adaptations. Preclinical findings and limited clinical evidence from non-invasive vagal stimulation support feasibility, but direct evidence that taVNS normalizes reward circuitry or prevents relapse in OUD remains lacking.
Cannabigerol (CBG) is a phytocannabinoid present in the plant Cannabis sativa that, similar to cannabidiol (CBD), does not cause psychotomimetic effects. It has shown potential therapeutic effects for relieving pain, inflammation, and anxiety, and it possesses antioxidant and neuroprotective properties. To date, few studies have investigated the potential of CBG in animal models of schizophrenia. Previous studies have demonstrated the antipsychotic-like profile of CBD in clinical and preclinical studies, with a lower induction of side effects when compared to conventional therapy. Although the pharmacological properties of CBG partially resemble those of CBD, some important differences could result in distinct clinical potential. In the present work, we investigated whether CBG could also show an antipsychotic-like profile in animal models of schizophrenia. Male Swiss mice received intraperitoneal injections of CBG followed by d-amphetamine (AMPH) or MK-801 and were exposed to different behavioral assays, including the open field, novel object recognition (NOR), social interaction, and prepulse inhibition (PPI) tests. CBG attenuated the disruptive effects of AMPH in the PPI and open field tests. In addition, pre-treatment with this compound also attenuated the impairments in the social interaction test, NOR, and PPI induced by MK-801. These results suggest that CBG therapeutic profile in behavioral assays. Notably, these benefits were observed at reduced concentrations, indicating that this compound represents a promising candidate for future translational and clinical investigations.
Methamphetamine (MA) use disorder represents a major global public health concern, yet effective pharmacological treatments remain unavailable. Betaine has previously been shown to ameliorate MA-induced depression, cognitive deficits, and behavioral sensitization, suggesting its potential as a therapeutic candidate for MA use disorder. However, its effects on relapse prevention remain unknown. Given that betaine activates adenosine monophosphate-activated protein kinase (AMPK) in peripheral tissues and that AMPK signaling regulates cocaine reinstatement, we hypothesized that betaine attenuates MA relapse through calcium/calmodulin-dependent protein kinase kinase 2 (CAMKK2)-dependent activation of AMPK in the nucleus accumbens (NAc) core. In SH-SY5Y cells, betaine increased AMPK phosphorylation through a CAMKK2-dependent mechanism. Consistently, betaine increased AMPK phosphorylation in the NAc of rats. Using a conditioned place preference (CPP) paradigm in male Sprague-Dawley rats, we found that betaine attenuated MA-primed reinstatement, without affecting CPP acquisition and locomotor activity. Bilateral intra-NAc core infusion of betaine similarly reduced MA-primed reinstatement. Furthermore, intra-NAc core administration of the AMPK inhibitor dorsomorphin or the CAMKK2 inhibitor STO-609 abolished the anti-reinstatement effect of betaine, indicating the involvement of CAMKK2-AMPK signaling. Collectively, these findings demonstrate that betaine suppresses MA relapse-like behavior possibly via CAMKK2-dependent AMPK activation in the NAc core and support its further evaluation as a potential pharmacotherapy for MA use disorder.
Problematic smartphone use (PSU) is an emerging behavioral concern linked to impaired cognitive control and heightened social sensitivity, with fear of missing out (FoMO) increasingly recognized as a core psychological driver. However, the neurobiological mechanisms underlying PSU remain unclear. In this study, we investigated the neural architecture of PSU by integrating functional connectome gradient mapping, psychological mediation, and transcriptomic annotation. PSU severity was associated with altered principal gradients of functional connectivity, including increased connectivity gradients in the frontal pole and angular gyrus, and decreased connectivity gradients in the superior parietal lobule. Inter-subject representational similarity analyses further revealed that PSU-related gradient alterations were distributed across the default mode, frontoparietal, dorsal and ventral attention, sensorimotor, and visual networks. Mediation analyses demonstrated that FoMO significantly mediated the association between gradient 1 alterations in the bilateral superior parietal lobule and PSU severity. Transcriptomic analysis identified 1273 genes spatially aligned with the neural gradients, enriched for synaptic and glial functions, with distinct developmental expression profiles. Notably, hub genes such as BDNF, SYN1, and MRPS11 pointed to neuroplasticity and developmental mechanisms. Together, these findings provide a multilevel framework for understanding PSU, bridging neural gradients, molecular architecture, and psychological vulnerability.
BACKGROUND:Depression exhibits substantial neurobiological heterogeneity. Conventional group-level EEG analyses often fail to identify reproducible biomarkers, limiting objective diagnosis and treatment. This study employed a normative modeling framework to characterize individual EEG abnormalities by quantifying deviations from a healthy reference distribution. METHOD:This study integrated multicenter resting-state EEG data from 1163 participants (participants with major depressive disorder [MDD] = 369; healthy controls [HC] = 794). Normative models were constructed using Dortmund Vital Study data (HC = 608). In the test set, individual deviations in time- and frequency-domain EEG features were quantified at both scalp and source levels. Abnormal patterns were subsequently characterized across electrode, regional, connectivity, and network levels. Group differences were assessed using permutation testing with FDR correction. RESULTS:Normative modeling revealed that EEG abnormalities in MDD were predominantly characterized by highly individualized deviation patterns, with limited overlap at the group level. Significant abnormalities were mainly observed in prefrontal regions and the orbitofrontal cortex (OFC), particularly in beta-band relative power. At the network level, only the limbic subnetwork remained significant after multiple-comparison correction, with approximately 25% of MDD patients showing significant deviations. CONCLUSION:Multilevel EEG analysis based on normative modeling reveals pronounced individualized neurofunctional abnormalities in MDD and identifies relatively stable alterations in the OFC and limbic network. This framework provides a robust basis for characterizing neurobiological heterogeneity and supporting individualized precision diagnosis in depression.
Repetitive transcranial magnetic stimulation (rTMS) has emerged as a potential intervention for post-traumatic stress disorder (PTSD), although the available evidence remains limited and heterogeneous. This study aimed to systematically review and meta-analyze the efficacy, safety, and potential moderators of rTMS for PTSD, focusing exclusively on randomized controlled trials. A systematic search of multiple databases was conducted from inception to February 2025 and updated to April 2026 to identify studies comparing rTMS with sham stimulation, pharmacological treatment, or alternative rTMS protocols (high versus low frequency stimulation). Random-effects meta-analyses were performed, along with subgroup and meta-regression analyses to explore potential moderators. Sixteen randomized controlled trials comprising 786 participants were included in the primary meta-analysis comparing rTMS with sham for self-reported PTSD symptoms. rTMS showed a statistically significant large effect in reducing symptoms relative to sham, although between-study heterogeneity was substantial and prediction intervals were wide. Findings based on interview-reported outcomes did not reach statistical significance. Follow-up analyses provided sparse and uncertain evidence of effects beyond the treatment period. Secondary analyses found limited evidence for improvements in comorbid symptoms of anxiety and depression, with significant effects restricted to specific comparisons or follow-up time points. Adverse events were generally mild, and serious adverse events were rare, with no clear evidence that they were attributable to rTMS. Given the substantial heterogeneity, limited follow-up evidence, and low or very low certainty of the evidence, these findings should be interpreted cautiously and do not allow firm conclusions regarding the efficacy, durability, or safety of rTMS for PTSD.
BACKGROUND:High-density transcranial direct current stimulation (HD-tDCS) shows efficacy in major depressive disorder (MDD), but underlying mechanisms remain unclear. Electroencephalogram (EEG) microstates reflect large-scale network dynamics, and MDD patients exhibit microstate abnormalities. Whether HD-tDCS modulates these states and their clinical relevance is unknown. METHODS:In a randomized controlled trial, 39 MDD patients were assigned to either drug or HD-tDCS groups. The HD-tDCS group received accelerated stimulation over the left dorsolateral prefrontal cortex, twice daily for 20 sessions across two weeks, in addition to antidepressant medication. Resting-state EEG were recorded pre- and post-treatment, and microstate dynamics were analyzed in relation to symptom change. RESULTS:Both groups identified five microstates (A-E). The HD-tDCS group showed significantly greater reductions in Hamilton Depression Rating Scale (HAMD) scores, response rates, and remission rates compared with medication group. Only HD-tDCS induced significant microstate changes: decreased coverage of A and D, reduced duration of D, and decreased occurrence of E, alongside increased coverage and occurrence of B. In the remission subgroup, D coverage decreased and E coverage increased. Reduced D coverage correlated with Hamilton Anxiety Rating Scale (HAMA) improvement, increased B occurrence with HAMD improvement, and transitions from E to B with improvements on both scales. In remitters, changes in D coverage were strongly linked to reductions in both HAMA and HAMD. CONCLUSIONS:Microstate D and B were specifically associated with anxiety and depression improvement, respectively, while E to B transitions related to both. These findings provide novel neuro-electrophysiological evidence for accelerated HD-tDCS mechanisms in MDD.
Sleep disturbances are prevalent in schizophrenia and are associated with symptom severity, yet findings remain inconsistent, reflecting unmeasured biological moderators. Chronotype and the BDNF genetic variant influence sleep regulation and schizophrenia-related phenotypes. However, their combined contribution to the sleep quality-symptom severity association remains unexplored. We aimed to characterise sleep quality and chronotype in schizophrenia, examine their associations with symptom severity, and determine whether BDNF Val66Met moderates these relationships. In this cross-sectional study, 217 individuals with schizophrenia were assessed for sleep quality (Pittsburgh Sleep Quality Index), chronotype (Morningness-Eveningness Questionnaire), and symptom severity (Positive and Negative Syndrome Scale) and genotyped for BDNF Val66Met. Multivariate regression evaluated the contributions of sleep quality, chronotype, and Val66Met genotype to symptom severity scores, adjusting for covariates. Plasma BDNF levels were measured in a subset of patients (n = 91). Poor sleep quality was prevalent in schizophrenia (69.12%) and was significantly associated with PANSS total (β = 0.35, P < 0.001), positive (β = 0.24, P < 0.001), negative (β = 0.27, P < 0.001), and general psychopathology scores (β = 0.31, P < 0.001). Schizophrenia patients with poor sleep quality had later illness onset (P = 0.02). Morning chronotype was associated with longer illness duration (P = 0.03) but lower general psychopathology (P = 0.02). BDNF Val66Met showed no association with sleep, chronotype, or symptom severity and did not moderate sleep-symptom relationships. Plasma BDNF levels were higher in poor sleepers (P < 0.001, r = 0.65), independent of Val66Met genotype. Our results showed poor sleep quality is associated with symptom severity in schizophrenia, including later illness onset, independent of chronotype or BDNF Val66Met variant.
OBJECTIVE:The present study aimed to investigate the relationship between endothelial microparticles (EMPs) and carotid atherosclerosis in geriatric patients with schizophrenia (SCH), as well as whether metabolic syndrome (MS) and immune-inflammatory markers play a mediating or moderating role on this pathway. METHODS:A total of 92 geriatric patients with SCH and 63 healthy controls (HC) were included in this study. Plasma levels of endothelial microparticles and inflammatory cytokines (interleukin (IL)-2, IL-4, IL-6, IL-10, tumor necrosis factor (TNF)-α, granulocyte-macrophage colony-stimulating factor (GM-CSF), IL-1β, interferon (IFN)-γ), blood glucose and lipid components, and carotid intima-media thickness (CIMT) were measured in all participants. Based on the diagnostic criteria for MS in the Chinese Type 2 Diabetes Prevention and Treatment Guidelines (2020 edition), participants were divided into MS and non-MS (NMS) groups. RESULTS:We found that geriatric SCH patients had significantly lower IL-4 levels (p < 0.001) and higher levels of IL-6, TNF, IL-1β, EMPs and CIMT (all p < 0.001) compared to the matched HC. Moreover, our results showed that geriatric SCH patients with MS had markedly higher IL-1β, CIMT and EMPs levels, but lower IL-4 than SCH-NMS group. Pearson correlation analysis showed that circulating EMPs counts, IL-6 and GM-CSF were positively, whereas IL-4 was negatively, correlated with CIMT in geriatric SCH. Moderated mediation analyses revealed that IL-4 and IL-6 mediated the relationship between EMPs and CIMT, and MS moderated this mediation model. CONCLUSION:Our study suggests that EMPs may be associated with carotid atherosclerosis in geriatric SCH, in which immune-inflammatory factors (IL-4 and IL-6) may play a mediating role and MS may act as a moderator in this pathway. These findings may provide preliminary insights into the potential pathogenesis of atherosclerosis-related cardiovascular diseases in geriatric patients with SCH.
Background and hypothesis Schizophrenia (SCZ) is characterized by deficits in emotional expression, with facial expressions serving as potential markers for diagnosis. Leveraging machine learning and computerized facial analysis, this study aimed to identify facial expression features in patients with SCZ and individuals with high social anhedonia (SocAnh), construct an explainable classification model, and explore the associations between facial features and negative symptoms. Study design Emotional expressions of 2 samples comprising 32 patients with SCZ and 34 Health controls (HC), and 56 participants with high SocAnh and 56 participants with low SocAnh were recorded based on an emotion elicitation paradigm combining film based and autobiographical methods. Facial features were extracted using FaceReader to develop classification models with a standardized machine learning pipeline. Study results Patients with SCZ showed lower intensity of sad facial expressions during neutral and positive film viewing than HC, whereas no significant differences were found between participants with high and low SocAnh. In the clinical sample, the Support Vector Machine model achieved a classification accuracy of 81.7%, with highly weighted features showing a mixed pattern across elicitation conditions. In contrast, the model distinguishing participants with high versus low SocAnh yielded lower classification performance. Exploratory correlation analyses showed modest associations between selected facial expression features and negative symptoms, but none remained statistically significant after Bonferroni correction. Conclusions Computational facial expression analysis with interpretable machine learning may help examine facial expression deficits in SCZ. Associations with negative symptoms were exploratory and require confirmation in larger independent samples.
BACKGROUND:Methamphetamine use disorder (MUD) is associated with profound neurobiological alterations involving dopaminergic dysregulation and oxidative stress. However, reliable molecular biomarkers reflecting these alterations remain limited. This study aimed to investigate the expression levels of trace amine-associated receptor 1 (TAAR1) and NAD(P)H quinone oxidoreductase 1 (NQO1) genes in individuals with methamphetamine use and to evaluate their potential as candidate molecular indicators. METHODS:A case-control study was conducted including individuals with methamphetamine use and a control group without substance use. Peripheral blood samples were collected, and gene expression levels of TAAR1 and NQO1 were quantified using quantitative real-time PCR. Relative expression levels were evaluated using ΔCt values, and fold changes were calculated using the 2^-ΔΔCt method. Group comparisons and effect sizes were analyzed to assess the magnitude and consistency of differences. RESULTS:Both TAAR1 and NQO1 expression levels were significantly reduced in the methamphetamine group compared to controls (p < 0.001). Increased ΔCt values indicated marked downregulation of both genes. The magnitude of differences was substantial, with large effect sizes observed for both TAAR1 and NQO1. These findings indicate concurrent downregulation of transcripts related to dopaminergic regulation and to the oxidative stress response in methamphetamine users. CONCLUSIONS:The observed downregulation of TAAR1 and NQO1 indicates concurrent transcriptional alterations in genes involved in dopaminergic regulation and antioxidant defense. Because these measurements were obtained at the transcript level, they cannot be equated with a reduction in enzymatic antioxidant capacity. These genes may represent candidate peripheral molecular indicators; however, their temporal and clinical significance requires validation in longitudinal and independent cohorts.
Multiple sclerosis (MS) is an autoimmune condition that affects the central nervous system (CNS) and leads to neuroinflammation. The chronic inflammatory cascades of the immune system damage the functions of the nerves and myelin tissue. The heterogeneity of MS symptoms depends on the anatomical location of the lesion within the CNS. The identification of the gut microbiota as a crucial modulator of neuroinflammation via the microbiota-gut-brain axis has been the subject of new studies. Butyrate, one of the well-known short-chain fatty acids (SCFAs) produced by gut bacteria through the fermentation of dietary fiber, exhibits notable immunomodulatory and neuroprotective properties. Along with the inhibition of histone deacetylases, the activation of G-protein-coupled receptors, and the facilitation of the transition of T helper 17 (Th17) cells to regulatory T (Treg) cells are some of the actions butyrate has. MS patients have fewer butyrate-producing bacteria and lower amounts of butyrate in their blood and intestines. It has been found that supplementing with butyrate in animal models can lead to the normalization of the blood-brain barrier (BBB) and the gut lining, the reduction of activated brain cells, the increase in myelin repair, and a decrease in T-cell responses to pathology. Butyrate also promotes regulatory B (Breg) cell function. The use of these therapies as an addition to traditional ones, such as dietary changes, the consumption of prebiotics or probiotics, and fecal microbiota transplantation, has shown favorable results in preliminary clinical studies. However, there are several challenges in translating these research-based preclinical evidences into clinical settings to treat MS. This review comprehensively summarizes the current evidence for butyrate-mediated mechanisms in MS and evaluates the potential of the gut microbiome as a therapeutic target.
OBJECTIVE:The individual remission timing for children with Benign Childhood Epilepsy with Centrotemporal Spikes (BECTS) is difficult to predict, presenting significant challenges for clinical medication management. This study aimed to investigate alterations in the cortical Morphometric Similarity Network (MSN) in children with BECTS and, on this basis, to construct a Connectome-based Predictive Model (CPM) for the precise prediction of individualized medication duration. METHODS:We employed a dual-center longitudinal design. The study recruited 79 children with BECTS and 72 healthy controls (HC) from Center 1 as the discovery cohort, and 29 children with BECTS from Center 2 as an independent validation cohort. All participants underwent high-resolution T1-weighted imaging. Based on the Desikan-Killiany atlas, multiple morphometric features were extracted from 308 brain regions to construct individual MSNs. We first compared MSN differences between patients with BECTS and HCs. Subsequently, using a leave-one-out cross-validation CPM framework within the discovery cohort, we identified MSN connectivity features associated with medication duration to build a predictive model, which was then tested for generalizability in the independent cohort. RESULTS:Relative to HCs, children with BECTS exhibited significant MSN abnormalities in key brain regions involving the sensorimotor, default mode, and frontoparietal control networks (p < 0.05, Bonferroni-corrected). Leveraging these network anomalies, the CPM successfully extracted predictive features from pre-treatment MSNs, significantly predicting individualized medication duration in the discovery cohort (r = 0.309, p = 0.006), with the positive feature set yielding the best performance (r = 0.325, p = 0.004). The model maintained significant predictive capacity in the independent validation cohort (r = 0.421, p = 0.023). CONCLUSIONS:This study is the first to reveal specific morphometric similarity network abnormalities in children with BECTS and to successfully construct a generalizable connectome-based predictive model based on these findings. The model enables individualized prediction of medication remission time based on pre-treatment brain structural characteristics, providing a potential objective tool for prognostic stratification and precision clinical management of BECTS.
