搜索结果：Translational neuroscience

Histone deacetylase 3 (HDAC3) is increasingly implicated in Alzheimer's disease (AD), yet its precise pathogenic role and therapeutic value remain unresolved. This mini-review critically examines the current evidence for HDAC3 in AD, with a focus on what is established, what remains uncertain, and what is needed for translation. We review the data linking HDAC3 to amyloid-β (Aβ) accumulation, Tau pathology, neuroinflammation, and synaptic dysfunction, while highlighting key limitations in the field, including weak causal evidence, inconsistent cell type-specific findings, insufficient human brain validation, and the lack of proof that HDAC3 serves as a central mechanistic node across AD-related pathways. We also discuss major translational challenges, including poor inhibitor selectivity, uncertain brain penetrance, potential safety concerns, and the absence of standardized preclinical benchmarks. We propose that future progress will require human evidence, cell type-specific causal studies, integrated mechanistic models, and more rigorous pharmacological validation. Together, these considerations define a clearer roadmap for evaluating HDAC3 as a biologically credible and clinically actionable target in AD.

Neuroticism, a Big Five trait characterized by emotional instability and susceptibility to negative affect, is a robust transdiagnostic predictor for the onset, severity, and persistence of anxiety disorders, major depressive disorder (MDD), and other affective conditions. Recent advances in functional magnetic resonance imaging (fMRI) techniques-including resting-state fMRI, multimodal neuroimaging, and their integration with machine learning-have enabled multi-perspective investigations into the neural substrates of neuroticism. Current research in this field primarily follows three complementary approaches: cross-sectional studies identifying key brain regions for emotional processing and cognitive control (e.g., amygdala (AMG), prefrontal cortex); longitudinal studies capturing neural mechanisms evolution across adolescence, middle age, and old age to elucidate relationships between neuroticism and brain plasticity; and intervention studies exploring plastic pathways for reshaping the neural representations of neuroticism, challenging the classic "trait stability" paradigm. This review synthesizes recent progress in the cognitive neuroscience of neuroticism across these three approaches, proposes a unified emotion-cognition neural model centered on the AMG-prefrontal-default mode network circuit, and outlines a hypothesized lifespan trajectory of Limbic Sensitivity → Regulatory Strain → Prefrontal Decline. While accumulated evidence broadly supports the cross-sectional and interventional pillars of this framework, the lifespan trajectory remains a theoretically informed working model requiring further longitudinal validation. The field still faces critical limitations, including small effect sizes, methodological heterogeneity, and unresolved questions regarding causality and circuit specificity. This review aims to provide a conceptual integration of existing findings, identify key uncertainties, and propose evidence-based future directions. We further link the proposed neural model to clinical phenotypic characteristics of high neuroticism and discuss its implications for targeted neural interventions, thereby advancing our understanding of the biological basis of neuroticism and providing a theoretical framework for prevention and intervention in neuroticism-related affective disorders.

Not all sleep loss is equal, and overlooking this limits progress in sleep and neurological disease research. We compared nine rodent sleep deprivation paradigms, gentle handling, multiple platform variants, disk-over-water, the Unpredictable Chronic Sleep Deprivation (UCSD) paradigm, novel object introduction, curling prevention by water, automated systems, and head-lifting, evaluating stress confounds, sleep stage specificity, chronicity, and neurobiological outcomes. Effects included hippocampal plasticity, prefrontal chemistry, glymphatic clearance, neuroinflammation, oxidative stress, neurogenesis, and circadian regulation, linked to Alzheimer's, Parkinson's, and psychiatric comorbidities. UCSD with caffeine produced antioxidant depletion, serotonin reduction, acetylcholinesterase upregulation, and synaptophysin loss, early neurodegeneration markers. We propose a disease-targeted framework with six translational priorities and reporting standards.

Pregnancy represents a critical eco-biological window during which maternal physiology integrates environmental exposures, lifestyle factors, and interconnected microbial ecosystems to shape fetal development and long-term health. From a One Health perspective, defined here as the interconnection between maternal health, environmental determinants, and microbial ecosystems across generations, the maternal microbiome functions as a dynamic interface linking the external environment to the intrauterine milieu, translating ecological signals into immunological, metabolic, and neuroendocrine pathways that influence placental function and developmental programming. Across gut, vaginal, oral, and mammary niches, maternal microbial communities operate as an integrated network regulating systemic inflammation, metabolic homeostasis, and the production of bioactive metabolites, including short-chain fatty acids, bile acids, and tryptophan derivatives. This review proposes an integrated systems framework in which pregnancy is viewed as a transient ecological system shaped by ten interconnected maternal determinants, encompassing microbial niches, nutrition, lifestyle factors, medical interventions, mode of delivery, and postnatal microbial transmission, that converge on shared microbiome-mediated signaling pathways affecting fetal and neonatal immune, metabolic, and neurodevelopmental trajectories. Broader macro-environmental drivers, including biodiversity loss, urbanization, pollution, and industrialized lifestyles, are considered as upstream modulators of maternal microbial ecology within a One Health context. A systems model is presented to illustrate how environmental inputs are biologically transduced through maternal microbial networks to influence placental function, fetal development, and early-life health trajectories. Framing pregnancy as an integrated eco-biological continuum highlights the maternal microbiome as a central hub of intergenerational health and may support microbiome-informed preventive strategies and public health approaches aimed at reducing the burden of non-communicable diseases (NCDs) of early-life origin.

In recent years, liquid biopsy has emerged as a promising non-invasive strategy for the identification of tumor-derived biomarkers. Among circulating analytes, cell-free DNA (cfDNA), including both nuclear and mitochondrial fractions, has been extensively investigated in a variety of biological fluids for its potential applications in cancer diagnosis, disease monitoring, and prognostic stratification. Owing to its higher copy number per cell compared with nuclear DNA, mitochondrial DNA (mtDNA) is typically present at higher concentrations in body fluids and is therefore potentially detectable. Circulating cell-free mitochondrial DNA (cfmtDNA) is closely associated with pro-inflammatory signaling pathways and cellular damage responses, including apoptosis, necrosis, and neutrophil extracellular trap formation (NETosis). This review provides a comprehensive overview of the reported alterations of cfmtDNA in the most prevalent gynecological malignancies, namely ovarian and endometrial cancers, which are characterized by a chronic inflammatory microenvironment. We further critically assess the current evidence supporting cfmtDNA as a potential non-invasive biomarker in these malignancies, highlighting current limitations and future research directions.

Circular RNAs (circRNAs) are covalently closed transcripts generated by spliceosome-mediated back-splicing. Their high stability and tissue-, cell-state-, and disease-context specificity support roles as cancer regulators and biomarkers. circMAN1A2, derived from the MAN1A2 locus, is recurrently dysregulated across malignancies. This review aims to summarize current knowledge of circMAN1A2 biology and evaluates its mechanistic and translational relevance in cancer. Emerging evidence indicates that circMAN1A2 should be considered an isoform-resolved RNA hub. Alternative circularization generates multiple isoforms, whereas cancer tissues often show dominance of a predominantly expressed isoform. Functionally, circMAN1A2 extends beyond microRNA sponging to protein binding, proteostasis regulation, and direct circRNA-mRNA pairing mediated by the back-splice junction. Its biological effects are highly context dependent: circMAN1A2 promotes oncogenic phenotypes in several epithelial cancers, but can suppress glioblastoma by inducing ferroptosis and remodeling the immune microenvironment. We integrate evidence on circMAN1A2 isoforms, biogenesis, interactome modules, and cancer-type-specific phenotypes; highlight experimental and quantitative limitations, particularly in competing endogenous RNA models; and propose a translational route for biomarker development and therapeutic targeting, including back-splice-junction-directed oligonucleotides and isoform restoration. Reproducible, isoform-specific standards will be essential for defining the clinical actionability of circMAN1A2.

Animal models remain essential in translational research, particularly in neuroscience, where behavioral assessments are used to investigate brain function and disease mechanisms. However, conventional statistical approaches, focused on frequencies, durations, and correlations, provide limited insight into the temporal organization and dependency structure of behavior. These methods treat behaviors as isolated or aggregated events, often overlooking the sequential, hierarchical, and dynamic nature of behavioral expression. This perspective advocates the adoption of analytical frameworks that integrate pattern-oriented and time-series dependency inference approaches. Tools such as THEME enable the detection of non-random, temporally structured behavioral sequences (T-patterns), revealing how behaviors are hierarchically organized over time. To further incorporate the possibility of assessing directed dependencies in behavioral sequences, tools such as Tigramite can be useful. Under specific assumptions, this tool allows the estimation of time-lagged, confounder-controlled, conditionally independent relationships between behaviors, representing them as dynamic interactions established over time. By combining these approaches, researchers can move beyond descriptive analyses toward generating hypotheses about the temporal organization and interdependence of behavioral events. Animal models of conditions such as drug addiction, autism spectrum disorder, anxiety disorders, and Alzheimer's disease, among others, in which traditional measures incompletely capture the richness of behavioral interactions, could benefit from this approach. Time-series inference methods may help identify candidate behavioral predictors and generate testable hypotheses about underlying pathophysiology. Overall, adopting this integrative, time-resolved analytical strategy may enable more comprehensive, reproducible, and biologically meaningful insights from animal models.

Background: Plasma biomarkers are promoted as scalable tools for the staging of Alzheimer's disease (AD), yet head-to-head comparisons against the clinical scales used to define diagnostic labels remain scarce. Reported gains from machine learning fusion of clinical and biomarker features may reflect label circularity rather than biological signals, and quantifying this circularity is a central aim of the present work. Methods: From the Alzheimer's Disease Neuroimaging Initiative (ADNI), we assembled 655 participants (CN = 296, MCI = 168, and AD = 191) with concurrent plasma biomarkers (pT217, Aβ42/40, NfL, and GFAP), clinical scales (MMSE, CDR-SB, and FAQ), APOE genotype, and demographics. Three pre-specified feature sets (clinical-only, biomarker plus demographic-genetic, and full fusion) were compared across four classifiers (Logistic Regression, SVM, Random Forest, and XGBoost) using repeated, nested cross-validation (5-fold × 3 outer, 5-fold inner) with balanced class weighting. Because the external Center for Neurodegeneration and Translational Neuroscience (CNTN) cohort (n=130) measures pT181 rather than pT217 and lacks Aβ42/40, external evaluation used a separate reduced feature panel (NfL, GFAP, APOE, age, sex, and education), not the proposed pT217-inclusive panel. Results: Clinical scales alone reached a three-class AUC-OVR of 0.9539±0.0041, and fusion reached 0.9559±0.0046, an indistinguishable gain. Because MMSE, CDR-SB, and FAQ partly determine ADNI diagnostic labels, both estimates are circularity-inflated upper bounds and do not reflect independent classification power. Independent of this circularity, the internal plasma plus demographic-genetic model still achieved AUC-OVR =0.7455±0.0150, with pT217 as the dominant contributor. Pairwise discrimination was excellent for CN vs. AD (1.0000) and MCI vs. AD (0.9739) but markedly weaker for CN vs. MCI (0.9302 for fused and 0.6972 for plasma only). The separate reduced-feature model, which contains neither pT217 nor Aβ42/40, transferred to CNTN with AUC-OVR =0.702 (95% CI 0.635-0.764). Conclusions: Apparent fusion gains in ADNI are largely a consequence of label circularity. After removing the circular clinical features, the internal pT217-inclusive plasma model supports three-class CN/MCI/AD screening at AUC ≈0.74 and a reduced panel without pT217 transfers to an independent cohort at AUC ≈0.70. These values provide a realistic performance estimate for blood-based AD staging under the current feature set, diagnostic label structure, and cohort design, and richer feature sets or pathology-anchored labels may shift this estimate. MCI detection remains the principal bottleneck.

Background: Auditory oddball paradigms are widely used to investigate neural responses to deviant stimuli and attentional processing. However, different paradigms involve deviant stimuli with varying levels of stimulus relevance, and the corresponding neural responses have rarely been directly compared within a unified experimental framework. The aim of this study was to compare neural responses elicited by three variants of the auditory oddball paradigm that differ in the type of deviant stimuli: tone, reversed speech, and self-name deviants. Methods: Electroencephalography (EEG) data were recorded from 38 healthy participants while they performed three paradigm variants. Event-related potentials (ERPs) were analyzed to examine neural responses to deviant stimuli. In addition, cortical activation patterns were identified via source reconstruction, and classification analyses were conducted to assess the discriminability of neural responses across the three variants. Results: ERP results revealed that the self-name paradigm elicited the largest ERP responses, characterized by a significant P300 amplitude (3.95 μV) and prominent MMN (-6.39 μV). Crucially, source-space analysis revealed a graded expansion of cortical recruitment: acoustic deviance (tone) and structural reanalysis (reversed speech) were associated with 7 and 6 significant clusters, respectively, primarily in the auditory and fronto-cingulate cortices, whereas the self-name paradigm engaged 12 significant clusters spanning a distributed network encompassing salience-processing regions and cortical midline structures associated with self-referential processing (including the insula and posterior cingulate cortex). Classification analyses mirrored these findings, with the self-name paradigm consistently yielding the highest neural separability (~80% accuracy) and greater robustness to interindividual variability, demonstrating the superior discriminability of self-referential neural patterns. Conclusions: These findings demonstrate that self-referential auditory stimuli elicit stronger and more discriminable neural responses than other auditory deviant stimuli in the oddball paradigm. These results provide a comparative perspective on how different dimensions of auditory relevance modulate neural processing and may inform the design of effective auditory paradigms for cognitive neuroscience and related translational applications.

Pain disorders such as neuropathic pain and headache remain areas of considerable unmet need and considered high risk by pharma. Human-induced pluripotent stem cells (iPSC)-derived sensory neurons have already been used to accelerate translational research but the current differentiation protocols produce non-peptidergic nociceptors. We demonstrate for the first time the robust differentiation of hiPSC into peptidergic nociceptor lineage with high yield. These nociceptors express CGRP and TRPV1 and show functional maturity including the expression of TTX-resistant currents and responding to TRPV1 and TRPA1 agonists. Importantly, they were able to release CGRP basally and upon stimulation by inflammatory soup, which was inhibited upon the application of the 5-HT1B/1D/1F agonist, sumatriptan, a migraine prophylactic drug. We report the successful generation of a novel in vitro functional peptidergic nociceptor model which will allow investigation of disease mechanisms in pain and translational phenotypic drug screening for new effective pain therapies.

Pathogenic variants in HCN1 are associated with a spectrum of epilepsies, including drug-resistant developmental and epileptic encephalopathies. However, evidence to guide antiseizure medication (ASM) selection in HCN1-related epilepsies remains limited. In this intentionally selected, retrospective case series, we identified seven patients with confirmed pathogenic or likely pathogenic HCN1 variants associated with gain-of-function effects. In all cases, treatment with sodium-channel-blocking ASMs including phenytoin, lamotrigine, oxcarbazepine, and lacosamide was associated with seizure worsening, with clinical improvement after drug discontinuation. These findings identify a clinically actionable gene-drug interaction in HCN1-related epilepsies and support a mechanism-based approach to ASM selection. Our data provide translational evidence to inform precision treatment strategies and help avoid potentially harmful therapies in affected children.

Vascular cognitive impairment (VCI) encompasses a spectrum of cerebrovascular diseases ranging from mild clinical cognitive impairment to advanced vascular dementia and is recognized as a major contributor to the global dementia burden. Frequently coexisting with Alzheimer's Disease (AD), VCI represents a complex, mixed-pathology neurodegenerative process driven by chronic cerebral hypoperfusion (CCH), white matter (WM) injury and volume loss, neurovascular dysfunction, and progressive cognitive decline. While numerous animal models have been developed to characterize the underlying mechanisms and identify therapeutic targets, the field is presently limited by the absence of a distinct framework to guide model selection based on unique pathophysiological features of recently delineated VCI subtypes. Surgical VCI models, including transient and permanent occlusion, stenosis, or gradual occlusion approaches, differ substantially in the duration of ischemic injury, severity of hypoperfusion, and mechanism of cerebral blood flow (CBF) reductions, generating diverse downstream effects on cerebral tissue damage, neuroinflammation, neurometabolic dysfunction, functional integrity, and, ultimately, memory function. No single model completely captures the heterogeneity of VCI pathology; however, each selectively captures unique aspects of disease subtypes. As such, this review aims to establish a clear, pathophysiology-driven framework to guide the selection of appropriate surgical VCI models for investigating specific VCI subtypes. To do so, we evaluate common models of carotid artery manipulation, integrating histological, neuroenergetic, and cognitive outcomes with clinically relevant imaging and patient data. This review provides practical guidance for model selection, enhancing the specificity and translational relevance of preclinical VCI investigation.

The integration of artificial intelligence (AI) into the life sciences has accelerated significantly between 2022 and 2026, accompanied by global investment exceeding USD 100 billion and widespread expectations of a transformative impact in drug discovery. Despite these advances, the extent to which AI has improved clinical outcomes remains unclear. This study presents a structured narrative review evaluating the economic, technical, clinical, and regulatory dimensions of AI adoption in drug discovery. Current evidence indicates that clinical attrition rates remain high, with approximately 90% of drug candidates entering clinical development failing to achieve regulatory approval. Although AI systems such as AlphaFold have achieved high structural prediction accuracy, with predicted local distance difference test (pLDDT) scores exceeding 90 for well-structured proteins and root mean square deviation (RMSD) values comparable to experimental methods, limitations persist in modelling protein dynamics, post-translational modifications, and protein-ligand interactions. Clinical case studies demonstrate that while AI can accelerate early-stage discovery timelines, these advantages do not consistently translate into improved late-stage success rates. Furthermore, reproducibility challenges, limited data transparency, and regulatory gaps continue to constrain reliable implementation. These findings suggest that AI in drug discovery is currently in a transitional phase characterised by high investment but limited validated clinical impact. Future progress will depend on strengthening validation frameworks, improving data sharing practices, and aligning regulatory standards with real-world clinical performance.

Nature-based interventions (NBIs) are increasingly used in mental health services, but their effectiveness in people with psychiatric disorders, and how these individuals experience them, remains unclear. This review synthesised quantitative and qualitative evidence on NBIs in psychiatric populations. Eligible studies evaluated outdoor NBIs against controlled comparators, excluding neurodevelopmental/degenerative conditions and indoor or virtual interventions. Quantitative outcomes were synthesised using random-effects meta-analysis; qualitative data were analysed using thematic synthesis. Twenty-eight studies were included, mostly involving people with diagnoses of schizophrenia or depression. NBIs were associated with greater improvements in clinical symptoms than controlled comparators (pooled effect size 0.71 [95% CI 0.29-1.12]; p = 0.0009), with moderate heterogeneity (I2 = 48.6%). The qualitative synthesis identified five themes: Being in Nature, Personal Growth, Psychological Wellbeing, Social Relationships, and Physical Benefits. Participants reported reduced stress, improved mood and coping, strengthened identity, enhanced social connection, and increased energy. NBIs, particularly horticultural programmes and guided outdoor activities, may offer promising recovery-oriented adjuncts to psychiatric care. The next step is to build a translational evidence base by harmonising recovery-relevant outcomes and developing pragmatic, scalable models of delivery that can be embedded within routine mental health services, informed by mixed methods evaluation.

Altered immune function is increasingly recognized as a contributor to Alzheimer's disease (AD); however, it remains unclear whether peripheral immune alterations reflect constitutive inflammation or stimulus-dependent changes in immune responsiveness. Addressing this distinction is critical for understanding immune dysregulation in neurodegenerative diseases. In this study, we applied a challenge-based ex vivo immune profiling approach to characterize functional immune responsiveness in patients with AD and in cognitively healthy older adults. Peripheral blood mononuclear cells were exposed to defined innate, antigenic, and mitogenic stimuli, and cytokine and β-amyloid responses were quantified in culture supernatants. Diagnosis-by-stimulus interaction effects were assessed using generalized estimating equation models, adjusted for age and sex. In parallel, exploratory correlation-based immune-amyloid network analyses and hypothesis-driven immunogenetic stratification were performed to investigate biomarker coordination patterns and context-dependent genetic influence. Baseline cytokine concentrations showed limited between-group differences, whereas ex vivo immune challenge revealed selective stimulus-dependent alterations in cytokine production. In contrast, immune stimulation revealed selective amplification of stimulus-evoked responses in AD, particularly involving IFN-γ, IL-4, and IL-10, whereas classical proinflammatory cytokines retained preserved inducibility. Exploratory genotype-stratified analyses suggested potential context-dependent differences in functional immune and β-amyloid responses to immune challenges. Together, these findings indicate that peripheral immune dysregulation in AD is characterized by stimulus-dependent differences in cytokine production that become apparent under immune challenge conditions, highlighting the value of ex vivo immune stimulation assays in translational immunology in neurodegenerative diseases.

The MTXPK.org webtool facilitates model-informed supportive care and glucarpidase use in patients receiving high-dose methotrexate, but its reliance on manual data entry limits workflow integration. We aimed to highlight how translational informatics can advance, automate, and enhance the usability of clinical decision support tools by embedding MTXPK.org within the electronic health record (EHR). A human factors study with 6 clinical providers guided iterative prototype development. MTXPK.org was rebuilt within the EHR using a 3-tier architecture with Fast Healthcare Interoperability Resources-based data retrieval, automated pharmacokinetic modeling, and interactive visualization. Two rounds of prototyping with task-based evaluations and contextual inquiry showed progressive improvements in information recognition and navigation. The final design achieved excellent usability, with a System Usability Scale score of 90.4 compared to 57 for the original MTXPK.org tool. The dashboard is now live, automating data entry and generating individualized pharmacokinetic profiles with interactive visualization. The Methotrexate Monitoring Tool is an integrated methotrexate dashboard that automates data entry, improves usability, and facilitates model-informed supportive care and glucarpidase use.

The blood-brain barrier (BBB) is essential for maintaining central nervous system homeostasis by regulating selective permeability and protecting neural tissue. Surgical interventions and anesthesia can compromise BBB integrity, particularly in elderly patients, contributing to postoperative cognitive dysfunction (POCD). This narrative review provides a BBB-centered perspective on postoperative cognitive dysfunction by critically evaluating blood-brain barrier dysfunction as a potential unifying mechanistic pathway linking surgical stress, systemic inflammation, anesthetic exposure, neuroinflammation, and cognitive decline. While previous reviews have examined these perioperative factors individually, this review integrates current experimental and clinical evidence to highlight BBB disruption as a central contributor to postoperative neurocognitive impairment and discusses its translational implications for perioperative neuroprotection. Surgical trauma triggers an acute inflammatory response with elevated cytokines, increasing BBB permeability and facilitating the entry of neurotoxic substances into the brain, thereby promoting neuroinflammation. Anesthetic agents may further exacerbate these effects. Emerging evidence suggests that BBB dysfunction may represent an important mechanistic contributor linking perioperative stress, neuroinflammation, and cognitive impairment; however, direct evidence establishing causality in humans remains limited. Interventions targeting BBB stabilization and perioperative neuroprotection may represent promising strategies to reduce the risk of POCD. A better understanding of these mechanisms is crucial for developing strategies to preserve cognitive function and improve postoperative outcomes. However, much of the current evidence is derived from preclinical studies, and direct causal relationships in humans remain limited. Future research should clarify neuroinflammatory pathways and identify effective protective therapies.

The medial prefrontal cortex (mPFC) plays a pivotal role in attention by exerting top-down control to allocate cognitive resources toward behaviorally relevant stimuli based on learned context and expectations. mPFC neurons project to multiple cortical and subcortical regions, including the locus coeruleus (LC)-the brain's primary source of norepinephrine (NE). The mPFC also receives inputs from the LC, which release NE to modulate mPFC neuronal activity and downstream cellular signaling. While enhanced functional connectivity between the mPFC and LC in mice during sustained attention tasks suggest an important role for the mPFC-LC circuit, and in particular for mPFC neurons projecting to the LC (mPFC-LC projectors), functional evidence directly implicating this population in attention is lacking. Here, we investigated the role of the mPFC-LC projectors in attention by comparing selective chemogenetic manipulation of these neurons to broad chemogenetic manipulation of mPFC neurons. Selective activation of mPFC-LC projectors in mice performing the rodent continuous performance test (rCPT), a translational sustained attention task, robustly improves attentional performance by enhancing discrimination while non-selective activation of mPFC neurons increases attentional performance by increasing responsiveness. Behavioral effects of mPFC-LC projector activation were mediated by recruitment of a microcircuit involving LC-NE neurons and glutamate and GABA peri-LC neurons that resulted in an increase in NE tone within the mPFC. while effects of non-selective activation of mPFC neurons were mediated by engaging downstream targets such as the nucleus accumbens (NAc) as well as the LC/peri-LC region. These findings demonstrate that subpopulations of mPFC neurons engaging distinct downstream targets control different domains of attentional performance, providing a circuit-level framework for understanding the mechanisms of sustained attention and for developing targeted therapies for attentional deficits across neuropsychiatric disorders.

Major depressive disorder remains a leading cause of disability, and decades of monoamine-centered pharmacology have yielded delayed and often incomplete relief. Rapid-acting antidepressants reshaped the field by linking swift symptom improvement to glutamatergic plasticity, yet durable benefit depends on how newly reconfigured circuits are stabilized and tuned. This review synthesizes evidence that antidepressant efficacy arises from the coordinated engagement of synaptic plasticity, spanning induction and consolidation, and intrinsic excitability, which provides gain control, and proposes an integrated framework to guide future discovery. It first outlines induction through N-methyl-D-aspartate receptors (NMDARs) and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs), exemplified by ketamine and esketamine, followed by consolidation mediated by tropomyosin receptor kinase B (TrkB) signaling, translational disinhibition via eukaryotic elongation factor 2 kinase (eEF2K), and presynaptic stabilization indexed by synaptic vesicle glycoprotein 2A (SV2A); together, these processes transform transient potentiation into persistent network change. It then highlights intrinsic excitability, emphasizing voltage-gated potassium channel subfamily Q (Kv7), hyperpolarization-activated cyclic nucleotide-gated (HCN), and G protein-gated inwardly rectifying potassium (GIRK) channels as circuit-level governors that normalize firing and limit relapse-prone hyperexcitability. Finally, it presents the Induction-Consolidation-Maintenance (ICM) framework as a hypothesis-generating roadmap for future studies, with SV2A positron emission tomography (PET), electroencephalography (EEG), and functional magnetic resonance imaging (fMRI) biomarkers discussed as candidate tools rather than validated guides for treatment timing or patient selection. The proposed contribution is not another list of plasticity pathways, but a phase-specific model that links synaptic induction, consolidation, and excitability-based maintenance to distinct therapeutic windows, biomarkers, and relapse-prevention strategies.