The dopaminergic system has long been a central focus of functional neuroimaging. Positron emission tomography (PET) with the D2/D3 receptor radioligand [11C]raclopride remains the most widely used method for indirectly quantifying striatal dopamine release in vivo. However, no previous meta-analysis has studied the relative magnitude and regional distribution of dopamine release across different interventions or cognitive interventions overall. To address this gap, in this meta-analysis of 92 [11C]raclopride PET studies (n = 1640), we compared the magnitude and regional distribution of dopamine release induced by amphetamine, methylphenidate, ketamine, alcohol, and cognitive challenges with and without reward. Amphetamine induced approximately four-fold greater dopamine release than cognitive challenges (10.9 vs. 2.7%, p < 0.001), and approximately twice that of alcohol (4.8%, p < 0.001), with effects comparable to methylphenidate (11.5%) and slightly greater than ketamine (9.8%). Psychostimulant-induced increase in synaptic dopamine was greater in putamen and ventral striatum than in caudate, whereas alcohol preferentially engaged ventral striatum. Dopamine release did not differ between rewarded and non-rewarded cognitive tasks in the ventral striatum (p > 0.14) or overall striatum (p > 0.10). Methylphenidate-induced increases in synaptic dopamine appeared to attenuate with advancing age, whereas cognitive challenges were associated with greater dopamine release in older individuals. These findings demonstrate that individual pharmacological and cognitive interventions differ markedly in both magnitude and regional pattern of dopamine release. They also suggest that [¹¹C]raclopride PET may have limited sensitivity for distinguishing reward-related from non-reward-related dopamine release. These findings have implications for the design and interpretation of future neuroimaging studies of dopaminergic function in health and disease.
Neuroplasticity and synaptic dysfunction are central to the pathophysiology of depression, yet their underlying mechanisms remain not fully understood. Here, we identify the m6A reader protein YTHDF1 as a key regulator of depression pathogenesis. Mice with genetic deletion of Ythdf1 exhibited anterior cingulate cortex (ACC) hyperactivity, hyperalgesia, heightened social stress sensitivity, and anxiodepressive-like behaviors following acute social stress. Using multi-omics approaches, we linked these effects to dysregulated actin cytoskeleton dynamics and identified cyclase-associated protein 1 (CAP1) as a downstream effector of YTHDF1. YTHDF1 deficiency reduced CAP1 expression from early postnatal stages, leading to F-actin accumulation, impaired synaptic function, and behavioral deficits. Strikingly, early postnatal re-expression of YTHDF1 in the ACC normalized CAP1 levels, restored synaptic integrity, and reversed depressive-like phenotypes. Similarly, CAP1 re-expression was sufficient to alleviate stress-induced psychomotor retardation and anxiety. Our findings establish the YTHDF1-CAP1 axis as a critical pathway governing synaptic function and depressive behaviors.
PTSD is marked by atypical coupling between emotion and learning, yet it remains unclear how threat that is situational (the presence of potential danger) shapes episodic memory. We investigated whether unpredictability-driven threat alters incidental memory formation as a function of PTSD symptom severity (PTSDss). Sixty male combat veterans underwent fMRI scanning during incidental encoding of everyday objects presented under unpredictable threat (U), predictable threat (P), or no threat (N). Threat was operationalized as potential exposure to a highly aversive sound. An unexpected recognition test followed 90 min later. We related memory (hit rates), subjective anxiety, and encoding-related BOLD activity to PTSDss assessed with CAPS-5. Greater PTSDss predicted heightened anxiety and poorer memory for items specifically presented under unpredictable threat. While amygdala and dorsal anterior cingulate activity during encoding tracked overall memory success, these responses were attenuated with increasing PTSDss in the U condition. By linking unpredictable threat to both behavioral and neural markers of disrupted episodic memory encoding, the study helps explain how memory-related symptoms may develop and persist in PTSD.
Schizophrenia (SZ) is a highly heritable neuropsychiatric condition with complex polygenic architecture. Elucidating the cellular and developmental substrates vulnerable to the genetic risk is essential for understanding the underlying neurobiological mechanisms. Here, we integrated genome-wide association study (GWAS) and whole-exome sequencing (WES) data with a developmental multi-omics atlas of the human cortex (including 5 cortical regions), comprising about 3 million single-nucleus RNA sequencing (snRNA-Seq) and single-nucleus assay for transposase-accessible chromatin using sequencing (snATAC-Seq) profiles across 8 neurodevelopmental processes, to map cell-type-specific enrichment of SZ genetic risk. Our enrichment analyses revealed that both common and rare genetic liabilities converged on broad excitatory and inhibitory neuronal classes. Across different statistical frameworks, we identified genetic enrichment within intratelencephalic (IT) projection neurons and layer 6b excitatory neurons (Ex-L6b) networks across multiple cortical regions. Stage-resolved developmental mapping in the frontal cortex showed that genetic liabilities, particularly the rare variants, are predominantly concentrated within early developmental processes, namely neurogenesis and neuronal migration. Differential expression analysis in postmortem frontal cortex snRNA-Seq datasets cross-validated the cellular substrates of the genetic liabilities. Collectively, our findings establish a high-resolution cellular and temporal framework of SZ susceptibility, implicating mature associative IT microcircuits, deep-layer thalamocortical-regulating networks, and early developmental specification windows as primary points of genetic convergence in SZ.
Neurovascular dysfunction is an early and critical contributor to Alzheimer’s disease (AD), yet the molecular mechanisms linking vascular pathology to metabolic dysregulation remain incompletely understood. Advanced glycation end products (AGEs), which accumulate during aging and metabolic stress, have been implicated in AD pathology; however, their role in cerebrovascular lipid homeostasis is unclear. Here, we demonstrate that AGE accumulation within cerebral microvessels promotes lipid droplet (LD) formation in endothelial cells through receptor for AGE (RAGE)-dependent disruption of cholesterol efflux pathways. In aged APP transgenic mice and human AD brains, we observe increased AGE deposition concomitant with elevated RAGE, DGAT1, and perilipin expression, alongside reduced ABCA1 levels. In human brain endothelial cells, AGE exposure induces lipid metabolic reprogramming characterized by enhanced LD accumulation, upregulation of lipogenic machinery, and suppression of cholesterol efflux. Mechanistically, RAGE silencing restores ABCA1 expression and attenuates LD formation, identifying RAGE as a key upstream regulator. Pharmacological activation of ABCA1 reverses AGE-induced lipid accumulation and reduces RAGE expression, highlighting a therapeutic axis. Furthermore, AGE exposure disrupts blood-brain barrier (BBB) integrity and impairs amyloid-β transport in an in vitro BBB model. In vivo, aging is associated with progressive microvascular LD accumulation, linking metabolic dysfunction to vascular pathology. Together, our findings establish an AGE-RAGE-ABCA1 signaling axis that drives endothelial lipid dysregulation and BBB impairment, providing a mechanistic framework connecting metabolic stress to neurovascular dysfunction in AD.
Marked interindividual heterogeneity in substance use disorder (SUD) impedes the identification of consistent neuroanatomical alterations, limiting the development of robust markers for clinical risk stratification. We applied a normative modeling framework to explore individual neuroanatomical deviations in individuals with SUD across two distinct substance classes, and assessed their potential for classification and risk prediction. We established a developmental normative model of gray matter volume (GMV) using T1-weighted images from 1209 healthy individuals aged 18-65 years. Deviations from this normative model were then estimated for 184 individuals with methamphetamine use disorder (MUD), 94 with ketamine use disorder (KUD), and 220 healthy controls (HC) collected from two independent sites. Based on these individual deviation scores, a machine learning model was constructed to identify individuals at risk for SUD. The normative model revealed high interindividual heterogeneity in GMV deviations among individuals with MUD and KUD. Cross-substance extreme negative deviations were primarily located in regions of the default mode network (DMN). Notably, individual-level deviations could successfully distinguish both MUD and KUD from HC, and replicated in an external dataset. We further identified significant associations between abnormal GMV trajectories and neurobiological features, particularly myelination and neurotransmitter systems. These findings enhance our understanding of the heterogeneous neurobiology underlying SUD, providing potential neurobiological biomarkers for clinical diagnosis, risk stratification, and prevention.
Treatment-resistant depression (TRD) affects approximately 30% of patients with major depressive disorder. Stanford Neuromodulation Therapy (SNT), a high-dose intermittent theta-burst transcranial magnetic stimulation protocol, produces rapid antidepressant effects, but its neurophysiological mechanisms remain unclear. Here, we used longitudinal TMS-EEG to characterize the progressive neurophysiological changes induced by SNT, assess their site-specificity, and explore whether baseline neural markers are associated with clinical response. We conducted a double-blind, randomized, sham-controlled trial at Stanford University (2017-2018; analysis August 2024-October 2025) in 24 TMS-naïve participants with TRD (Montgomery-Åsberg Depression Rating Scale ≥20; ≥1 failed antidepressant trial). Participants were randomized to active (n = 12) or sham (n = 12) SNT, consisting of 10 sessions per day over 5 consecutive days targeting the left dorsolateral prefrontal cortex (90,000 pulses). TMS-EEG was acquired at two baseline sessions, before and after each treatment session, and at 1-month follow-up (14 TMS-EEG sessions in total). Active SNT progressively reduced cortical excitability at the treatment site, with significant decreases by day 3 in the early window component (-27.9%; P < 0.01), while no changes were observed at the vertex control site. Site-specific comparisons confirmed early window reductions only at the left dorsolateral prefrontal cortex (t₂₂ = -3.82; P < 0.001). SNT also selectively decreased estimated medial prefrontal source activity consistent with the subgenual anterior cingulate cortex (sgACC) across sessions (F₁₃,₂₂₂ = 4.93; P < 0.001), with effects persisting at 1-month follow-up. In an exploratory analysis in the active group (n = 12), higher baseline estimated sgACC source activity was associated with greater clinical improvement (r = -0.67; P = 0.023); although promising, the latter preliminary finding requires replication in larger, adequately powered samples before predictive utility can be established. These findings indicate that SNT induces progressive, site-specific cortical modulation and selective downstream effects on estimated sgACC source activity. Early cortical excitability changes represent candidate neurophysiological markers of SNT response, while the observed association between baseline sgACC activity and clinical outcome, while preliminary, motivates prospective investigation of subcortical source activity as a potential predictor of treatment response in larger trials. ClinicalTrials.gov Identifier: NCT03068715.
Dopaminergic signalling is critical for regulating large-scale brain network dynamics and is implicated in the pathophysiology of psychotic disorders. 22q11.2 deletion syndrome (22q11DS), a high-penetrance genetic risk factor for schizophrenia, is associated with both dopaminergic alterations and disruptions in functional connectivity (FC), yet the degree to which these separate people with 22q11DS from healthy controls and their inter-relationship remains unclear. Eighteen individuals with 22q11DS (no history of psychosis or antipsychotic use) and 22 controls underwent [18F]-DOPA PET imaging to assess striatal dopamine synthesis (indexed by Kicer values) and resting-state fMRI to examine FC. Classification performance using FC, dopamine measures, and their combination was assessed via repeated 10-fold cross-validation. Associations between Kicer, connectivity, and psychotic symptoms were evaluated using linear regression. FC alone classified 22q11DS with 68% balanced accuracy (p = 0.004), Kicer alone with 77% (p < 0.001), and combined measures with 85% balanced accuracy (p < 0.001), indicating additive value. The somatomotor and auditory networks contributed most to group discrimination. Across individuals, higher Kicer was significantly associated with more control-like connectivity profiles (p = 0.011), with significant effects in all striatal subdivisions. No association was found between FC and subclinical psychotic symptoms. This multimodal study demonstrates a significant association between striatal dopamine synthesis capacity and functional brain network architecture in 22q11DS. Higher dopamine synthesis was linked to more normative FC, potentially suggesting FC differences reflect a compensatory, rather than pathogenic role. These findings bridge genetic risk at the 22q11.2 locus with dopamine dysfunction and large-scale networks, offering novel insights into the neurobiology of 22q11DS and related neuropsychiatric risk.
BACKGROUND:"Nutraceuticals" and "phytoceuticals" may have antidepressant activity, prompting an in-depth network meta-analysis of randomized clinical trials (RCTs). METHODS:Systematic review and network meta-analysis (PubMed/MEDLINE/EMBASE/Scopus/PsycINFO/CENTRAL/ClinicalTrials.gov (until 01/07/2025) of RCTs vs. placebo or treatment as usual (TAU = antidepressants) testing nutraceuticals/phytoceuticals in major depressive disorder (MDD). Change in depressive symptomatology (standardized-mean-difference = SMD), treatment response, and all-cause discontinuation (acceptability) (risk ratio = RR) were co-primary outcomes. Secondary outcomes were anxiety symptom changes, adverse event-related discontinuation ("tolerability"), and symptom remission. We conducted subgroup, transitivity, and sensitivity analyses to explore/reduce heterogeneity, and assessed global/local inconsistency, publication bias, risk of bias (RoB), and confidence in the evidence (CINeMA/AMSTAR-Plus). RESULTS:Across 163 studies (n = 15,757, distinct compounds' combinations n = 137), several molecules/classes outperformed placebo with very large effect sizes. Restricting analyses to compounds with ≥2RCTs/non-TAU antidepressants/low RoB/non-sponsored trials/non-outliers, higher-than-expected/unrealistic effect sizes emerged vs. placebo for eicosapentaenoic acid (EPA) (k = 20, n = 8483; SMD = -1.67;95%C.I. = -2.38;-0.97), vitamin D3 (k = 3, n = 344; SMD = -1.59;95%C.I. = -2.71;-0.46), and St. John's-wort-extract-ZE117 (k = 3,n = 207;SMD = -1.02;95%C.I = -1.90;-0.15) regarding depressive symptomatology. Substantial heterogeneity (I2 = 85.3%;95%C.I. = 81.9%;88.0%) and global inconsistency (Q-between-designs = 186.91, p < 0.0001) emerged, explained by subgroup and sensitivity analyses, without publication bias. Additionally, a medium effect size emerged vs. placebo for curcumin (k = 2,n = 89;SMD = -0.58;95%C.I. = -0.99;-0.18) regarding anxiety reduction in low RoB analyses. Shugan granules, EPA, EPA+Docosahexaenoic Acid, ZE117, and fluoxetine+EPA response and St. John's wort extract WS5570/ZE117 remission significantly outperformed placebo. Citicoline was associated with significantly more dropouts than placebo. A meta-regression of efficacy effect sizes against adapted AMSTAR-Plus scores showed that larger SMDs were associated with lower AMSTAR scores, indicating lower study quality. Confidence in the evidence was low to very low. CONCLUSIONS:Upon filtering low-quality evidence, only a few nutraceutical/phytoceuticals showed potential for depressive symptoms, warranting more rigorous trials.
Methamphetamine use disorder (MUD) involves persistent cue reactivity and impaired inhibitory control, posing major treatment challenges. Closed-loop noninvasive neuromodulation can tailor stimulation to dynamic brain states, but its application in MUD remains largely unexplored. This study developed a cue reactivity biomarker-guided closed-loop transcranial alternating current stimulation (tACS) system, examined its optimal stimulation frequency, and preliminarily evaluated its efficacy in improving inhibitory control and reducing cue reactivity. Three cohorts of individuals with severe MUD were enrolled. Drug-cue exposure increased parietal-occipital theta power in Cohort 1. Based on this biomarker, a closed-loop tACS system targeting the left dorsolateral prefrontal cortex was developed. In Cohort 2, a within-subject crossover design examined closed-loop tACS at 6, 10, and 40 Hz. Random-time and open-loop continuous stimulation at the identified optimal frequency served as controls. In Cohort 3, an open-label, single-arm study of five consecutive daily closed-loop stimulation sessions assessed preliminary clinical efficacy. The results showed that closed-loop tACS at 40 Hz was the only condition that simultaneously reduced cue-related electroencephalographic (EEG) activity (P = 0.007) and improved inhibitory control (P = 0.031). Random-time stimulation reduced cue-related activity but did not improve inhibitory control, whereas open-loop continuous stimulation showed no significant effects. Repeated 40 Hz closed-loop tACS over five days in severe MUD significantly reduced craving and improved inhibitory control. These findings suggest that biomarker-guided 40 Hz closed-loop tACS can modulate neural and behavioral dysfunction in MUD and support the feasibility, safety, and preliminary efficacy of this neuromodulation strategy.
Autism spectrum disorder (ASD) is a multifactorial neurodevelopmental condition marked by heterogeneity in its clinical presentations, which complicates both its diagnosis and treatment. Despite advancements, the molecular mechanisms underlying ASD remain largely unclear. In recent years, two independent yet highly promising technological domains have made significant advancements: organoids and artificial intelligence (AI). Brain organoids can simulate critical processes of human neurodevelopment in both physiological and pathological states, providing an unprecedented window into the mechanisms and potential therapeutics of ASD. Simultaneously, AI has demonstrated formidable capabilities for processing and analyzing large-scale, high-dimensional biomedical data, and has been effectively applied to ASD imaging analysis, genomics research, and behavioral data interpretation. However, these two fields have developed mainly in parallel, leaving their potential for cross-disciplinary integration untapped in ASD research. This review aims to fill this gap by reviewing the current status and limitations of both fields in the context of ASD, and to elucidate a promising research paradigm that integrates AI with brain organoids, called “organoid intelligence”, to enhance our understanding of predictive models, unravel ASD pathogenesis, and develop more effective and individualized therapies.
Postoperative delirium is associated with both gut microbiota alterations and Tau phosphorylation; however, how these factors interact and jointly contribute to postoperative delirium remains poorly understood. This prospective observational cohort study screened 491 patients aged ≥65 years undergoing elective laminectomy or hip or knee replacement under general or spinal anesthesia at Massachusetts General Hospital (2016-2020). The study aimed to assess the correlation between plasma Tau protein levels, specific gut microbiota, and the gut microbiota-derived metabolite indole-3-propionic acid (IPA) in participants with and without postoperative delirium. Exclusion criteria included major neurological disease, smoking history, sensory impairment, and recent antibiotic use. Delirium was assessed 24-48 h postoperatively. Out of 491 screened participants, 139 had blood biomarker data, and 86 had gut microbiota data included in the final analysis. Ten percent of the participants experienced postoperative delirium. Co-occurrence network analysis demonstrated that microbial interactions differed between postoperative delirium and non-postoperative delirium groups. Significant associations among gut microbiota, IPA, and Tau biomarkers were observed only in postoperative delirium participants. Additionally, three machine learning classifiers distinguished postoperative delirium cases from non-postoperative delirium cases with Area Under the Curve values above 50%. The results suggest that plasma Tau may interact with gut microbiota, and this interaction is associated with postoperative delirium, suggesting the involvement of a gut-blood-brain axis in postoperative delirium vulnerability. These findings implicate a microbiota-Tau interaction that may represent both a potential pathogenic mechanism and a therapeutic target for postoperative delirium pending confirmative studies. Furthermore, machine learning classifiers utilizing microbiome-derived features demonstrate a potential in distinguishing patients with and without postoperative delirium.
BACKGROUND:Lipids represent a significant component of the human brain, exerting crucial functions in both physiological and pathological conditions. Mapping brain lipids distribution is an emerging area of research, with mass spectrometry imaging allowing the detection of lipid species and their localization within tissue sections. However, comprehensive spatial mapping of lipids in the human brain remains to be achieved. This systematic review addresses this gap by critically synthesizing the available literature in the field. METHODS:A bibliographic search on PubMed, Scopus and Web of Science for original articles employing mass spectrometry imaging to analyze lipids and their distribution in the human brain was performed. The included articles were grouped according to the clinical characteristics of the studied populations, including healthy subjects and selected neurological and psychiatric disorders. Studies on human brain tissue from tumoral specimens, animals, or in vitro models such as organoids were excluded to maintain focus on translational findings directly applicable to human neurological and psychiatric diseases. RESULTS:Following the inclusion criteria, 34 articles were selected. Alzheimer's disease, schizophrenia and multiple sclerosis were the most frequently investigated conditions, alongside studies in healthy subjects describing lipid distribution under physiological conditions. We observed considerable heterogeneity across studies in terms of research questions and methodological approaches. Nevertheless, our critical synthesis allowed us to identify both consistencies and discrepancies in experimental strategies and in the lipid signatures reported. CONCLUSIONS:Studying lipids by mass spectrometry imaging enables the identification of spatially defined profiles in distinct brain areas, providing valuable insights into both human neurobiology and brain disorders' pathophysiology. In this context, spatial information is crucial for linking lipid alterations to specific structures or lesions, thereby supporting translational applications. Finally, we identified key methodological issues that must be addressed to advance this emerging field.
Brain iron homeostasis is indispensable for sustaining central nervous system development, neural circuit stability, and core neurobiological processes including neurotransmission, mitochondrial energetics, and synaptic plasticity. Compelling clinical and preclinical evidence has established that iron dyshomeostasis represents a pervasive, convergent pathological driver of multiple psychiatric disorders, spanning anxiety, depression, schizophrenia, and neurodevelopmental and affective spectrum conditions. Nevertheless, current literature remains fragmented by disease-specific investigation, with persistent unresolved contradictions regarding regional iron deficiency versus overload in disease pathogenesis. Moreover, the cell-type- and circuit-specific mechanisms linking iron imbalance to psychiatric phenotypes, lacks systematic integration—hindering mechanistic consensus and translational advancement. In this comprehensive review, we synthesize multimodal evidence from human neuroimaging, post-mortem tissue analysis, clinical cohorts, and preclinical animal studies to delineate the distinct regulatory logics governing peripheral and cerebral iron metabolism. We systematically dissect how disrupted iron homeostasis triggers a cascade of neurobiological impairments, encompassing oxidative stress, ferroptosis, neuroinflammation, myelination defects, neurotransmitter dysfunction, and maladaptive synaptic remodeling. Synthesizing these findings, we construct a unified mechanistic framework illustrating how diverse etiological factors—genetic predisposition, chronic psychosocial stress, neuroinflammatory insult, and developmental influences—converge to drive region- and cell-specific iron perturbations and subsequent circuit dysfunction. We further summarize the translational potential and inherent limitations of existing iron-targeted intervention strategies. Collectively, this synthesis positions brain iron dyshomeostasis as a core convergent node in the pathophysiological network of psychiatric disorders, bridges basic iron neurobiology with translational neuroscience, and provides a conceptual framework and practical roadmap for biomarker discovery, mechanistic dissection, and next-generation therapeutic development in psychiatry.
Schizophrenia (SCZ) is characterized by cognitive impairments, particularly in working memory (WM), which significantly impact patients' functional outcomes, yet effective interventions for these cognitive deficits remain limited. This randomized, double-blind, sham-controlled study aimed to investigate whether a single session of high-definition transcranial direct current stimulation (HD-tDCS) targeting the left dorsolateral prefrontal cortex (DLPFC) can improve WM in patients with SCZ and to elucidate the underlying neural mechanisms. A total of 69 patients with SCZ were randomly assigned to active HD-tDCS (n = 34) or sham HD-tDCS (n = 35). WM performance was assessed using a 2-back task, and neural activity was evaluated via TMS-EEG to measure changes in excitability and connectivity across the DLPFC, hippocampus (HPC), and entorhinal cortex (EC). Active HD-tDCS significantly improved WM task accuracy (p < 0.001) and reduced reaction times (p < 0.01) compared to the sham group. TMS-EEG analyses revealed reduced neural excitability in the EC and HPC following active stimulation, with changes in EC activity correlating with improved WM accuracy. Mediation analyses suggested that HD-tDCS modulates WM through a statistically modeled DLPFC-HPC-EC neural pathway, with the HPC acting as a full mediator. These findings provide evidence that a single session of HD-tDCS targeting the DLPFC enhances WM in SCZ patients, potentially by modulating the DLPFC-HPC-EC functional cascade, highlighting its potential as a non-invasive intervention for cognitive impairments in SCZ and offering insights into its underlying mechanisms.
Maternal sleep deprivation during late pregnancy is common and has been linked to increased neuropsychiatric risk in offspring, but the mechanisms by which it shapes developing hippocampal circuits remain unclear. The triggering receptor expressed on myeloid cells 2 (Trem2) is a key genetic risk factor for neurodegenerative disease and regulator of microglial survival and synaptic refinement, but it is unknown whether perinatal environmental stressors, such as maternal sleep loss, reprogram Trem2-associated microglial states in the developing hippocampus. Here, we combine single-nucleus RNA sequencing and spatial transcriptomics to generate a lifespan atlas of hippocampal cell populations in rat dams and offspring exposed to late-pregnancy maternal sleep deprivation (LSD) across postnatal day (P)1, 7, 14, and 56. LSD induces widespread, cell-type-specific transcriptional alterations across oligodendrocytes, astrocytes, and excitatory and inhibitory neurons, with a convergent and persistent disruption of microglial states characterized by downregulation of Trem2 and enrichment of gene modules shared with human neuropsychiatric and neurodegenerative risk loci. Behaviourally, LSD offspring display long-lasting deficits in Morris water maze performance, altered anxiety-related and exploratory behaviors in the open field and elevated plus maze, and impaired hippocampal long-term potentiation (LTP). AAV-mediated hippocampal Trem2 restoration, accompanied by recovery of Trem2 signal in Iba1-positive microglia, partially attenuates inflammatory remodeling and ameliorates LTP and behavioural abnormalities. Our findings identify maternal sleep deprivation as an early-life insult that programs a Trem2-centred microglial axis shaping hippocampal circuits and long-lasting cognitive and behavioral alterations, and suggest microglial Trem2 as a candidate target for mitigating neuropsychiatric risk associated with adverse sleep in pregnancy.
Post-traumatic stress disorder (PTSD) involves maladaptive aversion processing and anxiety disorders; however, the correlation between these components and their underlying neural circuit mechanisms remains unclear. The single prolonged stress and shock (SPS&S) model reliably induced PTSD-related aversive behaviors in mice through functional reorganization of the zona incerta (ZI)→lateral habenula (LHb) neural pathway. The ZI primarily projected to the lateral subregion of the LHb (LHbL) and consisted predominantly of GABAergic neurons. During the SPS&S expression phase, mice exhibit weakened ZI-LHb connectivity, accompanied by reduced miniature inhibitory postsynaptic current (mIPSC) frequency in ZI-targeted postsynaptic LHb neurons. Circuit-specific suppression of ZI→LHb neurons in naive mice recapitulated PTSD-related symptoms. Compared to control mice, the presynaptic and postsynaptic neurons in the ZI-LHb pathway of SPS&S expression mice show weakened and amplified activation, respectively, during aversive stimulus processing. During the SPS&S acquisition and expression phase, chemogenetic activation of this pathway rescued anxiety-like behaviors and aversion responses without affecting depression. Downstream tracing reveals divergent functions: the ZI-LHb-ventral tegmental area (VTA) pathway mediates aversion, while ZI-LHb-rostromedial tegmental nucleus (RMTg) primarily regulates anxiety manifestations. Our findings establish the ZI-LHb circuit as a dual-control hub in PTSD-related phenotype, offering new therapeutic targets.
White matter (WM) alterations are well documented in individuals at clinical high-risk for psychosis (CHR-P) and with recent-onset psychosis (ROP), yet it remains unclear whether they reflect vulnerability, psychosis-related symptom severity, or general impairments shared with other disorders such as depression. To disentangle these correlates, we analyzed diffusion MRI data from 882 individuals (457 females) of the multisite Personalized Prognostic Tools for Early Psychosis Management (PRONIA) study, including 183 CHR-P, 206 ROP, 298 healthy controls, and 195 individuals with depression who were assessed as a clinical comparison group of observed associations. Fractional anisotropy (FA) was extracted from 25 WM regions. Analyses of covariance tested group differences across CHR-P, ROP, and healthy controls. Canonical correlation analysis then identified multivariate correlation components between FA and a broad set of twelve clinical, two cognitive, and two risk-related measures in CHR-P and ROP, restricted to regions with group effects. To identify if these findings are not specific to psychosis, we additionally tested whether the correlation components were expressed in the depression comparison group. Group differences emerged in 17 of 25 regions. Canonical correlation analysis identified two significant correlation components. Component 1 linked widespread lower FA with impaired cognition and functioning, representing general impairment. Component 2 linked focal lower FA with more severe psychosis-related symptoms and absence of familial risk. Component 1, but not Component 2, showed also a significant association in the depression group emphasizing its relevance for general impairment. Disentangling general impairment from psychosis-related symptom severity suggests potentially different underlying processes. Developing imaging biomarkers accounting for these processes may guide early detection strategies and targeted interventions.
The catechol-O-methyltransferase (COMT) enzyme regulates dopamine signaling in the prefrontal cortex (PFC). Given the role of PFC dopamine in the regulation of different aspects of executive function, COMT has emerged as a potential modulator of such behaviours. Here, we review the evidence linking COMT with executive function, with a particular focus on human studies using the brain penetrant COMT inhibitor tolcapone. Although small in scale, multiple studies demonstrate improvements in working memory after COMT inhibition in a manner dependent on baseline PFC dopamine signaling, consistent with the well-established inverted-U shaped relationship between PFC dopamine signaling and working memory performance. However, the effects of COMT inhibition extend not only to other cognitive domains but also to other aspects of executive control, notably including the regulation of impulsive or risky behaviours. Findings from rodents are broadly consistent with the human data: animals with lower COMT activity, mediated either genetically or pharmacologically, also show relatively better cognitive performance and reduced impulsive behaviours, compared to wild type/vehicle-treated animals. Taken together, these data are consistent with a model whereby COMT regulates PFC dopamine signaling to modulate top-down control over multiple aspects of behaviour. These findings suggest COMT inhibition as an attractive therapeutic approach in individuals with neuropsychiatric disorders associated with aspects of executive dysfunction.
Early identification of Treatment-resistant schizophrenia (TRS) remains a challenge. Metabolomics offers a promising strategy for identifying biomarkers and uncovering the metabolic alterations underlying TRS. Machine learning (ML), increasingly applied in omics research, enables the development of predictive models from complex datasets. This study aimed to develop a metabolomics-based ML model for TRS identification and to examine its association with cognitive impairments. 112 TRS patients, 232 non-TRS patients, and 145 sex- and age-matched healthy controls were recruited in this study. Psychiatric symptoms were assessed using the Positive and Negative Syndrome Scale (PANSS), and cognitive function was evaluated with the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) and the Toronto Alexithymia Scale (TAS-26). Fasting blood samples were collected from all participants and analyzed by UHPLC-HRMS, enabling the quantification and profiling of 699 metabolites. The resulting data were then subjected to orthogonal partial least squares discriminant analysis (OPLS-DA), and a predictive model was constructed using a random forest algorithm, which was validated through 5 × 5 nested cross-validation with inner-loop LASSO feature selection. From 699 metabolites, 58 metabolites exhibited significant changes (VIP > 1.5, FDR p < 0.05). 5 × 5 nested cross-validation combining an inner-loop LASSO feature selection model yielded a pooled out-of-fold AUC of 0.958 (95% CI (DeLong): 0.940-0.977) for TRS classification. Further correlation analyses revealed distinct associations between the top five predictive metabolites and cognitive domains in TRS versus non-TRS patients. Gamma-glutamylthreonine (gamma-Glu-Thr) showed a significant negative correlation with visuospatial cognition in TRS patients (r = -0.25, p = 0.009). In contrast, it showed a positive correlation in the non-TRS group (r = 0.16, p = 0.015). However, these associations did not remain significant after FDR correction. This study established an RF-metabolomic model for identifying TRS and provided specific metabolic signatures associated with cognitive dysfunction.
