Nature Mental Health

ABSTRACT Background: Low intensity focused ultrasound (FUS) offers a noninvasive method for directly modulating deep brain structures with millimeter-scale precision, potentially addressing a major limitation of existing noninvasive neuromodulation approaches. The amygdala is a key node in affective neurocircuitry and a compelling target for psychiatric intervention. We conducted a first in human active controlled trial (NCT05147142) of amygdala-targeted FUS in patients with major depressive disorder, examining safety and target engagement. Methods: Ten participants with major depressive disorder completed a single-blind randomized crossover trial with blinded symptom ratings, comparing imaging guided FUS targeting the right amygdala versus left primary somatosensory cortex as an active control. FUS comprised two 10 minute applications within diagnostic ultrasound safety limits. Primary outcomes were safety and target engagement assessed with BOLD fMRI during sonication, post-sonication arterial spin labeling (ASL) and resting state functional connectivity. Secondary and exploratory outcomes included symptom change and imaging x symptom relationships. Results: Nine participants completed both sessions. No serious adverse events occurred; neurological, neuropsychological, and MRI safety assessments were unremarkable. Adverse events were more frequent after amygdala than control sonication (p=0.028); one participant experienced clinical worsening following amygdala sonication and required monitoring. Amygdala sonication produced greater perfusion change in the targeted amygdala compared to the control region (p<0.001), and ipsilateral hippocampus (p<0.001). BOLD fMRI during sonication demonstrated engagement of ventromedial prefrontal and rostral anterior cingulate regions, while post sonication resting state connectivity decreased between basolateral amygdala and sensorimotor regions (corrected ps<0.05). Symptom improvement did not differ between conditions, but exploratory imaging x symptom relationships were significant. Spontaneous reports of calm, clarity, or lightness occurred after amygdala but not control sonication (p<0.001). Conclusions: Amygdala targeted FUS produced anatomically specific, multimodal evidence of neuromodulation relative to active control, with a manageable safety profile. Convergent target and circuit level signals support further development of FUS as a precision approach for modulating deep brain targets in psychiatric disorders. Key words: low-intensity focused ultrasound, amygdala, neuroimaging, depression, anxiety

Psychiatric states manifest over multiple timescales, from transient fluctuations in disorder-related distress to long-term variations in disease severity, yet whether these expressions share common neural representations remains unknown. We addressed this question in obsessive-compulsive disorder (OCD) by comparing neural activity associated with momentary distress evoked by triggers versus that associated with clinically assessed chronic symptom severity. We analyzed more than 200 hours of intracranial recordings from the ventral capsule and orbitofrontal cortex throughout deep brain stimulation treatment, together with quantitative facial- and speech-derived measures. Neural signatures of OCD severity were dissociable from and stronger than signatures of momentary distress, indicating distinct underlying brain states. Integrating behavioral data into a predictive model improved neural decoding performance of both momentary distress and OCD severity. These findings demonstrate that clinically relevant psychiatric states across varying timescales are encoded by distinct neural processes. Future closed-loop neuromodulation strategies should consider these differences and choose neural signals associated with the appropriate target state.

Background. Resting-state fMRI (rs-fMRI) co-activation patterns (CAPs) capture recurrent whole-brain states that may index neurobiological substrates of affective psychopathology, yet whether their dynamics co-evolve with symptom severity over the long-term course of depression and anxiety remains unknown. Methods. We analyzed rs-fMRI and clinical data from 340 participants in the NESDA cohort (228 with a depressive and/or anxiety disorder, AFF) at baseline (Y0), 2-year (Y2), and 9-year (Y9) follow-up. Four CAPs were identified and validated. CAP dwell time and entries were related to depression (IDS), anxiety (BAI), and fear (FQ) severity between subjects and within individuals. Results. At baseline, attention-somatomotor (AT-SMCAP) dwell time was inversely associated with depression severity in CTR (rho = -0.29, q < 0.05) but not AFF (rho = 0.09, q = 0.79). Longitudinally, group-level trajectories showed no CAP-symptom coupling; it emerged only within individuals, where increases in frontoparietal-default-mode (FP-DMCAP) dwell time tracked worsening depression in AFF only (Y2 to Y9: rho = 0.47, q < 0.01). Conclusions. AT-SMCAP distinguished health from illness cross-sectionally, whereas FP-DMCAP indexed illness course within individuals, effects not captured by group-averaged analyses. These findings highlight individual-level CAP dynamics as relevant to longitudinal brain-symptom coupling, with implications for precision psychiatry.

Background: Autism spectrum disorder is a prevalent neurodevelopmental condition featuring marked social difficulties. Oxytocin supplementation shows promising therapeutic efficacy in alleviating autism-like traits in rodent models, but clinical effects in humans remain inconsistent. The rodent-derived social salience network (SSN) comprises several oxytocin-modulated brain regions implicated in social behavior, but its conservation has not been established in humans. Here we assess, for the first time, functional connectivity (FC) within a homologous human SSN in autistic men to examine its relationship with behavioral traits and modulation by oxytocin. Methods: The human SSN atlas was collated from open-access cortical and subcortical parcellations, and used to retrospectively analyze a resting-state fMRI dataset of adult men with autism from a previously published, randomized, placebo-controlled oxytocin trial. SSN-wide and sub-network ROI-to-ROI FC correlations with social trait expression and salivary oxytocin concentrations were performed at baseline and post-administration. Treatment specific outcomes on FC were calculated using ANCOVA. Results: We observed SSN sub-network FC correlations with social and repetitive behavioral scores and identified strong oxytocin sensitivity of nucleus accumbens-somatosensory and paraventricular nucleus-somatosensory circuits at baseline. Following nasal spray administration, a strengthening of amygdala-somatosensory circuit was detected as the largest oxytocin-induced FC shift. Notably, baseline connectivity within this circuit strongly predicted treatment response, with individuals having lower baseline FC showing greater post-treatment FC. Conclusions: These findings provide first evidence for clinical relevance of the SSN in humans with autism and highlight circuits that may represent promising biomarkers for predicting oxytocin responsiveness.

Background: Electroconvulsive therapy (ECT) induces widespread brain effects and remains the most effective intervention for severe major depressive disorder (MDD). However, how ECT reshapes the global organization of functional connectomes remains poorly understood. Edge-centric connectomics offers a framework for characterizing large-scale reconfiguration beyond conventional node-based analyses. Methods: Longitudinal resting-state fMRI data from a primary cohort (80 MDD patients, 75 healthy controls) and an independent validation cohort (30 MDD patients) were analyzed. Edge-centric normalized entropy was utilized to quantify connectomic topology at baseline and post-ECT. These topological changes were evaluated for clinical associations and multiscale spatial correlations encompassing cognitive dimensions, neurotransmitter maps, and transcriptomic profiles. Additionally, baseline edge-centric features were leveraged in a machine learning framework to predict treatment response. Results: At baseline, MDD patients showed increased entropy in the subcortical network and decreased entropy in the dorsal attention and sensorimotor networks. Following ECT, a further reduction in sensorimotor network (SMN) entropy was observed, which was replicated in the independent cohort. SMN reorganization was significantly associated with improvements in specific depressive symptoms. Multiscale decoding revealed that these topological shifts spatially aligned with broad monoaminergic receptor distributions and transcriptomic signatures governing neuroplasticity and specific cell types. Furthermore, baseline edge-centric features outperformed conventional fMRI metrics in predicting treatment response and maintained partial cross-site generalizability. Conclusions: ECT is associated with selective reorganization of the sensorimotor network rather than normalization of baseline abnormalities. Edge-centric connectomics combined with multiscale biological annotations provides a robust framework for characterizing therapeutic mechanisms and developing predictive biomarkers in MDD.

Ketamine and subcallosal cingulate (SCC) deep brain stimulation (DBS) have each shown antidepressant effects in treatment-resistant depression, yet how ketamine modulates SCC physiology during chronic stimulation remains poorly understood. We analyzed 5,907 hours of bilateral SCC sensing, 485 participant-triggered recordings across 193 days, 47 ketamine dosing episodes, and an in-clinic session. Across naturalistic and in-clinic recordings, ketamine was associated with a convergent bilateral shift toward 21-40 Hz fast-band activity. Post-dose snapshots showed lower aperiodic-adjusted 5-20 Hz power and higher 21-40 Hz power, while 23.44 Hz activity increased bilaterally after administration. Longitudinal 23.44 Hz tracking revealed larger bilateral increases around anxious and sad events, beginning before self-report and peaking approximately 15 minutes later, smaller decreases after neutral and happy events, and smaller lateralized changes around dosing, despite limited spectral separation between affective states at event onset. These findings provide direct human evidence that ketamine exposure and affective states can engage the same SCC frequency range with distinct spectral and temporal expressions. This positions SCC fast-band activity as a context-dependent signal whose interpretation may depend on its magnitude, laterality, and time course, motivating adaptive DBS strategies that decode evolving SCC dynamics rather than rely on simple band-power thresholds.

Depressive disorders are one of the most common psychiatric conditions worldwide. We systematically screened a prespecified exposure panel for associations with depressive symptoms and evaluated cross-wave replication among Brazilian adults. This preregistered exposure-wide association study used independent, nationally representative cross-sectional samples from the 2013 (n=60,202) and 2019 (n=88,531) Brazilian National Health Surveys. 31 general exposures were assessed with survey-weighted regression; four occupational exposures were analysed separately. The primary outcome was a positive Patient Health Questionnaire-9 screen (PHQ-9 >=10); continuous PHQ-9 score was secondary. Discoveries required a Benjamini-Yekutieli-adjusted p<0.05 in 2013; replication required the same coefficient direction and raw p<0.05 in 2019. 21 general exposures were primary discoveries, and all replicated. Associations spanned health status/health care (n=11), behaviour/participation (n=5), and social/material context (n=5). Poor or very poor vs very good self-rated health showed the largest association (adjusted prevalence ratio 10.97, 95% CI 8.79-13.69 in 2013; 12.33, 10.19-14.92 in 2019). Replicated correlates also included morbidity, smoking, prolonged television viewing, diet, group activities, education, income, sanitation, and nearby public space. All 25 continuous-outcome discoveries replicated. All four occupational associations retained the same direction and raw p<0.05 in 2019. This recurrent profile provides a reproducible map for prioritizing longitudinal research but, because both waves were cross-sectional and exposures were modelled separately, does not establish temporality, causality, or independent effects.

Background: Post-COVID Condition (PCC) affected 10-30% of individuals after SARS-CoV-2 infection, and pre-infection predictors of who goes on to develop it are not well established. We compared pre-infection structural brain variation and baseline psychiatric phenotype as candidate predictors of PCC within a common multi-modal framework in the German National Cohort (NAKO). Both are candidate "first hits" under the second-hit hypothesis, which motivates the comparison; the vulnerability-by-infection interaction that hypothesis turns on is not identifiable among infected participants, so this is a prediction study rather than a test of the hypothesis. Methods: In 8,464 SARS-CoV-2-infected NAKO neuroimaging participants, of whom 2,304 (27.2%) met PCC criteria (weighted post-COVID syndrome (PCS) score > 10.75) and the rest were symptom-free controls, ten pre-infection modalities (T1-weighted volumetric brain MRI across six parcellations, baseline mental health (PHQ-9, GAD-7), demographics, and seven further biomedical and socioeconomic domains) entered a stacked ensemble with 10x5 nested cross-validation (primary metric: the area under the precision-recall curve, PR-AUC). Pre-specified analyses assessed transportability to a non-imaging cohort and robustness to symptom trajectory and unmeasured confounding. SARS-CoV-2 infection status and the PCC outcome were ascertained by self-report; no clinically confirmed diagnoses were available. Results: Baseline mental health, assessed 3-8 years before infection, was the strongest predictor (38.2% of feature importance; standalone ROC-AUC = 0.640), whereas all six brain-MRI parcellations performed at or near chance (ROC-AUC 0.496-0.516). The multi-modal model achieved moderate, well-calibrated discrimination (ROC-AUC = 0.664, 95% CI 0.652-0.677; PR-AUC = 0.413, 0.392-0.434; ECE = 0.015). Applied without retraining to the non-imaging cohort it retained discrimination (ROC-AUC = 0.660, 0.654-0.665), meeting two of three pre-specified equivalence criteria. Adjusting for the full symptom trajectory shrank the baseline mental-health odds ratio from 2.06 (1.83-2.32) to a conservative lower bound of 1.38 (1.20-1.58; E-value 2.66) while leaving it independently significant, and was essentially unchanged under an alternative control definition (2.07). Baseline mental health did not predict objectively measured hyposmia in participants screened before their infection (0.89) while predicting self-reported smell loss in the same participants (1.92). Conclusions: Baseline mental health years before infection is the strongest pre-infection predictor of PCC among the infected, and the association held across the pre-specified sensitivity analyses. Whether it acts specifically on COVID-19 sequelae is a separate question this design cannot answer, and the indirect evidence points away from specificity: the association is undiminished after mild infection but absent among the hospitalised, and baseline mental health predicts current symptom load no more strongly in infected than in non-infected participants. The volumetric structural candidate is not supported: pre-pandemic T1-weighted volumetry carried no predictive signal in this single neuroimaging cohort, consistent with COVID-19-associated brain changes being acute-onset rather than pre-existing. Risk stratification may benefit from incorporating baseline psychiatric phenotype; whether treating it reduces PCC incidence requires interventional study.

Psychiatric diagnoses lack neurobiological grounding, and brain based abnormalities correspond poorly to established diagnostic categories. While multivariate algorithms extract transdiagnostic dimensions from brain behavior covariation, most studies rely on conventional structural morphometric measures such as cortical thickness and surface area, which fail to capture atypical developmental trajectories. Normative modeling (NM) quantifies individual-level brain deviation scores by contrasting brain structural features with population derived percentiles, yielding continuous indices of atypical brain development. However, whether these scores can identify transdiagnostic dimensions remains unclear. We applied sparse canonical correlation analysis (sCCA) to investigate covariation between brain deviation scores and psychiatric symptoms in 1095 children and adolescents from the Healthy Brain Network. We identified an emotion-dysregulation dimension (r=0.33). When generalized to the independent cohort, this dimension showed nominal significance (r=0.18, PFDR = 0.05, uncorrectedP=0.01, n=198). By contrast, no significant dimension was detected using conventional structural brain features. Emotion dysregulation was primarily driven by deviations in cortical thickness and surface area, with distinct regional distributions; reductions in cortical surface area in the middle frontal gyrus, cingulate gyrus, superior temporal gyrus, and insula carried the highest weights. Latent brain scores for emotion dysregulation, computed using sCCA weights for brain deviation scores, showed specific correlations with out-of-model emotion dysregulation related phenotypes. In addition, females exhibited significantly higher latent brain scores than males. Collectively, NM based brain deviation scores were more sensitive than conventional structural metrics in identifying transdiagnostic psychiatric dimensions. Furthermore, latent brain scores derived from sCCA estimated weights may serve as a straightforward metric that captures the degree of dimension-specific atypical brain deviation.

Approximately 30% of U.S. adolescents have consumed alcohol, and earlier onset is associated with faster escalation and greater risk of adult alcohol use disorder. Predicting not only who will initiate drinking but also when could enable more timely and targeted prevention. Existing machine-learning approaches have largely classified drinking status or predefined trajectory classes, requiring fully observed outcomes and often excluding adolescents who remain abstinent at last follow-up. Many also rely on neuroimaging or small, hand-selected feature sets, limiting scalability. We developed a stacked-encoder survival framework for predicting drinking-onset timing from baseline clinical data alone. The framework compresses 1,582 clinical features using an unsupervised denoising autoencoder and re-embeds them with FocalTab, a TabPFN encoder fine-tuned with focal loss; the encoders are then frozen for survival modeling using a neural Cox or random survival forest head. Among 661 NCANDA adolescents who were non-drinkers at baseline, 495 initiated drinking within six years. The stacked representation outperformed single-encoder and raw-feature models, achieving an Uno C-index of 0.7654 and time-dependent AUC of 0.8274. Focal-loss encoding outperformed cross-entropy encoding, while SHAP identified alcohol expectancies, substance access, and community prevention engagement among the most influential predictors. Unsupervised clustering further identified five biotypes with a monotonic gradient in mean time to onset (4.30 to 2.86 years) and differential limbic and thalamic neuroimaging phenotypes. These findings support the feasibility of scalable, clinically based prediction of drinking-onset timing and data-driven adolescent risk stratification.

Efficacy of Transcranial Magnetic Stimulation (TMS) for depression has been shown to correlate with the proximity of stimulation to the region of LDLPFC most anti-correlated with subgenual anterior cingulate cortex (sgACC). Using the Human Connectome Project (HCP) surface-based reconstruction from a structural MRI, we observed that the region of DLPFC most anti-correlated with sgACC falls in anterior parcel 46 and encompasses the junction between adjacent parcels. Individual parcel mapped TMS directed at this target showed high efficacy for treatment resistant depression in a group of 15 patients. Pre-treatment anti-correlation between DLPFC (parcel 46) and sgACC correlated significantly with treatment response. These findings, along with our prior study, demonstrate the feasibility of individual, parcel-guided rTMS and suggest response/remission rates similar to those observed using individualized fMRI localization of the peak anti-correlated region.

Robust sex differences in brain morphology and function exist from birth onwards. While traditionally conceptualized as categorical, they were recently shown to be best described along a female-to-male continuum. Here, we probed the under-explored implications of this proposal for understanding early life vulnerability to psychiatric conditions with sex-biased prevalence. We analyzed environmental, clinical and resting state functional neuroimaging data from 411 participants in Developing Human Connectome Project (dHCP) and 214 children from the Oregon ADHD 1000 study. In the dHCP, we identified complementary patterns of neonatal alignment with sex-typical young adult architecture, which overlapped areas rich in nicotinic cholinergic receptors and predicted the same psychiatric symptoms across sexes in toddlerhood. Specifically, for association (compared to sensorimotor) areas, greater alignment with female-typical functional connectivity (FC) patterns foreshadowed higher anxiety, whereas greater alignment with male-typical FC patterns forecasted autism-related problems. The latter effect was weaker among toddlers with an older epigenetic age at birth and exposure to fewer maternal health problems, but greater environmental enrichment. In the independent child sample, patterns of alignment with sex-typical FC, overlapping those observed in neonates, distinguished a history of depression and generalized anxiety (greater alignment with female-typical FC) from a history of separation anxiety, behavioral problems and current ADHD (greater alignment with male-typical FC). Our study speaks to the psychiatric relevance of sex-related functional brain architecture from birth onwards, its value in identifying vulnerable youths and its promise in monitoring the success of environmental interventions.

Real-time fMRI neurofeedback can modify clinically relevant brain states, but transferring brain dynamics between peers has not been tested, particularly in addiction, where social learning is central to recovery. Here we introduce peer-guided movie neurofeedback (PG-MovieNF), which trains participants to align their brain activity with treatment-related dynamics derived from peers further along in recovery viewing the same movie. Using longitudinal fMRI data from individuals with opioid use disorder watching Trainspotting before and after 8 weeks of adjunctive treatment, we derived a multivariate treatment-predictive signal in dorsolateral and dorsomedial prefrontal cortex. An independent cohort then received real PG-MovieNF or yoked sham feedback during the same film. Real feedback increased alignment with the peer-derived target signal, reduced craving and improved affective ratings. Target alignment was coupled to shared orbitofrontal cortex dynamics during real feedback and predicted craving reduction. Together, these findings establish proof of concept for PG-MovieNF to enhance addiction recovery.

Background: Substance use disorders (SUDs) remain common worldwide and inadequately treated. Neuromodulation targets neural circuits involved in reward, craving, and cognitive control. However, the primary research literature has not been systematically mapped regarding the relative contributions of clinical and preclinical studies. Methods: We conducted a multi-database bibliometric analysis of primary studies on neuromodulation for SUD. Web of Science, Scopus, and PubMed were searched covering 2000-2025. After scope classification and exclusion of secondary literature, 810 primary research documents remained. Performance analysis and science mapping were performed with bibliometrix and VOSviewer. Results: Scientific output grew at a compound annual growth rate of 14.16% (2001-2025), accelerating after 2015. Of 810 studies, 82.8% were clinical, 12.5% preclinical, and 4.7% mixed/translational. Alcohol (31.5%) and nicotine/tobacco (25.7%) dominated the literature and were overwhelmingly clinical (>92%), whereas cocaine and opioids retained larger preclinical shares (24-27%). Repetitive transcranial magnetic stimulation (rTMS) was the leading modality (31.6%), followed by deep brain stimulation (24.9%) and transcranial direct current stimulation (24.2%). Keyword co-occurrence revealed three clusters: a clinical neuromodulation core, a nicotine/tobacco axis, and a preclinical reward-circuitry module. The United States and China led in output. Conclusions: Neuromodulation research for SUD is expanding rapidly and is heavily skewed toward clinical investigations. A persistent clinical-preclinical asymmetry and limited explicitly translational work constitute structural features of the field. Greater integration between mechanistic and clinical research is needed to advance definitive trials.

Worry is an important contributor to anxiety and depressive disorders, particularly in adolescence. Underlying changes in brain networks are thought to drive development of pathological worry, yet the degree to which neural correlates of worry vary by age is unknown. Here, we use multivariate pattern analysis (MVPA) to investigate individual variance in brain activity during worry in a cross-sectional sample of adolescents and adults varying in worry symptoms. Participants (n=69; ages 12-31) completed a personalized worry-induction fMRI task. Brain activity was averaged across k=268 brain regions from the Shen atlas. Participants also completed self-reports assessing trait worry (PSWQ), anxiety (SCARED), and depression (MFQ) severity. A random-forest model was trained on estimated parameters from worry vs. neutral blocks using leave-one-subject-out cross-validation. Age and symptomatology measures were correlated with model performance metrics. The overall cross-validated model had accuracy=66.71%, balanced accuracy=62.61%, sensitivity=80.43%, and specificity=44.78%. Trial-level accuracy increased with in-scanner worry ratings regardless of condition (b =0.62, p<.001). Accuracy (r=0.30, p=.01) and balanced accuracy (r=0.31, p=.01) significantly correlated with age but did not relate to symptom severity. Brain patterns associated with worry become more differentiated across adolescence, suggesting that neural processes of worry consolidate across this period. Thus, early adolescence may be an opportune time for interventions to alter worry, but interventions adapted from adult populations may require tailoring for adolescent patients.

Background. Cord blood DNA methylation profile scores (MPSs) based on genetic and pre-/perinatal risk factors for neurodevelopmental conditions (NDCs) may capture downstream biological effects and help understand how combined exposure signals contribute to NDC risk. Methods. Using data from two longitudinal birth cohorts, Generation R (N-train = 1856, N-test = 476) and ALSPAC (N-validation= 832), we developed cord blood MPSs based on genetic and pre-/perinatal NDC risk factors. We assessed individual and combined predictive performance of risk factors and MPSs for eight childhood psychiatric outcomes (four broad, four specific), measured between ages 5 and 14 years. We also evaluated if the MPSs could be combined into a composite "transmission load" MPS. Results. We validated four novel MPSs: maternal age, birthweight, and genetic liability for ADHD and schizophrenia (r range = 0.08 to 0.29) and included two previously validated MPSs: maternal smoking and gestational age (r range = 0.42 to 0.63). Jointly modeling the six MPSs with their corresponding risk factors explained on average 3.3% of variance in outcomes, higher than that explained by risk factors (1.8%) or MPSs alone (1.6%), indicating complementary sources of risk. The "transmission load" MPS did not replicate due to heterogeneous contributions of the predictors across cohorts. Conclusions. The four novel MPSs based on genetic and pre-/perinatal risk factors can serve as valuable tools for future research. Integrating genetic and prenatal risk factors with DNA methylation at birth can provide insights into their individual and joint contributions to early psychiatric risk and may improve prediction.

N6-methyladenosine (m6A) is the most abundant internal mRNA modification, dynamically regulating post-transcriptional gene expression. Although m6A dysregulation has been implicated in addiction neurobiology, comprehensive characterization of m6A alterations in alcohol use disorder (AUD) remains unexplored. We profiled m6A methylomes across eight reward- and motor-related brain regions in postmortem tissue from 12 AUD subjects and 12 matched controls (192 samples total) using MazF-mediated enzymatic cleavage and microarray-based m6A site mapping. Region-specific differential analysis identified 1,403 differentially methylated m6A sites (P < 0.05, |fold change| [&ge;] 1.5): amygdala (n = 198), caudate nucleus (n = 91), cerebellum (n = 201), hippocampus (n = 89), nucleus accumbens (n = 276), prefrontal cortex (n = 362), putamen (n = 142), and ventral tegmental area (n = 241). Host transcripts were enriched for functional categories spanning intercellular communication, immune and inflammatory responses, RNA and protein synthesis, energy metabolism, and neurodegeneration. Gene set enrichment analysis revealed a coordinated inverse relationship between m6A methylation status and transcript abundance: hypermethylated transcripts were enriched among downregulated genes and hypomethylated transcripts among upregulated genes, consistent with m6A-mediated transcript destabilization. Cross-validation with Allen Human Brain Atlas region-specific expression profiles demonstrated that differential methylation patterns were primarily AUD-driven rather than secondary to regional transcriptional heterogeneity. These findings position m6A epitranscriptomics as a novel molecular signature of AUD and suggest that m6A regulatory machinery may serve as a therapeutic target. Future studies with larger validation cohorts and mechanistic interrogation are essential to establish causal relationships and facilitate translation to drug development.

The left dorsolateral prefrontal cortex (lDLPFC) is the standard transcranial magnetic stimulation (TMS) target for treatment-resistant depression (TRD), yet non-response rates remain high. TMS efficacy has been linked to the stimulation sites functional connectivity, particularly its anti-correlation with the subgenual cingulate cortex (SGC). While this pragmatic strategy has demonstrated clinical utility, it offers limited insight into how the lDLPFCs network interactions contribute to site-dependent variability in treatment response. Here, we used connectivity-based parcellation within a region of interest encompassing common TMS targets in the left prefrontal cortex (TMS-PFC) to delineate functionally distinct subregions and characterize their large-scale network connectivity and behavioral associations. Our results revealed a hierarchical organization: a coarse two-pole antagonism between anterior-central and superior-posterior subregions and a finer nine-cluster architecture exposing the heterogeneity along anterior-posterior and ventral-dorsal axes within TMS-PFC. Anterior-central areas were strongly anti-correlated with SGC and default-mode network, positively connected with salience, dorsal attention, and control networks, and associated with cognitive control. In contrast, superior-posterior subregions displayed the inverse pattern, while ventral clusters engaged somatomotor and visual networks, and language-related processes. Central and superior-anterior clusters showed differentiated profiles, including associations with inhibition, social cognition, and perceptual functions. To aid clinical translation, we derived a likelihood map integrating granularities, highlighting the anterior-central subregion as the strongest TMS candidate given its connectivity and behavioral relevance in depression, while indicating that neighboring subregions have distinct functions. These findings underscore the hierarchical and heterogeneous organization of TMS-PFC and provide a network-informed reference for developing individualized, symptom-specific TMS interventions. HighlightsO_LITMS targets within the left PFC encompass nine functionally distinct subregions C_LIO_LISubregions differ in SGC, DMN, salience, attention, and control network connectivity C_LIO_LIAnterior-central and superior-posterior subregions show antagonistic profiles C_LIO_LIA likelihood map identifies the anterior-central subregion as optimal TMS target C_LIO_LIFindings provide a network-informed reference for symptom-specific TMS targeting C_LI

Stress-related disorders are among the most common and burdensome mental health conditions worldwide, yet the mechanisms that allow most trauma-exposed individuals to maintain or regain mental health remain poorly understood. Decades of research have focused on identifying risk factors for psychopathology rather than the active processes that promote resilience and recovery. Here, we present the study protocol for Stress and Trauma Resilience: Opportunities for National Growth (STRONG), a multi-tiered, multi-domain, multi-level investigation of resilience conducted in Israel in the aftermath of the October 7, 2023 attack and the prolonged national adversity that followed. STRONG uses a nested design that integrates nationally representative longitudinal data with in-depth neurobehavioral assessment. STRONG-1 is a longitudinal, population-based study of approximately 4,600 Israeli adults assessed across five waves over three years, characterizing individual, social, and societal predictors of resilience trajectories. STRONG-2 is a controlled laboratory study of highly resilient and highly vulnerable individuals selected from STRONG-1, assessing behavioral and physiological mechanisms alongside cognitive tests and ecological momentary assessment. STRONG- 3 examines a subset of these individuals in the MRI scanner, capturing structural and functional neural markers with synchronized physiological and eye-tracking data. Advanced computational approaches will integrate data across tiers, levels, and domains into predictive models of resilience. STRONG will establish Israel's first nationally representative dataset on stress resilience and provide a rare opportunity to study human adaptation at scale and in a real-world context. These findings will inform early detection strategies and the development of empirically grounded, modifiable targets for intervention.

Major depressive disorder (MDD) is a highly prevalent psychiatric disorder that remains challenging to treat effectively. Difficulty predicting who will develop the disorder and which individuals will respond to treatment is most likely due to our limited mechanistic understanding and the lack of reliable biomarkers. The current literature suggests that lipid profiles differ in MDD; however, most studies do not integrate other omics layers to elucidate their potential causal relationship at the molecular level. Using phenotypic, genetic, epigenetic and mass-spectrometry lipid data from a Scottish-based cohort of approximately 1,000 participants, we examined associations with individual lipid species. We identified 77 common genetic variant associations ({beta} range = -0.46 to 0.69, p < 3.1x10-10) and 82 genome-wide DNA methylation associations ({beta} range = -1.88 to 1.39, p < 2.2x10-10). We identified 23 lipid species associated with lifetime MDD ({beta} range = -0.23 to 0.2, p < 0.05), including lipid classes such as lysophosphatidylcholine (LPC), diacylglycerol (DG) and phosphatidylcholine (PC). Our integration analysis showed that genetic variants and DNA methylation at sites annotated to the FADS gene cluster were linked with lipid species that were also associated with lifetime MDD status, including LPC(14:1) ({beta} = 0.15, p = 0.03) and PC(19:0)/(20:4) ({beta} = -0.14, p < 0.05). Our findings provide further support for the role of the FADS gene cluster in lipid metabolism and its association with MDD, while introducing DNA methylation as an additional molecular layer supporting this link.