Nature Mental Health

Patterns of brain circuit dysfunction underlying depression and anxiety have been increasingly characterized, including dimensional and subtype variation. A key challenge is determining how such patterns generalize across populations and measurement frameworks. Here, we introduce HARMONY, a harmonized multimodal neuroimaging dataset supporting large-scale investigation of brain behavior associations across symptom-defined dimensions. HARMONY integrates four Human Connectome Project style Connectomes Related to Human Disease cohorts spanning adolescence to later adulthood and capturing anxious misery symptoms. The resource combines standardized HCP style preprocessing, quality control, imaging-derived phenotypes, and harmonized symptom measures into a clinically enriched public dataset. Proof of concept analyses using HARMONY showed that pooling heterogeneous cohorts increased statistical power for detecting associations between imaging-derived phenotypes and anhedonia and depression severity. Effect sizes remained modest, consistent with symptom-based measures across heterogeneous samples. Functional imaging derived phenotypes showed the strongest multivariate predictive performance. In summary, HARMONY provides a large multi cohort resource for reproducible mental health neuroimaging research.

Identifying generalizable brain-based biotypes across independent cohorts is critical for parsing heterogeneity in Major Depressive Disorder (MDD), yet robust subtypes spanning micro- and macroscales remain poorly defined. We applied stability-based clustering to cortical thickness data from 1,531 MDD individuals in UK Biobank (UKB), with external validation in 144 inpatients from IRCCS Ospedale San Raffaele (HSR). Two distinguishable clusters emerged (accuracy=87.5%), with one showing widespread cortical thinning, anergy-related symptoms, childhood trauma, and diabetes comorbidity. This profile generalized with 96.5% accuracy in a hold-out UKB sample and 80.6% in HSR. Mapping clusters cortical profiles onto Neurosynth meta-analytic activation patterns revealed a ventral-dorsal gradient linked with emotion regulation, interoceptive, and motivational processes. Spatial correlations with 19 neurotransmitter receptors and transporters obtained from positron emission tomography identified dopamine transporter as the dominant contributor in UKB, and histamine receptor H3 in HSR. These findings provide a reproducible framework linking MDD subtypes to multiscale biological complexity.

The increased vulnerability to mental health problems in adolescence is frequently reported but poorly understood, hampered by a rigid diagnostic system which fails to capture intertwining symptoms and only loosely aligns with biological axes of variability. Here, we reconceptualised the mental health symptoms of young adolescents in the ABCD cohort (N=11862) as a latent topology of overlapping symptom dimensions, using an unsupervised machine learning algorithm to establish how transdiagnostic dimensions co-occur and overlap within individuals. Combining this with a novel classification approach, we delineated zones within this landscape, within which specific profiles of symptoms were robustly represented. These data-driven profiles were leveraged to establish associated resting-state functional connectivity and genetic characteristics. In doing so we recaptured the commonly reported p-factor axis as well as further symptom-subtype dimensions. Gene ontology analysis revealed that shared neurobiological and cellular mechanisms embedded in both the genome and transcriptome may confer risk for psychopathology.

Adolescence is a period of rapid neurodevelopment during which psychiatric symptoms may emerge, yet symptom-specific markers show inconsistent associations with cognition and brain structure and can rarely be generalised longitudinally. Using data from the ABCD Study, we derived a transdiagnostic mental-health burden measure that integrates multiple symptom domains and examined its cognitive and structural brain correlates in early adolescence longitudinally. Adolescents with higher burden showed consistently lower performance in vocabulary, memory, and processing-speed, alongside widespread reductions in whole-brain, cortical, and white-matter volumes at baseline and after 2 years. These effects were strongest in a subgroup with persistent high burden and replicated in cross-sectional analyses. After 4 years, mental-health differences remained robust, although brain-behaviour associations weakened, likely reflecting developmental reorganisation and reduced sample size. Our study demonstrates that global mental-health burden provides a scalable, developmentally appropriate marker of early psychiatric vulnerability that overcomes limitations of symptom-specific approaches.

This study examined whether connectivity-guided accelerated intermittent theta-burst stimulation (iTBS) improves depressive symptoms beyond routine multimodal inpatient care in hospitalized patients with treatment-resistant depression (TRD). In this randomized, double-blind, sham-controlled trial, patients with unipolar TRD received active or sham iTBS. Stimulation targeted an individualized left dorsolateral prefrontal cortex site showing most functional anticorrelation with the subgenual anterior cingulate cortex on resting-state functional MRI. Treatment was delivered as 3 daily sessions over 10 weekdays (30 sessions; 54,000 pulses) as an inpatient augmentation strategy. Primary and secondary outcomes were changes in Montgomery-Asberg Depression Rating Scale (MADRS) and Beck Depression Inventory-II (BDI-II) scores during the 2-week stimulation phase. Exploratory endpoints included response and remission rates. Of the 57 randomized patients, 51 completed treatment (active, n=27; sham, n=24). The cohort exhibited moderate-to-severe treatment resistance (mean Maudsley Staging Method score, 10.9) and high psychiatric comorbidity. Active iTBS was associated with significantly steeper MADRS improvement than sham (-3.54 points/week; 95% CI, -5.53 to -1.55; PFDR=.02), corresponding to model-estimated reductions of 12.06 versus 4.98 points with a large effect size (d=-0.89). BDI-II trajectories similarly favored active treatment, though with a smaller effect (group-by-time estimate, -0.23 points/day; 95% CI, -0.41 to -0.05; PFDR=.04; d=-0.22). MADRS response rates were higher with active iTBS (42.3% vs 13.0%), while remission rates were numerically but not significantly higher (26.9% vs 12.5%). No serious adverse events occurred. In conclusion, connectivity-guided iTBS produced significant add-on antidepressant effects during acute inpatient treatment of TRD. Larger multicenter trials are needed to establish durability and optimize implementation.

Major depressive disorder (MDD) is a leading cause of disability worldwide with risk of onset and recurrence linked to depressive ruminative thought patterns. Mindfulness-based cognitive therapy (MBCT) is an evidence-based treatment for depression that targets the ability to recognise, decenter, and disengage from ruminative thought patterns. Elucidating how MBCT impacts hierarchical brain organisation may be key to understanding the processes by which MBCT can modulate ruminative tendencies. In a randomised controlled functional magnetic resonance imaging (fMRI) trial on individuals with MDD (N=80) before and after MBCT in addition to treatment as usual (TAU), we investigated changes in hierarchical brain organisation during resting-state and rumination. We built whole-brain models to obtain generative connectivity (GEC) matrices per patient and quantified brain hierarchy by measuring the global directedness and regional trophic levels in each GEC, in which greater directedness reflects more directional information flow and less recurrence. Global directedness in MBCT+TAU compared to TAU increased during rumination, with no changes during resting-state. Furthermore, increased regional breadth of hierarchy during rumination was related to improvements in clinical and behavioural outcomes following MBCT+TAU. Increased brain hierarchy during rumination following mindfulness training may be consistent with a shift away from self-reinforcing negative mental loops towards more differentiated and less coupled cognitive and bodily cycles, supporting MBCT's ability to interrupt ruminative processes. Hierarchical brain dynamics may hold promise as a treatment-sensitive marker and a potential mechanism of therapeutic change in MBCT for depression.

Non-suicidal self-injury (NSSI) is common among adolescents with emotional disorders, yet biological indicators of current NSSI status remain limited. We developed a genome-aware multi-omics modeling framework in 107 adolescents with emotional disorders, including 53 without NSSI and 54 with current NSSI. The model integrated metabolomic, inflammatory, clinical blood and genome-derived features, with polygenic risk score and rare variant burden used as genetic-context variables. The fusion model achieved the strongest classification performance (mean AUC = 0.811) and outperformed single-omics alternatives, indicating that NSSI status was better represented by distributed multi-omics patterns than by a single biomarker layer. Repeated modeling prioritized 42 stable features, many of which were not significant in conventional univariate testing. Group-specific network reconstruction further revealed peripheral reorganization, including convergence of non-NSSI modules into an NSSI-associated module that linked inflammatory recruitment with weaker immune-communication, repair and support-related signals. Exploratory MRI, gut-related and stress-endocrine analyses provided additional biological anchors, while a compact sentinel marker panel translated the full model into clinically readable profiles. These findings support a distributed, genome-aware peripheral state associated with current NSSI and provide a framework for future validation of multi-omics state markers in adolescent emotional disorders.

Major depressive disorder (MDD) is a heterogeneous mental disorder characterised by rumination. Mindfulness-based cognitive therapy (MBCT) is an evidence-based treatment developed to target rumination and recurrence risk. Ongoing studies have begun to identify neural changes associated with treatment effects. However, the low-dimensional organisation underlying whole-brain dynamics remains largely unexplored and may provide a more complete characterisation of the neural processes through which MBCT exerts its therapeutic effects in MDD. Here, we investigated functional magnetic resonance imaging (fMRI) of a randomised controlled trial of MBCT with treatment as usual (TAU), or TAU alone, in a group of MDD patients (N=80). We applied a novel framework, complex harmonics decomposition (CHARM), to uncover low-dimensional manifolds in the spacetime domain, capturing local as well as non-local interactions made possible by brain criticality and amplified by the anatomical long-range connectivity. We successfully identified distinct distributed spatiotemporal manifolds across brain states and outperformed traditional dimensionality reduction techniques. During rumination after MBCT we found consistent recruitment of regions involved in bodily and interoceptive processing integrated within the whole-brain across manifolds, changes in latent configurations associated with clinical and behavioural improvements, and greater flexibility within the reduced space. Integration of bodily and interoceptive processing regions within distributed whole-brain manifolds and greater brain flexibility may be associated with reduced 'stickiness' of ruminative thinking patterns following mindfulness training in depression. Our findings highlight the promise of low-dimensional manifolds and long-range interactions arising from critical brain dynamics in understanding how mindfulness targets depressive ruminative processing.

Distinguishing causal biology from confounding or downstream consequences of psychiatric disorders remains a key barrier for drug development in psychiatry. We performed a large-scale proteogenomic investigation using a sequential triangulation framework integrating plasma proteomics, Mendelian randomisation, genetic colocalisation, transcriptomics, rare-variant analyses, and clinical phenotyping to identify causal proteins and prioritise therapeutic targets for depression, anxiety, bipolar disorder, and psychotic disorders. Using 2,920 plasma proteins measured in 52,615 UK Biobank participants, we identified 830 protein-disorder associations involving 574 proteins. Mendelian randomisation and colocalisation prioritised 26 proteins with putative causal effects, of which 17 are potentially druggable. Integrating multi-omic and phenotypic evidence ultimately resulted in five high-confidence causal candidates: DDR1 and LTB for depression, DDR1 for anxiety, DSG3 and PBXIP1 for bipolar disorder, and PDIA3 for psychosis. These findings provide convergent evidence implicating neuroimmune and neurodevelopmental pathways in psychiatric disorder biology, while also identifying potentially tractable targets for therapeutic development.

Background: Transcranial magnetic stimulation (TMS) is a promising treatment for substance use disorders (SUDs), although heterogeneous stimulation parameters hinder the identification of optimal strategies. Using meta modeling, we linked treatment effect sizes (Hedges' g) to simulated electric field (E field) distributions to identify brain regions associated with efficacy variability. Methods: TMS trials in individuals with SUDs published through the end of 2025 were identified through a systematic PubMed search. Studies reporting craving or consumption outcomes with quantifiable effect sizes were included. Objectives were to (i) examine associations between study-level effect sizes and simulated local E field strength in MNI space for craving and consumption outcomes; (ii) generate a combined E field effect size association map; and (iii) assess spatial overlap with fMRI drug cue reactivity patterns in 60 individuals with SUDs. Results: The analysis included 81 randomized controlled TMS studies, yielding 107 effect size estimates for craving and consumption (n = 75 and n = 32, respectively). Compared with sham stimulation, TMS produced small-to-moderate improvements in both outcomes. E-field modeling identified the pre-supplementary motor area (preSMA) and inferior frontal gyrus (IFG) as regions associated with variability in craving-related effect sizes, and the frontopolar cortex with variability in consumption-related effect sizes. Correlation maps were highly robust (mean leave one out similarity r = 0.996), and the frontopolar cluster showed significant spatial overlap with fMRI drug cue reactivity patterns (Dice coefficient = 0.37). Conclusion: These findings identify frontopolar, preSMA, and IFG regions where local E-field strength is associated with SUD treatment effects, supporting more precise neuromodulation strategies.

Finding suitable therapies for treatment-refractory neuropsychiatric disorders constitutes a major goal for translational neuroscience. Deep brain stimulation shows promise for treatment resistant depression, but treatment efficacy varies substantially across patients. Objective, electrophysiologically driven strategies to optimize deep brain stimulation for treatment resistant depression could greatly improve clinical efficacy by minimizing the trial-and-error approach needed to personalize stimulation settings. This may not only reduce the delay between the start of the treatment and symptom improvement, but also enable acute, real-time verification of circuit engagement, advancing our understanding of the mechanism mediating antidepressant effects. Here, we investigate whether cerebro-cerebral evoked potentials elicited through different deep brain stimulation configurations could be used to guide stimulation personalization for treatment resistant depression. Cerebro-cerebral evoked potentials offer a fast, objective way to identify regions engaged by stimulation, revealing the effective connectivity pattern of the stimulated location. Data were collected from eight patients with treatment resistant depression who received dual bilateral deep brain stimulation devices targeting the subcallosal cingulate and ventral capsule/ventral striatum. During an initial in-hospital monitoring period, single-pulse electrical stimulation was delivered through the deep brain stimulation devices and cerebro-cerebral evoked potentials were recorded through temporary stereo-electroencephalography probes across fronto-temporal regions. Patients underwent several outpatient stimulation programming sessions over the course of 9 months to identify the stimulation configurations leading to the greatest improvement in depressive symptoms. We retrospectively analysed cerebro-cerebral evoked potentials obtained in response to stimulation of different stimulation configurations to identify features distinguishing the clinically effective configurations. The deep brain stimulation configurations leading to the greatest improvement in depressive symptoms were associated with significantly larger evoked potentials in the orbitofrontal cortex and showed an increased number of evoked potentials across dorsal and ventral prefrontal regions. Waveform similarity analysis revealed a gradient in therapeutic effects, such that multiple alternative stimulation configurations led to similar symptom improvement. The vast deep brain stimulation parameter space might contain a configuration subspace defined by comparable therapeutic effects. In addition, evoked potentials obtained from single-pulse and from bursts of high-frequency stimulation displayed similar spatial patterns, suggesting that either method might be able to identify the configuration best engaging the circuit mediating the clinical response. Together, these findings provide proof-of-principle evidence that stimulation-evoked prefrontal responses reflect network engagement associated with antidepressant effects. Cerebro-cerebral evoked potentials may offer an objective and acute strategy to guide contact selection in deep brain stimulation for treatment resistant depression.

HighlightsO_LILeader-follower roles structure dyadic coordination mechanisms C_LIO_LIMirroring yields a robust Follower reaction time advantage C_LIO_LITonic-phasic analyses reveal scale-dependent condition asymmetry C_LIO_LIPhasic dynamics resolve into Leader anticipation and Follower post-response inhibition C_LIO_LIIntegrated information decomposition reveals a role-specific inter-brain predictive architecture C_LI How do leader and follower roles shape the brain mechanisms that support coordinated action between people? This question has direct therapeutic relevance for conditions such as schizophrenia and autism spectrum disorder, where the capacity for reciprocal social coordination is a defining vulnerability. Here, we propose a tetradic framework and examine sensorimotor coordination in 16 healthy adult pairs using simultaneous dual-brain EEG hyperscanning, a 2x2 within-subject design crossing Role (Leader/Follower) and Condition (Mirroring/Matching). Mirroring required resonance with a partners movement; Matching required its controlled transformation. This contrast was designed to dissociate automatic from controlled coordination processes across roles. Behaviourally, Mirroring produced a reaction-time advantage that was selective to Followers, a finding replicated in a combined cohort, and consistent with role-dependent attention-inhibition gating. At the neural level, sustained (tonic) activity was dominated by Matching-related frontoparietal engagement regardless of role, while time-resolved (phasic) activity revealed a Mirroring-dominant reorganization that differentiated into role-specific patterns: anticipatory gating in Leaders and post-response inhibitory rebound in Followers. Information-theoretic decomposition of inter-brain coupling identified a Leader-specific predictive signal in medial prefrontal and cingulate cortices, a Follower-specific adaptive signal across sensorimotor and temporal regions, and a shared redundancy scaffold in orbitofrontal and insular cortices. These findings characterize tetradic coordination within dyads as a multiscale, role-asymmetric architecture in which top-down predictive control and bottom-up adaptive regulation are functionally dissociable. The tetradic framework provides an organizing scaffold for this dissociation, and the role-specific signatures it reveals offer candidate biomarkers for clinical populations in whom interpersonal coordination is disrupted.

Background: Individuals with attention-deficit hyperactivity disorder (ADHD) are at increased risk for cannabis misuse, with increasing prevalence among young adults. Existing evidence suggests that cannabis can have therapeutic effects on ADHD symptoms, and continued use may be partly driven by perceived improvements in symptom-related deficits. To investigate the neural evidence for these associations, we integrated functional neuroimaging and Allen Human Brain Atlas transcriptomic data to assess neural correlates of ADHD in regions targeted by cannabinoids as predictors of cannabis use. We hypothesized that greater ADHD symptoms would lead to higher cannabis use frequency through associations of ADHD symptoms with functional deficits in cannabinoid receptor type 1 (CB1R; encoded by the CNR1 gene) expressing brain regions. Methods: We tested 466 college students (ages 18-19) with varying ADHD symptom severity and cannabis use, self-reported at baseline and three yearly-follow up questionnaires. ADHD-related neural deficits were tested in a subset of 144 participants using an fMRI stop-signal task at baseline. Growth mixture modelling categorized participants with similar cannabis use into three latent classes. The covariance between the CNR1 gene expression map and differences in stop-signal task activation were tested as a mediator linking ADHD symptoms and cannabis use. Results: Greater ADHD symptoms significantly predicted reduced activation within CNR1-expressing regions, which predicted higher-use cannabis class membership. Conclusions: Our results add support for the self-medication hypothesis for higher rates of cannabis use among individuals with greater ADHD symptoms, which may be mechanistically linked through CB1R-enriched attention and inhibitory networks, highlighting neural targets for prevention and treatment.

Background and Hypothesis: Schizophrenia (SZ) and bipolar disorder (BD) share overlapping yet distinct clinical profiles and system-wide brain alterations. Macroscale functional connectivity gradients capture principal axes of cortical organization, including the separation of unimodal and transmodal systems, offering a low-dimensional lens on individual differences in brain architecture. Whether these axes reflect shared or diagnosis-specific variation across the SZ-BD spectrum is unknown. Study Design: Using resting-state fMRI from 187 adults (110 HC, 37 SZ, 40 BD) from the UCLA Consortium for Neuropsychiatric Phenomics, we derived individual low-dimensional gradients and applied three analyses: case-control comparisons at both the cortical network and subcortical region-of-interest level, Partial Least Squares (PLS) regression linking gradients to clinical phenotypes, and individual-level similarity indices (SI-PLS) positioning participants within a gradient-behaviour space. Study Results: While the gradient structure (G1: visual-somatomotor and G2: unimodal-transmodal) was preserved across groups, patient groups showed greater deviations along both axes. Network analyses revealed transdiagnostic frontoparietal compression in G2, alongside disorder-specific effects: visual pole contraction and subcortical amygdala displacement in SZ, and somatomotor displacement in BD. PLS identified a BD-associated profile of preserved gradient architecture and lower symptom burden, contrasting with an SZ-associated profile of greater cognitive impairment and symptom severity. SI-PLS scores placed SZ and BD in distinct regions of a shared two-dimensional neural space, with HC between them. Conclusions: Differences across the SZ-BD spectrum organize along two principal axes, revealing transdiagnostic alterations in higher-order association systems alongside disorder-specific sensory signatures. These findings support a multi-axis dimensional framework for understanding clinical heterogeneity in psychosis.

Adolescent externalizing behavior is a major risk factor for later substance use and other psychiatric outcomes. Understanding its genetic architecture and its relationship with brain imaging phenotypes requires scalable genome-wide methods applied to youth cohorts. Using data from the Adolescent Brain Cognitive Development (ABCD) Study, we implemented a pipeline for genome-wide association studies (GWAS) of longitudinally measured externalizing traits and multimodal neuroimaging-derived phenotypes (IDPs). We performed quality-controlled genotype processing and constructed harmonized phenotype and covariate datasets. GWAS analyses were conducted using REGENIE in a two-step framework, with Step 1 ridge regression models trained on LD-pruned variants and Step 2 association testing performed genome-wide. Externalizing traits measured at baseline and summarized as longitudinal means and slopes, together with approximately 200 IDPs measured at baseline and summarized as longitudinal means and slopes, were analyzed. We further constructed a custom linkage disequilibrium (LD) reference panel using unrelated individuals and computed LD scores using LDSC. Genetic correlations between externalizing traits and imaging phenotypes were estimated using LD Score Regression. This exploratory study systematically evaluated genome-wide genetic correlations between regional cortical morphology and externalizing phenotypes in adolescence. Although several associations reached nominal significance, none remained significant after correction for multiple comparisons. These findings should not be interpreted as demonstrating an absence of shared genetic architecture. Rather, the precision of the estimates was constrained by the available imaging GWAS sample size, uncertainty in SNP-heritability estimates, and the large number of regional comparisons. Larger imaging-genetics samples and independent replication will be required to determine whether modest or regionally specific genetic correlations exist.

Background: Opioid use disorder (OUD) is heritable, yet most genome-wide association studies (GWAS) have focused on European populations, leaving the genetic architecture of OUD in non-European populations underexplored. Methods: We conducted GWAS of OUD across three ancestries using electronic health records and genomic data from 52,357 All of Us Research Program participants (8,912 cases; 43,445 matched opioid-exposed controls; 48.5% female). Participants were stratified into European (EUR), African (AFR), and Admixed American (AMR) ancestry groups for logistic regression GWAS, with independent replication in the Million Veteran Program. We then applied the deep-learning model AlphaGenome to predict the tissue-specific transcriptomic and splicing consequences of top risk variants across 13 reward-pathway brain regions. Results: We identified and replicated a novel DDX6 risk locus, alongside established OPRM1 and FURIN signals. AlphaGenome predicted the DDX6 regulatory allele downregulates the stress-resistance gene FOXR1 in the nucleus accumbens, while the protective OPRM1 variant (rs1799971) upregulates OPRM1 expression across reward networks. Other signals of interest included IL6R and SHISA9 (EUR); GHR (AFR); and ASTN2 (AMR). Conclusions: This study identifies DDX6 as a novel OUD risk locus, replicates associations with OPRM1 and FURIN, and highlights biologically plausible ancestry-specific signals in AFR and AMR populations. We also replicated top variants in an independent population. Finally, integrating GWAS with deep-learning annotations provides specific, localized biological hypotheses to guide future experimental validation and targeted therapeutics.

Inhibitory control matures progressively from childhood to early adulthood, yet the neural mechanisms driving this development and their relevance to psychiatric risk remain poorly understood. Guided by the Dual Cognitive Control model, we leveraged longitudinal fMRI from two independent cohorts in the US (ABCD, ages 9-12) and Europe (IMAGEN, ages 14-22) to map the spatiotemporal dynamics of reactive and proactive control using novel single-trial modeling and representational similarity analysis. We found both reactive and proactive stopping networks stabilize after mid-adolescence, tracking the developmental patterns of inhibitory control and behavioral stability. By decoding trial-by-trial fluctuations along a speed-caution continuum, we demonstrate that brain-behavior coupling to a proactive "Safe state" tightens progressively with age. Furthermore, network-level representational coherence of this Safe state emerged as a scanner-invariant, trait-like biomarker that robustly predicted inhibitory control, behavioral stability, and transdiagnostic psychopathology across multiple developmental windows, providing a validated neural phenotype for precision psychiatry.

Background: Long COVID is a syndrome characterized by symptoms and conditions across all biological systems. This breadth of Long COVID phenotypes impedes efforts to identify the mechanistic pathways of Long COVID. Low serotonin may play a role in long-term sequelae of COVID-19, and selective serotonin reuptake inhibitors (SSRIs) may prevent these sequelae. Evaluation of the relationship between SSRIs and distinct categories of symptoms and conditions associated with Long COVID can highlight the mechanistic pathways that drive these relationships. Methods: We evaluated electronic health record data from a retrospective cohort of patients in the National Clinical Cohort Collaborative with comorbid depression and COVID-19 between October 2021 and February 2024. We estimated the relationship between SSRI prescription (versus no SSRI prescription) during acute COVID-19 and the one-year cumulative incidence of Long COVID-related conditions and symptoms across 14 human phenotype ontology categories. We applied Super Learner and targeted maximum likelihood estimation to estimate risk ratios while adjusting for confounders of interest and correcting for false discoveries from repeated testing. Results: We evaluated EHR data from 542,938 patients. We found that patients who were prescribed SSRIs during COVID-19 had a significantly lower risk of symptoms and conditions related to gastrointestinal factors (adjusted risk ratio (aRR) 0.95, 95% CI 0.92, 0.97), general health (aRR 0.91, 95% CI 0.88, 0.95), headaches (aRR 0.96, 95% CI 0.92, 0.99) and skin (aRR 0.92, 95% CI 0.87, 0.98). Discussion: We found that the prescription of SSRIs during acute COVID-19 was associated with a significantly lower risk of post-COVID sequelae related to gastrointestinal, headache-related, skin-related, and general symptoms and conditions, compared with no SSRI prescription. These findings highlight the role of serotonin in Long COVID and specific sequelae that may be reduced by SSRIs.

Repetitive Transcranial Magnetic Stimulation (rTMS) is a promising treatment for tobacco use disorder (TUD). Although at a group level, active stimulation outperforms sham, at an individual level, variability exists in clinical response. The behavioral and neurobiological factors that differentiate those who respond to rTMS from those who do not remain unclear. To explore individual factors that influence acute responses to rTMS, N = 60 human participants received one session of rTMS to the dorsolateral prefrontal cortex (DLPFC) and to a control region (visual cortex; V5) in a randomized order. They completed behavioral assessments and neuroimaging before and after rTMS sessions. Hypotheses involving behavioral and neuroimaging predictors of response were pre-registered prior to completion of data collection. rTMS to the DLPFC led to significant reductions in self-reported cigarette craving compared with rTMS to a control brain region (p = 0.0006) and participants were classified as n = 38 responders and n = 22 nonresponders. Responders used significantly more cigarettes per day (M = 11.441) compared to nonresponders (M = 7.952), reported higher levels of cigarette craving (d = 1.059), and more severe nicotine withdrawal (d = 0.803) prior to rTMS. Neuroimaging analyses based on preregistered hypotheses indicated that DLPFC-frontoparietal and insula whole-brain functional connectivity did not differ significantly between responders and nonresponders. However, exploratory analyses revealed that responders had reduced pre-rTMS functional connectivity between the insula and nucleus accumbens, precuneus, and occipital pole. These findings suggest that response to rTMS for TUD is associated with greater baseline cigarette consumption, craving, and withdrawal, in addition to distinct functional connectivity patterns related to salience, reward, and self-referential processes, providing candidate behavioral and neural markers for personalized rTMS interventions for TUD.

Externalizing has been associated with differences in brain structure and function; however, it remains unclear whether these associations reflect shared common-variant genetic influences. Cross-trait linkage disequilibrium score regression was used to estimate genome-wide genetic correlations between externalizing GWAS results and 3,935 brain imaging-derived phenotypes from the UK Biobank BIG40 resource. Imaging phenotypes covered structural MRI, diffusion MRI, susceptibility-weighted imaging, resting-state functional MRI, and task functional MRI. Analyses were included in the primary dataset when the imaging phenotype had positive SNP heritability, a heritability Z statistic of at least 1.96, a mean GWAS chi-square statistic of at least 1.02, at least 200,000 regression SNPs, and a complete LDSC result without a fatal error. Technical imaging quality-control phenotypes were excluded from biological inference. Individual results were corrected using the Benjamini-Hochberg false discovery rate procedure. Aggregated Cauchy association tests were used to evaluate evidence across all imaging phenotypes and within predefined imaging categories. Power, simultaneous confidence bounds, and alternative quality-control definitions were examined in sensitivity analyses. Of 3,935 imaging phenotypes, 3,716 produced estimable genetic correlations, 2,980 met the primary LDSC quality-control criteria, and 2,967 were biological imaging phenotypes. No individual phenotype survived false discovery rate correction; the smallest unadjusted P value was 0.0005, and the minimum adjusted q value was 0.486. The distribution of genetic correlations was centered near zero, with a median genetic correlation of 0.0014 and a median absolute correlation of 0.0338. There was no aggregate evidence across all biological imaging phenotypes using ACAT (P = 0.302), and no predefined imaging category survived correction. The median minimum detectable genetic correlation at 80% power was 0.216. Bonferroni-adjusted simultaneous confidence intervals were contained within [-0.30, 0.30] for 80.0% of phenotypes in the primary analysis and 88.0% under stringent heritability quality control. Broad and stringent sensitivity analyses produced the same overall conclusions. In this study, no statistically robust evidence of global genetic correlations between externalizing and individual UK Biobank brain imaging phenotypes was found. Small, localized, mixed-direction, or developmentally specific genetic effects remain possible.