Biological Psychiatry: Cognitive Neuroscience and Neuroimaging

Background: Opioid use disorder (OUD) is associated with a wide range of cognitive, affective, and motivational impairments, suggesting a disruption of large-scale brain systems that support diverse domains of functioning. Resting-state brain network segregation quantifies the degree of functional specialization within brain networks, is age-related, has been linked to brain glucose metabolism, and has been shown to be reduced in substance use disorders. We examined brain network segregation in individuals with OUD and non-OUD controls and tested associations with the duration of opioid use. Methods: Resting-state functional MRI data were collected from 149 individuals with OUD and 126 non-OUD controls. Functional connectivity was computed between brain regions assigned to functionally specialized networks supporting higher-order "association" or "sensorimotor" processes. For each network, segregation was quantified as the extent to which within-network connectivity exceeded between-network connectivity. Results: Individuals with OUD demonstrated lower segregation of the association and sensorimotor networks than non-OUD controls. Within the OUD group, more years of opioid use was associated with lower segregation of the association network, but not the sensorimotor network. Conclusions: OUD is characterized by overall lower resting-state brain network segregation. More years of opioid exposure was associated with lower association-network segregation, consistent with there being cumulative effects of chronic opioid use on large-scale brain organization, though causation could not be examined in this cross-sectional dataset. These findings identify altered network segregation as a potential neurobiological marker of OUD and suggest that restoration of brain network specialization is a measurable target of OUD treatment and potentially recovery.

BackgroundSocial dysfunction is a major source of disability in schizophrenia, yet the neural mechanisms that contribute to impaired social understanding remain poorly understood. Converging evidence points to the role of the default mode network (DMN) in integrating social information over time to construct interpretations of social behaviors. Here, we tested the hypothesis that individuals with schizophrenia show reduced stimulus-driven coordination between brain regions within the DMN during free viewing of naturalistic social stimuli. MethodsA sample of 124 adults (schizophrenia: n=63; healthy controls: n=61) viewed naturalistic video clips during fMRI. Inter-subject functional connectivity (ISFC) was computed within the two groups. Group differences were identified via permutation testing. We also explored group differences in other brain networks to examine whether effects were specific to the DMN. ResultsIndividuals with schizophrenia showed weaker stimulus-driven coupling within the DMN compared to healthy controls, specifically between areas such as the parahippocampal gyrus, precuneus, and medial prefrontal cortex. Group differences in ISFC were specific to the DMN. Furthermore, no between-group differences emerged for within-participant functional connectivity in the DMN, suggesting that the observed effects reflect reduced stimulus-driven coordination among DMN regions when processing social stimuli rather than a more general decline in DMN connectivity. ConclusionsSchizophrenia is characterized by impaired coordination within the DMN as it dynamically integrates social information over time, which could contribute to difficulties in constructing coherent interpretations of real-world social situations. These findings suggest that disrupted stimulus-driven network coordination might underlie social cognitive impairments in schizophrenia, highlighting the value of naturalistic paradigms for revealing network-level dysfunction under conditions that closely approximate real-world experience.

Metacognition is the ability to reflect on and evaluate our own cognitive processes. It is often altered in psychopathology. Yet, the computational mechanisms underlying these alterations remain unclear. In this work, we extend Hierarchical Gaussian Filter (HGF) models to jointly fit trial-by-trial predictions and confidence ratings in a predictive inference task, providing an individualised characterisation on metacognitive processing. Applying our cognitive computational model to a large subclinical open dataset (N=430), we are able to achieve, on average, excellent fit of prediction responses [Formula] and a moderate to good fit of confidence ratings [Formula]. Analysis of experimental change-points revealed that our model accurately captures confidence self-reports dynamics around these change-points. Posterior parameter estimates reveal a negative effect of sensory input prediction errors and a positive effect of sensory input prediction precision on confidence ratings, respectively. In addition, we replicate state-of-the-art findings related to compulsivity as measured by a transdiagnostic factor score, such as inflated confidence and a decoupling of action updates (here, prediction errors) and confidence in compulsivity. These results demonstrate the robustness of our methodology and the potential of joint prediction-confidence modelling to uncover latent metacognitive alterations in psychopathology.

Severe worry is a transdiagnostic syndrome associated with significant morbidity in older adults. In this study, we aim to infer worry-related mental states though brain activity timeseries. We acquired fMRI on two cohorts (N=116 and N=88), using an in-scanner worry induction and reappraisal task. We trained a recurrent long short-term memory (LSTM) neural network, using the first cohort as the train/validation and the second cohort as an independent test set. We predicted worry induction, reappraisal, and neutral states (area under the curve 0.89, 0.77, 0.91 for the test set and 0.78, 0.63, 0.81 for the independent set). The model was most accurate when participants reported high worry during the induction state. Dorsal attention network, and networks seeded on the anterior hippocampus, and supplementary motor area were most important for predicting worry states. The LSTM approach may have critical translational implications for identifying and treating severe worry in older adults.

BackgroundHigh-frequency heart-rate variability (HF-HRV) is a downstream marker of cortico-autonomic regulation linked to executive function. A recent proposal suggests it indexes regulatory processes because it is both a product of and contributor to neural organization within the prefrontal cortex. The oscillatory phase of HF-HRV has been reported to modulate fronto-central EEG amplitude at rest, and this coupling is attenuated in individuals with schizophrenia relative to healthy controls. We evaluated this brain-body correspondence in relation to anxiety symptomatology, which is likewise associated with autonomic dysregulation. MethodsConcurrent EEG and electrocardiography (ECG) were recorded in 22 nonclinical adults at rest and during a mental arithmetic task. Participants rated anxiety severity using the Generalized Anxiety Disorder 7-item scale (GAD-7). We evaluated between-person associations between anxiety severity and HF-HRV-EEG coupling, as well as within-person differences in coupling between rest and mental arithmetic. ResultsAnxiety symptomatology was associated with decreased resting-state phase-amplitude coupling between HF-HRV phase and fronto-central theta amplitude, independent of EEG and HRV covariates. Relative to rest, HF-HRV-theta coupling increased during a cognitive task, independent of condition-related changes in EEG, HR, or respiration. Anxiety severity moderated the task-evoked changes in heart-brain coupling such that more anxious individuals exhibited larger condition-related differences. Simple-slope analyses indicated anxietys effect on coupling at rest was absent when engaged in a task. ConclusionsHF-HRV-theta phase-amplitude coupling captured anxiety-related and state-dependent variance not evident in conventional cardiac or neural indices. This coupling may index a state-sensitive component of cortico-autonomic regulation, although its putative functional role requires direct testing.

Current dimensional approaches to psychiatric disorders have largely focused on explaining differences between individuals, whereas it remains unknown whether symptom dynamics within individuals are organized by the same underlying dimensions. In PTSD, temporal symptom variability may represent a clinically meaningful source of heterogeneity relevant to spontaneous recovery, chronicity, and treatment response. Our reciprocal inhibition model of PTSD proposed that both between-individual heterogeneity and within-individual dynamics may be organized along a dimension reflecting the relative balance between re-experiencing and avoidance symptoms (symptom imbalance), potentially corresponding to shifts between states of emotional under- and overmodulation. Here, using seven longitudinal and two cross-sectional PTSD cohorts spanning disorder development, chronicity, and recovery, we examined whether symptom heterogeneity between individuals and within individuals over time is organized along shared latent symptom dimensions. Principal component analysis (PCA) performed separately on between-individual variability (individual differences) and within-individual variation (temporal variability) consistently recovered the same two axes: the first indexing overall symptom severity and the second reflecting the proposed symptom imbalance. To enable direct comparison across cohorts and between-individual and temporal scales, we integrated cohort-specific covariance structures using hierarchical multi-group PCA yielding universal axes (uPC1/uPC2). Mapping treatment trajectories onto this shared symptom space revealed that two first-line psychotherapies: cognitive processing therapy (CPT) and eye movement desensitization and reprocessing (EMDR): produced comparable reductions in overall symptom severity (uPC1), but opposite shifts along symptom imbalance (uPC2). These findings suggest treatment-related symptom trajectories that are not captured by severity alone and provide a quantitative basis for treatment stratification grounded in symptom imbalance dynamics, motivating prospective tests of state-dependent intervention in PTSD and related psychiatric disorders.

Post-traumatic stress disorder (PTSD) has been associated with impairments in cognitive function, including working memory, and may involve altered glutamatergic regulation in the prefrontal cortex. In this study, we used 7T functional magnetic resonance spectroscopy (fMRS) to examine dorsolateral prefrontal cortex (DLPFC) glutamate during working memory in individuals with PTSD, trauma exposure without PTSD (TE), and no trauma exposure (NT). Eighty participants (27 PTSD, 27 TE, 26 NT) underwent baseline MRS followed by fMRS during a letter n-back task. A linear mixed-effects model was used to evaluate glutamate concentrations across baseline, 0-back, 1-back, 2-back, and post-task fixation conditions. Behavioral performance was assessed using repeated-measures ANOVA for percentage correct, reaction time, and the discrimination index (d) across the 0-back, 1-back, and 2-back conditions. Glutamate differed significantly by group, condition, and the group x condition interaction. Individuals with PTSD exhibited lower glutamate than NT at baseline and during the 0-back, 1-back, and 2-back conditions. TE participants also showed lower glutamate than NT during the 1-back and 2-back conditions. Within-group analyses showed higher glutamate during the 0-back, 1-back, and 2-back conditions than at baseline in the NT group, whereas these baseline-to-task differences were limited in the PTSD and TE groups. Accuracy decreased and reaction time increased with increasing working memory load, and discrimination (d) was lower in PTSD than NT. These findings demonstrate altered DLPFC glutamate dynamics during working memory in PTSD and trauma-exposed individuals. Functional MRS provides complementary information beyond resting-state MRS by characterizing glutamatergic responses during cognitive engagement and may improve our understanding of neurochemical alterations associated with trauma and PTSD.

Background: Neuroimaging studies implicate network alterations in functional motor disorder (FND-motor), yet white matter remains poorly characterized. Objectives: To characterize white matter microstructure in FND-motor relative to healthy (HCs) and psychiatric (PCs) controls and examine symptom associations. Methods: Fifty individuals with FND-motor, 50 age- and sex-matched HCs, and 50 PCs matched on age, sex, depression, anxiety, and post-traumatic stress disorder severity underwent multi-shell diffusion MRI. Voxel-based analyses examined whole-brain white matter using diffusion tensor imaging (fractional anisotropy [FA], mean diffusivity [MD]) and neurite orientation dispersion and density imaging (NODDI) (neurite density index [NDI], orientation dispersion index, and free water fraction [FWF]) metrics. Cross-metric convergence was characterized using atlas-based tract overlap analyses and probabilistic tractography. Associations with FND symptoms and transdiagnostic physical symptoms were also evaluated. Results: Compared with HCs, FND-motor showed higher FA/NDI and lower MD/FWF, predominantly in the middle cerebellar peduncle. Compared with PCs, differences were limited to lower MD/FWF, involving the corpus callosum, middle cerebellar peduncle, and left inferior longitudinal fasciculus. Greater FND symptom severity was associated with a lower FA/NDI and higher MD/FWF in the corpus callosum and right-lateralized association and projection pathways, whereas greater transdiagnostic physical symptom burden across FND-motor and PCs was associated with higher FA and lower MD/FWF in the middle cerebellar peduncle. Conclusions: This study provides a comprehensive multi-metric diffusion-weighted characterization of white matter microstructure in FND-motor relative to both HCs and PCs - highlighting cortico-cerebellar connections via the middle cerebellar peduncle as distinct in FND-motor and associated transdiagnostically with physical symptom burden.

Modeling how functional network connectivity underlies transdiagnostic symptomatology has promised to advance psychiatric medicine by revealing neurobiological mechanisms related to comorbidity. However, network mapping methods have yet to yield clinically-actionable insights, largely due to complexities in the neurobiological underpinnings of symptom comorbidity across disorders and symptom heterogeneity within disorders. Here, we sought to address this problem by leveraging a large (n=317) transdiagnostic dataset of adults with extensive fMRI scanning (>50 min), using connectome-based predictive modeling (CPM) to identify network correlates of an array of psychiatric symptoms. The symptom networks spanned a complex web of shared and unique networks, in which individuals displayed significant heterogeneity in their edge-level dysfunction. We then constructed disordered circuit models that jointly accounted for an individuals symptom severity, the multivariate network space, and network heterogeneity. Although all the symptoms were highly comorbid and none showed specificity to any single diagnostic category, many features within the disordered circuit models were uniquely associated with individual diagnoses and comorbidity patters. These findings shed mechanistic insights into how transdiagnostic symptoms arise from different neurobiological processes depending on a patients diagnostic profile. Thus, this approach provides key insights into where an individuals disordered circuits are located, a critical first step in precision psychiatry frameworks.

Background. Serotonergic psychedelics show promise for treating psychiatric disorders, with symptom improvements lasting for weeks after a single dose. Clarifying the neural basis of these effects would benefit from an identification of empirical biomarkers of such lasting shifts in brain function. Resting state EEG offers a rapid (<5 minute), low-cost window into functional brain networks. However, connectivity is not static, but cycles between recurring semi-stable patterns that vary across frequency bands. Here we employed a dynamic function connectivity (dFC) framework to identify frequency-specific connectivity states and examine how they change in the weeks following psychedelic use. Methods. We collected resting-state EEG from individuals who had used one of two serotonergic psychedelic subclasses within the prior three weeks, psilocybin/LSD (typical; n=14) or 5-MeO-DMT (atypical; n=12), and age- and sex-matched controls (n=16). Frequency-band-specific spatial connectivity states (phase-lag index) were estimated across the full sample (5 per band). Groups were compared on proportion and dwell-time (per state) and state-to-state transitions. Results. A right frontoparietally-distributed beta synchrony state was dominant after both typical and atypical psychedelics use (proportion/dwell-time). This effect correlated with the number of days since using psychedelics. A globally-distributed theta-band state was prominent in recent users of typical psychedelics but occurred less often in atypical users. In contrast, neural entropy (Lempel-Ziv complexity; known to increase acutely during psychedelic dosing) was not altered in recent users of either subclass. Conclusion. These results reveal a beta-band signature of altered neural dynamics in the week following a psychedelic dose, consistent with a relaxation of brain network hierarchy after psychedelics.

Depression is marked by blunted affective responses to context, which interoceptive accounts trace to altered neural representations of bodily states. Yet this evidence mainly concerns response magnitude, not how quickly affect is updated when contexts change. Here we tested whether depressive symptom severity is related to delayed affective updating, and whether cortical dynamics tracking cardiac states account for this delay. To this end, we applied a movie-watching paradigm with independently defined contextual shifts, continuous affect ratings, electroencephalography, and electrocardiography in individuals spanning a continuum of depressive symptoms. Combining deep representation learning and a dynamical systems framework, we quantified how quickly (speed) and how sharply (angle) cardiac-coupled cortical representations reorganized at each shift. Greater symptom severity predicted longer latency to enter the context-congruent affective state across contextual shifts, regardless of valence. In a cross-sectional mediation analysis, slower speed, but not angle, accounted for this association. This mediation was specific to depressive symptoms, contextual shifts, and cardiac-coupled neural dynamics. These findings extend the embodied account of depression from blunted affective intensity toward its inflexible updating at moments of contextual shift, and offer a broadly applicable framework for quantifying brain-body dynamics across affective dysfunctions.

Background: Posttraumatic stress disorder (PTSD) has been associated with advanced biological age in DNA methylation data, but results have been inconsistent. This study evaluated PTSD in association with epigenetic age in the largest cohort to date (by about 20 fold). Methods: Participants were 45,091 US Veterans (92.76% male) enrolled in the VA Million Veteran Program, with VA electronic health record (EHR), self-report PTSD severity (n = 22,835), and DNA methylation and genotype data. PTSD diagnoses predated the blood draw for obtaining DNA by > = one year. Results: PTSD diagnosis, severity, and duration were associated with age-adjusted metrics of epigenetic age (age residuals) per the Horvath, Hannum, PhenoAge, GrimAge, and DunedinPACE epigenetic age algorithms after multiple testing adjustment. The strongest and most robust effect (to additional covariates) was evident for PTSD severity in association with GrimAge residuals (B = .029, adjusted-p = 2.48e-53, up to 2 years advanced age). The relationships between PTSD severity and GrimAge and PhenoAge residuals were stronger among younger vs. older Veterans. In stratified analyses, all PTSD variables were associated with all epigenetic age residuals in the European ancestry subgroup (n = 27,578), but significant associations only emerged for GrimAge and DunedinPACE in the African ancestry participants (n = 11,690). Conclusions: PTSD was associated with advanced epigenetic aging in the largest study to date to evaluate this question. Effects were generally small in magnitude, though meaningful when considering the personal and healthcare system impact of advanced aging in the large population of VA users with PTSD.

Background: Cognitive impairment is the leading cause of disability in schizophrenia with limited treatments. A major barrier to treatment development is the absence of reproducible, mechanistically grounded neural targets. Cross-sectional studies have identified dorsomedial prefrontal cortex (DMPFC)-somatomotor connectivity as a neural marker of cognitive performance on the Auditory Continuous performance task (ACPT), a measure of attention. To test the stability of this marker, we tested the relationship between DMPFC-somatomotor connectivity and ACPT performance in a longitudinal psychosis sample. Methods: Individuals with early psychosis (n=251) and matched controls (n=90) were enrolled and underwent resting-state neuroimaging and neurocognitive assessment. A subset completed longitudinal assessments over 2-4 years. We calculated DMPFC-somatomotor resting-state functional connectivity using a previously identified DMPFC region and a seed in the somatomotor cortex. We performed linear mixed effects models to predict ACPT performance based on connectivity, time, psychosis type, and their interaction. Results: In the psychosis sample, time (p=.0037) and affective psychosis diagnosis (p<.0001) predicted better ACPT performance. In a model predicting ACPT performance, we observed a significant interaction effect of DMPFC-somatomotor connectivity*psychosis subtype (p=.0079) such that DMPFC-somatomotor connectivity predicted ACPT performance only in individuals with non-affective psychosis (p=.0051). We then tested the specificity of this connectivity-cognitive performance relationship. In a model predicting DMPFC-somatomotor connectivity, only ACPT performance (p=.017), but not fluid cognition, was a significant predictor. Conclusions: DMPFC-somatomotor connectivity is longitudinally associated with cognitive performance in early psychosis. This relationship is strongest in nonaffective psychosis, suggesting a novel, reliable target for intervention for cognitive deficits in early psychosis.

Although non-invasive transcutaneous auricular vagus nerve stimulation (taVNS) has demonstrated a therapeutic-relevant potential by enhancing mood recovery and fear extinction, its influence on neural dynamics during naturalistic, sustained fear processing remains unclear. In this study, we employed a randomized, sham-controlled, parallel-group design involving 63 participants (taVNS: n = 33; sham: n = 30) who provided continuous subjective fear ratings (1170 timepoints) while watching a 10-minute fear-inducing video, with simultaneous fNIRS recordings. We employed: (1) a convolutional neural network (CNN) to decode fear ratings from frontal activations, (2) validation of stimulus-evoked activity comparing fNIRS with fMRI signal, (3) dynamic conditional correlation analysis to assess taVNS-induced connectivity changes, and (4) moderation analysis to examine anxiety state effects. Behaviorally, taVNS significantly attenuated fear responses during four threat phases by content analysis: T1 (ghost appearance), T2 (escape sequence), T3 (sudden threat emergence) and T4 (suicide scene). Neurally, taVNS suppressed medial prefrontal cortex (mPFC) activation during escape (T2) and disrupted the typical fear coupling between fear experience and brain activity. Furthermore, taVNS enhanced intra-mPFC functional connectivity, suggesting a potential neural basis for modulating subjective threat appraisal. Additionally, state anxiety significantly moderated brain-behavior relationships. These findings demonstrate that taVNS attenuates fear responses through modulation of mPFC engagement and strengthening frontal network integration. Our results highlight taVNS as a promising neuromodulatory intervention for fear-related disorders (e.g., anxiety disorder), particularly as an early adjunct to exposure-based therapies, warranting further clinical validation.

Depression and treatment-resistant depression (TRD) are significant public health issues, but the associated network-level neurobiological mechanisms remain poorly understood. This study used magnetoencephalography (MEG) to identify altered resting-state connectivity within the default mode (DMN), executive control (ECN), salience (SN), dorsal attention (DAN), motor (MN), and visual (VN) networks as potential biomarkers of depression and treatment resistance. The study recruited 168 participants (80 healthy volunteers (HVs) and 88 currently experiencing a major depressive episode (74 with TRD and 14 without TRD (noTRD))). Data Integration Analysis for Biomarker Discovery using Latent Variable Approaches for Omics Studies (DIABLO) was used to differentiate the depression, TRD, and HV subgroups and identify neural markers of depression and treatment resistance. For differentiating the depression and HV groups, the triple network model (area under the receiver operating curve (AUROC): 0.759-0.787) - which includes the DMN, ECN, and SN - outperformed the six-network model (AUROC: 0.747-0.762) across different bandwidths. For differentiating the TRD and HV groups, the triple network model demonstrated reasonable prediction across different bandwidths (AUROC: 0.737-0.807); potential within-network connectivity differences distinguished those with TRD from HVs, especially DMN within-network connectivity between the inferior parietal lobule and precuneus in the beta band (FDR-corrected p<.05). Hyperconnectivity within the SN (superior parietal lobule and frontal operculum in the alpha band) and DMN (inferior parietal lobule and lateral prefrontal cortex in the beta band) was associated with number of treatment failures (ps<.05). These findings highlight key brain regions and connectivity patterns, advancing our understanding of neural mechanisms underlying depression and treatment resistance.

BackgroundIndividuals with early psychosis (EP) have elevated risk for suicide, the leading cause of death in the first five years following diagnosis. Non-suicidal self-injury (NSSI) significantly predicts suicidal behavior, yet studies of self-injury often exclude participants with psychosis. We investigated effective connectivity in emotion regulation and reward network regions among participants with lifetime history of NSSI or suicide attempt (SA) with and without EP. MethodsResting-state fMRI data were acquired for 23 individuals with EP and 34 non-clinical controls (NCC). We estimated effective connectivity models for regions implicated in the self-injury literature: middle cingulate cortex (MCC), posterior cingulate cortex (PCC), caudate, putamen, posterior superior temporal gyrus (STG), orbitofrontal cortex (OFC), and insula. There were 3 models characterizing different groupings: diagnosis (NCC vs. EP); NSSI (present[+], n=21 vs. absent[-], n=36); and SA (present[+], n=21 vs. absent[-], n=36). ResultsEP was associated with increased STG to PCC and insula to putamen connectivity. NSSI+ (n=7 NCC, 14 EP) had increased PCC to insula lagged connectivity and increased contemporaneous bilateral putamen activity, relative to NSSI- (n=27 NCC, 9 EP). NSSI was positively correlated with lagged insula to putamen activity (p=0.016). SA and NSSI were associated with reduced PCC to caudate connectivity. ConclusionNSSI is associated with increased connectivity within emotion regulation regions and disrupted connectivity between emotion regulation and reward networks modulated by the STG and striatum. Findings are consistent with broader self-injury literature, supporting the utility of using similar interventions from other disorders to address self-injury within EP.

Background: Bipolar disorder is associated with persistent social, cognitive, and functional impairment during euthymia, yet the neural mechanisms underlying these deficits remain unclear. Alterations to self-referential processing are a candidate mechanism, but existing electrophysiological studies rely on emotionally valenced paradigms that potentially confound self-processing with emotional biases. Methods: We analysed electroencephalography from 28 patients with bipolar disorder (type I or II) and 28 age- and sex-matched healthy controls during an emotionally neutral colour judgment task with self-related (preference) and non-self-related (similarity) conditions. Late positive potentials, temporal generalisation decoding, and frequency band decoding (theta, alpha, beta) were used to characterise the temporal dynamics and oscillatory correlates of self versus non-self processing. Results: Controls showed higher overall event-related potential amplitudes and greater self versus non-self differentiation than patients (condition by group interaction, 337 to 946 ms). Broadband temporal generalisation decoding revealed extensive cross-temporal generalisation of the self versus non-self representation in controls, spanning most of the trial, but no significant generalisation in patients. Frequency analyses showed that alpha and beta carried self versus non-self information in both groups, with broader extent in controls, and that anterior theta carried this information in patients but not controls. Exploratory correlations linked decoding measures to rumination and anxiety but not to manic symptoms. Conclusions: The neural representation distinguishing self-referential from externally guided processing was both smaller in amplitude and less temporally sustained in bipolar disorder. Reduced persistence is not detectable by conventional amplitude analyses, and may bear on the self-related and social cognitive difficulties reported in this population.

Individuals with schizophrenia show well-documented impairment in metaphor comprehension, often exhibiting a bias toward concrete, literal interpretations. While this tendency has traditionally been linked to psychopathological and cognitive factors, the contribution of perceptual processes remains underexplored. Here, we tested the hypothesis that figurative language impairment reflects altered perceptual processing, whereby the visual representations evoked by metaphors remain abnormally active and hinder abstraction. A sample of 143 individuals with schizophrenia and healthy controls was administered a novel paradigm where metaphors (e.g., Wisdom is a flashlight) served as primes for target words related to the metaphor vehicle based on visual (e.g., microphone), action (e.g., remote), or semantic features (e.g., lamp). While in healthy participants metaphors activated semantically associated words, individuals with schizophrenia showed sustained visual priming, emerging 1000 ms after metaphor presentation and persisting up to 1400 ms, with both groups showing reverse priming for action targets. Critically, greater visual priming predicted lower metaphor comprehension in patients, whereas greater semantic priming was correlated with better metaphor skills in controls. These results suggest that visual-perceptual representations are not only overactivated in patients compared to controls during metaphor processing but may also interfere with figurative comprehension. We argue that concretism arises from an imbalance between bottom-up sensory signals and top-down contextual priors, leading to the persistence of the visual representations and impaired abstraction. More broadly, these results support multimodal and predictive accounts of metaphor processing and point to altered perceptual dynamics as a previously unappreciated mechanism contributing to pragmatic impairment in schizophrenia.

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.

Catatonia offers no principled basis for sequencing interventions when first-line benzodiazepines fail. Electroconvulsive therapy (ECT) is the established next step, but specifies what to escalate to, not what to stabilise first. Here we show that recovery is a constrained progression across five precision domains of hierarchical inference: sensory ({pi}s), policy ({beta}), motivational ({pi}m), and fast and slow volatility precision ({pi}v_fast, {pi}v_slow). In twenty-five consecutive inpatients managed without ECT, an order {pi}s [-&gt;] {beta} [-&gt;] {pi}m [-&gt;] {pi}v_fast [-&gt;] {pi}v_slow) held without inversion in every patient. Bush-Francis Catatonia Rating Scale (BFCRS) scores fell from 26.3 {+/-} 5.6 to 2.0 {+/-} 2.4 (p < 0.001), and functional recovery tracked restoration of organized action rather than symptom suppression. The framework predicts, untested in this uniformly remitting cohort, that interventions effective at one stage may destabilise another. Within stated conditions, a single inversion falsifies the ordering.