Biological Psychiatry: Cognitive Neuroscience and Neuroimaging

Background. Psychosis spectrum symptoms (PSS) are prevalent in youth and are associated with increased risk for psychotic disorder, suicidality, and functional impairment. Computationally, PSS may stem from altered predictive coding of basic sensory surprises and environmental volatility. Formalized as hierarchical precision-weighted prediction errors (pwPEs), this altered processing is a proposed mechanistic substrate of aberrant perceptual inference across disorders, including psychosis-risk populations. While the auditory mismatch negativity (MMN) provides an electrophysiological index of pwPEs, it remains unknown if distinct hierarchical pwPE components distinguish youth who endorse PSS. Methods. A sample of 131 participants (PSS-=66, PSS+=65; ages 11-24) from the ongoing Toronto Adolescent and Youth (TAY-CAMH) Cohort study were stratified by PSS status using the PRIME Screen-Revised and were assessed for their psychosocial functioning. 64-channel EEG was recorded during an auditory oddball paradigm with stable and volatile phases. A hierarchical Bayesian model applied to the stimulus stream generated trajectories of low-level sensory and high-level volatility-related pwPEs. Alongside standard phase-averaged event-related potentials (ERPs), Bayesian trajectories derived model-based ERPs. Results. Replicating prior findings in non-clinical controls, stable-phase MMN significantly exceeds volatile-phase MMN and lower psychosocial functioning was associated with reduced volatile-phase MMN amplitude. Age significantly modulated oddball MMN and unweighted prediction errors ({delta}1, {delta}2). Group differences between PSS+ and PSS- were statistically significant for volatility-level pwPE ({epsilon}3), peaking at ~180 ms Peri-Stimulus Time (pFWE-peak =.024). Conclusions. Independent of age-related developmental effects, volatility-level pwPE learning ({epsilon}3) constitutes a more sensitive EEG marker associated with PSS status in help-seeking youth than low-level sensory pwPE.

BackgroundCognitive restructuring, the process of identifying and challenging negative thoughts, is a key technique for treating depressive and anxiety disorders. Although neuroimaging studies have characterised the brain systems supporting cognitive restructuring in healthy individuals, it remains unclear how these systems are altered in depression and anxiety, or whether each disorder is associated with distinct neural dysfunction. MethodsSeventy-three clinical participants with depressive or anxiety disorders and 70 healthy controls completed a cognitive restructuring paradigm during 7 Tesla functional magnetic resonance imaging (fMRI). The task required participants to either repeat a series of negative statements or challenge them using Socratic questioning. Group-level fMRI analyses examined the effects of depressive and anxiety symptom severity on brain activation, while dynamic causal modelling characterized the directional neural influences between implicated regions. ResultsDuring challenging compared to repeating statements, greater depressive symptoms were associated with reduced dorsolateral prefrontal cortex (dlPFC) activation. Conversely, greater anxiety symptoms were associated with greater dlPFC activation. Effective connectivity results revealed that depressive symptoms were associated with greater inhibition from the ventrolateral prefrontal cortex (vlPFC) to the ventromedial prefrontal cortex, whereas anxiety symptoms were associated with greater excitation from the dlPFC to amygdala and greater inhibition from the vlPFC to amygdala. ConclusionsWhile clinical participants modified negative beliefs as effectively as healthy controls, depressive and anxiety symptoms were associated with dissociable neural signatures during restructuring. This suggests that cognitive behavioral therapy may engage partially distinct mechanisms depending on symptom profile, a possibility that warrants longitudinal investigation of treatment response.

The brains response to potential threats is shaped by the egocentric spatiotemporal distance to the threat. While distal threats recruit evaluative, cognitive-fear networks for strategic planning, proximal threats engage evolutionarily conserved reactive-fear circuitry to mount immediate defensive responses. Understanding this dual neural architecture is clinically relevant, as the spatial proximity to a traumatic event predicts subsequent psychiatric symptom severity and recovery. Despite the well-characterized disruptions to threat circuitry in posttraumatic stress disorder (PTSD) and major depressive disorder (MDD), how these networks respond to threats at distinct spatial distances has yet to be investigated in these disorders. Utilizing 3D virtual reality technology, we implemented a spatially-modulated fear conditioning paradigm by presenting human avatars at proximal (peripersonal) and distal (extrapersonal) distances, paired with shock, to trauma-exposed participants (n = 50) during functional MRI (fMRI). We modeled associations between PTSD and MDD symptom severity and task-evoked hemodynamic responses within cognitive-fear and reactive-fear threat networks. Our results support a transdiagnostic disruption of thalamic responses to proximal threats, driven by heightened activation to the safety stimulus and stronger deactivation to the threat stimulus. MDD showed a disorder-specific effect of disrupted activity across cognitive-fear and social cognition regions in response to proximal social threats, and illness severity-by-proximity effects across motor regions. PTSD symptom severity was uniquely associated with hyperactivity of the amygdala to distal threats. Transdiagnostic disruption of connectivity between the dorsal precuneus and several subcortical structures followed a disorder-specific gradient across the anterior-posterior axis. While thalamic disruptions to threats represent a shared transdiagnostic effect, PTSD and MDD are distinguished by differences in amygdala hyperactivation and cognitive-fear network hypoactivation, respectively.

Objective: Working memory (WM) deficits are a co-occurring feature to numerous neuropsychiatric disorders, particularly attention-deficit/hyperactivity disorder (ADHD), and there remain no treatments that directly target WM. The coupling between the phase of theta band activity and amplitude of gamma band activity (i.e., TGC) is an established neural correlate of WM. However, no studies have examined WM-related TGC in ADHD or whether neuromodulation can modulate these oscillatory dynamics in youth. This set of studies examined the effects of intermittent theta burst stimulation (iTBS) to the left dorsolateral prefrontal cortex (DLPFC) and left posterior parietal cortex (PPC) on TGC in youth with ADHD. Methods: In two randomized, double-blind, sham-controlled crossover trials, adolescents with ADHD and clinically significant parent-reported WM symptoms first completed a single-session study comparing DLPFC versus PPC iTBS targeting (n = 47) and then a multi-session clinical trial comparing 10 sessions of active versus sham left DLPFC iTBS (n = 29). Participants completed a computerized visuospatial Sternberg WM task with concurrent electroencephalography (EEG) before and after the single sessions, as well as at baseline, midway through treatment, and approximately 24 hours after the final session within the multi-session trial. Phase-amplitude coupling between theta phase and gamma amplitude was quantified using the Kullback Leibler modulation index at frontoparietal electrodes. Linear mixed-effects models examined treatment effects and associations between change in TGC and WM status (including accuracy, reaction time, and clinical symptoms). Results: Across participants, lower TGC was associated with lower symptoms and better WM performance, including higher accuracy, faster and more consistent RT. Active iTBS increased frontoparietal TGC relative to sham stimulation, with effects observed both acutely after a single session and ~24 hours after multiple sessions. DLPFC-targeted iTBS increased TGC, whereas PPC-iTBS had no measurable effect. Change in TGC was associated with change in WM, such that a decrease in TGC was associated with faster RT and decreased RT variability. Higher baseline TGC was associated with greater improvement in WM. Active iTBS decoupled the TGC-WM association observed during sham iTBS, and greater electric field intensity of iTBS was associated with greater improvement in WM accuracy and greater decrease in TGC. Conclusions: Active iTBS to the left DLPFC modulated WM-related TGC in youth with ADHD. These findings provide preliminary evidence that neuromodulation may improve WW by modifying oscillatory dynamics within frontoparietal networks. Larger clinical trials with higher stimulation doses are needed to determine whether targeting oscillatory coupling represents a potential therapeutic strategy for WM deficits.

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.

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.

Background The excitation-inhibition (E-I) balance is essential for normal brain functioning, while deviations from this balance have been implicated in several psychiatric disorders. However, the extent to which electroencephalography (EEG) and proton magnetic resonance spectroscopy (1H-MRS) E-I markers are altered in schizophrenia spectrum disorders (SSD), how they converge across modalities, and how they relate to cognitive performance and clinical symptoms remain insufficiently characterized. Methods We recruited 111 healthy controls (HC) and 113 individuals with SSD. All participants underwent resting-state EEG and 1H-MRS. Metabolites were measured either in the anterior cingulate cortex (ACC; NSSD = 63, NHC = 58) or in the left dorsolateral prefrontal cortex (lDLPFC; NSSD = 50, NHC = 53), from which gamma-aminobutyric acid (GABA), glutamate + glutamine (Glx), and the Glx/GABA ratio were extracted. Extracted EEG E-I markers included oscillatory activity, aperiodic activity, functional E-I, microstates, multiscale entropy, and neuronal avalanche criticality. Results MRS results showed no group differences in GABA, Glx, or the Glx/GABA ratio. In contrast, most EEG-derived E-I markers indicated increased cortical inhibition in SSD, including steeper aperiodic exponents, prolonged microstate durations, and greater prevalence of subcritical states. However, functional E-I showed a divergent pattern, suggesting balanced dynamics in SSD and relatively inhibition-weighted dynamics in HC. Across groups, higher ACC and lDLPFC GABA predicted a lower kappa index, whereas a higher lDLPFC Glx/GABA ratio was associated with a higher kappa index. In SSD, reduced avalanche criticality was associated with better cognition and less severe symptoms. Conclusion Several EEG-derived E-I proxies, but not MRS measures, indicate an increased cortical inhibition in SSD. Criticality indices best capture frontal neurochemical metabolites and improvements in clinical symptoms, potentially reflecting inhibitory compensation mechanisms in SSD.

Anxiety and depression are globally prevalent conditions associated with maladaptive decision making. However, whether affective symptoms primarily amplify threat avoidance or dampen motivational drive remains debated, and behavioural studies yield inconsistent findings. Here we show that both anxiety and depression impair the fundamental cognitive process of translating objective value into decision evidence. Across independent cohorts from the US and India, participants evaluated risky gambles while we assessed choice behaviour and the centroparietal positivity, an EEG marker of accumulating decision evidence. Prospect theory parameters, like risk and loss aversion, showed little association with symptom severity. Conversely, hierarchical drift-diffusion modelling revealed that higher symptom scores predicted attenuated value sensitivity during evidence accumulation, whereas decision caution remained intact. This reduction in value sensitivity suggests internalizing symptoms disrupt choice at the value-to-evidence interface, offering a unified mechanism underlying biased decision making in affective disorders.

Background: Anxiety-spectrum disorders (ANSD) are highly prevalent, yet the underlying neurovascular mechanisms remain unclear. Functional near-infrared spectroscopy (fNIRS) comprises a non-invasive method to assess cortical hemodynamics, neurovascular coupling, and network organization during cognitive processing. Methods: We investigated healthy controls (HC), generalized anxiety disorder (GAD), anxious depression (AD), and anxiety-depression comorbidity (CO) using multichannel fNIRS during a verbal fluency task. Multiple hemodynamic features were extracted, including peak response, temporal hemodynamic variability, {beta}activation, and HbO, HbR, and HbT signals. Functional connectivity, graph-theoretical network measures, machine-learning classification, and associations with depressive, anxiety and psychosomatic scores were examined. Results: Compared to controls, ANSD patients showed reduced task-evoked HbO and HbT responses, preserved HbR levels, increased temporal hemodynamic variability, and reduced {beta}activation. Activation deficits were most prominent in bilateral frontopolar and medial prefrontal cortices and followed a gradient, with the CO group exhibiting highest abnormalities. Functional connectivity was increased, whereas clustering coefficient, nodal local efficiency, and nodal efficiency were reduced, indicating maladaptive hyperconnectivity accompanied by inefficient network organization. The AD and CO groups showed the greatest network disintegration. Temporal hemodynamic variability emerged as the strongest predictor of anxiety, depressive, and physiosomatic symptom severity. Reduced prefrontal activation was significantly associated with higher symptom domain scores. Machine-learning analyses demonstrated adequate discrimination between HC and ANSD. Conclusions: ANSD are characterized by impaired neurovascular recruitment, increased hemodynamic instability, maladaptive hyperconnectivity, and disrupted cortical network topology. These abnormalities appear to represent transdiagnostic neurovascular processes underlying anxiety, depressive, and physiosomatic symptoms across the anxiety spectrum.

Background: Rett syndrome (RTT), a rare neurodevelopmental disorder caused primarily by pathogenic variants in the MECP2 gene, is characterized by severe cognitive, motor, and autonomic impairments. Atypical sensory processing, including co-occurring hypo- and hyper-responsivity, is a core yet poorly understood feature. While evoked potentials (EPs) show delayed and attenuated sensory responses in RTT, the underlying mechanisms of these impairments remain unclear. Inter-trial phase coherence (ITPC), which quantifies trial-by-trial neural response consistency, offers a promising functional biomarker of variability in sensory processing. Methods: We characterized caregiver-reported sensory responsivity in 32 individuals with RTT (all female) and 28 typically developing controls (26 female, 2 male). EPs were then recorded during passive visual and auditory stimulation and ITPC was computed to assess whether variability in the timing of neural responses could account for reduced EP amplitudes and atypical sensory responsivity. Results: Hypo- and hyper-responsivity to sensory stimuli were both significantly elevated in RTT and were positively correlated, co-occurring within individuals. ITPC was significantly reduced in RTT across visual and auditory modalities and was associated with reduced EP amplitudes. Notably, reduced ITPC in visual-evoked potentials was further associated with elevated visual responsivity and greater behavioral symptom severity. Conclusions: Increased variability in neural response timing may contribute to both reduced EPs and atypical sensory responsivity in RTT, supporting ITPC as a functional biomarker. Decreased temporal precision of neural activity may explain the co-occurrence of hypo- and hyper-responsivity and provide a unifying framework for sensory dysfunction across neurodevelopmental disorders.

ImportanceCallous Unemotional (CU) traits are associated with significant clinical and neurophysiological heterogeneity that may affect treatment effectiveness. ObjectiveTo uncover neuroanatomical subtypes of CU traits using weakly-supervised machine learning and assess whether emerging subtypes differ on relevant clinical, temperamental, and environmental constructs. Design, Setting, and ParticipantsImaging data was from the longitudinal Adolescent, Brain, Cognitive Development (ABCD) Study. Participants were 9-10 years old at baseline (M=9.925, 69.9% male) and included 222 children with CU traits and 234 typically developing controls matched on age, sex and income. Main Outcomes and MeasuresThe weakly-supervised heterogeneity through discriminative analysis (HYDRA) model was trained on grey matter (GM) volumes from 84 regions of interest (ROIs) and tested for reproducibility using cross-validation and permutation testing. Derived subtypes were compared cross-sectionally and prospectively on relevant clinical, temperamental, and environmental measures and subsequent GM volume at 2-year follow up. ResultsHYDRA revealed an optimal 2-subtype solution within children with CU traits. Subtypes showed comparable levels of aggression that were significantly higher than typically developing controls. At the same time, subtype 1 had larger GM volume, fewer internalizing symptoms, and less adversity exposure, while subtype 2 was characterized by smaller GM volume, more internalizing symptoms, and more adversity exposure. Conclusions and RelevanceThis study provides evidence of neuroanatomically distinct subtypes of CU traits characterized by different clinical and etiological profiles, with implications for diagnosis and treatment.

Suicidal thoughts and behaviours (STBs) are heterogeneous in their proximal dynamics, planning, and stress-sensitivity, yet most subtyping efforts remain symptom-driven and rarely validated across independent datasets. Computational mixture modelling offers a principled alternative: by fitting explicit models of learning and action selection and partitioning individuals by their latent parameter profiles, it can identify mechanistically distinct control strategies invisible to cross-sectional symptom measurement. We applied this approach to aversive Go/NoGo performance, jointly clustering two independently collected STB-enriched samples (N = 50 and N = 184) using tasks with the same structure but different duration, reversal timing, and clinical instrumentation. Two recurrent behavioural regimes emerged: a fast/adaptive regime characterised by rapid policy updating and elevated feedback reactivity, and a slow/perseverative regime characterised by slow updating, high choice determinism, and a pronounced cost following contingency reversal. These regimes were stable across initialisations, recovered more parsimoniously in joint than independent solutions, and were largely orthogonal to symptom-based stratification. Critically, stratification by regime exposed clinical-computational coupling structures substantially attenuated in pooled analyses. Pooled, population-level associations were modest and anchored by a broad affective burden axis. Within the slow/perseverative regime, coupling reorganised around learning dynamics and internalizing burden (depression, hopelessness, and active suicidal ideation) with markedly larger effect sizes. Within the fast/adaptive regime, a dissociation between anxious-compulsive and antisocial-disinhibitory profiles emerged along the same computational axis, invisible at the population level. These findings support a view of suicidality heterogeneity in which clinically similar individuals differ in the control strategies they recruit under aversive uncertainty - variation that symptom measurement alone cannot capture.

BackgroundEarly life adversity (ELA) is associated with notable negative consequences across development. Experiences of deprivation may affect neurocognitive development, while experiences of threat may alter emotion processing. Deprivation and threat may also differentially influence reward processing. However, unique consequences of deprivation and threat beyond low family resources are debated. MethodsWe employed an exposure vs. control data analytic approach to isolate deprivation and threat influences from socioeconomic resources. Adolescent Brain Cognitive Development (ABCD(R)) Study youth exposed to neither deprivation nor threat (N=2408-2962) were matched to youth exposed to deprivation-only (N=638-721), threat-only (N=198-232), or threat non-exclusively (threat+: N=382-464) based on family income, parental education, race/ethnicity, sex, and age. Multivariate analyses were used to distinguish each ELA group from their respective control groups in the neurocognitive domain (resting-state connectomic maturation, cognitive task performance, and cortical grey matter thickness at two timepoints) and in the neuroaffective domain (nucleus accumbens and caudate activation to reward anticipation and amygdala and insula activation to fearful faces). ResultsIn the neurocognitive domain, similar latent variables (LVs) differentiated the deprivation and threat+ groups from their respective matched control groups. This LV corresponded to neurocognitive maturation, loading positively on cortical functional maturation and task performance, and negatively on cortical grey matter thickness. This LV was weaker in the deprivation and threat+ groups compared to controls. In the neuroaffective domain, no significant LVs were found. ConclusionBoth threat and deprivation exposure during childhood may delay neurocognitive development in early adolescence beyond their co-occurrence with low socioeconomic resources.

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.

IntroductionAnhedonia is a transdiagnostic psychiatric symptom linked to increased functional connectivity between the prefrontal cortex and striatum. Here, we examined how dimensions of early adversity contribute to this profile of connectivity. MethodsIn a European community sample of young adults (IMAGEN), we examined cross-sectional (n=613) and longitudinal (n=332) associations of adversity dimensions with resting-state fMRI-derived connectivity. We selected 10 ROIs from anhedonia literature, defined in the functional images as 4mm-radius spheres. We then used network-based regression models to identify clusters of ROI-ROI connections associated with threat and deprivation scores, using interaction terms to examine sex and age-specific associations. We also examined associations between adversity and anhedonia, operationalized using factor analysis of six items from self-report surveys. ResultsAt age 18-22, we identified sex-specific associations between deprivation and connectivity for a cluster of 9 ROI-ROI connections (p-FWE=0.038), primarily involving the nucleus accumbens. Specifically, we observed positive associations between deprivation and connectivity in males, and negative associations in females. In the longitudinal analysis, negative deprivation associations in females attenuated with age for a cluster of 14 connections (p-FWE=0.009). A cluster of 17 connections also had initial positive associations with threat in females that attenuated with age (p-FWE=0.008). No such longitudinal changes were observed in males. Higher deprivation was linked to increased later anhedonia in males but not females (p=0.026). ConclusionCompared to females, young adult males may be more vulnerable to developing anhedonia after experiencing deprivation in childhood. Dimensions of early adversity are linked to distinct pathways of frontostriatal development.

Preliminary evidence suggests that the combination of magnesium and ibogaine, an atypical psychedelic, may be a promising treatment for post-traumatic stress disorder (PTSD), opioid use disorder, and traumatic brain injury (TBI). The "mystical" experience elicited by ibogaine, which is characterized by feelings of awe, selflessness, and transcendence, is correlated with improvements in PTSD symptoms. Mystical experiences with other psychedelics are associated with acute decreases in the activity and connectivity of the default mode network (DMN), which mediates self-related cognition. However, the dynamic effects of ibogaine on the DMN have not yet been studied. At baseline, immediately (3-4 days) after ibogaine, and one month after ibogaine, we acquired resting-state functional magnetic resonance imaging data in an open-label, observational trial of magnesium-ibogaine treatment for 30 U.S. veterans with TBI. Magnesium-ibogaine did not significantly alter static DMN connectivity at either the immediate-post or one-month timepoint. Since static measures cannot capture time-evolving changes in connectivity, we next used Hidden Markov Models (HMM) to measure the post-acute dynamics of DMN activity. Magnesium-ibogaine was associated with significant, sustained decreases in the switching rate (i.e., increases in the duration of) a dynamic DMN substate, which was significantly correlated with the mean score on the Revised Mystical Experience Questionnaire and clinical improvements at one month-post treatment. This DMN substate exhibited a lateral-medial spatial gradient, which was significantly associated with a gradient of externally oriented (i.e., directed to the environment) to internally oriented (i.e., self-related) perception and cognition. Taken together, our results indicate that magnesium-ibogaine alters specific dynamic substates of the DMN, which correlate with its subjective and therapeutic effects.

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.

Schizophrenia is characterized by persistent executive dysfunction and atypical engagement of prefrontal circuits underlying attentional control. However, the neural dynamics of executive processing during ecologically relevant tasks remain underexplored. This study examined frontal theta/beta oscillatory patterns and trial level EEG responsiveness as indices of adaptive cognitive control in schizophrenia compared to healthy controls. Thirty adults with schizophrenia (M age = 39.9, SD = 9.10 years) and a matched healthy control group (N = 30; M age = 32.25, SD = 6.50 years) underwent quantitative EEG during an eyes-open resting state and while performing two executive tasks: an augmented reality visuomotor challenge (LCAR) and a mobile guided daily routine task (B2B). Frontal theta/beta ratios (TBR) at Fz and Cz indexed attentional engagement. Trial level responsiveness was assessed via discrete Stimulus Response Events (SREs). Results: Both LCAR and B2B elicited significant TBR increases relative to eyes open rest at midline frontal sites (p < .001), reflecting elevated executive demand. Compared to healthy controls, participants with schizophrenia exhibited higher baseline TBR and reduced modulation across task segments. In contrast, controls showed stronger SRE linked variability and greater memory gains, indicating more efficient task locked cognitive adaptation. Age related effects were also observed, with participants under 40 years showing higher resting TBR at Fp1 (p =.01). Conclusion: Findings advance understanding of prefrontal theta/beta modulation as a neurophysiological marker of adaptive executive control during complex, ecologically valid tasks. By integrating real-world paradigms with trial level EEG analyses, this study contributes to models of dynamic information processing and cognitive resource allocation in schizophrenia. Keywords: schizophrenia; healthy controls; theta/beta ratio; QEEG; executive function; augmented reality; neural biomarkers

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.