Psychiatry and Clinical Neurosciences

ObjectiveWe examined the clinical utility of resting state electroencephalography (rsEEG) by evaluating its temporal stability, discriminant validity for B-SNIP psychosis Biotypes, and suitability as a treatment target for brain stimulation. MethodsWe collected 5 minutes of eyes-open rsEEG from 1401 participants with psychosis and 750 healthy persons. A subset of participants was re-tested after 6 months and 12 months (N=109). In a pilot target engagement study (n=5) we collected rsEEG before and after 2 high-definition transcranial direct current stimulation (HD-tDCS) interventions targeting the left dorsolateral prefrontal cortex (dlPFC) and temporoparietal junction (TPJ). Data were reduced with principal component analyses to delta/theta, alpha, beta, and gamma frequency bands, and compared between groups and timepoints. ResultsrsEEG frequency bands displayed good-to-excellent stability and significantly distinguished psychosis Biotypes with large effect sizes. Compared to healthy, Biotype-1 had low activity (average ES=-.58), Biotype-2 had high activity (ES=1.07), and Biotype-3 had slightly elevated activity (ES=.33). There were no rsEEG differences between DSM psychosis groups. After anodal dlPFC stimulation, alpha and gamma power slightly increased while positive symptoms and verbal fluency improved. After cathodal TPJ stimulation, delta/theta power slightly increased while psychoticism and digit sequencing improved. ConclusionsResting state brain activity is a trait-like marker that differentiates B-SNIP psychosis Biotypes, suggesting differing underlying neurophysiology. The pilot intervention supports the feasibility of targeting this underlying neurophysiology with HD-tDCS. Integrating rsEEG in diagnostic procedures and stratified intervention selection may be beneficial for psychosis patients.

There is a predicted increase in older adults presenting with mild to severe cognitive impairment. Screening tools with high sensitivity are the first frontier in identifying a cognitive pathology; however, to ensure that they are measuring the intended concept or criterion, thorough psychometric procedures should be followed. In this study, convergent criterion validity of Riga Cognitive Screening Task was measured, using cortical thickness of regions of interest as the criterion. 106 older adults (Mage = 70.49, SD =8.08, 35.8% male) with varying levels of cognitive functioning were involved in the study. All participants underwent cognitive assessment with the screening task and a 3T MRI. Cortical thickness of selected temporal and parietal regions was used as a brain measure. Behavioural Partial Least Squares Correlation was conducted and one latent variable was extracted. The results confirmed that Riga Cognitive Screening Task shows good criterion validity, suggesting successful use for screening.

AimsDue to the multifaceted nature of "impulsivity", its measurement remains fragmented. Here, we developed the Risky Social Choices task to provide evidence for its validity and reliability, while testing the hypothesis that impaired access to implicit knowledge of negative long-term consequences is of distinct importance for "impulsive" decision-making in a general population sample. MethodsForty participants chose whether to engage in risk-taking behaviors, which combined web-based AI-generated videos with narrated hypothetical scenarios and measured worries related to negative long-term consequences, approach-related motivation for short-term rewards, response time to and accuracy of recognizing degraded auditory prime words denoting negative long-term consequences. ResultsA pre-registered multi-step regression model was constructed with worry, motivation, response time and accuracy as predictors and percentage of risky choices as the outcome. Among all predictors, only prime word recognition accuracy was significantly negatively associated with risky choices, confirming our hypothesis of the role of reduced implicit access to negative long-term consequences in risk-taking decisions. In contrast, approach-related motivation for rewards was the only predictor significantly positively related to percentage of risky choices. DiscussionAs predicted, the negative association between risky choices and implicit access to negative long-term consequences supports its role as a distinct aspect of "impulsivity". The novel task successfully captured this aspect, paving the way for a more precise neurocognitive characterization of clinical conditions where "impulsivity" plays a key role. The findings unveil the importance of implicit social sequential knowledge for impulsivity in neurotypical populations, so far only investigated in patients with brain lesions.

ObjectivesAlarm fatigue is a patient safety concern in ICUs, yet no validated instrument exists to assess alarm fatigue among healthcare professionals in non-Western settings. This study aimed to cross-culturally adapt the Charite Alarm Fatigue Questionnaire (CAFQa) into Japanese and evaluate its reliability and validity among ICU nurses and physicians. MethodsThe Japanese CAFQa was cross-culturally adapted following the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) guidelines, including forward translation, back-translation, expert panel review, and cognitive interviews. A multicenter cross-sectional validation study was performed across eight ICUs at five hospitals in Japan. A total of 129 participants (103 nurses and 26 physicians) completed the Japanese CAFQa, the NIOSH Brief Job Stress Questionnaire, and the Insomnia Severity Index (ISI). Structural validity, internal consistency, test-retest reliability (n = 102), convergent validity, and known-groups validity were assessed. ResultsCFA confirmed the two-factor structure with acceptable fit (CFI = 0.922, RMSEA = 0.041, SRMR = 0.076), with standardized factor loadings ranging from 0.33 to 0.82. The two factors were not correlated (r = 0.05). Cronbachs alpha was 0.688 for the overall scale, 0.805 for Alarm Stress, and 0.649 for Alarm Coping. Test-retest ICCs ranged from 0.616 to 0.753. The CAFQa total score correlated with the NIOSH total (r = 0.261) and the ISI total (r = 0.338). Healthcare professionals with [≥]4 years of ICU experience had higher Alarm Coping scores than those with 1-3 years (median 7.0 vs 6.5), and physicians scored higher on Alarm Coping than nurses (median 8.0 vs 7.0). ConclusionsThe Japanese CAFQa demonstrated acceptable structural validity, reliability, and convergent and known-groups validity, providing the first validated tool for quantitatively measuring alarm fatigue in Japan. Implications for Clinical PracticeThe Japanese CAFQa enables ICU managers to quantify alarm fatigue at individual and unit levels, identify high-risk staff, and evaluate the effectiveness of alarm management interventions.

BackgroundEarly detection of individuals at risk for clinical deterioration in first-episode psychosis (FEP) remains a vital challenge in psychiatric care. Emerging evidence indicates that immune dysregulation might play a crucial role in the pathophysiology and progression of psychotic disorders. AimsThis study examined the predictive potential of a plasma cytokine and chemokine panel in anticipating clinical stage transition of FEP patients. MethodUsing multiplex immunoassays, plasma samples from a cohort of 35 FEP patients were screened for the quantification of 21 analytes. Participants were clinically assessed at baseline and follow-up and classified according to a validated staging model. Data was used to predict clinical stability over a 12-month follow-up period. ResultsIL-17A was found to be significantly increased in transitioning patients (p = 0.045), with a medium standardized effect size and wide confidence interval (Hedges g = - 0.687, 95% CI [-1.379, 0.004]). A logistic regression model was determined, which revealed that higher baseline levels of IL-17A were significantly linked to progression to a more advanced clinical stage, while higher baseline levels of MIP-3 and IFN-{gamma} were associated with clinical stability. This combined cytokine model exhibited strong predictive capacity (AUC = 0.853), indicating its potential as a biomarker panel for early risk assessment. ConclusionsThese findings highlight the importance of neuroimmune mechanisms in the development of psychotic disorders and advocate for the inclusion of immunological markers within staging-based models of care. Incorporating cytokine profiling into clinical practice could improve personalised treatment strategies and lead to better long-term outcomes for individuals with FEP.

BackgroundAnorexia nervosa (AN) is a severe psychiatric disorder associated with profound malnutrition, multisystem medical complications, and one of the highest mortality rates among mental illnesses. Despite decades of research into its biological and neurocognitive mechanisms, effective pharmacological treatments remain limited. While systematic reviews synthesize results from published studies, clinical trial registries offer a complementary perspective by capturing ongoing research efforts, discontinued studies, and emerging therapeutic strategies that may not yet be reflected in the published literature. ObjectiveThis study aimed to characterize the landscape of clinical research in AN by systematically analyzing studies registered on ClinicalTrials.gov. MethodsWe conducted a structured analysis of studies registered on ClinicalTrials.gov related to AN. Trial characteristics, including study design, intervention type, phase classification, geographic distribution, and recruitment status, were extracted and analyzed using an automated text-based classification pipeline. ResultsNearly 400 studies investigating AN were identified over the past 25 years. Approximately 71% were classified as interventional studies; however, a large proportion were not associated with conventional clinical trial phases, suggesting that many registered trials correspond to mechanistic or exploratory investigations rather than therapeutic development programs. The geographic distribution of studies revealed a strong predominance of North America and Western Europe. A substantial proportion of trials were terminated or discontinued, highlighting the significant challenges associated with conducting interventional studies in this population. Observational studies generally included larger sample sizes than interventional trials. ConclusionsRegistry-based analyses provide valuable insights into the evolving landscape of clinical research in AN. Despite considerable scientific activity, important gaps remain between mechanistic knowledge and the development of therapeutic interventions. Understanding these gaps may help inform future translational research strategies aimed at improving treatment options for this severe disorder.

ObjectiveCognitive behavioral therapy (CBT) is an effective first-line treatment for obsessive-compulsive disorder (OCD), yet it remains difficult to predict who will respond to this intervention. This study investigates associations between neural activity during inhibitory control tasks and CBT outcomes, and whether task-based fMRI data could serve as a predictive marker of individual CBT response. MethodsUsing fMRI data from individuals performing an inhibitory control task across five samples (n=130, age range=8-57, 54% female) of the ENIGMA-OCD consortium, univariate associations were analyzed between activity during response inhibition and error processing and three CBT outcomes: response, remission, and pre-post treatment change in symptom severity. Random forest and support vector machine models using leave-one-sample-out cross-validation were used for prediction of CBT response and remission from fMRI activity and clinical data. ResultsRemission after CBT was associated with weaker activity in default mode regions during response inhibition and in the right supramarginal gyrus during error processing. Greater symptom reduction was linked to weaker pre-treatment activity across frontoparietal, dorsal attention, visual, and subcortical regions during response inhibition, but to stronger default mode activity during error processing. Despite these robust group-level effects, machine learning models failed to predict individual outcomes above chance level with either neuroimaging or clinical data. ConclusionWeaker activity during response inhibition in a widespread network, as well as stronger activity in default mode regions during error processing before treatment, appear beneficial to CBT response. However, these findings cannot yet be translated into individually predictive markers of CBT outcome.

BackgroundNegative symptoms (NS) such as anhedonia, avolition, asociality, blunted affect and alogia are associated with poor functional outcomes in psychiatric and neurological disorders and are an unmet treatment need. ObjectiveThis protocol describes the design of an observational, single-center study aimed at characterizing negative symptoms across a transdiagnostic sample of individuals with mental disorders and related conditions, with a particular focus on avolition, its biological correlates, and associated neurocognitive and electrophysiological profiles. MethodsAn observational study (involving no invasive procedures or drug administration other than a routine blood draw) has been designed to examine negative symptoms across psychiatric disorders. A total of 300 participants with a primary diagnosis of schizophrenia, bipolar disorder, unipolar depressive disorder, autism, or dementia will be recruited, with a target of at least 50 individuals in each diagnostic group. Consenting inpatients and outpatients will complete a battery of non-invasive behavioral and cognitive assessments, undergo electroencephalogram (EEG) recording, and provide blood samples for the assessment of polygenic risk scores. ResultsThe initial version of the study protocol was developed in February 2024. The finalized protocol was completed on August 5, 2024, and subsequently updated on January 11, 2025, to incorporate minor methodological clarifications. Participant recruitment and data collection commenced on July 1, 2024, and are ongoing at the time of manuscript submission. Data quality control and preliminary analyses are performed concurrently with data collection, while final statistical analyses and dissemination of results are planned following completion of the recruitment phase. ConclusionsThis study will provide critical insights into the characterization and underlying mechanisms of negative symptoms across psychiatric disorders. By focusing on avolition, reward processing, and their interaction with neurocognitive and social cognitive deficits, it will help identify potential biological and electrophysiological markers of negative symptoms. The findings may guide the development of more precise assessment tools and inform novel therapeutic strategies, with broad translational impact for improving outcomes in individuals with serious mental illness and related conditions.

IntroductionLactate plays dual roles in neuronal energy metabolism and signalling. The dorsal anterior cingulate cortex (dACC), a region with high baseline glycolytic activity implicated in psychiatric disorders, may exhibit dynamic lactate responses to graded cognitive-emotional demands. Because mitochondrial function declines with age, aging may model whether fMRS-derived lactate dynamics can detect latent neurometabolic vulnerabilities. MethodsUsing fMRS, we monitored dACC metabolite changes in 34 healthy participants (aged 21-69) during an emotional face-processing task with escalating cognitive-emotional workload. The paradigm comprised a 2-minute baseline, 10-minute task of increasing intensity, and 10-minute recovery. ResultsdACC lactate increased significantly, tracking task intensity and peaking 19.5% above baseline at maximum cognitive load (z = 2.66, p = 0.004). The response showed both linear task-related increases (z = 2.08, p = 0.02) and a quadratic inverted-U profile (z = 2.72, p = 0.004). Total creatine, total NAA and Glx (Glutamate+Glutamine) exhibited no task-dependent changes. Age influenced task-period lactate AUC (z = 2.19, p = 0.014). Participants over 40 exhibited greater peak responses (54% vs 28%), steeper upslopes (14% vs 7% per block), and larger AUC (155% vs 16%) than those under 40. Sex differences were also observed. Baseline lactate did not correlate with age. ConclusionsdACC lactate dynamics are sensitive to cognitive-emotional demand, with evidence of age-and sex-dependent modulation. The dissociation between static and dynamic measures establishes a metabolic stress-testing paradigm for detecting latent neuroenergetic vulnerabilities, supporting fMRS utility for probing mitochondrial function in health and psychiatric disorders.

Deficits in inhibitory control are common across a wide range of psychiatric disorders and are closely linked to symptom severity, including emotional dysregulation, anxiety, substance misuse, and self-harm, making them an appealing target for intervention. Cognitive training offers a low-cost, scalable, and non-invasive strategy to strengthen inhibitory control; however, most existing paradigms target only a single facet of inhibition and rarely account for environmental influences, such as affective context. To address these gaps, we developed a computerized inhibitory control training paradigm to simultaneously engage three components of inhibition: preemptive, proactive, and reactive, while embedding trials within positive and negative affective contexts to assess the impact of emotional stimuli. Across two online experiments, participants completed the GAMBIT task in one session (Experiment 1, N = 300) or repeated over three sessions (Experiment 2, N = 65). The task included No-Go trials to train preemptive inhibition, stop-signal trials for reactive inhibition, and stop-signal anticipation trials to train proactive inhibition. Affective images of differing valence were presented as background stimuli to evaluate their impact on inhibitory performance. In Experiment 1, participants showed higher accuracy on No-Go versus reference Go trials ({beta}=1.45, SE=0.09, p<.001), confirming successful manipulation of preemptive inhibition. Reaction times were slower during anticipation trials across two different conditions ({beta}=0.16, SE=0.04, p<.001; {beta} = 0.07, SE = 0.04, p = 0.047), consistent with proactive slowing when anticipating a potential stop signal. Additionally, positive affective images ({beta} = 0.10, SE= 0.009, p < 0.001) further slowed RTs, indicating emotional interference with proactive control. In Experiment 2, the pattern of higher No-Go accuracy was replicated ({beta} = 0.91, SE = 0.11, p < .001) and accuracy generally improved over sessions ({beta} = 0.38, SE = 0.06, p < .001). In anticipation trials, RTs become shorter across sessions (session 2: {beta} = -0.25, SE = 0.06, p < .001; session 3: {beta} = -0.45, SE = 0.06, p < .001), reflecting practice-related gains, and SSRTs decreased over time (F(2,56) = 6.26, p = .004), consistent with enhanced reactive inhibition. Proactive inhibition was modulated by affective images, with both negative ({beta} = 0.04, SE = 0.02, p = .039) and positive ({beta} = 0.16, SE = 0.02, p < .001) affective images associated with slower RTs. Participants also reported reductions in self-assessed temper control by the last session (W = 25.5, p = .007, q = .037, d = -0.51) and usability ratings were high (all means [&ge;] 3.87/5). Together, these findings show that this paradigm recruits multiple forms of inhibitory control and yields training-related improvements in both performance and affective outcomes. This provides preliminary validation of a scalable, fully online inhibitory control training tool targeting multiple dissociable inhibitory processes within affective contexts. The approach holds promise as an accessible transdiagnostic intervention to support symptom improvement across psychiatric disorders, with future work needed to evaluate clinical efficacy in patient populations.

ObjectiveDespite substantial variability in the severity of post-anoxic encephalopathy, all comatose patients after cardiac arrest are usually treated according to the same standardized intensive care protocol, including sedation, mechanical ventilation, and targeted temperature management (TTM). We hypothesize that patients with a favourable EEG pattern (continuous EEG within 12 hours after cardiac arrest) may not benefit from prolonged sedation and TTM. We studied the feasibility and safety of early cessation of sedation and TTM in this subgroup. MethodsWe conducted a non-randomized, controlled intervention study including 40 adult patients admitted to the ICU with postanoxic encephalopathy after cardiac arrest and an early (< 12 hours) favourable EEG pattern. The control group received standard care with sedation and TTM for at least 24-48 hours, whereas the intervention group underwent early cessation of sedation and TTM as soon as possible after establishing a favourable EEG, followed by weaning from mechanical ventilation. The primary outcome was duration of mechanical ventilation. Secondary outcomes included ICU length of stay, total sedation time, number of ICU complications, and neurological outcomes at 3 and 6 months. ResultsDuration of mechanical ventilation was significantly shorter in the intervention than in the control group (median 12 vs 28 h, p < 0.001). Median ICU length of stay and median total sedation time were also reduced by more than 50% in the intervention group, from respectively 2.5 to 1.2 days (p = 0.001) and 27 to 12 h (p < 0.001). There was no increase in ICU complications in the intervention group. No statistically significant differences in neurological outcomes at 3 or 6 months were observed. ConclusionEarly withdrawal of sedation is feasible and safe in patients with an early favourable EEG following cardiac arrest. The study was underpowered to detect possible differences in long-term neurological recovery. SignificanceShortening sedation and mechanical ventilation is likely to result in direct reductions in healthcare costs and contribute to more appropriate care. Larger studies are needed to evaluate the impact on long-term neurological outcomes.

Individuals at clinical high risk for psychosis (CHR) are cognitively and neurobiologically heterogeneous, which encourages the use of a clustering approach to parse this heterogeneity. Multimodal approaches are assumed to be superior to unimodal approaches in identifying subgroups. With the success of the use of cognition and electrophysiological measures collectively in established psychotic disorders, and the lack of such an approach in CHR, we were motivated to address this gap. Using the North American Psychosis-Risk Longitudinal Study (NAPLS) 2 consortia (CHR (N=764)), we applied unsupervised cluster analysis on the combined cognitive and electrophysiology measures to identify CHR subgroups and assess their relationship with clinical and functional outcomes. A two-cluster solution with modest separability was found, which prompted the use of an alternative probabilistic, rather than discrete, clustering approach. Individuals who were more likely to be in Cluster 1 exhibited poorer cognitive performance, larger N100, mismatch negativity, and P300 amplitudes, and worse functioning, as well as a younger age of onset. These findings were largely replicated in NAPLS 3 (CHR (N=628)). Taken together, the results of our previous study of cognition-only clustering and the current study of combining cognition and electrophysiology indicate that multimodal clustering, if not developmentally informed, may obscure meaningful subtyping.

Background: The highly influential predictive processing theory of psychosis posits that symptoms arise from imbalances in the weighting of predictions (priors) and sensory evidence. Despite this theory's increasing prominence, studies often present conflicting results. This is particularly problematic as findings from single tasks with modest sample sizes are frequently used to advance a theory for a generalised altered reliance on priors in psychosis. Methods: This study presents a random-effects, multi-level meta-analysis (PROSPERO CRD42024574379) evaluating evidence for aberrant reliance on priors in psychosis across perceptual tasks. The search identified articles in Embase, MEDLINE, APA PsycINFO, and APA PsycArticles published between 1st January 2005 and 31st October 2024, with risk of bias assessed using the Newcastle-Ottawa Scale. Included articles (34 results from 27 studies) compared adults with schizophrenia-spectrum psychosis (SZ; n = 904) to healthy controls (n = 1,039) on behavioural measures representing reliance on priors. Results: Results provided no evidence for atypical reliance on priors in psychosis (g = .03, 95% CI [-0.27, 0.34]; p = .818) or associations with delusions (6 results; SZ = 183; r = -.16, 95% CI [-0.51, 0.19]; p = .293) or hallucinations (10 results; SZ = 370; r = .04, 95% CI [-0.28, 0.36]; p = .780). In contrast with the theory that psychosis may differentially affect priors at different levels of the cognitive hierarchy, a sub-group analysis indicated that a two-level hierarchical model of priors did not account for conflicting results (F(1,32) = 0.1, p = .758). Conclusion: These findings do not suggest that psychosis is associated with a generalised predictive processing deficit spanning multiple aspects of perception. Key words: psychosis, schizophrenia, predictive processing, prior expectations, perception

BackgroundTo clarify the working mechanisms of psychotherapy for obsessive-compulsive disorder (OCD), we studied the neural effects of two psychotherapies: cognitive behavioral therapy with exposure and response prevention (CBT-ERP) and inference-based cognitive behavioral therapy (I-CBT). MethodsFifty-five individuals with OCD completed an emotional processing task during fMRI before and after 20 weekly psychotherapy sessions, using general fear and OCD-related visual stimuli. Forty-two healthy controls performed the task once. We used Bayesian region-of-interest analyses to assess changes in brain activation in prefrontal, limbic, sensory, subcortical, and visual areas, and their association with symptom improvement. ResultsAfter treatment, the CBT-ERP group (N=28) showed strong credible evidence for decreased activation across all brain regions during fear (but not OCD) versus neutral stimuli, especially in treatment responders. Conversely, the I-CBT group (N=27) showed increased activation during fear versus neutral stimuli in the precentral gyrus and lateral occipital cortex (LOC), which correlated with symptom improvement. A similar but weaker pattern was observed for OCD-related stimuli. Across all ROIs, baseline fear-related activity was associated with symptom improvement in CBT-ERP, while lower baseline activity was associated with improvement in I-CBT in, amongst others, the precentral gyrus and dorsolateral prefrontal cortex. Lower baseline LOC activation during OCD-related stimuli was linked to symptom improvement after both psychotherapies. ConclusionsThe results support CBT-ERPs mechanism of fear reduction and I-CBTs mechanism of sensory engagement. Visual brain activity during emotional processing may predict treatment response across psychotherapies.

Adult ADHD is increasingly recognized across the lifespan, yet the psychometric equivalence of the Adult ADHD Self-Report Scale (ASRS) remains unverified for older populations. This study examined age-related Differential Item Functioning (DIF) in 600 adults (n = 100 per decade, ages 20-80) who completed the 18-item ASRS. Using a bi-factor Graded Response Model, we extracted latent ADHD trait scores ({omega}H = .895) and assessed DIF via ordinal logistic regression with adaptive age modeling. Five of 18 items exhibited significant uniform DIF. At equivalent latent severity, older adults were less likely to endorse hyperactivity symptoms in Part A (fidgeting, feeling "driven by a motor") but more likely to endorse specific symptoms in Part B (careless mistakes, misplacing items, interrupting). From ages 20 to 80, expected Part A scores decreased by 1.36 points (~0.27 per decade), while Part B scores increased by 1.15 points (~0.23 per decade). These findings indicate a phenotypic redistribution of ADHD symptoms as individuals age. Because the 6-item Part A screener serves as the primary clinical gatekeeper, its concentration of negative DIF suggests standard screening practice may systematically underestimate ADHD severity in older adults. We recommend using the full 18-item ASRS when screening older populations and suggest that developing age-adjusted norms would improve diagnostic accuracy.

Mild Behavioural Impairment (MBI) is defined by later-life onset of persistent behavioural changes and is recognized as a risk marker for cognitive decline and dementia. Apathy, a core MBI domain characterized by diminished interest, initiative, and emotional reactivity, can emerge before dementia and is hypothesized to be associated with structural brain changes. While previous studies have explored Alzheimer disease (AD)-related neuroanatomical substrates of apathy in the dementia clinical stage, few have investigated these associations in cognitively normal (CN) or mild cognitive impairment (MCI) individuals with persistent apathy consistent with MBI. Thus, this study explores structural brain differences between individuals with MBI-apathy and those without neuropsychiatric symptoms (no-NPS). Participants (n = 446; mean age = 69.6 years; 79.8% CN; 62.8% female) were drawn from the National Alzheimers Coordinating Center and categorized into MBI-apathy (n = 59) and no-NPS (n = 387) groups. Linear regressions were used to model associations between NPS group and regional brain measures, with adjustments for age, sex, years of education, apolipoprotein E4 carrier status, intracranial volume, and Mini-Mental State Examination score, with false discovery rate (FDR) correction for multiple comparisons. Primary outcomes included two predefined AD meta-regions-of-interest (ROIs): 1) thickness: a composite measure of mean cortical thickness across the entorhinal cortex, inferior temporal gyrus, middle temporal gyrus, inferior parietal lobule, fusiform gyrus, and precuneus; and 2) volume: a composite measure of mean cortical and subcortical grey matter volume across the hippocampus, entorhinal cortex, amygdala, middle temporal gyrus, inferior parietal lobule, and precuneus. Primary outcomes also included cortical thickness and grey matter volume among individual ROIs including the ventral striatum (VS), anterior cingulate cortex (ACC), orbitofrontal cortex (OFC), ventrolateral prefrontal cortex (vlPFC), and dorsolateral prefrontal cortex (dlPFC). MBI-apathy status was associated with significantly lower AD-meta-ROI cortical thickness (Z-score difference [95% CI]; FDR-corrected p-value, -0.43 [-0.73 - [-0.12]]; 0.025) and lower AD meta-ROI grey matter volume (-0.50 [-0.71 - [-0.30]]; <0.001). MBI-apathy was also associated with significantly lower dlPFC thickness (-0.40, [-0.70 - [-0.09]]; 0.02) and volume (-0.28 [-0.50- [-0.06]]; 0.026) and lower OFC volume (-0.32, [-0.57 - [-0.07]]; 0.026) compared to the no-NPS group. Within a non-dementia sample, MBI-apathy was more strongly associated with established AD-vulnerable regions than with regions that have been traditionally implicated in apathy in dementia. Results suggests that during CN and MCI stages, MBI-apathy may reflect early AD-related neurodegeneration, with conventional apathy-related structural changes becoming more prominent as disease progresses.

BackgroundScientific research remains disproportionately grounded in data from high-income countries (HICs), yet efforts to map the distribution of neuroimaging findings by income levels remain limited. MethodsUsing data from the ENIGMA Consortium, we conducted a systematic quantitative synthesis of 83 publications across nine psychiatric and neurological conditions, analyzing T1-weighted structural MRI data from 16,086 cases in 27 countries. Representation was mapped using World Bank income classifications and World Health Organization (WHO) regions. ResultsHICs contributed 90.5% of cases; upper-middle-income countries 7.9%; lower-middle-income countries 1.6%; and low-income countries none. Geographically, 85% of cases originated from North America and Europe, while Africa, South-East Asia, and the Eastern Mediterranean were underrepresented. Supplemental analyses of other datasets (Brain Growth Charts; fMRI meta-analysis) revealed similar disparities. ConclusionsEquitable neuroimaging science is critical to inform practice and policy decision-making that is context specific. This requires targeted investment in infrastructure, data sharing, and participation from underrepresented regions.

ImportanceLarge language models (LLMs) have demonstrated diagnostic potential in several medical specialties, but their application to psychiatry - where diagnosis relies heavily on clinical judgment, narrative interpretation, and reasoning under uncertainty - remains insufficiently evaluated. ObjectiveTo evaluate diagnostic accuracy and clinician-judged reasoning quality of multiple large language models using psychiatric case vignettes. DesignMixed-methods evaluation study of diagnostic accuracy across four LLMs using 196 psychiatric case vignettes (135 published and 61 novel). Clinical reasoning quality was evaluated on a randomly selected subset of 30 vignettes using structured clinician ratings along two reasoning dimensions. The highest-performing model was illustratively compared with psychiatry trainees on the same subset. Diagnostic correctness for the full vignette set was assessed by a separate adjudicator LLM. SettingPublicly available model interfaces, December 2025. ParticipantsFive board-certified psychiatrists evaluated model-generated clinical reasoning. Two psychiatry residents served as the illustrative human comparison. Main Outcomes and MeasuresDiagnostic accuracy and clinician-rated clinical reasoning quality. Diagnostic accuracy was assessed using top-1 accuracy, top-5 accuracy, recall@5, and mean reciprocal rank based on ranked lists of five differential diagnoses per vignette. Clinical reasoning quality was assessed using two 5-point Likert scales adapted from the American Council of Graduate Medical Education Psychiatry Residency Milestones, evaluating data extraction and diagnostic reasoning. ResultsAcross 196 psychiatric case vignettes, Claude Opus 4.5 (Anthropic) achieved the highest diagnostic accuracy (top-1 accuracy, 0.638; top-5 accuracy, 0.801; recall@5, 0.731; mean reciprocal rank, 0.710) and clinician-rated reasoning scores. Higher clinician-rated diagnostic reasoning quality was strongly associated with diagnostic correctness in mixed-effects logistic regression analyses ({beta} = 1.80; p < 0.001), corresponding to an approximately six-fold increase in odds of a correct diagnosis per 1-point increase in reasoning score. In an illustrative comparison, diagnostic accuracy of Claude Opus 4.5 fell within the range observed for psychiatry trainees. Conclusions and RelevanceLLMs demonstrated high diagnostic accuracy and generated clinical reasoning that clinicians judged to be largely coherent and safe. Diagnostic reasoning quality was more strongly associated with diagnostic correctness than data extraction quality, underscoring the importance of evaluating reasoning alongside accuracy when assessing LLMs for clinical decision support in psychiatry. Key PointsO_ST_ABSQuestionC_ST_ABSCan multiple large language models accurately diagnose psychiatric conditions and generate diagnostic reasoning that clinicians judge as coherent, safe, and clinically meaningful? FindingsAcross 196 psychiatric case vignettes, four large language models demonstrated high diagnostic accuracy. In a clinician-evaluated subset of 30 vignettes, model diagnostic accuracy fell within the range observed for psychiatry residents. Clinicians judged model-generated diagnostic reasoning to be largely coherent and safe. Higher clinician-rated reasoning quality was strongly associated with diagnostic correctness, independent of data extraction quality. MeaningEvaluating diagnostic reasoning, in addition to accuracy, may be important when assessing large language models for potential clinical decision support in psychiatry.

Background and HypothesisAbnormal default mode network (DMN) connectivity was observed in both tobacco use and psychotic spectrum disorders, but it remains unknown how psychosis impacts the relationship between connectivity and tobacco use. Interventions targeting the left lateral parietal DMN node (LLPDMN) have modulated DMN connectivity and nicotine craving in psychosis. We aimed to investigate relationships between DMN connectivity, psychotic illness, and tobacco use. Study Design336 participants (psychosis: n=161, control: n=175) reported their tobacco use history and underwent resting-state functional magnetic resonance imaging. We calculated connectivity within DMN and salience network (SN), between DMN-SN, and from LLPDMN to other DMN and SN nodes. Logistic and LASSO regression with bootstrapping were performed to investigate diagnosis-by-connectivity interactions on lifetime tobacco use. Exploratory brainwide analysis was conducted by regressing brainwide connectivity to LLPDMN against daily cigarette use. Study ResultsWe observed a significant diagnosis-by-DMN connectivity interaction for lifetime tobacco use (p=0.0281, coefficient=0.457, OR=1.579, 95% CI=[1.063, 2.411]); in the psychosis group, higher DMN connectivity was associated with higher odds of lifetime tobacco use. LASSO regression yielded four predictors of lifetime tobacco use: age, diagnosis, LLPDMN connectivity to a prefrontal SN node, and the interaction between diagnosis and LLPDMN connectivity to a right parietal DMN node. Brainwide analysis identified bilateral somatomotor clusters where higher connectivity to LLPDMN correlated with higher daily cigarette use (voxel-wise p<0.001, cluster p<0.05). ConclusionsPsychosis diagnosis modified relationship between DMN connectivity and tobacco use. Modulating DMN connectivity may provide a psychosis-specific treatment target for tobacco dependence.

In-scanner head motion is a recognized source of bias in structural magnetic resonance imaging (sMRI), yet it remains under-addressed in psychiatric neuroimaging where structural difference in patient populations are considered foundational. We examined motion-related bias in grey matter volume estimates across eight independent cohorts comprising 9,664 individuals, including 8,979 neurotypical controls (NC), 497 patients with schizophrenia (SCZ), and 188 patients with bipolar disorder (BD). Motion estimates were derived from multiple fMRI scans acquired within the same scanning session and summarized using principal component analysis. In NC, motion accounted for 1-6% of regional grey matter variance, a magnitude comparable to reported psychiatric case-control effect sizes. Adjusting for motion attenuated SCZ-NC group differences, reducing effect sizes in 85% of brain regions and yielding 5% fewer significant ROIs (pFDR<0.05). In BD, motion correction reduced effect sizes in 97% of regions, with a 24% reduction in significant ROIs. Cross-diagnostic spatial patterns were significantly correlated (r=0.63, p=3x10-{superscript 1}3), explaining a sizable portion of SCZ-BD commonalities. Critically, a falsification analysis in UK Biobank (N=5,123) showed that stratifying NC by motion alone produced grey matter differences accounting for 45-62% of SCZ case-control effect magnitude, underscoring how difficult it is to interpret SCZ-like morphometric differences as tissue properties rather than as motion-driven patterns. These findings urge caution in interpretations of sMRIdifferences in patient-control comparisons and use of systematic fMRI based motion control as standard practice in sMRI analyses.