Psychiatry Research: Neuroimaging

Background and Hypothesis: Although psychotic disorders are characterized by widespread cortical thinning, translating these findings into individualized biomarkers remains challenging. Regional Vulnerability Index (RVI) and Neuroscore are candidate individual-level biomarkers that quantify the similarity between an individual's structural brain pattern and generic psychosis-associated alterations. This study investigated cortical thickness (CT)-based RVI and Neuroscore, along with mean CT deviation from healthy individuals, across the psychosis spectrum. Study Design: Using structural 3T MRI data from 289 participants (clinical high risk for psychosis [CHR], n=39; first episode psychosis [FEP], n=44, chronic psychosis [CP], n=86; healthy controls [HC], n=120), RVI, Neuroscore, and mean CT deviation (z) scores were computed from region-wise CT measures quantified using FreeSurfer v7.2. Group differences and associations with symptoms were evaluated. Study Results: RVI and Neuroscore were significantly higher, and mean CT z-scores lower, in FEP and CP compared with CHR and HC, with moderate-to-large effect sizes (|Cohen's d| = 0.55-1.15). No differences were observed between FEP and CP or CHR and HC. Indices were not associated with psychosis-spectrum symptom severity in the CHR, FEP, or CP groups. RVI (r = -0.561, p < .001) and Neuroscore (r = -0.997, p < .001) were strongly negatively correlated with the mean CT z-score. Conclusions: CT-based RVI and Neuroscore appear to index established disease status but not risk status, while showing substantial overlap with mean CT deviation. These findings highlight the need to further refine these biomarkers through data-driven and multimodal approaches to improve their utility for risk and prognostic prediction.

Importance: Anhedonia, social withdrawal and executive deficits represent transdiagnostic symptoms in depression, schizophrenia and addition, yet transdiagnostic brain volumetric alterations and their contribution to these symptom domains have not been systematically characterized. Objective: To determine common and disorder-specific gray matter deficits in patients with schizophrenia (SZ), major depression disorder (MDD) and alcohol use disorder (AUD) compared to heathy controls. Data Sources: A comprehensive literature search using three databases including PubMed, Web of Science and PsycInfo (between January 2000 to March 2024) as well as searches on review articles. Study Selection: Studies had to be published in English, reporting whole-brain results, comparing patients and healthy controls. Data Extraction and Synthesis: This meta-analysis adhered to the PRISMA guidelines and was pre-registered (https://osf.io/s57uq) prior to data analysis. Data was independently extracted and verified by 3 contributors, and statistical brain maps were generated using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI) random-effects model. 147 VBM studies encompassing 90 Schizophrenia, 44 MDD and 13 AUD studies were finally included. Main Outcomes and Measures: Statistical maps of common and disorder-specific gray matter deficits in patients versus healthy controls are presented. Statistical inference utilized permutation tests, with threshold at p < .0025 and cluster extent of 10 voxels. Heterogeneity was evaluated using the I2 statistic and publication bias using Eggers regression test. Results: Capitalizing on a coordinate-based meta-analysis involving n=13,041 participants, we found shared GMV alterations in the ventromedial prefrontal cortex, ventral anterior cingulate cortex, insula, superior temporal gyrus. On the network level, we found robust functional interactions of the identified shared regions with the cortical midline structures as well as social and theory of mind-related behavioral functions. Subsequent characterization analyses highlighted the involvement of dopaminergic and serotonergic neurotransmitters as well as associated genetic annotations. Conclusions and Relevance: Overall, the present meta-analysis found common as well as separate GMV abnormalities in schizophrenia, major depressive disorder and alcohol use disorder suggesting a common neurostructural substrate and treatment target for social withdrawal and anhedonia across these disorders. Keywords: alcohol-use disorder, depression, meta-analysis, schizophrenia, voxel-based morphometry

Background: Predictive-processing accounts propose altered prediction-error signaling in psychosis. However, event-related potential (ERP) findings in first-episode psychosis (FEP) are inconsistent and focused predominantly on mismatch negativity (MMN). Examining anticipatory and later post-stimulus ERP responses alongside MMN within a single passive paradigm may broaden the assessment of predictive processing in FEP. Methods: 34 participants with non-affective FEP and 34 healthy controls (HC) underwent EEG during a passive auditory pattern-based paradigm. Mean amplitudes of contingent negative variation (CNV), MMN, and post-imperative negative variation (PINV) were quantified at five frontocentral electrodes. Exploratory analyses examined N1 and P2 mean amplitudes. Mixed models tested group, electrode, and group-by- electrode effects adjusting for age, sex and years of education. Regressions examined ERP associations with psychotic-like experiences, neurocognition, and social cognition across groups, and with symptoms, functioning, and antipsychotic dose within FEP. Results: FEP participants reported more psychotic-like experiences and showed poorer global and social cognition than HC (all p-FDR [&le;] .042). Standardized adjusted FEP-HC differences at FCz were nonsignificant for CNV ({beta} = -0.46), MMN ({beta} = -0.30), and PINV ({beta} = -0.10; all p-FDR [&ge;] .265). Exploratory analyses identified lower P2 amplitude in FEP at FCz ({beta} = -0.73; p-FDR = .019) and in the averaged frontocentral ROI ({beta} = -0.64; p-FDR = .027). No ERP-phenotype associations survived FDR correction. Conclusions: Despite phenotypic differences between FEP and HC, no significant differences emerged in the primary ERP indices of predictive processing examined. The exploratory P2 finding and its possible relationship to predictive processing and frontal dysfunction in FEP warrant further investigation.

Introduction: This study aimed to characterize the neurochemical profile of Alzheimer s disease (AD) continuum stages using proton magnetic resonance spectroscopy (1H-MRS) data for brain metabolites and macromolecules. We used Orthogonal Projection to Latent Structures Discriminant Analysis (OPLS-DA) to classify diagnostic groups and assess feature importance. Methods: Our dataset consisted of 7 T short-echo time 1H-MRS spectra of the posterior cingulate cortex/precuneus of individuals diagnosed with AD (n = 27) or Mild Cognitive Impairment (MCI, n = 27) and Healthy Controls (HC, n = 31). Classification used 100-round Monte Carlo cross-validation based on metabolite concentrations and macromolecular peak intensities. Variable importance in projection (VIP) was assessed across classification models to identify the most relevant neurochemical features. The resulting metabolite rankings were compared with previous literature to evaluate their biological plausibility. Results: Classification was strongest for AD versus HC (AUC=0.88) and lower for comparisons involving MCI. Metabolites contributing most strongly to classification included myo-inositol, ascorbate, and choline-containing compounds, consistent with previous studies on AD-related neurochemical alterations. In addition, specific macromolecular components were identified as relevant features, suggesting a potential contribution of MM signals to the characterization of AD-related neurochemistry. Discussion: These findings support the use of short-echo-time 1H-MRS as a complementary tool for characterizing neurochemical changes in the AD continuum, with potential applications in disease characterization and monitoring, warranting validation in larger cohorts.

Brain-imaging models predicting psychiatric outcomes are rarely tested where they would be used. Clinical high risk for psychosis is a demanding test case because services differ in whom they recruit and how they record transition. We re-estimated a published structural magnetic resonance imaging (MRI) risk model and applied it unchanged to four independent cohorts (685 participants, 106 transitions). Discrimination was weak (area under the curve, 0.56) and near chance in the two cohorts not previously used to evaluate the model (0.53 and 0.56). Predicted risks did not transfer (calibration slope, 0.17). Exploratory performance landscapes, which map local discrimination across participant characteristics, showed that discrimination varied with negative-symptom severity, also in external cohorts, whereas apparent age- and brain-age-related variation depended on single cohorts; no global test survived multiple-testing correction. Reporting where a model works, not only its average accuracy, can reveal when one diagnostic label hides several prediction problems.

Schizophrenia spectrum disorders (SSD) have been linked to immune dysregulation and altered intestinal barrier function, although findings across studies remain inconsistent. The present study investigated circulating markers of intestinal permeability and immune activation in individuals with early-stage SSD and healthy controls and examined their associations with clinical outcomes. Serum concentrations of intestinal fatty acid-binding protein (iFABP), zonulin, soluble CD14 (sCD14), bactericidal/permeability-increasing protein (BPI), and lipopolysaccharide-binding protein (LBP) were measured in 36 patients with SSD at baseline and one-year follow-up and in 36 matched healthy controls. Linear mixed-effects models adjusted for age, sex, and body mass index (BMI) were used to evaluate group differences and longitudinal changes. No biomarker differences between patients and controls, and no longitudinal changes within patients, remained statistically significant after correction for multiple testing. Similarly, biomarker concentrations were not associated with symptom severity, global functioning, or neutrophil-to-lymphocyte ratio (NLR). Exploratory analyses identified a robust positive association between BMI and zonulin concentrations and additional associations among iFABP, LBP, and sCD14. Exploratory mixed-effects models identified a negative non-significant association between iFABP and sCD14 after adjustment for age, sex, and BMI. Overall, the findings do not support a generalized alteration of gut permeability or immune activation markers in SSD. Instead, they suggest that variation in gut-related biomarkers may be more strongly influenced by metabolic and individual factors than by diagnosis or current symptom severity.

Objective To evaluate the association of early addiction consultation with withdrawal pharmacotherapy and complications in hospitalized patients with alcohol withdrawal. Methods Retrospective single-center cohort of adult hospitalizations with alcohol withdrawal (diagnosis code or CIWA-Ar [&ge;]10 within 24 hours) from July 2020 to January 2026. Exposure was an addiction consult service order within 24 hours of arrival, compared with no or later consultation. Primary outcomes were intensive care unit (ICU) transfer after the first 24 hours and delirium tremens or withdrawal seizure not present on admission. Process outcomes included phenobarbital and thiamine administration within 24 hours and benzodiazepine dose in the first 48 hours. Secondary outcomes included length of stay and 30-day death or return to hospital. Results Of 4,663 hospitalizations, 1,379 (29.6%) received early consultation. After adjustment, early consultation was associated with lower odds of ICU transfer (odds ratio [OR] 0.73; 95% CI 0.54-0.99) and fewer episodes of delirium tremens (risk difference -1.09 per 100; -1.45 to -0.73), with no significant difference in withdrawal seizure. Early consultation was associated with phenobarbital (OR 1.57; 1.32-1.86) and thiamine (OR 1.23; 1.07-1.41) within 24 hours, higher 48-hour lorazepam-equivalent dose (ratio of geometric means 1.44; 1.32-1.57), shorter stays (ratio 0.92; 0.88-0.97), and lower odds of 30-day death or return to hospital (OR 0.78; 0.66-0.91). Conclusions Early addiction consultation was associated with earlier, more intensive withdrawal pharmacotherapy, lower odds of ICU transfer, and fewer episodes of delirium tremens, although the complication associations were sensitive to cohort definition.

Deep brain stimulation (DBS) can be an effective treatment for severe obsessive-compulsive disorder (OCD), but response is variable, and objective, reliable predictors of who will respond positively are absent. In three individuals with OCD, we implanted intracranial electrodes across the cortico-striato-limbic network before DBS. Prefrontal gamma activity, most prominently in the orbitofrontal cortex (OFC), distinguished high- from low-symptom OCD states reproducibly across days. Acute stimulation of the ventral capsule/ventral striatum relieved OCD symptoms and, in one patient, significantly shifted this OFC gamma signal. Effective connectivity from therapeutic stimulation sites to these prefrontal regions defined three patient-specific OCD subnetworks; the baseline strength of the nucleus accumbens/ventral pallidum-OFC subnetwork predicted acute and long-term DBS response. Imaging-based modeling of current propagation in stimulation-activated ventral capsule to the OFC tracked acute response, suggesting that this prediction could be approximated from imaging alone. These findings outline a predictive, subnetwork-level framework for personalized DBS in OCD.

Background: Attention-deficit/hyperactivity disorder (ADHD) is a heterogeneous neurodevelopmental condition, and co-occurring autistic traits may shape its phenotypic expression. Although creativity has been discussed as a relative strength in ADHD, it remains unclear whether autistic traits are associated with different dimensions of creativity in adults with ADHD and whether large-scale brain networks contribute to these associations. Methods: We compared divergent thinking (DT) and real-life creativity between 65 adults with ADHD and 73 neurotypical controls. We used subgroup comparisons, partial correlation analyses, and multiple regression models to determine the effects of autistic traits (ATs) on creativity dimensions within the ADHD cohort. Guided by prior creativity-network research, we analyzed resting-state functional connectivity (FC) of the Default Mode Network (DMN) and Frontoparietal Network (FPN) to uncover the neural mechanisms. Results: The ADHD cohort demonstrated significantly higher scores on both DT (ps = 0.002-0.009) and real-life creativity measures (ps = 0.011-0.017) compared to the neurotypical control group. Importantly, increased ATs within the ADHD group were associated with a dissociated creativity profile, showing positive associations with DT but negative associations with real-life creativity. At the neural level, stronger right DMN FPN connectivity mediated positive associations between ATs and DT performance (ps = 0.007-0.018), but mediated a negative association between ATs and self-reported imagination (p = 0.029). Conclusions: In individuals with ADHD, co-occurring ATs exert a paradoxical effect on creativity, enhancing DT while compromising real-life creative achievement. In this subgroup with ADHD and elevated ATs, interventions should capitalize on ATs-associated ideational strengths while circumventing translational barriers, to enhance real-world creative outcomes.

Background: Cervical spinal cord (CSC) compression and myelopathy are frequent, severe complications in mucopolysaccharidosis (MPS) types I and VI. Early detection is critical to prevent irreversible neurological deficits and preserve quality of life. This prospective study aimed to identify quantitative neuroimaging biomarkers of structural CSC damage and myelopathy in MPS I and VI. Methods: Structural and diffusion magnetic resonance imaging (MRI) of the CSC was analyzed in 17 MPS I patients, 3 MPS VI patients (19{+/-}7 years), and 19 case-matched healthy controls (20{+/-}7 years). CSC macrostructure (cross-sectional area, diameters) and microstructure (fractional anisotropy [FA], radial [RD], mean, axial diffusivities) were evaluated within and beyond the maximal compression areas. Results: MPS patients demonstrated reduced CSC cross-sectional area and anterior-posterior diameter at upper cervical levels, alongside widespread microstructural alterations (lower FA, higher RD) throughout the CSC versus controls (pFDR<0.05). Restricted foramen magnum space was associated with microstructural damage at lower cervical levels without overt macrostructural compression. Morphological and microstructural metrics were sensitive to CSC compression and myelopathy severity (pFDR<0.05). Lower CSC integrity correlated with more advanced growth retardation (p<0.001). Among patients receiving hematopoietic stem cell transplantation, earlier treatment correlated with preserved upper CSC morphology (p<0.05). Distinct neuroimaging phenotypes were observed across MPS genotypes (pFDR<0.05). Conclusions: CSC MRI provides candidate neuroimaging biomarkers for early detection and characterization of myelopathy in MPS I and VI. Structural damage extends beyond primary maximal compression areas, highlighting these markers for clinical monitoring and future therapeutic trials.

Neuropsychiatric symptoms (NPS) are highly prevalent in Mild Cognitive Impairment (MCI), conceptualized as an intermediate clinical state between normal aging and dementia. Although neuroimaging studies have reported associations between NPS and brain alterations in MCI, longitudinal evidence on NPS and their relationship with multimodal MRI measures remains limited. We aimed to investigate (1) longitudinal changes in NPS, volumetric assessment and measurement of regional cerebral blood flow (CBF) within the medial temporal lobe (MTL); (2) associations between longitudinal changes in MRI measures and NPS; (3) whether baseline MRI measures predicted longitudinal changes in NPS. Thirty individuals with MCI aged over 65 years were recruited, of whom eighteen followed up for a mean period of 29 {+/-} 8.9 months. There were significant reductions in total grey matter and white matter volumes, together with volume loss in the bilateral hippocampus, bilateral parahippocampal gyrus and right entorhinal cortex (pFDR[&le;]0.05) over the follow up period. Volume loss in the right parahippocampal gyrus was associated with increase in aggression (pFDR<0.01), and CBF increase in the right hippocampus and parahippocampal gyrus was associated with worsening in agitation (pFDR<0.01). Furthermore, lower baseline volume of the right entorhinal cortex was associated with increase in aggression, and lower baseline CBF in the right hippocampus and parahippocampal gyrus was associated with worsening of agitation (pFDR<0.05). Our findings highlight the importance of identifying NPS in individuals with MCI and the role of structural and perfusion MRI measures as promising biomarkers for monitoring disease progression and improving risk prediction of conversion to dementia.

Background: Ultra-low field (ULF) magnetic resonance imaging (MRI) offers accessible point-of-care neuroimaging, yet its capability to support multivariate network analyses remains understudied. Methods: We systematically evaluated the feasibility and reliability of Morphometric Similarity Networks (MSN) and Morphometric Inverse Divergence (MIND) networks derived from paired high-field (HF, 3T/1.5T) and ULF (64mT) scans in a primary cohort(N=23) and an independent replication cohort (N=19). We tested the impact of super-resolution processing, cortical parcellation granularity (Desikan-Killiany vs. Destrieux), and mesh resolution (fsnative vs. fsaverage5) on edge-level cross-scanner agreement (Lin's concordance correlation coefficient, CCC) and subject-level identifiability. Results: MIND networks demonstrated higher edge-level HF-ULF agreement (mean CCC = 0.95) and ULF reproducibility (mean CCC = 0.98) compared to MSNs (mean CCC = 0.86-0.87 and 0.91-0.94, respectively). In contrast, MSNs yielded higher subject-level differential identifiability across field strengths due to a broader distinction between within- and between-subject similarity. Reconstructing MIND networks on native high-resolution meshes (fsnative) maximised cross-scanner agreement, whereas coarser meshes and finer cortical parcellations enhanced individual network identifiability. Conclusions: Structural similarity networks can be reliably constructed from ULF MRI data. Optimal analytic choices depend on study objectives: MIND on native meshes maximises cross-scanner stability, whereas MSNs excel at preserving individual uniqueness, paving the way for globally scalable connectomic research.

Objective: Amphetamine (AMP) and its derivates are commonly prescribed for the management of attention-deficit/hyperactivity disorder (ADHD). This study aimed to 1) quantify AMP's transfer into human milk in lactating women receiving therapeutic doses and 2) contextualize infant exposure. Methods: Milk samples and dyad health histories of thirteen lactating women treated with varying doses of either mixed AMP, dextroamphetamine, or lisdexamfetamine were released for this study from the InfantRisk Human Milk Biorepository. Milk drug concentrations were quantified by validated liquid chromatography-tandem mass spectrometry (LC-MS/MS) and used to calculate standard relative infant dose (RIDmat) and RID standardized to the defined daily dose (RIDDDD) of 15 mg AMP base. Results: Average milk concentrations (Cavg) of AMP base ranged from 23.3 to 297.0 ng/mL and increased linearly (R^2 = 0.92) with maternal daily dose. The RIDmat ranged 3.6-10.8% and RIDDDD ranged 1.4-21.8%. The fitted regression line crossed the 10% RIDDDD threshold at 1.17x DDD, with the upper 95% prediction limit crossing at 0.84x DDD. No adverse event reported by the mother-infant dyads were attributed to AMP exposure, notably including absence of reduced milk supply. Conclusions:The levels of AMP observed in human milk followed a dose predictable manner. The average patient was predicted to remain below the 10% RID DDD safety threshold at total daily doses at or below approximately 28 mg of mixed AMP salts or 59 mg of lisdexamfetamine. Ninety-five percent of patients were predicted to remain below the threshold at or below approximately 20 mg and 40 mg, respectively.

Biologically informed polygenic scores (PGSs) may help interpret genetic associations with treatment outcomes. Here, we computed neurotransmitter-based PGSs (NeuroPGSs) for patients with major depressive disorder (MDD) and assessed their association with SSRI treatment outcomes. By targeting genetic variants involved in key neurotransmission pathways, we developed PGSs for serotonergic (5228), gamma-aminobutyric acid (GABA, 4103), dopaminergic (7061), and glutamatergic (5690) variants. We leveraged data from two cohorts of patients who received SSRIs for at least 4 weeks: the International SSRI Pharmacogenomics Consortium (ISPC) and the Pharmacogenomics Research Network Antidepressants Medication Pharmacogenomics Study (PGRN-AMPS). Treatment response was defined as [&ge;]50% reduction from baseline depression symptoms, measured using 17-item Hamilton Rating Scale for Depression (HRSD-17) in ISPC and 16-item Quick Inventory of Depressive Symptomatology (QIDS-C16) in PGRN-AMPS. Remission was defined as HRSD-17 [&le;]7 (ISPC) or QIDS-C16[&le;]5 (PGRN-AMPS). The association between each NeuroPGS and antidepressant response or remission was evaluated using logistic regression models, adjusting for age, sex and five genetic principal components, with statistical significance determined at Benjamini Hochberg-adjusted p<0.05. The GABAergic-based PGS was associated with SSRI response (aOR per SD = 1.45, 95%CI: 1.14-1.85). Patients with the highest GABAergic PGS (10th decile) had 2.59-fold higher odds of response (95%CI: 1.46-4.60) compared to those with the lowest decile (1st decile). These exploratory findings support further evaluation of NeuroPGSs; independent replication and assessment of incremental predictive value are needed before clinical application. Keywords: Polygenic score; Neurotransmitters; GABAergic; depression; SSRIs; pharmacogenomics

Objectives: To determine how strongly clinical referral text, rather than image content, drives the decisions of multimodal large language models (LLMs) differentiating glioma from brain abscess on MRI. Materials and methods: This retrospective single-center study included 60 contrast-enhanced T1-weighted brain MRI examinations (30 gliomas, 30 abscesses; reference-standard diagnosis). One axial slice per case was paired with five referral texts (explicitly inflammatory to explicitly tumor-suggestive) under three prompt instructions and submitted 20 times each to GPT-4o, Claude Sonnet 5, Gemini 3.5 Flash, and Qwen 3.6 (72,000 answers). The primary outcome was the text-driven span (difference in tumor-call frequency between explicit inflammatory and explicit tumor text). Two blinded radiologists classified the images without text; sensitivity, specificity, and accuracy were compared descriptively. Results: Patients (29 women) had a mean age of 55.8 +/- 16.4 years. With neutral text, models answered tumor in 45-67% of queries (specificity, 0.38-0.78), versus 52% and 60% for radiologists (specificity, 0.77 and 0.63). The text-driven span was 0.79-1.00 (p < 0.001 for all models), whereas confirmed gliomas and abscesses differed by only 0.01-0.10 in three models and 0.25 in Gemini 3.5 Flash. Subtle wording produced at least 83% of the span in three models. An instruction to ignore the text reduced the span to 0.48-0.70 in three models and to 0.04 in Gemini 3.5 Flash, partly through reversion to a tumor default. Model accuracy was 0.52-0.73 versus 0.78 and 0.73 for radiologists. Conclusion: Multimodal LLMs deferred to referral text over imaging evidence in differentiating glioma from brain abscess, and prompt instructions did not reliably correct this.

Purpose: To evaluate whether visually assessed high-quality prostate MRI, as defined by the Prostate Imaging Quality (PI-QUAL) scoring system, is sufficient to ensure consistent AI model performance, or whether hidden acquisition-related technical effects introduce variability that cannot be detected through visual inspection. Methods: This retrospective study included 568 bpMRI scans from seven institutions, classified using PI-QUAL v2 into high-quality (scores 2-3) and low-quality (score 1) groups. Quantitative image quality features were extracted using MRQy from T2w and ADC sequences. Three experiments were conducted on high-quality scans: (1) unsupervised clustering to identify hidden technical batch effects; (2) V-Net models (M) for prostate capsule segmentation on T2w using single-cluster and balanced multi-cluster training strategies; and (3) VoxelMorph-based deformable registration (M2) to align ADC maps to T2w images. Performance metrics included DSC, precision, recall, volume ratio, HD95, ASSD, and TRE. Kruskal-Wallis tests assessed cross-cluster variability, Two One-Sided Tests (TOST) evaluated performance equivalence, and bootstrap resampling quantified training efficiency. Results: Clustering identified four clusters among 277 high-quality bpMRI scans. Single-cluster models showed variable performance (DSC=0.41-0.85) with significant batch effects (p<0.001), even with 140 training samples. The balanced multi-cluster model (n=60) eliminated batch effects (p=0.075), achieved equivalent performance (p<0.001), and demonstrated 2.23-fold greater training efficiency despite using 2.3-fold fewer samples. Registration performance also varied significantly across clusters (TRE=0.86-5.47 mm, p<0.001). Conclusion: Acquisition-related batch effects significantly impact AI generalizability in prostate MRI, even among high-quality scans. Quantitative clustering enables detection of technical variations and supports diversity-aware training strategies that achieve equivalent performance with 2.3-fold fewer samples while eliminating batch effects.

Pharmacological treatments targeting core autistic features remain limited. In neurotypical adults, cannabidiol (CBD) alters mismatch negativity (MMN), an electrophysiological marker of automatic auditory deviance detection that has been associated with autistic traits. The effects of CBD on MMN, as measured using electroencephalography (EEG), in autistic children remain unknown. This secondary analysis of a pilot randomised, double-blind, placebo-controlled crossover trial examined the effects of 12 weeks of oral CBD oil on MMN during a duration-deviant auditory oddball task. We hypothesised that CBD would produce more negative MMN amplitudes and shorter peak latencies relative to placebo. Nineteen autistic children (6-12 years, 8 female) received CBD (dose) and placebo in randomised order, separated by an 8-week washout. Age-adjusted linear mixed-effects models tested Time (baseline, follow-up) x Treatment (CBD, placebo) effects on mean MMN amplitude and peak latency separately at Fz and FCz. Exploratory Spearman correlations examined baseline associations between MMN and Developmental Behaviour Checklist-2 (DBC-2) Social Relating scores. At FCz, latency decreased following CBD but increased following placebo but did not survive FDR correction (p = .029, pFDR = .058) and no other interactions were significant. Higher baseline DBC-2 Social Relating scores were associated with more negative MMN amplitude at FCz (rho = -.58, pFDR = .028) and Fz (rho = -.59, pFDR = .028). These findings provide tentative evidence that CBD may modulate MMN latency in autistic children. Larger trials should clarify CBDs effects on MMN and further examine the association between MMN amplitude and social-relating difficulties.

Exposure to armed conflict can leave persistent effects on mental health, yet its neurophysiological correlates remain poorly characterized, particularly in populations exposed to prolonged violence outside narrowly defined post traumatic stress disorder samples. Here we examined resting state electroencephalographic activity in 122 Colombian adults, including 74 participants exposed to armed conflict and 48 controls. Eyes closed EEG was analysed for 240 seconds per participant. We quantified absolute and relative spectral power across delta, theta, alpha and beta bands and regional functional connectivity using weighted phase lag index in theta, alpha and beta. Group comparisons were adjusted for age, education, sex and assessment year, with correction across 78 EEG contrasts. Median occipital and global alpha power was lower in exposed participants, although adjusted estimates were close to the null and confidence intervals were broad. Temporal theta relative power was lower in exposed participants at nominal significance, but this association did not survive Holm correction. No spectral power or regional connectivity contrast remained significant after multiple testing correction. Exploratory associations between EEG measures and MoCA performance were modest in participants with confirmed scores. These findings provide a systematic characterization of resting EEG after exposure to armed conflict in a population shaped by prolonged violence and displacement. They also indicate that large or widespread alterations in resting spectral power and connectivity were not detectable with the current sample and analytical framework, while defining candidate signals and methodological priorities for larger studies of conflict related brain health.

PhenoAge, a blood-based biological aging clock, is associated with cancer outcomes, a finding taken as evidence that biological aging shapes prognosis. In 3,067 patients undergoing primary tumor resection, PhenoAge acceleration rose with tumor stage. One or two of its eight blood components, differing by cancer type, carried the prognostic signal, and replacing its general-population weights with equal weights did not weaken it. In cancer, PhenoAge may reflect disease burden rather than biological aging.

We report general acceptance (79.0%) of Ebola vaccination in the Democratic Republic of the Congo (DRC) between 2023-2024 (n=5,062). Acceptance varied significantly by province with acceptance ranging from 47.6% in Kongo Central to 92.1% in Tshuapa province and was highest among healthcare workers. While Ebola vaccine acceptance and disease knowledge are relatively strong nationally in the DRC, regional differences do exist and may impact future vaccine rollout.