Cerebral Cortex Communications

Background: Autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS) is a genetic disease characterized by spasticity and ataxia which reflects involvement of the corticospinal tracts (CST) and cerebellum. The primary involvement of the middle cerebellar peduncles (MCP) and transverse pontine fibers (TPF) at the crossing with the CST, and their role in the pathophysiology of the disease, is currently debated. Objectives: Advanced MRI techniques capable of isolating sub-voxel microstructural parameters can test the hypothesis that the MCP and TPF are abnormally large, compressing the CST at their crossing, and potentially impairing CST development. Methods: Tract macro- and micro-structural properties, including axon and tract caliber, axon density and geometry, and myelin content were estimated from diffusion-relaxometry and magnetization transfer imaging. These features were analyzed along segments of the CST, MCP, and TPF of 9 patients and 9 age-matched controls. Results: While the CST showed significant decreases in tract size, axon caliber, and myelination throughout its length compared to controls (p<0.01), the MCP and TPF were relatively unaffected. In our group, neither the MCP nor the pons were enlarged. The proximal MCP showed an increase in axon caliber. Conclusions: The increase in fractional anisotropy and axon density towards the center of the TPF could be driven by geometric confounds related to differences in the relative sizes of the CST and TPF compared to controls. This highlights the importance of investigating tract-specific microstructural profiles, particularly in regions of geometric complexity. The findings confirm the involvement of the CST, with a relatively limited involvement of the MCP and TPF.

Background: Prostate-specific membrane antigen (PSMA) PET/CT is central to prostate cancer staging and theranostic workflows. To our knowledge, no direct within-patient comparison of [18F]FC303 ([18F]Florastamin) and [68Ga]Ga-PSMA-11 has been reported. We performed a preliminary paired method-comparison study under non-harmonized acquisition protocols. Patients and Methods: Twenty patients with histologically confirmed prostate cancer underwent [68Ga]Ga-PSMA-11 PET/CT (185 +/- 37 MBq, 60 +/- 10 min) followed by [18F]FC303 PET/CT (370 +/- 37 MBq, 105 +/- 15 min) on the same PET/CT system within each patient (median interval, 29.5 days). Index targets were anatomically matched to the biopsied or surgically sampled lesion or target region. The primary malignant set included 18 histologically malignant targets; two histology-negative or indeterminate targets were included only in sensitivity analysis. Fixed [68Ga]Ga-PSMA-11-first scan order and the 45-min uptake-time difference were central interpretive constraints. Results: Across five predefined reference organs, [18F]FC303 showed lower SUVmean than [68Ga]Ga-PSMA-11 (all Benjamini-Hochberg-adjusted p < 0.001; [68Ga]/[18F]FC303 geometric mean ratio [GMR], 1.29-3.89). In the primary malignant set, [18F]FC303 lesion SUVmax was lower than [68Ga]Ga-PSMA-11 (median, 11.3 vs 18.1; paired median difference, -5.50; 95% CI, -6.85 to -2.90; Wilcoxon p = 8.4 x 10-4), with strong rank correlation (Spearman {rho} = 0.90). Passing-Bablok regression yielded {beta} = 1.13 (95% CI, 1.04-1.45), and log-Bland-Altman GMR (FC303/[68Ga]) was 0.75, consistent with proportional non-interchangeability. Tumor-to-liver and tumor-to-mediastinum ratios did not differ significantly (GMR, 1.17 [95% CI, 0.94-1.45] and 0.96 [0.80-1.15], respectively); the study was not powered for equivalence. The n = 20 sensitivity analysis showed consistent directionality. Conclusions: Under non-harmonized acquisition conditions, [18F]FC303 showed lower physiologic reference-organ SUVmean and malignant target-region SUVmax than [68Ga]Ga-PSMA-11, whereas tumor-to-liver and tumor-to-mediastinum ratios were not significantly different. Absolute SUVs were not interchangeable; [68Ga]Ga-PSMA-11-derived SUV thresholds should not be directly transferred to [18F]FC303 without tracer-specific calibration.

There is substantial interest in understanding neurological impact and recovery over time, but there is a dearth of longitudinal assessment extending from minutes to months surrounding neural system impact. We compared rare intraoperative recordings in three patients, obtained immediately before and after anterior temporal lobe (ATL) resection during a semantic prediction task, with longitudinal source-localized electroencephalography (EEG) obtained 2-6 weeks before and 2 and 6-14 months after surgery. Relative to controls (n = 20), task performance showed sustained impairment in the two left-hemisphere patients and delayed impact in the right-hemisphere patient. Consistent with theory on ipsilateral and contralateral hemisphere compensation, all three patients exhibited bilateral EEG alterations in speech responses and effective connectivity that did not recover to pre-operative levels. Direct comparison of the datasets for intrinsic neurophysiological biomarkers associated with timescales of processing ({tau}INT) and excitatory-inhibitory balance (aperiodic slope, {chi}SPEC) showed a striking months-long reduction in rapid timescale processing and gradually increasing aperiodic slope (e.g., putatively increased cortical inhibition) in the ipsilateral hemisphere of all three patients. Amidst these neurophysiological alterations, task performance did not return to pre-operative levels. These rare longitudinal patient data advance a framework to broadly evaluate neurological impact over multiple timeframes.

We present the TopBrain 2025 Challenge, the first benchmark for fine-grained multiclass segmentation of the whole brain vasculature in both computed tomography angiography (CTA) and magnetic resonance angiography (MRA). Building on the TopCoW challenge, TopBrain scales vessel annotation from the Circle of Willis to the entire brain, introducing a dataset of 90 annotated volumes across 48 landmark vessel classes spanning arterial and venous systems, of which 50 training volumes are publicly released. Vessel definitions were consolidated from established neuroanatomical references into a unified annotation scheme, and vessel caliber measurements along the centerline are reported for the first time across the whole brain vascular anatomy. To address the unique challenges of multiclass brain vessel segmentation, we propose an evaluation framework that accounts for detection in segmentation performance, assesses anatomical plausibility, and introduces novel contamination metrics that characterize inter-class prediction errors. Fifteen teams from over 220 registered participants submitted algorithms to the benchmark. The top-performing teams built on nnUNet with principled system design choices, achieving around 80% Dice scores, near-zero invalid neighbor counts, over 60% F1 scores for side-road vessels, and below 18% foreground contamination ratio. Larger vessels are easier to segment, while smaller and more complex vessels remain the true bottleneck. The annotated datasets and podium-finish algorithms are made publicly available on Zenodo.

Hypoxia-targeted BOLD MRI is a novel technique, which probes oxygenation physiology in response to a controlled transient hypoxia stimulus. In glioblastoma, the signal response is spatially and temporally heterogeneous. We developed a voxel-wise temporal decomposition framework for hypoxia-targeted BOLD MRI that separates the arrival of responses, transition phases, and steady state during controlled isocapnic hypoxia. Twenty healthy controls underwent 3-T BOLD MRI during a double hypoxic step challenge to establish a normative reference. Three patients with newly diagnosed glioblastoma were included as proof-of-concept cases. For each voxel, we estimated response arrival delay (Delaycorr), delay to plateau, delay to return and an O2-normalized steady-state response (HypoxiaSS). Healthy-control maps were used to construct a voxel-wise normative atlas and, for HypoxiaSS, a global-response-adjusted model for patient deviation mapping. In healthy controls, HypoxiaSS showed lower supratentorial between-subject variabilitythan both whole-stimulus comparators (coefficient of variation: 1.77 versus 2.36 for Hypoxiaavg) and higher voxel-level step-to-step agreement (ICC(2,1): median 0.951 versus 0.792 for Hypoxiaavg). Whole-stimulus averaging exhibited a systematic step-2 signal amplification present in 19 of 20 subjects, which was absent from HypoxiaSS. Asingle global response scalar explained a median 72.5% of voxel-wise between-subject variance in HypoxiaSS. In proof-of-concept patient analyses, G-adjusted HypoxiaSS deviation maps and timing maps identified spatially coherentabnormalities that were partly complementary and extended beyond conventional MRI-defined lesion margins.Temporal decomposition improves the stability and interpretability of hypoxia-targeted BOLD MRI and provides a practical framework for population-referenced physiological mapping and atlas-based deviation mapping in glioblastoma.

One of the prominent methods in neuroimaging data processing is SSM-PCA, which is based on principal component analysis and allows for the identification of diagnostically significant patterns in the form of statistical maps. We developed software, PIE Toolbox, employs SSM-PCA and classification based on the obtained diagnostic patterns revealed from functional and structural tomographic brain imaging. The program supports the entire analysis pipeline including preprocessing of brain images, diagnostic patterns extraction, building classification models, and prediction based on them. The resulting diagnostic patterns are weighted principal components obtained through SSM-PCA, or their linear combinations. PIE Toolbox allows selection of relevant structural and functional brain patterns, computation of their expression values in regions of interest, classification using support vector machines, and evaluation of model performance via cross-validation. This approach enables the use of patterns as features of intergroup differences for individual diagnosis. The software has been validated on both simulated and ADNI datasets.

Purpose. The choroid plexus (ChP) is the primary source of cerebrospinal fluid and an emerging marker of cerebral health, with enlargement and hypoperfusion reported in aging and neurodegeneration. However, frequent ChP calcifications can confound volumetric and perfusion measures. Although computed tomography (CT) is the gold standard for detecting calcification, it is rarely available in research MRI. Quantitative susceptibility mapping (QSM) offers an alternative sensitive to diamagnetic mineralization but lacks validated susceptibility thresholds. Method. Participants underwent CT and MRI within four weeks, including 3D T1-weighted and a multi-echo gradient echo QSM MRI. ChP calcifications were identified on CT using standard diagnostic criteria. Using the Bayes decision boundary framework, we identified optimal susceptibility thresholds for detecting diamagnetic signals consistent with calcification and compared these thresholds with multiple density levels measured on gold standard CT images. Results. Across all participants (n=20; age=62.2+-12.0 yrs), the optimal susceptibility threshold separating background ChP signal from calcifications was -0.10 ppm at 60 HU (low-density) and -0.15 ppm at 100 HU (high-density). Susceptibility values within calcified tissue exhibited a linear relationship with CT-derived tissue density. A significant positive association was observed between ChP volume and calcification volume among participants with detectable calcification (beta=2.26, p=0.047). Conclusion. This work should provide a practical framework for quantifying ChP calcifications routinely from MRI. The observed relationship between ChP volume and calcification volume highlights the importance of accounting for calcified tissue, particularly when calcification burden is substantial, when investigating ChP abnormalities in aging and neurodegenerative disease.

Neuroimaging based pain decoding faces two underappreciated challenges: between subject variability that prevents classifiers from generalizing across patients, and within session cross validation designs that inflate reported accuracy by conflating within person and between person variance. Here we address both using portable functional near infrared spectroscopy (fNIRS) during pharmacologically verified local nerve anesthesia. Twentyfive patients with clinically painful teeth underwent 36 channel bilateral fNIRS during percussion before ("Pre") and after ("Post") local nerve anesthesia. In 13 block-success patients, a paired Pre versus Post comparison with healthy tooth control identified three temporal hemodynamic response function (HRF) features (late slope, mean first derivative, and baseline normalized amplitude) whose analgesia interaction effects (d = 0.63 to 0.79) exceeded that of raw general linear model (GLM) amplitude (d = 0.56), with a significant difference-in-differences interaction (p = 0.011). Per-patient calibration with these features yielded leave one subject out (LOSO) AUC = 0.68 to 0.76 for nonlinear classifiers (permutation p = 0.002), with HbO-specific feature selection achieving the best performance (RF AUC = 0.760); a healthy tooth negative control was non-significant. End to end deep learning on raw time series (CNN LSTM AUC = 0.719) was competitive with feature based classifiers, while linear models did not reach significance. Critically, head to head comparison of within-session CV and LOSO on the same data revealed mean inflation of +0.13 AUC across all model types, including deep learning, demonstrating that high within session accuracy alone does not establish subject-independent validity. Exploratory analyses suggested complementary roles for oxyhemoglobin (HbO; within patient analgesia detection) and deoxyhemoglobin (HbR; cross patient information), and that trial to trial response variability may complement amplitude for cross patient pain detection. These results show that per patient calibration with temporal HRF features supports subject independent analgesic-state detection under strict LOSO evaluation, and that within-session validation (standard in the fNIRS pain- decoding literature) can substantially overestimate performance.

The proliferation of gambling advertising has intensified concerns regarding its influence on vulnerable populations, yet the neural mechanisms underlying cue-reactivity to these stimuli remain underexplored in ecologically valid settings. This study protocol proposes a novel methodological framework to investigate prefrontal cortical responses to gambling advertisements in individuals with varying degrees of gambling experience. Materials and methods: This cross-sectional study will recruit 44 participants, divided into a clinical group (individuals with high-frequency gambling or gambling disorder) and a matched control group. Neural activity will be recorded using fNIRS while participants view gambling-related, neutral, violent, and sexual stimuli. Secondary measures include validated scales for gambling severity (SOGS), impulsivity, sensation seeking, and alexithymia. Data analysis will primarily utilize inter-subject correlation (ISC) to quantify neural synchronization and multiband frequency decomposition to capture dynamic affective processing. Advanced preprocessing, including short-channel regression, will be applied to ensure signal robustness. Discussion: By combining portable neuroimaging with a data-driven ISC approach, this study aims to identify objective neural markers of gambling vulnerability. The findings will provide novel insights into the idiosyncratic processing of commercial stimuli, potentially informing public health policies and the development of more effective evidence-based regulations for gambling marketing.

Background: Individuals with body dysmorphic disorder misperceive defects of their physical appearance. Current evidence suggests that visual processing abnormalities may underlie this core symptom. Separate pre-clinical studies testing perceptual and attentional interventions and non-invasive neuromodulation suggest that these visual processing abnormalities may be modifiable, but their combined effects on neural connectivity and perceptual processing remain unclear. Methods: Thirty-nine unmedicated men and women with body dysmorphic disorder or subclinical body dysmorphic disorder received intermittent theta burst stimulation and continuous theta burst stimulation targeting the lateral parietal cortex combined with a visual attention modification paradigm during functional magnetic resonance imaging, in a crossover design. Dynamic effective connectivity within dorsal and ventral visual stream pathways was calculated, and global visual processing biases were assessed using the face inversion effect before and after stimulation plus attention modification. Results: Intermittent theta burst stimulation resulted in increased connectivity in higher-level dorsal visual stream pathways during naturalistic viewing following attention modification, whereas continuous theta burst stimulation was associated with reduced connectivity in lower-level dorsal pathways and increased connectivity in ventral stream pathways. These changes were accompanied by differential effects on global visual processing, with stimulation type modulating the magnitude of the face inversion effect. Conclusions: Combined neuromodulation and visual attention modification modulate visual system connectivity and perceptual processing in individuals with body dysmorphic disorder symptoms. These findings support a mechanistic link between dorsal-ventral stream dynamics and perceptual biases. Integrating neuromodulation with perceptual retraining may represent a viable approach for targeting core symptoms of distorted appearance perception.

Background: The specific 3D morphological substrates distinguishing the newly defined massive and torrential functional tricuspid regurgitation (FTR) phenotypes from standard severe disease remain under-characterized. Objectives: This study investigates the 3D geometric changes of the tricuspid valve (TV) apparatus across the spectrum of FTR, specifically focusing on the structural definition of massive and torrential grades. Methods: Three-dimensional (3D) transesophageal echocardiography (TEE) was performed in 322 patients with FTR secondary to left-sided heart disease. Patients were stratified into mild-moderate (n=166), severe (n=82), and massive-torrential (n=74) groups. TV geometry, including annular dimensions, leaflet tethering, and subvalvular apparatus, was quantified using 3D modeling software. Results: Patients with massive-torrential TR were characterized by advanced age, female predominance, and atrial fibrillation (75%). 3D analysis demonstrated that massive-torrential TR represents a distinct phenotype defined by extreme annular circularization (ellipticity index 1.0) and planar flattening (P < 0.001). Furthermore, these patients exhibited a critical leaflet-annulus uncoupling, where compensatory leaflet growth (relative length < 80%) failed to match the massive annular dilation. Consequently, the regurgitant orifice in massive-torrential grades appeared highly complex, frequently manifesting as multiple irregular orifices. Conclusions: Massive and torrential FTR are characterized by a unique geometric profile involving extreme annular circularization, severe leaflet tethering, and leaflet-annulus uncoupling. These morphological insights suggest that conventional repair strategies may be insufficient for these advanced phenotypes, highlighting the necessity for pre-procedural 3D TEE to guide device selection.

The surge in medical imaging has spurred the development of vision-language models (VLMs) to alleviate radiologist workloads. However, clinical deployment is hindered by the lack of meaningful evaluation frameworks. Current metrics - ranging from semantic similarity to large language model (LLM) based judges - often fail to distinguish between clinically trivial and critical discrepancies, poorly reflecting real-world clinical judgment. To address this, we introduce DISCERN (Discordance and Significance-aware Entity-level Radiology Report Comparison). DISCERN is a significance-aware framework that weighs report errors based on their potential impact on patient care. Our results demonstrate that DISCERN powered by closed source LLMs aligns more closely with expert radiologist assessments than traditional metrics or current LLM evaluators, providing a more interpretable and clinically relevant benchmark. By modeling radiologist prioritization and entity-level feedback, DISCERN facilitates targeted model refinement and ensures the safer integration of generative AI into clinical workflows.

Pediatric patients with cardiac implantable electronic devices (CIEDs) face limited MRI access due to RF-induced heating, and computational modeling is increasingly used to characterize this risk. The validity of these simulations, however, depends on pairing body models with clinically realistic lead configurations, guidance that is currently lacking. We retrospectively analyzed 302 CIED surgeries in 281 pediatric patients to derive weight-based constraints for simulation design. Weight alone discriminated epicardial from endocardial lead implantation with AUC = 0.90, and adding age and height yielded no improvement, supporting weight as a sufficient single-parameter selection metric. The probabilistic crossover between approaches occurred at 44~kg, substantially higher than the 10 to 15~kg threshold commonly cited in the literature, with a broad transition zone of 21 to 66~kg in which both lead types were routinely used. Lead length was likewise weight-constrained: only 25~cm leads were observed in patients below 6~kg, and leads of 45~cm or longer were uncommon below 50~kg. These findings yield a three-tier framework, with epicardial-only configurations below 21~kg, dual configurations within 21 to 66~kg, and weight-thresholded lead lengths throughout, enabling MRI safety simulations to focus on clinically realizable anatomy and device combinations.

Objective: Interictal spikes have been proposed as a biomarker for both localizing seizure onset zones (SOZ) and tracking changes in seizure risk with neurostimulation in patients with drug-resistant epilepsy. Electrical stimulation can modulate spike rates acutely, and it has been proposed that measuring this modulation can help localize the SOZ. However, it is unclear whether stimulation-induced spike rate changes reflect epilepsy-specific pathology in the stimulated network or simply intrinsic regional excitability, which limits our understanding of their utility in epilepsy surgery planning. Methods: We analyzed low-frequency stimulation (LFS; 1 Hz) applied during a clinical seizure-induction protocol systematically targeting multiple brain regions in 43 patients with drug-resistant epilepsy undergoing intracranial EEG monitoring. A validated, automated spike detector was used to quantify pre-, during-, and post-stimulation spike rates. We tested whether the stimulation-evoked spike rate response (i) tracks the expected change in seizure risk from a seizure induction protocol, (ii) varies with anatomical stimulation site and epilepsy localization, (iii) localizes the SOZ beyond baseline spike rate, and (iv) is accompanied by changes in spike morphology. Results: Nearby LFS acutely increased spike rates in high-spiking channels (inter-stimulation median 2.25 vs. during-stimulation 4.25 spikes/min; p < 0.001), with effects attenuating with distance and resolving within approximately 30 seconds of stimulation offset. Mesial temporal lobe stimulation produced the largest increase in nearby spike rates relative to temporal neocortex and other cortex (Kruskal-Wallis p = 0.003), but this effect did not differ between patients with and without mesial temporal lobe epilepsy. A random forest classifier incorporating stimulation-evoked modulation features achieved an AUC of 0.787, comparable to a resting-state spike model (AUC 0.747; DeLong p = 0.81), indicating that stimulation-evoked spike changes do not add localizing information beyond resting-state spike rates. Stimulation produced a small but significant shift in spike morphology toward broader, higher-amplitude discharges (PERMANOVA p < 0.001), consistent with recruitment of a broader neuronal population. Significance: LFS-evoked increases in interictal spike rates reflect intrinsic regional excitability, greatest in the mesial temporal lobe, rather than epilepsy-specific pathology, and do not improve SOZ localization over resting-state spike rates. These results argue against using the change in spikes with stimulation to localize the SOZ. On the other hand, the transient spike rate increase induced by a pro-epileptic protocol supports the acute change in spike rate as a biomarker of the effect of stimulation on seizure risk, with potential to guide parameter selection for epilepsy neuromodulation.

Background: Cannabis use is highly prevalent among emerging adults (18-25 years), a developmental period marked by ongoing neurodevelopment and heightened risk for cannabis use disorder (CUD). Structural alterations in the orbitofrontal cortex (OFC) and medial prefrontal/anterior cingulate cortex (mPFC/ACC) have been linked to cannabis use, though findings remain inconsistent in directionality. To address this, we examined cortical thickness and surface area of the OFC and mPFC/ACC subregions using the high-resolution Glasser atlas, allowing for more granular characterization of associations with CUD severity. Method: One hundred eleven emerging adults (41% male, aged=20.6{+/-}1.1 years) reporting significant alcohol and/or cannabis use completed clinical assessments and structural MRI. The OFC and mPFC/ACC were segmented into seven and six subregions per hemisphere, respectively. Multiple linear regressions tested associations between cortical thickness or surface area and DSM-5 CUD symptom count, controlling for alcohol use and intracranial volume. Subregions surviving false discovery rate correction were examined in relation to depression, trauma-related symptoms, impulsivity, and cannabis use motives. Results: Greater CUD severity was associated with lower cortical surface area and greater cortical thickness in OFC and mPFC/ACC subregions. Lower OFC surface area was correlated with coping- and enhancement-related cannabis use motives. Lower mPFC/ACC surface area and greater thickness were associated with more severe depression, trauma-related symptoms, and impulsivity. Conclusion: In high-risk emerging adults, greater CUD symptom burden is associated with lower surface area and greater thickness in OFC and mPFC/ACC subregions. Using the high-resolution Glasser atlas, these findings provide a more precise characterization of structural correlates of CUD and highlight potential neurobiological markers linked to affective and motivational processes underlying cannabis use.

Background: Non-communicable diseases (NCDs) represent a critical public health challenge in Kenya, responsible for over 50% of inpatient admissions and 40% of deaths. While digital health tools and artificial intelligence offer promising ways to improve prevention, diagnosis, and management, little is known about how these tools are perceived and used in practice. There is limited research exploring the views and lived experiences of young people in Kenya, who are a strategic priority for NCD prevention because behavioral risk factors are established in this window, and for Community Health Providers (CHPs) who provide health services within the community. This study aims to address this gap by examining the perspectives of the burden of non-communicable diseases and the potential role of digital health technologies, including artificial intelligence, for preventing and managing these conditions in these specific populations. Methods: A qualitative research design using focus group discussions (FGDs) was employed in Nairobi (urban) and Busia (rural) counties between March and July 2024. Eight FGDs were conducted with 60 participants purposively sampled from three stakeholder groups: community health promoters (CHPs), healthcare workers (HCWs), and youth aged 18-35 years. A semi-structured guide, co-developed with a Community Advisory Board, explored beliefs about NCDs, health-seeking behaviors, lifestyle practices, and attitudes toward digital health and AI. Audio recordings were transcribed verbatim, translated where necessary, and analyzed thematically using grounded theory principles on NVivo software (v12). Results: Six consolidated themes emerged: (1) understanding of NCDs and perceived risk; (2) barriers to NCD prevention and care; (3) the role of CHPs; (4) adoption of AI tools for NCD management; (5) trust, ethics and access concerns; and (6) community-driven recommendations for AI integration. Significant barriers including stigma, economic constraints, and barriers to care were documented alongside enthusiasm for AI tools among youth and CHPs in both urban and rural areas. Conclusion: This study shows that AI tools are being used for NCD prevention and management through spontaneous community adoption. However, it emphasizes the need for culturally relevant, equitable, and community-driven solutions. Effective scaling requires the identification and bridging of digital literacy gaps, the establishment of affordable infrastructure, the protection of data privacy, and the integration of artificial intelligence tools into existing community health frameworks. This process should involve the collaboration of trusted intermediaries, such as CHPs and community leaders, to ensure successful outcomes. Future initiatives should prioritize participatory design, policy frameworks for ethical governance, and targeted capacity building to enhance acceptance and sustainability of digital health innovations in low- and middle-income country settings.

Background: Conventional psychiatric screening instruments summarize symptoms within individual scales and prioritize cases with high single-instrument additive score severity. This design treats items as independent within instruments and ignores cross-instrument covariance structure, making it insensitive to respondents whose responses are distributed across multiple domains in unusual combinations that remain below threshold on every individual scale. Methods: We analyzed two cohorts spanning older and younger adults. Item prompts from depression, stress, anxiety, and sleep instruments were embedded into a shared semantic space using a pretrained sentence encoder. Principal component analysis of the item-prompt embeddings alone---with no use of respondent data at this stage---was used to construct a low-dimensional subspace retaining 80\% of variance in the item embedding matrix. Normalized participant responses were then projected into this subspace, with Jaccard-based stability analysis used as a check on dimensional robustness. Multivariate deviation from the cohort norm was quantified with Mahalanobis distance using Ledoit-Wolf covariance regularization. Candidate outliers were defined by the empirical 95th percentile of the cohort-specific distance distribution. To isolate response configurations not already captured by conventional single-instrument extreme-value logic, we excluded all outlier respondents who had endorsed any individual item at the maximum value of its Likert scale on any instrument. For the remaining outliers, anomalous components were backtracked to their original item loadings for interpretation. Results: In the older-adult Health and Retirement Study (HRS) cohort, principal component analysis of 27 item-prompt embeddings showed that a 10-dimensional subspace provided a stable representation of cross-instrument semantic structure. In the younger-adult Xinxiang cohort the corresponding stable solution was 16-dimensional. In each cohort, seven respondents remained as multivariate outliers despite falling below every single-instrument extreme-value threshold. These cases were not characterized by uniformly severe symptom scores but by unusual cross-domain response configurations that became visible only in the shared semantic covariance subspace. The response structure of the retained configurations differed across cohorts: older-adult cases more often involved weak endorsement of mood-labeled items alongside nonzero body- and sleep-related responses, whereas younger-adult cases more often involved incomplete response configurations spanning mood, sleep, stress, and self-harm-related items. Conclusions: A semantically aligned, auditable covariance subspace provides a practical tool for flagging unusual multivariate response configurations that single-instrument additive screening may not flag. The method is interpretable at the level of original item contributions. It should be understood as a hypothesis-generating screen for unusual response configurations requiring further clinical assessment, not as a diagnostic instrument. Outcome validity remains to be established by prospective study.

Lead is a toxic metal ubiquitous in our environment. While dramatic reductions in lead sources have paralleled equivalent decreases in lead-poisoning rates, chronic lead exposure remains a critical public health concern. Childhood lead exposure (at its lowest levels) is liked to changes in cognitive development but less is known about lead's effects on children's brain structure, especially as a result of in utero exposure. We measured prenatal and early-postnatal lead exposure in shed deciduous teeth of 448 9- and 10-year-old children (from 20 United States cities) and linked those lead levels to childhood brain structure, cognition/behavior, and neighborhood- and family-level socioeconomic characteristics. Here we show negative associations between tooth-lead levels and the thickness of the brain's cortex, particularly in regions linked to language processing. With increasing tooth-lead levels, children of lower-income (versus higher-income) families showed steeper declines in receptive vocabulary. Caregiver-reported behavioral problems exhibited similar associations. With in utero exposure linked to adverse neurodevelopmental outcomes (well before lead exposure and its risks are evaluated by healthcare professionals), prenatal screening of maternal lead levels/exposure, coupled with recommended strategies to reduce its placental transmission, may help reduce lead's effects on future generations.

Intro: Characteristics of the pulse wave transmitted through the carotid arteries are predictive of cognitive decline and cerebrovascular health in humans. This study aimed to identify risk factor trajectories in childhood, adolescence and early adulthood that are associated with forward compression wave intensity (FCWI) in the common carotid artery in adults aged 28 years. Methods: Systolic blood pressure (SBP), body mass index (BMI) and fasting blood glucose (FBG) measured at multiple time-points when participants were aged between 8-20 years were included in a trajectory analysis. At age 28 years, FCWI was measured in 402 (M=206, F=196) participants who underwent a Duplex ultrasound assessment of the common carotid artery. Statistical analysis assessed differences in FCWI between each trajectory group for males and females separately. Results: In males, four trajectory groups were identified for BMI, three for SBP, and two for FBG. In females, three trajectory groups were identified for BMI, SBP, and FG. In males, having higher BMI (P=0.006), SBP (P=0.021) and FBG (P=0.002) from ages 8-20 years was associated with greater FCWI at age 28 years. In females, no associations were found between FCWI at age 28-years and trajectory groups for BMI (P=0.185), SBP (P=0.289) or FBG (P=0.070). Conclusion: Having high BMI, SBP and FBG throughout childhood, adolescence and early adulthood was associated with higher FCWI in the carotid artery at age 28 years in males, but not females. This may have a direct impact on the etiology of cognitive decline and cerebrovascular disease in later life.

Background Atrial fibrillation (AF) is a leading cause of stroke, cardiovascular morbidity, and mortality. Atrial myopathy, characterized by progressive metabolic, electrical, and structural changes, creates the arrhythmogenic substrate that drives AF. Defining the key drivers of atrial myopathic processes is essential for targeted therapies that can mitigate AF progression. Here we explore how reduced ERBB4 expression contributes to the development of left atrial myopathy. Methods We analyzed the Cleveland Clinic Biobank to compare left atrial ERBB4 levels in patients grouped by AF diagnosis. To investigate the impact of reduced ERBB4 levels on atrial tissue substrate, we created mouse models of cardiac-specific Erbb4 deficiency using Mlc2a (myosin light chain 2a)-Cre. Comprehensive physiological assessments were performed. Transcriptomic analyses of the left atrium were performed in an Erbb4 haploinsufficient mouse model and compared with human atrial datasets. Molecular validation of key dysregulated pathways was performed. Results We found that left atrial ERBB4 levels are reduced in patients with AF. Adult cardiomyocyte-specific Erbb4 heterozygous (Erbb4fl/+;Mlc2a-Cre) mice exhibited prolonged P-wave duration in the absence of ventricular dysfunction. Left atrial transcriptomic analysis in Erbb4 haploinsufficient mice showed upregulation of pathways related to fibrosis, apoptosis, and coagulation, and downregulation of pathways related to fatty acid metabolism and mitochondrial function, mirroring changes observed in pressure overload mouse models. A cross-species transcriptomic comparison revealed significant overlap between ERBB4-correlated gene expression and functional pathways in adult human atria and mice with Erbb4 haploinsufficiency. Validating the transcriptomic data, protein and functional assays demonstrated increased fibrosis, apoptosis, and oxidative stress in the mutant left atrial tissue. Conclusion Left atrial ERBB4 levels are reduced in AF patients. A mouse model of Erbb4 deficiency and human atrial transcriptomic analyses highlight a role for ERBB4 in supporting normal atrial metabolism while protecting against inflammation, apoptosis, and fibrosis.