Nature Neuroscience

Pathological activity within frontal cortical circuits is common in many neuropsychiatric disorders, such as obsessive-compulsive disorder (OCD). We developed an invasive brain mapping protocol in which temporary electrodes are implanted in candidate sites to identify personalized stimulation targets that can acutely relieve OCD symptoms. We found that stimulation within segments of the anterior limb of the internal capsule (ALIC) focally suppressed the structurally and functionally connected region of prefrontal and cingulate cortex. By leveraging the topographic organization of the ALIC, we reversibly inactivated frontal cortical sites with ALIC stimulation to determine which cortical regions are necessary for sustaining OCD symptoms. Stimulation of ventral capsule (VC) near the globus pallidus within the ALIC was associated with suppression of lateral orbitofrontal cortex activity and acute and long-term improvements in OCD symptoms. These results provide a paradigm for leveraging ALIC topography to deliver targeted connectomic neuromodulation to frontal cortex to treat neuropsychiatric disorders.

Non-coding variants are important contributors to human traits and diseases but linking them to molecular mechanisms and phenotypes at scale remains challenging. G-quadruplexes (G4s) are four-stranded structures formed by guanine-rich sequences and have emerged as key functional elements within the non-coding genome. G4s are enriched in regulatory regions and can modulate gene expression at both the DNA and RNA levels, influencing transcription, replication, and RNA processing, positioning them as key mediators linking non-coding variation to complex biological traits. Here, we profile putative G4s across five regulatory regions in 459,449 UK Biobank genomes and perform phenome-wide association analyses spanning 2,941 plasma protein abundances, 13,321 binary traits, and 1,682 quantitative traits. We show that putative G4-modifying variants are depleted under purifying selection despite elevated local mutability and drive large, bidirectional associations with plasma proteins and clinical traits, including associations not captured by coding variants. Using a mechanism-aware collapsing strategy that groups rare non-coding variants by their predicted impact on G4 stability, we achieved stronger gene-level signals than those obtained with standard rare-variant collapsing approaches. Integrating non-coding and protein-truncating variants (PTVs) increases discovery power, revealing 843 significant associations missed by the PTV-only model. Replication in the Alliance for Genomic Discovery cohort demonstrates cross-cohort robustness. Our study suggests G4s as widespread mediators of non-coding regulation and provides a framework for mechanism-informed target discovery and prioritization across the non-coding genome.

Understanding the regulatory consequences of genetic variation in the aging human brain requires molecular maps that span brain regions, cell types and regulatory modalities. We present the Alzheimer's Disease Sequencing Project Functional Genomics (FunGen-AD) xQTL Atlas, a harmonized resource of molecular quantitative trait loci from four postmortem brain studies, ROSMAP, MSBB, Knight-ADRC and MiGA. The atlas integrates histone acetylation, DNA methylation, gene expression, splicing and protein abundance QTLs across 14 brain regions, 7 major cell types and 17,566 samples, with standardized association, significance-filtered and fine-mapping outputs. To expand discovery beyond conventional 1-Mb cis windows, we include variants within Topologically Associating Domains (TAD) and their boundaries where appropriate, identifying on average 21% more variant-molecular-trait associations per dataset. Statistical fine-mapping reduced broad association sets by 95% into credible sets of candidate regulatory variants. Distributed through the NIAGADS xQTL portal and bulk-download services, the atlas provides a comprehensive functional-genomic foundation for interpreting genetic risk variants in Alzheimer's disease and aging-brain research.

Emerging evidence links the gut microbiome to sleep quality, yet measuring sleep at scale remains challenging. Commercial wearables, such as Fitbit, capture objective sleep and activity data in naturalistic settings. We integrated Fitbit data from a large, deeply-phenotyped cohort with paired lifestyle and health questionnaires. Wearable-derived measures aligned well with self-reported sleep, activity, and happiness. We identified dozens of covariate-adjusted associations between Fitbit-derived sleep features, lifestyle factors, and multi-omic data. Among molecular feature sets, the gut microbiome showed the greatest number of associations with sleep quality: butyrate-producing genera were positively associated with sleep and amplified the benefits of physical activity. Oscillospira, in particular, was consistently associated with better sleep. In blood, insulin, omega-3, and cortisol correlated with poorer sleep, whereas lower alcohol intake and mineral supplements correlated with better sleep. These robust, covariate-adjusted findings advance mechanistic understanding of the gut-sleep axis and broader molecular and lifestyle determinants of sleep quality.

Can we decode Alzheimers disease (AD) heterogeneity into a few portable axes that capture how amyloid-{beta}, tau and neurodegeneration (A-T-N) spatially co vary in vivo? To answer this question, we built a pipeline that harmonizes longitudinal amyloid-{beta}/tau PET and T1 MRI (gray matter) from ADNI cohort (12,430 images) with mixed effects modeling and then derived stage specific multimodal axes (mVCs) using linked component analysis, with robustness tested in simulations and external validation in the OASIS cohort (4,958 images). We identified a small set of multimodal axes that (i) recapitulate early tau weighted variation in cognitively unimpaired (CU) individuals, AD like A-T-N coupling in cognitively impaired (CI) individuals and atypical CU and CI participants with posterior (precuneus/occipitoparietal) and fronto insular/frontal weighted patterns, (ii) map onto domain specific cognition, APOE e4, and blood/CSF biomarkers of neurodegeneration, neuroaxonal injury and astrocyte activation, (iii) predict clinical transitions, (iv) generalize in an independent cohort, and (v) demonstrate modelling robustness to missing data, high dimensionality, and cross-cohort variability, enabling direct application of the extracted axes to new datasets for biomarker discovery and stratification. Multimodal axes provide a portable, interpretable layer for quantifying amyloid-{beta}-tau-neurodegeneration coupling at the individual level, complementing current biomarker-based staging frameworks based on A-T-N status and tau PET topography, and can be computed on new datasets to aid clinical assessment and trial enrichment.

Maternal infection, immune disease, and delivery mode are plausible influences on early brain development. We analyzed 1,179,611 US Merative MarketScan mother-child pairs (2003-2024), including 259,339 non-twin siblings in 123,926 families. Population models screened 18 perinatal exposures against 13 childhood psychiatric/neurodevelopmental diagnosis-count outcomes; sibling fixed effects tested robustness to stable family-level confounding. Cesarean delivery was associated with higher composite neurodevelopmental diagnosis counts in pairs (23.4%) and siblings (25.0%) and with ADHD in siblings (38.8%; FDR q = 0.025). Autism was elevated in pairs (20.0%) but not supported within families (5.0%; p = 0.87). Claims-defined no-labor/no-repeat cesarean showed stronger lower-risk-birth associations for composite neurodevelopmental burden (48.0%), autism (44.9%), speech/language disorders (41.0%), and ADHD (24.1%). Maternal infection/immune-mediated disease, preterm birth, and advanced maternal age were additional population signals.

Vocal biomarkers, encompassing voice and speech, have largely been developed for individual conditions in isolation, limiting their generalizability across diseases and recording settings. To address this, we introduce VoiceFM, a contrastive model that learns general-purpose clinical voice representations by aligning audio embeddings with rich clinical metadata. Using the Bridge2AI-Voice dataset (984 primarily English-speaking adult participants, 846 used for training and 138 held out as a temporally separated validation cohort, 40,056 recordings totaling 176 hours across 5 academic medical centers), VoiceFM pairs a fine-tuned Whisper large-v2 encoder with a tabular transformer over 44 clinical features via symmetric InfoNCE loss. Linear probes on frozen VoiceFM embeddings achieve mean AUROC 0.952 +/- 0.005 across five evaluation tasks (control vs disease screening plus four disease categories), significantly outperforming Frozen Whisper (0.926 +/- 0.013, p = 0.013), Frozen HuBERT (0.885 +/- 0.017, p = 0.0009), and the contrastively trained VoiceFM-HuBERT (0.938 +/- 0.006, p = 0.012). On the 138-participant held-out cohort, VoiceFM-Whisper achieves AUROCs of 0.99 for Alzheimer's/dementia/MCI and 0.89 for airway stenosis, demonstrating that the learned representations generalize to participants the model has never seen. VoiceFM representations transfer to three external datasets without retraining and improve few-shot classification. Recording task attribution identifies a small set of speech tasks that match or exceed the full battery's performance, suggesting shorter screening protocols are feasible. Trained predominantly on English audio, VoiceFM transfers without fine-tuning to Spanish-language Parkinson's disease (PD) detection (NeuroVoz, 107 participants, AUROC 0.93 +/- 0.02), with the signal dominated by articulatory rather than phonatory features. A fine-tuned classifier achieves participant-level AUROC 0.87 (sustained 0.85, countdown 0.80) on the mPower smartphone study (585 held-out participants). Together, these results show that contrastive alignment between voice and rich clinical metadata can serve as the basis for a clinical voice foundation model, producing a single set of transferable representations that generalize across diseases, languages, recording conditions, and patients enrolled after model freeze.

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease affecting more than 450,000 individuals worldwide and is frequently diagnosed more than 12 months after symptom onset, delaying intervention during a critical early window. Because up to 80% of patients develop dysarthria within two years, subtle changes in speech provide a signal of early bulbar motor neuron degeneration. However, existing speech-based systems rely on supervised classification trained on limited datasets, achieving moderate sensitivity and depending heavily on labeled disease examples, which restrict scalability and early detection. This study introduces SPEAK-NORM, the first-ever normative speech modeling framework for early ALS diagnosis, which learns age- and sex-conditioned motor-speech distributions exclusively from healthy individuals. A conditional variational autoencoder models coordination of hypoglossal, laryngeal, and respiratory motor pathways, and deviation from this healthy manifold is quantified through latent representations and reconstruction error to form a 354-dimensional profile. A calibrated linear Support Vector Machine performs subject-level classification under subject-disjoint validation. On the VOC-ALS database (n = 153), SPEAK-NORM achieves 98% accuracy with balanced sensitivity and specificity, significantly outperforming established clinical acoustic indices and prior systems. The framework maintains strong performance under cross-task generalization and when retrained on healthy controls in independent dementia and Parkinson disease cohorts, demonstrating disease-specific deviation patterns rather than generic neurodegenerative change. Spectral, temporal, and latent separations further support interpretability. By modeling healthy speech instead of memorizing disease examples, SPEAK-NORM enables scalable early neuromotor screening using recording devices, with potential to support earlier diagnosis, differential classification, and monitoring of ALS progression.

Yang et al. recently published a systematic comparison of genetic effects on disease susceptibility and disease-specific mortality across nine common diseases and seven biobanks, concluding that susceptibility and survival architectures overlap only modestly. This is an important resource, but we argue that the current mortality genome-wide association studies (GWAS) require explicit power calibration before limited overlap can be interpreted biologically. Using two-sample Mendelian randomization (MR) with positive-control exposures, we show that even a well-powered positive control, body mass index (BMI), instrumented by 855 genome-wide-significant variants, produces a clearly detectable effect for heart failure (HF) mortality, with only weaker evidence for chronic kidney disease (CKD) mortality. However, when BMI instruments were stratified into quartiles by exposure-association strength, the heart failure association remained nominally significant only in the two strongest quartiles and was not significant in the two weakest quartiles. Further, using household income as a weakly instrumented socio-economic contrast has insufficient power to detect moderate effects on any disease mortality outcome. These analyses indicate that current disease mortality GWAS may be insufficiently powered to detect shared effects. In contrast, the same BMI instrument set produced large and directionally coherent effects when applied to case-control GWAS of the matched six diseases, with the HF and prostate cancer associations preserved under a within-family BMI sensitivity analysis, and nominal support for CKD. The HF mortality association was also preserved in a within-family BMI sensitivity analysis. Similarly, genetically proxied household income was associated with HF risk in the case-control GWAS despite null associations with disease-specific mortality, consistent with limited power in the mortality GWAS. These findings indicate that the limited BMI-mortality evidence across several outcomes is unlikely to reflect a weak BMI instrument or dynastic artefacts alone and instead supports limited effective power in current disease-mortality GWAS.

The course of multiple sclerosis (MS) is highly heterogeneous, yet the biological mechanisms underlying this variability remain incompletely understood. Although metabolic alterations have increasingly been associated with disease progression, existing observational evidence is limited by confounding, reverse causation, and an inability to establish causal mechanisms. To bridge this gap, we used a metabolome-wide Mendelian Randomization (MR) framework, including thorough sensitivity analyses, to identify metabolites genetically linked to MS severity that can causally affect it. Bidirectional MR analyses revealed a subset of amino acid and lipid pathways with strong, consistent effects across different MR approaches, confirmed by tests for heterogeneity, horizontal pleiotropy, and LD confounding. For metabolites prioritized by metabolome-wide MR with evidence of causal effects, we conducted genetic colocalization at loci encompassing proximal enzyme-encoding genes, leveraging the corresponding instrumental variants to assess shared underlying genetic signals. This process revealed shared genetic signals between metabolite levels and MS severity, mapped to the FADS1/2 and CYP4F2 loci. A subsequent pathway-resolved set of cis-MR analyses across FADS1/2-derived polyunsaturated fatty acid (PUFA) metabolites, using a functional variant that proxies reduced {triangleup}5-desaturase activity, showed consistent effects indicating that FADS1 perturbation is associated with MS severity. Collectively, these results highlight FADS1 as a key driver of PUFA-related causal effects on MS severity in both systemic (circulating metabolites) and brain cell-specific contexts. Additional supportive cis-MR evidence implicates the disruption of CYP4F2 as another PUFA-metabolizing enzyme.

A closed-loop quality system deployed across thirteen US hospital sites resolved physician complaints with zero regressions on 42 tracked cases across 1,089 optimization iterations, while a deterministic assembly-agent replacement cut H+P trace latency from 19.6 s to 10.8 s (-8.8 s, 95% CI [-10.5, -7.1] s; n = 100 pre, n = 100 post). We report four observations and an architectural follow-through. First, the same binary-check instrument produces opposite outcomes depending on the question asked: "maximize this score" produces structurally-correct notes that physicians reject (Spearman rho = -0.077, 95% CI [-0.40, 0.26], n = 36); "did this specific fabrication stop?" produces rater-invariant deployment decisions. Second, in our pipeline, assembly-stage agents did not respond to prompt optimization the way reasoning agents did: four consecutive optimization attempts produced 18-28 point regressions. Third, physician preference is rater-fragile at typical clinical-AI calibration sample sizes (Cohen's kappa = 0.028 between two board-certified physicians, 95% CI [-0.30, 0.36] on n = 35 overlapping pairs). Fourth, the architectural punchline: six weeks after the prediction, the LLM call at the chart-assembly step was replaced with a deterministic renderer (sub-500-character template plus sandboxed scripting), lifting the defect-free rate on a 51-case holdout from 49% to 84%. We introduce a Pareto-with-absolute-floors acceptance rule (multi-axis commit with severity-class categorical vetoes) as a methodological contribution distinct from scalar-reward acceptance in standard prompt-optimization frameworks. Cross-iteration rejection memory prevents the loop from re-proposing edits already rejected three or more times. A reproducibility bundle (anonymized ablation per-case counts, bootstrap-CI data, analysis scripts) is released under CC BY 4.0 at github.com/sayvant/SQS-Auditor-paper-data.

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.

Advances in single-cell and spatial assays have revolutionized the scale and resolution of molecular tissue profiling. Here we present MetaPlaq, a multimodal atlas of human atherosclerotic arterial beds comprising over a million cells across single-cell transcriptomics, epigenomics and high-resolution spatial expression assays. We map granular cell states and disease-relevant transcriptional programs within the native tissue context of coronary arteries. Furthermore, we map cardiovascular GWAS signals to smooth muscle cells (SMCs) and endothelial cells (ECs) and uncover the cis-regulatory architecture governing their phenotypic transitions. Our comprehensive epigenomic reference allowed us to build cell-specific enhancer-gene link maps and multimodal gene regulatory networks (GRNs) underlying disease-relevant states such as osteogenic SMCs and ECs undergoing mesenchymal transition. We also integrate SMC and EC disease-associated gene sets with GRNs to nominate key transcription factors such as PRRX1, BNC2 and ELK3 regulating atherosclerosis-relevant transcriptional programs. Finally, we layer single-cell and spatial modalities to fine-map GWAS variants with improved cell and anatomical context. We highlight candidate cell-specific regulatory mechanisms at less characterized CAD loci, including FGD5 and MCF2L in ECs. Together, this atlas represents an important step towards fully interpreting genetic risk loci and informing new therapeutic strategies for cardiovascular disease.

Background. Something in discourse with a person experiencing psychosis often "feels off" before formal assessment is completed, yet this disturbance has not been quantified at the level of ongoing dyadic conversation. Prior work has largely treated patient speech in isolation, limiting our capacity to measure how communicative disruption emerges within clinical exchange. Methods. We applied a three-level decomposition of conversational alignment in 109 patients with psychotic disorders (26 female) and 60 healthy controls (22 female) at baseline and 12 months (n = 115). Register divergence (dAUCnorm) captured lexical distance between interviewer and patient; embedding-based synchrony (rembed) measured semantic trajectory coupling; within-speaker coherence was computed separately for each speaker. We used linear mixed-effects models adjusted for timepoint and participant clustering. Results. Patients showed significantly greater lexical-semantic divergence from the interviewer (d = 0.48, p < .001) and reduced embedding-based synchrony (d = -0.59, p < .001), both effects replicating at each time point. Critically, the interviewer's within-speaker coherence was reduced during conversations with patients (d = -0.33, p = .016), indicating that the disruption extends beyond the patient to the interaction itself. Register divergence tracked impoverished thinking and synchrony tracked disorganized thinking (both FDR-corrected q = .038). Group differences were persistent at 12 months, indicating a partially stable profile. Conclusions. Conversational alignment in psychosis reveals a dyadic failure of semantic coordination that destabilizes the interviewing clinician's coherence even when patient narrative continuity is preserved. These transcript-derived alignment metrics offer a scalable approach to quantifying interpersonal communicative function from routine clinical encounters.

The choroid is critical for maintaining vision and implicated in several ocular diseases, being the sole source of nutrients and waste removal for the outer retina. Genetic discovery can help elucidate the pathways through which choroidal features influence disease risk. Our meta-analysis of genome-wide association studies (n= 78,682 participants) identified 30 genomic regions, including 20 novel loci, associated with choroidal thickness. Findings suggest inflammatory and vascular processes drive choroidal thickness, with overlapping mechanisms shared with refractive error. Genome-wide independently significant SNPs accounted for 18.7% of the genetic variance in choroidal thickness. Mendelian randomisation analyses showed a causal effect of age-related macular degeneration on choroidal thickness, and suggest a bidirectional causal effect between choroidal thickness and primary angle-closure glaucoma. These findings provide insight into the shared genetic architecture and biological pathways linking choroidal thickness and related diseases.

Most routine clinical markers are interpreted using population-based reference intervals, despite being regulated around patient-specific homeostatic setpoints. This mismatch obscures physiologic shifts, inhibiting detection of early disease signatures. Here, we develop a novel Bayesian inference method that adaptively constructs personalized reference intervals using each patients existing health records. In analysis of >100 million lab tests in >800,000 patients, these personalized intervals can be accurately constructed with only minimal prior data, meaning this method can be applied near universally. We show that across 43 common lab markers, patient setpoints are strongly associated with future morbidity, with signal strength increasing as more test data is collected. Deviation from personalized reference intervals provides strong and novel risk signatures across diverse disease states, including hypothyroidism, hematologic cancers, kidney disease, and pregnancy complications. Importantly, personalized reference intervals capture a different risk signature to existing population-based approaches, with the highest risk patients being those who deviate from both intervals simultaneously. In a targeted clinical use case study of iron infusion, use of personalized reference intervals greatly improved prediction of treatment efficacy and allowed precise tracking of treatment responses. Our results illustrate how existing health records can be used to construct personalized benchmarks for nearly all common clinical tests, driving a new paradigm for precision laboratory medicine.

KCNT1-related disorders represent clinically heterogeneous severe epilepsies associated with profound neurodevelopmental impairment. The full phenotypic spectrum and longitudinal disease trajectory remain incompletely characterized, which is a critical gap limiting the establishment of quantifiable endpoints necessary for future clinical trials. Compounding this challenge, identical pathogenic variants result in phenotypically distinct syndromes, including early infantile developmental and epileptic encephalopathy (EIDEE) and autosomal dominant sleep-related hypermotor epilepsy (ADSHE), underscoring unresolved genotype-phenotype relationships. To address these gaps, we performed a comprehensive analysis of 159 individuals with KCNT1-related disorders, including a longitudinally characterized subgroup of 62 individuals across 390 patient years, systematically defining disease progression, seizure trajectories, developmental outcomes, and treatment response across the full spectrum of the disorder. Seizures were nearly universal, affecting 157 of 159 individuals, with 81% (n=126/156) having seizure onset within the first year of life. Stratification by clinical subgroup revealed divergent seizure onset patterns. Recurrent variants did not significantly differ in age of seizure onset yet exhibited variant-specific clinical fingerprints, such as the preponderance of focal clonic seizures (OR=5.03, 95% CI 1.60-15.7, f=0.47) in those with the p.Gly288Ser variant. Comparison with a broader cohort of 14,893 individuals with neurodevelopmental disorders revealed phenotypic features such as migrating focal seizures (OR=21716, 95% CI 2409-Inf, f=0.42) and hypertonia (OR=26.5, 95% CI 18.2-38.3, f=0.45) to be more common in EIDEE, and nocturnal seizures (OR=29787, 95% CI 3062-Inf, f=0.5) and hyperactivity (OR=13.7, 95% CI 4.70-35.9, f=0.32) to be more common in ADSHE. These findings corroborate and extend those reported in the existing literature. Developmental milestones revealed marked delays across all domains. Analysis of longitudinal medication prescription patterns exposed striking therapeutic variability, reflecting the absence of a consistent treatment framework. Several anti-seizure medications frequently cited as beneficial, quinidine and cannabidiol, were not associated with seizure improvement or sustained seizure freedom in our cohort. In contrast, clobazam (OR=1.39, 95% CI 1.12-1.72, f=0.85), ketogenic diet (OR=1.30, 95% CI 1.07-1.57, f=0.75), and lacosamide (OR=2.03, 95% CI 1.54-2.66, f=0.59) demonstrated positive comparative effectiveness. Quantitative EEG analysis distinguished individuals with KCNT1-related disorders from age-matched controls with high accuracy (AUC=0.906), with key discriminating spectral features, including alpha power in the central and parietal regions, demonstrating significant reduction across childhood and adolescence. Collectively, these findings expand the phenotypic and genotypic landscape of KCNT1-related disorders through large-scale real-world clinical data, establish quantifiable longitudinal clinical endpoints, and provide actionable insights into genotype-phenotype relationships and differential treatment response. Together, these findings will help identify outcome measures and biomarkers to inform future clinical trial design.

We assembled a multimodal clinical dataset describing demographics, placement history, prenatal substance exposure (PSE), birth characteristics, adverse childhood experiences (ACEs), International Classification of Diseases (ICD) diagnoses, and laboratory results for 3,685+ pediatric patients evaluated between 2014 and 2024 at the University of Minnesotas Adoption Medicine Clinic (AMC). Data were curated from electronic medical records through a combined manual and automated extraction protocol using a standardized operating procedure. The resulting dataset integrates structured EMR fields including neuropsychological, laboratory, and diagnostic information with manually pulled fields of ACE scores, PSE history, and placement history. We provide an overview of the population represented and describe the datasets structure, variable definitions, and validation procedures. This resource enables investigations into how early adversity impacts medical and developmental outcomes, and provides one of the largest standardized clinical placement history, PSE, and ACE datasets in an adoption and foster care pediatric population.

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.

Quantifying uncertainty in polygenic score (PGS)-based phenotype prediction is crucial for the integration of genomic data into precision medicine. While the PGS provides a fundamental pivot for point estimation, clinical decision-making necessitates the construction of well-calibrated prediction intervals that reliably encompass the true phenotypic values. However, phenotypic residuals are frequently characterized by complex heteroscedasticity and stratified variance structures across diverse demographic contexts. Existing approaches often rely on global calibration mechanisms, which fail to account for such localized variance structures and lead to systematic miscalibration within specific subpopulations. To bridge this gap, we propose Clustering-based Split Conformal Prediction with Normalized Residuals (C-SCNR), a versatile framework based on Split Conformal Prediction. By adopting residual normalization and incorporating a repetitive `split-and-cluster` mechanism, C-SCNR dynamically identifies latent error strata and applies fine-grained adjustments to the resulting intervals. Our framework requires no distributional assumptions regarding the phenotype, is compatible with any PGS method, and flexibly accommodates biologically-informed grouping. Simulation studies demonstrate that our framework consistently outperforms existing methods across diverse error distributions. In real-data applications analyzing Body mass index (BMI), Low-density lipoprotein (LDL) cholesterol, and High-density lipoprotein (HDL) cholesterol in the UK Biobank, C-SCNR effectively resolves the coverage deficiencies of existing methods in specific subgroups and consistently yields superior localized calibration. Overall, C-SCNR represents a flexible and powerful framework for constructing high-resolution context-specific prediction intervals, thereby facilitating more reliable clinical interpretations of polygenic risk.