Human Brain Mapping

Clinical brain MRIs from pediatric health systems represent a viable resource for modeling early neurodevelopmental trajectories and studying neurodevelopmental risk in real-world populations. However, a limitation to date has been the performance of existing segmentation tools for measuring various brain phenotypes in clinical scans. In particular, many tools underperform in infant scans due to morphological and physical changes such as rapid myelination. Here, we introduce ClinSeg: a robust segmentation approach tailored to early-life clinical MRIs with variable orientation, resolution, and contrast. We leverage existing registration and synthetic data generation tools to construct a training corpus for a 3d U-Net spanning anatomical and contrast diversity, including scans with morphological abnormalities from a pediatric hospital. Validated against manual segmentations, ClinSeg outperforms existing models in infancy while matching them in childhood and adolescence. Finally, ClinSeg enables the construction of reference brain growth trajectories in 11,699 individuals from 0-21 years of age, leading to the detection of more nuanced age-related findings in clinical groups.

Rib-cage morphology is a determinant of thoracic biomechanics, ventilation, and injury response, yet statistical shape models (SSMs) of the rib cage have relied on small cohorts (~100s of individuals) imaged by clinical computed tomography, which over-represents injury and disease. We constructed a surface-based SSM of the complete 24-rib cage from 26,275 standardised whole-body magnetic resonance imaging (MRI) scans of adults aged 19-74 years from the population-based German National Cohort (NAKO). Ribs were segmented with a deep-learning pipeline (a rib-extended SPINEPS model), reconstructed as per-rib surface meshes, and brought into dense vertex-wise correspondence by Gaussian-process morphable registration in Scalismo; the aligned ensemble was summarised by generalised Procrustes analysis and principal component analysis (PCA). Fourteen per-rib geometric descriptors provided a quantitative cross-walk between the abstract PCA modes and named shape features, and associations with sex, age, body size and composition (including body-fat percentage), and smoking exposure were estimated by multivariable regression with Benjamini-Hochberg false-discovery-rate control. Shape variation was strongly concentrated: 28 modes captured 95% of the total variance, and the first three alone accounted for 69.4% (PC1, 42.6%; PC2, 16.3%; PC3, 10.5%) and admitted consistent anatomical readings - a sexually dimorphic axis (PC1), a slender-versus-stout body-habitus contrast (PC2), and a free-rib-size axis at ribs 11-12 (PC3). The sexes were nearly fully separated along PC1 (Cohen's d = 2.52). Body mass and body-fat percentage were the dominant modifiable correlates of rib-cage shape, whereas the association with cumulative smoking exposure was comparatively small. The model is released as a population-representative geometric reference for benchmarking and morphing donor-derived finite-element human-body models and for further large-cohort shape analysis.

Automated lesion segmentation is increasingly central to PSMA PET/CT interpretation, supporting staging, treatment planning, and response assessment at a scale that outpaces available nuclear-medicine expertise. However, automated PSMA-PET/CT whole-body lesion segmentation models are trained on images alone, with no knowledge of where in the body prostate metastases actually tend to occur. Radiologists use clinical domain knowledge of metastatic spread, but its absence in machine learning models produces false positives in anatomically implausible locations and missed lesions in high-risk sites such as the liver. In this work, we explore whether population-level spatial knowledge of metastatic spread can be used to augment deep learning segmentation predictions, and how such a prior should be fused with a network's output, without additional training. We build a data-driven metastasis atlas from 375 expert-annotated whole-body PSMA PET/CT scans and investigate its fusion with a trained segmentation network under a Bayesian framework, in which prediction probabilities from an nnU-Net-based lesion segmentation model serve as the likelihood and the data-driven atlas as the prior. Because metastases occupy only a small fraction of whole-body voxels, the atlas's peak probability is too low, and standard power-scaled or naive Bayesian pooling references lack the tools to deal with this shortcoming. This causes these standard fusion strategies to fail and, in the naive Bayesian case, to sharply degrade performance. We instead derive a calibrated, background-referenced log-odds fusion, one of many possible approaches to combine a population atlas with a deep learning model's predictions, distinct from classical multi-atlas label fusion in that it fuses a single population prior with a trained network's softmax rather than combining several registered atlases. Furthermore, this approach is neutral outside atlas support by construction, reduces exactly to the baseline network when unweighted, and requires no retraining. This atlas fusion significantly improved mean Dice over the baseline nnU-Net on a disjoint internal test set ($+0.011$, Holm-adjusted $p=0.021$) and on an independent external cohort ($+0.0129$, Holm-adjusted $p=3.8\times10^{-16}$), with lesion sensitivity improving from 0.849 to 0.861 internally and Dice improving over baseline in every stratified anatomic region, including the rare, high-risk sites motivating this work, while naive Bayesian pooling degrades performance sharply and power-scaled pooling underperforms it throughout. Our findings suggest that population-level spatial priors can meaningfully augment deep learning predictions in whole-body oncologic segmentation, provided the fusion rule is calibrated to where the prior actually carries signal.

Background and Objectives: Recurrence following burr-hole drainage of chronic subdural haematoma (cSDH) occurs in 10-25% of cases, sustained by neovascularisation of the subdural neomembrane supplied by the middle meningeal artery (MMA). MMA embolisation reduces recurrence; whether incidental burr-hole intersection of MMA branches during drainage confers similar benefit is unknown. Methods: We performed a multicentre retrospective cohort study of consecutive adults undergoing burr-hole drainage for cSDH at two UK tertiary neurosurgical centres. Postoperative thin-slice CT was used to classify burr-hole intersection of the underlying MMA groove (no hit, distal-branch hit or main-branch hit) and measure perpendicular burr-hole-to-MMA-groove distance. Co-primary outcomes were radiological recurrence and recurrence requiring intervention. Patient-clustered multivariable logistic regression adjusted for prespecified clinical covariates and treating site. Results: 227 patients (284 operated hemispheres) were included. Radiological recurrence decreased from 34.4% with no branch hit to 22.9% with main-branch intersection, with the gradient confined predominantly to unilateral cSDH. Main-branch intersection was associated with lower adjusted odds of radiological recurrence in unilateral cSDH (adjusted OR 0.30, 95% CI 0.11- 0.81; P = .018), with a similar but non-significant association in the overall cohort (adjusted OR 0.53, 95% CI 0.26-1.07; P = .075). Burr-hole-to-MMA-groove distance demonstrated a more consistent association: in the overall cohort, each 5-mm increase independently increased the odds of radiological recurrence (adjusted OR 1.38, 95% CI 1.04-1.82; P = .025). In unilateral cSDH, each 5-mm increase was independently associated with both radiological recurrence (adjusted OR 1.45, 95% CI 1.03-2.04; P = .034) and recurrence requiring intervention (adjusted OR 1.52, 95% CI 1.05-2.20; P = .027). Conclusion: Main-branch intersection of the middle meningeal artery during routine burr-hole surgery is associated with lower recurrence of unilateral cSDH, while the accompanying burr-hole-to-MMA-groove distance gradient provides biologically plausible support for a dose-response relationship. Together, these findings provide mechanistic rationale for prospective evaluation of intentional neuronavigation-guided MMA targeting (BURR-MMA; NCT07549893).

Background: Storage symptoms after stroke-isolated urgency, urgency with frequency, and isolated frequency are common but traditionally attributed to a single overactive bladder mechanism via suprapontine disinhibition. However, clinical heterogeneity in symptom presentation suggests distinct underlying mechanisms. We aimed to characterize the neural substrates of three storage symptom subtypes after stroke using comprehensive lesion-symptom mapping. Methods: We prospectively evaluated 1,498 consecutive subacute stroke patients admitted for inpatient rehabilitation (1,105 men, 73.8%; median age 61 years). Storage symptoms were classified into three subtypes: isolated urgency (n=109), urgency with frequency (n=32), and isolated frequency (n=19). Multivariable logistic regression models with Bonferroni correction identified independent predictors across demographic, clinical, white matter hyperintensity (WMH), brain atrophy, and lesion location variables. Results: The three subtypes demonstrated largely distinct sets of independent predictors. The left genu of the corpus callosum (aOR=20.06, 95% CI 7.78-51.74, P<0.001) and the inferior frontal gyrus (aOR=3.48, 95% CI 1.81-6.67, P<0.001) were independently associated with isolated urgency and survived Bonferroni correction, together with a right IFG-insula synergistic effect (OR=21.46, 95% CI 10.49-43.88, P<0.001). Urgency with frequency was associated with a broad fronto-cingulate network-the IFG (aOR=11.45, 95% CI 3.10-42.33, P<0.001, surviving Bonferroni correction) and the ACC (aOR=11.53, 95% CI 2.40-55.49, P=0.002) with diffuse right-hemisphere dominance, older age and brain atrophy. Isolated frequency was associated with anterior corona radiata involvement (aOR=5.46, 95% CI 1.92-15.54, P=0.002) and male sex (aOR=10.62, 95% CI 1.36-82.98, P=0.024), though none reached the strict Bonferroni threshold. Conclusions: These findings identify three mechanistically distinct post-stroke storage symptom subtypes with separable neural substrates, lateralization profiles, and clinical determinants. The triple dissociation across subtypes supports a discrete pathway model over the traditional unitary OAB framework, providing a neuroanatomically grounded basis for subtype-stratified treatment Keywords: storage symptoms; subacute stroke; hemispheric lateralization; structural synergy; lesion-syndrome mapping

Background: Hippocampal sclerosis (HS) is a common cause of drug-resistant focal epilepsy (DRFE) and amenable to neurosurgical treatment. Detection relies on MRI but can be challenging. 7 Tesla (T) ultra-high field MRI and automated MRI post-processing tools have independently been shown to improve radiological diagnosis of HS. However, combining these approaches remains underexplored. This study evaluated whether AID-HS, a tool for HS detection developed using 3T MRI, generalises to 7T MRI data. Methods: We collated a dataset of paired 3T and 7T T1-weighted MRI from four epilepsy centres, including 23 patients with HS, 39 healthy controls, and 23 individuals with focal cortical dysplasia as disease controls. Histopathology served as the gold standard for defining HS where available (n=7), otherwise radiological findings (n=16). AID-HS was applied to images acquired at both field strengths, and sensitivity and specificity for detection and lateralisation of HS were compared. Additionally, agreement of hippocampal features across 3T and 7T was evaluated. Results: We found no evidence of a difference in performance of AID-HS between 3T and 7T. Sensitivity for detection of unilateral HS was 63% (12/19) at 3T and 68% (13/19) at 7T (McNemar's exact test p=1.0). Specificity in controls was 97% (60/62) at 3T and 100% (62/62) at 7T (p=0.5). Bilateral HS was correctly flagged in 3 of 4 cases using feature-based criteria, with high specificity in controls. Quantitative hippocampal features showed moderate to good agreement across field strengths (ICC 0.70 to 0.98), with small differences observed for volume and thickness estimates. Conclusion: AID-HS provides robust detection and lateralisation of HS across multiple 7T MRI centres, highlighting its potential to enhance lesion detection. Future work is needed to investigate whether models trained on 7T data can leverage the improved image quality for further gains in HS detection performance.

Background Phenotypic age acceleration (PhenoAgeAccel), derived from PhenoAge, and MetaboHealth are composite exposures of biological ageing and metabolic health associated with dementia-related outcomes. Whether these associations are causal and reflect the exposures, constituent biomarkers, or both remains unclear. Methods This study included UK Biobank participants of White British genetic ancestry. MetaboHealth was derived from nuclear magnetic resonance (NMR) metabolomics and PhenoAgeAccel from clinical biomarkers and chronological age. Genome-wide association studies (GWAS) were conducted for MetaboHealth (n=272,568) and PhenoAgeAccel (n=274,077). Independent genome-wide significant variants were used as genetic instruments in two-sample Mendelian randomisation (MR) with FinnGen all-cause dementia summary statistics. Inverse-variance weighting was the primary MR method. Causal network analysis estimated relationships among constituent biomarkers and dementia. Findings GWAS identified 126 and 141 independent genome-wide significant variants for MetaboHealth and PhenoAgeAccel, of which 109 and 141 were retained as genetic instruments. MR found no evidence of a causal effect of genetically predicted MetaboHealth (per unit: OR 0.83, 95% CI 0.49-1.42; p=0.51) or PhenoAgeAccel (per year: OR 0.99, 95% CI 0.95-1.02; p=0.44) on all-cause dementia, with consistent findings across sensitivity analyses and robust MR methods. Lower lymphocyte percentage and higher NMR-derived glucose had direct relationships with dementia in the joint constituent-biomarker network. Interpretation MR provided no evidence that either composite exposure causally influenced dementia. The network prioritised lymphocyte percentage and NMR-derived glucose, supporting examination of composite exposures alongside their constituent biomarkers. Funding NIHR, UKRI, MRC, UK Dementia Research Institute, Innovate UK, and European Union. Full funding details are provided in the acknowledgements.

Objective: Published P300-speller fusion schemes fix prior trust regardless of trial reliability; we tested whether a reliability estimate improves on it. Methods: We reanalyzed 3,373 archived P300-speller selections from 47 people with ALS (BigP3BCI). A fair, matched-search-space comparison, tuning both a fixed weight and an adaptive policy out-of-fold, was evaluated across 22 evaluable language-model priors up to 46.7B parameters. Two representative priors, GPT-2 and a classical 5-gram, additionally received detailed naive and mechanistic analyses. Results: No prior's 95% CI favored adaptive fusion under the fair comparison, despite unexploited oracle headroom at every scale. Under GPT-2, the naive comparison was significantly worse for adaptive fusion; both anchors converged to a degenerate or near-degenerate fair-comparison solution. For the representative anchors, three further controllers failed to convert that headroom into benefit; the fixed-fused posterior's output probability outperformed the best controller for flagging errors (2.8- to 3.8-fold enrichment). Conclusion: A tuned fixed weight is a difficult-to-beat default across the tested scale range; reliability estimation gave no deployable adaptive advantage. Significance: Adaptive weighting should be validated against a fairly tuned baseline across model families and scales; in this dataset, the fused output's confidence identified high-risk selections better than the tested purpose-built ranker.

Adolescent substance use is a major public health concern due to increased risk of future physical and mental health conditions. Fronto-striatal functioning - particularly regarding inhibition and reward processing - may increase vulnerability to high-risk substance use. However, it remains unclear if these neurobiological differences precede substance use or are consequences of it. The ongoing Adolescent Brain Cognitive Development (ABCD) Study follows over 10000 youth, offering an unprecedented opportunity to longitudinally examine substance use patterns throughout development. We utilized family-clustered time-varying Cox proportional hazard models to prospectively examine main and interaction effects of right Inferior Frontal Gyrus (IFG) inhibitory control and bilateral nucleus accumbens (NAc) reward response, alongside early life adversity and peer substance use as predictors of alcohol and cannabis onset in the ABCD Study. We identified a significant crossover interaction such that left NAc activity had a slight positive association with first full alcoholic drink in the context of higher right IFG activity but a negative association in the context of lower right IFG activity. However, peer alcohol and cannabis use emerged as the strongest predictors of outcomes. Alcohol onset was also more common in females, and early life adversity was associated only with cannabis onset. Findings indicate that interactions between inhibition- and reward-related brain regions may impact risk for early substance use onset, but these effects may be modest relative to socioenvironmental factors. Additionally, divergent alcohol and cannabis findings suggest that risk profiles are substance specific. Peer-focused strategies should be considered in preventive efforts.

Major depressive disorder (MDD) has been associated with accelerated structural brain aging, yet whether this reflects a pre-existing neurobiological vulnerability, a dynamic acute state effect, or an accumulating biological residual remains unresolved. Across two longitudinal cohorts (N=3220), including a unique sample of 78 initially healthy individuals who transitioned into their first depressive episode during the study course, we systematically tested all three hypotheses. Patients with diagnosed MDD showed elevated MRI-derived brain age relative to healthy controls (1.4 and 2.5 years across cohorts). For the vulnerability hypothesis, individuals scanned prior to their first episode showed no baseline elevation, despite already demonstrating subclinical elevations in self-reported symptom severity, indicating that advanced brain age does not precede illness onset. For the state hypothesis, we found no acceleration of brain aging following the first depressive episode, and longitudinal brain age trajectories were independent of acute clinical symptom severity. Finally, neither episode duration nor recurrence scaled with brain age. Accelerated brain aging in depression is therefore neither an antecedent vulnerability nor an acute state marker of the first episode, but rather a stable biological feature of a long term illness course.

Diffusion models achieve high reconstruction quality in low-dose computed tomography (LDCT), but their iterative sampling trajectories impose substantial computational costs. Unlike unconditional generation, paired LDCT reconstruction starts from an image that already contains the anatomy and spatial structure of the standard-dose CT (SDCT) target; reconstruction primarily requires correcting dose-related noise and artifacts. We therefore introduce Residual Endpoint Flow Matching (REFM), an LDCT reconstruction method that learns to transport an LDCT image directly toward its paired SDCT endpoint rather than defining a noise-to-image trajectory. REFM predicts the residual velocity along linear interpolations between both images and supports single-step and multi-step reconstruction using the same trained network. We evaluate five model capacities using 1 to 50 Euler steps against deterministic U-Net and diffusion-based baselines. Across all REFM variants, one-step inference consistently provides the highest reconstruction quality. On the TCIA validation set, REFM Base achieves 50.98 dB PSNR and 0.9865 SSIM at 94.54 fps, compared with 50.92 dB, 0.9847, and 9.26 fps for DDPM-10. REFM Small retains 50.71 dB while increasing throughput to 198.56 fps. Without fine-tuning, REFM Base also matches the 25-step DDPM baseline on the external Mayo Clinic dataset, although DDPM remains stronger on synthetically degraded CRLM images. Thus, our results show that exploiting paired anatomical correspondence enables diffusion-level LDCT reconstruction with a single step reconstruction.

Background: Brain age is a biomarker of brain tissue integrity associated with disability in multiple sclerosis. While new lesion formation is central to MS diagnosis and treatment monitoring, its direct relationship to brain aging has not been established. Methods: We analyzed 163 people with MS with clinical and MRI assessments at baseline and years 3, 6, and 8. Brain age was estimated using BrainAgeNeXt. Annualized brain age acceleration was modeled as a function of radiological activity using generalized estimating equations, adjusting for age, sex, disease duration, baseline T2 lesion volume, normalized brain volume (NBV), brain age difference (BAD), and disease-modifying therapy. Secondary analyses examined dose-response effects, post-activity recovery, paramagnetic rim lesion (PRL) associations, and disability associations. Results: 105 participants had at least one new T2 lesion over 8 years. Radiologically active intervals (138 of 333) were associated with +0.19 yr/yr greater brain age acceleration than stable intervals (95% CI: 0.03-0.37; p=0.022), scaling with lesion count (beta=+0.18; p=0.001) and volume. Older age, greater baseline BAD, and NBV were independently associated with reduced brain age acceleration. Brain age acceleration in individuals with new lesions normalized during subsequent stable intervals (0.41 vs -0.06 yr/yr; p=0.001). Both PRLs and non-PRL lesions were associated with greater brain age acceleration than stable intervals. Baseline BAD, but not annualized acceleration, predicted Expanded Disability Status Scale (EDSS) and Nine-Hole Peg Test (9HPT) worsening. Conclusions: New focal lesion formation is associated with a quantifiable, dose-response acceleration of brain aging in MS that normalizes once lesion activity is suppressed.

Background: India has a rising incidence of chronic non-communicable diseases, making it a major healthcare burden today. Growing evidence suggests that chronic low-grade inflammation links ageing with cardiometabolic disorders, captured by the emerging concept of inflammaging. However, most evidence on biological ageing comes from Western populations, with no similar models developed for the Indian population. Given the country's distinctive genetic makeup, unique exposome, and heterogeneous NCD presentation, Western models may not capture inflammaging and its effects in the Indian population. Methods: We analysed baseline data from 4,240 adults in the Phenome India CSIR Health Cohort Knowledgebase (PI CheCK), a nationwide multi-centre cohort. Participants were stratified into eight cardiometabolic phenotype groups by BMI (Asian cut off), blood pressure and HbA1c status. We trained a Super Learner ensemble to predict chronological age in the lean normotensive-normoglycaemic reference group (n=615) using 44 plasma cytokines, sex, haemoglobin, and bioimpedance-derived visceral fat area, per cent body fat, and total body water. Performance was assessed by repeated five-fold cross-validation and in a held-out healthy test set. Calibrated biological age acceleration was then estimated in the remaining 3,625 participants. Results: Median age was 51.0 years (IQR 41.0 to 62.0) and 49.4% were female. The Super Learner outperformed elastic net and XGBoost comparators. Permutation importance identified visceral fat area, per cent body fat, CTACK, SDF1a, haemoglobin and sex as leading contributors, with body composition measures accounting for the largest share, indicating an immune-metabolic rather than cytokine-only signal. Biological age acceleration was concentrated in overweight/obese phenotypes. Lean phenotypes showed acceleration close to the reference (0.32 0.50 years). Conclusions: Cytokine and body composition measures capture a quantifiable immunometabolic ageing signal in a South Asian cohort, with acceleration driven predominantly by adiposity. External validation and longitudinal follow up are required.

Background Understanding the mechanisms underlying brain aging and age-related pathological changes is essential for advancing brain health research. Our group previously developed a mechanistic mathematical model of healthy brain, Chamberland et al. (2024) that integrates key biological processes involved in normal aging, from which Alzheimer's disease (AD) related changes may emerge naturally. Objectives To characterize and validate this brain model by evaluating its sensitivity, calibrating its parameters, and assessing generalizability in independent populations. Methods The model represents the evolution of key biological processes associated with brain aging, including amyloid beta (A{beta}), tau pathologies, neuroinflammation, and neuronal death. After identifying the 30 most influential parameters, we calibrated the model using cognitively normal (CN) participants from the AD Neuroimaging Initiative (ADNI) database (n = 211) by minimizing a loss function composed of three outcomes (AB) plaques, tau tangles, and neuronal density). The calibrated model was then applied to the UK Biobank cohort (n = 35,899) of normal controls (aged 44-82 years). The effects of sex and APOE were evaluated using stratified simulations. Results Parameter calibration significantly reduced the prediction errors for A{beta} and tau. Neuronal density predictions showed strong agreement in the UK Biobank cohort. The variance decomposition identified APOE status as a major contributor to variability in A{beta}. Conclusion Our validated brain health model links mechanistic pathways with population data and reproduces neuronal density patterns in an independent cohort. These findings support its use as a framework for studying brain aging and investigating how Alzheimer's disease related pathological changes may emerge with aging.

Stroke remains a leading cause of mortality and long-term disability worldwide, yet rapid diagnosis is often limited by the shortage of trained radiologists, particularly in resource-constrained settings. Automated analysis of CT imaging offers a potential solution, but existing methods often struggle to achieve clinically generalisable performance while jointly addressing multiple diagnostic tasks. Here we present the Intelligent Integrated Stroke Diagnosis System IISDS, an end-to-end deep learning framework built upon StrokeGNN, a graph-based architecture that integrates 3D contextual feature extraction with U-Net-based 2D lesion segmentation to enable comprehensive stroke analysis from non-contrast CT scans. IISDS performs stroke subtype classification, lesion segmentation and lesion volume estimation within a unified pipeline. To develop and validate the system, we collected and curated BGD-ISD through a collaboration between AI researchers, neurologists, radiologists and clinicians, resulting in a large multi-centre dataset comprising 1,507 CT scans from 597 stroke cases acquired across six hospitals and medical centres in Bangladesh. Across BGD-ISD and multiple publicly available datasets, IISDS achieves state-of-the-art performance on all tasks, improving segmentation accuracy by [&ge;]0.011 Dice score, reducing lesion volume estimation error by [&ge;]0.3 average symmetric surface distance (ASSD), and increasing classification performance by [&ge;]0.018 area under the receiver operating characteristic curve (AUC) compared with existing approaches. These results demonstrate the potential of graph-based deep learning to enable clinically generalisable, automated and scalable stroke diagnosis from CT imaging, supporting rapid clinical decision-making, particularly in healthcare environments with limited access to expert radiological interpretation.

Purpose: The primary objective of the Stanford Knee Osteoarthritis PET/MRI Evaluation (SKOPE) study is to develop and evaluate a multimodal, dynamic [18F]NaF PET-MRI framework for characterizing whole-joint physiology and its relationship to osteoarthritis (OA) risk, pain, and disease progression. Specifically, we aim to integrate dynamic PET with quantitative and anatomical MRI, to characterize structural, compositional, and metabolic features across the knee and surrounding musculoskeletal system, evaluate acute tissue responses to exercise, and identify imaging biomarkers associated with OA risk, pain, and disease progression. Methods: The SKOPE study includes multimodal PET-MRI of the knee and surrounding musculoskeletal tissues, with imaging of the knee, tibia, ankle, thigh, hip, pelvis, and lumbosacral spine. Dynamic [18F]NaF PET is combined with conventional anatomical MRI and quantitative MRI techniques, including quantitative double-echo steady-state (qDESS) T2 mapping of cartilage, Dixon fat-fraction imaging, ultrashort echo time (UTE) T2* mapping of short-T2 tissues, UTE imaging of tibial bone, and zero echo time (ZTE) imaging for bone morphology and pseudo-CT generation. Additional MRI sequences characterize muscle composition, bone and joint anatomy, intervertebral discs, and regional vascular anatomy. Selected scans are acquired before and after a standardized exercise protocol to assess the acute physiological response of the joint. Automated segmentation is used to generate subject-specific masks of muscles, bones, vertebrae, and intervertebral discs. A subset of the MRI protocol is repeated at 1- and 2-year follow-up to assess longitudinal changes. Expected Impact: By combining dynamic bone metabolic imaging with quantitative measures of cartilage, menisci, muscle, bone, fat, vascular structures, and the spine and hip, the SKOPE protocol provides a whole-joint and multijoint framework for studying the structural, metabolic, and physiological processes associated with OA and pain. Exercise and longitudinal imaging further enable assessment of acute tissue responses and changes over time, supporting the development of quantitative imaging biomarkers for OA risk, pain, and disease progression.

Background: The Centiloid (CL) scale standardizes global amyloid PET quantification and is widely used to define amyloid positivity. As a global summary measure, however, CL may not fully reflect the regional distribution of amyloid deposition, which can carry additional prognostic information about the rate of cognitive decline. Objective: To develop and externally validate a fixed, regional amyloid PET composite score that complements CL for predicting cognitive decline in Alzheimer's disease. Methods: The Regional Amyloid PET Score (RAPS) was derived from 82 FreeSurfer regions using machine learning with bootstrap stability selection to predict the rate of change in CDR-Sum of Boxes (CDR-SB) in 433 amyloid-positive ADNI [18F]florbetapir participants. The fixed nine-region weights were applied without retraining in a cross-tracer ADNI [18F]florbetaben subset (N = 71; largely overlapping the discovery participants) and two external validation cohorts, NACC SCAN (N = 1531; four tracers) and OASIS-3 (N = 428). Results: RAPS comprised nine regions. In ADNI, RAPS correlated more strongly with CDR-SB slope than CL and showed higher discrimination of rapid decliners (AUC 0.813 vs 0.713). Performance was directionally consistent across validation cohorts; in NACC SCAN, RAPS and CL independently predicted clinical progression. Cross-cohort meta-analysis of the three independent cohorts supported incremental discrimination beyond CL (pooled {Delta}AUC +0.066; I2 = 0%). Conclusions: RAPS, a fixed regional amyloid PET-derived score, may complement CL for prognostic stratification in Alzheimer's disease research.

Neighbourhood deprivation is one of the few potential policy-modifiable risk factors for psychiatric and neurological disorders, but the neurobiological pathways underlying these associations remain unclear. We investigated these relationships across three cohorts spanning the life span: the Healthy Brain and Child Development (HBCD) Study (n = 84, aged 0 to 4 weeks postnatal), the Adolescent Brain Cognitive Development (ABCD) Study (n = 4,792, aged 9 to 10 years), and the UK Biobank (UKB; approximately 500,000 adults, aged 44 to 87 years). Neighbourhood deprivation was associated with elevated disease risk, and individual lifestyle factors accounted for only a small fraction of this burden, indicating that the much larger residual effect reflects broader contextual characteristics of deprived environments rather than individual behaviours alone. Across all cohorts, greater deprivation consistently predicted lower cortical and subcortical brain volume, with effects detectable in early development and substantially stronger in adulthood. Across disorders, regional brain volume emerged as a consistent neuroanatomical mediator linking neighbourhood deprivation to neuropsychiatric disease. We further showed that deprivation preferentially affects brain regions intrinsically vulnerable to neuropsychiatric disorders. Spatial decoding analyses implicated dopaminergic and serotonergic neurotransmitter systems together with specific excitatory and inhibitory neuronal classes. Importantly, both the deprivation effects and their neuroanatomical mediation patterns were replicated across independent populations. Our study delivers a translational framework linking neighbourhood deprivation to brain health, which could inform public health policies and preventive interventions.

Purpose: Identifying head impacts linked to brain injury in sport remains challenging. Instrumented mouthguards quantify head-impact kinematics, and finite element (FE) modelling can transform these data into brain strain estimates, which may better reflect injury risk than kinematics alone. Here, we examined associations between mouthguard-measured kinematics, FE-derived strain, and plasma brain injury biomarker GFAP following head impacts. Methods: We analysed 41 video-verified impacts from male Australian football players, including 22 assessed for concussion (17 diagnosed) and 19 unassessed. Instrumented mouthguards recorded peak linear acceleration (PLA), peak rotational acceleration, and peak rotational velocity (PRV). Brain strain was estimated using the Imperial College FE brain model, and plasma GFAP was quantified using Simoa. Biomechanical-GFAP associations were examined using Spearman correlations and segmented regression. Results: For impacts overall, plasma GFAP was moderately correlated with PLA ({rho}=0.46, 95% CI: 0.20-0.66), PRV ({rho}=0.53, 95% CI: 0.20-0.78), and strain ({rho}=0.60, 95% CI: 0.32-0.80). Associations were stronger within concussion cases for strain ({rho}=0.86, 95% CI: 0.58-0.97) and PRV ({rho}=0.64, 95% CI: 0.15-0.93). Piecewise regression identified strain levels above which strain-GFAP relationships steepened across the whole-brain and brainstem. In concussion cases, supra-threshold brainstem strain was associated with greater symptoms. Conclusion: Finite element brain strain may better predict brain injury risk following a sport-related head impact than peak acceleration metrics. Stronger associations with plasma GFAP, particularly among concussion cases, and evidence of a biomechanical threshold, support the use of biomarker-informed strain measures in future risk modelling and the development of brain injury screening thresholds.

Background: The relative merits of infrainguinal bypass and primary major lower limb amputation (MLLA) for chronic limb-threatening ischaemia (CLTI) remain uncertain, and the baseline profiles of patients selected for each strategy are poorly described. Methods: A systematic review and meta-analysis were undertaken in accordance with PRISMA 2020 and prospectively registered (PROSPERO: CRD42022356094). MEDLINE, Embase, CENTRAL, and CINAHL were searched from inception to March 2025. Prospective studies of adults with CLTI undergoing primary infrainguinal bypass or primary MLLA were eligible. Mortality, major adverse cardiovascular events (MACE) and subsequent amputation outcomes were synthesised using random-effects meta-analysis of proportions. Baseline comorbidity profiles were also extracted. Results: Twenty-seven studies involving 6,576 patients were included: 5,779 underwent infrainguinal bypass and 797 underwent MLLA. After bypass, pooled mortality was 3.7% at 30 days (95% CI 2.8%-4.9%, I2 = 49.4%), 18.5% at 1 year (95% CI 15.6%-21.9%, I2 = 62.3%), and 54.3% at 5 years (95% CI 50.5%-58.0%, I2 = 0%). After MLLA, pooled mortality was 9.2% at 30 days (95% CI 4.1%-19.3%, I2 = 73.5%), 28.5% at 1 year (95% CI 13.3%-51.0, I2 = 70.8%), and 39.9% at 2 years (95% CI 0.3%-99.3, I2 = 90.5%), although longer-term estimates were limited by sparse data and marked heterogeneity. Thirty-day MACE was 6.5% (95% CI 4.3%-9.7, I2 = 63.5%) after bypass and 2.8% after MLLA (95% CI 0.1%-37.6%, I2 = 0%). Early subsequent major amputation after bypass occurred in 3.9% of patients (95% CI 2.0%-7.7%, I2 = 91.2%), rising to 16.2% at 1 year (95% CI 12.6%-20.5%, I2 = 82.0%) and 33.3% at 3 years (95% CI 20.1%-49.8%, I2 = 0%). Early re-amputation after MLLA occurred in 10.9% of patients (95% CI 4.5%-24.4%, I2 = 40.3%). Baseline comorbidity burden was high in both groups, with substantial heterogeneity across studies. Conclusions: CLTI carries a poor prognosis regardless of treatment strategy. Infrainguinal bypass is associated with lower early mortality and better early limb preservation than primary MLLA, but long-term survival remains poor and later limb failure is common. Primary MLLA is not a low-risk alternative. Better contemporary comparative evidence utilising modern causal inference approaches is needed to support individualised decision-making.