Muthu Rama Krishnan Mookiah

Background: We present the design and implementation of a data curation framework to generate a large-scale clinical brain imaging dataset suitable for artificial intelligence (AI) enabled image analysis. Findings: The dataset is accessible through the Brain Health Data (BHD) initiative, which includes ~417,341 magnetic resonance imaging (MRI) and 846,077 computerized tomography head studies, linked electronic health records, and associated free-text imaging reports from clinical practice between 2010 and 2018 in Scotland, exceeding 185 TB in size. The data curation framework was developed during the SCottish AI in Neuroimaging to Predict Dementia and Neurodegenerative Disease (SCANDAN) study, which used a subset of 41,966 MRI series from the BHD for dementia prediction. We describe the processing of the BHD metadata and our multilabel classification output. We discuss the strengths of the BHD, including clinical relevance thanks to its unprecedented scale, population-wide representativeness of a national free-at-the-point-of-delivery healthcare, long-term follow-up to neurodegenerative disease, and real-world variability. We describe the challenges and lessons learnt in developing a framework to curate data, including the time needed to obtain permissions, the need for easily accessible, secure, responsive and affordable computational environments, the variability of clinical data, and the challenge of extracting linked clinical data and images at scale. Conclusion: This resource will be crucial for clinical research, fostering the development of personalized medicine approaches, and fast-tracking the implementation of AI models in clinical workflows. We encourage the use of the BHD data through a streamlined application to the Public Benefit and Privacy Panel for Health and Care via the electronic Data Research and Innovation Service of Public Health Scotland (eDRIS).

Importance: Early identification of dementia risk is essential for preventive care and timely enrolment into disease-modifying interventions. Current approaches rely on costly, invasive, or research-only methods not feasible at scale within public health systems. Objective: To test whether routinely acquired NHS brain MRI scans can be used to predict future dementia diagnosis and whether confidence-based stratification improves prediction reliability and clinical interpretability. Design, Setting, and Participants: Retrospective case-control study conducted entirely within a secure NHS Trusted Research Environment. Routine T1-weighted MRI brain scans were linked with electronic health records for participants from Tayside and Fife, Scotland. The study included 259 individuals who subsequently developed dementia and 259 age- and sex-matched controls. Data were processed and modelled between January and June 2025. Exposure: MRI-derived structural brain features analysed using a support-vector-machine model with nested cross-validation and distance-from-hyperplane (DFH) confidence calibration. Main Outcomes and Measures: Primary outcomes were prediction accuracy and area under the receiver-operating-characteristic curve (AUC). Secondary analyses assessed DFH-stratified performance and time from scan to clinical diagnosis. Results: Confidence-based filtering identified a high-confidence subgroup (≈35% of scans) with ≈80% accuracy. Overall, the model predicted future dementia up to five years before first recorded NHS diagnosis, achieving 66.8% accuracy (AUC = 0.71). Model sensitivity increased for shorter time-to-diagnosis intervals. Analyses were generalisable across heterogeneous routine NHS scanners and datasets. Conclusions and Relevance: This study provides, to our knowledge, the first demonstration that routinely collected NHS MRI data can predict future dementia years before clinical diagnosis. Incorporating confidence calibration transforms a standard classifier into a safety-aware, clinically interpretable framework, supporting scalable early detection, risk stratification, and recruitment to preventive or disease-modifying trials across population health systems. Key Points: Question: Can routinely acquired NHS magnetic resonance imaging (MRI) brain scans and linked health records be used to predict future dementia, with confidence calibration suitable for clinical and population-level use? Findings: In this retrospective case-control study of 518 individuals, a support-vector-machine model trained on routine NHS MRI data predicted future dementia up to five years before diagnosis. Confidence-based stratification identified a high-confidence subgroup (∼35% of scans) with ∼80% accuracy. Meaning: Routine NHS imaging data can be transformed into a population-scale, privacy-preserving early detection framework, where confidence calibration allows safe, clinically interpretable application for preventive care and research recruitment.

With the increasing proportion of elderly individuals, understanding biological mechanisms of aging is critical. Retinal vascular complexity, measured as fractal dimension ( D _f ) from fundus photographs, has emerged as a vascular aging indicator. We conducted a genome-wide association study of D _f on 74,434 participants from the Canadian Longitudinal Study on Aging, Genetics of Diabetes Audit and Research in Tayside Scotland, and UK Biobank cohorts. We identified a novel locus near DAAM1 . We found negative genetic correlations between D _f and cardiovascular disease, stroke, and inflammation but a positive correlation with life span. By combining the genetic determinants of 1159 circulating proteins from the Prospective Urban and Rural Epidemiological cohort with those of D _f using Mendelian randomization, we identified eight causal mediators, including MMP12 and IgG–Fc receptor IIb, which link higher inflammation to lower D _f , increased cardiovascular disease risk, and shorter life span. These results extend our understanding of the biological pathways underlying aging processes and inform targets to prevention and treatment.

We present the design and implementation of a data curation framework to generate a large-scale clinical brain imaging dataset suitable for artificial intelligence (AI) enabled image analysis, accessible through the Brain Health Data (BHD) initiative. The raw data accessible through the BHD includes approximately 417K magnetic resonance imaging (MRI) and 846K computerized tomography (CT) head scans, linked electronic health records (EHRs), and associated free-text imaging reports from clinical practice between 2010 and 2018 in Scotland, totally exceeding 185 TB storage of brain imaging and associated data. We present the work curating the dataset and the strengths of the BHD, including clinical relevance thanks to its unprecedented scale, population-wide representativeness of a national free-at-the-point-of-service healthcare, long-term follow-up to neurodegenerative disease, and real-world variability. We discuss challenges and lessons learnt in developing the framework to curate the data initially available, including the time needed to obtain relevant permissions, the need for easily accessible, secure, responsive and affordable computational environments, the variability and inconsistencies of clinical data and records, and the challenge of extracting linked clinical data and images at scale, among others. This resource will be crucial for clinical research, fostering the development of personalized medicine approaches, and fast-tracking the implementation of AI models in clinical workflows. We encourage the use of the BHD data through a streamlined application to the Data Research and Innovation Service (eDRIS) of Public Health Scotland (PHS).

There is increasing evidence that the complexity of the retinal vasculature measured as fractal dimension, D_f, might offer earlier insights into the progression of coronary artery disease (CAD) before traditional biomarkers can be detected. This association could be partly explained by a common genetic basis; however, the genetic component of D_f is poorly understood. We present a genome-wide association study (GWAS) of 38,000 individuals with white British ancestry from the UK Biobank aimed to comprehensively study the genetic component of D_f and analyse its relationship with CAD. We replicated 5 D_f loci and found 4 additional loci with suggestive significance (P < 1e−05) to contribute to D_f variation, which previously were reported in retinal tortuosity and complexity, hypertension, and CAD studies. Significant negative genetic correlation estimates support the inverse relationship between D_f and CAD, and between D_f and myocardial infarction (MI), one of CAD’s fatal outcomes. Fine-mapping of D_f loci revealed Notch signalling regulatory variants supporting a shared mechanism with MI outcomes. We developed a predictive model for MI incident cases, recorded over a 10-year period following clinical and ophthalmic evaluation, combining clinical information, D_f, and a CAD polygenic risk score. Internal cross-validation demonstrated a considerable improvement in the area under the curve (AUC) of our predictive model (AUC = 0.770 ± 0.001) when comparing with an established risk model, SCORE, (AUC = 0.741 ± 0.002) and extensions thereof leveraging the PRS (AUC = 0.728 ± 0.001). This evidences that D_f provides risk information beyond demographic, lifestyle, and genetic risk factors. Our findings shed new light on the genetic basis of D_f, unveiling a common control with MI, and highlighting the benefits of its application in individualised MI risk prediction.

Purpose: Based on conventional and quantitative magnetic resonance imaging (MRI), texture analysis (TA) has shown encouraging results as a biomarker for tissue structure. Chemical shift encoding-based water–fat MRI (CSE-MRI)-derived proton density fat fraction (PDFF) of thigh muscles has been associated with musculoskeletal, metabolic, and neuromuscular disorders and was demonstrated to predict muscle strength. The purpose of this study was to investigate PDFF-based TA of thigh muscles as a predictor of thigh muscle strength in comparison to mean PDFF. Methods: 30 healthy subjects (age = 30 ± 6 years; 15 females) underwent CSE-MRI of the lumbar spine at 3T, using a six-echo 3D spoiled gradient echo sequence. Quadriceps (EXT) and ischiocrural (FLEX) muscles were segmented to extract mean PDFF and texture features. Muscle flexion and extension strength were measured with an isokinetic dynamometer. Results: Of the eleven extracted texture features, Variance(global) showed the highest significant correlation with extension strength (p < 0.001, R²_adj = 0.712), and Correlation showed the highest significant correlation with flexion strength (p = 0.016, R²_adj = 0.658). Multivariate linear regression models identified Variance(global) and sex, but not PDFF, as significant predictors of extension strength (R²_adj = 0.709; p < 0.001), while mean PDFF, sex, and BMI, but none of the texture features, were identified as significant predictors of flexion strength (R²_adj = 0.674; p < 0.001). Conclusions: Prediction of quadriceps muscle strength can be improved beyond mean PDFF by means of TA, indicating the capability to quantify muscular fat infiltration patterns.

Texture analysis (TA) has shown promise as a surrogate marker for tissue structure, based on conventional and quantitative MRI sequences. Chemical-shift-encoding-based MRI (CSE-MRI)-derived proton density fat fraction (PDFF) of paraspinal muscles has been associated with various medical conditions including lumbar back pain (LBP) and neuromuscular diseases (NMD). Its application has been shown to improve the prediction of paraspinal muscle strength beyond muscle volume. Since mean PDFF values do not fully reflect muscle tissue structure, the purpose of our study was to investigate PDFF-based TA of paraspinal muscles as a predictor of muscle strength, as compared to mean PDFF. We performed 3T-MRI of the lumbar spine in 26 healthy subjects (age = 30 ± 6 years; 15 females) using a six-echo 3D spoiled gradient echo sequence for chemical-shift-encoding-based water–fat separation. Erector spinae (ES) and psoas (PS) muscles were segmented bilaterally from level L2–L5 to extract mean PDFF and texture features. Muscle flexion and extension strength was measured with an isokinetic dynamometer. Out of the eleven texture features extracted for each muscle, Kurtosis(global) of ES showed the highest significant correlation (r = 0.59, p = 0.001) with extension strength and Variance(global) of PS showed the highest significant correlation (r = 0.63, p = 0.001) with flexion strength. Using multivariate linear regression models, Kurtosis(global) of ES and BMI were identified as significant predictors of extension strength (R²_adj = 0.42; p < 0.001), and Variance(global) and Skewness(global) of PS were identified as significant predictors of flexion strength (R²_adj = 0.59; p = 0.001), while mean PDFF was not identified as a significant predictor. TA of CSE-MRI-based PDFF maps improves the prediction of paraspinal muscle strength beyond mean PDFF, potentially reflecting the ability to quantify the pattern of muscular fat infiltration. In the future, this may help to improve the pathophysiological understanding, diagnosis, monitoring and treatment evaluation of diseases with paraspinal muscle involvement, e.g., NMD and LBP.