BACKGROUND:Longitudinal studies of seed-based functional connectivity (SBFC) in young adult Huntington's disease gene-expanded (HDGE) individuals are rare, and none, to our knowledge, have examined adult cohorts decades from predicted clinical motor diagnosis. OBJECTIVES:To examine longitudinal functional connectivity (FC) changes over ~4.8 years in adult HDGE individuals before clinical motor diagnosis compared with matched controls, all selected from the HD Young Adult Study (HD-YAS) cohort, focusing on bilateral caudate and putamen as seeds. METHODS:A subset of 71 right-handed individuals (43 HDGEs and 28 controls) from the HD-YAS underwent resting-state functional magnetic resonance imaging (fMRI) at two visits ~4.8 years apart. SBFC analyses focused on bilateral caudate and putamen seeds. Mixed-effects analysis of variance (ANOVA) tested main effects of group, time, and group × time interactions, with false discovery rate (FDR) correction. Post hoc tests explored significant findings. RESULTS:HDGEs showed reduced FC between the putamen and cerebellar vermis/lobules and brainstem (P-FDR ≤ 0.01), alongside increased connectivity with precuneus (P-FDR = 0.04), supramarginal, and angular gyri (P-FDR = 0.04). Over ~4.8 years, HDGEs exhibited different FC trajectories compared with controls, displaying FC reductions between the right caudate and paracingulate, frontal (P-FDR = < 0.001), occipital (P-FDR = < 0.01), and striatal regions (P-FDR = ≤ 0.03). CONCLUSIONS:These findings provide the first longitudinal evidence of early cortico-striatal and cerebellar functional network changes in adult HDGEs decades before clinical motor diagnosis, alongside possible compensatory processes in key hubs of the default mode network. These early FC changes likely reflect a dynamic interplay between neurodegenerative processes and adaptive reorganization, a balance that may ultimately fail as pathology progresses. © 2026 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

The gold standard in the treatment of ischaemic stroke is set by evidence from randomized controlled trials, typically using simple estimands of presumptively homogeneous populations. Yet the manifest complexity of the brain's functional, connective, and vascular architectures introduces heterogeneities that violate the underlying statistical premisses, potentially leading to substantial errors at both individual and population levels. The counterfactual nature of interventional inference renders quantifying the impact of this defect difficult. Here we conduct a comprehensive series of semi-synthetic, biologically plausible, virtual interventional trials across 100M+ distinct simulations. We generate empirically grounded virtual trial data from large-scale meta-analytic connective, functional, genetic expression, and receptor distribution data, with high-resolution maps of 4K+ acute ischaemic lesions. Within each trial, we estimate treatment effects using models varying in complexity, in the presence of increasingly confounded outcomes and noisy treatment responses. Individualized prescriptions inferred from simple models, fitted to unconfounded data, are less accurate than those from complex models, even when fitted to confounded data. Our results indicate that complex modelling with richly represented lesion data may substantively enhance individualized prescriptive inference in ischaemic stroke.

Hyperconnectivity in functional brain networks occurs decades before disease onset in Huntington's disease. However, the biological mechanisms remain unknown. We investigate connectivity in Huntington's disease using Morphometric INverse Divergence (MIND) in three Huntington's disease cohorts (N = 512) spanning from two decades before the onset of symptoms through to functional decline. Here, we identify stage-specific profiles, with hyperconnectivity 22 years from predicted motor onset, progressing to hypoconnectivity through the late premanifest and manifest stages, showing that hypoconnectivity is correlated with neurofilament light concentrations. To understand the biological mechanisms, we investigate associations with cortical organization principles including disease epicentres and cell-autonomous systems, in particular neurotransmitter distribution. The contribution from disease epicentres is limited to late premanifest while cell-autonomous associations are demonstrated across the Huntington's disease lifespan. Specific relationships to cholinergic and serotoninergic systems localized to granular and infragranular cortical layers are identified, consistent with serotoninergic layer 5a neuronal vulnerability previously identified in post-mortem brains.

This viewpoint paper addresses the ongoing challenges and opportunities within the data-for-health ecosystem, drawing insights from a multistakeholder workshop. Despite notable progress in the digitization of health care systems, data sharing and interoperability remain limited, so the full potential of health care data is not realized. There is a critical need for data ecosystems that can enable the timely, safe, efficient, and sustainable collection and sharing of health care data. However, efforts to meet this need face risks related to privacy, data protection, security, democratic governance, and exclusion. Key challenges include poor interoperability, inconsistent approaches to data governance, and concerns about the commodification of data. While emerging platforms such as social media play a growing role in gathering and sharing health information, their integration into formal data systems remains limited. A robust and secure data-for-health ecosystem requires stronger frameworks for data governance, interoperability, and citizen engagement to build public trust. This paper argues that reframing health care data as a common good, improving the transparency of data acquisition and processing, and promoting the use of application programming interfaces (APIs) for real-time data access are essential to overcoming these challenges. In addition, it highlights the need for international norms and standards guided by multisector leadership, given the multinational nature of data sharing. Ultimately, this paper emphasizes the need to balance risks and opportunities to create a socially acceptable, secure, and effective data-sharing ecosystem in health care.

Huntington’s disease (HD) is an autosomal dominant neurodegenerative disease with the age at which characteristic symptoms manifest strongly influenced by inherited HTT CAG length. Somatic CAG expansion occurs throughout life and understanding the impact of somatic expansion on neurodegeneration is key to developing therapeutic targets. In 57 HD gene expanded (HDGE) individuals, ~23 years before their predicted clinical motor diagnosis, no significant decline in clinical, cognitive or neuropsychiatric function was observed over 4.5 years compared with 46 controls (false discovery rate (FDR) > 0.3). However, cerebrospinal fluid (CSF) markers showed very early signs of neurodegeneration in HDGE with elevated neurofilament light (NfL) protein, an indicator of neuroaxonal damage (FDR = 3.2 × 10−12), and reduced proenkephalin (PENK), a surrogate marker for the state of striatal medium spiny neurons (FDR = 2.6 × 10−3), accompanied by brain atrophy, predominantly in the caudate (FDR = 5.5 × 10−10) and putamen (FDR = 1.2 × 10−9). Longitudinal increase in somatic CAG repeat expansion ratio (SER) in blood was a significant predictor of subsequent caudate (FDR = 0.072) and putamen (FDR = 0.148) atrophy. Atypical loss of interruption HTT repeat structures, known to predict earlier age at clinical motor diagnosis, was associated with substantially faster caudate and putamen atrophy. We provide evidence in living humans that the influence of CAG length on HD neuropathology is mediated by somatic CAG repeat expansion. These critical mechanistic insights into the earliest neurodegenerative changes will inform the design of preventative clinical trials aimed at modulating somatic expansion. ClinicalTrials.gov registration: NCT06391619 . A comprehensive longitudinal analysis of individuals with preclinical Huntington’s disease identifies biomarkers of neurodegeneration and somatic expansion in blood DNA, detectable years before symptom onset.

Accurate outcome prediction in acute ischaemic stroke is critical to clinical decision-making and optimal trial design. Predictive performance may, however, vary inequitably across multidimensionally defined patient subpopulations shaped by the intersection of diverse demographic, clinical, and anatomical factors such as the lesion distribution. Here we investigate the variation in achievable predictive performance— the epistemic equity of the model—across a multi-site cohort of 4083 richly characterised stroke episodes. Flexible machine learning models of demographic, clinical, and diffusion-weighted imaging data were used to predict an array of care needs and clinical outcomes. To identify systematic multidimensional subpopulations, deep representation learning was used to derive low-dimensional latent projections of high-dimensional clinical and imaging data. The distinct, consistent, interpretable multidimensional subpopulations thereby identified were used to quantify model epistemic equity across the population. Predictive performance varied substantially across subpopulations, revealing disparities invisible to simple, unidimensional measures of equity. Combining deep representation learning with stratified performance evaluation, we demonstrate the need for expressive, multidimensional representations in assuring the equity of predictive models in acute stroke care and research.

Radiological reports typically summarize the content and interpretation of imaging studies in unstructured form that precludes quantitative analysis. This limits the monitoring of radiological services to throughput undifferentiated by content, impeding specific, targeted operational optimization. Here we present Neuradicon, a natural language processing (NLP) framework for quantitative analysis of neuroradiological reports. Our framework is a hybrid of rule-based and artificial intelligence models to represent neurological reports in succinct, quantitative form optimally suited to operational guidance. We demonstrate the application of Neuradicon to operational phenotyping of a corpus of 336,569 reports, and report excellent generalizability across time and two independent healthcare institutions.

Ischaemic stroke, a leading cause of death and disability, critically relies on neuroimaging for characterising the anatomical pattern of injury. Diffusion-weighted imaging (DWI) provides the highest expressivity in ischemic stroke but poses substantial challenges for automated lesion segmentation: susceptibility artefacts, morphological heterogeneity, age-related comorbidities, time-dependent signal dynamics, instrumental variability, and limited labelled data. Current U-Net-based models therefore underperform, a problem accentuated by inadequate evaluation metrics that focus on mean performance, neglecting anatomical, subpopulation, and acquisition-dependent variability. Here, we present a high-performance DWI lesion segmentation tool addressing these challenges through optimized vision transformer-based architectures, integration of 3563 annotated lesions from multi-site data, and algorithmic enhancements, achieving state-of-the-art results. We further propose a novel evaluative framework assessing model fidelity, equity (across demographics and lesion subtypes), anatomical precision, and robustness to instrumental variability, promoting clinical and research utility. This work advances stroke imaging by reconciling model expressivity with domain-specific challenges and redefining performance benchmarks to prioritize equity and generalizability, critical for personalized medicine and mechanistic research.

A robust learning system balances adaptability to new experiences with stability of its foundational architecture. To investigate how the human brain implements this we used a new approach to study plasticity and stability across hierarchical processing stages in visual cortex. We compare the rod system of individuals born with rod-only photoreceptor inputs (achromatopsia) to the typically developed rod system, allowing us to dissociate impacts of life-long versus transient responses to altered input. Cortical input stages (V1) exhibited high stability, with structural hallmarks of deprivation and no retinotopic reorganisation. However, plasticity manifested as reorganised read-out of these inputs by higher-order cortex, in a pattern that could compensate for the lower resolution of a rod-only system and its lack of high-density foveal input. We propose that these hierarchical dynamics robustly optimize processing of available input and could reflect a broader principle of brain organisation with important implications for emerging sight-recue therapies.

The quantification of cognitive powers rests on identifying a behavioural task that depends on them. Such dependence cannot be assured, for the powers a task invokes cannot be experimentally controlled or constrained a priori, resulting in unknown vulnerability to failure of specificity and generalisability. Evaluating a compact version of Raven's Advanced Progressive Matrices (RAPM), a widely used clinical test of fluid intelligence, we show that LaMa, a self-supervised artificial neural network trained solely on the completion of partially masked images of natural environmental scenes, achieves representative human-level test scores a prima vista, without any task-specific inductive bias or training. Compared with cohorts of healthy and focally lesioned participants, LaMa exhibits human-like variation with item difficulty, and produces errors characteristic of right frontal lobe damage under degradation of its ability to integrate global spatial patterns. LaMa's narrow training and limited capacity suggest matrix-style tests may be open to computationally simple solutions that need not necessarily invoke the substrates of reasoning.

Achromatopsia is an inherited retinal disease that affects 1 in 30,000–50,000 individuals and is characterised by an absence of functioning cone photoreceptors from birth. This results in severely reduced visual acuity, no colour vision, marked sensitivity to light and involuntary oscillations of the eyes (nystagmus). In most cases, a single gene mutation prevents normal development of cone photoreceptors, with mutations in the CNGB3 or CNGA3 gene being responsible for ∼80 % of all patients with achromatopsia. There are a growing number of studies investigating recovery of cone function after targeted gene therapy. These studies have provided some promise for patients with the CNGA3 mutation, but thus far have found limited or no recovery for patients with the CNGB3 mutation. Here, we developed colour-calibrated visual stimuli designed to isolate cone photoreceptor responses. We combined these with adapted fMRI techniques and pRF mapping to identify if cortical responses to cone-driven signals could be detected in 9 adult patients with the CNGB3 mutation after receiving gene therapy. We did not detect any change in brain activity after gene therapy when the 9 patients were analysed as a group. However, on an individual basis, one patient self-reported a change in colour perception, corroborated by improved performance on a psychophysical task designed to selectively identify cone function. This suggests a level of cone sensitivity that was lacking pre-treatment, further supported by a subtle but reliable change in cortical activity within their primary visual cortex.

Background The presence of bias in artificial intelligence has garnered increased attention, with inequities in algorithmic performance being exposed across the fields of criminal justice, education, and welfare services. In health care, the inequitable performance of algorithms across demographic groups may widen health inequalities. Objective Here, we identify and characterize bias in cardiology algorithms, looking specifically at algorithms used in the management of heart failure. Methods Stage 1 involved a literature search of PubMed and Web of Science for key terms relating to cardiac machine learning (ML) algorithms. Papers that built ML models to predict cardiac disease were evaluated for their focus on demographic bias in model performance, and open-source data sets were retained for our investigation. Two open-source data sets were identified: (1) the University of California Irvine Heart Failure data set and (2) the University of California Irvine Coronary Artery Disease data set. We reproduced existing algorithms that have been reported for these data sets, tested them for sex biases in algorithm performance, and assessed a range of remediation techniques for their efficacy in reducing inequities. Particular attention was paid to the false negative rate (FNR), due to the clinical significance of underdiagnosis and missed opportunities for treatment. Results In stage 1, our literature search returned 127 papers, with 60 meeting the criteria for a full review and only 3 papers highlighting sex differences in algorithm performance. In the papers that reported sex, there was a consistent underrepresentation of female patients in the data sets. No papers investigated racial or ethnic differences. In stage 2, we reproduced algorithms reported in the literature, achieving mean accuracies of 84.24% (SD 3.51%) for data set 1 and 85.72% (SD 1.75%) for data set 2 (random forest models). For data set 1, the FNR was significantly higher for female patients in 13 out of 16 experiments, meeting the threshold of statistical significance (–17.81% to –3.37%; P<.05). A smaller disparity in the false positive rate was significant for male patients in 13 out of 16 experiments (–0.48% to +9.77%; P<.05). We observed an overprediction of disease for male patients (higher false positive rate) and an underprediction of disease for female patients (higher FNR). Sex differences in feature importance suggest that feature selection needs to be demographically tailored. Conclusions Our research exposes a significant gap in cardiac ML research, highlighting that the underperformance of algorithms for female patients has been overlooked in the published literature. Our study quantifies sex disparities in algorithmic performance and explores several sources of bias. We found an underrepresentation of female patients in the data sets used to train algorithms, identified sex biases in model error rates, and demonstrated that a series of remediation techniques were unable to address the inequities present.

Our knowledge of the organisation of the human brain at the population-level is yet to translate into power to predict functional differences at the individual-level, limiting clinical applications, and casting doubt on the generalisability of inferred mechanisms. It remains unknown whether the difficulty arises from the absence of individuating biological patterns within the brain, or from limited power to access them with the models and compute at our disposal.Here we comprehensively investigate the resolvability of such patterns with data and compute at unprecedented scale. Across 23 810 unique participants from UK Biobank, we systematically evaluate the predictability of 25 individual biological characteristics, from all available combinations of structural and functional neuroimaging data. Over 4526 GPU*hours of computation, we train, optimize, and evaluate out-of-sample 700 individual predictive models, including fully-connected feed-forward neural networks of demographic, psychological, serological, chronic disease, and functional connectivity characteristics, and both uni- and multi-modal 3D convolutional neural network models of macro- and micro-structural brain imaging.We find a marked discrepancy between the high predictability of sex (balanced accuracy 99.7%), age (mean absolute error 2.048 years, R2 0.859), and weight (mean absolute error 2.609Kg, R2 0.625), for which we set new state-of-the-art performance, and the surprisingly low predictability of other characteristics. Neither structural nor functional imaging predicted an individual's psychology better than the coincidence of common chronic disease (p<0.05). Serology predicted chronic disease (p<0.05) and was best predicted by it (p<0.001), followed by structural neuroimaging (p<0.05).Our findings suggest either more informative imaging or more powerful models will be needed to decipher individual level characteristics from the human brain. We make our models and code openly available.

Our knowledge of the organisation of the human brain at the population-level is yet to translate into power to predict functional differences at the individual-level, limiting clinical applications, and casting doubt on the generalisability of inferred mechanisms. It remains unknown whether the difficulty arises from the absence of individuating biological patterns within the brain, or from limited power to access them with the models and compute at our disposal. Here we comprehensively investigate the resolvability of such patterns with data and compute at unprecedented scale. Across 23 810 unique participants from UK Biobank, we systematically evaluate the predictability of 25 individual biological characteristics, from all available combinations of structural and functional neuroimaging data. Over 4526 GPU hours of computation, we train, optimize, and evaluate out-of-sample 700 individual predictive models, including fully-connected feed-forward neural networks of demographic, psychological, serological, chronic disease, and functional connectivity characteristics, and both uni- and multi-modal 3D convolutional neural network models of macro- and micro-structural brain imaging. We find a marked discrepancy between the high predictability of sex (balanced accuracy 99.7%), age (mean absolute error 2.048 years, R2 0.859), and weight (mean absolute error 2.609Kg, R2 0.625), for which we set new state-of-the-art performance, and the surprisingly low predictability of other characteristics. Neither structural nor functional imaging predicted psychology better than the coincidence of chronic disease (p<0.05). Serology predicted chronic disease (p<0.05) and was best predicted by it (p<0.001), followed by structural neuroimaging (p<0.05). Our findings suggest either more informative imaging or more powerful models are needed to decipher individual level characteristics from the human brain.

Study on how the visual brain groups fragmented stimuli into coherent shapes &amp; the role of stimulus awareness.