Biology subjects

Henderson, V. W.

Publications and source records attributed to Henderson, V. W..

4 recordsLinked to original sources

bioRxiv2026

Unlocking Sensitive Data with SPHERE in the Age of AI

Sensitive human data underpin discoveries across medicine, biology and the social sciences, yet privacy regulation often prevents sharing them with collaborators or artificial intelligence (AI) systems. We introduce SPHERE, a model-free method that makes sensitive datasets directly usable by AI and shareable for open science as a synthetic twin, while the original records never leave the local environment. Across 33 datasets spanning five scientific domains, SPHERE protects individual privacy against adversarial re-identification attacks while preserving the data's statistical structure: means, variances and correlations are reproduced exactly, effect size and P value in linear statistical analysis is numerically identical, nonlinear machine-learning utility is retained, and each twin is generated in seconds on a laptop. Frontier AI agents running on the twin reach the same scientific conclusions as on the original records. Analyses of the twin reproduce genome- and proteome-wide results at UK Biobank scale and recover the findings of landmark studies across three independent cohorts and consortia. The approach also extends to deep-learning embeddings across language, vision and time-series, with minimal utility loss. We make the Stanford Alzheimer's Disease Research Center cohort openly available for the first time, as a SPHERE twin spanning nine modalities that any registered researcher can analyze without an approval process. We release SPHERE with certification of each twin's privacy and fidelity, and an AI agent that autonomously executes research tasks on sensitive data without ever accessing it. Sensitive datasets that are currently closed to research could thus become routine inputs to open science and AI to enable key discoveries.

bioinformatics↗

bioRxiv2026

Plasma proteomics reveals divergent sex-specific senescence and bone biology signatures across neurodegenerative diseases

Neurodegenerative diseases are often accompanied by systemic comorbidities, including changes in bone health, but the molecular relationship between neurodegeneration, skeletal decline, and cellular senescence remains poorly understood. In this study, we investigated sex-specific changes in circulating bone- and senescence-related proteins across the spectrum of Alzheimers disease(AD), Lewbody dementia(LB) and Parkinsons disease(PD). Plasma proteomic profiling was performed on samples from 408 participants deeply phenotyped for neurodegenerative diseases, followed by differential protein and pathway analyses. This study reveals sex-dependent alterations in bone and senescence-related circulating proteins in AD-related, PD and LB-related neurodegenerative diseases, providing insights into the complex relationship between neurodegeneration and bone health. Several candidate proteins were also associated with established plasma neurodegeneration biomarkers, particularly pTau181. Pathway analyses revealed shared mitochondrial and metabolic dysfunction across neurodegenerative diseases, with disease-specific features including vesicle trafficking disruption in AD and inflammatory-senescence pathways in LB, plus sex-divergent patterns in inflammatory signaling and bone-related pathways.

neuroscience↗

bioRxiv2026

Blood-brain barrier dysfunction predicts cognitive trajectory after ischemic stroke

Ischemic stroke doubles the risk of dementia.1-4 Stroke severity and location affect cognition early,5,6 but late dementia risk is not related to infarct characteristics, nor is it reduced by preventing additional strokes,3,6,7 and its mechanism is unknown. We identified a plasma proteomic signature of chronic stroke that was consistent with blood-brain barrier (BBB) dysfunction, including a 58% decrease in plasma levels of platelet-derived growth factor B and downregulation of its pathway compared to healthy controls. During 2 years of follow-up, the stroke-specific proteome was accentuated in stroke survivors who subsequently declined in the processing speed/executive function cognitive domain. To test BBB function, we performed dynamic contrast-enhanced MRI 6-9 months after stroke in an additional cohort and found 1.7-fold higher whole brain BBB leakage compared to controls. Finally, we compared autopsy tissue from people with infarcts and dementia at death to those with infarcts and no dementia. Those who died with dementia had dramatic loss of vascular mural cell coverage compared to those without dementia (median 0.7% vs. 27%). Thus, our proteomic, functional, and structural data implicate chronic BBB dysfunction in cognitive decline late after stroke, revealing potential proteomic and imaging biomarkers and, importantly, a novel target for intervention.

neuroscience↗

bioRxiv2025

Denoising 7T Structural MRI with Conditional Generative Diffusion Models

Purpose7T MRI offers ultra-high resolution and improved sensitivity for iron deposition in neurodegenerative disorders, but commonly used acquisitions are long and hence challenging, especially for elderly subjects. Efficiently denoising a short acquisition to achieve the image quality of a longer acquisition would be of translational benefit. Materials and MethodsWe introduce a conditional diffusion model derived from generative AI (a 7T Conditional Diffusion Model, 7TCDM) that was trained on native single-acquisition 2D reconstructions and referenced multi-repetition images to guide the denoising process and improve SNR and contrast. 7TCDM model was tested on 2D T2-weighted gradient-echo imaging from 19 participants, including healthy controls and individuals with mild cognitive impairment or Alzheimers disease (AD). 7TCDMs performance was assessed using Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and comprehensive reader studies. ResultsReferencing the multi-repetition ground truth, 7TCDM improved the single-acquisition original image by 29.1% in MSE, 5.8% in PSNR, and 9.4% in SSIM, and outperformed convolutional neural network-based models in all metrics. Expert rater evaluations confirmed superior image quality, with significantly enhanced detail and contrast preservation in regions such as the hippocampi, white matter lesions, and small cortical veins. The model also demonstrated robust performance in both the concurrently acquired and publicly available 3D multi-echo gradient echo acquisitions, which the model was not trained on. ConclusionsThe 7T Conditional Diffusion Model provides high-quality denoised images from shorter scans, increasing the feasibility of scanning patients in shorter times while preserving essential anatomical and pathological details.

neuroscience↗