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NeuroVNN models the graph-like nature of brain morphology to produce interpretable indicators from T1-weighted MRI.
EmailThe NeuroVNN project proposes a foundation AI model that leverages coVariance neural networks (VNNs) to capture the graph-theoretic characteristics of brain atrophy from T1-weighted MRI. Trained on healthy adult MRI-derived features and applied to neurodegenerative conditions, the model aims to provide transparent, data-driven biomarkers and visualizations of heterogeneity across and within AD/ADRD. The work addresses limitations of black-box MRI models and supports decision-making for researchers, clinicians, and caregivers.
Founder of Care Daily developing AI-powered caregiving and home monitoring tools.
Harvard psychiatrist researching digital markers of mood and cognitive disorders.
Neuropsychologist at Johns Hopkins studying cognitive and neuropsychiatric measurement in aging.
CEO of CareYaya using AI to scale caregiver and elder support technologies.
Johns Hopkins expert in vestibular and balance rehabilitation for older adults.
McLean Hospital researcher studying digital tools for cognitive and emotional health in aging.
