Keywords: Diagnosis/Prediction, AI/ML Image Reconstruction, Aging, CBV, Medical Image Synthesis, 3D Mamba
Motivation: Aging impacts brain regions differently, leading to cognitive decline and vascular changes. Identifying the most susceptible or resilient regions is crucial for understanding neurodegeneration and developing interventions.
Goal(s): To identify aging-sensitive and resilient brain regions by developing a pipeline generating AI-derived CBV (AICBV) maps from standard T1-weighted MRI.
Approach: We used DeepContrast AI model to generate AICBV maps from T1-weighted MRI scans of 2,851 subjects. FreeSurfer provided segmentation for regional CBV analysis.
Results: Dentate gyrus and inferior frontal gyrus subregions are highly vulnerable to aging; the entorhinal cortex shows resilience. Our findings align with prior studies, extending them to a larger cohort.
Impact: This non-invasive AICBV mapping technique offers an efficient alternative to traditional CBV fMRI scans, enabling large-scale and longitudinal studies of brain aging. It allows early detection of high-risk regions and guides targeted interventions to preserve cognitive health and resilience.
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