Neurology and Neurosurgery Department, Montreal Neurological Institute, McGill University, Montreal, Canada; McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada; Ludmer Centre for Neuroinformatics and Mental Health, McGill University, Montreal, Canada
Ahmed F Khan
Neurology and Neurosurgery Department, Montreal Neurological Institute, McGill University, Montreal, Canada; McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada; Ludmer Centre for Neuroinformatics and Mental Health, McGill University, Montreal, Canada
Neurology and Neurosurgery Department, Montreal Neurological Institute, McGill University, Montreal, Canada; McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada; Ludmer Centre for Neuroinformatics and Mental Health, McGill University, Montreal, Canada
Both healthy aging and Alzheimer’s disease (AD) are characterized by concurrent alterations in several biological factors. However, generative brain models of aging and AD are limited in incorporating the measures of these biological factors at different spatial resolutions. Here, we propose a personalized bottom-up spatiotemporal brain model that accounts for the direct interplay between hundreds of RNA transcripts and multiple macroscopic neuroimaging modalities (PET, MRI). In normal elderly and AD participants, the model identifies top genes modulating tau and amyloid-β burdens, vascular flow, glucose metabolism, functional activity, and atrophy to drive cognitive decline. The results also revealed that AD and healthy aging share specific biological mechanisms, even though AD is a separate entity with considerably more altered pathways. Overall, this personalized model offers novel insights into the multiscale alterations in the elderly brain, with important implications for identifying effective genetic targets for extending healthy aging and treating AD progression.
