Connecting across Scales: Integration of Cell-Specific Microstructure within Long-Range Brain Networks in the Macaque Monkey

Project Summary Structural alterations in brain networks are driven by changes in cortical microstructure, including neuronal death, the disappearance or rewiring of a synapse, and glial changes like decreases in myelination or astrocytic swelling. Disentangling cell-type specific signatures using accessible tools, such as in vivo MRI, and

determining how signatures of microstructure vary across individuals, is an important objective for the advancement of mechanistic understanding of age-related decline and developing clinically relevant biomarkers. Such work requires a more accurate “ground truth” mapping of brain networks and their cellular composition

using specialized analysis techniques to integrate multi-scale whole brain models. For this study, we propose to investigate network architecture across scale – from cellular-level microstructure to network based structural connectivity to better understand the unique demands of healthy aging. We will use carefully aligned multi-scale

reference data of rhesus macaque that includes multi-parametric in vivo MRI, high-resolution ex vivo MRI, and histological markers of neuronal cell bodies (Nissl), myelination (Gallyas), astrocytes (GFAP), and microglial (Iba-1). We will perform a whole brain network analysis to first establish the morphological and microstructural

signatures of regions connected by long-range anatomical projections. We hypothesize that regions connected over long distances experience high neural and metabolic demand, leading to accelerated age-related decline. We will investigate age-related decline in these cortical areas using a normative cohort of n=28 middle to older

aged adult macaques (equivalent to 25-80+ human years). The aims of this project will contribute towards the development of a highly translational nonhuman primate reference data and network-based analyses for lifespan health research, enhancing our ability to interpret multiscale features of the aging brain to allow for future

mechanistic studies that cannot be completed in humans.