UC Irvine

Title: Diffusion to Densities: Using Diffusion-Weighted Imaging to Study Gray Matter Microstructure.Name: Hamsanandini Radhakrishnan Degree: Doctor of Philosophy University: University of California, Irvine Year: 2022 Committee Chair: Dr. Craig Stark

The brain goes through a large set of structural changes at the onset of aging, resulting in sometimes devastating cognitive and behavioral consequences. Targeting these changes at an early stage is key to protecting against later cognitive decline or even pathology. However, studying tissue microstructure in the brain non-invasively is not trivial, especially in humans. Most of our non-invasive metrics derived from neuroimaging can detect only large-scale changes like gross atrophy or cortical thinning, which are usually only observable when it is too late to intervene. Diffusion imaging, popularized for studying white matter microstructure, has recently advanced to the stage that it might be sensitive to gray matter cytoarchitectural properties as well. However, these diffusion metrics, especially the newer ones derived from biophysical modelling techniques like Neurite Orientation Dispersion and Density Imaging (NODDI), have not been adequately evaluated, especially in the context of cognitive aging.

In this thesis, with a series of both human and animal studies, we aim to fill some of these gaps in knowledge, focusing mainly on cognitive aging in the hippocampus. We first identify a novel aging biomarker in the dentate gyrus, that might be partially mediating aging-related cognitive decline. We then show that a combination of diffusion metrics is far better than traditional MRI metrics in predicting age or cognition associated properties. We also demonstrate that these metrics can also be used as non-invasive probes to measure the efficiency of intervention studies designed to protect against aging-related structural changes! Finally, we establish a pipeline to estimate cellular properties non-invasively through the diffusion metrics alone. These results together not only shine light on the power of diffusion MRI to study gray matter changes in aging, but also present a framework to extend this method to other domains.