Precision Mapping of Functional Networks in Healthy and Pathological Aging

Project Summary Functional Magnetic Resonance Imaging (fMRI) has become a powerful tool for studying the underlying functional architecture of brain networks by tracking temporal correlations in the activity of different brain regions; a technique called resting-state functional connectivity (rs-FC). Recently, individualized methods of rs-FC have

revealed reliable differences in functional organization between individuals and group averages that have been implicated with differences in behavior. These precision fMRI methods involve collecting extended amounts of rs-FC data across multiple sessions for each subject and the use of advanced denoising techniques to improve

the quality of the data. Individual-specific networks have not been examined in older adults yet, even though group-average studies suggest that brain networks change systematically over the course of the lifespan and as a function of disease, both in cortical and cerebellar regions. I propose the use of a dataset of highly sampled

older adults, ages 60-75 (N = 38), and younger adults, ages 18-30 (N = 38), to create individual-specific parcellations and network representations using high-quality anatomical and rs-FC data. In Aim 1, I will examine whether the properties of individualized networks in older adults differ compared to young adults. In Aim 2, I will

examine whether the networks affected in Alzheimer’s Disease (AD) differ from those affected in healthy aging, particularly in the cerebellum. Preliminary data suggests that, with sufficient high-quality data, cortical networks in young adults are stable across days (r > 0.85), supporting their endophenotypic nature and potential for use

as biomarkers. This study will be the first to use individualized measures in older adults to provide a better understanding of neurodevelopmental changes to rs-FC that may be relevant to behavior. Individualized network topology has previously been found to be predictive of behavioral and cognitive measurements, suggesting that

it may be a promising avenue to search for biomarkers of cognitive decline. My pre-doctoral work (Aim 1) will set the benchmark for using precision fMRI with an older population to study the relationship between brain network variability and cognitive decline. My post-doctoral goal (Aim 2) is to apply these methodologies to the study of

AD-related changes to the functional architecture of the brain and how these changes drive hallmark cognitive symptoms. This project will also provide ample opportunities for additional scientific and professional training. My training goals will focus on gaining theoretical and practical knowledge of defining individualized functional

networks and brain parcellations, ensuring the quality of anatomical images using FreeSurfer, and applying special considerations to obtain reliable signal from cerebellar data. Professional development goals will center on mentoring practices and science communication. These skills will be key to my future career as an

independent researcher dedicated to elucidating the relationship between individual differences in networks and cognitive changes in healthy and pathological aging and fostering diversity and inclusion in academia.