Precision neuroimaging study of segregation of brain networks with age - Project Summary On average, aging is associated with decline in fluid cognitive functions such as cognitive control, which past research has linked to the desegregation of large-scale brain networks. Yet, aging is also associated with substantial individual variability: some people remain cognitively intact as they age, while others show evidence of prominent declines, leading into Alzheimer’s Disease (AD) and related dementias. Given these findings, brain network measures related to desegregation may serve as a natural target to track these differences and, perhaps, to suggest avenues for intervention. However, in past work these measures have been subject to interpretative confounds: small amounts of fMRI data collected from each individual leads to high measurement noise and the inability to reliably track individual brain networks. Here, we propose to overcome these issues by using precision fMRI to provide highly reliable cross-sectional and longitudinal measures of brain networks across the adult lifespan. Precision fMRI involves collecting extensive fMRI data, across multiple sessions and task states, allowing researchers to separate different forms of variation in functional MRI data. These data are then combined with advanced denoising and network identification methods to produce highly reliable maps of individual brain networks. In preliminary data, we demonstrate that precision fMRI can track individual functional networks in young adults, even across years. We also show that it is feasible to collect precision fMRI data in older adults, achieving similar levels of reliability to young adults’. Here, we propose to collect precision fMRI data from a sample of younger (25-45 yo, N = 30), middle aged (45-65 yo, N = 30) and older (65-85 yo, N = 30) adults. Each individual will participate in 5 MRI and 3 behavioral sessions; middle-age and older adults will complete longitudinal data collection at 2 timepoints with a ~3-year gap. These data will allow us to address confounds in prior studies and determine (Aim 1) how brain network desegregation and individual differences and (Aim 2) how fMRI task activations linked to brain networks vary over the adult lifespan and how they change over time within individuals, and (Aim 3) how individual differences in brain networks relate to cognitive control performance with age. Our framework is that health, environmental, and genetic influences accumulate throughout the adult lifespan, which will result in larger (and more variable) longitudinal changes in brain networks in older individuals, that in turn will link to alterations in task responses and variable cognitive performance, especially in cognitive control. This work will have important impact on establishing reliable markers of variability and change in large-scale networks in the adult lifespan, with relevance to healthy aging and declines in AD.
