Dynamic approaches to understanding social cognitive aging: A social network neuroscience approach

PROJECT SUMMARY Social connectedness is critical for promoting healthy aging, including delaying the onset of Alzheimer’s disease (AD). Developing and maintaining social relationships relies on social cognitive function – the process by which people understand, store, and apply information about others. However, healthy aging and AD are associated with declines in social cognitive function. Identifying the mechanisms underlying this decline is essential for ultimately improving the clinical course of AD. Neuroscience is uniquely suited to identify these mechanisms because the brain regions underlying social behavior have been well-characterized. However, the limited work in this domain has fallen short in elucidating how brain activation relates to older adults’ social cognitive deficits. One reason for this might be its reliance on relatively narrow measures of brain activation and impoverished stimuli, which neglect the dynamic nature of brain function and of social interactions. The current proposal addresses these gaps by applying cutting-edge methods from the field of network neuroscience to social cognitive aging to examine how age-related changes in brain networks – collections of brain regions that communicate disproportionately more with each other – affect social cognitive function. Using this novel social network neuroscience framework will ultimately transform our understanding of the mechanisms by which healthy aging and AD disrupt social cognitive function. In Aim 1, we compare traditional approaches of focusing on activation in specific brain regions to a brain networks approach in order to determine which better relates to social cognitive deficits (e.g., theory of mind; the ability to infer others’ mental states). Aim 2 explores whether older adults’ less stable brain networks predict their theory of mind deficits. An exploratory goal of this aim is to determine whether dynamic (more naturalistic) stimuli provide greater insight into age-related social cognitive deficits than traditional, static stimuli. Finally, Aim 3 challenges current assumptions that older adults’ social cognitive deficits are limited to how their brains engage during tasks. Specifically, we examine whether older adults’ baseline brain network structure (during resting state) predicts their subsequent task performance, and extend this question to an AD sample. The proposed study combines cutting-edge network neuroscience methods with social cognitive aging to advance our understanding of healthy aging and AD. Ultimately, this project will help identify novel targets for intervention to slow the progression of AD.