Aging-related cognitive decline has been independently linked to alterations in resting-state functional connectivity (RSFC) and cognitive task activations using functional MRI (fMRI), yet it is now well established that there is a strong statistical relationship between RSFC and task activations. This is despite these being quite distinct measures: RSFC is calculated as correlations among distributed brain activity time series during rest, while task activations are localized brain activity amplitude changes during active task performance. The network mechanisms underlying the RSFC-activation relationship are unknown, yet understanding this relationship would clarify how aging alters both RSFC and cognitive task activations. For RSFC, linking to cognitive task activations may help explain why RSFC is associated with cognitive processes despite being measured independently of tasks testing those processes. For cognitive task activations, linking to RSFC may bring a unified network-based understanding to the varied aging-related activation changes identified across many brain regions and tasks. Thus, there is a critical need to determine the network mechanisms underlying the relationship between RSFC and cognitive task activations. Without such knowledge, obtaining a unified understanding of the neural basis of aging-related cognitive decline is unlikely. The overall objective of this proposal is to identify network mechanisms that can account for the alterations in both RSFC and cognitive task activations that occur with aging-related decline (from ages 18-28 to 65-75) of cognitive control abilities among healthy older adults. This focus on cognitive control reflects its importance to adaptive, goal-directed behavior in daily life. Further, cognitive control network (CCN) regions have RSFC network ?hub? properties well suited to regulate general cognitive ability. Of particular relevance to aging, cognitive control is one of the abilities most affected by both healthy aging and Alzheimer?s disease, and it plays a role in long-term memory deficits (a key feature of Alzheimer?s disease). This proposal?s central hypothesis is that aging-related alterations in RSFC reflect changes in intrinsic network pathways that influence brain activations during task performance, and hence mediate disruption of cognitive control abilities. Three approaches will be utilized across three aims to test our central hypothesis. Briefly, the first will utilize an innovative approach to predict activation abnormalities based on RSFC abnormalities in healthy older adults. The second will utilize individual differences to determine the contribution of CCN hub disruption to aging-related cognitive decline. The third will utilize the established influence of cognitive training on RSFC to investigate the role of functional network plasticity in aging-related cognitive decline. This project is expected to markedly improve understanding of the brain network basis of cognitive decline from healthy aging, in addition to improving general understanding of the large-scale network mechanisms underlying cognition.

Public Health Relevance

The proposed project is relevant to public health because it investigates the brain basis of aging-related cognitive decline, a major public health problem. This project contributes to the long-term goal of understanding the large-scale brain network mechanisms underlying aging-related cognitive decline in healthy older adults, as well as those with aging-related neurodegenerative diseases such as Alzheimer?s disease.

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Research Project (R01)
Project #
5R01AG055556-02
Application #
9440323
Study Section
Cognition and Perception Study Section (CP)
Program Officer
Wagster, Molly V
Project Start
2017-03-01
Project End
2022-02-28
Budget Start
2018-03-01
Budget End
2019-02-28
Support Year
2
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Rutgers University
Department
Other Basic Sciences
Type
Schools of Arts and Sciences
DUNS #
130029205
City
Newark
State
NJ
Country
United States
Zip Code
Ito, Takuya; Kulkarni, Kaustubh R; Schultz, Douglas H et al. (2017) Cognitive task information is transferred between brain regions via resting-state network topology. Nat Commun 8:1027