Understanding the factors contributing to optimal cognitive function throughout the aging process is essential to better understand successful cognitive aging. Processing speed is an age sensitive cognitive domain that usually declines early in the aging process; however, this cognitive skill is essential for other cognitive tasks and everyday functioning. Evaluating brain network interactions in cognitively healthy older adults can help us understand how brain characteristics variations affect cognitive functioning. Functional connections among groups of brain areas give insight into the brain’s organization, and the cognitive effects of aging may relate to this large-scale organization. To follow-up on our prior work, we sought to replicate our findings regarding network segregation’s relationship with processing speed. In order to address possible influences of node location or network membership we replicated the analysis across 4 different node sets.

Data were acquired as part of a multi-center study of 85+ cognitively normal individuals, the McKnight Brain Aging Registry (MBAR). For this analysis, we included 146 community-dwelling, cognitively unimpaired older adults, ages 85-99, who had undergone structural and BOLD resting state MRI scans and a battery of neuropsychological tests. Exploratory factor analysis identified the processing speed factor of interest. We preprocessed BOLD scans using fmriprep, Ciftify, and XCPEngine algorithms. We used 4 different sets of connectivity-based parcellation: 1)MBAR data used to define nodes and Power (2011) atlas used to determine node network membership, 2) Younger adults data used to define nodes (Chan 2014) and Power (2011) atlas used to determine node network membership, 3) Older adults data from a different study (Han 2018) used to define nodes and Power (2011) atlas used to determine node network membership, and 4) MBAR data used to define nodes and MBAR data based community detection used to determine node network membership.

Segregation (balance of within-network and between-network connections) was measured within the association system and three wellcharacterized networks: Default Mode Network (DMN), Cingulo-Opercular Network (CON), and Fronto-Parietal Network (FPN). Correlation between processing speed and association system and networks was performed for all 4 node sets.

We replicated prior work and found the segregation of both the cortical association system, the segregation of FPN and DMN had a consistent relationship with processing speed across all node sets (association system range of correlations: r=.294 to .342, FPN: r=.254 to .272, DMN: r=.263 to .273). Additionally, compared to parcellations created with older adults, the parcellation created based on younger individuals showed attenuated and less robust findings as those with older adults (association system r=.263, FPN r=.255, DMN r=.263).

This study shows that network segregation of the oldest-old brain is closely linked with processing speed and this relationship is replicable across different node sets created with varied datasets. This work adds to the growing body of knowledge about age-related dedifferentiation by demonstrating replicability and consistency of the finding that as essential cognitive skill, processing speed, is associated with differentiated functional networks even in very old individuals experiencing successful cognitive aging.

To evaluate the construct validity of the NIH Toolbox Cognitive Battery (NIH TB-CB) in the healthy oldest-old (85+ years old).

Our sample from the McKnight Brain Aging Registry consists of 179 individuals, 85 to 99 years of age, screened for memory, neurological, and psychiatric disorders. Using previous research methods on a sample of 85 + y/o adults, we conducted confirmatory factor analyses on models of NIH TB-CB and same domain standard neuropsychological measures. We hypothesized the five-factor model (Reading, Vocabulary, Memory, Working Memory, and Executive/Speed) would have the best fit, consistent with younger populations. We assessed confirmatory and discriminant validity. We also evaluated demographic and computer use predictors of NIH TB-CB composite scores.

Findings suggest the six-factor model (Vocabulary, Reading, Memory, Working Memory, Executive, and Speed) had a better fit than alternative models. NIH TB-CB tests had good convergent and discriminant validity, though tests in the executive functioning domain had high inter-correlations with other cognitive domains. Computer use was strongly associated with higher NIH TB-CB overall and fluid cognition composite scores.

The NIH TB-CB is a valid assessment for the oldest-old samples, with relatively weak validity in the domain of executive functioning. Computer use’s impact on composite scores could be due to the executive demands of learning to use a tablet. Strong relationships of executive function with other cognitive domains could be due to cognitive dedifferentiation. Overall, the NIH TB-CB could be useful for testing cognition in the oldest-old and the impact of aging on cognition in older populations.