Differences Between Resting-State fMRI BOLD Variability and Default Mode Network Connectivity in Healthy Older and Younger Adults

Abstract

Background:

Resting-state fMRI analyses have been used to examine functional connectivity in the aging brain. Recently, fluctuations in the fMRI BOLD signal have been used as a potential marker of integrity in neural systems. Despite its increasing popularity, the results of BOLD variability analyses and traditional seed-based functional connectivity analyses have rarely been compared. The current study examined fMRI BOLD signal variability and default mode network seed-based analyses in healthy older and younger adults to better understand the unique contributions of these methodological approaches.

Methods:

Thirty-four healthy participants were separated into a younger adult group (age 25–35, n = 17) and an older adult group (age 65+, n = 17). For each participant, a map of the standard deviation of the BOLD signal (SDBOLD) was derived. Group comparisons examined differences in resting-state SDBOLD in younger versus older adults. Seed-based analyses were used to examine differences between younger and older adults in the default mode network.

Results:

Between-group comparisons revealed significantly greater BOLD variability in widespread brain regions in older relative to younger adults. There were no significant differences between younger and older adults in the default mode network connectivity.

Conclusion:

The current findings align with an increasing number of studies reporting greater BOLD variability in older relative to younger adults. The current results also suggest that the traditional resting state examination methods may not detect nuanced age-related differences. Further large-scale studies in an adult lifespan sample are needed to better understand the functional relevance of the BOLD variability in normative aging.

Impact Statement

Examining functional connectivity helps us to better understand the healthy aging brain. The traditional means of examining functional connectivity have relied on a traditional seed-based analysis. More novel approaches, such as BOLD variability, have been gaining popularity; however, it has rarely been compared with other methods. By comparing the impact of both methodologies on the same dataset, we contribute to the growing number of studies that use BOLD variability and further the understanding of this methodology. Furthermore, we can directly examine the resulting differences between the two approaches and expand our understanding of functional connectivity across the lifespan.