Evaluating the Sensitivity of Resting-State BOLD Variability to Age and Cognition after Controlling for Motion and Cardiovascular Influences: A Network-Based Approach

Peter R. Millar, Steven E. Petersen, Beau M. Ances, Brian A. Gordon, Tammie L.S. Benzinger, John C. Morris, David A. Balota

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Recent functional magnetic resonance imaging (fMRI) studies report that moment-to-moment variability in the BOLD signal is related to differences in age and cognition and, thus, may be sensitive to age-dependent decline. However, head motion and/or cardiovascular health (CVH) may contaminate these relationships. We evaluated relationships between resting-state BOLD variability, age, and cognition, after characterizing and controlling for motion-related and cardiovascular influences, including pulse, blood pressure, BMI, and white matter hyperintensities (WMH), in a large (N = 422) resting-state fMRI sample of cognitively normal individuals (age 43-89). We found that resting-state BOLD variability was negatively related to age and positively related to cognition after maximally controlling for head motion. Age relationships also survived correction for CVH, but were greatly reduced when correcting for WMH alone. Our results suggest that network-based machine learning analyses of resting-state BOLD variability might yield reliable, sensitive measures to characterize age-related decline across a broad range of networks. Age-related differences in resting-state BOLD variability may be largely sensitive to processes related to WMH burden.

Original languageEnglish
Pages (from-to)5686-5701
Number of pages16
JournalCerebral Cortex
Volume30
Issue number11
DOIs
StatePublished - Nov 1 2020

Keywords

  • BOLD variability
  • aging
  • machine learning
  • resting-state fMRI

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