The effects of age on resting‐state BOLD signal variability is explained by cardiovascular and cerebrovascular factors

Abstract

Accurate identification of brain function is necessary to understand neurocognitive aging, and thereby promote health and well-being. Many studies of neurocognitive aging have investigated brain function with the blood-oxygen level-dependent (BOLD) signal measured by functional magnetic resonance imaging. However, the BOLD signal is a composite of neural and vascular signals, which are differentially affected by aging. It is, therefore, essential to distinguish the age effects on vascular versus neural function. The BOLD signal variability at rest (known as resting state fluctuation amplitude, RSFA), is a safe, scalable, and robust means to calibrate vascular responsivity, as an alternative to breath-holding and hypercapnia. However, the use of RSFA for normalization of BOLD imaging assumes that age differences in RSFA reflecting only vascular factors, rather than age-related differences in neural function (activity) or neuronal loss (atrophy). Previous studies indicate that two vascular factors, cardiovascular health (CVH) and cerebrovascular function, are insufficient when used alone to fully explain age-related differences in RSFA. It remains possible that their joint consideration is required to fully capture age differences in RSFA. We tested the hypothesis that RSFA no longer varies with age after adjusting for a combination of cardiovascular and cerebrovascular measures. We also tested the hypothesis that RSFA variation with age is not associated with atrophy. We used data from the population-based, lifespan Cam-CAN cohort. After controlling for cardiovascular and cerebrovascular estimates alone, the residual variance in RSFA across individuals was significantly associated with age. However, when controlling for both cardiovascular and cerebrovascular estimates, the variance in RSFA was no longer associated with age. Grey matter volumes did not explain age differences in RSFA, after controlling for CVH. The results were consistent between voxel-level analysis and independent component analysis. Our findings indicate that cardiovascular and cerebrovascular signals are together sufficient predictors of age differences in RSFA. We suggest that RSFA can be used to separate vascular from neuronal factors, to characterize neurocognitive aging. We discuss the implications and make recommendations for the use of RSFA in the research of aging. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Age differences in the functional architecture of the human brain

“…Among the most critical issues remaining involves resolving, or at least accurately modeling, age differences in neurovascular coupling. Altered neurovascular coupling with age can introduce spurious RSFC differences that are difficult to detect with standard imaging protocols (Tsvetanov et al, 2020). While ME-ICA methods, which separate neural from non-neural sources in the BOLD signal, are a significant advance, implementation of multimodal methods such as simultaneous arterial spin labeling and echo-planar imaging may be necessary to resolve this issue (Tsvetanov et al, 2020).…”

“…Altered neurovascular coupling with age can introduce spurious RSFC differences that are difficult to detect with standard imaging protocols (Tsvetanov et al, 2020). While ME-ICA methods, which separate neural from non-neural sources in the BOLD signal, are a significant advance, implementation of multimodal methods such as simultaneous arterial spin labeling and echo-planar imaging may be necessary to resolve this issue (Tsvetanov et al, 2020). Despite these limitations, we suggest that the methodological innovations implemented here provide a comprehensive, multi-faceted, and high-fidelity representation of age-differences in the functional network architecture of the brain.…”

“…After this point, continued reductions in dimensionality may no longer be driven by network integration, but rather global network disintegration, and associated loss of coherent network components in the BOLD signal. While beyond the scope of the current report, such global shifts may result from systemic structural, neurophysiological, metabolic or cerebrovascular changes known to occur with advancing age (e.g., Tsvetanov et al, 2020).…”

Separating vascular and neuronal effects of age on fMRI BOLD signals

“…Resting state ASL studies of ageing support the presence of age-related atrophy-independent decreases in resting CBF throughout the cortex [117][118][119]. Some studies also suggest a nonlinear effect across the lifespan [120].…”

Language Disorder in Progressive Supranuclear Palsy and Corticobasal Syndrome: Neural Correlates and Detection by the MLSE Screening Tool

“…To assess the multivariate relationship between MLSE domain scores and brain structures, we adopted a two-level procedure (Tsvetanov et al, 2018(Tsvetanov et al, , 2019Passamonti et al, 2019). First, canonical correlation analysis (Hotelling, 1936) identified the linear relationship between the two multivariate datasets, namely structural values (cortical thickness and subcortical volume) and MLSE domain scores, providing pairs of latent variables (Structure-LV and MLSE-LV).…”