Static and dynamic functional connectivity analysis of cerebrovascular reactivity: An fMRI study.

Noah Lewis,Xirui Hou,Peiying Liu,Armin Iraji,Eswar Damaraju,Vince Calhoun,Hanzhang Lu

doi:10.1002/brb3.1516

Abstract

BackgroundCerebrovascular reactivity (CVR) is an important aspect of brain function, and as such it is important to understand relationship between CVR and functional connectivity.MethodsThis research studied the role of CVR, or the brain's ability to react to vasoactive stimuli on brain functional connectivity by scanning subjects with blood‐oxygenation‐level‐dependent (BOLD) functional magnetic resonance imaging (fMRI) while they periodically inhale room air and a CO 2‐enriched gas mixture. We developed a new metric to measure the effect of CVR on each intrinsic connectivity network (ICN), which contrasts to voxel‐wise CVR. We also studied the changes in whole‐brain connectivity patterns using both static functional network connectivity (sFNC) and dynamic FNC (dFNC).ResultsWe found that network connectivity is generally weaker during vascular dilation, which is supported by previous research. The dFNC analysis revealed that participants did not return to the pre‐CO 2 inhalation state, suggesting that one‐minute periods of room‐air inhalation is not enough for the CO 2 effect to fully dissipate.ConclusionsCerebrovascular reactivity is one tool that the cerebrovascular system uses to ensure the constant, finely‐tuned flow of oxygen to function properly. Understanding the relationship between CVR and brain dynamism can provide unique information about cerebrovascular diseases and general brain function. We observed that CVR has a wide, but consistent relationship to connectivity patterns between functional networks.

Highlights

cerebrovascular reactivity (CVR) reflects dilation and constriction capacity of blood vessels in response to different stimuli, stress
We examined the static functional network connectivity, or the temporal iew correlations between ICNs across the entire time course (Jafri, Pearlson et al 2008)
We investigated the dynamic functional network connectivity, or the functional network connectivity with a sliding-window approach (Allen E.A. 2012)

Summary

Introduction

CVR reflects dilation and constriction capacity of blood vessels in response to different stimuli, stress. CVR can be measured by inducing vasodilation, such as inhalation of a CO2 gas mixture, while monitoring perfusion-sensitive MRI signals such as blood oxygenation level dependent (BOLD) MRI (Lu, Xu et al 2011). These methods have been used in previous research to study cerebrovascular disease (Yezhuvath, Uh et al 2012, Marshall, Lu et al 2014) and to study brain networks related to CVR (Liu, Welch et al 2016). The relationship between vasodilation and network level analysis could provide deeper insight into how between-network connectivity is altered, moving beyond spatial patterns to provide information about the ongoing dynamics. We accomplished this by calculating the correlation between the EtCO2 time courses and each network time course and averaging this correlation across all subjects

Objectives

Methods

Results

Discussion

Conclusion