Age-related and sex-specific reduction in cerebral arterial-venous cardiac pulse delay in fMRI
Poster No:
2116
Submission Type:
Abstract Submission
Authors:
Adam Wright1, Tianyin Xu1, John Koo2, Yi Zhao2, Yunjie Tong1, Qiuting Wen2
Institutions:
1Purdue University, West Lafayette, IN, 2Indiana University School of Medicine, Indianapolis, IN
First Author:
Co-Author(s):
Introduction:
Each heartbeat generates a cardiac pressure wave that propagates through the brain and travels from large arteries through cerebrospinal fluid and brain tissue, ultimately compressing the venous sinuses. The delay between the arterial and venous pulsation (A-V delay) is an insightful marker of intracranial mechanics, with studies revealing its reduction in cerebral pathologies (G. Bateman 2000, 2002, 2004; Rivera-Rivera et al., 2021). However, the need for phase-contrast MRI has restricted the availability of A-V delay studies with large datasets, particularly those covering wide age ranges. In this work, we developed a novel method to measure the A-V delay using functional MRI (fMRI) and analyzed it in participants 36-90 years old.
Methods:
Participant information: This analysis used 578 participants (36-90 years old, 43.8% biological men) from HCP-Aging (Bookheimer et al., 2019).
Image acquisition: T1-weighted and fMRI images were acquired with a 3T Prisma Siemens scanner. Two repeated fMRI scans were acquired with simultaneous finger photoplethysmography (PPG): TR/TE = 800/37 msec, FA = 52º, voxel size = 2.0x2.0x2.0 mm³, volumes = 488, multiband factor = 8.
Arterial-venous pulse delay calculation: We developed a fully automated three-step signal-processing pipeline to quantify A-V delay by utilizing the fMRI signal's sensitivity to blood flow changes in large arteries and venous sinuses, along with simultaneous finger PPG (Figure 1). First, we used a data-driven pipeline to segment the major cerebral arteries and the superior sagittal sinus (SSS) in fMRI space (Wright et al., 2024). Second, the time shift between the pulse wave arrival in the brain and the finger was measured by cross-correlating voxel-wise fMRI signal and finger PPG. The voxel-wise time shift represents the time delay between the cardiac pulsation reaching the brain and the finger, referred to as LeadTime (similar to Wen et al. 2023). Third, the mean voxel-wise LeadTime in the arteries and SSS were used to calculate the A-V delay:
Arterial-venous delay (A-V delay) = LeadTime Artery – LeadTime SSS
Statistical Analysis: A best-fit linear mixed-effects model assessed the relationship between A-V delay and covariates. The model was required to include demographics (age, biological sex, race, ethnicity), body mass index (BMI), and heart rate. The initial model included all covariates, which were then sequentially removed to achieve the best fit by optimizing the Bayesian information criterion. The covariates included all ambulatory measures (i.e. blood pressure) and blood tests (i.e. blood glucose, cholesterol) in the HCP-aging dataset.
Image acquisition: T1-weighted and fMRI images were acquired with a 3T Prisma Siemens scanner. Two repeated fMRI scans were acquired with simultaneous finger photoplethysmography (PPG): TR/TE = 800/37 msec, FA = 52º, voxel size = 2.0x2.0x2.0 mm³, volumes = 488, multiband factor = 8.
Arterial-venous pulse delay calculation: We developed a fully automated three-step signal-processing pipeline to quantify A-V delay by utilizing the fMRI signal's sensitivity to blood flow changes in large arteries and venous sinuses, along with simultaneous finger PPG (Figure 1). First, we used a data-driven pipeline to segment the major cerebral arteries and the superior sagittal sinus (SSS) in fMRI space (Wright et al., 2024). Second, the time shift between the pulse wave arrival in the brain and the finger was measured by cross-correlating voxel-wise fMRI signal and finger PPG. The voxel-wise time shift represents the time delay between the cardiac pulsation reaching the brain and the finger, referred to as LeadTime (similar to Wen et al. 2023). Third, the mean voxel-wise LeadTime in the arteries and SSS were used to calculate the A-V delay:
Arterial-venous delay (A-V delay) = LeadTime Artery – LeadTime SSS
Statistical Analysis: A best-fit linear mixed-effects model assessed the relationship between A-V delay and covariates. The model was required to include demographics (age, biological sex, race, ethnicity), body mass index (BMI), and heart rate. The initial model included all covariates, which were then sequentially removed to achieve the best fit by optimizing the Bayesian information criterion. The covariates included all ambulatory measures (i.e. blood pressure) and blood tests (i.e. blood glucose, cholesterol) in the HCP-aging dataset.
Results:
Arterial-venous pulse delay decreased with age and was lower in men than women:
Figure 2 summarizes the relationship between A-V delay, age, and biological sex. A-V delay decreased with age (β=-0.40 msec/year), heart rate (β=-0.30 msec/bpm), and was lower in men (β=-11.61 msec). A-V delay increased with normalized brain volume (brain volume/total intracranial volume, β=0.635 msec/% volume) and SSS volume (β=4.73 msec/cm³). Covariates associated with vascular health, including blood pressure, hemoglobin A1c, and cholesterol, were not significant predictors of A-V delay. The spatial representation of the A-V delay in Figure 2CD shows that the venous pulsation propagates from posterior to anterior, with an earlier arrival in older participants.
Figure 2 summarizes the relationship between A-V delay, age, and biological sex. A-V delay decreased with age (β=-0.40 msec/year), heart rate (β=-0.30 msec/bpm), and was lower in men (β=-11.61 msec). A-V delay increased with normalized brain volume (brain volume/total intracranial volume, β=0.635 msec/% volume) and SSS volume (β=4.73 msec/cm³). Covariates associated with vascular health, including blood pressure, hemoglobin A1c, and cholesterol, were not significant predictors of A-V delay. The spatial representation of the A-V delay in Figure 2CD shows that the venous pulsation propagates from posterior to anterior, with an earlier arrival in older participants.
Conclusions:
We developed a novel fMRI method to quantify the A-V delay. In this study, the mean A-V delay was 77 ± 31 msec; it shortened by 4 msec per decade of aging and was 11 msec faster in men than women, highlighting age-related and sex-specific differences. This delay reflects the mechanical transfer of arterial pulsation that compresses venous structures and is likely sensitive to variations in intracranial compliance. Thus, our fMRI-based A-V delay measurement has value in studies of broader conditions to investigate disease-related biomechanical changes in the brain.
Lifespan Development:
Aging 2
Modeling and Analysis Methods:
Methods Development
Physiology, Metabolism and Neurotransmission:
Neurophysiology of Imaging Signals 1
Keywords:
Aging
Data analysis
Design and Analysis
fMRI CONTRAST MECHANISMS
FUNCTIONAL MRI
MRI
Multivariate
Other - Neurofluids, arterial-venous pulse delay, intracranial compliance
1|2Indicates the priority used for review
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Provide references using APA citation style.
Bateman, G A. (2000). Vascular compliance in normal pressure hydrocephalus. AJNR. American Journal of Neuroradiology, 21(9), 1574–85.
Bateman, Grant A. (2004). Pulse wave encephalopathy: a spectrum hypothesis incorporating Alzheimer’s disease, vascular dementia and normal pressure hydrocephalus. Medical Hypotheses, 62(2), 182–187.
Bookheimer, S. (2019). The Lifespan Human Connectome Project in Aging: An overview. NeuroImage, 185, 335–348.
Rivera‐Rivera, L. A. (2021). Cerebrovascular stiffness and flow dynamics in the presence of amyloid and tau biomarkers. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 13(1).
Wen, Q. (2023). Paravascular fluid dynamics reveal arterial stiffness assessed using dynamic diffusion‐weighted imaging. NMR in Biomedicine.
Wright, A. M. (2024). Robust data-driven segmentation of pulsatile cerebral vessels using functional magnetic resonance imaging. Interface Focus, 14(6).