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The association between resting-state functional magnetic resonance imaging and aortic pulse-wave velocity in healthy adults.

ABSTRACT: Resting-state functional magnetic resonance imaging (rs-fMRI) is frequently used to study brain function; but, it is unclear whether BOLD-signal fluctuation amplitude and functional connectivity are associated with vascular factors, and how vascular-health factors are reflected in rs-fMRI metrics in the healthy population. As arterial stiffening is a known age-related cardiovascular risk factor, we investigated the associations between aortic stiffening (as measured using pulse-wave velocity [PWV]) and rs-fMRI metrics. We used cardiac MRI to measure aortic PWV (an established indicator of whole-body vascular stiffness), as well as dual-echo pseudo-continuous arterial-spin labeling to measure BOLD and CBF dynamics simultaneously in a group of generally healthy adults. We found that: (1) higher aortic PWV is associated with lower variance in the resting-state BOLD signal; (2) higher PWV is also associated with lower BOLD-based resting-state functional connectivity; (3) regions showing lower connectivity do not fully overlap with those showing lower BOLD variance with higher PWV; (4) CBF signal variance is a significant mediator of the above findings, only when averaged across regions-of-interest. Furthermore, we found no significant association between BOLD signal variance and systolic blood pressure, which is also a known predictor of vascular stiffness. Age-related vascular stiffness, as measured by PWV, provides a unique scenario to demonstrate the extent of vascular bias in rs-fMRI signal fluctuations and functional connectivity. These findings suggest that a substantial portion of age-related rs-fMRI differences may be driven by vascular effects rather than directly by brain function.

SUBMITTER: Hussein A

PROVIDER: S-EPMC7268071 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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The association between resting-state functional magnetic resonance imaging and aortic pulse-wave velocity in healthy adults.

Hussein Ahmad A Matthews Jacob L JL Syme Catriona C Macgowan Christopher C MacIntosh Bradley J BJ Shirzadi Zahra Z Pausova Zdenka Z Paus Tomáš T Chen J Jean JJ

Human brain mapping 20200207 8

Resting-state functional magnetic resonance imaging (rs-fMRI) is frequently used to study brain function; but, it is unclear whether BOLD-signal fluctuation amplitude and functional connectivity are associated with vascular factors, and how vascular-health factors are reflected in rs-fMRI metrics in the healthy population. As arterial stiffening is a known age-related cardiovascular risk factor, we investigated the associations between aortic stiffening (as measured using pulse-wave velocity [PW ...[more]

PMID: 32034832

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Project description:BACKGROUND:Arterial pulse wave velocity (PWV) is associated with increased mortality in aging and disease. Several studies have shown the accuracy of applanation tonometry carotid-femoral PWV (Cf-PWV) and the relevance of evaluating central aorta stiffness using 2D cardiovascular magnetic resonance (CMR) to estimate PWV, and aortic distensibility-derived PWV through the theoretical Bramwell-Hill model (BH-PWV). Our aim was to compare various methods of aortic PWV (aoPWV) estimation from 4D flow CMR, in terms of associations with age, Cf-PWV, BH-PWV and left ventricular (LV) mass-to-volume ratio while evaluating inter-observer reproducibility and robustness to temporal resolution. METHODS:We studied 47 healthy subjects (49.5?±?18?years) who underwent Cf-PWV and CMR including aortic 4D flow CMR as well as 2D cine SSFP for BH-PWV and LV mass-to-volume ratio estimation. The aorta was semi-automatically segmented from 4D flow data, and mean velocity waveforms were estimated in 25 planes perpendicular to the aortic centerline. 4D flow CMR aoPWV was calculated: using velocity curves at two locations, namely ascending aorta (AAo) and distal descending aorta (DAo) aorta (S1, 2D-like strategy), or using all velocity curves along the entire aortic centreline (3D-like strategies) with iterative transit time (TT) estimates (S2) or a plane fitting of velocity curves systolic upslope (S3). For S1 and S2, TT was calculated using three approaches: cross-correlation (TTc), wavelets (TTw) and Fourier transforms (TTf). Intra-class correlation coefficients (ICC) and Bland-Altman biases (BA) were used to evaluate inter-observer reproducibility and effect of lower temporal resolution. RESULTS:4D flow CMR aoPWV estimates were significantly (p?<?0.05) correlated to the CMR-independent Cf-PWV, BH-PWV, age and LV mass-to-volume ratio, with the strongest correlations for the 3D-like strategy using wavelets TT (S2-TTw) (R?=?0.62, 0.65, 0.77 and 0.52, respectively, all p?<?0.001). S2-TTw was also highly reproducible (ICC?=?0.99, BA?=?0.09?m/s) and robust to lower temporal resolution (ICC?=?0.97, BA?=?0.15?m/s). CONCLUSIONS:Reproducible 4D flow CMR aoPWV estimates can be obtained using full 3D aortic coverage. Such 4D flow CMR stiffness measures were significantly associated with Cf-PWV, BH-PWV, age and LV mass-to-volume ratio, with a slight superiority of the 3D strategy using wavelets transit time (S2-TTw).

Project description:Functional magnetic resonance imaging (fMRI) during a resting-state condition can reveal the co-activation of specific brain regions in distributed networks, called resting-state networks, which are selected by independent component analysis (ICA) of the fMRI data. One of the major difficulties with component analysis is the automatic selection of the ICA features related to brain activity. In this study we describe a method designed to automatically select networks of potential functional relevance, specifically, those regions known to be involved in motor function, visual processing, executive functioning, auditory processing, memory, and the default-mode network. To do this, image analysis was based on probabilistic ICA as implemented in FSL software. After decomposition, the optimal number of components was selected by applying a novel algorithm which takes into account, for each component, Pearson's median coefficient of skewness of the spatial maps generated by FSL, followed by clustering, segmentation, and spectral analysis. To evaluate the performance of the approach, we investigated the resting-state networks in 25 subjects. For each subject, three resting-state scans were obtained with a Siemens Allegra 3 T scanner (NYU data set). Comparison of the visually and the automatically identified neuronal networks showed that the algorithm had high accuracy (first scan: 95%, second scan: 95%, third scan: 93%) and precision (90%, 90%, 84%). The reproducibility of the networks for visual and automatic selection was very close: it was highly consistent in each subject for the default-mode network (?92%) and the occipital network, which includes the medial visual cortical areas (?94%), and consistent for the attention network (?80%), the right and/or left lateralized frontoparietal attention networks, and the temporal-motor network (?80%). The automatic selection method may be used to detect neural networks and reduce subjectivity in ICA component assessment.

Dataset Information

The association between resting-state functional magnetic resonance imaging and aortic pulse-wave velocity in healthy adults.

Publications

The association between resting-state functional magnetic resonance imaging and aortic pulse-wave velocity in healthy adults.

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