Effect of hypertension and diabetes on CVR follows networks in grey and white matter

2603 

Abstract Submission 

Nuwan Nanayakkara¹, Liesel-Ann Meusel¹, Nicole Anderson^1,2, J. Jean Chen^1,2

¹Baycrest Health Sciences, Toronto, Ontario, Canada, ²University of Toronto, Toronto, Ontario, Canada

Nuwan Nanayakkara  
Baycrest Health Sciences
Toronto, Ontario, Canada

Liesel-Ann Meusel  
Baycrest Health Sciences
Toronto, Ontario, Canada

Nicole Anderson  
Baycrest Health Sciences|University of Toronto
Toronto, Ontario, Canada|Toronto, Ontario, Canada

J. Jean Chen  
Baycrest Health Sciences|University of Toronto
Toronto, Ontario, Canada|Toronto, Ontario, Canada

Cerebrovascular reactivity (CVR) changes in gray matter (GM) have been reported in hypertension and diabetes (Ivankovic et al., 2013; Tchistiakova et al., 2014; Li et al., 2021), but have only been reported sparsely in the white matter (WM) (Nanayakkara et al., 2023b). Studies of CVR changes in functionally and structurally connected brain circuits are not reported even with recent trends for integrated analysis of regions within functional networks. This study assessed differences in the amplitude and time delay of CVR among patients with hypertension (HT), hypertension and type 2 diabetes (HT+DM), and age-matched controls (CTL) in joint WM and GM atlas contributing to resting-state (RS) networks (Nozais et al., 2023).

The study consisted of 18 CTL, 20 HT, and 11 HT+DM subjects with mean ages of 71.42, 72.22 and 70.22 years, respectively. Each subject followed a 30 s resting and 2 s exhale followed by a 15 s breath-hold (period = 47 s), repeated 6 times during an fMRI image acquisition with T2*-weighted EPI (voxel size = 3.4x3.4x6.0 mm, TE1/TE2/TR = 9.1/25/4000 ms). Respiratory bellows were recorded using the scanner's built-in belt. A T1-anatomical scan was acquired to generate brain parcellations by registering to standard atlases. The time series data were preprocessed using FSL and AFNI tools. AFNI's 3dretroicor was used to generate BOLD data corrected for physiological noise (i.e., heartbeat, respiration). Neighbouring control and tag frames were averaged in a sliding-window manner to produce 78 frames of BOLD data. These were then high-pass filtered at a 0.01 Hz cut-off frequency, from which CVR metrics were derived using the Fourier transform using our recently published method (Nanayakkara et al., 2023a, 2023c). CVR amplitude and time delay were computed in corresponding WM and GM atlases (Nozais et al., 2023) contributing to RS networks, with GM separated from WM using dilated FreeSurfer GM parcellations. The CVR time delay was expressed in reference to the respiratory belt signal. The 2-way ANOVA was used in each RS network to compare CVR metrics between groups corrected for multiple comparisons by controlling the false discovery rate.

Figure 1 shows RS network-wise mean CVR amplitudes and time delays compared between subject groups in the GM, the WM and the combined GM and WM regions. All regions show similar differences between groups. The CVR amplitude is significantly higher in CTL than in both HT and HT+DM and the time delay is significantly shorter in HT than in both CTL and HT+DM. HT+DM subjects have the lowest CVR amplitudes and longest time delays. Figure 2 shows the network-wise comparison of the GM-WM combined CVR amplitudes and time delays between groups. CVR time delays show more significant differences between groups than amplitudes in RS networks.

In this work, we demonstrate that CVR varies across CTL, HT and HT+DM groups in a network-specific manner, in line with vascular-density differences across tracts (Bernier, Viessmann and Ohringer, 2020). This complements our previous work in which CVR amplitude and delay were shown to differ across these groups in a parcellation-specific manner (Nanayakkara et al., 2023c). The CVR amplitude was lowest in HT+DM for both GM and WM, consistent with previous studies in the GM (Tchistiakova et al., 2014), whereas the HT group exhibited a lower CVR than CTL, also consistent with previous GM studies(Lee et al., 2021; Li et al., 2021). Interestingly, the CVR time delay in RS networks was far more sensitive than CVR amplitude to differences across the groups; this was the case in both GM and WM. While HT+DM seems to confer longer CVR delays, HT seems to confer shorter delays than CTL, suggesting the unique effect of hypertension on vascular dynamics. These findings suggest that the cerebrovasculature may be affected by disease in a network-wise manner, spanning both GM and WM that are connected within the same networks.

Anatomy and Functional Systems ²

BOLD fMRI

Cerebral Metabolism and Hemodynamics ¹

Neurophysiology of Imaging Signals

Cerebral Blood Flow

White Matter