Synopsis

Keywords: Signal Modeling, fMRI (task based)

The limitation of estimating Dynamic Cerebral Autoregulation (dCA) using Transcranial Doppler ultrasound is the lack of whole-brain estimation capabilities. In this study, the data acquisition of whole-brain task-fMRI scan during a novel Passive Cyclical Leg Raise (PCLR) task allows us to induce blood pressure (BP) changes. A surrogate BP signal was acquired from the brain stem and depicts expected fluctuations based on the PCLR-task design. The whole-brain dCA was estimated using voxel-wise TFA to obtain the transfer gain, coherence, and phase-offset. These results show similar significant brain regions but with distinct values based on the participant’s varying level of constitution.

Introduction: Dynamic Cerebral Autoregulation (dCA) is the brain’s inherent ability to modulate cerebral blood flow (CBF) in response to changes in arterial blood pressure (BP). The dCA modulatory signals have been previously discovered to be a frequency-dependent phenomena1 and thus Transfer Function analysis (TFA) techniques are most commonly utilized to estimate the modulation between the changes in CBF following changes in the BP2). The most common approach of estimating the CBF the has been through the continuous measurements of changes using a Transcranial Doppler ultrasound (TCD) over the temples, and BP has been obtained through arterial volume clamping of a finger artery2. However, the limitation of this approach is that it not only cumbersome, but also the lack of whole-brain estimation of dCA due to the limitations of TCD. In this study, we employed a novel Passive Cyclical Leg Raise (PCLR) task to induce blood pressure changes during a whole-brain rapid fMRI scan to acquire a surrogate BP signal from the brain stem and then employ voxel-wise TFA to estimate whole-brain dCA. Methods: The task-fMRI images were acquired from a healthy participant and a participant with hypertension. The novel PCLR task occurred was time synced with a rapid fMRI acquisition (TR=0.605sec) using an in-house apparatus to passively raise and lower the participant legs at 0.05 and 0.1Hz. The apparatus was designed to allow 60º elevation to induce cardiac load and thereby altering the BP. The 0.05Hz oscillation occurred for five minutes before a five-minute washout and followed up by five minutes of the 0.1Hz oscillation. The images were slice time corrected before being separated into the 0.05Hz, 0.05Hz-washout, 0.1Hz, and 0.1Hz-washout resulting in four images (each oscillation will be referred as conditions). The 0.1Hz-washout condition was removed from this analysis due to a sub five-minute acquisition time2. The separated images were corrected for rigid body motion followed by ICA denoising for various fMRI artifacts. The denoised fMRI images were co-registered to the MPRAGE using FSL tools, temporally filtered and then smoothed. The surrogate BP signal was estimated by extracting the BOLD signal from the rostral Ventrolateral Medullary due to its relationship with regulating blood pressure3. The signal was averaged within this ROI using a five-millimeter sphere and then decomposed using a maximum overlap discrete wavelet transform (MODWT) into six frequency bands4. The frequency bands encompassing 0.05Hz and 0.1Hz was extracted as the surrogate BP signals and used as the surrogate BP signal for its corresponding condition. The temporal filtering steps were as follows: the 0.05Hz condition was low-pass filtered at 0–0.06Hz, the washout low-pass filtered at 0-0.15Hz, and 0.1Hz band-pass filtered at 0.08-0.12Hz. Considering that BOLD physiology inherently involves CBF, and that BOLD offers improved temporal resolution and SNR, we chose BOLD signal to index CBF changes. Finally, using in-house scripts for TFA algorithm, the transfer gain, phase-offset, and coherence were estimated for every voxel in the whole-brain image for every condition. The coherence images were thresholded at a minimum of 0.34 to indicate a confidence value of 95% or higher2, and the transfer gain and phase-offset were also thresholded and clusterized to depict significant clusters of voxels. For a closer inspection of TFA-based dCA functioning, we extracted the estimated gain, coherence, and phase offset from left insula, as it has been shown to be in the baroreflex control of sympathetic nerve activity3. Results: Figure-1 depicts the expected BP fluctuations at the designed PCLR frequency. Figure-2 and 3 depicts, the transfer gain, phase-offset, and coherence images for the 0.05Hz and 0.1Hz condition where we observe that for both conditions, each participant had similar significant brain regions in the transfer gain maps. Figure-4 shows TFA outputs at 0.05Hz condition. The coherence index was greater in participant-2 at 0.015Hz and similar in other frequencies. The transfer gain was greater in participant-1 but maintained peaks at similar frequencies. The phase-offset in participant-1 was more positive at 0.05Hz in comparison to participant-2, but otherwise, shares similar trend. Figure-5 portrays TFA outputs at 0.1Hz condition. The coherence index is greater in participant-2 at 0.1Hz. Further, the transfer gain values are greater in participant-1, but the two participant’s transfer gain values differ only by a small factor across all frequencies. The phase-offset depicts larger shifts in participant-2 across all frequencies. Discussion: Our preliminary findings show results supporting that our novel PCLR approach combined with advanced signal processing tools to estimate surrogate BP and dynamic CA are promising and meaningful. While we note that the TFA outputs are represented in similar brain regions between both participants, the values of said TFA outputs are distinct between the participants. Participant-1 had larger and more consistent values as compared to participant-2 with hypertension. Thus, the capabilities of obtaining meaningful TFA outputs is also demonstrated in participants with varying degree of associated health constitution. Conclusion: Our novel MRI compatible PCLR approach along with TFA modeling for dCA will be useful in acute stroke and inpatient neurological settings to diagnose and regulate BP fluctuations, and thereby minimizing secondary and/or severe damage from stroke and other complications.

Acknowledgements

We thank Dr. Keith McGregor for his assistance in data collection.