Vasomotion as an oscillatory sign of functional impairment in the human internal thoracic artery: A study based on risk factors and vessel reactivity

ZHAO

Front. Bioeng. Biotechnol.

2022

Vasomotion is the spontaneous time-dependent contraction and relaxation of micro arteries and the oscillating frequency is about 0.01–0.1 Hz. The physiological mechanism of vasomotion has not been thoroughly understood. From the dynamics point of view, the heartbeat is the only external loading exerted on the vascular system. We speculate that the nonlinear vascular system and the variable period of the heartbeat might induce the low-frequency vasomotion. In this study, the laser Doppler flowmeter is used to measure the time series of radial artery blood flow and reconstructed modified time series that has the same period as the measured time series but different heartbeat curves. We measured the time series of radial artery blood flow in different conditions by adding different noise disturbances on the forearm, and we decomposed the experiment pulse signal by Hilbert–Huang transform. The wavelet spectral analyses showed that the low-frequency components were induced by the variable period but independent of the shape of the heartbeat curve. Furthermore, we simulated the linear flow in a single pipe and the nonlinear flow in a piping network and found that the nonlinear flow would generate low-frequency components. From the results, we could deduce that the variable period of heartbeat and the nonlinearity of the vascular system induce vasomotion. The noise has effects on the blood signals related to the respiratory activities (∼0.3 Hz) but little influence on that related to the cardiac activities (∼1 Hz). Adding white noise and then stopping would induce an SNR increase in the frequency band related to vasomotion (∼0.1 Hz).

Section: Introductionmentioning

confidence: 99%

The Origin of Vasomotion and Stochastic Resonance in Vasomotion

ZHAO

Front. Bioeng. Biotechnol.

2022

“…Norepinephrine [ 13 ] receptors are present on the pial arterial smooth muscle cells [ 14 , 15 ]. In fact, vasomotion can be elicited via a contractile stimulation of single-dose norepinephrine in internal thoracic artery segments [ 16 ]. Such evoked responses can provide biomarkers, e.g., vasomotion is associated with endothelial dysfunction [ 16 ], while norepinephrine deficiency has been linked to the pathogenesis of Alzheimer’s disease (AD), which can be related to reduced vessel pulsatility and amyloid-beta clearance [ 17 ] via the perivascular pathways [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

“…In fact, vasomotion can be elicited via a contractile stimulation of single-dose norepinephrine in internal thoracic artery segments [ 16 ]. Such evoked responses can provide biomarkers, e.g., vasomotion is associated with endothelial dysfunction [ 16 ], while norepinephrine deficiency has been linked to the pathogenesis of Alzheimer’s disease (AD), which can be related to reduced vessel pulsatility and amyloid-beta clearance [ 17 ] via the perivascular pathways [ 18 ]. Additionally, 0.01–0.02 Hz oscillations are known to be crucial for supporting higher oxygen concentrations distant from the small vessels [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Human-in-the-Loop Optimization of Transcranial Electrical Stimulation at the Point of Care: A Computational Perspective

Arora

Dutta

2022

Brain Sciences

Individual differences in the responsiveness of the brain to transcranial electrical stimulation (tES) are increasingly demonstrated by the large variability in the effects of tES. Anatomically detailed computational brain models have been developed to address this variability; however, static brain models are not “realistic” in accounting for the dynamic state of the brain. Therefore, human-in-the-loop optimization at the point of care is proposed in this perspective article based on systems analysis of the neurovascular effects of tES. First, modal analysis was conducted using a physiologically detailed neurovascular model that found stable modes in the 0 Hz to 0.05 Hz range for the pathway for vessel response through the smooth muscle cells, measured with functional near-infrared spectroscopy (fNIRS). During tES, the transient sensations can have arousal effects on the hemodynamics, so we present a healthy case series for black-box modeling of fNIRS–pupillometry of short-duration tDCS effects. The block exogeneity test rejected the claim that tDCS is not a one-step Granger cause of the fNIRS total hemoglobin changes (HbT) and pupil dilation changes (p < 0.05). Moreover, grey-box modeling using fNIRS of the tDCS effects in chronic stroke showed the HbT response to be significantly different (paired-samples t-test, p < 0.05) between the ipsilesional and contralesional hemispheres for primary motor cortex tDCS and cerebellar tDCS, which was subserved by the smooth muscle cells. Here, our opinion is that various physiological pathways subserving the effects of tES can lead to state–trait variability, which can be challenging for clinical translation. Therefore, we conducted a case study on human-in-the-loop optimization using our reduced-dimensions model and a stochastic, derivative-free covariance matrix adaptation evolution strategy. We conclude from our computational analysis that human-in-the-loop optimization of the effects of tES at the point of care merits investigation in future studies for reducing inter-subject and intra-subject variability in neuromodulation.

“…Norepinephrine [13] receptors are present on the pial arterial smooth muscle cells [14,15]. In fact, vasomotion can be elicited with a contractile stimulation of single-dose norepinephrine in internal thoracic artery segments [16]. Such evoked responses can provide a biomarker, e.g., vasomotion is associated with endothelial dysfunction [16] and norepinephrine deficiency has been linked to the pathogenesis of the Alzheimer's Disease (AD) that can be related to a reduced vessel pulsatility and amyloid-beta clearance [17] via perivascular pathways [18].…”

mentioning

confidence: 99%

Human-In-The-Loop Optimization of Transcranial Electrical Stimulation Effects: A Computational Perspective Based on Systems Analysis

Arora¹,

Dutta²

2022

Preprint

Individual differences in the responsiveness of the brain to transcranial electrical stimulation (tES) is increasingly demonstrated in large variability in the tES effects. Anatomically detailed computational brain models have been developed to address this variability; however, static brain models are not ‘realistic’ in accounting for the dynamic state of the brain. Therefore, human-in-the-loop optimization is proposed in this perspective article based on an extensive systems analysis of the tES neurovascular effects. First, modal analysis was conducted using a physiologically detailed neurovascular model that found stable modes in the 0 Hz to 0.05 Hz range for the pathway for vessel response through the smooth muscle cells, measured with functional near-infrared spectroscopy (fNIRS). tES effects in the 0 Hz to 0.05 Hz range can also be measured with functional magnetic resonance imaging (fMRI)-tDCS data with a maximum TR=10sec. Therefore, we investigated an open-source fMRI-tDCS dataset that used a TR=3.36sec. We found that both the anodal tDCS condition and sham tDCS condition had similar Finite Impulse Response at the region of interest underlying the anode and a remote location, which indicated a global hemodynamic effect of sham tDCS beyond the intended transient sensations. Here, transient sensations can have arousal effects on the hemodynamics so we conducted a healthy case series for black box modeling of fNIRS-pupillometry of short-duration tDCS effects. The block exogeneity test rejected the claim that tDCS is not a 1-step Granger-cause of the fNIRS total hemoglobin changes (HbT) and pupil dilation changes (p<0.05). Also, grey-box modeling using fNIRS of the tDCS effects in chronic stroke showed HbT response to be significantly different (paired-sample t-test, p<0.05) between the ipsilesional and the contralesional hemisphere for primary motor cortex tDCS and cerebellar tDCS which was subserved by the smooth muscle cells. Here, our perspective is that various physiological pathways subserving tES effects can lead to state-trait variability that can be challenging for clinical translation. Therefore, we conducted a case study on human-in-the-loop optimization using our reduced dimension model and a stochastic, derivative-free Covariance Matrix Adaptation Evolution Strategy. Future studies need to investigate human-in-the-loop optimization of tES for reducing inter-subject and intra-subject variability in tES effects.