Phase Relationship Between Cerebral Blood Flow Velocity and Blood Pressure

Diehl, Rolf R.; Linden, Dieter; Lücke, Dorothee; Berlit, Peter

doi:10.1161/01.str.26.10.1801

Cited by 364 publications

(387 citation statements)

References 16 publications

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“…According to Diehl et al, a decrease of the phase angle below 60-90°i ndicates a more passive behavior of the cerebral vessel bed and is an indicator of impaired cerebral autoregulation [20,22,24]. In both our groups, the phase shift remained stable during LBNP confirming the assumptions of intact cerebral autoregulation during LBNP.…”

Section: Discussionsupporting

confidence: 77%

“…Both parameters were calculated only, if coherence between BP and CBFV was above 0.5, i.e. the two signals had a stable phase relation for a given frequency of oscillation and the signals were thought to be synchronized with each other [20,22,23]. We normalized the transfer function gain by dividing the LF transfer function gain by the mean values of the input signal BP and the output signal CBFV of cerebral autoregulation, i.e.…”

Section: Lbnp Protocolmentioning

confidence: 99%

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Cardiovascular and cerebrovascular responses to lower body negative pressure in type 2 diabetic patients

Marthol¹,

Zikeli²,

Brown³

et al. 2007

Journal of the Neurological Sciences

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In diabetic patients, vascular disease and autonomic dysfunction might compromise cerebral autoregulation and contribute to orthostatic intolerance. The aim of our study was to determine whether impaired cerebral autoregulation contributes to orthostatic intolerance during lower body negative pressure in diabetic patients.Thirteen patients with early-stage type 2 diabetes were studied. We continuously recorded RR-interval, mean blood pressure and mean middle cerebral artery blood flow velocity at rest and during lower body negative pressure applied at −20 and −40 mm Hg. Spectral powers of RR-interval, blood pressure and cerebral blood flow velocity were analyzed in the sympathetically mediated low (LF: 0.04-0.15 Hz) and the high (HF: 0.15-0.5 Hz) frequency ranges. Cerebral autoregulation was assessed from the transfer function gain and phase shift between LF oscillations of blood pressure and cerebral blood flow velocity. In the diabetic patients, lower body negative pressure decreased the RRinterval, i.e. increased heart rate, while blood pressure and cerebral blood flow velocity decreased. Transfer function gain and phase shift remained stable.Lower body negative pressure did not induce the normal increase in sympathetically mediated LF-powers of blood pressure and cerebral blood flow velocity in our patients indicating sympathetic dysfunction. The stable phase shift, however, suggests intact cerebral autoregulation. The dying back pathology in diabetic neuropathy may explain an earlier and greater impairment of peripheral vasomotor than cerebrovascular control, thus maintaining cerebral blood flow constant and protecting patients from symptoms of presyncope.

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Section: Discussionsupporting

confidence: 77%

Section: Lbnp Protocolmentioning

confidence: 99%

Cardiovascular and cerebrovascular responses to lower body negative pressure in type 2 diabetic patients

Marthol¹,

Zikeli²,

Brown³

et al. 2007

Journal of the Neurological Sciences

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“…this is demonstrated by the findings that the amplitude variations of the spontaneous LF oscillation can be caused by frequency variations of respiration. Respiration with the same frequency as the LF oscillation (0.1 Hz) leads to a resonance amplification (Cheng et al, 2012;Diehl et al, 1995;Obrig et al, 2000a;Reinhard et al, 2006) and the choice of the length of the stimulation period influences the amplitude substantially (Toronov et al, 2000). Furthermore the LF oscillation seems to be interconnected with stimulus/task-evoked functional brain activity (i.e.…”

Section: Classification Of Signal Componentsmentioning

confidence: 99%

A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology

Scholkmann¹,

Kleiser²,

Metz³

et al. 2014

NeuroImage

1,418

1,394

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This year marks the 20th anniversary of functional near-infrared spectroscopy and imaging (fNIRS/fNIRI). As the vast majority of commercial instruments developed until now are based on continuous wave technology, the aim of this publication is to review the current state of instrumentation and methodology of continuous wave fNIRI. For this purpose we provide an overview of the commercially available instruments and address instrumental aspects such as light sources, detectors and sensor arrangements. Methodological aspects, algorithms to calculate the concentrations of oxy-and deoxyhemoglobin and approaches for data analysis are also reviewed. From the single-location measurements of the early years, instrumentation has progressed to imaging initially in two dimensions (topography) and then three (tomography). The methods of analysis have also changed tremendously, from the simple modified Beer-Lambert law to sophisticated image reconstruction and data analysis methods used today. Due to these advances, fNIRI has become a modality that is widely used in neuroscience research and several manufacturers provide commercial instrumentation. It seems likely that fNIRI will become a clinical tool in the foreseeable future, which will enable diagnosis in single subjects. This year marks the 20th anniversary of functional near-infrared spectroscopy and imaging (fNIRS/fNIRI). As the vast majority of commercial instruments developed until now are based on continuous wave technology, the aim of this publication is to review the current state of instrumentation and methodology of continuous wave fNIRI. For this purpose we provide an overview of the commercially available instruments and address instrumental aspects such as light sources, detectors and sensor arrangements. Methodological aspects, algorithms to calculate the concentrations of oxy-and deoxyhemoglobin and approaches for data analysis are also reviewed. From the single-location measurements of the early years, instrumentation has progressed to imaging initially in two dimensions (topography) and then three (tomography). The methods of analysis have also changed tremendously, from the simple modified Beer-Lambert law to sophisticated image reconstruction and data analysis methods used today. Due to these advances, fNIRI has become a modality that is widely used in neuroscience research and several manufacturers provide commercial instrumentation. It seems likely that fNIRI will become a clinical tool in the foreseeable future, which will enable diagnosis in single subjects. Contents

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“…Conventional approaches model autoregulation with blood pressure as input and blood flow as output [8,9]. A transfer function is typically used to explore the relationship between blood pressure (BP) and blood flow velocity (BFV) by calculating gain and phase shift between the BP and BFV power spectra [2,8,[10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…A transfer function is typically used to explore the relationship between blood pressure (BP) and blood flow velocity (BFV) by calculating gain and phase shift between the BP and BFV power spectra [2,8,[10][11][12][13][14][15][16]. In this approach, it is presumed that signals are stationary, and are composed of superimposed sinusoidal oscillations of constant amplitude and period at a pre-determined frequency range.…”

Section: Introductionmentioning

confidence: 99%

Altered phase interactions between spontaneous blood pressure and flow fluctuations in type 2 diabetes mellitus: Nonlinear assessment of cerebral autoregulation

Peng

Huang

et al. 2008

Physica A: Statistical Mechanics and its Applications

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Cerebral autoregulation (CA) is an important mechanism that involves dilation and constriction in arterioles to maintain relatively s cerebral blood flow in response to changes of systemic blood pressure. Traditional assessments of CA focus on the changes of cerebral blood flow velocity in response to large blood pressure fluctuations induced by interventions. This approach is not feasible for patients with impaired autoregulation or cardiovascular regulation. Here we propose a newly developed technique-the multimodal pressure-flow (MMPF) analysis, which assesses CA by quantifying nonlinear phase interactions between spontaneous oscillations in blood pressure and flow velocity during resting conditions. We show that CA in healthy subjects can be characterized by specific phase shifts between spontaneous blood pressure and flow velocity oscillations, and the phase shifts are significantly reduced in diabetic subjects. Smaller phase shifts between oscillations in the two variables indicate more passive dependence of blood flow velocity on blood pressure, thus suggesting impaired cerebral autoregulation. Moreover, the reduction of the phase shifts in diabetes is observed not only in previously-recognized effective region of CA (<0.1Hz), but also over the higher frequency range from ~0.1 to 0.4Hz. These findings indicate that Type 2 diabetes alters cerebral blood flow regulation over a wide frequency range and that this alteration can be reliably assessed from spontaneous oscillations in blood pressure and blood flow velocity during resting conditions. We also show that the MMPF method has better performance than traditional approaches based on Fourier transform, and is more sui for the quantification of nonlinear phase interactions between nonstationary biological signals such as blood pressure and blood flow.

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Phase Relationship Between Cerebral Blood Flow Velocity and Blood Pressure

Cited by 364 publications

References 16 publications

Cardiovascular and cerebrovascular responses to lower body negative pressure in type 2 diabetic patients

Cardiovascular and cerebrovascular responses to lower body negative pressure in type 2 diabetic patients

A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology

Altered phase interactions between spontaneous blood pressure and flow fluctuations in type 2 diabetes mellitus: Nonlinear assessment of cerebral autoregulation

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