Linearity and non-linearity in cerebral hemodynamics

Modeling the Mechanisms of Non-Neurogenic Dynamic Cerebral Autoregulation

OBJECTIVE

Dynamic cerebral autoregulation (dCA) refers to a collection of mechanisms that act to maintain steady state cerebral blood flow (CBF) near constant despite changes in arterial blood pressure (ABP), but which is known to become impaired in various cerebrovascular diseases. Currently, the mechanisms of dCA and how they are affected in different physiological conditions are poorly understood. The objective of this study was to disentangle the magnitudes and time scales of the myogenic and metabolic responses of dCA, in order to investigate how each mechanism is affected in impaired dCA.

METHODS

A physiological model of dCA was developed, where both the myogenic and metabolic responses were represented by a gain and time constant. Model parameters were optimized with pressure-flow impulse responses under normocapnic, thigh cuff, and hypercapnic conditions. The impulse responses were derived by applying transfer function analysis (TFA) to experimental recordings of ABP (Finapres), end-tidal CO2 (capnograph), and CBF velocity (transcranial doppler ultrasound in bilateral middle cerebral arteries).

RESULTS

The myogenic gain to time constant ratio was significantly smaller (p-values < 0.001 using both univariate and multivariate TFA), and the metabolic time constant was significantly larger (p-values < 0.001 using both univariate and multivariate TFA) in hypercapnia compared to normocapnia.

CONCLUSION

Both the myogenic and metabolic responses were shown to be affected in impaired dCA, and the metabolic response was shown to be slowed down.

SIGNIFICANCE

This study contributes to the understanding of the complexities of dCA and how it is affected in different physiological conditions.

Time-domain methods for quantifying dynamic cerebral blood flow autoregulation: Review and recommendations. A white paper from the Cerebrovascular Research Network (CARNet)

Cerebral Autoregulation (CA) is an important physiological mechanism stabilizing cerebral blood flow (CBF) in response to changes in cerebral perfusion pressure (CPP). By maintaining an adequate, relatively constant supply of blood flow, CA plays a critical role in brain function. Quantifying CA under different physiological and pathological states is crucial for understanding its implications. This knowledge may serve as a foundation for informed clinical decision-making, particularly in cases where CA may become impaired. The quantification of CA functionality typically involves constructing models that capture the relationship between CPP (or arterial blood pressure) and experimental measures of CBF. Besides describing normal CA function, these models provide a means to detect possible deviations from the latter. In this context, a recent white paper from the Cerebrovascular Research Network focused on Transfer Function Analysis (TFA), which obtains frequency domain estimates of dynamic CA. In the present paper, we consider the use of time-domain techniques as an alternative approach. Due to their increased flexibility, time-domain methods enable the mitigation of measurement/physiological noise and the incorporation of nonlinearities and time variations in CA dynamics. Here, we provide practical recommendations and guidelines to support researchers and clinicians in effectively utilizing these techniques to study CA.

Test-retest reliability of transfer function analysis metrics for assessing dynamic cerebral autoregulation to spontaneous blood pressure oscillations

Transfer function analysis (TFA) is a widely used method for assessing dynamic cerebral autoregulation in humans. In the present study, we assessed the test-retest reliability of established TFA metrics derived from spontaneous blood pressure oscillations and based on 5 min recordings. The TFA-based gain, phase and coherence in the low-frequency range (0.07-0.20 Hz) from 19 healthy volunteers, 37 patients with subarachnoid haemorrhage and 19 patients with sepsis were included. Reliability assessments included the smallest real difference (SRD) and the coefficient of variance for comparing consecutive 5 min recordings, temporally separated 5 min recordings and consecutive recordings with a minimal length of 10 min. In healthy volunteers, temporally separating the 5 min recordings led to a 0.38 (0.01-0.79) cm s-1 mmHg-1 higher SRD for gain (P = 0.032), and extending the duration of recordings did not affect the reliability. In subarachnoid haemorrhage, temporal separation led to a 0.85 (-0.13 to 1.93) cm s-1 mmHg-1 higher SRD (P = 0.047) and a 20 (-2 to 41)% higher coefficient of variance (P = 0.038) for gain, but neither metric was affected by extending the recording duration. In sepsis, temporal separation increased the SRD for phase by 94 (23-160)° (P = 0.006) but was unaffected by extending the recording. A recording duration of 8 min was required to achieve stable gain and normalized gain measures in healthy individuals, and even longer recordings were required in patients. In conclusion, a recording duration of 5 min appears insufficient for obtaining stable and reliable TFA metrics when based on spontaneous blood pressure oscillations, particularly in critically ill patients with subarachnoid haemorrhage and sepsis.

Phenylephrine alters phase synchronization between cerebral blood velocity and blood pressure in ME/CFS with orthostatic intolerance

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) with orthostatic intolerance (OI) is characterized by neurocognitive deficits perhaps related to upright hypocapnia and loss of cerebral autoregulation (CA). We performed N-back neurocognition testing and calculated the phase synchronization index (PhSI) between arterial pressure (AP) and cerebral blood velocity (CBV) as a time-dependent measurement of cerebral autoregulation in 11 control (mean age = 24.1 yr) and 15 patients with ME/CFS (mean age = 21.8 yr). All patients with ME/CFS had postural tachycardia syndrome (POTS). A 10-min 60° head-up tilt (HUT) significantly increased heart rate (109.4 ± 3.9 vs. 77.2 ± 1.6 beats/min, P < 0.05) and respiratory rate (20.9 ± 1.7 vs. 14.2 ± 1.2 breaths/min, P < 0.05) and decreased end-tidal CO2 (ETCO2; 33.9 ± 1.1 vs. 42.8 ± 1.2 Torr, P < 0.05) in ME/CFS versus control. In ME/CFS, HUT significantly decreased CBV compared with control (-22.5% vs. -8.7%, P < 0.005). To mitigate the orthostatic CBV reduction, we administered supplemental CO2, phenylephrine, and acetazolamide and performed N-back testing supine and during HUT. Only phenylephrine corrected the orthostatic decrease in neurocognition by reverting % correct n = 4 N-back during HUT in ME/CFS similar to control (ME/CFS = 38.5 ± 5.5 vs. ME/CFS + PE= 65.6 ± 5.7 vs. Control 56.9 ± 7.5). HUT in ME/CFS resulted in increased PhSI values indicating decreased CA. Although CO2 and acetazolamide had no effect on PhSI in ME/CFS, phenylephrine caused a significant reduction in PhSI (ME/CFS = 0.80 ± 0.03 vs. ME/CFS + PE= 0.69 ± 0.04, P < 0.05) and improved cerebral autoregulation. Thus, PE improved neurocognitive function in patients with ME/CFS, perhaps related to improved neurovascular coupling, cerebral autoregulation, and maintenance of CBV.NEW & NOTEWORTHY We evaluated cognitive function before and after CO2, acetazolamide, and phenylephrine, which mitigate orthostatic reductions in cerebral blood velocity. Neither CO2 nor acetazolamide affected N-back testing (% correct answers) during an orthostatic challenge. Only phenylephrine improved upright N-back performance in ME/CFS, as it both blocked hyperventilation and increased CO2 significantly compared with those untreated. And only phenylephrine resulted in improved PSI values in both ME/CFS and control while upright, suggesting improved cerebral autoregulation.

Dynamic cerebral autoregulation is preserved during orthostasis and intrathoracic pressure regulation in healthy subjects: A pilot study

Resistance breathing may restore cardiac output (CO) and cerebral blood flow (CBF) during hypovolemia. We assessed CBF and cerebral autoregulation (CA) during tilt, resistance breathing, and paced breathing in 10 healthy subjects. Blood velocities in the internal carotid artery (ICA), middle cerebral arteries (MCA, four subjects), and aorta were measured by Doppler ultrasound in 30° and 60° semi-recumbent positions. ICA blood flow and CO were calculated. Arterial blood pressure (ABP, Finometer), and end-tidal CO2 (ETCO2) were recorded. ICA blood flow response was assessed by mixed-models regression analysis. The synchronization index (SI) for the variable pairs ABP-ICA blood velocity, ABP-MCA velocities in 0.005-0.08 Hz frequency interval was calculated as a measure of CA. Passive tilting from 30° to 60° resulted in 12% decrease in CO (p = 0.001); ICA blood flow tended to fall (p = 0.04); Resistance breathing restored CO and ICA blood flow despite a 10% ETCO2 drop. ETCO2 and CO contributed to ICA blood flow variance (adjusted R2: 0.9, p < 0.0001). The median SI was low (<0.2) indicating intact CA, confirmed by surrogate date testing. The peak SI was transiently elevated during resistance breathing in the 60° position. Resistance breathing may transiently reduce CA efficiency. Paced breathing did not restore CO or ICA blood flow.

Transfer function analysis of dynamic cerebral autoregulation: A CARNet white paper 2022 update

Cerebral autoregulation (CA) refers to the control of cerebral tissue blood flow (CBF) in response to changes in perfusion pressure. Due to the challenges of measuring intracranial pressure, CA is often described as the relationship between mean arterial pressure (MAP) and CBF. Dynamic CA (dCA) can be assessed using multiple techniques, with transfer function analysis (TFA) being the most common. A 2016 white paper by members of an international Cerebrovascular Research Network (CARNet) that is focused on CA strove to improve TFA standardization by way of introducing data acquisition, analysis, and reporting guidelines. Since then, additional evidence has allowed for the improvement and refinement of the original recommendations, as well as for the inclusion of new guidelines to reflect recent advances in the field. This second edition of the white paper contains more robust, evidence-based recommendations, which have been expanded to address current streams of inquiry, including optimizing MAP variability, acquiring CBF estimates from alternative methods, estimating alternative dCA metrics, and incorporating dCA quantification into clinical trials. Implementation of these new and revised recommendations is important to improve the reliability and reproducibility of dCA studies, and to facilitate inter-institutional collaboration and the comparison of results between studies.

Spectral decomposition of cerebrovascular and cardiovascular interactions in patients prone to postural syncope and healthy controls

We present a framework for the linear parametric analysis of pairwise interactions in bivariate time series in the time and frequency domains, which allows the evaluation of total, causal and instantaneous interactions and connects time- and frequency-domain measures. The framework is applied to physiological time series to investigate the cerebrovascular regulation from the variability of mean cerebral blood flow velocity (CBFV) and mean arterial pressure (MAP), and the cardiovascular regulation from the variability of heart period (HP) and systolic arterial pressure (SAP). We analyze time series acquired at rest and during the early and late phase of head-up tilt in subjects developing orthostatic syncope in response to prolonged postural stress, and in healthy controls. The spectral measures of total, causal and instantaneous coupling between HP and SAP, and between MAP and CBFV, are averaged in the low-frequency band of the spectrum to focus on specific rhythms, and over all frequencies to get time-domain measures. The analysis of cardiovascular interactions indicates that postural stress induces baroreflex involvement, and its prolongation induces baroreflex dysregulation in syncope subjects. The analysis of cerebrovascular interactions indicates that the postural stress enhances the total coupling between MAP and CBFV, and challenges cerebral autoregulation in syncope subjects, while the strong sympathetic activation elicited by prolonged postural stress in healthy controls may determine an increased coupling from CBFV to MAP during late tilt. These results document that the combination of time-domain and spectral measures allows us to obtain an integrated view of cardiovascular and cerebrovascular regulation in healthy and diseased subjects.

Machine Learning Models and Statistical Complexity to Analyze the Effects of Posture on Cerebral Hemodynamics

The mechanism of cerebral blood flow autoregulation can be of great importance in diagnosing and controlling a diversity of cerebrovascular pathologies such as vascular dementia, brain injury, and neurodegenerative diseases. To assess it, there are several methods that use changing postures, such as sit-stand or squat-stand maneuvers. However, the evaluation of the dynamic cerebral blood flow autoregulation (dCA) in these postures has not been adequately studied using more complex models, such as non-linear ones. Moreover, dCA can be considered part of a more complex mechanism called cerebral hemodynamics, where others (CO2 reactivity and neurovascular-coupling) that affect cerebral blood flow (BF) are included. In this work, we analyzed postural influences using non-linear machine learning models of dCA and studied characteristics of cerebral hemodynamics under statistical complexity using eighteen young adult subjects, aged 27 ± 6.29 years, who took the systemic or arterial blood pressure (BP) and cerebral blood flow velocity (BFV) for five minutes in three different postures: stand, sit, and lay. With models of a Support Vector Machine (SVM) through time, we used an AutoRegulatory Index (ARI) to compare the dCA in different postures. Using wavelet entropy, we estimated the statistical complexity of BFV for three postures. Repeated measures ANOVA showed that only the complexity of lay-sit had significant differences.

The INfoMATAS project: Methods for assessing cerebral autoregulation in stroke

Cerebral autoregulation refers to the physiological mechanism that aims to maintain blood flow to the brain approximately constant when blood pressure changes. Impairment of this protective mechanism has been linked to a number of serious clinical conditions, including carotid stenosis, head trauma, subarachnoid haemorrhage and stroke. While the concept and experimental evidence is well established, methods for the assessment of autoregulation in individual patients remains an open challenge, with no gold-standard having emerged. In the current review paper, we will outline some of the basic concepts of autoregulation, as a foundation for experimental protocols and signal analysis methods used to extract indexes of cerebral autoregulation. Measurement methods for blood flow and pressure are discussed, followed by an outline of signal pre-processing steps. An outline of the data analysis methods is then provided, linking the different approaches through their underlying principles and rationale. The methods cover correlation based approaches (e.g. Mx) through Transfer Function Analysis to non-linear, multivariate and time-variant approaches. Challenges in choosing which method may be 'best' and some directions for ongoing and future research conclude this work.

Cerebral autoregulation assessed by near-infrared spectroscopy: validation using transcranial Doppler in patients with controlled hypertension, cognitive impairment and controls

PURPOSE

Cerebral autoregulation (CA) aims to attenuate the effects of blood pressure variation on cerebral blood flow. This study assessed the criterion validity of CA derived from near-infrared spectroscopy (NIRS) as an alternative for Transcranial Doppler (TCD).

METHODS

Measurements of continuous blood pressure (BP), oxygenated hemoglobin (O₂Hb) using NIRS and cerebral blood flow velocity (CBFV) using TCD (gold standard) were performed in 82 controls, 27 patients with hypertension and 94 cognitively impaired patients during supine rest (all individuals) and repeated sit to stand transitions (cognitively impaired patients). The BP-CBFV and BP-O₂Hb transfer function phase shifts (TF_φ) were computed as CA measures. Spearman correlations (ρ) and Bland Altman limits of agreement (BAloa) between NIRS- and TCD-derived CA measures were computed. BAloa separation < 50° was considered a high absolute agreement.

RESULTS

NIRS- and TCD-derived CA estimates were significantly correlated during supine rest (ρ = 0.22-0.30, N = 111-120) and repeated sit-to-stand transitions (ρ = 0.46-0.61, N = 19-32). BAloa separation ranged between 87° and 112° (supine rest) and 65°-77° (repeated sit to stand transitions).

CONCLUSION

Criterion validity of NIRS-derived CA measures allows for comparison between groups but was insufficient for clinical application in individuals.

Two-Tiered Response of Cardiorespiratory-Cerebrovascular Network to Orthostatic Challenge

Dynamic interdependencies within and between physiological systems and subsystems are key for homeostatic mechanisms to establish an optimal state of the organism. These interactions mediate regulatory responses elicited by various perturbations, such as the high-pressure baroreflex and cerebral autoregulation, alleviating the impact of orthostatic stress on cerebral hemodynamics and oxygenation. The aim of this study was to evaluate the responsiveness of the cardiorespiratory-cerebrovascular networks by capturing linear and nonlinear interdependencies to postural changes. Ten young healthy adults participated in our study. Non-invasive measurements of arterial blood pressure (from that cardiac cycle durations were derived), breath-to-breath interval, cerebral blood flow velocity (BFV, recorded by transcranial Doppler sonography), and cerebral hemodynamics (HbT, total hemoglobin content monitored by near-infrared spectroscopy) were performed for 30-min in resting state, followed by a 1-min stand-up and a 1-min sit-down period. During preprocessing, noise was filtered and the contribution of arterial blood pressure was regressed from BFV and HbT signals. Cardiorespiratory-cerebrovascular networks were reconstructed by computing pair-wise Pearson-correlation or mutual information between the resampled signals to capture their linear and/or nonlinear interdependencies, respectively. The interdependencies between cardiac, respiratory, and cerebrovascular dynamics showed a marked weakening after standing up persisting throughout the sit-down period, which could mainly be attributed to strikingly attenuated nonlinear coupling. To summarize, we found that postural changes induced topological changes in the cardiorespiratory-cerebrovascular network. The dissolution of nonlinear networks suggests that the complexity of key homeostatic mechanisms maintaining cerebral hemodynamics and oxygenation is indeed sensitive to physiological perturbations such as orthostatic stress.

Cerebrovascular Dynamics During Continuous Motor Task

We investigated the cerebral autoregulation (CA) dynamics parameter phase and gain change when exposed to a longlasting motor task. 25 healthy subjects (mean age ± SE, 38±2.6 years, 13 females) underwent simultaneous recordings of spontaneous fluctuations in blood pressure (BP), cerebral blood flow velocity (CBFV), and end-tidal CO(2) (ETCO(2)) over 5 min of rest followed by 5 min of left elbow flexion at a frequency of 1 Hz. Tansfer function gain and phase between BP and CBFV were assessed in the frequency ranges of very low frequencies (VLF, 0.02-0.07 Hz), low frequencies (LF, 0.07-0.15), and high frequencies (HF, >0.15). CBFV increased on both sides rapidly to maintain an elevated steady state until movement stopped. Cerebrovascular resistance fell on the right side (rest 1.35±0.06, movement 1.28±0.06, p<0.01), LF gain decreased from baseline (right side 0.97±0.07 %/mm Hg, left 1.01±0.09) to movement epoch (right 0.73±0.08, left 0.76±0.06, p</=0.01). VLF phase decreased from baseline (right 1.03±0.05 radians, left 1.10±0.06) to the movement epoch (right 0.81±0.07, left 0.82±0.10, p?0.05). CA regulates continuous motor efforts by changes in resistance, gain and phase.

Can interhemispheric desynchronization of cerebral blood flow anticipate upcoming vasospasm in aneurysmal subarachnoid haemorrhage patients?

BACKGROUND

Asymmetry of cerebral autoregulation (CA) was demonstrated in patients after aneurysmal subarachnoid haemorrhage (aSAH). A classical method for CA assessment requires simultaneous measurement of both arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV). In this study, we have proposed a cerebral blood flow asymmetry index based only on CBFV and analysed its association with the occurrence of vasospasm after aSAH.

NEW METHOD

The phase shifts (PS) between slow oscillations in left and right CBFV (side-to-side PS) and between ABP and CBFV (CBFV-ABP PS) were estimated using multichannel matching pursuit (MMP) and cross-spectral analysis.

RESULTS

We retrospectively analysed data collected from 45 aSAH patients (26 with vasospasm). Data were analysed up to 7th day after aSAH unless the vasospasm was detected earlier. A progressive asymmetry, manifested by a gradual increase in side-to-side PS on consecutive days after aSAH, was observed in patients who developed vasospasm (R_adj² = 0.14, p = 0.009). In these patients, early side-to-side PS was more positive than in patients without vasospasm (2.8° ± 5.6° vs -1.7° ± 5.7°, p = 0.011). No such a difference was found in CBFV-ABP PS. Patients with positive side-to-side PS were more likely to develop vasospasm than patients with negative side-to-side PS (21/7 vs 5/12, p = 0.0047).

COMPARISON WITH EXISTING METHOD

MMP, in contrast to the spectral approach, accounts for non-stationarity of analysed signals. MMP applied to the PS estimation reflects the cerebral blood flow asymmetry in aSAH better than the spectral analysis.

CONCLUSIONS

Changes in side-to-side PS might be helpful to identify patients who are at risk of vasospasm.

Revisiting human cerebral blood flow responses to augmented blood pressure oscillations

KEY POINTS

Cerebral autoregulation is most effective in buffering against pressure fluctuations slower than 0.03 Hz (∼30 s). This suggests that frequency bands for characterizing cerebral autoregulation should be redefined Low cross-spectral coherence below 0.03 Hz highlights the limitations of transfer function approaches Haemodynamic changes induced by lower body pressure could not fully explain the differences in autoregulation estimated from spontaneous vs. augmented fluctuations, and thus, observations of spontaneous fluctuations should not be relied on whenever possible.

ABSTRACT

There is currently little empirical basis for time scales that are considered to be most significant in cerebrovascular counter-regulation of changes in arterial pressure. Although it is well established that cerebral autoregulation behaves as a 'high-pass' filter, recommended frequency bands have been largely arbitrarily determined. To test effectiveness of cerebral autoregulation, we refined oscillatory lower body pressure (LBP) to augment resting pressure fluctuations below 0.1 Hz by a factor of two in 13 young male volunteers, and thoroughly characterized the time and frequency responses of cerebral autoregulation. We observed that despite a threefold increase in arterial pressure power <0.03 Hz with oscillatory LBP, there was no change in cerebral blood flow power, indicating near perfect counter-regulation. By contrast, in the range 0.03-0.10 Hz, both cerebral blood flow and arterial pressure power more than doubled. Our data demonstrate that cerebral autoregulation is most effective in buffering against pressure fluctuations slower than 0.03 Hz (∼30 s). This suggests that frequency bands of interest should be redefined and recording length should be increased considerably to account for this. Furthermore, low cross-spectral coherence below 0.03 Hz, even when pressure fluctuations were augmented, highlights the uncertainty in transfer function approaches and the need to either report precision or use non-linear approaches. Finally, haemodynamic changes induced by LBP could not fully explain the differences in autoregulation estimated from spontaneous vs. augmented fluctuations, and thus, observations of spontaneous fluctuations should not be relied on whenever possible.

Neonatal cerebrovascular autoregulation

Cerebrovascular pressure autoregulation is the physiologic mechanism that holds cerebral blood flow (CBF) relatively constant across changes in cerebral perfusion pressure (CPP). Cerebral vasoreactivity refers to the vasoconstriction and vasodilation that occur during fluctuations in arterial blood pressure (ABP) to maintain autoregulation. These are vital protective mechanisms of the brain. Impairments in pressure autoregulation increase the risk of brain injury and persistent neurologic disability. Autoregulation may be impaired during various neonatal disease states including prematurity, hypoxic-ischemic encephalopathy (HIE), intraventricular hemorrhage, congenital cardiac disease, and infants requiring extracorporeal membrane oxygenation (ECMO). Because infants are exquisitely sensitive to changes in cerebral blood flow (CBF), both hypoperfusion and hyperperfusion can cause significant neurologic injury. We will review neonatal pressure autoregulation and autoregulation monitoring techniques with a focus on brain protection. Current clinical therapies have failed to fully prevent permanent brain injuries in neonates. Adjuvant treatments that support and optimize autoregulation may improve neurologic outcomes.

Human cerebrovascular function in health and disease: insights from integrative approaches

BACKGROUND

The marked increase in the size of the brain, and consequently, in neural processing capability, throughout human evolution is the basis of the higher cognitive function in humans. However, greater neural, and thus information processing capability, comes at a significant metabolic cost; despite its relatively small size, the modern human brain consumes almost a quarter of the glucose and oxygen supply in the human body. Fortunately, several vascular mechanisms ensure sufficient delivery of glucose and oxygen to the active neural tissue (neurovascular coupling), prompt removal of neural metabolic by-products (cerebral vasoreactivity), and constant global blood supply despite daily variations in perfusion pressure (cerebral autoregulation). The aim of this review is to provide an integrated overview of the available data on these vascular mechanisms and their underlying physiology. We also briefly review modern experimental approaches to assess these mechanisms in humans, and further highlight the importance of these mechanisms for humans' evolutionary success by providing examples of their healthy adaptations as well as pathophysiological alterations.

CONCLUSIONS

Data reviewed in this paper demonstrate the importance of the cerebrovascular function to support humans' unique ability to form new and different interactions with each other and their surroundings. This highlights that there is much insight into the neural and cognitive functions that could be gleaned from interrogating the cerebrovascular function.

Applying time-frequency analysis to assess cerebral autoregulation during hypercapnia

OBJECTIVE

Classic methods for assessing cerebral autoregulation involve a transfer function analysis performed using the Fourier transform to quantify relationship between fluctuations in arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV). This approach usually assumes the signals and the system to be stationary. Such an presumption is restrictive and may lead to unreliable results. The aim of this study is to present an alternative method that accounts for intrinsic non-stationarity of cerebral autoregulation and the signals used for its assessment.

METHODS

Continuous recording of CBFV, ABP, ECG, and end-tidal CO2 were performed in 50 young volunteers during normocapnia and hypercapnia. Hypercapnia served as a surrogate of the cerebral autoregulation impairment. Fluctuations in ABP, CBFV, and phase shift between them were tested for stationarity using sphericity based test. The Zhao-Atlas-Marks distribution was utilized to estimate the time-frequency coherence (TFCoh) and phase shift (TFPS) between ABP and CBFV in three frequency ranges: 0.02-0.07 Hz (VLF), 0.07-0.20 Hz (LF), and 0.20-0.35 Hz (HF). TFPS was estimated in regions locally validated by statistically justified value of TFCoh. The comparison of TFPS with spectral phase shift determined using transfer function approach was performed.

RESULTS

The hypothesis of stationarity for ABP and CBFV fluctuations and the phase shift was rejected. Reduced TFPS was associated with hypercapnia in the VLF and the LF but not in the HF. Spectral phase shift was also decreased during hypercapnia in the VLF and the LF but increased in the HF. Time-frequency method led to lower dispersion of phase estimates than the spectral method, mainly during normocapnia in the VLF and the LF.

CONCLUSION

The time-frequency method performed no worse than the classic one and yet may offer benefits from lower dispersion of phase shift as well as a more in-depth insight into the dynamic nature of cerebral autoregulation.

Indexes of cerebral autoregulation do not reflect impairment in syncope: insights from head-up tilt test of vasovagal and autonomic failure subjects

PURPOSE

The study of dynamic cerebral autoregulation (CA), which adapts cerebral blood flow to arterial blood pressure (ABP) fluctuations, has been limited in orthostatic intolerance syndromes, mainly due to its stationary prerequisites hardly to meet during maneuvers to provoke syncope itself. New techniques of continuous estimates of CA could overcome this pitfall. We aimed to evaluate CA during head-up tilt test in common conditions causing syncope.

METHODS

We compared three groups: eight controls; eight patients with autonomic failure due to familial amyloidotic polyneuropathy; eight patients with vasovagal syncope (VVS). ABP and cerebral blood flow velocity (CBFV) were measured with Finometer^® and transcranial Doppler. We calculated cerebrovascular resistance index (CVRi), critical closing pressure (CrCP) and resistance area product (RAP), and derived CA continuously from autoregulation index [ARI(t)].

RESULTS

With HUTT, AF subjects showed a pronounced decrease in CBFV (-36 ± 17 versus -7 ± 6%, p < 0.0001), ABP (-29 ± 27 versus 7 ± 12%, p < 0.0001) and RAP (-17 ± 23 versus 3 ± 18%, p < 0.0001) but not CVRi (p = 0.110). VVS subjects showed progressive cerebral vasoconstriction prior to syncope, (reduced CBFV 19 ± 15 versus 1 ± 6, p < 0.000; increased RAP 12 ± 18 versus 2 ± 3%, p = 0.024 and CVRi 12 ± 18 versus 2 ± 3%, p = 0.005). ARI(t) increased significantly in AF patients (5.7 ± 1.2 versus 6.9 ± 1.2, p = 0.040) and VVS (5.8 ± 1.2 versus 7.3 ± 1.2, p = 0.015) in response to ABP fall during syncope.

CONCLUSIONS

Our data suggest that dynamic cerebral autoregulatory response to orthostatic challenge is neither affected by autonomic dysfunction nor in neutrally mediated syncope. This study also emphasizes that RAP + CrCP model is more informative than CVRi, mainly during cerebral vasodilatory response to orthostatic hypotension.

Comparison of Model-Based Indices of Cerebral Autoregulation and Vasomotor Reactivity Using Transcranial Doppler versus Near-Infrared Spectroscopy in Patients with Amnestic Mild Cognitive Impairment

We recently introduced model-based "physiomarkers" of dynamic cerebral autoregulation and CO2 vasomotor reactivity as an aid for diagnosis of early-stage Alzheimer's disease (AD) [1], where significant impairment of dynamic vasomotor reactivity (DVR) was observed in early-stage AD patients relative to age-matched controls. Milder impairment of DVR was shown in patients with amnestic mild cognitive impairment (MCI) using the same approach in a subsequent study [2]. The advocated approach utilizes subject-specific data-based models of cerebral hemodynamics to quantify the dynamic effects of resting-state changes in arterial blood pressure and end-tidal CO2 (the putative inputs) upon cerebral blood flow velocity (the putative output) measured at the middle cerebral artery via transcranial Doppler (TCD). The obtained input-output models are then used to compute model-based indices of DCA and DVR from model-predicted responses to an input pressure pulse or an input CO2 pulse, respectively. In this paper, we compare these model-based indices of DVR and DCA in 46 amnestic MCI patients, relative to 20 age-matched controls, using TCD measurements with their counterparts using Near-Infrared Spectroscopy (NIRS) measurements of blood oxygenation at the lateral prefrontal cortex in 43 patients and 22 age-matched controls. The goal of the study is to assess whether NIRS measurements can be used instead of TCD measurements to obtain model-based physiomarkers with comparable diagnostic utility. The results corroborate this view in terms of the ability of either output to yield model-based physiomarkers that can differentiate the group of aMCI patients from age-matched healthy controls.

Multiple-input nonlinear modelling of cerebral haemodynamics using spontaneous arterial blood pressure, end-tidal CO2 and heart rate measurements

In order to examine the effect of changes in heart rate (HR) upon cerebral perfusion and autoregulation, we include the HR signal recorded from 18 control subjects as a third input in a two-input model of cerebral haemodynamics that has been used previously to quantify the dynamic effects of changes in arterial blood pressure and end-tidal CO2upon cerebral blood flow velocity (CBFV) measured at the middle cerebral arteries via transcranial Doppler ultrasound. It is shown that the inclusion of HR as a third input reduces the output prediction error in a statistically significant manner, which implies that there is a functional connection between HR changes and CBFV. The inclusion of nonlinearities in the model causes further statistically significant reduction of the output prediction error. To achieve this task, we employ the concept of principal dynamic modes (PDMs) that yields dynamic nonlinear models of multi-input systems using relatively short data records. The obtained PDMs suggest model-driven quantitative hypotheses for the role of sympathetic and parasympathetic activity (corresponding to distinct PDMs) in the underlying physiological mechanisms by virtue of their relative contributions to the model output. These relative PDM contributions are subject-specific and, therefore, may be used to assess personalized characteristics for diagnostic purposes.

Can the Assessment of Spontaneous Oscillations by Near Infrared Spectrophotometry Predict Neurological Outcome of Preterm Infants?

The aim was to assess the correlation between cerebral autoregulation and outcome. Included were 31 preterm infants, gestational age 26 1/7 to 32 2/7 and <24 h life. Coherence between cerebral total haemoglobin (tHb) or oxygenation index (OI) measured by near-infrared spectrophotometry (NIRS) and systemic heart rate (HR) or arterial blood pressure (MAP) was calculated as a measure of autoregulation. In contrast to previous studies, low coherences in the first 24 h were significantly associated with intraventricular haemorrhage, death or abnormal neurodevelopmental outcome at 18 months or later. We suggest that our results can be explained by the concept of a multi-oscillatory-functions-order.

Model-based indices describing cerebrovascular dynamics

Understanding the dynamic relationship between cerebral blood flow (CBF) and the circulation of cerebrospinal fluid (CSF) can facilitate management of cerebral pathologies. For this reason, various hydrodynamic models have been introduced in order to simulate the phenomena governing the interaction between CBF and CSF. The identification of hydrodynamic models requires an array of signals as input, with the most common of them being arterial blood pressure, intracranial pressure, and cerebral blood flow velocity; monitoring all of them is considered as a standard practice in neurointensive care. Based on these signals, physiological parameters like cerebrovascular resistance, compliances of cerebrovascular bed, and CSF space could then be estimated. Various secondary model-based indices describing cerebrovascular dynamics have been introduced, like the cerebral arterial time constant or critical closing pressure. This review presents model-derived indices that describe cerebrovascular phenomena, the nature of which is both physiological (carbon dioxide reactivity and arterial hypotension) and pathological (cerebral artery stenosis, intracranial hypertension, and cerebral vasospasm). In a neurointensive environment, real-time monitoring of a patient with these indices may be able to provide a detection of the onset of a cerebrovascular phenomenon, which could have otherwise been missed. This potentially "early warning" indicator may then prove to be important for the therapeutic management of the patient.

Complexity of cerebral blood flow velocity and arterial blood pressure in subarachnoid hemorrhage using time-frequency analysis

We investigated changes of time-frequency (TF) complexity, in terms of Rényi entropy and a measure of concentration, of middle cerebral blood flow velocity (CBFV) and arterial blood pressure in relation to the development of cerebral vasospasm in 15 patients after aneurysmal subarachnoid hemorrhage. Interhemispheric differences in the period of no vasospasm and vasospasm were also compared. Results show reduced complexity of TF representations of CBFV on the side of aneurysm before vasospasm was identified. This potentially can serve as an early-warning indicator of future derangement of cerebral circulation.

Assessing cerebral autoregulation via oscillatory lower body negative pressure and projection pursuit regression

The process by which cerebral perfusion is maintained constant over a wide range of systemic pressures is known as "cerebral autoregulation." Effective dampening of flow against pressure changes occurs over periods as short as ~15 sec and becomes progressively greater over longer time periods. Thus, slower changes in blood pressure are effectively blunted and faster changes or fluctuations pass through to cerebral blood flow relatively unaffected. The primary difficulty in characterizing the frequency dependence of cerebral autoregulation is the lack of prominent spontaneous fluctuations in arterial pressure around the frequencies of interest (less than ~0.07 Hz or ~15 sec). Oscillatory lower body negative pressure (OLBNP) can be employed to generate oscillations in central venous return that result in arterial pressure fluctuations at the frequency of OLBNP. Moreover, Projection Pursuit Regression (PPR) provides a nonparametric method to characterize nonlinear relations inherent in the system without a priori assumptions and reveals the characteristic non-linearity of cerebral autoregulation. OLBNP generates larger fluctuations in arterial pressure as the frequency of negative pressure oscillations become slower; however, fluctuations in cerebral blood flow become progressively lesser. Hence, the PPR shows an increasingly more prominent autoregulatory region at OLBNP frequencies of 0.05 Hz and below (20 sec cycles). The goal of this approach it to allow laboratory-based determination of the characteristic nonlinear relationship between pressure and cerebral flow and could provide unique insight to integrated cerebrovascular control as well as to physiological alterations underlying impaired cerebral autoregulation (e.g., after traumatic brain injury, stroke, etc.).

A comparison of dynamic cerebral autoregulation across changes in cerebral blood flow velocity for 200 s

Objectives: The dynamic interaction between blood pressure (BP) and cerebral blood flow velocity (CBFV) is not fully understood, especially for CBFV changes lasting longer than 50 s. The interaction between BP and CBFV is relatively well characterized for periods <50 s using transfer function (TF) estimations of phase, gain, and coherence. We used TF estimations to compare the phase and gain for periods >50 s with those for periods <50 s.

Materials and Methods: BP and CBFV (of the middle cerebral artery) were simultaneously recorded in 23 healthy subjects (10 men, 13 women, mean age 35 ± 10 years) under normo- and hypocapnia (induced by hyperventilation). TF and coherence estimations were based on Welch's periodogram method with a windowing of 200 s (frequency resolution, 0.005 Hz, corresponding to a period of 200 s). Means of the phase, gain, and coherence were calculated over frequency periods of 0.005–0.02 Hz (sVLF), 0.02–0.07 Hz (VLF), 0.07–0.15 Hz (LF), and 0.15–0.40 Hz (HF) and analyzed using the t-test and Pearson correlation.

Results: Compared with the VLF range, normo- and hypocapnia phases were slightly but significantly lower in sVLF, while gain and coherence were not different. Hypocapnia induced significant (mostly p < 0.01) phase increases and gain decreases as well as coherence decreases in all frequency ranges. The phase and gain correlated significantly (−0.87 < r > −0.99) (p < 0.001) and inversely in all frequency ranges <0.15 Hz under both respiratory conditions. In some instances, the phase indicated disturbed autoregulation.

Conclusion: In the frequency range <0.15 Hz, the phase and gain correlate highly and linearly with high consistency. The phase, gain, and coherence were similar in sVLF and VLF ranges. The phase was slightly lower in the sVLF range than in the VLF range. Notably, the data suggest that autoregulatory failure may occur in healthy persons.

Autonomic dysfunction affects dynamic cerebral autoregulation during Valsalva maneuver: comparison between healthy and autonomic dysfunction subjects

The role of autonomic nervous system (ANS) in adapting cerebral blood flow (CBF) to arterial blood pressure (ABP) fluctuations [cerebral autoregulation (CA)] is still controversial. We aimed to study the repercussion of autonomic failure (AF) on dynamic CA during the Valsalva maneuver (VM). Eight AF subjects with familial amyloidotic polineuropahty (FAP) were compared with eight healthy controls. ABP and CBF velocity (CBFV) were measured continuously with Finapres and transcranial Doppler, respectively. Cerebrovascular response was evaluated by cerebrovascular resistance index (CVRi), critical closing pressure (CrCP), and resistance-area product (RAP) changes. Dynamic CA was derived from continuous estimates of autoregulatory index (ARI) [ARI(t)]. During phase II of VM, FAP subjects showed a more pronounced decrease in normalized CBFV (78 ± 19 and 111 ± 16%; P = 0.002), ABP (78 ± 19 and 124 ± 12%; P = 0.0003), and RAP (67 ± 17 and 89 ± 17%; P = 0.019) compared with controls. CrCP and CVRi increased similarly in both groups during strain. ARI(t) showed a biphasic variation in controls with initial increase followed by a decrease during phase II but in FAP this response was blunted (5.4 ± 3.0 and 2.0 ± 2.9; P = 0.033). Our data suggest that dynamic cerebral autoregulatory response is a time-varying phenomena during VM and that it is disturbed by autonomic dysfunction. This study also emphasizes the fact that RAP + CrCP model allowed additional insights into understanding of cerebral hemodynamics, showing a higher vasodilatory response expressed by RAP in AF and an equal CrCP response in both groups during the increased intracranial and intrathoracic pressure, while classical CVRi paradoxically suggests a cerebral vasoconstriction.

Model-based physiomarkers of cerebral hemodynamics in patients with mild cognitive impairment

In our previous studies, we have introduced model-based "functional biomarkers" or "physiomarkers" of cerebral hemodynamics that hold promise for improved diagnosis of early-stage Alzheimer's disease (AD). The advocated methodology utilizes subject-specific data-based dynamic nonlinear models of cerebral hemodynamics to compute indices (serving as possible diagnostic physiomarkers) that quantify the state of cerebral blood flow autoregulation to pressure-changes (CFAP) and cerebral CO2 vasomotor reactivity (CVMR) in each subject. The model is estimated from beat-to-beat measurements of mean arterial blood pressure, mean cerebral blood flow velocity and end-tidal CO2, which can be made reliably and non-invasively under resting conditions. In a previous study, it was found that a CVMR index quantifying the impairment in CO2 vasomotor reactivity correlates with clinical indications of early AD, offering the prospect of a potentially useful diagnostic tool. In this paper, we explore the use of the same model-based indices for patients with amnestic Mild Cognitive Impairment (MCI), a preclinical stage of AD, relative to a control subjects and clinical cognitive assessments. It was found that the model-based CVMR values were lower for MCI patients relative to the control subjects.

A novel use of transfer function estimation for early assessment of brain injury outcome

Normal blood flow in the brain as a response to pressure fluctuations is commonly referred to as cerebral autoregulation. Linear, nonparametric models of cerebral autoregulation were estimated for 77 human subjects afflicted with brain injury, with mean arterial pressure used as input, and invasively measured regional cerebral blood flow used as output. The data were continuously monitored from the beginning of subject hospital stay. Mean transfer function gain as a function of frequency was calculated for each subject over a limited time window spanning 48 h, starting postsurgery. The mean transfer function gain of the cerebral autoregulation model provided a highly accurate, statistically significant, assessment of patient outcome. Subjects were accurately grouped by outcome, with a high significance ( ) across the entire measurement spectrum (0.005-0.25 Hz). In addition, the frequency band from 0.1 to 0.25 Hz contains particularly low variance in transfer function gain. This spectral region provides a highly statistically significant ( ) assessment of brain injury patient health that is useful for outcome prediction.

Model-based quantification of cerebral hemodynamics as a physiomarker for Alzheimer's disease?

Previous studies have found that Alzheimer's disease (AD) impairs cerebral vascular function, even at early stages of the disease. This offers the prospect of a useful diagnostic method for AD, if cerebral vascular dysfunction can be quantified reliably within practical clinical constraints. We present a recently developed methodology that utilizes a data-based dynamic nonlinear closed-loop model of cerebral hemodynamics to compute "physiomarkers" quantifying the state of cerebral flow autoregulation to pressure-changes (CA) and cerebral CO2 vasomotor reactivity (CVMR) in each subject. This model is estimated from beat-to-beat measurements of mean arterial blood pressure, mean cerebral blood flow velocity and end-tidal CO2, which can be made reliably and non-invasively under resting conditions. This model may also take an open-loop form and comparisons are made with the closed-loop counterpart. The proposed model-based physiomarkers take the form of two indices that quantify the gain of the CA and CVMR processes in each subject. It was found in an initial set of clinical data that the CVMR index delineates AD patients from control subjects and, therefore, may prove useful in the improved diagnosis of early-stage AD.

Time-varying modeling of cerebral hemodynamics

The scientific and clinical importance of cerebral hemodynamics has generated considerable interest in their quantitative understanding via computational modeling. In particular, two aspects of cerebral hemodynamics, cerebral flow autoregulation (CFA) and CO2 vasomotor reactivity (CVR), have attracted much attention because they are implicated in many important clinical conditions and pathologies (orthostatic intolerance, syncope, hypertension, stroke, vascular dementia, mild cognitive impairment, Alzheimer's disease, and other neurodegenerative diseases with cerebrovascular components). Both CFA and CVR are dynamic physiological processes by which cerebral blood flow is regulated in response to fluctuations in cerebral perfusion pressure and blood CO2 tension. Several modeling studies to date have analyzed beat-to-beat hemodynamic data in order to advance our quantitative understanding of CFA-CVR dynamics. A confounding factor in these studies is the fact that the dynamics of the CFA-CVR processes appear to vary with time (i.e., changes in cerebrovascular characteristics) due to neural, endocrine, and metabolic effects. This paper seeks to address this issue by tracking the changes in linear time-invariant models obtained from short successive segments of data from ten healthy human subjects. The results suggest that systemic variations exist but have stationary statistics and, therefore, the use of time-invariant modeling yields "time-averaged models" of physiological and clinical utility.

Integrative physiological and computational approaches to understand autonomic control of cerebral autoregulation

The brain requires steady delivery of oxygen and glucose, without which neurodegeneration occurs within minutes. Thus, the ability of the cerebral vasculature to maintain relatively steady blood flow in the face of changing systemic pressure, i.e. cerebral autoregulation, is critical to neurophysiological health. Although the study of autoregulation dates to the early 20th century, only the recent availability of cerebral blood flow measures with high temporal resolution has allowed rapid, beat-by-beat measurements to explore the characteristics and mechanisms of autoregulation. These explorations have been further enhanced by the ability to apply sophisticated computational approaches that exploit the large amounts of data that can be acquired. These advances have led to unique insights. For example, recent studies have revealed characteristic time scales wherein cerebral autoregulation is most active, as well as specific regions wherein autonomic mechanisms are prepotent. However, given that effective cerebral autoregulation against pressure fluctuations results in relatively unchanging flow despite changing pressure, estimating the pressure-flow relationship can be limited by the error inherent in computational models of autoregulatory function. This review focuses on the autonomic neural control of the cerebral vasculature in health and disease from an integrative physiological perspective. It also provides a critical overview of the current analytical approaches to understand cerebral autoregulation.

Closed-loop dynamic modeling of cerebral hemodynamics

The dynamics of cerebral hemodynamics have been studied extensively because of their fundamental physiological and clinical importance. In particular, the dynamic processes of cerebral flow autoregulation (CFA) and CO2 vasomotor reactivity have attracted broad attention because of their involvement in a host of pathologies and clinical conditions (e.g., hypertension, syncope, stroke, traumatic brain injury, vascular dementia, Alzheimer's disease, mild cognitive impairment etc.). This raises the prospect of useful diagnostic methods being developed on the basis of quantitative models of cerebral hemodynamics, if cerebral vascular dysfunction can be quantified reliably from data collected within practical clinical constraints. This paper presents a modeling method that utilizes beat-to-beat measurements of mean arterial blood pressure, cerebral blood flow velocity and end-tidal CO2 (collected non-invasively under resting conditions) to quantify the dynamics of CFA and cerebral vasomotor reactivity (CVMR). The unique and novel aspect of this dynamic model is that it is nonlinear and operates in a closed-loop configuration.

Phase-amplitude investigation of spontaneous low-frequency oscillations of cerebral hemodynamics with near-infrared spectroscopy: a sleep study in human subjects

We have investigated the amplitude and phase of spontaneous low-frequency oscillations (LFOs) of the cerebral deoxy- and oxy-hemoglobin concentrations ([Hb] and [HbO]) in a human sleep study using near-infrared spectroscopy (NIRS). Amplitude and phase analysis was based on the analytic signal method, and phasor algebra was used to decompose measured [Hb] and [HbO] oscillations into cerebral blood volume (CBV) and flow velocity (CBFV) oscillations. We have found a greater phase lead of [Hb] vs. [HbO] LFOs during non-REM sleep with respect to the awake and REM sleep states (maximum increase in [Hb] phase lead: ~π/2). Furthermore, during non-REM sleep, the amplitudes of [Hb] and [HbO] LFOs are suppressed with respect to the awake and REM sleep states (maximum amplitude decrease: 87%). The associated cerebral blood volume and flow velocity oscillations are found to maintain their relative phase difference during sleep, whereas their amplitudes are attenuated during non-REM sleep. These results show the potential of phase-amplitude analysis of [Hb] and [HbO] oscillations measured by NIRS in the investigation of hemodynamics associated with cerebral physiology, activation, and pathological conditions.

Multichannel near infrared spectroscopy indicates regional variations in cerebral autoregulation in infants supported on extracorporeal membrane oxygenation

Assessing noninvasively cerebral autoregulation, the protective mechanism of the brain to maintain constant cerebral blood flow despite changes in blood pressure, is challenging. Infants on life support system (ECMO) for cardiorespiratory failure are at risk of cerebral autoregulation impairment and consequent neurological problems. We measured oxyhaemoglobin concentration (HbO(2)) by multichannel (12 channels) near-infrared spectroscopy (NIRS) in six infants during sequential changes in ECMO flow. Wavelet cross-correlation (WCC) between mean arterial pressure (MAP) and HbO(2) was used to construct a time-frequency representation of the concordance between the two signals to assess the nonstationary aspect of cerebral autoregulation and investigate regional variations. Group data showed that WCC increases with decreasing ECMO flow indicating higher concordance between MAP and HbO(2) and demonstrating loss of cerebral autoregulation at low ECMO flows. Statistically significant differences in WCC were observed between channels placed on the right and left scalp with channels on the right exhibiting higher values of WCC suggesting that the right hemisphere was more susceptible to disruption of cerebral autoregulation. Multichannel NIRS in conjunction with wavelet analysis methods can be used to assess regional variations in dynamic cerebral autoregulation with important clinical application in the management of critically ill children on life support systems.

Linear and Nonlinear Modeling of Cerebral Flow Autoregulation Using Principal Dynamic Modes

Cerebral Flow Autoregulation (CFA) is the dynamic process by which cerebral blood flow is maintained within physiologically acceptable bounds during fluctuations of cerebral perfusion pressure. The distinction is made with “static” flow autoregulation under steady-state conditions of perfusion pressure, described by the celebrated “autoregulatory curve” with a homeostatic plateau. This paper studies the dynamic CFA during changes in perfusion pressure, which attains critical clinical importance in patients with stroke, traumatic brain injury and neurodegenerative disease with a cerebrovascular component. Mathematical and computational models have been used to advance our quantitative understanding of dynamic CFA and to elucidate the underlying physiological mechanisms by analyzing the relation between beat-to-beat data of mean arterial blood pressure (viewed as input) and mean cerebral blood flow velocity(viewed as output) of a putative CFA system. Although previous studies have shown that the dynamic CFA process is nonlinear, most modeling studies to date have been linear. It has also been shown that blood CO2 tension affects the CFA process. This paper presents a nonlinear modeling methodology that includes the dynamic effects of CO2 tension (or its surrogate, end-tidal CO2) as a second input and quantifies CFA from short data-records of healthy human subjects by use of the modeling concept of Principal Dynamic Modes (PDMs). The PDMs improve the robustness of the obtained nonlinear models and facilitate their physiological interpretation. The results demonstrate the importance of including the CO2 input in the dynamic CFA study and the utility of nonlinear models under hypercapnic or hypocapnic conditions.

Linear and nonlinear modeling of cerebral flow autoregulation using principal dynamic modes

Cerebral Flow Autoregulation (CFA) is the dynamic process by which cerebral blood flow is maintained within physiologically acceptable bounds during fluctuations of cerebral perfusion pressure. The distinction is made with “static” flow autoregulation under steady-state conditions of perfusion pressure, described by the celebrated “autoregulatory curve” with a homeostatic plateau. This paper studies the dynamic CFA during changes in perfusion pressure, which attains critical clinical importance in patients with stroke, traumatic brain injury and neurodegenerative disease with a cerebrovascular component. Mathematical and computational models have been used to advance our quantitative understanding of dynamic CFA and to elucidate the underlying physiological mechanisms by analyzing the relation between beat-to-beat data of mean arterial blood pressure (viewed as input) and mean cerebral blood flow velocity(viewed as output) of a putative CFA system. Although previous studies have shown that the dynamic CFA process is nonlinear, most modeling studies to date have been linear. It has also been shown that blood CO2 tension affects the CFA process. This paper presents a nonlinear modeling methodology that includes the dynamic effects of CO2 tension (or its surrogate, end-tidal CO2) as a second input and quantifies CFA from short data-records of healthy human subjects by use of the modeling concept of Principal Dynamic Modes (PDMs). The PDMs improve the robustness of the obtained nonlinear models and facilitate their physiological interpretation. The results demonstrate the importance of including the CO2 input in the dynamic CFA study and the utility of nonlinear models under hypercapnic or hypocapnic conditions.

Oscillations in cerebral blood flow and cortical oxygenation in Alzheimer's disease

In Alzheimer's disease (AD) cerebrovascular function is at risk. Transcranial Doppler, near-infrared spectroscopy, and photoplethysmography are noninvasive methods to continuously measure changes in cerebral blood flow velocity (CBFV), cerebral cortical oxygenated hemoglobin (O(2)Hb), and blood pressure (BP). In 21 patients with mild to moderate AD and 20 age-matched controls, we investigated how oscillations in cerebral blood flow velocity (CBFV) and O(2)Hb are associated with spontaneous and induced oscillations in blood pressure (BP) at the very low (VLF = 0.05 Hz) and low frequencies (LF = 0.1 Hz). We applied spectral and transfer function analysis to quantify dynamic cerebral autoregulation and brain tissue oxygenation. In AD, cerebrovascular resistance was substantially higher (34%, AD vs. control: Δ = 0.69 (0.25) mm Hg/cm/second, p = 0.012) and the transmission of very low frequency (VLF) cerebral blood flow (CBF) oscillations into O(2)Hb differed, with increased phase lag and gain (Δ phase 0.32 [0.15] rad; Δ gain 0.049 [0.014] μmol/cm/second, p both < 0.05). The altered transfer of CBF to cortical oxygenation in AD indicates that properties of the cerebral microvasculature are changed in this disease.

Spontaneous fluctuations in cerebral blood flow regulation: contribution of PaCO2

To investigate the temporal variability of dynamic cerebral autoregulation (CA), the transient response of cerebral blood flow to rapid changes in arterial blood pressure, a new approach was introduced to improve the temporal resolution of dynamic CA assessment. Continuous bilateral recordings of cerebral blood flow velocity (transcranial Doppler, middle cerebral artery), end-tidal Pco2 (PetCO2, infrared capnograph), and blood pressure (Finapres) were obtained at rest and during breath hold in 30 young subjects (25 ± 6 yr old) and 30 older subjects (64 ± 4 yr old). Time-varying estimates of the autoregulation index [ARI( t)] were obtained with an autoregressive-moving average model with coefficients expanded by orthogonal decomposition. The temporal pattern of ARI( t) varied inversely with PetCO2, decreasing with hypercapnia. At rest, ARI( t) showed spontaneous fluctuations that were significantly different from noise and significantly correlated with spontaneous fluctuations in PetCO2 in the majority of recordings (young: 72% and old: 65%). No significant differences were found in ARI( t) due to aging. This new approach to improve the temporal resolution of dynamic CA parameters allows the identification of physiologically meaningful fluctuations in dynamic CA efficiency at rest and in response to changes in arterial CO2.

Non-linear multivariate modeling of cerebral hemodynamics with autoregressive Support Vector Machines

Cerebral blood flow (CBF) is normally controlled by myogenic and metabolic mechanisms that can be impaired in different cerebrovascular conditions. Modeling the influences of arterial blood pressure (ABP) and arterial CO(2) (PaCO(2)) on CBF is an essential step to shed light on regulatory mechanisms and extract clinically relevant parameters. Support Vector Machines (SVM) were used to model the influences of ABP and PaCO(2) on CBFV in two different conditions: baseline and during breathing of 5% CO(2) in air, in a group of 16 healthy subjects. Different model structures were considered, including innovative non-linear multivariate autoregressive (AR) models. Results showed that AR models are significantly superior to finite impulse response models and that non-linear models provide better performance for both structures. Correlation coefficients for multivariate AR non-linear models were 0.71 ± 0.11 at baseline, reaching 0.91 ± 0.06 during 5% CO(2). These results warrant further work to investigate the performance of autoregressive SVM in patients with cerebrovascular conditions.

Autonomic neural control of cerebral hemodynamics

Despite the rich innervation of the cerebral vasculature by both sympathetic and parasympathetic nerves, the role of autonomic control in cerebral circulation and, particularly, cerebral hemodynamics is not entirely clear. Previous animal studies have reported inconsistent results regarding the effects of electrical stimulation or denervation on cerebral blood flow (CBF), cerebral pressure-flow relationship, and cerebral vessel response to metabolic stimuli. Moreover, with the advance of transcranial Doppler ultrasound (TCD), which yields accurate measurements of CBF velocity (CBFV) with high time resolution, it has been found that in humans CBFV in the middle cerebral artery decreased substantially during lower body negative pressure (LBNP) and head-up tilt in the absence of systemic hypotension, which suggests the presence of cerebral vasoconstriction associated with augmented sympathetic nerve activity during orthostatic stress. These observations were based on assessing static measures of cerebral circulation, i.e., mean values of artevial blood pressure (ABP) and CBF with a low time resolution.

Dynamic cerebral autoregulation in the old using a repeated sit-stand maneuver

The aim of this study was to assess the feasibility and reproducibility of a simple and nonobtrusive repeated sit-stand maneuver to assess cerebral autoregulation (CA) in healthy old subjects >70 years. In 27 subjects aged 76 (SD 4) years, we continuously measured blood pressure using photoplethysmography and cerebral blood flow velocity in the middle cerebral artery (transcranial Doppler ultrasonography) during 5 min of sitting rest and again during repeated sit-stand maneuvers at 10 s (0.05 Hz) and 5 s (0.1 Hz) intervals. In 11 randomly selected subjects, these measurements were repeated after 3 months. Both maneuvers induced substantial periodic oscillations in pressure and flow. For example, the maneuvers at 0.05 Hz increased the power spectral density (magnitude) of blood pressure and cerebral blood flow velocity oscillations with 16.3 (mm Hg)(2) and 14.5 (cm/s)(2), respectively (p<0.001). These larger oscillations led to an increase in transfer function coherence compared with spontaneous oscillations from 0.46 to 0.60 for 0.05 Hz maneuvers and from 0.56 to 0.76 for 0.1 Hz maneuvers (p<0.01), allowing for more confident assessment of CA through transfer function phase and gain. This increased coherence was not associated with improved reproducibility however. In conclusion, we were able to investigate CA in old patients using these repeated sit-stand maneuvers, which, compared with spontaneous oscillations, produced a stronger and more clinically relevant hemodynamic challenge for CA.

Continuous estimates of dynamic cerebral autoregulation during transient hypocapnia and hypercapnia

Dynamic cerebral autoregulation (CA) is the transient response of cerebral blood flow (CBF) to rapid blood pressure changes: it improves in hypocapnia and becomes impaired during hypercapnia. Batch-processing techniques have mostly been used to measure CA, providing a single estimate for an entire recording. A new approach to increase the temporal resolution of dynamic CA parameters was applied to transient hypercapnia and hypocapnia to describe the time-varying properties of dynamic CA during these conditions. Thirty healthy subjects (mean +/- SD: 25 +/- 6 yr, 9 men) were recruited. CBF velocity was recorded in both middle cerebral arteries (MCAs) with transcranial Doppler ultrasound. Arterial blood pressure (Finapres), end-tidal CO(2) (ET(CO(2)); infrared capnograph), and a three-lead ECG were also measured at rest and during repeated breath hold and hyperventilation. A moving window autoregressive moving average model provided continuous values of the dynamic CA index [autoregulation index (ARI)] and unconstrained gain. Breath hold led to significant increase in ET(CO(2)) (+5.4 +/- 6.1 mmHg), with concomitant increase in CBF velocity in both MCAs. Continuous dynamic CA parameters showed highly significant changes (P < 0.001), with a temporal pattern reflecting a delayed dynamic response of CA to changes in arterial Pco(2) and a maximal reduction in ARI of -5.1 +/- 2.4 and -5.1 +/- 2.3 for the right and left MCA, respectively. Hyperventilation led to a marked decrease in ET(CO(2)) (-7.2 +/- 4.1 mmHg, P < 0.001). Unexpectedly, CA efficiency dropped significantly with the inception of the metronome-controlled hyperventilation, but, after approximately 30 s, the ARI increased gradually to show a maximum change of 5.7 +/- 2.9 and 5.3 +/- 3.0 for the right and left MCA, respectively (P < 0.001). These results confirm the potential of continuous estimates of dynamic CA to improve our understanding of human cerebrovascular physiology and represent a promising new approach to improve the sensitivity of clinical applications of dynamic CA modeling.