Nonlinear modeling of the dynamic effects of arterial pressure and CO2 variations on cerebral blood flow in healthy humans

Spiking Laguerre Volterra networks-predicting neuronal activity from local field potentials

Objective.Understanding the generative mechanism between local field potentials (LFP) and neuronal spiking activity is a crucial step for understanding information processing in the brain. Up to now, most approaches have relied on simply quantifying the coupling between LFP and spikes. However, very few have managed to predict the exact timing of spike occurrence based on LFP variations.Approach.Here, we fill this gap by proposing novel spiking Laguerre-Volterra network (sLVN) models to describe the dynamic LFP-spike relationship. Compared to conventional artificial neural networks, the sLVNs are interpretable models that provide explainable features of the underlying dynamics.Main results.The proposed networks were applied on extracellular microelectrode recordings of Parkinson's Disease patients during deep brain stimulation (DBS) surgery. Based on the predictability of the LFP-spike pairs, we detected three neuronal populations with unique signal characteristics and sLVN model features.Significance.These clusters were indirectly associated with motor score improvement following DBS surgery, warranting further investigation into the potential of spiking activity predictability as an intraoperative biomarker for optimal DBS lead placement.

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.

Time-Series Modeling and Forecasting of Cerebral Pressure-Flow Physiology: A Scoping Systematic Review of the Human and Animal Literature

The modeling and forecasting of cerebral pressure-flow dynamics in the time-frequency domain have promising implications for veterinary and human life sciences research, enhancing clinical care by predicting cerebral blood flow (CBF)/perfusion, nutrient delivery, and intracranial pressure (ICP)/compliance behavior in advance. Despite its potential, the literature lacks coherence regarding the optimal model type, structure, data streams, and performance. This systematic scoping review comprehensively examines the current landscape of cerebral physiological time-series modeling and forecasting. It focuses on temporally resolved cerebral pressure-flow and oxygen delivery data streams obtained from invasive/non-invasive cerebral sensors. A thorough search of databases identified 88 studies for evaluation, covering diverse cerebral physiologic signals from healthy volunteers, patients with various conditions, and animal subjects. Methodologies range from traditional statistical time-series analysis to innovative machine learning algorithms. A total of 30 studies in healthy cohorts and 23 studies in patient cohorts with traumatic brain injury (TBI) concentrated on modeling CBFv and predicting ICP, respectively. Animal studies exclusively analyzed CBF/CBFv. Of the 88 studies, 65 predominantly used traditional statistical time-series analysis, with transfer function analysis (TFA), wavelet analysis, and autoregressive (AR) models being prominent. Among machine learning algorithms, support vector machine (SVM) was widely utilized, and decision trees showed promise, especially in ICP prediction. Nonlinear models and multi-input models were prevalent, emphasizing the significance of multivariate modeling and forecasting. This review clarifies knowledge gaps and sets the stage for future research to advance cerebral physiologic signal analysis, benefiting neurocritical care applications.

Detection of Blood CO2 Influences on Cerebral Hemodynamics Using Transfer Entropy

Cerebral hemodynamics describes an important physiological system affected by components such as blood pressure, CO2 levels, and endothelial factors. Recently, novel techniques have emerged to analyse cerebral hemodynamics based on the calculation of entropies, which quantifies or describes changes in the complexity of this system when it is affected by a pathological or physiological influence. One recently described measure is transfer entropy, which allows for the determination of causality between the various components of a system in terms of their flow of information, and has shown positive results in the multivariate analysis of physiological signals. This study aims to determine whether conditional transfer entropy reflects the causality in terms of entropy generated by hypocapnia on cerebral hemodynamics. To achieve this, non-invasive signals from 28 healthy individuals who undertook a hyperventilation maneuver were analyzed using conditional transfer entropy to assess the variation in the relevance of CO2 levels on cerebral blood velocity. By employing a specific method to discretize the signals, it was possible to differentiate the influence of CO2 levels during the hyperventilation phase (22.0% and 20.3% increase for the left and right hemispheres, respectively) compared to normal breathing, which remained higher during the recovery phase (15.3% and 15.2% increase, respectively).

Can Alterations in Cerebrovascular CO2 Reactivity Be Identified Using Transfer Function Analysis without the Requirement for Carbon Dioxide Inhalation?

The present study aimed to examine the validity of a novel method to assess cerebrovascular carbon dioxide (CO₂) reactivity (CVR) that does not require a CO₂ inhalation challenge, e.g., for use in patients with respiratory disease or the elderly, etc. In twenty-one healthy participants, CVR responses to orthostatic stress (50° head-up tilt, HUT) were assessed using two methods: (1) the traditional CO₂ inhalation method, and (2) transfer function analysis (TFA) between middle cerebral artery blood velocity (MCA V) and predicted arterial partial pressure of CO₂ (PaCO₂) during spontaneous respiration. During HUT, MCA V steady-state (i.e., magnitude) and MCA V onset (i.e., time constant) responses to CO₂ inhalation were decreased (p < 0.001) and increased (p = 0.001), respectively, indicative of attenuated CVR. In contrast, TFA gain in the very low-frequency range (VLF, 0.005-0.024 Hz) was unchanged, while the TFA phase in the VLF approached zero during HUT (-0.38 ± 0.59 vs. 0.31 ± 0.78 radians, supine vs. HUT; p = 0.003), indicative of a shorter time (i.e., improved) response of CVR. These findings indicate that CVR metrics determined by TFA without a CO₂ inhalation do not track HUT-evoked reductions in CVR identified using CO₂ inhalation, suggesting that enhanced cerebral blood flow response to a change in CO₂ using CO₂ inhalation is necessary to assess CVR adequately.

Cerebral blood flow response to cardiorespiratory oscillations in healthy humans

Dynamic cerebral autoregulation (CA) characterizes the cerebral blood flow (CBF) response to abrupt changes in arterial blood pressure (ABP). CA operates at frequencies below 0.15 Hz. ABP regulation and probably CA are modified by autonomic nervous activity. We investigated the CBF response and CA dynamics to mild increase in sympathetic activity. Twelve healthy volunteers underwent oscillatory lower body negative pressure (oLBNP), which induced respiratory-related ABP oscillations at an average of 0.22 Hz. We recorded blood velocity in the internal carotid artery (ICA) by Doppler ultrasound and ABP. We quantified variability and peak wavelet power of ABP and ICA blood velocity by wavelet analysis at low frequency (LF, 0.05-0.15 Hz) and Mayer waves (0.08-0.12 Hz), respectively. CA was quantified by calculation of the wavelet synchronization gamma index for the pair ABP-ICA blood velocity in the LF and Mayer wave band. oLBNP increased ABP peak wavelet power at the Mayer wave frequency. At the Mayer wave, ABP peak wavelet power increased by >70 % from rest to oLBNP (p < 0.05), while ICA blood flow velocity peak wavelet power was unchanged, and gamma index increased (from 0.49 to 0.69, p < 0.05). At LF, variability in both ABP and ICA blood velocity and gamma index were unchanged from rest to oLBNP. Despite an increased gamma index at Mayer wave, ICA blood flow variability was unchanged during increased ABP variability. The increased synchronization during oLBNP did not cause less stable CBF or less active CA. Sympathetic activation seems to improve the mechanisms of CA.

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.

Physiological noise modeling in fMRI based on the pulsatile component of photoplethysmograph

The blood oxygenation level-dependent (BOLD) contrast mechanism allows the noninvasive monitoring of changes in deoxyhemoglobin content. As such, it is commonly used in functional magnetic resonance imaging (fMRI) to study brain activity since levels of deoxyhemoglobin are indirectly related to local neuronal activity through neurovascular coupling mechanisms. However, the BOLD signal is severely affected by physiological processes as well as motion. Due to this, several noise correction techniques have been developed to correct for the associated confounds. The present study focuses on cardiac pulsatility fMRI confounds, aiming to refine model-based techniques that utilize the photoplethysmograph (PPG) signal. Specifically, we propose a new technique based on convolution filtering, termed cardiac pulsatility model (CPM) and compare its performance with the cardiac-related RETROICOR (Card-RETROICOR), which is a technique commonly used to model fMRI fluctuations due to cardiac pulsatility. Further, we investigate whether variations in the amplitude of the PPG pulses (PPG-Amp) covary with variations in amplitude of pulse-related fMRI fluctuations, as well as with the systemic low frequency oscillations (SLFOs) component of the fMRI global signal (GS - defined as the mean signal across all gray matter voxels). Capitalizing on 3T fMRI data from the Human Connectome Project, CPM was found to explain a significantly larger fraction of the fMRI signal variance compared to Card-RETROICOR, particularly for subjects with larger heart rate variability during the scan. The amplitude of the fMRI pulse-related fluctuations did not covary with PPG-Amp; however, PPG-Amp explained significant variance in the GS that was not attributed to variations in heart rate or breathing patterns. Our results suggest that the proposed approach can model high-frequency fluctuations due to pulsation as well as low-frequency physiological fluctuations more accurately compared to model-based techniques commonly employed in fMRI studies.

Determinants of cerebral blood flow velocity change during squat-stand maneuvers

The large changes in mean arterial blood pressure (MABP) and cerebral blood flow velocity (CBFV) induced by squat-stand maneuvers (SSM) make this approach particularly suited for studying dynamic cerebral autoregulation (CA). However, the role of other systemic determinants of CBFV has not been described and could provide alternative physiological interpretations of SSM results. In 32 healthy subjects (16 female), continuous recordings of MABP (Finometer), bilateral CBFV (transcranial Doppler, MCA), end-tidal CO₂ (EtCO₂; capnography), and heart rate (HR; electrocardiogram) were performed for 5 min standing at rest, and during 15 SSM at the frequency of 0.05 Hz. A time-domain, multivariate dynamic model estimated the CBFV variance explained by different inputs, corresponding to significant contributions from MABP (P < 0.00001), EtCO₂ (P < 0.0001), and HR (P = 0.041). The autoregulation index (ARI; range 0-9) was estimated from the CBFV response to a step change in MABP. At rest, ARI values (typically 5.7) were independent of the number of model inputs, but during SSM, ARI was reduced compared with baseline (P < 0.0001), and the three input model yielded lower values for the right and left MCA (3.4 ± 1.2, 3.1 ± 1.3) when compared with the single-input MABP-CBFV model (4.1 ± 1.1, 3.9 ± 1.0; P < 0.0001). The high coherence of the MABP-CBFV transfer function at 0.05 Hz (typically 0.98) was considerably reduced (around 0.71-0.73; P < 0.0001) when the contribution of CBFV covariates was taken into account. Not taking into consideration other determinants of CBFV, in addition to MABP, could be misleading and introduce biases in physiological and clinical studies.

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.

Fluctuations in intracranial pressure can be estimated non-invasively using near-infrared spectroscopy in non-human primates

Intracranial pressure (ICP) is typically measured invasively through a sensor placed inside the brain or a needle inserted into the spinal canal, limiting the patient population on which this assessment can be performed. Currently, non-invasive methods are limited due to lack of sensitivity and thus only apply to extreme cases of increased ICP, instead of use in general clinical practice. We demonstrate a novel application for near-infrared spectroscopy (NIRS) to accurately estimate ICP changes over time. Using a non-human primate (Rhesus Macaque) model, we collected optical data while we induced ICP oscillations at multiple ICP levels obtained by manipulating the height of a fluid column connected via a catheter to the lateral ventricle. Hemodynamic responses to ICP changes were measured at the occipital pole and compared to changes detected by a conventional intraparenchymal ICP probe. We demonstrate that hemoglobin concentrations are highly correlated with induced ICP oscillations and that this response is frequency dependent. We translated the NIRS data into non-invasive ICP measurements via a fitted non-parametric transfer function, demonstrating a match in both magnitude and time alignment with an invasively measured reference. Our results demonstrate that NIRS has the potential for non-invasive ICP monitoring.

Phase dynamics of cerebral blood flow in subarachnoid haemorrhage in response to sodium nitrite infusion

Aneurysmal subarachnoid haemorrhage (SAH) is a devastating subset of stroke. One of the major determinates of morbidity is the development of delayed cerebral ischemia (DCI). Disruption of the nitric oxide (NO) pathway and consequently the control of cerebral blood flow (CBF), known as cerebral autoregulation, is believed to play a role in its pathophysiology. Through the pharmacological manipulation of in vivo NO levels using an exogenous NO donor we sought to explore this relationship. Phase synchronisation index (PSI), an expression of the interdependence between CBF and arterial blood pressure (ABP) and thus cerebral autoregulation, was calculated before and during sodium nitrite administration in 10 high-grade SAH patients acutely post-rupture. In patients that did not develop DCI, there was a significant increase in PSI around 0.1 Hz during the administration of sodium nitrite (33%; p-value 0.006). In patients that developed DCI, PSI did not change significantly. Synchronisation between ABP and CBF at 0.1 Hz has been proposed as a mechanism by which organ perfusion is maintained, during periods of physiological stress. These findings suggest that functional NO depletion plays a role in impaired cerebral autoregulation following SAH, but the development of DCI may have a distinct pathophysiological aetiology.

Assessment of dynamic cerebral autoregulation in humans: Is reproducibility dependent on blood pressure variability?

We tested the influence of blood pressure variability on the reproducibility of dynamic cerebral autoregulation (DCA) estimates. Data were analyzed from the 2^nd CARNet bootstrap initiative, where mean arterial blood pressure (MABP), cerebral blood flow velocity (CBFV) and end tidal CO2 were measured twice in 75 healthy subjects. DCA was analyzed by 14 different centers with a variety of different analysis methods. Intraclass Correlation (ICC) values increased significantly when subjects with low power spectral density MABP (PSD-MABP) values were removed from the analysis for all gain, phase and autoregulation index (ARI) parameters. Gain in the low frequency band (LF) had the highest ICC, followed by phase LF and gain in the very low frequency band. No significant differences were found between analysis methods for gain parameters, but for phase and ARI parameters, significant differences between the analysis methods were found. Alternatively, the Spearman-Brown prediction formula indicated that prolongation of the measurement duration up to 35 minutes may be needed to achieve good reproducibility for some DCA parameters. We conclude that poor DCA reproducibility (ICC<0.4) can improve to good (ICC > 0.6) values when cases with low PSD-MABP are removed, and probably also when measurement duration is increased.

Quantification of dynamic cerebral autoregulation and CO2 dynamic vasomotor reactivity impairment in essential hypertension

The study of dynamic cerebral autoregulation (DCA) in essential hypertension has received considerable attention because of its clinical importance. Several studies have examined the dynamic relationship between spontaneous beat-to-beat arterial blood pressure data and contemporaneous cerebral blood flow velocity measurements (obtained via transcranial Doppler at the middle cerebral arteries) in the form of a linear input-output model using transfer function analysis. This analysis is more reliable when the contemporaneous effects of changes in blood CO₂ tension are also taken into account, because of the significant effects of CO₂ dynamic vasomotor reactivity (DVR) upon cerebral flow. In this article, we extract such input-output predictive models from spontaneous time series hemodynamic data of 24 patients with essential hypertension and 20 normotensive control subjects under resting conditions, using the novel methodology of principal dynamic modes (PDMs) that achieves improved estimation accuracy over previous methods for relatively short and noisy data. The obtained data-based models are subsequently used to compute indexes and markers that quantify DCA and DVR in each subject or patient and therefore can be used to assess the effects of essential hypertension. These model-based DCA and DVR indexes were properly defined to capture the observed effects of DCA and VR and found to be significantly different (P < 0.05) in the hypertensive patients. We also found significant differences between patients and control subjects in the relative contribution of three PDMs to the model output prediction, a finding that offers the prospect of identifying the physiological mechanisms affected by essential hypertension when the PDMs are interpreted in terms of specific physiological mechanisms.NEW & NOTEWORTHY This article presents novel model-based methodology for obtaining diagnostic indexes of dynamic cerebral autoregulation and dynamic vasomotor reactivity in hypertension.

Modulation of simultaneously collected hemodynamic and electrophysiological functional connectivity by ketamine and midazolam

The pharmacological modulation of functional connectivity in the brain may underlie therapeutic efficacy for several neurological and psychiatric disorders. Functional magnetic resonance imaging (fMRI) provides a noninvasive method of assessing this modulation, however, the indirect nature of the blood‐oxygen level dependent signal restricts the discrimination of neural from physiological contributions. Here we followed two approaches to assess the validity of fMRI functional connectivity in developing drug biomarkers, using simultaneous electroencephalography (EEG)/fMRI in a placebo‐controlled, three‐way crossover design with ketamine and midazolam. First, we compared seven different preprocessing pipelines to determine their impact on the connectivity of common resting‐state networks. Independent components analysis (ICA)‐denoising resulted in stronger reductions in connectivity after ketamine, and weaker increases after midazolam, than pipelines employing physiological noise modelling or averaged signals from cerebrospinal fluid or white matter. This suggests that pipeline decisions should reflect a drug's unique noise structure, and if this is unknown then accepting possible signal loss when choosing extensive ICA denoising pipelines could engender more confidence in the remaining results. We then compared the temporal correlation structure of fMRI to that derived from two connectivity metrics of EEG, which provides a direct measure of neural activity. While electrophysiological estimates based on the power envelope were more closely aligned to BOLD signal connectivity than those based on phase consistency, no significant relationship between the change in electrophysiological and hemodynamic correlation structures was found, implying caution should be used when making cross‐modal comparisons of pharmacologically‐modulated functional connectivity.

Cerebrovascular Pulsatility During Rest and Exercise Reflects Hemodynamic Impairment in Stroke and Cerebral Small Vessel Disease

Although aerobic exercise is recommended as a core component of stroke rehabilitation, knowledge of acute cerebrovascular responses in patients is limited. This study tested the hypothesis that older adults with chronic stroke or cerebral small vessel disease (SVD) exhibit a greater increase in pulsatile hemodynamics during exercise compared with young and age-matched healthy adults. Middle cerebral artery blood flow velocity was acquired during 20 min of moderate intensity cycling in 51 participants from four groups (young, old, SVD and stroke). During rest, only the stroke group had a higher pulsatility index (PI) compared with the young group (1.02 ± 0.17 vs 0.83 ± 0.13; p = 0.038). During exercise, however, the SVD group exhibited a larger increase in PI (68 ± 20% relative to rest) than the young (47 ± 19%), old (45 ± 17%) and stroke (40 ± 25%) groups (p < 0.05, for each). The stress of aerobic exercise may reveal arterial dysfunction associated with latent and overt cerebrovascular disease.

Alternative representation of neural activation in multivariate models of neurovascular coupling in humans

Neural stimulation leads to increases in cerebral blood flow (CBF), but simultaneous changes in covariates, such as arterial blood pressure (BP) and [Formula: see text], rule out the use of CBF changes as a reliable marker of neurovascular coupling (NVC) integrity. Healthy subjects performed repetitive (1 Hz) passive elbow flexion with their dominant arm for 60 s. CBF velocity (CBFV) was recorded bilaterally in the middle cerebral artery with transcranial Doppler, BP with the Finometer device, and end-tidal CO2 (EtCO2) with capnography. The simultaneous effects of neural stimulation, BP, and [Formula: see text] on CBFV were expressed with a dynamic multivariate model, using BP, EtCO2, and stimulation [ s( t)] as inputs. Two versions of s( t) were considered: a gate function [ sG( t)] or an orthogonal decomposition [ sO( t)] function. A separate CBFV step response was extracted from the model for each of the three inputs, providing estimates of dynamic cerebral autoregulation [CA; autoregulation index (ARI)], CO2 reactivity [vasomotor reactivity step response (VMRSR)], and NVC [stimulus step response (STIMSR)]. In 56 subjects, 224 model implementations produced excellent predictive CBFV correlation (median r = 0.995). Model-generated sO( t), for both dominant (DH) and nondominant (NDH) hemispheres, was highly significant during stimulation (<10−5) and was correlated with the CBFV change ( r = 0.73, P = 0.0001). The sO( t) explained a greater fraction of CBFV variance (~50%) than sG( t) (44%, P = 0.002). Most CBFV step responses to the three inputs were physiologically plausible, with better agreement for the CBFV-BP step response yielding ARI values of 7.3 for both DH and NDH for sG( t), and 6.9 and 7.4 for sO( t), respectively. No differences between DH and NDH were observed for VMRSR or STIMSR. A new procedure is proposed to represent the contribution from other aspects of CBF regulation than BP and CO2 in response to sensorimotor stimulation, as a tool for integrated, noninvasive, assessment of the multiple influences of dynamic CA, CO2 reactivity, and NVC in humans.

NEW & NOTEWORTHY A new approach was proposed to identify the separate contributions of stimulation, arterial blood pressure (BP), and arterial CO2 ([Formula: see text]) to the cerebral blood flow (CBF) response observed in neurovascular coupling (NVC) studies in humans. Instead of adopting an empirical gate function to represent the stimulation input, a model-generated function is derived as part of the modeling process, providing a representation of the NVC response, independent of the contributions of BP or [Formula: see text]. This new marker of NVC, together with the model-predicted outputs for the contributions of BP, [Formula: see text] and stimulation, has considerable potential to both quantify and simultaneously integrate the separate mechanisms involved in CBF regulation, namely, cerebral autoregulation, CO2 reactivity and other contributions.

Dynamic Cerebral Autoregulation Reproducibility Is Affected by Physiological Variability

Parameters describing dynamic cerebral autoregulation (DCA) have limited reproducibility. In an international, multi-center study, we evaluated the influence of multiple analytical methods on the reproducibility of DCA. Fourteen participating centers analyzed repeated measurements from 75 healthy subjects, consisting of 5 min of spontaneous fluctuations in blood pressure and cerebral blood flow velocity signals, based on their usual methods of analysis. DCA methods were grouped into three broad categories, depending on output types: (1) transfer function analysis (TFA); (2) autoregulation index (ARI); and (3) correlation coefficient. Only TFA gain in the low frequency (LF) band showed good reproducibility in approximately half of the estimates of gain, defined as an intraclass correlation coefficient (ICC) of >0.6. None of the other DCA metrics had good reproducibility. For TFA-like and ARI-like methods, ICCs were lower than values obtained with surrogate data (p < 0.05). For TFA-like methods, ICCs were lower for the very LF band (gain 0.38 ± 0.057, phase 0.17 ± 0.13) than for LF band (gain 0.59 ± 0.078, phase 0.39 ± 0.11, p ≤ 0.001 for both gain and phase). For ARI-like methods, the mean ICC was 0.30 ± 0.12 and for the correlation methods 0.24 ± 0.23. Based on comparisons with ICC estimates obtained from surrogate data, we conclude that physiological variability or non-stationarity is likely to be the main reason for the poor reproducibility of DCA parameters.

Cerebral Autoregulation Evidenced by Synchronized Low Frequency Oscillations in Blood Pressure and Resting-State fMRI

Resting-state functional magnetic resonance imaging (rs-fMRI) is a widely used technique for mapping the brain’s functional architecture, so delineating the main sources of variance comprising the signal is crucial. Low frequency oscillations (LFO) that are not of neural origin, but which are driven by mechanisms related to cerebral autoregulation (CA), are present in the blood-oxygenation-level-dependent (BOLD) signal within the rs-fMRI frequency band. In this study we use a MR compatible device (Caretaker, Biopac) to obtain a non-invasive estimate of beat-to-beat mean arterial pressure (MAP) fluctuations concurrently with rs-fMRI at 3T. Healthy adult subjects (n = 9; 5 male) completed two 20-min rs-fMRI scans. MAP fluctuations were decomposed into different frequency scales using a discrete wavelet transform, and oscillations at approximately 0.1 Hz show a high degree of spatially structured correlations with matched frequency fMRI fluctuations. On average across subjects, MAP fluctuations at this scale of the wavelet decomposition explain ∼2.2% of matched frequency fMRI signal variance. Additionally, a simultaneous multi-slice multi-echo acquisition was used to collect 10-min rs-fMRI at three echo times at 7T in a separate group of healthy adults (n = 5; 5 male). Multiple echo times were used to estimate the R₂^∗ decay at every time point, and MAP was shown to strongly correlate with this signal, which suggests a purely BOLD (i.e., blood flow related) origin. This study demonstrates that there is a significant component of the BOLD signal that has a systemic physiological origin, and highlights the fact that not all localized BOLD signal changes necessarily reflect blood flow supporting local neural activity. Instead, these data show that a proportion of BOLD signal fluctuations in rs-fMRI are due to localized control of blood flow that is independent of local neural activity, most likely reflecting more general systemic autoregulatory processes. Thus, fMRI is a promising tool for studying flow changes associated with cerebral autoregulation with high spatial resolution.

Reproducibility of dynamic cerebral autoregulation parameters: a multi-centre, multi-method study

OBJECTIVE

Different methods to calculate dynamic cerebral autoregulation (dCA) parameters are available. However, most of these methods demonstrate poor reproducibility that limit their reliability for clinical use. Inter-centre differences in study protocols, modelling approaches and default parameter settings have all led to a lack of standardisation and comparability between studies. We evaluated reproducibility of dCA parameters by assessing systematic errors in surrogate data resulting from different modelling techniques.

APPROACH

Fourteen centres analysed 22 datasets consisting of two repeated physiological blood pressure measurements with surrogate cerebral blood flow velocity signals, generated using Tiecks curves (autoregulation index, ARI 0-9) and added noise. For reproducibility, dCA methods were grouped in three broad categories: 1. Transfer function analysis (TFA)-like output; 2. ARI-like output; 3. Correlation coefficient-like output. For all methods, reproducibility was determined by one-way intraclass correlation coefficient analysis (ICC).

MAIN RESULTS

For TFA-like methods the mean (SD; [range]) ICC gain was 0.71 (0.10; [0.49-0.86]) and 0.80 (0.17; [0.36-0.94]) for VLF and LF (p  =  0.003) respectively. For phase, ICC values were 0.53 (0.21; [0.09-0.80]) for VLF, and 0.92 (0.13; [0.44-1.00]) for LF (p  <  0.001). Finally, ICC for ARI-like methods was equal to 0.84 (0.19; [0.41-0.94]), and for correlation-like methods, ICC was 0.21 (0.21; [0.056-0.35]).

SIGNIFICANCE

When applied to realistic surrogate data, free from the additional exogenous influences of physiological variability on cerebral blood flow, most methods of dCA modelling showed ICC values considerably higher than what has been reported for physiological data. This finding suggests that the poor reproducibility reported by previous studies may be mainly due to the inherent physiological variability of cerebral blood flow regulatory mechanisms rather than related to (stationary) random noise and the signal analysis methods.

Modeling of dynamic cerebrovascular reactivity to spontaneous and externally induced CO2 fluctuations in the human brain using BOLD-fMRI

In this work, we investigate the regional characteristics of the dynamic interactions between arterial CO₂ and BOLD (dynamic cerebrovascular reactivity - dCVR) during normal breathing and hypercapnic, externally induced step CO₂ challenges. To obtain dCVR curves at each voxel, we use a custom set of basis functions based on the Laguerre and gamma basis sets. This allows us to obtain robust dCVR estimates both in larger regions of interest (ROIs), as well as in individual voxels. We also implement classification schemes to identify brain regions with similar dCVR characteristics. Our results reveal considerable variability of dCVR across different brain regions, as well as during different experimental conditions (normal breathing and hypercapnic challenges), suggesting a differential response of cerebral vasculature to spontaneous CO₂ fluctuations and larger, externally induced CO₂ changes that are possibly associated with the underlying differences in mean arterial CO₂ levels. The clustering results suggest that anatomically distinct brain regions are characterized by different dCVR curves that in some cases do not exhibit the standard, positive valued curves that have been previously reported. They also reveal a consistent set of dCVR cluster shapes for resting and forcing conditions, which exhibit different distribution patterns across brain voxels.

Investigation Of Interaction Between Physiological Signals And fMRI Dynamic Functional Connectivity Using Independent Component Analysis

The blood oxygen level dependent (BOLD) fMRI signal is influenced not only by neuronal activity but also by fluctuations in physiological signals, including respiration, arterial CO2 and heart rate/ heart rate variability (HR/HRV). Even spontaneous physiological signal fluctuations have been shown to influence the BOLD fMRI signal in a regionally specific manner. Consequently, estimates of functional connectivity between different brain regions, performed when the subject is at rest, may be confounded by the effects of physiological signal fluctuations. In addition, resting functional connectivity has been shown to vary with respect to time (dynamic functional connectivity - DFC), with the sources of this variation not fully elucidated. The effect of physiological factors on dynamic (time-varying) resting-state functional connectivity has not been studied extensively, to our knowledge. In our previous study, we investigated the effect of heart rate (HR) and end-tidal CO2 (PETCO2) on the time-varying network degree of three well-described RSNs (DMN, SMN and Visual Network) using mask-based and seed-based analysis, and we identified brain-heart interactions which were more pronounced in specific frequency bands. Here, we extend this work, by estimating DFC and its corresponding network degree for the RSNs, employing a data-driven approach to extract the RSNs (low-and high-dimensional Independent Component Analysis (ICA)), which we subsequently correlate with the characteristics of simultaneously collected physiological signals. The results confirm that physiological signals have a modulatory effect on resting-state, fMRI-based DFC.

Dynamic cerebral autoregulation is preserved during isometric handgrip and head-down tilt in healthy volunteers

In healthy humans, cerebral blood flow (CBF) is autoregulated against changes in arterial blood pressure. Spontaneous fluctuations in mean arterial pressure (MAP) and CBF can be used to assess cerebral autoregulation. We hypothesized that dynamic cerebral autoregulation is affected by changes in autonomic activity, MAP, and cardiac output (CO) induced by handgrip (HG), head‐down tilt (HDT), and their combination. In thirteen healthy volunteers, we recorded blood velocity by ultrasound in the internal carotid artery (ICA), HR, MAP and CO‐estimates from continuous finger blood pressure, and end‐tidal CO₂. Instantaneous ICA beat volume (ICABV, mL) and ICA blood flow (ICABF, mL/min) were calculated. Wavelet synchronization index γ (0–1) was calculated for the pairs: MAP–ICABF, CO–ICABF and HR–ICABV in the low (0.05–0.15 Hz; LF) and high (0.15–0.4 Hz; HF) frequency bands. ICABF did not change between experimental states. MAP and CO were increased during HG (+16% and +15%, respectively, P < 0.001) and during HDT + HG (+12% and +23%, respectively, P < 0.001). In the LF interval, median γ for the MAP–ICABF pair (baseline: 0.23 [0.12–0.28]) and the CO–ICABF pair (baseline: 0.22 [0.15–0.28]) did not change with HG, HDT, or their combination. High γ was observed for the HR–ICABV pair at the respiratory frequency, the oscillations in these variables being in inverse phase. The unaltered ICABF and the low synchronization between MAP and ICABF in the LF interval suggest intact dynamic cerebral autoregulation during HG, HDT, and their combination.

Non-linear models for the detection of impaired cerebral blood flow autoregulation

The ability to discriminate between normal and impaired dynamic cerebral autoregulation (CA), based on measurements of spontaneous fluctuations in arterial blood pressure (BP) and cerebral blood flow (CBF), has considerable clinical relevance. We studied 45 normal subjects at rest and under hypercapnia induced by breathing a mixture of carbon dioxide and air. Non-linear models with BP as input and CBF velocity (CBFV) as output, were implemented with support vector machines (SVM) using separate recordings for learning and validation. Dynamic SVM implementations used either moving average or autoregressive structures. The efficiency of dynamic CA was estimated from the model's derived CBFV response to a step change in BP as an autoregulation index for both linear and non-linear models. Non-linear models with recurrences (autoregressive) showed the best results, with CA indexes of 5.9 ± 1.5 in normocapnia, and 2.5 ± 1.2 for hypercapnia with an area under the receiver-operator curve of 0.955. The high performance achieved by non-linear SVM models to detect deterioration of dynamic CA should encourage further assessment of its applicability to clinical conditions where CA might be impaired.

Comparing Models of Spontaneous Variations, Maneuvers and Indexes to Assess Dynamic Cerebral Autoregulation

OBJECTIVE

We analyzed the performance of linear and nonlinear models to assess dynamic cerebral autoregulation (dCA) from spontaneous variations in healthy subjects and compared it with the use of two known maneuvers to abruptly change arterial blood pressure (BP): thigh cuffs and sit-to-stand.

MATERIALS AND METHODS

Cerebral blood flow velocity and BP were measured simultaneously at rest and while the maneuvers were performed in 20 healthy subjects. To analyze the spontaneous variations, we implemented two types of models using support vector machine (SVM): linear and nonlinear finite impulse response models. The classic autoregulation index (ARI) and the more recently proposed model-free ARI (mfARI) were used as measures of dCA. An ANOVA analysis was applied to compare the different methods and the coefficient of variation was calculated to evaluate their variability.

RESULTS

There are differences between indexes, but not between models and maneuvers. The mfARI index with the sit-to-stand maneuver shows the least variability.

CONCLUSIONS

Support vector machine modeling of spontaneous variation with the mfARI index could be used for the assessment of dCA as an alternative to maneuvers to introduce large BP fluctuations.

Effects of continuous positive airway pressure and isocapnic-hypoxia on cerebral autoregulation in patients with obstructive sleep apnoea

KEY POINTS

Altered cerebral autoregulation (CA) in obstructive sleep apnoea (OSA) patients may contribute to increased stroke risk in this population; the gold standard treatment for OSA is continuous positive airway pressure, which improves cerebrovascular regulation and may decrease the risk of stroke. Isocapnic-hypoxia impairs CA in healthy subjects, but it remains unknown in OSA whether impaired CA is further exacerbated by isocapnic-hypoxia and whether it is improved by treatment with continuous positive airway pressure. During normoxia, CA was altered in the more severe but not in the less severe OSA patients, while, in contrast, during isocapnic-hypoxia, CA was similar between groups and tended to improve in patients with more severe OSA compared to normoxia. From a clinical perspective, one month of continuous positive airway pressure treatment does not improve CA. From a physiological perspective, this study suggests that sympathetic overactivity may be responsible for altered CA in the more severe OSA patients.

ABSTRACT

Cerebral autoregulation (CA) impairment may contribute to the increased risk of stroke associated with obstructive sleep apnoea (OSA). It is unknown if impaired CA is further exacerbated by isocapnic-hypoxia and whether it is improved by treatment of OSA with continuous positive airway pressure (CPAP). CA was assessed during wakefulness in 53 OSA patients (50.3 ± 9.3 years) and 21 controls (49.8 ± 8.6 years) at baseline and following a minimum of 1 month of effective CPAP therapy (OSA patients, n = 40). Control participants (n = 21) performed a follow-up visit to control for time effects within OSA patients between baseline and the post-CPAP visit. Beat-by-beat middle cerebral artery blood flow velocity and mean arterial blood pressure (MBP), and breath-by-breath end-tidal partial pressure of CO₂ (P ET ,CO2) were monitored. CA was determined during normoxia and isocapnic-hypoxia using transfer function (phase and gain) and coherence analysis (including multiple and partial coherence (using MBP and P ET ,CO2 as inputs)) in the very low frequency range (0.03-0.07 Hz). OSA patients were divided into two subgroups (less severe and more severe) based upon the median respiratory disturbance index (RDI). During normoxia, the more severe OSA patients (RDI 45.9 ± 10.3) exhibited altered CA compared to controls and the less severe OSA patients (RDI 24.5 ± 5.9). In contrast, during isocapnic-hypoxia, CA was similar between groups. CPAP had no effect on CA. In conclusion, CA is altered in the more severe OSA patients during normoxia but not during isocapnic-hypoxia and CPAP treatment does not impact CA.

Methodology of Recurrent Laguerre-Volterra Network for Modeling Nonlinear Dynamic Systems

In this paper, we have introduced a general modeling approach for dynamic nonlinear systems that utilizes a variant of the simulated annealing algorithm for training the Laguerre-Volterra network (LVN) to overcome the local minima and convergence problems and employs a pruning technique to achieve sparse LVN representations with l₁ regularization. We tested this new approach with computer simulated systems and extended it to autoregressive sparse LVN (ASLVN) model structures that are suitable for input-output modeling of nonlinear systems that exhibit transitions in dynamic states, such as the Hodgkin-Huxley (H-H) equations of neuronal firing. Application of the proposed ASLVN to the H-H equations yields a more parsimonious input-output model with improved predictive capability that is amenable to more insightful physiological/biological interpretation.

Estimation of voxel-wise dynamic cerebrovascular reactivity curves from resting-state fMRI data

In this work, we investigate the linear dynamic interactions between fluctuations in arterial CO2 that occur during normal breathing, and the BOLD fMRI signal. We cast this problem within a systems-theoretic framework, where we employ functional expansions for the estimation of the impulse responses in large regions of interest, as well as in individual voxels. We also implement classification schemes in order to identify different brain regions with similar cerebrovascular reactivity characteristics. Our results reveal that it is feasible to obtain reliable estimates of cerebrovascular reactivity curves from resting-state data and that these curves exhibit considerable variability across different brain regions that may be related to the underlying anatomy.

Increased blood pressure variability upon standing up improves reproducibility of cerebral autoregulation indices

Dynamic cerebral autoregulation, that is the transient response of cerebral blood flow to changes in arterial blood pressure, is currently assessed using a variety of different time series methods and data collection protocols. In the continuing absence of a gold standard for the study of cerebral autoregulation it is unclear to what extent does the assessment depend on the choice of a computational method and protocol. We use continuous measurements of blood pressure and cerebral blood flow velocity in the middle cerebral artery from the cohorts of 18 normotensive subjects performing sit-to-stand manoeuvre. We estimate cerebral autoregulation using a wide variety of black-box approaches (including the following six autoregulation indices ARI, Mx, Sx, Dx, FIR and ARX) and compare them in the context of reproducibility and variability. For all autoregulation indices, considered here, the intra-class correlation was greater during the standing protocol, however, it was significantly greater (Fisher's Z-test) for Mx (p < 0.03), Sx (p < 0.003) and Dx (p < 0.03). In the specific case of the sit-to-stand manoeuvre, measurements taken immediately after standing up greatly improve the reproducibility of the autoregulation coefficients. This is generally coupled with an increase of the within-group spread of the estimates.

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.

Compartmental and Data-Based Modeling of Cerebral Hemodynamics: Nonlinear Analysis

OBJECTIVE

As an extension to our study comparing a putative compartmental and data-based model of linear dynamic cerebral autoregulation (CA) and CO2-vasomotor reactivity (VR), we study the CA-VR process in a nonlinear context.

METHODS

We use the concept of principal dynamic modes (PDM) in order to obtain a compact and more easily interpretable input-output model. This in silico study permits the use of input data with a dynamic range large enough to simulate the classic homeostatic CA and VR curves using a putative structural model of the regulatory control of the cerebral circulation. The PDM model obtained using theoretical and experimental data are compared.

RESULTS

It was found that the PDM model was able to reflect accurately both the simulated static CA and VR curves in the associated nonlinear functions (ANFs). Similar to experimental observations, the PDM model essentially separates the pressure-flow relationship into a linear component with fast dynamics and nonlinear components with slow dynamics. In addition, we found good qualitative agreement between the PDMs representing the dynamic theoretical and experimental CO2-flow relationship.

CONCLUSION

Under the modeling assumption and in light of other experimental findings, we hypothesize that PDMs obtained from experimental data correspond with passive fluid dynamical and active regulatory mechanisms.

SIGNIFICANCE

Both hypothesis-based and data-based modeling approaches can be combined to offer some insight into the physiological basis of PDM model obtained from human experimental data. The PDM modeling approach potentially offers a practical way to quantify the status of specific regulatory mechanisms in the CA-VR process.

Detection of Impaired Sympathetic Cerebrovascular Control Using Functional Biomarkers Based on Principal Dynamic Mode Analysis

This study sought to determine whether models of cerebrovascular function based on Laguerre-Volterra kernels that account for nonlinear cerebral blood flow (CBF) dynamics can detect the effects of functional cerebral sympathetic blockade. We retrospectively analyzed continuous beat-to-beat blood pressure, middle cerebral blood velocity, and partial-pressure of end-tidal CO₂ (P_ETCO₂) recordings from eighteen healthy individuals who were treated with either an oral dose of the α₁-adrenergic receptor blocker Prazosin or a placebo treatment. The global principal dynamic modes (PDMs) were analyzed using Laguerre-Volterra kernels to examine the nonlinear system dynamics. Our principal findings were: (1) very low frequency (<0.03 Hz) linear components of first-order kernels for BP and P_ETCO₂ are mutually coupled to CBF dynamics with the ability to separate individuals between control and blockade conditions, and (2) the gains of the nonlinear functions associated with low-pass and ≈0.03 Hz global PDMs for the BP are sensitive to sympathetic blockade. Collectively these results suggest that very low frequency global PDMs for BP may have potential utility as functional biomarkers of sympathetic neurovascular dysfunction which can occur in conditions like autonomic failure, stroke and traumatic brain injury.

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.

Identification of human sympathetic neurovascular control using multivariate wavelet decomposition analysis

The dynamic regulation of cerebral blood flow (CBF) is thought to involve myogenic and chemoreflex mechanisms, but the extent to which the sympathetic nervous system also plays a role remains debated. Here we sought to identify the role of human sympathetic neurovascular control by examining cerebral pressure-flow relations using linear transfer function analysis and multivariate wavelet decomposition analysis that explicitly accounts for the confounding effects of dynamic end-tidal Pco2 (PetCO2) fluctuations. In 18 healthy participants randomly assigned to the α1-adrenergic blockade group ( n = 9; oral Prazosin, 0.05 mg/kg) or the placebo group ( n = 9), we recorded blood pressure, middle cerebral blood flow velocity, and breath-to-breath PetCO2. Analyses showed that the placebo administration did not alter wavelet phase synchronization index (PSI) values, whereas sympathetic blockade increased PSI for frequency components ≤0.03 Hz. Additionally, three-way interaction effects were found for PSI change scores, indicating that the treatment response varied as a function of frequency and whether PSI values were PetCO2 corrected. In contrast, sympathetic blockade did not affect any linear transfer function parameters. These data show that very-low-frequency CBF dynamics have a composite origin involving, not only nonlinear and nonstationary interactions between BP and PetCO2, but also frequency-dependent interplay with the sympathetic nervous system.

Dynamic cerebral autoregulation in young athletes following concussion

The purpose of this study was to examine cerebral autoregulation (CA) in young athletes experiencing concussion. The subjects were monitored and repeatedly tested 72 hours, 2 weeks and 1 month post-injury. Mean arterial blood pressure (MABP), end-tidal partial pressure of carbon dioxide (PETCO2) and cerebral blood flow velocity (CBFV) in the middle and posterior cerebral arteries were monitored during mental activation paradigms. In order to characterize CA we employed autoregressive models with exogenous inputs (ARX) and impulse response models based on the Laguerre expansion technique (LET). By extracting gain and phase estimates from the obtained models we were able to detect disruptions in CA 72 hours following concussion and a slow recovery within a time period of one month.

Cerebral blood flow and autoregulation: current measurement techniques and prospects for noninvasive optical methods

Cerebral blood flow (CBF) and cerebral autoregulation (CA) are critically important to maintain proper brain perfusion and supply the brain with the necessary oxygen and energy substrates. Adequate brain perfusion is required to support normal brain function, to achieve successful aging, and to navigate acute and chronic medical conditions. We review the general principles of CBF measurements and the current techniques to measure CBF based on direct intravascular measurements, nuclear medicine, X-ray imaging, magnetic resonance imaging, ultrasound techniques, thermal diffusion, and optical methods. We also review techniques for arterial blood pressure measurements as well as theoretical and experimental methods for the assessment of CA, including recent approaches based on optical techniques. The assessment of cerebral perfusion in the clinical practice is also presented. The comprehensive description of principles, methods, and clinical requirements of CBF and CA measurements highlights the potentially important role that noninvasive optical methods can play in the assessment of neurovascular health. In fact, optical techniques have the ability to provide a noninvasive, quantitative, and continuous monitor of CBF and autoregulation.

Nonlinear effects of respiration on the crosstalk between cardiovascular and cerebrovascular control systems

Cardiovascular and cerebrovascular regulatory systems are vital control mechanisms responsible for guaranteeing homeostasis and are affected by respiration. This work proposes the investigation of cardiovascular and cerebrovascular control systems and the nonlinear influences of respiration on both regulations through joint symbolic analysis (JSA), conditioned or unconditioned on respiration. Interactions between cardiovascular and cerebrovascular regulatory systems were evaluated as well by performing correlation analysis between JSA indexes describing the two control systems. Heart period, systolic and mean arterial pressure, mean cerebral blood flow velocity and respiration were acquired on a beat-to-beat basis in 13 subjects experiencing recurrent syncope episodes (SYNC) and 13 healthy individuals (non-SYNC) in supine resting condition and during head-up tilt test at 60° (TILT). Results showed that JSA distinguished conditions and groups, whereas time domain parameters detected only the effect of TILT. Respiration affected cardiovascular and cerebrovascular regulatory systems in a nonlinear way and was able to modulate the interactions between the two control systems with different outcome in non-SYNC and SYNC groups, thus suggesting that the analysis of the impact of respiration on cardiovascular and cerebrovascular regulatory systems might improve our understanding of the mechanisms underpinning the development of postural-related syncope.

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.

Spontaneous physiological variability modulates dynamic functional connectivity in resting-state functional magnetic resonance imaging

It is well known that the blood oxygen level-dependent (BOLD) signal measured by functional magnetic resonance imaging (fMRI) is influenced-in addition to neuronal activity-by fluctuations in physiological signals, including arterial CO2, respiration and heart rate/heart rate variability (HR/HRV). Even spontaneous fluctuations of the aforementioned physiological signals have been shown to influence the BOLD fMRI signal in a regionally specific manner. Related to this, estimates of functional connectivity between different brain regions, performed when the subject is at rest, may be confounded by the effects of physiological signal fluctuations. Moreover, resting functional connectivity has been shown to vary with respect to time (dynamic functional connectivity), with the sources of this variation not fully elucidated. In this context, we examine the relation between dynamic functional connectivity patterns and the time-varying properties of simultaneously recorded physiological signals (end-tidal CO2 and HR/HRV) using resting-state fMRI measurements from 12 healthy subjects. The results reveal a modulatory effect of the aforementioned physiological signals on the dynamic resting functional connectivity patterns for a number of resting-state networks (default mode network, somatosensory, visual). By using discrete wavelet decomposition, we also show that these modulation effects are more pronounced in specific frequency bands.

Lack of correlation between cerebral vasomotor reactivity and dynamic cerebral autoregulation during stepwise increases in inspired CO2 concentration

Cerebral vasomotor reactivity (CVMR) and dynamic cerebral autoregulation (CA) are measured extensively in clinical and research studies. However, the relationship between these measurements of cerebrovascular function is not well understood. In this study, we measured changes in cerebral blood flow velocity (CBFV) and arterial blood pressure (BP) in response to stepwise increases in inspired CO2 concentrations of 3 and 6% to assess CVMR and dynamic CA in 13 healthy young adults [2 women, 32 ± 9 (SD) yr]. CVMR was assessed as percentage changes in CBFV (CVMRCBFV) or cerebrovascular conductance index (CVCi, CVMRCVCi) in response to hypercapnia. Dynamic CA was estimated by performing transfer function analysis between spontaneous oscillations in BP and CBFV. Steady-state CBFV and CVCi both increased exponentially during hypercapnia; CVMRCBFV and CVMRCVCi were greater at 6% (3.85 ± 0.90 and 2.45 ± 0.79%/mmHg) than at 3% CO2 (2.09 ± 1.47 and 0.21 ± 1.56%/mmHg, P = 0.009 and 0.005, respectively). Furthermore, CVMRCBFV was greater than CVMRCVCi during either 3 or 6% CO2 (P = 0.017 and P < 0.001, respectively). Transfer function gain and coherence increased in the very low frequency range (0.02-0.07 Hz), and phase decreased in the low-frequency range (0.07-0.20 Hz) when breathing 6%, but not 3% CO2 There were no correlations between the measurements of CVMR and dynamic CA. These findings demonstrated influences of inspired CO2 concentrations on assessment of CVMR and dynamic CA. The lack of correlation between CVMR and dynamic CA suggests that cerebrovascular responses to changes in arterial CO2 and BP are mediated by distinct regulatory mechanisms.

GPU technology as a platform for accelerating physiological systems modeling based on Laguerre-Volterra networks

The use of a GPGPU programming paradigm (running CUDA-enabled algorithms on GPU cards) in biomedical engineering and biology-related applications have shown promising results. GPU acceleration can be used to speedup computation-intensive models, such as the mathematical modeling of biological systems, which often requires the use of nonlinear modeling approaches with a large number of free parameters. In this context, we developed a CUDA-enabled version of a model which implements a nonlinear identification approach that combines basis expansions and polynomial-type networks, termed Laguerre-Volterra networks and can be used in diverse biological applications. The proposed software implementation uses the GPGPU programming paradigm to take advantage of the inherent parallel characteristics of the aforementioned modeling approach to execute the calculations on the GPU card of the host computer system. The initial results of the GPU-based model presented in this work, show performance improvements over the original MATLAB model.

Assessing cerebral blood flow control from variability in blood pressure and arterial CO2 levels

Blood flow to the brain is controlled by a number of physiological mechanisms that respond to changes in arterial blood pressure, arterial CO2 levels and many other factors. Assessing the integrity of this control system is a major challenge. We report on repeatability of measures based on single and multiple input models during spontaneous and enhanced fluctuations in blood pressure.

Hemodynamic variability and cerebrovascular control after transient cerebral ischemia

We investigated if hemodynamic variability, cerebral blood flow (CBF) regulation, and their interrelationships differ between patients with transient ischemic attack (TIA) and controls. We recorded blood pressure (BP) and bilateral middle cerebral artery flow velocity (MCAv) in a cohort of TIA patients (n = 17), and age-matched controls (n = 15). Spontaneous fluctuations in BP and MCAv were characterized by spectral power analysis, and CBF regulation was assessed by wavelet phase synchronization analysis in the very low- (0.02–0.07 Hz), low- (0.07–0.20 Hz), and high-frequency (0.20–0.40 Hz) ranges. Furthermore, cerebrovascular CO₂ reactivity was assessed as a second metric of CBF regulation by inducing hypercapnia with 8% CO₂ inhalation followed by hyperventilation driven hypocapnia. We found that TIA was associated with higher BP power (group effect, P < 0.05), but not MCAv power (P = 0.11). CBF regulation (assessed by wavelet phase synchronization and CO₂ reactivity) was intact in patients (all P ≥ 0.075) across both hemispheres (all P ≥ 0.51). Pooled data (controls and affected hemisphere of patients) showed that BP and MCAv power were positively correlated at all frequency ranges (R² = 0.20–0.80, all P < 0.01). Furthermore, LF phase synchronization index was a significant determinant of MCAv power (P < 0.05), while VLF and HF phase synchronization index, and TIA were not (all P ≥ 0.50). These results indicate that CBF stability and control is maintained in TIA patients, but BPV is markedly elevated. BPV attenuation may be an important therapeutic strategy for enhancing secondary stroke prevention in patients who suffer a TIA.

Compartmental and Data-Based Modeling of Cerebral Hemodynamics: Linear Analysis

Compartmental and data-based modeling of cerebral hemodynamics are alternative approaches that utilize distinct model forms and have been employed in the quantitative study of cerebral hemodynamics. This paper examines the relation between a compartmental equivalent-circuit and a data-based input-output model of dynamic cerebral autoregulation (DCA) and CO2-vasomotor reactivity (DVR). The compartmental model is constructed as an equivalent-circuit utilizing putative first principles and previously proposed hypothesis-based models. The linear input-output dynamics of this compartmental model are compared with data-based estimates of the DCA-DVR process. This comparative study indicates that there are some qualitative similarities between the two-input compartmental model and experimental results.

Cerebrovascular effects of the thigh cuff maneuver

Arterial hypotension can be induced by sudden release of inflated thigh cuffs (THC), but its effects on the cerebral circulation have not been fully described. In nine healthy subjects [aged 59 (9) yr], bilateral cerebral blood flow velocity (CBFV) was recorded in the middle cerebral artery (MCA), noninvasive arterial blood pressure (BP) in the finger, and end-tidal CO2 (ETCO2) with nasal capnography. Three THC maneuvers were performed in each subject with cuff inflation 20 mmHg above systolic BP for 3 min before release. Beat-to-beat values were extracted for mean CBFV, BP, ETCO2 , critical closing pressure (CrCP), resistance-area product (RAP), and heart rate (HR). Time-varying estimates of the autoregulation index [ARI(t)] were also obtained using an autoregressive-moving average model. Coherent averages synchronized by the instant of cuff release showed significant drops in mean BP, CBFV, and RAP with rapid return of CBFV to baseline. HR, ETCO2 , and ARI(t) were transiently increased, but CrCP remained relatively constant. Mean values of ARI(t) for the 30 s following cuff release were not significantly different from the classical ARI [right MCA 5.9 (1.1) vs. 5.1 (1.6); left MCA 5.5 (1.4) vs. 4.9 (1.7)]. HR was strongly correlated with the ARI(t) peak after THC release (in 17/22 and 21/24 recordings), and ETCO2 was correlated with the subsequent drop in ARI(t) (19/22 and 20/24 recordings). These results suggest a complex cerebral autoregulatory response to the THC maneuver, dominated by myogenic mechanisms and influenced by concurrent changes in ETCO2 and possible involvement of the autonomic nervous system and baroreflex.

Assessment of cerebrovascular reactivity during resting state breathing and its correlation with cognitive function in hypertension

BACKGROUND

Hypertension is associated with cognitive deficits, particularly executive function, and decreased cerebral microvascular responsiveness to CO2 (CO2 vasoreactivity). The relation between CO2 vasoreactivity and executive function is not known. Protocols to assess CO2 vasoreactivity are cumbersome and require inhaling a CO2-enriched gas. We explored the ability to measure CO2 vasoreactivity using end-tidal CO2 fluctuations during normal breathing and the association of this measure with cognitive function in hypertension.

METHODS

Executive function (Trail-Making Test parts A/B), memory, attention and blood flow velocity (BFV) in the middle cerebral artery using transcranial Doppler were measured in hypertensive subjects who were tapered off their treatment for 3 weeks. BFV was measured while sitting and normally breathing for 5 min, followed by breathing 5% CO2 gas and hyperventilation for 2 min each. We calculated CO2 vasoreactivity as the rate of BFV change from hypoventilation to hyperventilation, and as a model-derived measure using the normal breathing data. The latter was derived using nonlinear principal dynamic modes (PDM), which modelled the dynamic effect of fluctuations in end-tidal CO2 and blood pressure upon BFV during normal room-air respiration. Multiple regression analyses were used to correlate cerebral hemodynamics with cognitive measures.

RESULTS

Data were collected from 41 individuals with hypertension (mean age 71 years, 24% African Americans, 61% women, off antihypertensive therapy). Lower CO2 vasoreactivity was associated with a worse executive function test score using both calculation methods: p value using the hyper/hypoventilation data was 0.04 and from the PDM analysis was 0.009. PDM calculations showed a stronger correlation with executive function (0.41 vs. 0.21 using the hyper/hypoventilation data). There were no associations with memory or attention measures. There was a weak but statistically significant correlation between the two calculation methods of CO2 vasoreactivity (R(2) = 14%, p = 0.02).

CONCLUSION

This study suggests that the decrease in CO2 vasoreactivity in hypertension is associated with lower executive function. This may offer new insight into the vascular underpinning of cognitive decline in hypertension. We demonstrate that calculating CO2 vasoreactivity is possible during normal breathing. If replicated in future studies, this may offer a more convenient clinical way to assess CO2 vasoreactivity in hypertension and cognitive disorders.

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.