Individual Patient Data Meta-Analysis of Dynamic Cerebral Autoregulation and Functional Outcome After Ischemic Stroke.

BACKGROUND: The relationship between dynamic cerebral autoregulation (dCA) and functional outcome after acute ischemic stroke (AIS) is unclear. Previous studies are limited by small sample sizes and heterogeneity. METHODS: We performed a 1-stage individual patient data meta-analysis to investigate associations between dCA and functional outcome after AIS. Participating centers were identified through a systematic search of the literature and direct invitation. We included centers with dCA data within 1 year of AIS in adults aged over 18 years, excluding intracerebral or subarachnoid hemorrhage. Data were obtained on phase, gain, coherence, and autoregulation index derived from transfer function analysis at low-frequency and very low-frequency bands. Cerebral blood velocity, arterial pressure, end-tidal carbon dioxide, heart rate, stroke severity and sub-type, and comorbidities were collected where available. Data were grouped into 4 time points after AIS: <24 hours, 24 to 72 hours, 4 to 7 days, and >3 months. The modified Rankin Scale assessed functional outcome at 3 months. Modified Rankin Scale was analyzed as both dichotomized (0 to 2 versus 3 to 6) and ordinal (modified Rankin Scale scores, 0-6) outcomes. Univariable and multivariable analyses were conducted to identify significant relationships between dCA parameters, comorbidities, and outcomes, for each time point using generalized linear (dichotomized outcome), or cumulative link (ordinal outcome) mixed models. The participating center was modeled as a random intercept to generate odds ratios with 95% CIs. RESULTS: The sample included 384 individuals (35% women) from 7 centers, aged 66.3±13.7 years, with predominantly nonlacunar stroke (n=348, 69%). In the affected hemisphere, higher phase at very low-frequency predicted better outcome (dichotomized modified Rankin Scale) at <24 (crude odds ratios, 2.17 [95% CI, 1.47-3.19]; P<0.001) hours, 24-72 (crude odds ratios, 1.95 [95% CI, 1.21-3.13]; P=0.006) hours, and phase at low-frequency predicted outcome at 3 (crude odds ratios, 3.03 [95% CI, 1.10-8.33]; P=0.032) months. These results remained after covariate adjustment. CONCLUSIONS: Greater transfer function analysis-derived phase was associated with improved functional outcome at 3 months after AIS. dCA parameters in the early phase of AIS may help to predict functional outcome.

Spatiotemporal patterns of sleep spindle activity in human anterior thalamus and cortex.

Sleep spindles (8 - 16 Hz) are transient electrophysiological events during non-rapid eye movement sleep. While sleep spindles are routinely observed in the cortex using scalp electroencephalography (EEG), recordings of their thalamic counterparts have not been widely studied in humans. Based on a few existing studies, it has been hypothesized that spindles occur as largely local phenomena. We investigated intra-thalamic and thalamocortical spindle co-occurrence, which may underlie thalamocortical communication. We obtained scalp EEG and thalamic recordings from 7 patients that received bilateral deep brain stimulation (DBS) electrodes to the anterior thalamus for the treatment of drug resistant focal epilepsy. Spindles were categorized into subtypes based on their main frequency (i.e., slow (10±2 Hz) or fast (14±2 Hz)) and their level of thalamic involvement (spanning one channel, or spreading uni- or bilaterally within the thalamus). For the first time, we contrasted observed spindle patterns with permuted data to estimate random spindle co-occurrence. We found that multichannel spindle patterns were systematically coordinated at the thalamic and thalamocortical level. Importantly, distinct topographical patterns of thalamocortical spindle overlap were associated with slow and fast subtypes of spindles. These observations provide further evidence for coordinated spindle activity in thalamocortical networks.

Hemodilution Combined With Hypercapnia Impairs Cerebral Autoregulation During Normothermic Cardiopulmonary Bypass.

OBJECTIVE: To investigate the influence of hemodilution and arterial pCO2 on cerebral autoregulation and cerebral vascular CO2 reactivity. DESIGN: Prospective interventional study. SETTING: University hospital-based single-center study. PARTICIPANTS: Forty adult patients undergoing elective cardiac surgery using normothermic cardiopulmonary bypass. INTERVENTIONS: Blood pressure variations induced by 6/minute metronome-triggered breathing (baseline) and cyclic 6/min changes of indexed pump flow at 3 levels of arterial pCO2. MEASUREMENTS AND MAIN RESULTS: Based on median hematocrit on bypass, patients were assigned to either a group of a hematocrit ≥28% or<28%. The autoregulation index was calculated from cerebral blood flow velocity and mean arterial blood pressure using transfer function analysis. Cerebral vascular CO2 reactivity was calculated using cerebral tissue oximetry data. Cerebral autoregulation as reflected by autoregulation index (baseline 7.5) was significantly affected by arterial pCO2 (median autoregulation index amounted to 5.7, 4.8, and 2.8 for arterial pCO2 of 4.0, 5.3, and 6.6 kPa, p≤0.002) respectively. Hemodilution resulted in a decreased autoregulation index; however, during hypocapnia and normocapnia, there were no significant differences between the two hematocrit groups. Moreover, the autoregulation index was lowest during hypercapnia when hematocrit was<28% (autoregulation index 3.3 versus 2.6 for hematocrit ≥28% and<28%, respectively, p = 0.014). Cerebral vascular CO2 reactivity during hypocapnia was significantly lower when perioperative hematocrit was<28% (p = 0.018). CONCLUSIONS: Hemodilution down to a hematocrit of<28% combined with hypercapnia negatively affects dynamic cerebral autoregulation, which underlines the importance of tight control of both hematocrit and paCO2 during CPB.

A lumped parameter model of cerebral blood flow control combining cerebral autoregulation and neurovascular coupling.

Cerebral blood flow regulation is based on a variety of different mechanisms, of which the relative regulatory role remains largely unknown. The cerebral regulatory system expresses two regulatory properties: cerebral autoregulation and neurovascular coupling. Since partly the same mechanisms play a role in cerebral autoregulation and neurovascular coupling, this study aimed to develop a physiologically based mathematical model of cerebral blood flow regulation combining these properties. A lumped parameter model of the P2 segment of the posterior cerebral artery and its distal vessels was constructed. Blood flow regulation is exerted at the arteriolar level by vascular smooth muscle and implements myogenic, shear stress based, neurogenic, and metabolic mechanisms. In eight healthy subjects, cerebral autoregulation and neurovascular coupling were challenged by squat-stand maneuvers and visual stimulation using a checkerboard pattern, respectively. Cerebral blood flow velocity was measured using transcranial Doppler, whereas blood pressure was measured by finger volume clamping. In seven subjects, the model proposed fits autoregulation and neurovascular coupling measurement data well. Myogenic regulation is found to dominate the autoregulatory response. Neurogenic regulation, although only implemented as a first-order mechanism, describes neurovascular coupling responses to a great extent. It is concluded that our single, integrated model of cerebral blood flow control may be used to identify the main mechanisms affecting cerebral blood flow regulation in individual subjects.

Thalamocortical coherence and causality in different sleep stages using deep brain stimulation recordings.

Previous research has shown an interplay between the thalamus and cerebral cortex during NREM sleep in humans, however the directionality of the thalamocortical synchronization is as yet unknown. In this study thalamocortical connectivity during different NREM sleep stages using sleep scalp electroencephalograms and local field potentials from the left and right anterior thalamus was measured in three epilepsy patients implanted with deep brain stimulation electrodes. Connectivity was assessed as debiased weighted phase lag index and granger causality between the thalamus and cortex for the NREM sleep stages N1, N2 and N3. Results showed connectivity was most prominently directed from cortex to thalamus. Moreover, connectivity varied in strength between the different sleep stages, but barely in direction or frequency. These results imply relatively stable thalamocortical connectivity during NREM sleep directed from the cortex to the thalamus.

An Automated Algorithm for Optic Nerve Sheath Diameter Assessment from B-mode Ultrasound Images.

BACKGROUND AND PURPOSE: The optic nerve sheath diameter (ONSD) is a promising surrogate marker for the detection of raised intracranial pressure (ICP). However, inconsistencies in manual ONSD assessment are thought to affect ONSD and the corresponding ONSD cutoff values for the diagnosis of elevated ICP, hereby hampering the full potential of ONSD. In this study, we developed an image intensity-invariant algorithm to automatically estimate ONSD from B-mode ultrasound images at multiple depths. METHODS: The outcomes of the algorithm were validated against manual ONSD measurements by two human experts. Each expert analyzed the images twice (M1 and M2) in unknown order. RESULTS: The algorithm proved capable of segmenting the ONSD in 39 of 42 images, hereby showing mean differences of -.08 ± .45 and -.05 ± .41 mm compared to averaged ONSD values (M1 + M2/2) of Operator 1 and Operator 2, respectively, whereas the mean difference between the two experts was .03 ± .26 mm. Moreover, differences between algorithm-derived and expert-derived ONSD values were found to be much smaller than the 1 mm difference that is expected between patients with normal and elevated ICP, making it likely that our algorithm can distinguish between these patient groups. CONCLUSIONS: Our algorithm has the potential to improve the accuracy of ONSD as a surrogate marker for elevated ICP because it has no intrinsic variability. However, future research should be performed to validate if the algorithm does indeed result in more accurate noninvasive ICP predictions.

Optic nerve sheath diameter assessment by neurosonology: A review of methodologic discrepancies.

BACKGROUND AND PURPOSE: Reported cutoff values of the optic nerve sheath diameter (ONSD) for the diagnosis of elevated intracranial pressure (ICP) are inconsistent. This hampers ONSD as a possible noninvasive bedside monitoring tool for ICP. Because the influence of methodological differences on variations in cutoff values is unknown, we performed a narrative review to identify discrepancies in ONSD assessment methodologies and to investigate their effect on reported ONSD values. METHODS: We used a structured and quantitative approach in which each ONSD methodology found in the reviewed articles was categorized based on the characteristic appearance of the ultrasound images and ultrasound marker placement. Subsequently, we investigated the influence of the different methodologies on ONSD values by organizing the ONSDs with respect to these categories. RESULTS: In a total of 63 eligible articles, we could determine the applied ONSD assessment methodology. Reported ultrasound images either showed the optic nerve and its sheath as a dark region with hyperechoic striped band at its edges or as a single dark region surrounded by lighter retrobulbar fat. Four different ultrasound marker positions were used to delineate the optic nerve sheath, which resulted in different ONSD values and more importantly, different sensitivities to changes in ICP. CONCLUSIONS: Based on our observations, we recommend to place ultrasound markers at the outer edges of the hyperechoic striped bands or at the transitions from the single dark region to the hyperechoic retrobulbar fat because these locations yielded the highest sensitivity of ONSD measurements for increased ICP.

Deep Brain Stimulation in Epilepsy: A Role for Modulation of the Mammillothalamic Tract in Seizure Control?

BACKGROUND: Deep brain stimulation of the anterior nucleus of the thalamus (ANT-DBS) can improve seizure control for patients with drug-resistant epilepsy (DRE). Yet, one cannot overlook the high discrepancy in efficacy among patients, possibly resulting from differences in stimulation site. OBJECTIVE: To test the hypothesis that stimulation at the junction of the ANT and mammillothalamic tract (ANT-MTT junction) increases seizure control. METHODS: The relationship between seizure control and the location of the active contacts to the ANT-MTT junction was investigated in 20 patients treated with ANT-DBS for DRE. Coordinates and Euclidean distance of the active contacts relative to the ANT-MTT junction were calculated and related to seizure control. Stimulation sites were mapped by modelling the volume of tissue activation (VTA) and generating stimulation heat maps. RESULTS: After 1 yr of stimulation, patients had a median 46% reduction in total seizure frequency, 50% were responders, and 20% of patients were seizure-free. The Euclidean distance of the active contacts to the ANT-MTT junction correlates to change in seizure frequency (r2 = 0.24, P = .01) and is ∼30% smaller (P = .015) in responders than in non-responders. VTA models and stimulation heat maps indicate a hot-spot at the ANT-MTT junction for responders, whereas non-responders had no evident hot-spot. CONCLUSION: Stimulation at the ANT-MTT junction correlates to increased seizure control. Our findings suggest a relationship between the stimulation site and therapy response in ANT-DBS for epilepsy with a potential role for the MTT. DBS directed at white matter merits further exploration for the treatment of epilepsy.

Cross-database evaluation of EEG based epileptic seizures detection driven by adaptive median feature baseline correction.

OBJECTIVE: In long-term electroencephalogram (EEG) signals, automated classification of epileptic seizures is desirable in diagnosing epilepsy patients, as it otherwise depends on visual inspection. To the best of the author's knowledge, existing studies have validated their algorithms using cross-validation on the same database and less number of attempts have been made to extend their work on other databases to test the generalization capability of the developed algorithms. In this study, we present the algorithm for cross-database evaluation for classification of epileptic seizures using five EEG databases collected from different centers. The cross-database framework helps when sufficient epileptic seizures EEG data are not available to build automated seizure detection model. METHODS: Two features, namely successive decomposition index and matrix determinant were extracted at a segmentation length of 4 s (50% overlap). Then, adaptive median feature baseline correction (AM-FBC) was applied to overcome the inter-patient and inter-database variation in the feature distribution. The classification was performed using a support vector machine classifier with leave-one-database-out cross-validation. Different classification scenarios were considered using AM-FBC, smoothing of the train and test data, and post-processing of the classifier output. RESULTS: Simulation results revealed the highest area under the curve-sensitivity-specificity-false detections (per hour) of 1-1-1-0.15, 0.89-0.99-0.82-2.5, 0.99-0.73-1-1, 0.95-0.97-0.85-1.7, 0.99-0.99-0.92-1.1 using the Ramaiah Medical College and Hospitals, Children's Hospital Boston-Massachusetts Institute of Technology, Temple University Hospital, Maastricht University Medical Centre, and University of Bonn databases respectively. CONCLUSIONS: We observe that the AM-FBC plays a significant role in improving seizure detection results by overcoming inter-database variation of feature distribution. SIGNIFICANCE: To the best of the author's knowledge, this is the first study reporting on the cross-database evaluation of classification of epileptic seizures and proven to be better generalization capability when evaluated using five databases and can contribute to accurate and robust detection of epileptic seizures in real-time.

Influence of Anesthesia and Clinical Variables on the Firing Rate, Coefficient of Variation and Multi-Unit Activity of the Subthalamic Nucleus in Patients with Parkinson's Disease.

BACKGROUND: Microelectrode recordings (MER) are used to optimize lead placement during subthalamic nucleus deep brain stimulation (STN-DBS). To obtain reliable MER, surgery is usually performed while patients are awake. Procedural sedation and analgesia (PSA) is often desirable to improve patient comfort, anxiolysis and pain relief. The effect of these agents on MER are largely unknown. The objective of this study was to determine the effects of commonly used PSA agents, dexmedetomidine, clonidine and remifentanil and patient characteristics on MER during DBS surgery. METHODS: Data from 78 patients with Parkinson's disease (PD) who underwent STN-DBS surgery were retrospectively reviewed. The procedures were performed under local anesthesia or under PSA with dexmedetomidine, clonidine or remifentanil. In total, 4082 sites with multi-unit activity (MUA) and 588 with single units were acquired. Single unit firing rates and coefficient of variation (CV), and MUA total power were compared between patient groups. RESULTS: We observed a significant reduction in MUA, an increase of the CV and a trend for reduced firing rate by dexmedetomidine. The effect of dexmedetomidine was dose-dependent for all measures. Remifentanil had no effect on the firing rate but was associated with a significant increase in CV and a decrease in MUA. Clonidine showed no significant effect on firing rate, CV or MUA. In addition to anesthetic effects, MUA and CV were also influenced by patient-dependent variables. CONCLUSION: Our results showed that PSA influenced neuronal properties in the STN and the dexmedetomidine (DEX) effect was dose-dependent. In addition, patient-dependent characteristics also influenced MER.

Combining Gamma With Alpha and Beta Power Modulation for Enhanced Cortical Mapping in Patients With Focal Epilepsy.

About one third of patients with epilepsy have seizures refractory to the medical treatment. Electrical stimulation mapping (ESM) is the gold standard for the identification of "eloquent" areas prior to resection of epileptogenic tissue. However, it is time-consuming and may cause undesired side effects. Broadband gamma activity (55-200 Hz) recorded with extraoperative electrocorticography (ECoG) during cognitive tasks may be an alternative to ESM but until now has not proven of definitive clinical value. Considering their role in cognition, the alpha (8-12 Hz) and beta (15-25 Hz) bands could further improve the identification of eloquent cortex. We compared gamma, alpha and beta activity, and their combinations for the identification of eloquent cortical areas defined by ESM. Ten patients with intractable focal epilepsy (age: 35.9 ± 9.1 years, range: 22-48, 8 females, 9 right handed) participated in a delayed-match-to-sample task, where syllable sounds were compared to visually presented letters. We used a generalized linear model (GLM) approach to find the optimal weighting of each band for predicting ESM-defined categories and estimated the diagnostic ability by calculating the area under the receiver operating characteristic (ROC) curve. Gamma activity increased more in eloquent than in non-eloquent areas, whereas alpha and beta power decreased more in eloquent areas. Diagnostic ability of each band was close to 0.7 for all bands but depended on multiple factors including the time period of the cognitive task, the location of the electrodes and the patient's degree of attention to the stimulus. We show that diagnostic ability can be increased by 3-5% by combining gamma and alpha and by 7.5-11% when gamma and beta were combined. We then show how ECoG power modulation from cognitive testing can be used to map the probability of eloquence in individual patients and how this probability map can be used in clinical settings to optimize ESM planning. We conclude that the combination of gamma and beta power modulation during cognitive testing can contribute to the identification of eloquent areas prior to ESM in patients with refractory focal epilepsy.

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 2nd 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.

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.

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.

Assessment of dynamic cerebral autoregulation and cerebral carbon dioxide reactivity during normothermic cardiopulmonary bypass.

Despite increased risk of neurological complications after cardiac surgery, monitoring of cerebral hemodynamics during cardiopulmonary bypass (CPB) is still not a common practice. Therefore, a technique to evaluate dynamic cerebral autoregulation and cerebral carbon dioxide reactivity (CO2R) during normothermic nonpulsatile CPB is presented. The technique uses continuous recording of invasive arterial blood pressure, middle cerebral artery blood flow velocity, absolute cerebral tissue oxygenation, in-line arterial carbon dioxide levels, and pump flow measurement in 37 adult male patients undergoing elective CPB. Cerebral autoregulation is estimated by transfer function analysis and the autoregulation index, based on the response to blood pressure variation induced by cyclic 6/min changes of indexed pump flow from 2.0 to 2.4 up to 2.8 L/min/m(2). CO2R was calculated from recordings of both cerebral blood flow velocity and cerebral tissue oxygenation. Cerebral autoregulation and CO2R were estimated at hypocapnia, normocapnia, and hypercapnia. CO2R was preserved during CPB, but significantly lower for hypocapnia compared with hypercapnia (p < 0.01). Conversely, cerebral autoregulation parameters such as gain, phase, and autoregulation index were significantly higher (p < 0.01) during hypocapnia compared with both normocapnia and hypercapnia. Assessing cerebral autoregulation and CO2R during CPB, by cyclic alteration of pump flow, showed an impaired cerebral autoregulation during hypercapnia.

Visually evoked blood flow responses and interaction with dynamic cerebral autoregulation: correction for blood pressure variation.

Visually evoked flow responses recorded using transcranial Doppler ultrasonography are often quantified using a dynamic model of neurovascular coupling. The evoked flow response is seen as the model's response to a visual step input stimulus. However, the continuously active process of dynamic cerebral autoregulation (dCA) compensating cerebral blood flow for blood pressure fluctuations may induce changes of cerebral blood flow velocity (CBFV) as well. The effect of blood pressure variability on the flow response is evaluated by separately modeling the dCA-induced effects of beat-to-beat measured blood pressure related CBFV changes. Parameters of 71 subjects are estimated using an existing, well-known second order dynamic neurovascular coupling model proposed by Rosengarten et al., and a new model extending the existing model with a CBFV contributing component as the output of a dCA model driven by blood pressure as input. Both models were evaluated for mean and systolic CBFV responses. The model-to-data fit errors of mean and systolic blood pressure for the new model were significantly lower compared to the existing model: mean: 0.8%±0.6 vs. 2.4%±2.8, p<0.001; systolic: 1.5%±1.2 vs. 2.2%±2.6, p<0.001. The confidence bounds of all estimated neurovascular coupling model parameters were significantly (p<0.005) narrowed for the new model. In conclusion, blood pressure correction of visual evoked flow responses by including cerebral autoregulation in model fitting of averaged responses results in significantly lower fit errors and by that in more reliable model parameter estimation. Blood pressure correction is more effective when mean instead of systolic CBFV responses are used. Measurement and quantification of neurovascular coupling should include beat-to-beat blood pressure measurement.

Baroreflex function is reduced in Alzheimer's disease: a candidate biomarker?

The baroreflex (BR) reflects autonomic blood pressure control. Alzheimer's disease (AD) affects the autonomic system. Detailed properties of BR in AD are unknown. We hypothesized that BR is reduced in AD, and is influenced by autonomic effects of cholinesterase inhibitors (ChEI). BR was determined in 18 AD patients, 11 patients with mild cognitive impairment (MCI) and 19 healthy control subjects. In AD, BR was measured again after ChEI treatment. Receiver operating characteristic analysis was used to define a BR cutoff value, which was then tested in an independent validation sample of 16 AD, 18 MCI, and 18 control subjects. BR was lower in AD compared with MCI (p < 0.05) and in MCI compared with healthy control subjects (p < 0.01). Receiver operating characteristic analysis between AD and healthy control subjects yielded a sensitivity of 89% and a specificity of 94%. ChEI treatment increased BR with 66% (p < 0.01). BR was reduced in AD and increased after treatment with ChEI. BR might be a good biomarker to further explore the link between cardiovascular disease and AD.

Dynamic cerebral autoregulation in subjects with Alzheimer's disease, mild cognitive impairment, and controls: evidence for increased peripheral vascular resistance with possible predictive value.

Cerebrovascular dysfunction plays a role not only in vascular causes of cognitive impairment but also in Alzheimer's disease (AD). We hypothesized that cerebral autoregulation is impaired in patients with AD compared to subjects with mild cognitive impairment (MCI) and controls. Dynamic cerebral autoregulation (dCA) was investigated in 17 AD patients, 19 MCI subjects, and 20 controls (C). Groups were matched for age, gender, and level of education. Electrocardiogram and non-invasive finger arterial blood pressure were measured and transcranial doppler ultrasonography was used to measure cerebral blood flow velocity in right and left middle cerebral artery (MCA). Cerebrovascular resistance index (CVRi) was also computed. dCA in supine position was quantified based on spontaneous blood pressure variations by computation of the linear transfer function between arterial blood pressure and MCA cerebral blood flow velocity. dCA gain and phase were evaluated for different frequency bands. Results were also evaluated using a 3-parameter windkessel model (WKM). CVRi was significantly higher in AD (2.9 ± 0.2) compared to both MCI (2.3 ± 0.1, p = 0.02) and C (2.1 ± 0.1 mmHgs/cm, p = 0.002). Five MCI patients who converted to AD during the course of the study also had higher CVRi compared to non-converters (2.8 ± 0.6 versus 2.1 ± 0.5 mmHgs/cm, p < 0.05). No significant differences in dCA gain and phase were found. In terms of the WKM approach, in the order C→MCI→AD groups showed about equal arterial resistance and peripheral compliance, but increased peripheral vasculature resistance (26 ± 2 versus 36 ± 3 mmHgs/ml in C resp. AD, p = 0.004). In conclusion, AD patients compared to MCI patients and controls have increased CVRi, whereas dCA parameters do not seem to differentiate AD patients. For MCI patients, CVRi might have predictive value in developing AD.

Dynamic cerebral autoregulation: different signal processing methods without influence on results and reproducibility.

Cerebral autoregulation controls cerebral blood flow under changing cerebral perfusion pressure. Standards for measurement and analysis of dynamic cerebral autoregulation (dCA) are lacking. In this study, dCA reproducibility, quantified by intraclass correlation coefficient, is evaluated for different methodological approaches of transfer function analysis (TFA) and compared with multimodal pressure flow analysis (MMPF). dCA parameters were determined in 19 healthy volunteers during three 15-min lasting epochs of spontaneous breathing. Every spontaneous breathing epoch was followed by 5 min of paced breathing at 6 cycles/min. These six measurements were performed in both a morning and an afternoon session. Analysis compared raw data pre-processing by mean subtraction versus smoothness priors detrending. The estimation of spectral density was either performed by averaging of subsequent time windows or by smoothing the spectrum of the whole recording. No significant influence of pre-processing and spectral estimation on dCA parameters was found. Therefore, there seems to be no need to prescribe a specific signal-processing regime. Poor reproducibility of gain and phase was found for TFA as well as for MMPF. Based on reproducibility, no preference can be made for morning versus afternoon measurements, neither for spontaneous versus paced breathing. Finally, reproducibility results are not in favour of TFA or MMPF.