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

Quantification of dynamic cerebral autoregulation: welcome to the jungle!

PURPOSE

Patients with dysautonomia often experience symptoms such as dizziness, syncope, blurred vision and brain fog. Dynamic cerebral autoregulation, or the ability of the cerebrovasculature to react to transient changes in arterial blood pressure, could be associated with these symptoms.

METHODS

In this narrative review, we go beyond the classical view of cerebral autoregulation to discuss dynamic cerebral autoregulation, focusing on recent advances pitfalls and future directions.

RESULTS

Following some historical background, this narrative review provides a brief overview of the concept of cerebral autoregulation, with a focus on the quantification of dynamic cerebral autoregulation. We then discuss the main protocols and analytical approaches to assess dynamic cerebral autoregulation, including recent advances and important issues which need to be tackled.

CONCLUSION

The researcher or clinician new to this field needs an adequate comprehension of the toolbox they have to adequately assess, and interpret, the complex relationship between arterial blood pressure and cerebral blood flow in healthy individuals and clinical populations, including patients with autonomic disorders.

Hemodynamics of Prefrontal Cortex in Ornithine Transcarbamylase Deficiency: A Twin Case Study

Ornithine transcarbamylase deficiency (OTCD) is the most common form of urea cycle disorder characterized by the presence of hyperammonemia (HA). In patients with OTCD, HA is known to cause impairments in domains of executive function and working memory. Monitoring OTCD progression and investigating neurocognitive biomarkers can, therefore, become critical in understanding the underlying brain function in a population with OTCD. We used functional near infrared spectroscopy (fNIRS) to examine the hemodynamics of prefrontal cortex (PFC) in a fraternal twin with and without OTCD. fNIRS is a non-invasive and wearable optical technology that can be used to assess cortical hemodynamics in a realistic clinical setting. We quantified the hemodynamic variations in total-hemoglobin as assessed by fNIRS while subjects performed the N-back working memory (WM) task. Our preliminary results showed that the sibling with OTCD had higher variation in a very low frequency band (<0.03 Hz, related to mechanism of cerebral autoregulation) compared to the control sibling. The difference between these variations was not as prominent in the higher frequency band, indicating the possible role of impaired autoregulation and cognitive function due to presence of HA. We further examined the functional connectivity in PFC, where the OTCD sibling showed lower interhemispheric functional connectivity as the task load increased. Our pilot results are the first to show the utility of fNIRS in monitoring OTCD cortical hemodynamics, indicating the possibility of inefficient neurocognitive function. This study provides a novel insight into the monitoring of OTCD focusing on the contribution of physiological process and neurocognitive function in this population.

Network analysis identifies consensus physiological measures of neurovascular coupling in humans

Intimate communication between neural and vascular structures is required to match neuronal metabolism to blood flow, a process termed neurovascular coupling. The number of laboratories assessing neurovascular coupling in humans is increasing due to clinical interest in disease states, and basic science interest in a non-anesthetized, non-craniotomized, unrestrained, in vivo model. However, there is a lack of knowledge regarding how best to characterize the neurovascular response. To address this knowledge gap, we have amassed a highly powered human neurovascular coupling dataset, and deployed a network-based approach to reveal the most powerful and consistent metrics for quantifying neurovascular coupling. Using dimensionality reduction, community-based clustering, and majority-voting of traditional metrics (e.g. peak response, time to peak) and non-traditional metrics (e.g. varying time windows, pulsatility), we have identified which of the existing metrics predominantly characterize the neurovascular coupling response, are stable within and across participants, and explain the vast majority of the variance within our dataset of over 300 trials. We then harnessed our empirical approach to generate powerful novel metrics of neurovascular coupling, termed iAmplitude, iRate, and iPulsatility, which increase sensitivity when capturing population differences. These metrics may be useful to optimally understand neurovascular coupling in health and disease.

A Strategy for Classification of "Vaginal vs. Cesarean Section" Delivery: Bivariate Empirical Mode Decomposition of Cardiotocographic Recordings

We propose objective and robust measures for the purpose of classification of "vaginal vs. cesarean section" delivery by investigating temporal dynamics and complex interactions between fetal heart rate (FHR) and maternal uterine contraction (UC) recordings from cardiotocographic (CTG) traces. Multivariate extension of empirical mode decomposition (EMD) yields intrinsic scales embedded in UC-FHR recordings while also retaining inter-channel (UC-FHR) coupling at multiple scales. The mode alignment property of EMD results in the matched signal decomposition, in terms of frequency content, which paves the way for the selection of robust and objective time-frequency features for the problem at hand. Specifically, instantaneous amplitude and instantaneous frequency of multivariate intrinsic mode functions are utilized to construct a class of features which capture nonlinear and nonstationary interactions from UC-FHR recordings. The proposed features are fed to a variety of modern machine learning classifiers (decision tree, support vector machine, AdaBoost) to delineate vaginal and cesarean dynamics. We evaluate the performance of different classifiers on a real world dataset by investigating the following classifying measures: sensitivity, specificity, area under the ROC curve (AUC) and mean squared error (MSE). It is observed that under the application of all proposed 40 features AdaBoost classifier provides the best accuracy of 91.8% sensitivity, 95.5% specificity, 98% AUC, and 5% MSE. To conclude, the utilization of all proposed time-frequency features as input to machine learning classifiers can benefit clinical obstetric practitioners through a robust and automatic approach for the classification of fetus dynamics.

Wavelet decomposition analysis is a clinically relevant strategy to evaluate cerebrovascular buffering of blood pressure after spinal cord injury

The capacity of the cerebrovasculature to buffer changes in blood pressure (BP) is crucial to prevent stroke, the incidence of which is three- to fourfold elevated after spinal cord injury (SCI). Disruption of descending sympathetic pathways within the spinal cord due to cervical SCI may result in impaired cerebrovascular buffering. Only linear analyses of cerebrovascular buffering of BP, such as transfer function, have been used in SCI research. This approach does not account for inherent nonlinearity and nonstationarity components of cerebrovascular regulation, often depends on perturbations of BP to increase the statistical power, and does not account for the influence of arterial CO₂ tension. Here, we used a nonlinear and nonstationary analysis approach termed wavelet decomposition analysis (WDA), which recently identified novel sympathetic influences on cerebrovascular buffering of BP occurring in the ultra-low-frequency range (ULF; 0.02-0.03Hz). WDA does not require BP perturbations and can account for influences of CO₂ tension. Supine resting beat-by-beat BP (Finometer), middle cerebral artery blood velocity (transcranial Doppler), and end-tidal CO₂ tension were recorded in cervical SCI ( n = 14) and uninjured ( n = 16) individuals. WDA revealed that cerebral blood flow more closely follows changes in BP in the ULF range ( P = 0.0021, Cohen's d = 0.89), which may be interpreted as an impairment in cerebrovascular buffering of BP. This persisted after accounting for CO₂. Transfer function metrics were not different in the ULF range, but phase was reduced at 0.07-0.2 Hz ( P = 0.03, Cohen's d = 0.31). Sympathetically mediated cerebrovascular buffering of BP is impaired after SCI, and WDA is a powerful strategy for evaluating cerebrovascular buffering in clinical populations.

Characterisation of ictal and interictal states of epilepsy: A system dynamic approach of principal dynamic modes analysis

Epilepsy is a brain disorder characterised by the recurrent and unpredictable interruptions of normal brain function, called epileptic seizures. The present study attempts to derive new diagnostic indices which may delineate between ictal and interictal states of epilepsy. To achieve this, the nonlinear modeling approach of global principal dynamic modes (PDMs) is adopted to examine the functional connectivity of the temporal and frontal lobes with the occipital brain segment using an ensemble of paediatric EEGs having the presence of epileptic seizure. The distinct spectral characteristics of global PDMs are found to be in line with the neural rhythms of brain dynamics. Moreover, we find that the linear trends of associated nonlinear functions (ANFs) associated with the 2nd and 4th global PDMs (representing delta, theta and alpha bands) of Fp1–F3 may differentiate between ictal and interictal states of epilepsy. These findings suggest that global PDMs and their associated ANFs may offer potential utility as diagnostic neural measures for ictal and interictal states of epilepsy.