Dynamic cerebral autoregulation assessment using chaotic analysis in diabetic autonomic neuropathy

Reduced cardiovagal baroreflex sensitivity is associated with postural orthostatic tachycardia syndrome (POTS) and pain chronification in patients with headache

Background

Non-cephalgic symptoms including orthostatic intolerance, fatigue, and cognitive impairment, are common in patients with chronic headache disorders and may result from alterations in the autonomic nervous system. However, little is known about the function of autonomic reflexes, which regulate cardiovascular homeostasis and cerebral perfusion in patients with headache.

Methods

Autonomic function testing data from patients with headache collected between January 2018 and April 2022 was retrospectively analyzed. Through review of EMR we determined headache pain chronicity and patient self-report of orthostatic intolerance, fatigue, and cognitive impairment. Composite Autonomic Severity Score (CASS), CASS subscale scores, and cardiovagal and adrenergic baroreflex sensitivities were used to quantify autonomic reflex dysfunction. Descriptive analyses (Mann-Whitney-U or χ2, as appropriate) determined associations between autonomic reflex dysfunction and POTS as well as chronic headache. Binomial logistic regression adjusted for age and sex. Spearman’s rank correlation determined the association between the total CASS score and the number of painless symptoms reported by each participant.

Results

We identified 34 patients meeting inclusion criteria, of whom there were 16 (47.0%) with orthostatic intolerance, 17 (50.0%) with fatigue, 11 (32.4%) with cognitive complaints, and 11 (32.4%) with Postural Orthostatic Tachycardia Syndrome (POTS). The majority of participants had migraine (n = 24, 70.6%), were female (n = 23, 67.6%) and had a chronic (>15 headache days in a month) headache disorder (n = 26, 76.5%). Reduced cardiovagal baroreflex sensitivity (BRS-V) independently predicted chronic headache [aOR: 18.59 (1.16, 297.05), p = 0.039] and POTS [aOR: 5.78 (1.0, 32.5), p = 0.047]. The total CASS was correlated with the total number of non-painful features in the expected direction (r = 0.46, p = 0.007).

Conclusion

Abnormal autonomic reflexes may play an important role in pain chronification and the development of POTS in patients with headache.

EEG-Driven Prediction Model of Oxcarbazepine Treatment Outcomes in Patients With Newly-Diagnosed Focal Epilepsy

Objective: Antiseizure medicine (ASM) is the first choice for patients with epilepsy. The choice of ASM is determined by the type of epilepsy or epileptic syndrome, which may not be suitable for certain patients. This initial choice of a particular drug affects the long-term prognosis of patients, so it is critical to select the appropriate ASMs based on the individual characteristics of a patient at the early stage of the disease. The purpose of this study is to develop a personalized prediction model to predict the probability of achieving seizure control in patients with focal epilepsy, which will help in providing a more precise initial medication to patients.

Methods: Based on response to oxcarbazepine (OXC), enrolled patients were divided into two groups: seizure-free (52 patients), not seizure-free (NSF) (22 patients). We created models to predict patients' response to OXC monotherapy by combining Electroencephalogram (EEG) complexities and 15 clinical features. The prediction models were gradient boosting decision tree-Kolmogorov complexity (GBDT-KC) and gradient boosting decision tree-Lempel-Ziv complexity (GBDT-LZC). We also constructed two additional prediction models, support vector machine-Kolmogorov complexity (SVM-KC) and SVM-LZC, and these two models were compared with the GBDT models. The performance of the models was evaluated by calculating the accuracy, precision, recall, F1-score, sensitivity, specificity, and area under the curve (AUC) of these models.

Results: The mean accuracy, precision, recall, F1-score, sensitivity, specificity, AUC of GBDT-LZC model after five-fold cross-validation were 81%, 84%, 91%, 87%, 91%, 64%, 81%, respectively. The average accuracy, precision, recall, F1-score, sensitivity, specificity, AUC of GBDT-KC model with five-fold cross-validation were 82%, 84%, 92%, 88%, 83%, 92%, 83%, respectively. We used the rank of absolute weights to separately calculate the features that have the most significant impact on the classification of the two models.

Conclusion: (1) The GBDT-KC model has the potential to be used in the clinic to predict seizure-free with OXC monotherapy. (2). Electroencephalogram complexity, especially Kolmogorov complexity (KC) may be a potential biomarker in predicting the treatment efficacy of OXC in newly diagnosed patients with focal epilepsy.

Hypertension and Stroke Cardiovascular Control Evaluation by Analyzing Blood Pressure, Cerebral Blood Flow, Blood Vessel Resistance and Baroreflex

Cardiovascular diseases have been the leading causes of mortality in Taiwan and the world at large for decades. The composition of cardiovascular and cerebrovascular systems is quite complicated. Therefore, it is difficult to detect or trace the related signs of cardiovascular and cerebrovascular diseases. The characteristics and changes in cardiopulmonary system disease can be used to track cardiovascular and cerebrovascular disease prevention and diagnosis. This can effectively reduce the occurrence of cardiovascular and cerebrovascular diseases. This study analyzes the variability in blood pressure, cerebral blood flow velocity and the interaction characteristics using linear and nonlinear approaches in stroke, hypertension and healthy groups to identify the differences in cardiovascular control in these groups. The results showed that the blood pressure and cerebral blood flow of stroke patients and hypertensive patients were significantly higher than those of healthy people (statistical differences (p < 0.05). The cerebrovascular resistance (CVR) shows that the CVR of hypertensive patients is higher than that of healthy people and stroke patients (p < 0.1), indicating that the cerebral vascular resistance of hypertensive patients is slightly higher. From the patient's blood flow and vascular characteristics, it can be observed that the cardiovascular system is different from those in healthy people. Baroreflex sensitivity (BRS) decreased in stroke patients (p < 0.05). Chaotic analysis revealed that the blood pressure disturbance in hypertensive patients has a higher chaotic behavior change and the difference in initial state sensitivity. Cross-correlation (CCF) analysis shows that as the course of healthy→hypertension→stroke progresses, the maximum CCF value decreases significantly (p < 0.05). That means that blood pressure and cerebral blood flow are gradually not well controlled by the self-regulation mechanism. In conclusion, cardiovascular control performance in hypertensive and stroke patients displays greater variation. This can be observed by the bio-signal analysis. This analysis could identify a measure for detecting and preventing the risk for hypertension and stroke in clinical practice. This is a pilot study to analyze cardiovascular control variation in healthy, hypertensive and stroke groups.

Variability Predictors of Vasospasm in Subarachnoid Hemorrhage: A Feasibility Study

BACKGROUND

Mean cerebral blood flow velocity (mean-CBFV) obtained from Transcranial Doppler (TCD) poorly predicts cerebral vasospasm in patients with aneurysmal subarachnoid hemorrhage (aSAH). Variability descriptors of mean-CBFV obtained during extended TCD recordings may improve this prediction. We assessed the feasibility of generating reliable linear and non-linear descriptors of mean-CBFV variability using extended recordings in aSAH patients and in healthy controls. We also explored which of those metrics might have the ability to discriminate between aSAH patients and healthy controls, and among patients who would go on to develop vasospasm and those who would not.

METHODS

Bilateral mean-CBFV, blood pressure, and heart rate were continuously recorded for 40 minutes in aSAH patients (n = 8) within the first 5 days after ictus, in age-matched healthy controls (n = 8) and in additional young controls (n = 8). We obtained linear [standard deviation, coefficient of variations, and the very-low (0.003-0.040 Hz), low (0.040-0.150 Hz), and high-frequency (0.15-0.4 Hz) power spectra] and non-linear (Fractality, deterministic Chaos analyses) variability metrics.

RESULTS

We successfully obtained TCD recordings from patients and healthy controls and calculated the desired metrics of mean-CBFV variability. Differences were appreciable between aSAH patients and healthy controls, as well as between aSAH patients who later developed vasospasm and those who did not.

CONCLUSIONS

A 40-minute TCD recording provides reliable variability metrics in aSAH patients and healthy controls. Future studies are required to determine if mean-CBFV variability metrics remain stable over time, and whether they may serve to identify patients who are at greatest risk of developing cerebral vasospasm after aSAH.

COMPREHENSIVE CHARACTERIZATION OF GAIT VARIABILITY IN PATIENTS WITH KNEE OSTEOARTHRITIS FOR ALTERED VELOCITIES

This study focuses on quantifying the alterations in human gait variability in individuals with knee Osteoarthritis (OA). To assess the stride-to-stride variation, estimating the dynamical stability discloses the encrypted behavior of the locomotion system, at times facing perturbations, and for that the Largest Lyapunov Exponent is extracted for both Short ([Formula: see text]) and Long ([Formula: see text]) term. Information about the complexity of movement further bolsters the variation-driven conclusions which are calculated for Correlation Index and Kolmogorov–Sinai (K-S) entropy. Using gait analysis, knee angular displacement is the considered data tested for different walking speeds. In the result, [Formula: see text] revealed significant differences between the groups and their walking velocities while [Formula: see text] failed to represent any distinguishable differences. In contrast to the control group, increases in the walking velocity did not affect the amount of locomotion variability for the patients. The stride features also verified the vicissitudes in the gait pattern of knee OA suffering individuals.

Neural Vascular Mechanism for the Cerebral Blood Flow Autoregulation after Hemorrhagic Stroke

During the initial stages of hemorrhagic stroke, including intracerebral hemorrhage and subarachnoid hemorrhage, the reflex mechanisms are activated to protect cerebral perfusion, but secondary dysfunction of cerebral flow autoregulation will eventually reduce global cerebral blood flow and the delivery of metabolic substrates, leading to generalized cerebral ischemia, hypoxia, and ultimately, neuronal cell death. Cerebral blood flow is controlled by various regulatory mechanisms, including prevailing arterial pressure, intracranial pressure, arterial blood gases, neural activity, and metabolic demand. Evoked by the concept of vascular neural network, the unveiled neural vascular mechanism gains more and more attentions. Astrocyte, neuron, pericyte, endothelium, and so forth are formed as a communicate network to regulate with each other as well as the cerebral blood flow. However, the signaling molecules responsible for this communication between these new players and blood vessels are yet to be definitively confirmed. Recent evidence suggested the pivotal role of transcriptional mechanism, including but not limited to miRNA, lncRNA, exosome, and so forth, for the cerebral blood flow autoregulation. In the present review, we sought to summarize the hemodynamic changes and underline neural vascular mechanism for cerebral blood flow autoregulation in stroke-prone state and after hemorrhagic stroke and hopefully provide more systematic and innovative research interests for the pathophysiology and therapeutic strategies of hemorrhagic stroke.

Analysis of the Complexity Measures in the EEG of Schizophrenia Patients

Complexity measures have been enormously used in schizophrenia patients to estimate brain dynamics. However, the conflicting results in terms of both increased and reduced complexity values have been reported in these studies depending on the patients' clinical status or symptom severity or medication and age status. The objective of this study is to investigate the nonlinear brain dynamics of chronic and medicated schizophrenia patients using distinct complexity estimators. EEG data were collected from 22 relaxed eyes-closed patients and age-matched healthy controls. A single-trial EEG series of 2 min was partitioned into identical epochs of 20 s intervals. The EEG complexity of participants were investigated and compared using approximate entropy (ApEn), Shannon entropy (ShEn), Kolmogorov complexity (KC) and Lempel-Ziv complexity (LZC). Lower complexity values were obtained in schizophrenia patients. The most significant complexity differences between patients and controls were obtained in especially left frontal (F3) and parietal (P3) regions of the brain when all complexity measures were applied individually. Significantly, we found that KC was more sensitive for detecting EEG complexity of patients than other estimators in all investigated brain regions. Moreover, significant inter-hemispheric complexity differences were found in the frontal and parietal areas of schizophrenics' brain. Our findings demonstrate that the utilizing of sensitive complexity estimators to analyze brain dynamics of patients might be a useful discriminative tool for diagnostic purposes. Therefore, we expect that nonlinear analysis will give us deeper understanding of schizophrenics' brain.

A stochastic delay differential model of cerebral autoregulation

Mathematical models of the cardiovascular system and of cerebral autoregulation (CAR) have been employed for several years in order to describe the time course of pressures and flows changes subsequent to postural changes. The assessment of the degree of efficiency of cerebral auto regulation has indeed importance in the prognosis of such conditions as cerebro-vascular accidents or Alzheimer. In the quest for a simple but realistic mathematical description of cardiovascular control, which may be fitted onto non-invasive experimental observations after postural changes, the present work proposes a first version of an empirical Stochastic Delay Differential Equations (SDDEs) model. The model consists of a total of four SDDEs and two ancillary algebraic equations, incorporates four distinct delayed controls from the brain onto different components of the circulation, and is able to accurately capture the time course of mean arterial pressure and cerebral blood flow velocity signals, reproducing observed auto-correlated error around the expected drift.

Review and classification of variability analysis techniques with clinical applications

Analysis of patterns of variation of time-series, termed variability analysis, represents a rapidly evolving discipline with increasing applications in different fields of science. In medicine and in particular critical care, efforts have focussed on evaluating the clinical utility of variability. However, the growth and complexity of techniques applicable to this field have made interpretation and understanding of variability more challenging. Our objective is to provide an updated review of variability analysis techniques suitable for clinical applications. We review more than 70 variability techniques, providing for each technique a brief description of the underlying theory and assumptions, together with a summary of clinical applications. We propose a revised classification for the domains of variability techniques, which include statistical, geometric, energetic, informational, and invariant. We discuss the process of calculation, often necessitating a mathematical transform of the time-series. Our aims are to summarize a broad literature, promote a shared vocabulary that would improve the exchange of ideas, and the analyses of the results between different studies. We conclude with challenges for the evolving science of variability analysis.

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.

Autonomic neuropathy is associated with impairment of dynamic cerebral autoregulation in type 1 diabetes

HYPOTHESIS

The mechanisms underlying impairment of dynamic cerebral autoregulation in diabetes are not well known. Cardiovascular autonomic neuropathy (CAN) could contribute to dynamic cerebral autoregulation impairment. In this study, we assessed the association between CAN and impairment of dynamic cerebral autoregulation in patients with type 1 diabetes.

METHODS

We evaluated dynamic cerebral autoregulation (DCA) in patients with type 1 diabetes and no history of cerebrovascular disease. DCA was assessed with transcranial Doppler using the correlation coefficient index Mx method. Mx was calculated from slow changes in mean cerebral blood flow velocity and mean arterial blood pressure. Increase in Mx indicates weaker DCA, with a threshold for impaired DCA above 0.3. Moderate CAN was defined as reduced heart rate variability (HRV) on the following tests: deep controlled breathing, Valsalva maneuver or initiation of active standing. Severe CAN was defined as reduced HRV associated with orthostatic hypotension.

RESULTS

60 patients were included (M/F: 33/27; mean age ± SD: 46 years ± 11.5). 23 patients had moderate CAN and 15 patients severe CAN. DCA was impaired in 37 patients. CAN was associated with impaired DCA (p = 0.005). Impairment of DCA was more pronounced in patients with severe CAN (p = 0.019). Glycosylated haemoglobin (HbA1c) was associated with impaired DCA in univariate analysis (p = 0.05). In multivariate analysis, only CAN was associated with impaired DCA (p = 0.007) whereas HbA1c was not (p = 0.161).

CONCLUSIONS

CAN was associated with impaired DCA in type 1 diabetes. The magnitude of DCA impairment increased with the severity of CAN.