Excitability and recruitment patterns of spinal motoneurons in human sleep as assessed by F-wave recordings

This study examines the excitability and recruitment of spinal motoneurons in human sleep. The main objective was to assess whether supraspinal inhibition affects the different subpopulations of the compound spinal motoneuron pool in the same way or rather in a selective fashion in the various sleep stages. To this end, we studied F-conduction velocities (FCV) and F-tacheodispersion alongside F-amplitudes and F-persistence in 22 healthy subjects in sleep stages N2, N3 (slow-wave sleep), REM and in wakefulness. Stimuli were delivered on the ulnar nerve, and F-waves were recorded from the first dorsal interosseus muscle. Repeated sets of stimuli were stored to obtain at least 15 F-waves for each state of vigilance. F-tacheodispersion was calculated based on FCVs using the modified Kimura formula. Confirming the only previous study, excitability of spinal motoneurons was generally decreased in all sleep stages compared with wakefulness as indicated by significantly reduced F-persistence and F-amplitudes. More importantly, F-tacheodispersion showed a narrowed range of FCV in all sleep stages, most prominently in REM. In non-REM, this narrowed range was associated with a shift towards significantly decreased maximal FCV and mean FCV as well as with a trend towards lower minimal FCV. In REM, the lowering of mean FCV was even more pronounced, but contrary to non-REM sleep without a shift of minimal and maximal FCV. Variations in F-tacheodispersion between sleep stages suggest that different supraspinal inhibitory neuronal circuits acting on the spinal motoneuron pool may contribute to muscle hypotonia in human non-REM sleep and to atonia in REM sleep.

Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients

To derive tests for randomness, nonlinear-independence, and stationarity, we combine surrogates with a nonlinear prediction error, a nonlinear interdependence measure, and linear variability measures, respectively. We apply these tests to intracranial electroencephalographic recordings (EEG) from patients suffering from pharmacoresistant focal-onset epilepsy. These recordings had been performed prior to and independent from our study as part of the epilepsy diagnostics. The clinical purpose of these recordings was to delineate the brain areas to be surgically removed in each individual patient in order to achieve seizure control. This allowed us to define two distinct sets of signals: One set of signals recorded from brain areas where the first ictal EEG signal changes were detected as judged by expert visual inspection ("focal signals") and one set of signals recorded from brain areas that were not involved at seizure onset ("nonfocal signals"). We find more rejections for both the randomness and the nonlinear-independence test for focal versus nonfocal signals. In contrast more rejections of the stationarity test are found for nonfocal signals. Furthermore, while for nonfocal signals the rejection of the stationarity test increases the rejection probability of the randomness and nonlinear-independence test substantially, we find a much weaker influence for the focal signals. In consequence, the contrast between the focal and nonfocal signals obtained from the randomness and nonlinear-independence test is further enhanced when we exclude signals for which the stationarity test is rejected. To study the dependence between the randomness and nonlinear-independence test we include only focal signals for which the stationarity test is not rejected. We show that the rejection of these two tests correlates across signals. The rejection of either test is, however, neither necessary nor sufficient for the rejection of the other test. Thus, our results suggest that EEG signals from epileptogenic brain areas are less random, more nonlinear-dependent, and more stationary compared to signals recorded from nonepileptogenic brain areas. We provide the data, source code, and detailed results in the public domain.

Postreperfusion cardiac arrest and resuscitation during orthotopic liver transplantation: dynamic visualization and analysis of physiologic recordings

We recently reported on the Multi Wave Animator (MWA), a novel open-source tool with capability of recreating continuous physiologic signals from archived numerical data and presenting them as they appeared on the patient monitor. In this report, we demonstrate for the first time the power of this technology in a real clinical case, an intraoperative cardiopulmonary arrest following reperfusion of a liver transplant graft. Using the MWA, we animated hemodynamic and ventilator data acquired before, during, and after cardiac arrest and resuscitation. This report is accompanied by an online video that shows the most critical phases of the cardiac arrest and resuscitation and provides a basis for analysis and discussion. This video is extracted from a 33-min, uninterrupted video of cardiac arrest and resuscitation, which is available online. The unique strength of MWA, its capability to accurately present discrete and continuous data in a format familiar to clinicians, allowed us this rare glimpse into events leading to an intraoperative cardiac arrest. Because of the ability to recreate and replay clinical events, this tool should be of great interest to medical educators, researchers, and clinicians involved in quality assurance and patient safety.

Parameter-free extraction of the functional network topology from intracranial EEG recordings: a time-resolved study of graph properties in focal onset seizures

Rationale: Focal onset epileptic seizures are due to abnormal interactions between distributed brain areas. By estimating the cross-correlation matrix of multi-site intra-cerebral EEG recordings (iEEG), one can quantify these interactions. To assess the topology of the underlying functional network, the binary connectivity matrix has to be derived from the cross-correlation matrix by use of a threshold. Classically, a unique threshold is used that constrains the topology [1]. Our method aims to set the threshold in a data-driven way by separating genuine from random cross-correlation. We compare our approach to the fixed threshold method and study the dynamics of the functional topology. Methods: We investigate the iEEG of patients suffering from focal onset seizures who underwent evaluation for the possibility of surgery. The equal-time cross-correlation matrices are evaluated using a sliding time window. We then compare 3 approaches assessing the corresponding binary networks. For each time window: * Our parameter-free method derives from the cross-correlation strength matrix (CCS)[2]. It aims at disentangling genuine from random correlations (due to finite length and varying frequency content of the signals). In practice, a threshold is evaluated for each pair of channels independently, in a data-driven way. * The fixed mean degree (FMD) uses a unique threshold on the whole connectivity matrix so as to ensure a user defined mean degree. * The varying mean degree (VMD) uses the mean degree of the CCS network to set a unique threshold for the entire connectivity matrix. * Finally, the connectivity (c), connectedness (given by k, the number of disconnected sub-networks), mean global and local efficiencies (Eg, El, resp.) are computed from FMD, CCS, VMD, and their corresponding random and lattice networks. Results: Compared to FMD and VMD, CCS networks present: *topologies that are different in terms of c, k, Eg and El. *from the pre-ictal to the ictal and then post-ictal period, topological features time courses that are more stable within a period, and more contrasted from one period to the next. For CCS, pre-ictal connectivity is low, increases to a high level during the seizure, then decreases at offset. k shows a ‘‘U-curve’’ underlining the synchronization of all electrodes during the seizure. Eg and El time courses fluctuate between the corresponding random and lattice networks values in a reproducible manner. Conclusions: The definition of a data-driven threshold provides new insights into the topology of the epileptic functional networks.

Circadian blood pressure during the early course of type 1 diabetes. Analysis of 1,011 ambulatory blood pressure recordings in 354 adolescents and young adults

OBJECTIVE Little information is available on the early course of hypertension in type 1 diabetes. The aim of our study, therefore, was to document circadian blood pressure profiles in patients with a diabetes duration of up to 20 years and relate daytime and nighttime blood pressure to duration of diabetes, BMI, insulin therapy, and HbA1c. RESEARCH DESIGN AND METHODS Ambulatory profiles of 24-h blood pressure were recorded in 354 pediatric patients with type 1 diabetes (age 14.6 +/- 4.2 years, duration of diabetes 5.6 +/- 5.0 years, follow-up for up to 9 years). A total of 1,011 profiles were available for analysis from patients not receiving antihypertensive medication. RESULTS Although daytime mean systolic pressure was significantly elevated in diabetic subjects (+3.1 mmHg; P < 0.0001), daytime diastolic pressure was not different from from the height- and sex-adjusted normal range (+0.1 mmHg, NS). In contrast, both systolic and diastolic nighttime values were clearly elevated (+7.2 and +4.2 mmHg; P < 0.0001), and nocturnal dipping was reduced (P < 0.0001). Systolic blood pressure was related to overweight in all patients, while diastolic blood pressure was related to metabolic control in young adults. Blood pressure variability was significantly lower in girls compared with boys (P < 0.01). During follow-up, no increase of blood pressure was noted; however, diastolic nocturnal dipping decreased significantly (P < 0.03). Mean daytime blood pressure was significantly related to office blood pressure (r = +0.54 for systolic and r = +0.40 for diastolic pressure); however, hypertension was confirmed by ambulatory blood pressure measurement in only 32% of patients with elevated office blood pressure. CONCLUSIONS During the early course of type 1 diabetes, daytime blood pressure is higher compared with that of healthy control subjects. The elevation of nocturnal values is even more pronounced and nocturnal dipping is reduced. The frequency of white-coat hypertension is high among adolescents with diabetes, and ambulatory blood pressure monitoring avoids unnecessary antihypertensive treatment.

The optimal lead insertion depth for esophageal ECG recordings with respect to atrial signal quality

BACKGROUND Diagnosing supraventricular arrhythmias by conventional long-term ECG can be cumbersome because of poor p-waves. Esophageal long-term electrocardiography (eECG) has an excellent sensitivity for atrial signals and may overcome this limitation. However, the optimal lead insertion depth (OLID) is not known. METHODS We registered eECGs at different lead insertion depths in 27 patients and analyzed 199,716 atrial complexes with respect to signal amplitude and slope. Correlation and regression analyses were used to find a criterion for OLID. RESULTS Atrial signal amplitudes and slopes significantly depend on lead insertion depth. OLID correlates with body height (rSpearman=0.71) and can be estimated by OLID [cm]=0.25*body height[cm]-7cm. At this insertion depth, we recorded the largest esophageal atrial signal amplitudes (1.27±0.86mV), which were much larger compared to conventional surface lead II (0.19±0.10mV, p<0.0001). CONCLUSION The OLID depends on body height and can be calculated by a simple regression formula.

Localization of Epileptogenic Zone on Pre-surgical Intracranial EEG Recordings: Toward a Validation of Quantitative Signal Analysis Approaches

In patients diagnosed with pharmaco-resistant epilepsy, cerebral areas responsible for seizure generation can be defined by performing implantation of intracranial electrodes. The identification of the epileptogenic zone (EZ) is based on visual inspection of the intracranial electroencephalogram (IEEG) performed by highly qualified neurophysiologists. New computer-based quantitative EEG analyses have been developed in collaboration with the signal analysis community to expedite EZ detection. The aim of the present report is to compare different signal analysis approaches developed in four different European laboratories working in close collaboration with four European Epilepsy Centers. Computer-based signal analysis methods were retrospectively applied to IEEG recordings performed in four patients undergoing pre-surgical exploration of pharmaco-resistant epilepsy. The four methods elaborated by the different teams to identify the EZ are based either on frequency analysis, on nonlinear signal analysis, on connectivity measures or on statistical parametric mapping of epileptogenicity indices. All methods converge on the identification of EZ in patients that present with fast activity at seizure onset. When traditional visual inspection was not successful in detecting EZ on IEEG, the different signal analysis methods produced highly discordant results. Quantitative analysis of IEEG recordings complement clinical evaluation by contributing to the study of epileptogenic networks during seizures. We demonstrate that the degree of sensitivity of different computer-based methods to detect the EZ in respect to visual EEG inspection depends on the specific seizure pattern.

Muscle velocity recovery cycles: Comparison between surface and needle recordings.

INTRODUCTION Recording of muscle velocity recovery cycles (MVRCs) has been developed as a technique to investigate the pathophysiology of muscle diseases. MVRCs have been measured by direct muscle stimulation and concentric electromyographic needle recording. This study was undertaken to determine whether recordings can be made with surface electrodes. METHODS MVRCs with 1 and 2 conditioning stimuli were recorded simultaneously with concentric needle and surface electrodes from the brachioradialis muscle in 12 healthy volunteers. Muscle relative refractory period, early and late supernormality, and extra-late supernormality were compared between the recording techniques. RESULTS Surface recordings were possible in all subjects. The multifiber action potentials recorded with surface electrodes were smaller than those recorded with needles, but there was no significant difference between any of their MVRC properties. CONCLUSIONS MVRCs can be recorded with surface electrodes in healthy subjects. The use of surface electrodes may facilitate the technique of recording MVRCs. Muscle Nerve 53: 205-208, 2016.