Best Practices in Facial Nerve Monitoring.

OBJECTIVES/HYPOTHESIS: Facial nerve monitoring (FNM) has evolved into a widely used adjunct for many surgical procedures along the course of the facial nerve. Even though majority opinion holds that FNM reduces the incidence of iatrogenic nerve injury, there are few if any studies yielding high-level evidence and no practice guidelines on which clinicians can rely. Instead, a review of the literature and medicolegal cases reveals significant variations in methodology, training, and clinical indications. STUDY DESIGN: Literature review and expert opinion. METHODS: Given the lack of standard references to serve as a resource for FNM, we assembled a multidisciplinary group of experts representing more than a century of combined monitoring experience to synthesize the literature and provide a rational basis to improve the quality of patient care during FNM. RESULTS: Over the years, two models of monitoring have become well-established: 1) monitoring by the surgeon using a stand-alone device that provides auditory feedback of facial electromyography directly to the surgeon, and 2) a team, typically consisting of surgeon, technologist, and interpreting neurophysiologist. Regardless of the setting and the number of people involved, the reliability of monitoring depends on the integration of proper technical performance, accurate interpretation of responses, and their timely application to the surgical procedure. We describe critical steps in the technical set-up and provide a basis for context-appropriate interpretation and troubleshooting of recorded signals. CONCLUSIONS: We trust this initial attempt to describe best practices will serve as a basis for improving the quality of patient care while reducing inappropriate variations. LEVEL OF EVIDENCE: 4 Laryngoscope, 131:S1-S42, 2021.

Electroencephalography at the time of Covid-19 pandemic in Italy.

OBJECTIVE: During the Covid-19 pandemic, government restrictions limited health care to urgent needs. Neurophysiology centers had to suddenly reschedule their activities, with a lack of specific recommendations about electroencephalography (EEG) execution. During the pandemic phase 1, we launched an online survey to understand the flaws and strengths of the EEG management in Italy at the time of Covid-19 pandemic. METHODS: A 45-item online survey (published from April 16 to 30, 2020), endorsed by the Italian Society of Clinical Neurophysiology (SINC), the Italian League Against epilepsy (LICE), and the Italian Association of Neurophysiology technologists (AITN), collected EEG management data (EEG's number and type, indications, personnel and patients safety, devices' sanification) during the Covid-19 pandemic. RESULTS: We received responses from 206 centers. The number of EEGs performed was reduced by 76 ± 20%, and several types of specific EEG (video-EEG, ambulatory-EEG, LTM, polysomnography) were reduced at a minimum. Half of the centers performed inpatient EEGs only for urgencies. Repetitive seizures, encephalitis, and non-convulsive status epilepticus were the most common indications. Covid-19-positive patients received less EEG than negative ones (p < 0.0001). EEG requests came mainly not only from neurologists (n = 176) but also from general practitioners (n = 40), emergentists (n = 79), intensivists (n = 72), and other specialists (n = 53). Those centers which continued performing outpatient EEG examinations were instructed to perform the EEG after a Covid-19-related symptom screening for patients and using personal protective equipment (PPE) through all the procedure. Inpatient EEGs were performed using FFP2/FFP3 masks by neurophysiology technologists in only 50% of cases. Patients executed hyperventilation only for real clinical needs, but often (56%) with a mask. CONCLUSIONS: Italian neurophysiology centers strongly adhered to government restrictions of lockdown. Some issues emerged, ranging from the evaluation of a proper indication for EEG, technical procedures of EEG recording, and protection of neurophysiology technicians.

Single trial classification of magnetoencephalographic recordings using Granger causality.

The use of Granger causality (GC) for studying dependencies in neuroimaging data has recently been gaining popularity. Several frameworks exist for applying GC to neurophysiological questions but many rely heavily on specific statistical assumptions regarding autoregressive (AR) models for hypothesis testing. Since it is often difficult to satisfy these assumptions in practical settings, this study proposes an alternative statistical methodology based on the classification of individual trials of data. Instead of testing for significance using statistics based on estimated AR models or prediction errors, hypotheses were tested by determining whether or not individual magnetoencephalography (MEG) recording segments belonging to either of two experimental conditions can be successfully classified using features derived from AR and GC concepts. Using this novel approach, we show that bivariate temporal GC can be used to distinguish button presses based on whether they were experimentally forced or free. Additionally, the methodology was used to determine useful parameter settings for various steps of the analysis and this revealed surprising insight into several aspects of AR and GC analysis which, previously, could not be obtained in a comparable manner. A final mean accuracy of 79.2% was achieved for classifying forced and free button presses for 6 subjects suggesting that classification using GC features is a viable option for studying MEG signals and useful for evaluating the effectiveness of parameter variations in GC analysis.

iRaster: a novel information visualization tool to explore spatiotemporal patterns in multiple spike trains.

Over the last few years, simultaneous recordings of multiple spike trains have become widely used by neuroscientists. Therefore, it is important to develop new tools for analysing multiple spike trains in order to gain new insight into the function of neural systems. This paper describes how techniques from the field of visual analytics can be used to reveal specific patterns of neural activity. An interactive raster plot called iRaster has been developed. This software incorporates a selection of statistical procedures for visualization and flexible manipulations with multiple spike trains. For example, there are several procedures for the re-ordering of spike trains which can be used to unmask activity propagation, spiking synchronization, and many other important features of multiple spike train activity. Additionally, iRaster includes a rate representation of neural activity, a combined representation of rate and spikes, spike train removal and time interval removal. Furthermore, it provides multiple coordinated views, time and spike train zooming windows, a fisheye lens distortion, and dissemination facilities. iRaster is a user friendly, interactive, flexible tool which supports a broad range of visual representations. This tool has been successfully used to analyse both synthetic and experimentally recorded datasets. In this paper, the main features of iRaster are described and its performance and effectiveness are demonstrated using various types of data including experimental multi-electrode array recordings from the ganglion cell layer in mouse retina. iRaster is part of an ongoing research project called VISA (Visualization of Inter-Spike Associations) at the Visualization Lab in the University of Plymouth. The overall aim of the VISA project is to provide neuroscientists with the ability to freely explore and analyse their data. The software is freely available from the Visualization Lab website (see www.plymouth.ac.uk/infovis).

Statistical analysis of large-scale neuronal recording data.

Relating stimulus properties to the response properties of individual neurons and neuronal networks is a major goal of sensory research. Many investigators implant electrode arrays in multiple brain areas and record from chronically implanted electrodes over time to answer a variety of questions. Technical challenges related to analyzing large-scale neuronal recording data are not trivial. Several analysis methods traditionally used by neurophysiologists do not account for dependencies in the data that are inherent in multi-electrode recordings. In addition, when neurophysiological data are not best modeled by the normal distribution and when the variables of interest may not be linearly related, extensions of the linear modeling techniques are recommended. A variety of methods exist to analyze correlated data, even when the data are not normally distributed and the relationships are nonlinear. Here we review expansions of the Generalized Linear Model designed to address these data properties. Such methods are used in other research fields, and the application to large-scale neuronal recording data will enable investigators to determine the variable properties that convincingly contribute to the variances in the observed neuronal measures. Standard measures of neuron properties such as response magnitudes can be analyzed using these methods, and measures of neuronal network activity such as spike timing correlations can be analyzed as well. We have done just that in recordings from 100-electrode arrays implanted in the primary somatosensory cortex of owl monkeys. Here we illustrate how one example method, Generalized Estimating Equations analysis, is a useful method to apply to large-scale neuronal recordings.

Neurophysiological intraoperative monitoring in neurosurgery: aid or handicap? An international survey.

OBJECT: Neurophysiological intraoperative monitoring (IOM) is regarded as a useful tool to provide information about physiological changes during surgery in eloquent areas of the nervous system, to increase safety and reduce morbidity. Nevertheless, numerous older studies report that very few patients benefit from IOM, and that there are high rates of false-positive and false-negative changes of neurophysiological parameters during surgery. There is an ongoing discussion about the effectiveness of neurophysiological IOM. This questionnaire study was performed to evaluate the attitude of neurosurgeons toward neurophysiological IOM and the availability of this tool. METHODS: One hundred fifty neurosurgeons from 60 institutions in 16 countries were asked to answer anonymously a questionnaire with 11 questions. The questionnaire covered aspects of personal experience, the neurosurgical institution, and availability of neurophysiological IOM as well as asking the surgeon's opinion of the procedure. RESULTS: One hundred nine questionnaires were returned (73%). Seven questionnaires were excluded because of failure to complete the form correctly or completely, leaving 102 respondents from 44 institutions in 16 countries in the study; 79.5% of the included institutions provided neurophysiological IOM. Young neurosurgeons did not put more trust in IOM than experienced neurosurgeons. With growing IOM experience, surgeons seem to allow less influence of the findings on the course of their operation. At large institutions in which > 1500 operations per year are done, IOM is performed by the neurosurgeons themselves in most cases. In institutions with fewer operations, the IOM team consists mostly of nonneurosurgeons. Regardless of the availability of neurophysiological IOM, all surgeons stated that IOM is gaining increasing importance. CONCLUSIONS: Neurophysiological IOM represents an established tool in neurosurgery. Although the importance of IOM is emphasized by the majority of neurosurgeons, the relevance of this tool to the course of the operation changes with increasing neurophysiological IOM experience.

Intraoperative neurophysiological monitoring during spine surgery: a review.

Spinal surgery involves a wide spectrum of procedures during which the spinal cord, nerve roots, and key blood vessels are frequently placed at risk for injury. Neuromonitoring provides an opportunity to assess the functional integrity of susceptible neural elements during surgery. The methodology of obtaining and interpreting data from various neuromonitoring modalities-such as somatosensory evoked potentials, motor evoked potentials, spontaneous electromyography, and triggered electromyography-is reviewed in this report. Also discussed are the major benefits and limitations of each modality, as well as the strength of each alone and in combination with other modalities, with regard to its sensitivity, specificity, and overall value as a diagnostic tool. Finally, key clinical recommendations for the interpretation and step-wise decision-making process for intervention are discussed. Multimodality neuromonitoring relies on the strengths of different types of neurophysiological modalities to maximize the diagnostic efficacy in regard to sensitivity and specificity in the detection of impending neural injury. Thorough knowledge of the benefits and limitations of each modality helps in optimizing the diagnostic value of intraoperative monitoring during spinal procedures. As many spinal surgeries continue to evolve along a pathway of minimal invasiveness, it is quite likely that the value of neuromonitoring will only continue to become more prominent.

Impact of anesthesia on transcranial electric motor evoked potential monitoring during spine surgery: a review of the literature.

OBJECT: Transcranial motor evoked potential (TcMEP) monitoring is frequently used in complex spinal surgeries to prevent neurological injury. Anesthesia, however, can significantly affect the reliability of TcMEP monitoring. Understanding the impact of various anesthetic agents on neurophysiological monitoring is therefore essential. METHODS: A literature search of the National Library of Medicine database was conducted to identify articles pertaining to anesthesia and TcMEP monitoring during spine surgery. Twenty studies were selected and reviewed. RESULTS: Inhalational anesthetics and neuromuscular blockade have been shown to limit the ability of TcMEP monitoring to detect significant changes. Hypothermia can also negatively affect monitoring. Opioids, however, have little influence on TcMEPs. Total intravenous anesthesia regimens can minimize the need for inhalational anesthetics. CONCLUSIONS: In general, selecting the appropriate anesthetic regimen with maintenance of a stable concentration of inhalational or intravenous anesthetics optimizes TcMEP monitoring.

Intraoperative neurophysiological monitoring in vestibular schwannoma surgery: advances and clinical implications.

OBJECT: Intraoperative neurophysiological monitoring has become an integral part of vestibular schwannoma surgery. The aim of this article was to review the different techniques of intraoperative neurophysiological monitoring in vestibular schwannoma surgery, identify the clinical impact of certain pathognomonic patterns on postoperative outcomes of facial nerve function and hearing preservation, and highlight the role of postoperative medications in improving delayed cranial nerve dysfunction in the different reported series. METHODS: The authors performed a review of the literature regarding intraoperative monitoring in acoustic/vestibular schwannoma surgery. The different clinical series representing different monitoring techniques were reviewed. All the data from clinical series were analyzed in a comprehensive and comparative model. RESULTS: Intraoperative brainstem auditory evoked potential monitoring, direct cochlear nerve action potential monitoring, and facial nerve electromyography are the main tools used to assess the functional integrity of an anatomically intact cranial nerve. The identification of pathognomonic brainstem auditory evoked potential and electromyography patterns has been correlated with postoperative functional outcome. Recently, perioperative administration of intravenous hydroxyethyl starch and nimodipine as vasoactive and neuroprotective agents was shown to improve vestibular schwannoma functional outcome in few reported studies. CONCLUSIONS: Recent advances in electrophysiological technology have considerably contributed to improvement in functional outcome of vestibular neuroma surgery in terms of hearing preservation and facial nerve paresis. Perioperative intravenous nimodipine and hydroxyethyl starch may be valuable additions to surgery.

Clinical, functional, and neurophysiologic assessment of dysplastic cortical networks: Implications for cortical functioning and surgical management.

Cortical malformations are highly epileptogenic lesions associated with complex, unanticipated, and often aberrant electrophysiologic and functional relationships. These relationships are inextricably linked to widespread cortical networks subserving eloquent functions, particularly language and motor ability. Cytomegalic neurons but not balloon cells in Palmini type 2 dysplastic cortex are intrinsically hyperexcitable and contribute to local epileptogenesis and functional responsiveness. However, there is much evidence that focal cortical dysplasia is rarely a localized or even regional process, and is a functionally, electrophysiologically, and ultimately clinically integrated neural network disorder. Not surprisingly, malformed cortex is implicated in cognitive dysfunction, particularly disturbances of linguistic processing. An understanding of these relationships is critical for successful epilepsy surgery. Gains in surgical prognosis rely on multiple diagnostic modalities to delineate complex anatomic, electrophysiologic, and functional relationships in magnetic resonance imaging (MRI)-negative patients with rates of seizure-freedom roughly comparable to lesional patients.

Iron states and cognitive abilities in young adults: neuropsychological and neurophysiological assessment.

Many investigators found that iron deficiency anemia (IDA) had a great influence on cognitive functions in infants and children. However, studies of such topic in adults are few and controversial. We prospectively assessed the possible influence of IDA and iron supplementation (for 3 months) on cognitive function and intelligence of 28 young adults with IDA. We used group of hematological, cognitive, neurophysiological tests for assessment including: mini-mental state examination (MMSE), Wechsler memory scale-revised (WMS-R), Wechsler adult intelligence scale-revised (WAIS-R), event-related potentials (ERPs), and electroencephalography (EEG). Compared to controls, patients demonstrated lower scores of different cognitive tests (MMSE, WMS-R, and WAIS-R), which showed significant improvement after treatment. Prolongation of ERPs latencies (N200 and P300) and reduction in their amplitudes (P200 and P300) were identified with significant increase in amplitude occurred after treatment. EEG abnormalities were observed in 55% of patients which showed improvement in 35% after treatment. Positive correlation was identified before and after treatment between hemoglobin levels and MMSE (P=0.01, 0.05), total verbal (P=0.04) and performance (P=0.05, 0.04) IQ scores. Negative correlation was identified between before and after treatment between P300 latency and total IQ of WAIS-R (P=0.03, 0.008) and hemoglobin level (P=0.4, 0.01). Positive correlation was found before and after treatment between P300 amplitude and total IQ (P=0.028, 0.01) and serum iron (P=0.01, 0.001). In conclusion, IDA is a significant factor in cognitive performance in adult population, which can be partially reversed by treatment.

Temporal receptive field estimation using wavelets.

A standard goal of many neurophysiological investigations is to obtain enough insight into a neuron's behavior that it becomes possible to predict responses to arbitrary stimuli. Techniques have been developed to solve this system identification problem, and the numerical method presented here adds to this toolbox. Stimuli and responses, beginning as functions of time, are transformed to complex-valued functions of both time and temporal frequency, giving amplitude and phase at each frequency and time point. The transformation is implemented by wavelets. The kernel describing the system is then derived by simply dividing the response wavelet by the stimulus wavelet. The results are averaged over time, incorporating median filtering to remove artifacts. Estimated kernels match well to the actual kernels, with little data needed. Noise tolerance is excellent, and the method works on a wide range of kernels and stimulus types. The algorithm is easy to implement and understand, but can be applied broadly.

Cognitive neurophysiology: beyond averaging.

Averaging of repeated responses to sensory stimuli is the standard approach in cognitive electrophysiology. This procedure can give rise to inappropriate interpretations in some situations, because two factors contribute to the average ERP responses: the amplitude of the responses during the individual experimental trials, and the concentration of the phases (phase-locking) across responses. Larger poststimulus single-trial amplitudes compared to prestimulus baseline are thought to correspond to a stimulus-related increase of postsynaptic potentials or/and activation of an increased amount of neural assemblies. But the functional interpretation of an enhanced inter-trial phase-locking is unclear. BOLD responses are probably related to single-trial EEG amplitudes, but not to the phase concentration across trials. Therefore, separation of amplitude and phase contributions is indispensable to avoid misinterpretations and to gain a deeper understanding of the relation between event-related EEG and fMRI.

Correcting for the sampling bias problem in spike train information measures.

Information Theory enables the quantification of how much information a neuronal response carries about external stimuli and is hence a natural analytic framework for studying neural coding. The main difficulty in its practical application to spike train analysis is that estimates of neuronal information from experimental data are prone to a systematic error (called "bias"). This bias is an inevitable consequence of the limited number of stimulus-response samples that it is possible to record in a real experiment. In this paper, we first explain the origin and the implications of the bias problem in spike train analysis. We then review and evaluate some recent general-purpose methods to correct for sampling bias: the Panzeri-Treves, Quadratic Extrapolation, Best Universal Bound, Nemenman-Shafee-Bialek procedures, and a recently proposed shuffling bias reduction procedure. Finally, we make practical recommendations for the accurate computation of information from spike trains. Our main recommendation is to estimate information using the shuffling bias reduction procedure in combination with one of the other four general purpose bias reduction procedures mentioned in the preceding text. This provides information estimates with acceptable variance and which are unbiased even when the number of trials per stimulus is as small as the number of possible discrete neuronal responses.

Single-trial multiwavelet coherence in application to neurophysiological time series.

A method of single-trial coherence analysis is presented, through the application of continuous multiwavelets. Multiwavelets allow the construction of spectra and bivariate statistics such as coherence within single trials. Spectral estimates are made consistent through optimal time-frequency localization and smoothing. The use of multiwavelets is considered along with an alternative single-trial method prevalent in the literature, with the focus being on statistical, interpretive and computational aspects. The multiwavelet approach is shown to possess many desirable properties, including optimal conditioning, statistical descriptions and computational efficiency. The methods are then applied to bivariate surrogate and neurophysiological data for calibration and comparative study. Neurophysiological data were recorded intracellularly from two spinal motoneurones innervating the posterior biceps muscle during fictive locomotion in the decerebrated cat.

Gene therapy of metachromatic leukodystrophy reverses neurological damage and deficits in mice.

Metachromatic leukodystrophy (MLD) is a demyelinating lysosomal storage disorder for which new treatments are urgently needed. We previously showed that transplantation of gene-corrected hematopoietic stem progenitor cells (HSPCs) in presymptomatic myeloablated MLD mice prevented disease manifestations. Here we show that HSC gene therapy can reverse neurological deficits and neuropathological damage in affected mice, thus correcting an overt neurological disease. The efficacy of gene therapy was dependent on and proportional to arylsulfatase A (ARSA) overexpression in the microglia progeny of transplanted HSPCs. We demonstrate a widespread enzyme distribution from these cells through the CNS and a robust cross-correction of neurons and glia in vivo. Conversely, a peripheral source of enzyme, established by transplanting ARSA-overexpressing hepatocytes from transgenic donors, failed to effectively deliver the enzyme to the CNS. These results indicate that the recruitment of gene-modified, enzyme-overexpressing microglia makes the enzyme bioavailable to the brain and makes therapeutic efficacy and disease correction attainable. Overall, our data provide a strong rationale for implementing HSPC gene therapy in MLD patients.

A distributed computing system for multivariate time series analyses of multichannel neurophysiological data.

We present a client-server application for the distributed multivariate analysis of time series using standard PCs. We here concentrate on analyses of multichannel EEG/MEG data, but our method can easily be adapted to other time series. Due to the rapid development of new analysis techniques, the focus in the design of our application was not only on computational performance, but also on high flexibility and expandability of both the client and the server programs. For this purpose, the communication between the server and the clients as well as the building of the computational tasks has been realized via the Extensible Markup Language (XML). Running our newly developed method in an asynchronous distributed environment with random availability of remote and heterogeneous resources, we tested the system's performance for a number of different univariate and bivariate analysis techniques. Results indicate that for most of the currently available analysis techniques, calculations can be performed in real time, which, in principle, allows on-line analyses at relatively low cost.

Testing for directed influences among neural signals using partial directed coherence.

One major challenge in neuroscience is the identification of interrelations between signals reflecting neural activity. When applying multivariate time series analysis techniques to neural signals, detection of directed relationships, which can be described in terms of Granger-causality, is of particular interest. Partial directed coherence has been introduced for a frequency domain analysis of linear Granger-causality based on modeling the underlying dynamics by vector autoregressive processes. We discuss the statistical properties of estimates for partial directed coherence and propose a significance level for testing for nonzero partial directed coherence at a given frequency. The performance of this test is illustrated by means of linear and non-linear model systems and in an application to electroencephalography and electromyography data recorded from a patient suffering from essential tremor.

Testing equality of two functions using BARS.

This article presents two methods of testing the hypothesis of equality of two functions H(0):f(1)(t)=f(2)(t) for all t, in a generalized non-parametric regression framework using a recently developed generalized non-parametric regression method called Bayesian adaptive regression splines (BARS). Of particular interest is the special case of testing equality of two Poisson process intensity functions lambda(1) (t)=lambda(2) (t), which arises frequently in neurophysiological applications. The first method uses Bayes factors, and the second method uses a modified Hotelling T(2) test. Both methods are applied to the analysis of 347 motor cortical neurons and, for certain choices of test criteria, the two methods lead to the same conclusions for all but 7 neurons. A small simulation study of power indicates that the Bayes factor can be somewhat more powerful in small samples. The T(2)-type test should be useful in screening large number of neurons for condition-related activity, while the Bayes factor will be especially helpful in assessing evidence in favour of H(0).

Accuracy of tetrode spike separation as determined by simultaneous intracellular and extracellular measurements.

Simultaneous recording from large numbers of neurons is a prerequisite for understanding their cooperative behavior. Various recording techniques and spike separation methods are being used toward this goal. However, the error rates involved in spike separation have not yet been quantified. We studied the separation reliability of "tetrode" (4-wire electrode)-recorded spikes by monitoring simultaneously from the same cell intracellularly with a glass pipette and extracellularly with a tetrode. With manual spike sorting, we found a trade-off between Type I and Type II errors, with errors typically ranging from 0 to 30% depending on the amplitude and firing pattern of the cell, the similarity of the waveshapes of neighboring neurons, and the experience of the operator. Performance using only a single wire was markedly lower, indicating the advantages of multiple-site monitoring techniques over single-wire recordings. For tetrode recordings, error rates were increased by burst activity and during periods of cellular synchrony. The lowest possible separation error rates were estimated by a search for the best ellipsoidal cluster shape. Human operator performance was significantly below the estimated optimum. Investigation of error distributions indicated that suboptimal performance was caused by inability of the operators to mark cluster boundaries accurately in a high-dimensional feature space. We therefore hypothesized that automatic spike-sorting algorithms have the potential to significantly lower error rates. Implementation of a semi-automatic classification system confirms this suggestion, reducing errors close to the estimated optimum, in the range 0-8%.