Journal of Clinical Neurophysiology最新文献

Summary: The NeuroPace responsive neurostimulation system (RNS) has revolutionized the care of patients suffering from focal epilepsy since its approval in 2014. One major advantage of this device is its innate ability to gather long-term electrocorticographic (ECoG) data that the device uses in its novel closed-loop treatment paradigm. Beyond the standard stimulation treatments, which have been demonstrated to be safe and well-tolerated, the data collected by the RNS provide valuable information, such as the long-term circadian and ultradian variations that affect seizure risk, obtained under naturalistic conditions. Additionally, these data inform future surgical procedures, supplementing clinically reported seizures by patients, assessing the response to newly added anti-seizure medications, helping to forecast the risk of future seizures, and understanding the mechanisms of certain long-term outcomes in patients with postsurgical epilepsy. By leveraging these data, the delivery of high-quality clinical care for patients with epilepsy can only be enhanced. Finally, these data open significant avenues of research, including machine learning and artificial intelligence algorithms, which may also translate to improved outcomes in patients who struggle with recurrent seizures.

Purpose: There is a lack of clinical and epidemiological knowledge about nonconvulsive status epilepticus (NCSE) in developing countries including Mexico, which has the highest prevalence of epilepsy in the Americas. Our aim was to describe the clinical findings, EEG features, and outcomes of NCSE in a tertiary center in Mexico.

Methods: We conducted a retrospective case series study (2010-2020) including patients (≥15 years old) with NCSE according to the modified Salzburg NCSE criteria 2015 with at least 6 months of follow-up. We extracted the clinical data (age, sex, history of epilepsy, antiseizure medications, clinical manifestations, triggers, and etiology), EEG patterns of NCSE, and outcome. Descriptive statistics and multinomial logistic regression were used.

Results: One hundred thirty-four patients were analyzed; 74 (54.8%) women, the total mean age was 39.5 (15-85) years, and 71% had a history of epilepsy. Altered state of consciousness was found in 82% (including 27.7% in coma). A generalized NCSE pattern was the most common (32.1%). The NCSE etiology was mainly idiopathic (56%), and previous uncontrolled epilepsy was the trigger in 48% of patients. The clinical outcome was remission with clinical improvement in 54.5%. Multinomial logistic regression showed that the patient's age (P = 0.04), absence of comorbidities (P = 0.04), history of perinatal hypoxia (P = 0.04), absence of clinical manifestations (P = 0.01), and coma (P = 0.03) were negatively correlated with the outcome and only the absence of generalized slowing in the EEG (P = 0.001) had a significant positive effect on the prognosis.

Conclusions: Age, history of perinatal hypoxia, coma, and focal ictal EEG pattern influence negatively the prognosis of NCSE.

Introduction: The sympathetic skin response (SSR) reflects the function of sudomotor sympathetic unmyelinated fibers. This study evaluates SSR abnormalities in ulnar neuropathy at the elbow (UNE).

Methods: Sympathetic skin response was obtained after electrical stimulation of the glabella recording simultaneously from the hand palm (P), third digit (M3) and fifth digit ipsilateral (U5) and contralateral (cU5) to the side of UNE. Ten consecutive SSRs were recorded from each recording side of all participants.

Results: The authors enrolled 31 patients (mean age 54.3 ± 11.4 years) and 25 subjects of a control group (mean age 52.6 ± 11.3 years). The mean of the areas and the area of the largest response of U5-SSR were significantly lower in the patients (106.9 ± 68.9 and 127.8 ± 79.7 μV/s, respectively) than in control group (161.8 ± 116.6 and 197.2 ± 143.3 μV/s, respectively) and in the affected than in the unaffected sides of the patients (155.3 ± 84.8 and 197.7 ± 103.3 μV/s, respectively); there were no differences in U5-SSR latencies and P-SSR and M3-SSR parameters. U5-SSR mean areas and U5-SSR largest area were reduced in 29% and 26% of patients, respectively. The differences between patients and control group and the number of patients with U5-SSR abnormalities increased when the ratios of M3/U5-SSR and U5/cU5-SSR areas were considered. U5-SSR area was related to UNE clinical severity and to some parameters of the ulnar nerve conduction velocity and cutaneous silent period.

Conclusions: Sympathetic skin response is useful to demonstrate abnormalities of sympathetic fibers even if UNE patients do not complain for sympathetic symptoms. The SSR abnormalities were evident only if electrophysiological damage of myelinated fibers was moderate or severe.

Purpose: Repetitive ocular vestibular evoked myogenic potentials (ROVEMP) are a novel diagnostic test to quantify neuromuscular transmission deficits in extraocular muscles in myasthenia gravis. We aimed to investigate the test-retest reliability of the ROVEMP and the effect of amplitude and age.

Methods: We performed the ROVEMP test twice in 19 patients with myasthenia gravis (52.7 ± 19.8 years) and in 15 healthy control subjects (46.5 ± 16 years). The Bland-Altman level of agreement was determined. The relationship between test-retest reliability and signal quality, participant age and signal amplitude was studied.

Results: Limits of agreement were from -179.9 to 139.3 in myasthenia gravis patients and from -56.9 to 89.5 in healthy control subjects. Difference between measurements correlated with signal amplitude ( r = -0.50, P < 0.001). Combining the primary cohort with previously published data from 114 subjects, we found a significant negative correlation between age and reference amplitude ( r = -0.163, P = 0.045).

Conclusions: This study shows that in our hands, the test-retest reliability of the ROVEMP is not optimal. Measurements with higher reference amplitude had a better quality, higher reproducibility, and increased diagnostic yield. We caution against the use of ROVEMP measurements of lower amplitude in clinical practice. In addition, given the correlation between age and amplitude, age matching of healthy control subjects and patients is essential in future studies.

Summary: Electroencephalography (EEG) monitoring has served as a cornerstone in the diagnostic and therapeutic evaluation of epilepsy since its development. This has been accomplished with short-term inpatient video-EEG hospitalization enabling observation of both the semiological and the electrographic features of seizures or with short-term home ambulatory EEG or video-EEG. The advantages of inpatient video-EEG monitoring are limited by high cost, inconvenience, and inability to monitor patients for long periods (weeks or months) as might be done in the outpatient setting. This limitation has impelled the development of wearable EEG devices, which aim to capture high-quality long-term EEG data in a user-friendly and unobtrusive manner. This review article aims to summarize three broad categories of wearable EEG devices, including scalp, subcutaneous, and intracranial EEG. In this review, we will discuss the features of each type of device and the implications for the management of epilepsy. This review does not aim to describe every wearable EEG device on the market but instead seeks to provide a broad overview of the various categories of device that are available, giving examples of each and those in development (with no intention to recommend or advocate for any particular product).

Purpose: To characterize the epilepsy network as reflected in intracranial electroencephalography (iEEG) across the full spectrum of iEEG frequencies and different phases of epilepsy, using a single, conceptually straightforward mathematical measure.

Methods: The authors applied the spectral Granger causality techniques to intracranial electroencephalography recordings and computed contact-by-contact inward, outward, and total causal flow across frequencies and seizure phases in a selected group of three patients with well-defined, nonlesional seizure foci and prolonged responses to invasive procedures. One seizure and one interictal sample were analyzed per subject.

Results: A prominent intracranial electroencephalography network was identified by Granger causality at both high and low frequencies. This network persists during the preictal and interictal phases of epilepsy and closely matches the visible seizure onset. The causal inflow network corresponded to seizure onset electrode contacts in 8 of 12 conditions, including ripple, infraslow, preictal, and interictal phases of epilepsy. Its most striking feature is the consistent dominance of causal inflow rather than outflow in the vicinity of the seizure onset zone.

Conclusions: Findings of this study indicate that a stable intracranial electroencephalography epilepsy network persists, and it can be characterized by a single Granger causality measure from infraslow to ripple frequencies and from the interictal to the immediate preictal phases of epilepsy.

Purpose: Nerve conduction study (NCS) is essential for subclassifying Guillain-Barré syndrome (GBS). It is well known that the GBS subclassification can change through serial NCSs. However, the usefulness of serial NCSs is debatable, especially in patients with early stage GBS.

Methods: Follow-up NCS data within 3 weeks (early followed NCS, EFN) and within 3 to 10 weeks (late-followed NCS, LFN) were collected from 60 patients with GBS who underwent their first NCS (FN) within 10 days after symptom onset. Each NCS was classified into five subtypes (normal, demyelinating, axonal, inexcitable, and equivocal), according to Hadden's and Rajabally's criteria. We analyzed the frequency of significant changes in classification (SCCs) comprising electrodiagnostic aggravation and subtype shifts between demyelinating and axonal types according to follow-up timing.

Results: Between FN and EFN, 33.3% of patients with Hadden's criteria and 18.3% with Rajabally's criteria showed SCCs. Between FN and LFN, 23.3% of patients with Hadden's criteria and 21.7% with Rajabally's criteria showed SCCs, of which 71.4% (Hadden's criteria) and 46.2% (Rajabally's criteria) already showed SCCs from the EFN. The conditions of delayed SCCs between EFN and LFN were very early FN, mild symptoms at the FN, or persistent electrophysiological deterioration 3 weeks after symptom onset.

Conclusions: A substantial proportion of patients with GBS showed significant changes in neurophysiological classification at the early stage. Serial NCS may be helpful for precise neurophysiological classification. This study suggests that follow-up NCSs should be performed within 3 weeks of symptom onset in patients with GBS in whom FN was performed within 10 days of symptom onset.

Purpose: Early posttraumatic seizures (EPTS) occur after pediatric traumatic brain injury and have been associated with unfavorable outcomes. We aimed to characterize the relationship among quantitative EEG characteristics of early posttraumatic seizures, cerebral and somatic physiologic measures.

Methods: Differences in baseline physiologic, neuroimaging, and demographic characteristics between those with and without early posttraumatic seizures were investigated using Mann-Whitney U test or Fisher exact test. Multivariable dynamic structural equations modeling was used to investigate time series associations between ictal quantitative EEG characteristics with intracranial pressure, arterial blood pressure, heart rate (HR), and cerebral regional oximetry. Quantitative EEG characteristics included amplitude, total power, spectral edge frequency, peak value frequency, complexity, and periodicity.

Results: Among 72 children, 146 seizures were identified from 19 patients. Early posttraumatic seizures were associated with younger age ( P = 0.0034), increased HR ( P = 0.0018), and increased Glasgow Outcome Scale-Extended scores ( P = 0.0377). Group dynamic structural equations modeling analysis of the first seizure for patients demonstrated that intracranial pressure is negatively associated with spectral edge frequency (standardized regression coefficient -0.12, 99% credible interval [-0.21 to -0.04]), and HR is positively associated with peak value frequency (standardized regression coefficient 0.16, [0.00-0.31]). Among nine patients with seizures arising over the frontal lobe regions, HR was positively associated with peak value frequency (standardized regression coefficient 0.26 [0.02-0.50]) and complexity (standardized regression coefficient 0.14 [0.03-0.26]). Variation in strength and direction of associations was observed between subjects for relationships that were significant during group analysis.

Conclusions: Quantitative EEG characteristics of pediatric early posttraumatic seizures are associated with variable changes in cerebral and systemic physiology, with spectral edge frequency negatively associated with intracranial pressure and peak value frequency positively associated with HR.

Summary: Among the many fears associated with seizures, patients with epilepsy are greatly frustrated and distressed over seizure's apparent unpredictable occurrence. However, increasing evidence have emerged over the years to support that seizure occurrence is not a random phenomenon as previously presumed; it has a cyclic rhythm that oscillates over multiple timescales. The pattern in rises and falls of seizure rate that varies over 24 hours, weeks, months, and years has become a target for the development of innovative devices that intend to detect, predict, and forecast seizures. This article will review the different tools and devices available or that have been previously studied for seizure detection, prediction, and forecasting, as well as the associated challenges and limitations with the utilization of these devices. Although there is strong evidence for rhythmicity in seizure occurrence, very little is known about the mechanism behind this oscillation. This article concludes with early insights into the regulations that may potentially drive this cyclical variability and future directions.

Purpose: To investigate the prognostic value of a simple stratification system of electroencephalographical (EEG) patterns and spectral types for patients after cardiac arrest.

Methods: In this prospectively enrolled cohort, using manually selected EEG segments, patients after cardiac arrest were stratified into five independent EEG patterns (based on background continuity and burden of highly epileptiform discharges) and four independent power spectral types (based on the presence of frequency components). The primary outcome is cerebral performance category (CPC) at discharge. Results from multimodal prognostication testing were included for comparison.

Results: Of a total of 72 patients, 6 had CPC 1-2 by discharge, all of whom had mostly continuous EEG background without highly epileptiform activity at day 3. However, for the same EEG background pattern at day 3, 19 patients were discharged at CPC 3 and 15 patients at CPC 4-5. After adding spectral analysis, overall sensitivity for predicting good outcomes (CPC 1-2) was 83.3% (95% confidence interval 35.9% to 99.6%) and specificity was 97.0% (89.5% to 99.6%). In this cohort, standard prognostication testing all yielded 100% specificity but low sensitivity, with imaging being the most sensitive at 54.1% (36.9% to 70.5%).

Conclusions: Adding spectral analysis to qualitative EEG analysis may further improve the diagnostic accuracy of EEG and may aid developing novel measures linked to good outcomes in postcardiac arrest coma.