Journal of Clinical Neurophysiology最新文献

Purpose: Complex repetitive discharges (CRDs) are incompletely understood needle electromyography (EMG) waveforms seen in both myopathic and neurogenic disorders including radiculopathies. This study aimed to clarify the significance of CRDs in patients with radiculopathies.

Methods: This case-control study randomly identified 100 patients with needle EMG evidence of radiculopathy demonstrating at least one CRD in the electrodiagnostically involved myotome between January 2017 and January 2022. These patients were compared with 100 randomly selected patients with EMG evidence of radiculopathy without CRDs controlled for sex, age at EMG testing, and affected nerve root segment. Patient clinical symptoms, neurologic examination, EMG features, and imaging were analyzed. A paired sample t -test for categorial data and χ 2 test for nonparametric data were used for statistical analysis with significance defined as P < 0.05.

Results: Patients with radiculopathies with CRDs had longer disease duration averaging 59 months (range 1-480) compared with patients with radiculopathies without CRDs averaging 26 months (range 1-192, P < 0.01). Clinical symptoms of paresthesias and weakness were both significantly more common in patients with radiculopathies with CRDs than those without CRDs ( P < 0.01 and 0.01, respectively). Needle EMG demonstrated a greater average number of muscles with neurogenic motor unit potentials per radiculopathy in patients with radiculopathies with CRDs compared with those without CRDs. Imaging studies of patients with radiculopathies with CRDs were more likely to reveal evidence of nerve root compression ( P < 0.01).

Conclusions: The presence of CRDs in patients with radiculopathies is consistent with clinically more symptomatic radiculopathies and a longer duration of nerve root compromise.

Purpose: EEG reactivity (EEG-R) has become widely used in intensive care units for diagnosing and prognosticating patients with disorders of consciousness. Despite efforts toward standardization, including the establishment of terminology for critical care EEG in 2012, the processes of testing and interpreting EEG-R remain inconsistent.

Methods: A review was conducted on PubMed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Inclusion criteria consisted of articles published between January 2012, and November 2022, testing EEG-R on adult intensive care unit patients. Exclusion criteria included articles focused on highly specialized stimulation equipment or animal, basic science, or small case report studies. The Quality In Prognostic Studies tool was used to assess risk of bias.

Results: One hundred and five articles were identified, with 26 variables collected for each. EEG-R testing varied greatly, including the number of stimuli (range: 1-8; 26 total described), stimulus length (range: 2-30 seconds), length between stimuli (range: 10 seconds-5 minutes), frequency of stimulus application (range: 1-9), frequency of EEG-R testing (range: 1-3 times daily), EEG electrodes (range: 4-64), personnel testing EEG-R (range: neurophysiologists to nonexperts), and sedation protocols (range: discontinuing all sedation to no attempt). EEG-R interpretation widely varied, including EEG-R definitions and grading scales, personnel interpreting EEG-R (range: EEG specialists to nonneurologists), use of quantitative methods, EEG filters, and time to detect EEG-R poststimulation (range: 1-30 seconds).

Conclusions: This study demonstrates the persistent heterogeneity of testing and interpreting EEG-R over the past decade, and contributing components were identified. Further many institutional efforts must be made toward standardization, focusing on the reproducibility and unification of these methods, and detailed documentation in the published literature.

Purpose: While hyperventilation (HV) increases the diagnostic yield of EEG in children, there is conflicting evidence to support its application in adults. For the first time in history, a large cohort of patients has undergone EEGs without HV during the COVID-19 pandemic. Utilizing this opportunity, we sought to investigate whether HV increases the diagnostic yield of EEG in children compared with adults.

Methods: Patients aged six years and above who had routine EEGs at Monash Health between January 2019 and December 2020 were studied. The cohort was divided into two, pediatric (younger than 18 years) and adult (18 years or older). Epileptiform abnormalities (ictal and interictal) were the outcomes investigated. The effect of HV was examined with logistic regression to determine odds ratios with 95% confidence intervals.

Results: In total, we studied 3,273 patients (pediatric = 830, adult = 2,443). In the pediatric cohort, HV significantly increased the diagnostic yield of absence seizures ( p = 0.01, odds ratios 2.44, 95% confidence intervals 1.21-4.93). In adults, HV did not increase the yield of absence seizures ( p = 0.34, odds ratios 0.36, 95% confidence intervals 0.05-2.88). Interictal epileptiform discharges during HV were significantly more frequent in children compared with adults ( p < 0.001, odds ratios 3.81, 95% confidence intervals 2.51-5.77).

Conclusions: Hyperventilation is useful to increase the yield of interictal epileptiform discharges and absence seizures in pediatric patients but not in adults. Hence, routine EEG may be recorded in adults without HV when it is unsafe to perform.

Purpose: Sleep studies are important to evaluate sleep and sleep-related disorders. The standard test for evaluating sleep is polysomnography, during which several physiological signals are recorded separately and simultaneously with specialized equipment that requires a technologist. Simpler recordings that can model the results of a polysomnography would provide the benefit of expanding the possibilities of sleep recordings.

Methods: Using the publicly available sleep data set from the multiethnic study of atherosclerosis and 1769 nights of sleep, we extracted a distinct data subset with engineered features of the biomarkers collected by actigraphic, oxygenation, and electrocardiographic sensors. We then applied scalable models with recurrent neural network and Extreme Gradient Boosting (XGBoost) with a layered approach to produce an algorithm that we then validated with a separate data set of 177 nights.

Results: The algorithm achieved an overall performance of 0.833 accuracy and 0.736 kappa in classifying into four states: wake, light sleep, deep sleep, and rapid eye movement (REM). Using feature analysis, we demonstrated that heart rate variability is the most salient feature, which is similar to prior reports.

Conclusions: Our results demonstrate the potential benefit of a multilayered algorithm and achieved higher accuracy and kappa than previously described approaches for staging sleep. The results further the possibility of simple, wearable devices for sleep staging. Code is available at https://github.com/NovelaNeuro/nEureka-SleepStaging .

Purpose: This study aims to show the impact of multimodal intraoperative neurophysiologic monitoring (IOM) in glioma surgery in preventing severe neurologic injury and increasing tumor removal by comparing the historical cases where IOM was not used.

Methods: Fifty-nine patients with glial tumors located nearby the eloquent area, operated by the same surgeon, were included in the study. Between 2008 and 2012, 21 patients were operated on without IOM (non-IOM); between 2018 and 2021, 38 patients were operated on with IOM.

Results: The preoperative Karnofsky performance status scale (KPSS) scores were not statistically significant between non-IOM and IOM groups (P = 0.351). Postoperative KPSS (mean 97.9) scores were 15.7% higher than preoperative KPSS (mean 84.6) in the IOM group (P < 0.001). Conversely, there was no significant difference between preoperative and postoperative KPSS scores (mean 78.5 and 81.5, respectively) in the non-IOM group (P = 0.472). Moreover, postoperative KPSS scores were 20% higher in the IOM group than in the non-IOM group (P < 0.001). Preoperative tumor sizes were double the size in the non-IOM group compared with those in the IOM group (P = 0.007). Nevertheless, the postsurgery tumor residue volume was almost four times higher in the non-IOM group than that in the IOM group (P = 0.035). A median of 93.35% of the tumor volume was resected in the IOM group, but only 77.26% of the tumor was removed in the non-IOM group (P < 0.001).

Conclusions: Intraoperative neurophysiologic monitoring helps in a more radical tumor resection in glial tumors located close to the eloquent area, improves postoperative neurologic outcomes, and maintains the patient's quality of life.

Purpose: Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is an autoimmune reaction involving Immunoglobulin G antibodies against GluN1 subunit of NMDAR. Absence of biomarkers for early diagnosis and prognosis poses a challenge. Several small case-control studies have emphasized the prospect of quantitative EEG measurements. This study aimed to analyze and identify novel scalp quantitative EEG biomarkers and their implications on outcome of NMDRA encephalitis compared with a control group.

Methods: Retrospective (2012-2021) case-control study of patients with NMDRA encephalitis and with acute/subacute encephalitis from other causes. Clinical variables and outcomes were assessed with modified Rankin Scale at admission, discharge, and follow-up. All patients underwent extensive diagnostic workup, including scalp EEG within 72 hours of admission. Quantitative EEG was calculated for Renyi, Tsalis entropy, Hjorth complexity, mean energy, and spectral power of the following frequency bands and ratios: delta (0.5-4 Hz), theta (5-8 Hz), alpha (9-14 Hz), beta (15-30 Hz), gamma (31-45 Hz), gamma-beta, beta/alpha, beta/theta, and beta/delta. Descriptive statistics, power frequency bands, complexity measures, and Wilcoxon rank sum test were used.

Results: Patients with anti-NMDAR encephalitis had significantly higher delta frequency peak power, higher beta/alpha and gamma/beta frequency ratios, lower alpha and beta peak power, and lower beta/delta frequency ratio than the control group. In patients with anti-NMDAR encephalitis, higher delta and alpha peak power had the worst clinical outcome, at discharge and follow-up, and patients with higher gamma peak power had better outcomes.

Conclusions: Quantitative EEG is a valuable tool to differentiate anti-NMDAR encephalitis from other inflammatory encephalitis and predict outcomes in patients with anti-NMDAR encephalitis.

Purpose: Bereitschaftspotential (BP) or readiness potential in people with functional movement disorders can aid diagnostic workup. We evaluated the diagnostic value of BP as an interictal EEG marker in people with functional seizures (FS).

Methods: We recorded and analyzed BP interictal before intended movements in 17 adults with FS and 17 controls with alternative diagnoses. We evaluated the signals for the presence of BP, latency, amplitude, and early versus late BP.

Results: Bereitschaftspotential was present in all except one person with FS. We found no significant differences in the latency and amplitude of BP between participants with FS and controls. The early BP showed the most significant variance in amplitude, latency, and presence.

Conclusions: We found interictal typical BP values in participants with FS and variable semiology, while earlier research found interictal no BP in functional movement disorders. These findings do not support the use of BP as an interictal diagnostic tool for FS. Differences in early BP and focus on FS with pure motor semiology are starting points for further research evaluating potential interictal markers in people with FS.

Purpose: We demonstrate that different regions of the cerebral cortex have different diurnal rhythms of spontaneously occurring high-frequency oscillations (HFOs).

Methods: High-frequency oscillations were assessed with standard-of-care stereotactic electroencephalography in patients with drug-resistant epilepsy. To ensure generalizability of our findings beyond patients with drug-resistant epilepsy, we excluded stereotactic electroencephalography electrode contacts lying within seizure-onset zones, epileptogenic lesions, having frequent epileptiform activity, and excessive artifact. For each patient, we evaluated twenty-four 5-minute stereotactic electroencephalography epochs, sampled hourly throughout the day, and obtained the HFO rate (number of HFOs/minute) in every stereotactic electroencephalography channel. We analyzed diurnal rhythms of the HFO rates with the cosinor model and clustered neuroanatomic parcels in a standard brain space based on similarity of their cosinor parameters. Finally, we compared overlap among resting-state networks, described in the neuroimaging literature, and chronobiological spatial clusters discovered by us.

Results: We found five clusters that localized predominantly or exclusively to the left perisylvian, left perirolandic and left temporal, right perisylvian and right parietal, right frontal, and right insular-opercular cortices, respectively. These clusters were characterized by similarity of the HFO rates according to the time of the day. Also, these chronobiological spatial clusters preferentially overlapped with specific resting-state networks, particularly default mode network (clusters 1 and 3), frontoparietal network (cluster 1), visual network (cluster 1), and mesial temporal network (cluster 2).

Conclusions: This is probably the first human study to report clusters of cortical regions with similar diurnal rhythms of electrographic activity. Overlap with resting-state networks attests to their functional significance and has implications for understanding cognitive functions and epilepsy-related mortality.

Purpose: Ictal high-frequency oscillations (HFOs) are a reliable indicator of a seizure onset zone for intracranial EEG recordings. Interictal HFOs often are also observed and may be a useful biomarker to supplement ictal data, but distinguishing pathologic from physiologic HFOs continues to be a challenging task. We present a method of classifying HFOs based on morphologic contrast to the background.

Methods: We retrospectively screened 31 consecutive patients who underwent intracranial recordings for epilepsy at Stanford Medical Center during a 2-year period, and 13 patients met the criteria for inclusion. Interictal EEG data were analyzed using an automated event detector followed by morphologic feature extraction and k-means clustering. Instead of only using event features, the algorithm also incorporated features of the background adjacent to the events. High-frequency oscillations with higher morphologic contrast to the background were labeled as pathologic, and "hotspots" with the most active pathologic HFOs were identified and compared with clinically determined seizure onset zones.

Results: Clustering with contrast features produced groups with better separation and more consistent boundaries. Eleven of the 13 patients proceeded to surgery, and patients whose hotspots matched seizure onset zones had better outcomes, with 4 out of 5 "match" patients having no disabling seizures at 1+ year postoperatively (Engel I or International League Against Epilepsy Class 1-2), while all "mismatch" patients continued to have disabling seizures (Fisher exact test P-value = 0.015).

Conclusions: High-frequency oscillations with higher contrast to background more likely represent paroxysmal bursts of pathologic activity. Patients with HFO hotspots outside of identified seizure onset zones may not respond as well to surgery.