Quantitative EEG biomarkers for epilepsy and their relation to chemical biomarkers

The Role of EEG microstates in predicting oxcarbazepine treatment outcomes in patients with newly-diagnosed focal epilepsy

PURPOSE

Microstates represent the global and topographical distribution of electrical brain activity from scalp-recorded EEG. This study aims to explore EEG microstates of patients with focal epilepsy prior to medication, and employ extracted microstate metrics for predicting treatment outcomes with Oxcarbazepine monotherapy.

METHODS

This study involved 25 newly-diagnosed focal epilepsy patients (13 females), aged 12 to 68, with various etiologies. Patients were categorized into Non-Seizure-Free (NSF) and Seizure-Free (SF) groups according to their first follow-up outcomes. From pre-medication EEGs, four representative microstates were identified by using clustering. The temporal parameters and transition probabilities of microstates were extracted and analyzed to discern group differences. With generating sample method, Support Vector Machine (SVM), Logistic Regression (LR), and Naïve Bayes (NB) classifiers were employed for predicting treatment outcomes.

RESULTS

In the NSF group, Microstate 1 (MS1) exhibited a significantly higher duration (mean±std. = 0.092±0.008 vs. 0.085±0.008, p = 0.047), occurrence (mean±std. = 2.587±0.334 vs. 2.260±0.278, p = 0.014), and coverage (mean±std. = 0.240±0.046 vs. 0.194±0.040, p = 0.014) compared to the SF group. Additionally, the transition probabilities from Microstate 2 (MS2) and Microstate 3 (MS3) to MS1 were increased. In MS2, the NSF group displayed a stronger correlation (mean±std. = 0.618±0.025 vs. 0.571±0.034, p < 0.001) and a higher global explained variance (mean±std. = 0.083±0.035 vs. 0.055±0.023, p = 0.027) than the SF group. Conversely, Microstate 4 (MS4) in the SF group demonstrated significantly greater coverage (mean±std. = 0.388±0.074 vs. 0.334±0.052, p = 0.046) and more frequent transitions from MS2 to MS4, indicating a distinct pattern. Temporal parameters contribute major predictive role in predicting treatment outcomes of Oxcarbazepine, with area under curves (AUCs) of 0.95, 0.70, and 0.86, achieved by LR, NB and SVM, respectively.

CONCLUSION

This study underscores the potential of EEG microstates as predictive biomarkers for Oxcarbazepine treatment responses in newly-diagnosed focal epilepsy patients.

The effects of slow wave sleep characteristics on semantic, episodic, and procedural memory in people with epilepsy

Slow wave sleep (SWS) is highly relevant for verbal and non-verbal/spatial memory in healthy individuals, but also in people with epilepsy. However, contradictory findings exist regarding the effect of seizures on overnight memory retention, particularly relating to procedural and non-verbal memory, and thorough examination of episodic memory retention with ecologically valid tests is missing. This research explores the interaction of SWS duration with epilepsy-relevant factors, as well as the relation of spectral characteristics of SWS on overnight retention of procedural, verbal, and episodic memory. In an epilepsy monitoring unit, epilepsy patients (N = 40) underwent learning, immediate and 12 h delayed testing of memory retention for a fingertapping task (procedural memory), a word-pair task (verbal memory), and an innovative virtual reality task (episodic memory). We used multiple linear regression to examine the impact of SWS duration, spectral characteristics of SWS, seizure occurrence, medication, depression, seizure type, gender, and epilepsy duration on overnight memory retention. Results indicated that none of the candidate variables significantly predicted overnight changes for procedural memory performance. For verbal memory, the occurrence of tonic-clonic seizures negatively impacted memory retention and higher psychoactive medication load showed a tendency for lower verbal memory retention. Episodic memory was significantly impacted by epilepsy duration, displaying a potential nonlinear impact with a longer duration than 10 years negatively affecting memory performance. Higher drug load of anti-seizure medication was by tendency related to better overnight retention of episodic memory. Contrary to expectations longer SWS duration showed a trend towards decreased episodic memory performance. Analyses on associations between memory types and EEG band power during SWS revealed lower alpha-band power in the frontal right region as significant predictor for better episodic memory retention. In conclusion, this research reveals that memory modalities are not equally affected by important epilepsy factors such as duration of epilepsy and medication, as well as SWS spectral characteristics.

The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives

Epilepsy is now considered a network disease that affects the brain across multiple levels of spatial and temporal scales. The paradigm shift from an epileptic focus-a discrete cortical area from which seizures originate-to a widespread epileptic network-spanning lobes and hemispheres-considerably advanced our understanding of epilepsy and continues to influence both research and clinical treatment of this multi-faceted high-impact neurological disorder. The epileptic network, however, is not static but evolves in time which requires novel approaches for an in-depth characterization. In this review, we discuss conceptual basics of network theory and critically examine state-of-the-art recording techniques and analysis tools used to assess and characterize a time-evolving human epileptic brain network. We give an account on current shortcomings and highlight potential developments towards an improved clinical management of epilepsy.

Application of quantitative electroencephalography in predicting early cerebral ischemia in patients undergoing carotid endarterectomy

Background

Quantitative electroencephalography (QEEG) has emerged as a promising monitoring method in cerebral ischemia, but the feasibility of QEEG in intraoperative cerebral perfusion-related ischemia monitoring is still uncertain. The purpose of this study was to investigate the value of QEEG monitoring in Carotid Endarterectomy (CEA) and the thresholds for intraoperative cerebral perfusion-related ischemia monitoring.

Methods

Sixty-three patients who underwent carotid endarterectomy with continuous Transcranial Doppler ultrasound (TCD) monitoring and QEEG monitoring at Xuanwu Hospital Capital Medical University from January 2021 to August 2021 were enrolled in this study. Each patient received total intravenous anesthesia. Middle cerebral artery blood flow velocity (V-MCA) was obtained by TCD. Relative alpha percentage (RA) and alpha-delta ratio (ADR) were obtained by QEEG monitoring. Patients were divided into ischemic and non-ischemic groups using a decline of more than 50% in the V-MCA monitored by TCD as the gold standard.

Results

Of the 63 patients, twenty patients were divided into the ischemic group, and forty-three patients into the non-ischemic group. Ipsilateral post-clamp RA and ADR values of QEEG were decreased for all patients in the ischemic group. The optimal threshold for RA and ADR to predict cerebral ischemia was a 14% decrease from baseline (sensitivity 90.0%, specificity 90.7%, Kappa value 0.786), a 21% decrease from baseline (sensitivity 85.0%, specificity 81.4%, Kappa value 0.622), respectively, indicated by TCD monitoring.

Conclusions

Our study demonstrated that QEEG is a promising monitoring method undergoing CEA under general anesthesia and has good consistency with TCD.

Intermittent theta burst stimulation vs. high-frequency repetitive transcranial magnetic stimulation for post-stroke cognitive impairment: Protocol of a pilot randomized controlled double-blind trial

Introduction

Intermittent theta burst stimulation (iTBS), a novel mode of transcranial magnetic stimulation (TMS), has curative effects on patients with post-stroke cognitive impairment (PSCI). However, whether iTBS will be more applicable in clinical use than conventional high-frequency repetitive transcranial magnetic stimulation (rTMS) is unknown. Our study aims to compare the difference in effect between iTBS and rTMS in treating PSCI based on a randomized controlled trial, as well as to determine its safety and tolerability, and to further explore the underlying neural mechanism.

Methods

The study protocol is designed as a single-center, double-blind, randomized controlled trial. Forty patients with PSCI will be randomly assigned to two different TMS groups, one with iTBS and the other with 5 Hz rTMS. Neuropsychological evaluation, activities of daily living, and resting electroencephalography will be conducted before treatment, immediately post-treatment, and 1 month after iTBS/rTMS stimulation. The primary outcome is the change in the Montreal Cognitive Assessment Beijing Version (MoCA-BJ) score from baseline to the end of the intervention (D11). The secondary outcomes comprise changes in resting electroencephalogram (EEG) indexes from baseline to the end of the intervention (D11) as well as the Auditory Verbal Learning Test, the symbol digit modality test, the Digital Span Test findings, and the MoCA-BJ scores from baseline to endpoint (W6).

Discussion

In this study, the effects of iTBS and rTMS will be evaluated using cognitive function scales in patients with PSCI as well as data from resting EEG, which allows for an in-depth exploration of underlying neural oscillations. In the future, these results may contribute to the application of iTBS for cognitive rehabilitation of patients with PSCI.

Prognostic interictal electroencephalographic biomarkers and models to assess antiseizure medication efficacy for clinical practice: A scoping review

Antiseizure medication (ASM) is the primary treatment for epilepsy. In clinical practice, methods to assess ASM efficacy (predict seizure freedom or seizure reduction), during any phase of the drug treatment lifecycle, are limited. This scoping review identifies and appraises prognostic electroencephalographic (EEG) biomarkers and prognostic models that use EEG features, which are associated with seizure outcomes following ASM initiation, dose adjustment, or withdrawal. We also aim to summarize the population and context in which these biomarkers and models were identified and described, to understand how they could be used in clinical practice. Between January 2021 and October 2022, four databases, references, and citations were systematically searched for ASM studies investigating changes to interictal EEG or prognostic models using EEG features and seizure outcomes. Study bias was appraised using modified Quality in Prognosis Studies criteria. Results were synthesized into a qualitative review. Of 875 studies identified, 93 were included. Biomarkers identified were classed as qualitative (visually identified by wave morphology) or quantitative. Qualitative biomarkers include identifying hypsarrhythmia, centrotemporal spikes, interictal epileptiform discharges (IED), classifying the EEG as normal/abnormal/epileptiform, and photoparoxysmal response. Quantitative biomarkers were statistics applied to IED, high-frequency activity, frequency band power, current source density estimates, pairwise statistical interdependence between EEG channels, and measures of complexity. Prognostic models using EEG features were Cox proportional hazards models and machine learning models. There is promise that some quantitative EEG biomarkers could be used to assess ASM efficacy, but further research is required. There is insufficient evidence to conclude any specific biomarker can be used for a particular population or context to prognosticate ASM efficacy. We identified a potential battery of prognostic EEG biomarkers, which could be combined with prognostic models to assess ASM efficacy. However, many confounders need to be addressed for translation into clinical practice.

Entropy of eye movement during rapid automatized naming

Numerous studies have focused on the understanding of rapid automatized naming (RAN), which can be applied to predict reading abilities and developmental dyslexia in children. Eye tracking technique, characterizing the essential ocular activities, might have the feasibility to reveal the visual and cognitive features of RAN. However, traditional measures of eye movements ignore many dynamical details about the visual and cognitive processing of RAN, and are usually associated with the duration of time spent on some particular areas of interest, fixation counts, revisited fixation counts, saccadic velocities, or saccadic amplitudes. To cope with this drawback, we suggested an entropy-based method to measure eye movements for the first time, which first mapped eye movements during RAN in a time-series and then analyzed the time-series by a proper definition of entropy from the perspective of information theory. Our findings showed that the entropy was more sensitive to reflect small perturbation (e.g., rapid movements between focuses in the presence of skipping or omitting some stimulus during RAN) of eye movements, and thus gained better performance than traditional measures. We also verified that the entropy of eye movements significantly deceased with the age and the task complexity of RAN, and significantly correlated with traditional eye-movement measures [e.g., total time of naming (TTN)] and the RAN-related skills [e.g., selective attention (SA), cognitive speed, and visual-motor integration]. Our findings may bring some new insights into the understanding of both RAN and eye tracking technique itself.

Prediction Value of Epilepsy Secondary to Inferior Cavity Hemorrhage Based on Scalp EEG Wave Pattern in Deep Learning

Objective

To search the predictive value of epilepsy secondary to acute subarachnoid hemorrhage (aSAH) based on EEG wave pattern in deep learning.

Methods

A total of 156 cases of secondary epilepsy with lower cavity hemorrhage in our hospital were selected and divided into the late epilepsy group and the early epilepsy group according to seizure time, and the nonseizure group and the seizure group according to seizure condition. General data of patients were collected, the EEG types of each group were analyzed, and the disease recurrence rate, treatment effect, and symptom onset time were compared.

Results

Rapid and slow and rapid blood flow velocity were the main abnormal manifestations of epilepsy secondary to inferior cavity hemorrhage, accounting for 33.3% and 18.6%, respectively. Compared with the seizure group, the proportion of type ii and type iii in the nonseizure group was higher, and the proportion of type ii and type iii in the early epilepsy group was higher than in the late epilepsy group (P < 0.05). The diagnostic accuracy, missed diagnosis rate, misdiagnosis rate, specificity, and sensitivity of the EEG wave pattern were 94.9%, 3.2%, 1.9%, 91.7%, and 96.2%, respectively. Compared with the early epilepsy group, the recurrence rate of type iii and type iv in the late epilepsy group was higher (P < 0.05). The effective rates of the attack group and the nonattack group were 72.7% and 97.0%, respectively. Compared with the attack group, the effective rate of the nonattack group was higher (P < 0.05). The effective rates of the early epilepsy group and the late epilepsy group were 91.7% and 85.0%, respectively. Compared with the late epilepsy group, the effective rate of the early epilepsy group was higher (P < 0.05). Compared with the early epilepsy group, the late epilepsy group had longer tonic-clonic seizures, atonic seizures, and absent seizures, and the difference between the groups was statistically significant (P < 0.05).

Conclusion

In aSAH secondary epilepsy disease prediction, based on indepth study of the scalp EEG wave type prediction, they play an important role, including aSAH high-risk secondary epilepsy wave types for V, III, and IV types, as well as early and late epilepsy associated with disease stage. Through the diagnosis method to predict the severity of disease, this builds a good foundation for clinical treatment. It is beneficial to improve the effective rate of treatment.

Comparison of the In Vitro and In Vivo Electrochemical Performance of Bionic Electrodes

The electrochemical performance of platinum electrodes was assessed in vitro and in vivo to determine the impact of electrode implantation and the relevance of in vitro testing in predicting in vivo behaviour. A significant change in electrochemical response was seen after electrode polarisation. As a result, initial in vitro measurements were poor predictors of subsequent measurements performed in vitro or in vivo. Charge storage capacity and charge density measurements from initial voltammetric measurements were not correlated with subsequent measurements. Electrode implantation also affected the electrochemical impedance. The typically reported impedance at 1 kHz was a very poor predictor of electrode performance. Lower frequencies were significantly more dependent on electrode properties, while higher frequencies were dependent on solution properties. Stronger correlations in impedance at low frequencies were seen between in vitro and in vivo measurements after electrode activation had occurred. Implanting the electrode increased the resistance of the electrochemical circuit, with bone having a higher resistivity than soft tissue. In contrast, protein fouling and fibrous tissue formation had a minimal impact on electrochemical response. In vivo electrochemical measurements also typically use a quasi-reference electrode, may operate in a 2-electrode system, and suffer from uncompensated resistance. The impact of these experimental conditions on electrochemical performance and the relevance of in vitro electrode assessment is discussed. Recommended in vitro testing protocols for assessing bionic electrodes are presented.

Microdialysis and microperfusion electrodes in neurologic disease monitoring

Contemporary biomarker collection techniques in blood and cerebrospinal fluid have to date offered only modest clinical insights into neurologic diseases such as epilepsy and glioma. Conversely, the collection of human electroencephalography (EEG) data has long been the standard of care in these patients, enabling individualized insights for therapy and revealing fundamental principles of human neurophysiology. Increasing interest exists in simultaneously measuring neurochemical biomarkers and electrophysiological data to enhance our understanding of human disease mechanisms. This review compares microdialysis, microperfusion, and implanted EEG probe architectures and performance parameters. Invasive consequences of probe implantation are also investigated along with the functional impact of biofouling. Finally, previously developed microdialysis electrodes and microperfusion electrodes are reviewed in preclinical and clinical settings. Critically, current and precedent microdialysis and microperfusion probes lack the ability to collect neurochemical data that is spatially and temporally coincident with EEG data derived from depth electrodes. This ultimately limits diagnostic and therapeutic progress in epilepsy and glioma research. However, this gap also provides a unique opportunity to create a dual-sensing technology that will provide unprecedented insights into the pathogenic mechanisms of human neurologic disease.