Pharmaco-EEG: A history of progress and a missed opportunity

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.

Resting-state electroencephalography delta and theta bands as compensatory oscillations in chronic neuropathic pain: a secondary data analysis

Chronic neuropathic pain (CNP) remains a significant clinical challenge, with complex neurophysiological underpinnings that are not fully understood. Identifying specific neural oscillatory patterns related to pain perception and interference can enhance our understanding and management of CNP. To analyze resting electroencephalography data from individuals with chronic neuropathic pain to explore the possible neural signatures associated with pain intensity, pain interference, and specific neuropathic pain characteristics. We conducted a secondary analysis from a cross-sectional study using electroencephalography data from a previous study, and Brief Pain Inventory from 36 patients with chronic neuropathic pain. For statistical analysis, we modeled a linear or logistic regression by dependent variable for each model. As independent variables, we used electroencephalography data with such brain oscillations: as delta, theta, alpha, and beta, as well as the oscillations low alpha, high alpha, low beta, and high beta, for the central, frontal, and parietal regions. All models tested for confounding factors such as age and medication. There were no significant models for Pain interference in general activity, walking, work, relationships, sleep, and enjoyment of life. However, the model for pain intensity during the past four weeks showed decreased alpha oscillations, and increased delta and theta oscillations were associated with decreased levels of pain, especially in the central area. In terms of pain interference in mood, the model showed high oscillatory Alpha signals in the frontal and central regions correlated with mood impairment due to pain. Our models confirm recent findings proposing that lower oscillatory frequencies, likely related to subcortical pain sources, may be associated with brain compensatory mechanisms and thus may be associated with decreased pain levels. On the other hand, higher frequencies, including alpha oscillations, may disrupt top-down compensatory mechanisms.

Effects of antiepileptic drugs on electroencephalography (EEG): Insights and applicability

OBJECTIVE

The objective of the study was to assess the effects of antiepileptic drugs (AEDs) on posterior alpha rhythm and determine whether they produce pathological slow waves in patients with epilepsy.

METHODS

Outpatient electroencephalographs (EEGs) in alert patients were selected. The three compared groups include 1) patients with an interested AED (monotherapy or combined with other AEDs); 2) patients with AEDs other than the interested AED; and 3) patients who did not take AEDs. Outcomes were frequency of posterior alpha rhythm and presence of generalized continuous (CSWs) and generalized intermittent slow waves (ISWs). Analysis of variance was used to assess which AED was associated with slower posterior alpha rhythm. Chi-square and logistic regression were employed to assess association and odds ratio (OR) with 95% confidence interval (CI) between pathological generalized slow waves and each AED.

RESULTS

Among 1050 EEG tracings, 638 EEGs met our criteria. Electroencephalographs requested because of cognitive decline and psychiatric symptoms were excluded, leaving 616 EEGs for analysis. Four hundred thirty-seven patients received at least one AED, whereas the remaining 179 patients did not take AED. Conventional AEDs [carbamazepine (CBZ), p = 0.024; phenobarbital (PB), p = 0.013; phenytoin (PHT), p = 0.001] except valproic acid (VPA) were associated with slower alpha frequency. Carbamazepine [adjusted OR: 5.74 (95% CI: 2.07, 15.94)] and PB [adjusted OR: 2.58 (95% CI: 1.15, 5.78)] were significantly associated with generalized ISWs. None were associated with generalized CSWs.

CONCLUSIONS

Phenytoin, CBZ, and PB are associated with slower posterior alpha frequency. The latter 2 AEDs also produced pathological generalized ISWs. Valproic acid, benzodiazepines, and new-generation AEDs are not associated with either outcome. The presence of generalized ISWs in patients taking CBZ or PB should be cautiously interpreted since there can be drug effects. Association with cognitive side effects of these slow waves should be further studied.

Electroencephalogram Modifications Associated With Atypical Strict Antipsychotic Monotherapies

BACKGROUND

Antipsychotics produce electroencephalogram (EEG) modifications and increase the risk of epileptic seizure. These modifications remain sparsely studied specifically for atypical antipsychotics. In this context, our study focuses on EEG modifications associated with atypical strict antipsychotic monotherapies.

METHODS

Electroencephalogram recordings of 84 psychiatric patients treated with atypical antipsychotics in strict monotherapy (clozapine, n = 22; aripiprazole, n = 22; olanzapine, n = 17; risperidone, n = 9; quetiapine, n = 8; risperidone long-acting injection, n = 4; and paliperidone long-acting injection, n = 2) were analyzed. The modifications were ranked according to both slowing and excitability scores.

RESULTS

Electroencephalogram modifications (in 51 subjects, 60.71%) were graded according to 4 stages combining general slowing and sharp slow waves and/or epileptiform activities. The presence of sharp or epileptiform activities was significantly greater for clozapine (90.9%) compared with other second-generation antipsychotics (aripiprazole, 50%; olanzapine, 58.8%; quetiapine, 37.5%; risperidone, 44.4%). Age, duration of disease progression, and diagnosis were not associated as risk factors. Electroencephalogram modifications were associated with lower doses for treatment with quetiapine but not for specific antipsychotics. Electroencephalogram modifications and severe excitability were associated with higher chlorpromazine equivalent doses.

CONCLUSIONS

Atypical antipsychotics (clozapine, aripiprazole, quetiapine, olanzapine, and risperidone) induce EEG modifications, and these are significantly greater for clozapine and appear dependent on chlorpromazine equivalent dose. No encephalopathy was observed in these antipsychotic monotherapies, whatever dose.

Clinical correlates of quantitative EEG in Parkinson disease: A systematic review

OBJECTIVE

To assess the relevance of quantitative EEG (qEEG) measures as outcomes of disease severity and progression in Parkinson disease (PD).

METHODS

Main databases were systematically searched (January 2018) for studies of sufficient methodologic quality that examined correlations between clinical symptoms of idiopathic PD and cortical (surface) qEEG metrics.

RESULTS

Thirty-six out of 605 identified studied were included. Results were classified into 4 domains: cognition (23 studies), motor function (13 studies), responsiveness to interventions (7 studies), and other (10 studies). In cross-sectional studies, EEG slowing correlated with global cognitive impairment and with diffuse deterioration in other domains. In longitudinal studies, decreased dominant frequency and increased θ power, reflecting EEG slowing, were biomarkers of cognitive deterioration at an individual level. Results on motor dysfunction and treatment yielded contrasting findings. Studies on functional connectivity at an individual level and longitudinal studies on other domains or on connectivity measures were lacking.

CONCLUSION

qEEG measures reflecting EEG slowing, particularly decreased dominant frequency and increased θ power, correlate with cognitive impairment and predict future cognitive deterioration. qEEG could provide reliable and widely available biomarkers for nonmotor disease severity and progression in PD, potentially promoting early diagnosis of nonmotor symptoms and an objective monitoring of progression. More studies are needed to clarify the role of functional connectivity and network analyses.

Quantitative Pharmaco-Electroencephalography in Antiepileptic Drug Research

Pharmaco-electroencephalography (pharmaco-EEG) has never gained great popularity in epilepsy research. Nevertheless, the electroencephalogram (EEG) is the most important neurological examination technique in this patient population. Development and investigation of antiepileptic drugs (AEDs) involves EEG for diagnosis and outcome evaluation. In contrast to the common use of the EEG for documenting the effect of AEDs on the presence of interictal epileptiform activities or seizures, quantitative analysis of drug responses in the EEG are not yet standard in pharmacological studies. We provide an overview of dedicated pharmaco-EEG studies with AEDs in humans. A systematic search in PubMed yielded 43 articles, which were reviewed for their relevance. After excluding studies according to our exclusion criteria, nine studies remained. These studies plus the retrieved references from the bibliographies of the identified studies yielded 37 studies to be included in the review. The most prominent method in pharmaco-EEG research for AEDs was analysis of the frequency content in response to drug intake, often with quantitative methods such as spectral analysis. Despite documenting the effect of the drug on brain activity, some studies were conducted in order to document treatment response, detect neurotoxic effects, and measure reversibility of AED-induced changes. There were some attempts to predict treatment response or side effects. We suggest that pharmaco-EEG deserves more attention in AED research, specifically because the newest drugs and techniques have not yet been subject to investigation.

Quantitative methods in electroencephalography to access therapeutic response

Pharmacometrics or Quantitative Pharmacology aims to quantitatively analyze the interaction between drugs and patients whose tripod: pharmacokinetics, pharmacodynamics and disease monitoring to identify variability in drug response. Being the subject of central interest in the training of pharmacists, this work was out with a view to promoting this idea on methods to access the therapeutic response of drugs with central action. This paper discusses quantitative methods (Fast Fourier Transform, Magnitude Square Coherence, Conditional Entropy, Generalised Linear semi-canonical Correlation Analysis, Statistical Parametric Network and Mutual Information Function) used to evaluate the EEG signals obtained after administration regimen of drugs, the main findings and their clinical relevance, pointing it as a contribution to construction of different pharmaceutical practice. Peter Anderer et. al in 2000 showed the effect of 20mg of buspirone in 20 healthy subjects after 1, 2, 4, 6 and 8h after oral ingestion of the drug. The areas of increased power of the theta frequency occurred mainly in the temporo-occipital - parietal region. It has been shown by Sampaio et al., 2007 that the use of bromazepam, which allows the release of GABA (gamma amino butyric acid), an inhibitory neurotransmitter of the central nervous system could theoretically promote dissociation of cortical functional areas, a decrease of functional connectivity, a decrease of cognitive functions by means of smaller coherence (electrophysiological magnitude measured from the EEG by software) values. Ahmad Khodayari-Rostamabad et al. in 2015 talk that such a measure could be a useful clinical tool potentially to assess adverse effects of opioids and hence give rise to treatment guidelines. There was the relation between changes in pain intensity and brain sources (at maximum activity locations) during remifentanil infusion despite its potent analgesic effect. The statement of mathematical and computational aspects in the use of clinical data is frequent and elucidation of these aspects we use PhysioNet https://www.physionet.org/, Clinical Database online supported by the National Institutes of Health (National Institutes of Health of United States of America/NIH-USA) for the acquisition of EEG data and the Matlab program to do the simulations with the methods and thus create opportunities greater understanding.

Pharmaco-EEG: A Study of Individualized Medicine in Clinical Practice

Pharmaco-electroencephalography (Pharmaco-EEG) studies using clinical EEG and quantitative EEG (qEEG) technologies have existed for more than 4 decades. This is a promising area that could improve psychotropic intervention using neurological data. One of the objectives in our clinical practice has been to collect EEG and quantitative EEG (qEEG) data. In the past 5 years, we have identified a subset of refractory cases (n = 386) found to contain commonalities of a small number of electrophysiological features in the following diagnostic categories: mood, anxiety, autistic spectrum, and attention deficit disorders, Four abnormalities were noted in the majority of medication failure cases and these abnormalities did not appear to significantly align with their diagnoses. Those were the following: encephalopathy, focal slowing, beta spindles, and transient discharges. To analyze the relationship noted, they were tested for association with the assigned diagnoses. Fisher's exact test and binary logistics regression found very little (6%) association between particular EEG/qEEG abnormalities and diagnoses. Findings from studies of this type suggest that EEG/qEEG provides individualized understanding of pharmacotherapy failures and has the potential to improve medication selection.

Neurophysiological pharmacodynamic measures of groups and individuals extended from simple cognitive tasks to more "lifelike" activities

OBJECTIVE

Extend a method to track neurophysiological pharmacodynamics during repetitive cognitive testing to a more complex "lifelike" task.

METHODS

Alcohol was used as an exemplar psychoactive substance. An equation, derived in an exploratory analysis to detect alcohol's EEGs effects during repetitive cognitive testing, was validated in a Confirmatory Study on a new group whose EEGs after alcohol and placebo were recorded during working memory testing and while operating an automobile driving simulator.

RESULTS

The equation recognized alcohol by combining five times beta plus theta power. It worked well (p < .0001) when applied to both tasks in the confirmatory group. The maximum EEG effect occurred 2-2.5 h after drinking (>1 h after peak BAC) and remained at 90% at 3.5-4 h (BAC < 50% of peak). Individuals varied in the magnitude and timing of the EEG effect.

CONCLUSION

The equation tracked the EEG response to alcohol in the Confirmatory Study during both repetitive cognitive testing and a more complex "lifelike" task. The EEG metric was more sensitive to alcohol than several autonomic physiological measures, task performance measures or self-reports.

SIGNIFICANCE

Using EEG as a biomarker to track neurophysiological pharmacodynamics during complex "lifelike" activities may prove useful for assessing how drugs affect integrated brain functioning.

Effects of Psychotropic Drugs on Quantitative EEG among Patients with Schizophrenia-spectrum Disorders

OBJECTIVE

We examined how psychotropic medications affected quantitative EEG (qEEG) results among patients with a schizophrenia-spectrum disorder.

METHODS

The drugs were clustered into nine groups depending on their mechanism. We hypothesized that drugs would affect the relative power shown in qEEG results independently and investigated the effect of each drug group on relative power using multiple linear regression analysis and independent samples t-tests.

RESULTS

We found that antipsychotics other than clozapine induced an increase in the relative power of alpha activity. Clozapine markedly increased slow waves and decreased alpha activity in the occipital area. The main findings for antidepressants and antiepileptic drugs were the beta increment and lithium increased the power of delta and theta activity. However, we found no evident changes in power due to benzodiazepine.

CONCLUSION

Our results are generally consistent with previous pharmaco-EEG studies, despite some differences. Therefore, the EEG effect in each drug group could be singled out even under the polypharmacy condition, with the possible exception of benzodiazepines. Our results support using a new methodological approach to identify the qEEG effects of various psychotropic drugs in clinical settings.

Advances in electrophysiology in the diagnosis of behavioral disorders

INTRODUCTION

Diagnosis in psychiatry remains largely subjective. Developing biological observations in psychiatric disorders into laboratory-based diagnostic tests can significantly impact diagnosis and management of these disorders. Diagnostic electrophysiological techniques are non-invasive and relatively inexpensive.

AREAS COVERED

In this review, the authors propose that enough knowledge has accumulated to allow the establishment of psychiatry-based clinical electrophysiology laboratories (PCELs). A brief summary of established clinical indications for electrophysiology tests, summary of highly promising technologies and a presentation of a proposed four-step approach to facilitate the translation of promising biological observations into diagnostic tests are provided. The reader should develop an appreciation of the current status of the clinical applications of psychiatric electrophysiology. The authors propose to capitalize on the widely accepted indication to rule out medical causes of psychiatric symptoms (e.g., epileptic activity) to begin developing PCELs as the equipment and skills necessary are basic to the entire discipline. The potential impact of the growing knowledge on the practice of psychiatry is explored to update clinicians and administrators as they develop laboratory and service plans.

EXPERT OPINION

Psychiatric electrophysiology currently plays a limited role in the diagnosis and management in psychiatry. This status is not supported by the existing literature. The underutilization of electrophysiological tests in psychiatry is propagated by the fact that the laboratories providing the service are not managed by psychiatrists. The authors propose that the first steps are to establish such laboratories and train psychiatrists to competently provide the service.

Increasing synchronization may promote seizure termination: Evidence from status epilepticus

OBJECTIVE

To test whether increasing synchronization of neuronal activity might be causally related to seizure termination.

METHODS

Neuronal synchronization was assessed by the relative changes of the eigenvalue spectrum of the equal-time correlation matrix computed from a short window sliding along multi-channel EEGs, recorded with either intracranial or surface electrodes.

RESULTS

Synchronization dynamics of six status epilepticus EEG recordings from six patients were assessed. In all six recordings EEG synchronization fluctuated around relatively low levels during ongoing epileptiform activity. Synchronization only persistently increased before, or in one case, at the end of status epilepticus. Ongoing seizure activity stopped without pharmacological intervention in one patient. In four of the five other cases, the persistent increase of synchronization followed administration of anticonvulsant drugs.

CONCLUSIONS

Our findings support the hypothesis that increasing synchronization of neuronal activity may be considered as an emergent self-regulatory mechanism for seizure termination.

SIGNIFICANCE

The traditional concept is challenged that increasing neuronal synchronization during epileptic seizures is always pathological and should be suppressed. On the contrary, our findings imply that therapeutic interventions to increase synchronization during seizures might be beneficial.