Interictal brain activity changes in temporal lobe epilepsy: A quantitative electroencephalogram analysis

Quantitative EEG biomarkers of cognitive performance in drug-resistant temporal lobe epilepsy

BACKGROUND

This study aimed to explore quantitative electroencephalography (qEEG) biomarkers of cognitive performance in drug-resistant temporal lobe epilepsy (TLE) and analyze their relationship with clinical characteristics.

METHODS

Cross-sectional study including adult patients with drug-resistant TLE and a control group. Resting-state, eyes-closed qEEG samples were analyzed using the fast Fourier transform approach. Power spectral density was calculated for four frequency bands: delta (1-3.9 Hz), theta (4-7.9 Hz), alpha (8-12.9 Hz), and beta (13-18 Hz). Neuropsychological tests were administered to TLE patients.

RESULTS

Twenty-nine TLE patients (mean age 42 ± 8.2 years; 44.8 % women) and 23 age- and sex-matched controls were enrolled. Clinically significant cognitive impairment was found in 86.2 % of patients (58.6 % amnestic). Compared to controls, TLE patients showed increased ipsilateral power spectral density for the theta (p = 0.045), alpha (p = 0.023) and beta bands in the anterior region (p = 0.029) and for the delta band in the posterior region (p = 0.03). Alpha/theta ratio (ATR) was lower in the posterior quadrant of the epileptogenic hemisphere (p = 0.013), and higher seizure frequency correlated with a lower ATR in the ipsilateral temporal region (r: -0.425; p = 0.021). Patients with amnestic cognitive impairment exhibited higher power spectral density across most frequency bands (p < 0.005). Impaired verbal memory and executive function were associated with increased power density.

CONCLUSION

Increased power spectral density was evident in all frequency bands in the epileptogenic hemisphere, particularly in those patients with amnestic cognitive impairment. Moreover, higher seizure frequency correlated with a lower ATR in the temporal region. Power spectral analysis can provide useful information in drug-resistant TLE patients.

Utility of Quantitative EEG in Neurological Emergencies and ICU Clinical Practice

The electroencephalogram (EEG) is a cornerstone tool for the diagnosis, management, and prognosis of selected patient populations. EEGs offer significant advantages such as high temporal resolution, real-time cortical function assessment, and bedside usability. The quantitative EEG (qEEG) added the possibility of long recordings being processed in a compressive manner, making EEG revision more efficient for experienced users, and more friendly for new ones. Recent advancements in commercially available software, such as Persyst, have significantly expanded and facilitated the use of qEEGs, marking the beginning of a new era in its application. As a result, there has been a notable increase in the practical, real-world utilization of qEEGs in recent years. This paper aims to provide an overview of the current applications of qEEGs in daily neurological emergencies and ICU practice, and some elementary principles of qEEGs using Persyst software in clinical settings. This article illustrates basic qEEG patterns encountered in critical care and adopts the new terminology proposed for spectrogram reporting.

Differences and potential mechanisms of theta oscillation and temporoparietal and temporal-central networks in temporal lobe epilepsy patients with unilateral hippocampal sclerosis

BACKGROUND

There is a lack of further exploration of the epileptogenic network of specific types of epilepsy, such as unilateral hippocampal sclerosis (HS), and there is an urgent need to find exact evidence to confirm the consistency of its brain network changes.

METHODS

We enrolled 22 mesial temporal lobe epilepsy with hippocampal sclerosis (mTLE-HS) patients to compare the differences in brain activity between 22 healthy controls (HCs) and them. Resting-state electroencephalography (EEG) was also measured. Then, we calculated the power spectral density and phase locking values in and between these electrodes.

RESULTS

The results showed the increased theta power was related to the high severity of epilepsy in the temporal, parietal, and central regions in mTLE-HS patients, and there were positive correlations between theta power in the contralateral temporal region and seizure frequency. Theta power in the ipsilateral parietal lobe is positively correlated with the number of anti-seizure medications (ASMs), but not with the usage of third-generation ASMs. Meanwhile, the temporal lobe of mTLE-HS patients had more connectivity with parietal lobe and central region.

CONCLUSIONS

Theta power is an important EEG indicator of mTLE-HS, positively correlates with epilepsy severity and seizure frequency, and has network properties that can be observed outside the lesion. Moreover, the usage of third-generation ASMs did not affect the risk of increased theta power. Lastly, the temporoparietal and temporal-central networks are likely to be causative pathways in epilepsy patients with cognitive impairment. This study provides a potential guideline for the treatment of mTLE-HS in clinical practice.

The predicative value of early quantitative electroencephalograph in epilepsy after severe traumatic brain injury in children

Objective

To explore whether early quantitative electroencephalograph (EEG) can predict the development of epilepsy in pediatric patients with severe traumatic brain injury (TBI).

Methods

A total of 78 children with severe TBI who were admitted to our hospital were divided into post-traumatic epilepsy (PTE) and non-PTE groups according to whether or not they developed PTE. EEGs of frontal, central and parietal lobes were recorded at the time of their admission. The power values of each frequency band, odds ratio and peak envelope power values of each brain region were statistically analyzed. In addition, the patients were followed up for two years, and the occurrence of PTE was documented.

Results

During the follow-up period, PTE occurred in 8 patients. Analysis of EEG signals across different brain regions (frontal, central, and parietal lobes) revealed significant differences between the PTE and non-PTE groups. Patients with PTE exhibited significantly higher δ and θ power values (P < 0.01), lower α/θ ratios (P < 0.01), and elevated θ/β, (δ + θ)/(α + β), and peak envelope power (P < 0.01) compared to those in the non-PTE group.

Conclusion

In children with severe TBI, the parameter characterization of early quantitative EEG has potential application in predicting PTE.

Quantitative analysis of visually normal EEG reveals spectral power abnormalities in temporal lobe epilepsy

OBJECTIVE

To compare quantitative spectral parameters of visually-normal EEG between Mesial Temporal Lobe Epilepsy (MTLE) patients and healthy controls (HC).

METHOD

We enrolled 26 MTLE patients and 26 HC. From each recording we calculated total power of all frequency bands and determined alpha-theta (ATR) and alpha-delta (ADR) power ratios in different brain regions. Group-wise differences between spectral parameters were investigated (p < 0.05). To test for associations between spectral-power and cognitive status, we evaluated correlations between neuropsychological tests and quantitative EEG (qEEG) metrics.

RESULTS

In all comparisons, ATR and ADR were significantly decreased in MTLE patients compared to HC, particularly over the hemisphere ipsilateral to epileptic activity. A positive correlation was seen in MTLE patients between ATR in ipsilateral temporal lobe, and results of neuropsychological tests of auditory verbal learning (RAVLT and RAVLT-D), short term verbal memory (Digit span backwards), and executive function (Weigl's sorting test). ADR values in the contralateral posterior region correlated positively with RAVLT-D and Digit span backwards tests.

DISCUSSION

Results confirmed that the power spectrum of qEEG is shifted towards lower frequencies in MTLE patients compared to HC.

CONCLUSION

Of note, our results were found in visually-normal recordings, providing further evidence of the value of qEEG for longitudinal monitoring of MTLE patients over time. Exploratory analysis of associations between qEEG and neuropsychological data suggest this could be useful for investigating effects of antiseizure medications on cognitive integrity in patients.

Bibliometric Analysis of Quantitative Electroencephalogram Research in Neuropsychiatric Disorders From 2000 to 2021

BACKGROUND

With the development of quantitative electroencephalography (QEEG), an increasing number of studies have been published on the clinical use of QEEG in the past two decades, particularly in the diagnosis, treatment, and prognosis of neuropsychiatric disorders. However, to date, the current status and developing trends of this research field have not been systematically analyzed from a macroscopic perspective. The present study aimed to identify the hot spots, knowledge base, and frontiers of QEEG research in neuropsychiatric disorders from 2000 to 2021 through bibliometric analysis.

METHODS

QEEG-related publications in the neuropsychiatric field from 2000 to 2021 were retrieved from the Web of Science Core Collection (WOSCC). CiteSpace and VOSviewer software programs, and the online literature analysis platform (bibliometric.com) were employed to perform bibliographic and visualized analysis.

RESULTS

A total of 1,904 publications between 2000 and 2021 were retrieved. The number of QEEG-related publications in neuropsychiatric disorders increased steadily from 2000 to 2021, and research in psychiatric disorders requires more attention in comparison to research in neurological disorders. During the last two decades, QEEG has been mainly applied in neurodegenerative diseases, cerebrovascular diseases, and mental disorders to reveal the pathological mechanisms, assist clinical diagnosis, and promote the selection of effective treatments. The recent hot topics focused on QEEG utilization in neurodegenerative disorders like Alzheimer's and Parkinson's disease, traumatic brain injury and related cerebrovascular diseases, epilepsy and seizure, attention-deficit hyperactivity disorder, and other mental disorders like major depressive disorder and schizophrenia. In addition, studies to cross-validate QEEG biomarkers, develop new biomarkers (e.g., functional connectivity and complexity), and extract compound biomarkers by machine learning were the emerging trends.

CONCLUSION

The present study integrated bibliometric information on the current status, the knowledge base, and future directions of QEEG studies in neuropsychiatric disorders from a macroscopic perspective. It may provide valuable insights for researchers focusing on the utilization of QEEG in this field.