Generalized background qEEG abnormalities in localized symptomatic epilepsy

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.

Electroencephalography characteristics related to risk of sudden unexpected death in epilepsy in patients with Dravet syndrome

Objective

To investigate the quantitative electroencephalography (EEG) features associated with a high risk of sudden unexpected death in epilepsy (SUDEP) in patients with Dravet syndrome (DS).

Methods

Patients with DS and healthy controls (HCs) who underwent EEG were included in the study. EEG signals were recorded using a 21 channel digital EEG system, and pre-processed data were analyzed to identify quantitative EEG features associated with a high SUDEP risk. To assess the risk of SUDEP, SUDEP-7 scores were used.

Results

A total of 64 patients with DS [38 males and 26 females, aged: 128.51 ± 75.50 months (range: 23-380 months)], and 13 HCs [7 males and 6 females, aged: 95.46 ± 86.48 months (range: 13-263 months)] were included. For the absolute band power, the theta power was significantly higher in the high-SUDEP group than in the low-SUDEP group in the central brain region. For the relative band power, the theta power was also significantly higher in the high-SUDEP group than in the low-SUDEP group in the central and occipital brain regions. The alpha power was significantly lower in the high-SUDEP group than in the low-SUDEP group in the central and parietal brain regions.

Conclusion

Patients with high SUDEP-7 scores have different EEG features from those with low SUDEP-7 scores, suggesting that EEG may be used as a biomarker of SUDEP in DS.

Significance

Early intervention in patients with DS at a high risk of SUDEP can reduce mortality and morbidity. Patients with high theta band powers warrant high-level supervision.

Machine-learning for the prediction of one-year seizure recurrence based on routine electroencephalography

Predicting seizure recurrence risk is critical to the diagnosis and management of epilepsy. Routine electroencephalography (EEG) is a cornerstone of the estimation of seizure recurrence risk. However, EEG interpretation relies on the visual identification of interictal epileptiform discharges (IEDs) by neurologists, with limited sensitivity. Automated processing of EEG could increase its diagnostic yield and accessibility. The main objective was to develop a prediction model based on automated EEG processing to predict one-year seizure recurrence in patients undergoing routine EEG. We retrospectively selected a consecutive cohort of 517 patients undergoing routine EEG at our institution (training set) and a separate, temporally shifted cohort of 261 patients (testing set). We developed an automated processing pipeline to extract linear and non-linear features from the EEGs. We trained machine learning algorithms on multichannel EEG segments to predict one-year seizure recurrence. We evaluated the impact of IEDs and clinical confounders on performances and validated the performances on the testing set. The receiver operating characteristic area-under-the-curve for seizure recurrence after EEG in the testing set was 0.63 (95% CI 0.55-0.71). Predictions were still significantly above chance in EEGs with no IEDs. Our findings suggest that there are changes other than IEDs in the EEG signal embodying seizure propensity.

Epileptic diathesis: An EEG-LORETA study

OBJECTIVE

Epileptic diathesis is an inherited neurophysiological trait that contributes to the development of all types of epilepsy. The amount of resting-state electroencephalography (EEG) theta activity is proportional to the degree of cortical excitability and epileptic diathesis. Our aim was to explore the amount and topographic distribution of theta activity in epilepsy groups. We hypothesized that the anatomical distribution of increased theta activity is independent of the epilepsy type.

METHODS

Patients with unmedicated idiopathic generalized epilepsy (IGE, n = 92) or focal epilepsy (FE, n = 149) and non-seizure patients with mild to moderate cerebral lesions (NONEP, n = 99) were compared to healthy controls (NC, n = 114). We analysed artifact-free EEG activity and defined multiple distributed sources of theta activity in the source space via low resolution electromagnetic tomography software. Age-corrected and Z-transformed theta values were compared across the groups.

RESULTS

The rank of increased theta activity was IGE > FE > NONEP > NC. Both epilepsy groups showed significantly more theta activity than did the NC group. Maximum theta abnormality occurred in the medial-basal prefrontal and anterior temporal cortex in both epilepsy groups.

CONCLUSIONS

We confirmed the hypothesis outlined above.

SIGNIFICANCE

The common topographical pattern of increased EEG theta activity is correlated with epileptic diathesis, regardless of the epilepsy type.

Possible Mechanisms Underlying Neurological Post-COVID Symptoms and Neurofeedback as a Potential Therapy

Theoretical considerations related to neurological post-COVID complications have become a serious issue in the COVID pandemic. We propose 3 theoretical hypotheses related to neurological post-COVID complications. First, pathophysiological processes responsible for long-term neurological complications caused by COVID-19 might have 2 phases: (1) Phase of acute Sars-CoV-2 infection linked with the pathogenesis responsible for the onset of COVID-19-related neurological complications and (2) the phase of post-acute Sars-CoV-2 infection linked with the pathogenesis responsible for long-lasting persistence of post-COVID neurological problems and/or exacerbation of another neurological pathologies. Second, post-COVID symptoms can be described and investigated from the perspective of dynamical system theory exploiting its fundamental concepts such as system parameters, attractors and criticality. Thirdly, neurofeedback may represent a promising therapy for neurological post-COVID complications. Based on the current knowledge related to neurofeedback and what is already known about neurological complications linked to acute COVID-19 and post-acute COVID-19 conditions, we propose that neurofeedback modalities, such as functional magnetic resonance-based neurofeedback, quantitative EEG-based neurofeedback, Othmer's method of rewarding individual optimal EEG frequency and heart rate variability-based biofeedback, represent a potential therapy for improvement of post-COVID symptoms.

Signal complexity indicators of health status in clinical EEG

Brain signal variability changes across the lifespan in both health and disease, likely reflecting changes in information processing capacity related to development, aging and neurological disorders. While signal complexity, and multiscale entropy (MSE) in particular, has been proposed as a biomarker for neurological disorders, most observations of altered signal complexity have come from studies comparing patients with few to no comorbidities against healthy controls. In this study, we examined whether MSE of brain signals was distinguishable across patient groups in a large and heterogeneous set of clinical-EEG data. Using a multivariate analysis, we found unique timescale-dependent differences in MSE across various neurological disorders. We also found MSE to differentiate individuals with non-brain comorbidities, suggesting that MSE is sensitive to brain signal changes brought about by metabolic and other non-brain disorders. Such changes were not detectable in the spectral power density of brain signals. Our findings suggest that brain signal complexity may offer complementary information to spectral power about an individual's health status and is a promising avenue for clinical biomarker development.

Resting-state EEG theta activity reflects degree of genetic determination of the major epilepsy syndromes

OBJECTIVE

To explore relationship between EEG theta activity and clinical data that imply the degree of genetic determination of epilepsy.

METHODS

Clinical data of interest were epilepsy diagnosis and positive / negative family history of epilepsy. Study groups were: idiopathic generalized epilepsy (IGE), focal epilepsy (FE); FE of unknown etiology (FEUNK), FE of postnatal-acquired etiology (FEPA); all patients with positive / negative family history of epilepsy (FAPALL, FANALL, respectively), disregarding of the syndrome; FAP patients with 1st degree affected relative (FAP1) and those with 2nd degree epileptic relative only (FAP2). Quantitative EEG analysis assessed amount of theta (3.5-7.0 Hz) activity in 180 seconds of artifact-free waking EEG background activity for each patient and group. Group comparison was carried out by nonparametric statistics.

RESULTS

Differences of theta activity were: FAPALL > FANALL (p = 0.01); FAP1 > FAP2 (p = 0.2752). IGE > FE (p = 0.02); FEUNK > FEPA (p = 0.07).

CONCLUSIONS

This was the first attempt to explore and quantitatively ascertain relationship between an EEG variable and clinical data that imply greater or lesser degree of genetic determination in epilepsy.

SIGNIFICANCE

Theta activity is endophenotype that bridges the gap between epilepsy susceptibility genes and clinical phenotypes. Amount of theta activity is indicative of degree of genetic determination of the epilepsies.

Spectral power of interictal EEG in the diagnosis and prognosis of idiopathic generalized epilepsies

INTRODUCTION

Idiopathic generalized epilepsies (IGE) are characterized by generalized interictal epileptiform discharges (IEDs) on a normal background electroencephalography (EEG). However, the yield of IEDs can be low. Approximately 20% of patients with IGE fail to achieve seizure control with antiepileptic drug (AED) treatment. Currently, there are no reliable prognostic markers for early identification of drug-resistant epilepsy (DRE). We examined spectral power of the interictal EEG in patients with IGE and healthy controls, to identify potential diagnostic and prognostic biomarkers of IGE.

METHODS

A 64-channel EEG was recorded under standard conditions in patients with well-controlled IGE (WC-IGE, n = 19), drug-resistant IGE (DR-IGE, n = 18), and age-matched controls (n = 20). After preprocessing, fast Fourier transform was performed to obtain 1D frequency spectra for each EEG channel. The 1D spectra (averaged over channels) and 2D topographic maps (averaged over canonical frequency bands) were computed for each participant. Power spectra in the 3 cohorts were compared using one-way analysis of variance (ANOVA), and power spectra images were compared using T-contrast tests. A post hoc analysis compared peak alpha power between the groups.

RESULTS

Compared with controls, participants with IGE had higher interictal EEG spectral power in the delta band in the midline central region, in the theta band in the midline, in the beta band over the left hemisphere, and in the gamma band over right hemisphere and left central regions. There were no differences in spectral power between cohorts with WC-IGE and DR-IGE. Peak alpha power was lower in WC-IGE and DR-IGE than controls.

CONCLUSIONS

Electroencephalography spectral power analysis could form part of a clinically useful diagnostic biomarker for IGE; however, it did not correlate with response to AED in this study.

Quantitative EEG biomarkers for epilepsy and their relation to chemical biomarkers

The electroencephalogram (EEG) is the most important method to diagnose epilepsy. In clinical settings, it is evaluated by experts who identify patterns visually. Quantitative EEG is the application of digital signal processing to clinical recordings in order to automatize diagnostic procedures, and to make patterns visible that are hidden to the human eye. The EEG is related to chemical biomarkers, as electrical activity is based on chemical signals. The most well-known chemical biomarkers are blood laboratory tests to identify seizures after they have happened. However, research on chemical biomarkers is much less extensive than research on quantitative EEG, and combined studies are rarely published, but highly warranted. Quantitative EEG is as old as the EEG itself, but still, the methods are not yet standard in clinical practice. The most evident application is an automation of manual work, but also a quantitative description and localization of interictal epileptiform events as well as seizures can reveal important hints for diagnosis and contribute to presurgical evaluation. In addition, the assessment of network characteristics and entropy measures were found to reveal important insights into epileptic brain activity. Application scenarios of quantitative EEG in epilepsy include seizure prediction, pharmaco-EEG, treatment monitoring, evaluation of cognition, and neurofeedback. The main challenges to quantitative EEG are poor reliability and poor generalizability of measures, as well as the need for individualization of procedures. A main hindrance for quantitative EEG to enter clinical routine is also that training is not yet part of standard curricula for clinical neurophysiologists.

Neuronal network dysfunction precedes storage and neurodegeneration in a lysosomal storage disorder

Accumulation of lysosomal storage material and late-stage neurodegeneration are hallmarks of lysosomal storage disorders (LSDs) affecting the brain. Yet, for most LSDs, including CLN3 disease, the most common form of childhood dementia, it is unclear what mechanisms drive neurologic symptoms. Do deficits arise from loss of function of the mutated protein or toxicity from storage accumulation? Here, using in vitro voltage-sensitive dye imaging and in vivo electrophysiology, we find progressive hippocampal dysfunction occurs before notable lysosomal storage and neuronal loss in 2 CLN3 disease mouse models. Pharmacologic reversal of lysosomal storage deposition in young mice does not rescue this circuit dysfunction. Additionally, we find that CLN3 disease mice lose an electrophysiologic marker of new memory encoding - hippocampal sharp-wave ripples. This discovery, which is also seen in Alzheimer's disease, suggests the possibility of a shared electrophysiologic signature of dementia. Overall, our data describe new insights into previously unknown network-level changes occurring in LSDs affecting the central nervous system and highlight the need for new therapeutic interventions targeting early circuit defects.

Correlations Between Cognitive Aspects and Quantitative EEG in Adults With Epilepsy

Introduction. Cognitive impairment frequently occurs in adult patients with epilepsy (PWE), but its pathophysiological basis is not well known. This study assessed cognition and its correlations with quantitative EEG coherence (QEEG) of patients with epilepsy. Method. Eighty PWE seen consecutively in the clinic and 40 normal subjects (NC) were assessed by neurological evaluation, Mini Mental State Examination, immediate and delayed recall of 10 simple figures, phonemic verbal fluency (FAS), category fluency test (VF animals), clock drawing, and QEEG. The mean global inter- and intrahemispheric coherences for the delta, theta, alpha, and beta bands were calculated. Cognitive functions and QEEG coherence of the PWE and the NC were compared, and logistic regression analysis determined the factors associated with impaired cognitive functions in PWE. The significance level was set at P < .05. Results. Regression analysis showed that FAS impairment (14.5 ± 8.6 vs 24.3 ± 15.7, respectively) and delayed recall of figures in PWE (7.3 ± 2.07 vs 8.6 ± 1.48) differed significantly between the PWE and the NC (Nagelkerke R² = 0.266). Absolute power was greater in all the frequency bands in PWE. Interhemispheric and intrahemispheric beta coherences in the theta frequency was higher in the PWE than in the NC. Logistic regression analysis showed a significant association between interhemispheric delta coherence and VF animal impairment (cutoff point of 12), and between an interhemipheric beta coherence with level of education and delayed recall of figures impairment (cutoff point of 7) (Nagelkerke R² = 0.297). Other variables were not associated. Conclusions. There was cognitive impairment of PWE and it was significantly associated with QEEG, which suggests that QEEG measures may contribute to the understanding of physiopathological events and as a marker for cognitive alterations in epilepsy.

Effect of short-term transcutaneous trigeminal nerve stimulation on EEG activity in drug-resistant epilepsy

BACKGROUND

Transcutaneous trigeminal nerve stimulation (TNS) has antiepileptic effects in patients with drug-resistant epilepsy (DRE). However, whether and how TNS is able to modulate the electroencephalogram (EEG) background activity in patients with DRE is still unknown.

OBJECTIVES

To investigate the effect of short-term TNS on EEG background activity in DRE by qualitative and quantitative analyses.

METHODS

Twenty-nine DRE patients participated in the study. Twenty-two were randomly divided into a "sham-TNS" or "real-TNS" group; seven patients underwent stimulation of the median nerve (MNS) at the wrist. Real-TNS was delivered bilaterally to the infraorbital nerve (trains of 1-20 mA, 120 Hz, cyclic modality for 20 min). The sham-TNS protocol mimicked the real-TNS one but at a zero intensity. For MNS, the same parameters as real-TNS were used. EEG was continuously acquired for 40 min: 10' pre, 20' during and 10' post stimulation. EEG was visually inspected for interictal epileptiform discharge (IEDs) changes and processed by spectral analysis for changes in mean frequency and absolute power of each frequency band.

RESULTS

A significant increase of EEG absolute alpha power was observed during real-TNS compared with the sham-TNS (F_34,680 = 1.748; p = 0.006). Conversely, no significant effects were noticed either for quantitative analysis of other frequency bands or for IEDs detection. MNS proved unable to modulate EEG activity.

CONCLUSIONS

Short-term TNS induces an acute and specific effect on background EEG of DRE by increasing the absolute alpha band power. EEG alpha rhythm enhancement may index a cortical functional inhibition and act as a seizure-preventing mechanism.

Early Seizure Detection Algorithm Based on Intracranial EEG and Random Forest Classification

The goal of this study is to provide a seizure detection algorithm that is relatively simple to implement on a microcontroller, so it can be used for an implantable closed loop stimulation device. We propose a set of 11 simple time domain and power bands features, computed from one intracranial EEG contact located in the seizure onset zone. The classification of the features is performed using a random forest classifier. Depending on the training datasets and the optimization preferences, the performance of the algorithm were: 93.84% mean sensitivity (100% median sensitivity), 3.03 s mean (1.75 s median) detection delays and 0.33/h mean (0.07/h median) false detections per hour.

Childhood abuse and EEG source localization in crack cocaine dependence

Fourteen subjects with histories of sexual and/or physical abuse in childhood and 13 matched control subjects were selected from a consecutive series of clients in residential treatment for crack cocaine dependence. Standardized low-resolution electromagnetic brain tomography (sLORETA) was used to estimate the source generators of the EEG in a cortical mask with voxel z-scores referenced to normative data at frequency intervals of 039 Hz, with nonparametric permutation to correct by randomization for the number of comparisons and the intercorrelations and variance of distribution of voxel values. Subjects with histories of abuse in childhood had significantly greater EEG power than controls in the theta frequency range (3.51-7.41 Hz), with greatest differences in the 3.90-Hz band distributed mainly in the parahippocampal, fusiform, lingual, posterior cingulate, and insular gyri. The groups did not differ significantly with regard to delta (1.56-3.12 Hz), alpha (7.81-12.48 Hz), beta (12.87-19.89 Hz), and gamma (20.28-35.10 Hz) frequency power. In excess, theta EEG power, a bandwidth of transactions among hippocampus and amygdala and paralimbic and visual association cortex, may be a correlate of childhood exposure to abuse.

Gamma power is phase-locked to posterior alpha activity

Neuronal oscillations in various frequency bands have been reported in numerous studies in both humans and animals. While it is obvious that these oscillations play an important role in cognitive processing, it remains unclear how oscillations in various frequency bands interact. In this study we have investigated phase to power locking in MEG activity of healthy human subjects at rest with their eyes closed. To examine cross-frequency coupling, we have computed coherence between the time course of the power in a given frequency band and the signal itself within every channel. The time-course of the power was calculated using a sliding tapered time window followed by a Fourier transform. Our findings show that high-frequency gamma power (30-70 Hz) is phase-locked to alpha oscillations (8-13 Hz) in the ongoing MEG signals. The topography of the coupling was similar to the topography of the alpha power and was strongest over occipital areas. Interestingly, gamma activity per se was not evident in the power spectra and only became detectable when studied in relation to the alpha phase. Intracranial data from an epileptic subject confirmed these findings albeit there was slowing in both the alpha and gamma band. A tentative explanation for this phenomenon is that the visual system is inhibited during most of the alpha cycle whereas a burst of gamma activity at a specific alpha phase (e.g. at troughs) reflects a window of excitability.

Power spectral frequency and coherence abnormalities in patients with intractable epilepsy and their usefulness in long-term remediation of seizures using neurofeedback

Medically intractable seizures appear to be highly correlated with focal slow activity (delta or theta). They also correlate highly with decreases in the coherence of theta. Normalization of focal slowing and of decreased theta coherence will probably be the neurofeedback approaches most likely to decrease or eliminate seizures in future cases. Neurofeedback has been used for over 35 years to reduce the incidence and severity of seizures. With power training to decrease theta and increase the sensorimotor rhythm (12-15 Hz), an average of 82% of patients experienced a significant reduction in seizure frequency, and occasional remissions were seen. Recent improvements using QEEG to guide neurofeedback training have made it possible to eliminate seizures in most patients, even those with intractable seizures. Following our previous study in 2005, we report an additional 25 patients so treated. We also report an analysis of the frequency of QEEG abnormalities in this patient group. All of the intractable epileptic patients had one or more slow foci (excessive theta or delta compared with the normal database). One third had a relative deficiency of beta power. One fourth had a deficiency of absolute delta. Eighteen percent had excessive absolute alpha power, 18% had deficient absolute alpha power, 18% percent had excessive absolute beta power, and 18% percent had deficient absolute beta power. Hypocoherence of theta was found in 75%, and decreases in alpha coherence were noted in 42%. Hypocoherence of beta was found in 50%, and hypocoherence of delta was found in 25%. Increases in alpha coherence were noted in 33%. Seventeen percent had no coherence abnormalities. When most of the power and coherence abnormalities were normalized with neurofeedback training, all the patients became seizure-free; 76% no longer required an anticonvulsant for seizure control.

Focal epileptiform activity described by a large computerised EEG database

OBJECTIVE

To study the age-related topographical tendency of expressing epileptiform activity, and the effect of focal epileptiform activity (FEA) on the general cortical brain activity.

METHODS

1647 consecutive routine EEGs containing FEA were visually assessed for FEA location and asymmetry. Background activity was compared with that in normal EEGs from 3268 drug-free outpatient controls.

RESULTS

FEA localisation was age-related (p<0.0005) except for the temporal region (p=0.22) where FEA was found equally often in the young and the old. The left hemisphere was more prone to FEA (p=0.018). The left-right asymmetry varied by age (p=0.013). FEA asymmetry occurred most frequently in EEGs from patients older than 80 years, and least frequent in the age-group 20-39 years. FEA was associated with lower alpha rhythm (AR) frequencies (p=0.0041) and higher AR amplitudes (p=0.0023), as well as higher general background activity (GBA) amplitude (p<0.0005), while GBA frequencies were the same (p=0.96).

CONCLUSIONS

Topographical localisation of FEA was age-dependent. There was an overall left dominance, but the side asymmetry was modest and varied by age. FEA was associated with changes in AR and GBA.

SIGNIFICANCE

The results demonstrate that FEA is associated with cerebral cortical dysfunction also distant from the epileptic focus.

Detection of EEG background abnormalities in epilepsy by a new spectral index

OBJECTIVE

In epileptic disorders, EEG background activity is disorganized in or near the epileptogenic focus and spectral EEG analysis (SEA) can provide useful information about the focus. We tried to develop a new spectral index from basic spectral parameters to detect the epileptic abnormalities at EEG background activity.

METHODS

A new spectral EEG index, epileptic abnormality index (EAI), was constructed from frequency band power and power asymmetry parameters. Within the index, parameters were weighted due to both conventional EEG knowledge and their power in discrimination healthy subjects from patients. EEG background activity from 99 epileptic patients and 146 healthy subjects was examined both by EAI and by a conventional SEA method, by using z-scoring statistic. Each test results were compared with visual EEG interpretation of subjects.

RESULTS

In patient groups, EAI was most successful in lateralization of epileptic abnormalities. It was also helpful in discrimination of epileptic patients from normals in the case where visual EEG interpretation was 'normal'.

CONCLUSIONS

EAI depends on basic spectral parameters and it combines statistical methods and clinical knowledge about EEG. It increases the analysis capacity of SEA in evaluation of EEG background activity.

SIGNIFICANCE

EAI is a new and useful approach in detection of EEG background abnormalities in epilepsy and its logical base can also be used in the detection of brain electrical activity abnormalities other than epileptic disorders.

Assessing acute middle cerebral artery ischemic stroke by quantitative electric tomography

This paper focuses on the application of quantitative electric tomography (qEEGT) to map changes in EEG generators for detection of early signs of ischemia in patients with acute middle cerebral artery stroke. Thirty-two patients were studied with the diagnosis of acute ischemic stroke of the left middle cerebral artery territory, within the first 24 hours of their clinical evolution. Variable Resolution Electrical Tomography was used for estimating EEG source generators. High resolution source Z-spectra and 3- dimensional images of Z values for all the sources at each frequency were obtained for all cases. To estimate statistically significant increments and decrements of brain electric activity within the frequency spectra, the t-Student vs. Zero test was performed. A significant increment of delta activity was observed on the affected vascular territory, and a more extensive increment of theta activity was detected. A significant alpha decrement was found in the parieto-occipital region of the affected cerebral hemisphere (left), and in the medial and posterior region of the right hemisphere. These findings suggest that qEEGT Z delta images are probably related to the main ischemic core within the affected arterial territory; penumbra, diaschisis, edema, might explain those observed theta and alpha abnormalities. It was concluded that qEEGT is useful for the detection of early signs of ischemia in acute ischemic stroke.

Objective assessment of neurotoxicity while shifting from carbamazepine to oxcarbazepine

OBJECTIVES

Objective assessment of non-overt neurotoxicity of carbamazepine (CBZ) vs oxcarbazepine (OXC) in patients with difficult-to-treat partial epilepsy, who were resistant to CBZ treatment and were converted from CBZ monotherapy to OXC monotherapy.

MATERIAL AND METHODS

Therapeutically equivalent doses (150 mg OXC for every 100 mg CBZ) were compared in 20 adult patients. Neurological investigation, conventional and spectral EEG analysis, brainstem auditory evoked responses (BAER) were carried out in both treatment conditions. EEG and BAER data of 20 age-matched healthy controls helped interpretation. Primary target variables (electrophysiological parameters) were evaluated blindly.

RESULTS

There were no significant differences between treatment conditions concerning the neurological condition, lack of clinically evident neurotoxicity, seizure frequency and EEG spike frequency. OXC treatment was characterized by less delta, theta, and alpha power, more beta power, and significantly greater mean alpha frequency (P = 0.03 and 0.05 for the left and right occipital leads, respectively), than CBZ treatment. Interpeak latencies were prolonged in the CBZ condition as compared with normals (P = 0.01) and OXC (P = 0.02).

CONCLUSION

In this cohort of patients substitution of OXC for CBZ was associated with significant normalization of electrophysiological parameters, indicating decreasing neurotoxicity while shifting from CBZ to OXC monotherapy.

Quantitative analysis of EEG background activity in patients with rolandic spikes

OBJECTIVE

The aim was to compare the background activity, through quantitative EEG analysis, of patients with rolandic spikes and normal age-matched controls.

MATERIAL AND METHODS

Twenty-one channel EEG of 23 children with rolandic spikes and 39 normal children, with ages ranging from 7 to 12 years, were submitted to quantitative analysis (FFT) of discharge-free epochs. Patients and controls were divided in groups according to age (7-9 and 10-12 years old). Delta, theta, alpha and beta frequency ranges were compared between groups for all electrode positions.

RESULTS

Comparing normals, the 7-9 years old group showed power reduction in the alpha and beta ranges. Comparing patients and normal age-matched groups, the patients showed power increase, at all frequency ranges in the 7-9 years old group and at delta and theta frequency ranges in the 10-12 years old group.

CONCLUSIONS

Our findings agree with recent evidences that these children may differ from normal (besides the eventual occurrence of seizures); but they also suggest that these differences can be related to immaturity and not necessarily related to the epileptiform discharge.

Spatio-temporal characterizations of non-linear changes in intracranial activities prior to human temporal lobe seizures

Recent studies have shown that non-linear analysis of intracranial activities can detect a 'pre-ictal phase' preceding the epileptic seizure. Nevertheless, the dynamical nature of the underlying neuronal process and the spatial extension of this pre-ictal phase still remain unknown. In this paper, we address these aspects using a new non-linear measure of dynamic similarity between different parts of intracranial recordings of nine patients with medial temporal lobe epilepsy recorded during transitions to seizure. Our results confirm that non-linear changes in neuronal dynamics allow, in most cases (16 out of 17), a seizure anticipation several minutes in advance. Furthermore, we show that the spatial distribution of pre-ictal changes often involves an extended network projecting beyond the limits of the epileptogenic region. Finally, the pre-ictal phase could frequently (13 out of 17) be characterized with a marked shift toward slower frequencies in upper delta or theta frequency range.