Interictal electroencephalographic functional network topology in drug-resistant and well-controlled idiopathic generalized epilepsy

The landscape of drug resistant absence seizures in adolescents and adults: Pathophysiology, electroclinical spectrum and treatment options

The persistence of typical absence seizures (AS) in adolescence and adulthood may reduce the quality of life of patients with genetic generalized epilepsies (GGEs). The prevalence of drug resistant AS is probably underestimated in this patient population, and treatment options are relatively scarce. Similarly, atypical absence seizures in developmental and epileptic encephalopathies (DEEs) may be unrecognized, and often persist into adulthood despite improvement of more severe seizures. These two seemingly distant conditions, represented by typical AS in GGE and atypical AS in DEE, share at least partially overlapping pathophysiological and genetic mechanisms, which may be the target of drug and neurostimulation therapies. In addition, some patients with drug-resistant typical AS may present electroclinical features that lie in between the two extremes represented by these generalized forms of epilepsy.

Predicting the therapeutic response to valproic acid in childhood absence epilepsy through electroencephalogram analysis using machine learning

Childhood absence epilepsy (CAE) is a common type of idiopathic generalized epilepsy, manifesting as daily multiple absence seizures. Although seizures in most patients can be adequately controlled with first-line antiseizure medication (ASM), approximately 25 % of patients respond poorly to first-line ASM. In addition, an accurate method for predicting first-line medication responsiveness is lacking. We used the quantitative electroencephalogram (QEEG) features of patients with CAE along with machine learning to predict the therapeutic effects of valproic acid in this population. We enrolled 25 patients with CAE from multiple medical centers. Twelve patients who required additional medication for seizure control or who were shifted to another ASM and 13 patients who achieved seizure freedom with valproic acid within 6 months served as the nonresponder and responder groups. Using machine learning, we analyzed the interictal background EEG data without epileptiform discharge before ASM. The following features were analyzed: EEG frequency bands, Hjorth parameters, detrended fluctuation analysis, Higuchi fractal dimension, Lempel-Ziv complexity (LZC), Petrosian fractal dimension, and sample entropy (SE). We applied leave-one-out cross-validation with support vector machine, K-nearest neighbor (KNN), random forest, decision tree, Ada boost, and extreme gradient boosting, and we tested the performance of these models. The responders had significantly higher alpha band power and lower delta band power than the nonresponders. The Hjorth mobility, LZC, and SE values in the temporal, parietal, and occipital lobes were higher in the responders than in the nonresponders. Hjorth complexity was higher in the nonresponders than in the responders in almost all the brain regions, except for the leads FP1 and FP2. Using KNN classification with theta band power in the temporal lobe yielded optimal performance, with sensitivity of 92.31 %, specificity of 76.92 %, accuracy of 84.62 %, and area under the curve of 88.46 %.We used various EEG features along with machine learning to accurately predict whether patients with CAE would respond to valproic acid. Our method could provide valuable assistance for pediatric neurologists in selecting suitable ASM.

Limited clinical validity of univariate resting-state EEG markers for classifying seizure disorders

Differentiating between epilepsy and psychogenic non-epileptic seizures presents a considerable challenge in clinical practice, resulting in frequent misdiagnosis, unnecessary treatment and long diagnostic delays. Quantitative markers extracted from resting-state EEG may reveal subtle neurophysiological differences that are diagnostically relevant. Two observational, retrospective diagnostic accuracy studies were performed to test the clinical validity of univariate resting-state EEG markers for the differential diagnosis of epilepsy and psychogenic non-epileptic seizures. Clinical EEG data were collected for 179 quasi-consecutive patients (age > 18) with a suspected diagnosis of epilepsy or psychogenic non-epileptic seizures who were medication-naïve at the time of EEG; 148 age- and gender-matched patients subsequently received a diagnosis from specialist clinicians and were included in the analyses. Study 1 is a hypothesis-driven study testing the ability of theta power and peak alpha frequency to classify people with epilepsy and people with psychogenic non-epileptic seizures, with an advanced machine learning pipeline. The next study (Study 2) is data-driven; a high number of quantitative EEG features are extracted and a similar machine learning approach as Study 1 assesses whether previously unexplored univariate EEG measures show promise as diagnostic markers. The results of Study 1 suggest that EEG markers that were previously identified as promising diagnostic indicators (i.e. theta power and peak alpha frequency) have limited clinical validity for the classification of epilepsy and psychogenic non-epileptic seizures (mean accuracy: 48%). The results of Study 2 indicate that identifying univariate markers that show good correlation with a categorical diagnostic label is challenging (mean accuracy: 45-60%). This is due to a considerable overlap in neurophysiological features between the diagnostic classes considered in this study, and to the presence of more dominant EEG dynamics such as alterations due to temporal proximity to epileptiform discharges. Markers that were identified in the context of previous epilepsy research using visually normal resting-state EEG were found to have limited clinical validity for the classification task of distinguishing between people with epilepsy and people with psychogenic non-epileptic seizures. A search for alternative diagnostic markers uncovered the challenges involved and generated recommendations for further research.

Revisiting the concept of drug-resistant epilepsy: A TASK1 report of the ILAE/AES Joint Translational Task Force

Despite progress in the development of anti-seizure medications (ASMs), one third of people with epilepsy have drug-resistant epilepsy (DRE). The working definition of DRE, proposed by the International League Against Epilepsy (ILAE) in 2010, helped identify individuals who might benefit from presurgical evaluation early on. As the incidence of DRE remains high, the TASK1 workgroup on DRE of the ILAE/American Epilepsy Society (AES) Joint Translational Task Force discussed the heterogeneity and complexity of its presentation and mechanisms, the confounders in drawing mechanistic insights when testing treatment responses, and barriers in modeling DRE across the lifespan and translating across species. We propose that it is necessary to revisit the current definition of DRE, in order to transform the preclinical and clinical research of mechanisms and biomarkers, to identify novel, effective, precise, pharmacologic treatments, allowing for earlier recognition of drug resistance and individualized therapies.

Heterozygous RELN missense variants associated with genetic generalized epilepsy

PURPOSE

The RELN gene encodes the secreted glycoprotein Reelin and has important functions in both developing and adult brains. In this study, we aimed to explore the association between the RELN and genetic generalized epilepsy (GGE).

METHODS

We performed whole-exome sequencing on a cohort of 92 patients with GGE. Based on amino acid sequence alignments, allele frequency, pedigree validation and computational modeling, the RELN variants were identified and clinical features of cases were summarized. Cell-based Reelin secretion assays were examined by Western blotting. Alterations of mutant Reelin transport through the secretion pathway were detected by immunofluorescence staining.

RESULTS

Three novel pathogenic RELN variants (3.26%; c.2260C>T/p.R754W, c.2914C>G/p.P972A and c.3029G>A/p.R1010H) were identified. All probands showed adolescence-onset generalized seizures characterized by generalized epileptiform discharges with normal EEG backgrounds, no or mild cognitive impairment, and responded well to anti-seizure medications. All these variants were located in the central regions from 1B to 2A consecutive repeats, and protein modeling demonstrated structural alterations in Reelin. Moreover, we found that these heterozygous missense variants significantly decreased the secretion of mutant proteins in HEK-293T cells, and this impairment was due to the altered transport of mutant Reelin in the secretion pathway.

CONCLUSION

These results suggest that RELN is potentially associated with GGE. The phenotype of GGE caused by RELN variants is relatively mild, and the pathogenic mechanism may involve a loss-of-function.

Excessively increased thalamocortical connectivity and poor initial antiseizure medication response in epilepsy patients

Objectives

The network mechanism underlying the initial response to antiseizure medication in epilepsy has not been revealed yet. Given the central role of the thalamus in the brain network, we conducted a case-control study to investigate the association between thalamic connectivity and medication response.

Methods

We recruited 39 patients with newly diagnosed and medication-naïve epilepsy of genetic or unknown etiology, including 26 with a good response (GR group) and 13 with a poor response (PR group), and 26 matched healthy participants (control group). We measured the gray matter density (GMD) and the amplitude of low-frequency fluctuation (ALFF) of bilateral thalami. We then set each thalamus as the seed region of interest (ROI) to calculate voxel-wise functional connectivity (FC) and assessed ROI-wise effective connectivity (EC) between the thalamus and targeted regions.

Results

We found no significant difference between groups in the GMD or ALFF of bilateral thalami. However, we observed that the FC values of several circuits connecting the left thalamus and the cortical areas, including the bilateral Rolandic operculum, the left insula, the left postcentral gyrus, the left supramarginal gyrus, and the left superior temporal gyrus, differed among groups (False Discovery Rate correction, P < 0.05), with a higher value in the PR group than in the GR group and/or the control group (Bonferroni correction, P < 0.05). Similarly, both the outflow and the inflow EC in each thalamocortical circuit were higher in the PR group than in the GR group and the control group, although these differences did not remain statistically significant after applying the Bonferroni correction (P < 0.05). The FC showed a positive correlation with the corresponding outflow and inflow ECs for each circuit.

Conclusion

Our finding suggested that patients with stronger thalamocortical connectivity, potentially driven by both thalamic outflowing and inflowing information, may be more likely to respond poorly to initial antiseizure medication.

Variation in prognosis and treatment outcome in juvenile myoclonic epilepsy: a Biology of Juvenile Myoclonic Epilepsy Consortium proposal for a practical definition and stratified medicine classifications

Reliable definitions, classifications and prognostic models are the cornerstones of stratified medicine, but none of the current classifications systems in epilepsy address prognostic or outcome issues. Although heterogeneity is widely acknowledged within epilepsy syndromes, the significance of variation in electroclinical features, comorbidities and treatment response, as they relate to diagnostic and prognostic purposes, has not been explored. In this paper, we aim to provide an evidence-based definition of juvenile myoclonic epilepsy showing that with a predefined and limited set of mandatory features, variation in juvenile myoclonic epilepsy phenotype can be exploited for prognostic purposes. Our study is based on clinical data collected by the Biology of Juvenile Myoclonic Epilepsy Consortium augmented by literature data. We review prognosis research on mortality and seizure remission, predictors of antiseizure medication resistance and selected adverse drug events to valproate, levetiracetam and lamotrigine. Based on our analysis, a simplified set of diagnostic criteria for juvenile myoclonic epilepsy includes the following: (i) myoclonic jerks as mandatory seizure type; (ii) a circadian timing for myoclonia not mandatory for the diagnosis of juvenile myoclonic epilepsy; (iii) age of onset ranging from 6 to 40 years; (iv) generalized EEG abnormalities; and (v) intelligence conforming to population distribution. We find sufficient evidence to propose a predictive model of antiseizure medication resistance that emphasises (i) absence seizures as the strongest stratifying factor with regard to antiseizure medication resistance or seizure freedom for both sexes and (ii) sex as a major stratifying factor, revealing elevated odds of antiseizure medication resistance that correlates to self-report of catamenial and stress-related factors including sleep deprivation. In women, there are reduced odds of antiseizure medication resistance associated with EEG-measured or self-reported photosensitivity. In conclusion, by applying a simplified set of criteria to define phenotypic variations of juvenile myoclonic epilepsy, our paper proposes an evidence-based definition and prognostic stratification of juvenile myoclonic epilepsy. Further studies in existing data sets of individual patient data would be helpful to replicate our findings, and prospective studies in inception cohorts will contribute to validate them in real-world practice for juvenile myoclonic epilepsy management.

Frontotemporal phase lag index correlates with seizure severity in patients with temporal lobe epilepsy

Objectives

To find the brain network indicators correlated with the seizure severity in temporal lobe epilepsy (TLE) by graph theory analysis.

Methods

We enrolled 151 patients with TLE and 36 age- and sex-matched controls with video-EEG monitoring. The 90-s interictal EEG data were acquired. We adopted a network analyzing pipeline based on graph theory to quantify and localize their functional networks, including weighted classical network, minimum spanning tree, community structure, and LORETA. The seizure severities were evaluated using the seizure frequency, drug-resistant epilepsy (DRE), and VA-2 scores.

Results

Our network analysis pipeline showed ipsilateral frontotemporal activation in patients with TLE. The frontotemporal phase lag index (PLI) values increased in the theta band (4–7 Hz), which were elevated in patients with higher seizure severities (P &lt; 0.05). Multivariate linear regression analysis showed that the VA-2 scores were independently correlated with frontotemporal PLI values in the theta band (β = 0.259, P = 0.001) and age of onset (β = −0.215, P = 0.007).

Significance

This study illustrated that the frontotemporal PLI in the theta band independently correlated with seizure severity in patients with TLE. Our network analysis provided an accessible approach to guide the treatment strategy in routine clinical practice.

Quantitative pretreatment EEG predicts efficacy of ACTH therapy in infantile epileptic spasms syndrome

OBJECTIVE

This study aimed to determine the correlation between outcomes following adrenocorticotrophic hormone (ACTH) therapy and measurements of relative power spectrum (rPS), weighted phase lag index (wPLI), and graph theoretical analysis on pretreatment electroencephalography (EEG) in infants with non-lesional infantile epileptic spasms syndrome (IESS).

METHODS

Twenty-eight patients with non-lesional IESS were enrolled. Outcomes were classified based on seizure recurrence following ACTH therapy: seizure-free (F, n = 21) and seizure-recurrence (R, n = 7) groups. The rPS, wPLI, clustering coefficient, and betweenness centrality were calculated on pretreatment EEG and were statistically analyzed to determine the correlation with outcomes following ACTH therapy.

RESULTS

The rPS value was significantly higher in the delta frequency band in group R than in group F (p < 0.001). The wPLI values were significantly higher in the delta, theta, and alpha frequency bands in group R than in group F (p = 0.007, <0.001, and <0.001, respectively). The clustering coefficient in the delta frequency band was significantly lower in group R than in group F (p < 0.001).

CONCLUSIONS

Our findings demonstrate the significant differences in power and functional connectivity between outcome groups.

SIGNIFICANCE

This study may contribute to an early prediction of ACTH therapy outcomes and thus help in the development of appropriate treatment strategies.

Drug resistance in idiopathic generalized epilepsies: Evidence and concepts

Although approximately 10%-15% of patients with idiopathic generalized epilepsy (IGE)/genetic generalized epilepsy remain drug-resistant, there is no consensus or established concept regarding the underlying mechanisms and prevalence. This review summarizes the recent data and the current hypotheses on mechanisms that may contribute to drug-resistant IGE. A literature search was conducted in PubMed and Embase for studies on mechanisms of drug resistance published since 1980. The literature shows neither consensus on the definition nor a widely accepted model to explain drug resistance in IGE or one of its subsyndromes. Large-scale genetic studies have failed to identify distinct genetic causes or affected genes involved in pharmacokinetics. We found clinical and experimental evidence in support of four hypotheses: (1) "network hypothesis"-the degree of drug resistance in IGE reflects the severity of cortical network alterations, (2) "minor focal lesion in a predisposed brain hypothesis"-minor cortical lesions are important for drug resistance, (3) "interneuron hypothesis"-impaired functioning of γ-aminobutyric acidergic interneurons contributes to drug resistance, and (4) "changes in drug kinetics"-genetically impaired kinetics of antiseizure medication (ASM) reduce the effectiveness of available ASMs. In summary, the exact definition and cause of drug resistance in IGE is unknown. However, published evidence suggests four different mechanisms that may warrant further investigation.

Temporal Evolution of Multiday, Epileptic Functional Networks Prior to Seizure Occurrence

Epilepsy is one of the most common neurological disorders, characterized by the occurrence of repeated seizures. Given that epilepsy is considered a network disorder, tools derived from network neuroscience may confer the valuable ability to quantify the properties of epileptic brain networks. In this study, we use well-established brain network metrics (i.e., mean strength, variance of strength, eigenvector centrality, betweenness centrality) to characterize the temporal evolution of epileptic functional networks over several days prior to seizure occurrence. We infer the networks using long-term electroencephalographic recordings from 12 people with epilepsy. We found that brain network metrics are variable across days and show a circadian periodicity. In addition, we found that in 9 out of 12 patients the distribution of the variance of strength in the day (or even two last days) prior to seizure occurrence is significantly different compared to the corresponding distributions on all previous days. Our results suggest that brain network metrics computed fromelectroencephalographic recordings could potentially be used to characterize brain network changes that occur prior to seizures, and ultimately contribute to seizure warning systems.

EEG Markers of Treatment Resistance in Idiopathic Generalized Epilepsy: From Standard EEG Findings to Advanced Signal Analysis

Idiopathic generalized epilepsy (IGE) represents a common form of epilepsy in both adult and pediatric epilepsy units. Although IGE has been long considered a relatively benign epilepsy syndrome, a remarkable proportion of patients could be refractory to treatment. While some clinical prognostic factors have been largely validated among IGE patients, the impact of routine electroencephalography (EEG) findings in predicting drug resistance is still controversial and a growing number of authors highlighted the potential importance of capturing the sleep state in this setting. In addition, the development of advanced computational techniques to analyze EEG data has opened new opportunities in the identification of reliable and reproducible biomarkers of drug resistance in IGE patients. In this manuscript, we summarize the EEG findings associated with treatment resistance in IGE by reviewing the results of studies considering standard EEGs, 24-h EEG recordings, and resting-state protocols. We discuss the role of 24-h EEG recordings in assessing seizure recurrence in light of the potential prognostic relevance of generalized fast discharges occurring during sleep. In addition, we highlight new and promising biomarkers as identified by advanced EEG analysis, including hypothesis-driven functional connectivity measures of background activity and data-driven quantitative findings revealed by machine learning approaches. Finally, we thoroughly discuss the methodological limitations observed in existing studies and briefly outline future directions to identify reliable and replicable EEG biomarkers in IGE patients.

Heterogeneity of resting-state EEG features in juvenile myoclonic epilepsy and controls

Abnormal EEG features are a hallmark of epilepsy, and abnormal frequency and network features are apparent in EEGs from people with idiopathic generalized epilepsy in both ictal and interictal states. Here, we characterize differences in the resting-state EEG of individuals with juvenile myoclonic epilepsy and assess factors influencing the heterogeneity of EEG features. We collected EEG data from 147 participants with juvenile myoclonic epilepsy through the Biology of Juvenile Myoclonic Epilepsy study. Ninety-five control EEGs were acquired from two independent studies [Chowdhury et al. (2014) and EU-AIMS Longitudinal European Autism Project]. We extracted frequency and functional network-based features from 10 to 20 s epochs of resting-state EEG, including relative power spectral density, peak alpha frequency, network topology measures and brain network ictogenicity: a computational measure of the propensity of networks to generate seizure dynamics. We tested for differences between epilepsy and control EEGs using univariate, multivariable and receiver operating curve analysis. In addition, we explored the heterogeneity of EEG features within and between cohorts by testing for associations with potentially influential factors such as age, sex, epoch length and time, as well as testing for associations with clinical phenotypes including anti-seizure medication, and seizure characteristics in the epilepsy cohort. P-values were corrected for multiple comparisons. Univariate analysis showed significant differences in power spectral density in delta (2-5 Hz) (P = 0.0007, hedges' g = 0.55) and low-alpha (6-9 Hz) (P = 2.9 × 10-8, g = 0.80) frequency bands, peak alpha frequency (P = 0.000007, g = 0.66), functional network mean degree (P = 0.0006, g = 0.48) and brain network ictogenicity (P = 0.00006, g = 0.56) between epilepsy and controls. Since age (P = 0.009) and epoch length (P = 1.7 × 10-8) differed between the two groups and were potential confounders, we controlled for these covariates in multivariable analysis where disparities in EEG features between epilepsy and controls remained. Receiver operating curve analysis showed low-alpha power spectral density was optimal at distinguishing epilepsy from controls, with an area under the curve of 0.72. Lower average normalized clustering coefficient and shorter average normalized path length were associated with poorer seizure control in epilepsy patients. To conclude, individuals with juvenile myoclonic epilepsy have increased power of neural oscillatory activity at low-alpha frequencies, and increased brain network ictogenicity compared with controls, supporting evidence from studies in other epilepsies with considerable external validity. In addition, the impact of confounders on different frequency-based and network-based EEG features observed in this study highlights the need for careful consideration and control of these factors in future EEG research in idiopathic generalized epilepsy particularly for their use as biomarkers.

Quantitative Analysis of Visually Reviewed Normal Scalp EEG Predicts Seizure Freedom Following Anterior Temporal Lobectomy

Objective

Anterior temporal lobectomy (ATL) is a widely performed and successful intervention for drug‐resistant temporal lobe epilepsy (TLE). However, up to one third of patients experience seizure recurrence within 1 year after ATL. Despite the extensive literature on presurgical electroencephalography (EEG) and magnetic resonance imaging (MRI) abnormalities to prognosticate seizure freedom following ATL, the value of quantitative analysis of visually reviewed normal interictal EEG in such prognostication remains unclear. In this retrospective multicenter study, we investigate whether machine learning analysis of normal interictal scalp EEG studies can inform the prediction of postoperative seizure freedom outcomes in patients who have undergone ATL.

Methods

We analyzed normal presurgical scalp EEG recordings from 41 Mayo Clinic (MC) and 23 Cleveland Clinic (CC) patients. We used an unbiased automated algorithm to extract eyes closed awake epochs from scalp EEG studies that were free of any epileptiform activity and then extracted spectral EEG features representing (a) spectral power and (b) interhemispheric spectral coherence in frequencies between 1 and 25 Hz across several brain regions. We analyzed the differences between the seizure‐free and non–seizure‐free patients and employed a Naïve Bayes classifier using multiple spectral features to predict surgery outcomes. We trained the classifier using a leave‐one‐patient‐out cross‐validation scheme within the MC data set and then tested using the out‐of‐sample CC data set. Finally, we compared the predictive performance of normal scalp EEG‐derived features against MRI abnormalities.

Results

We found that several spectral power and coherence features showed significant differences correlated with surgical outcomes and that they were most pronounced in the 10–25 Hz range. The Naïve Bayes classification based on those features predicted 1‐year seizure freedom following ATL with area under the curve (AUC) values of 0.78 and 0.76 for the MC and CC data sets, respectively. Subsequent analyses revealed that (a) interhemispheric spectral coherence features in the 10–25 Hz range provided better predictability than other combinations and (b) normal scalp EEG‐derived features provided superior and potentially distinct predictive value when compared with MRI abnormalities (>10% higher F1 score).

Significance

These results support that quantitative analysis of even a normal presurgical scalp EEG may help prognosticate seizure freedom following ATL in patients with drug‐resistant TLE. Although the mechanism for this result is not known, the scalp EEG spectral and coherence properties predicting seizure freedom may represent activity arising from the neocortex or the networks responsible for temporal lobe seizure generation within vs outside the margins of an ATL.

Aberrant dynamic structure-function relationship of rich-club organization in treatment-naïve newly diagnosed juvenile myoclonic epilepsy

Neuroimaging studies have shown that juvenile myoclonic epilepsy (JME) is characterized by impaired brain networks. However, few studies have investigated the potential disruptions in rich‐club organization—a core feature of the brain networks. Moreover, it is unclear how structure–function relationships dynamically change over time in JME. Here, we quantify the anatomical rich‐club organization and dynamic structural and functional connectivity (SC–FC) coupling in 47 treatment‐naïve newly diagnosed patients with JME and 40 matched healthy controls. Dynamic functional network efficiency and its association with SC–FC coupling were also calculated to examine the supporting of structure–function relationship to brain information transfer. The results showed that the anatomical rich‐club organization was disrupted in the patient group, along with decreased connectivity strength among rich‐club hub nodes. Furthermore, reduced SC–FC coupling in rich‐club organization of the patients was found in two functionally independent dynamic states, that is the functional segregation state (State 1) and the strong somatomotor‐cognitive control interaction state (State 5); and the latter was significantly associated with disease severity. In addition, the relationships between SC–FC coupling of hub nodes connections and functional network efficiency in State 1 were found to be absent in patients. The aberrant dynamic SC–FC coupling of rich‐club organization suggests a selective influence of densely interconnected network core in patients with JME at the early phase of the disease, offering new insights and potential biomarkers into the underlying neurodevelopmental basis of behavioral and cognitive impairments observed in JME.

Feature Extraction of the Brain's Dynamic Complex Network Based on EEG and a Framework for Discrimination of Pediatric Epilepsy

Most of the current complex network studies about epilepsy used the electroencephalogram (EEG) to directly construct the static complex network for analysis and discarded the dynamic characteristics. This study constructed the dynamic complex network on EEG from pediatric epilepsy and pediatric control when they were asleep by the sliding window method. Dynamic features were extracted and incorporated into various machine learning classifiers to explore their classification performances. We compared these performances between the static and dynamic complex network. In the univariate analysis, the initially insignificant topological characteristics in the static complex network can be transformed to be significant in the dynamic complex network. Under most connectivity calculation methods between leads, the accuracy of using dynamic complex network features for discrimination was higher than that of static complex network features. Particularly in the imaginary part of the coherency function (iCOH) method under the full-frequency band, the discrimination accuracies of most machine learning classifiers were higher than 95%, and the discrimination accuracies in the higher-frequency band (beta-frequency band) and the full-frequency band were higher than that of the lower-frequency bands. Our proposed method and framework could efficiently summarize more time-varying features in the EEG and improve the accuracies of the discrimination of the machine learning classifiers more than using static complex network features.

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

OBJECTIVES

To evaluate the usefulness of quantitative electroencephalography (qEEG) in the analysis of baseline activity in patients with temporal lobe epilepsy (TLE) and identify measures potentially associated with disease duration and drug resistance.

MATERIALS AND METHODS

Cross-sectional study of adult patients with TLE and controls who underwent video-EEG monitoring. Representative artifact-free resting wakefulness baseline EEG segments were selected for quantitative analysis. The fast Fourier transform (FFT) approach was used for the power spectral analysis, with computation of FFT power ratios and alpha-delta and alpha-theta ratios for both hemispheres. The resulting measures were compared between TLE patients and controls and their values as predictors of epilepsy duration and drug resistance analyzed.

RESULTS

Thirty-nine TLE patients and 23 controls were included. The TLE patients had a lower alpha-delta ratio in the posterior quadrant ipsilateral to the epileptic focus and a lower alpha-theta ratio in the ipsilateral anterior/posterior quadrants and temporal region. A younger age at onset and longer epilepsy duration correlated with a higher theta power ratio in the contralateral anterior and posterior quadrants and temporal region. No qEEG measures predicted drug resistance.

CONCLUSIONS

Quantitative electroencephalography background activity may contribute to the diagnosis of TLE and provide useful information on disease duration. A lower alpha-delta and alpha-theta ratio may be reliable baseline qEEG measures for identifying patients with TLE. A higher contralateral theta power ratio may be indicative of longer epilepsy duration.

Different circuitry dysfunction in drug-naive patients with juvenile myoclonic epilepsy and juvenile absence epilepsy

RATIONALE

Juvenile myoclonic epilepsy (JME) and juvenile absence epilepsy (JAE) are generalized epileptic syndromes presenting in the same age range. To explore whether uneven network dysfunctions may underlie the two different phenotypes, we examined drug-naive patients with JME and JAE at the time of their earliest presentation.

METHODS

Patients were recruited based on typical JME (n = 23) or JAE (n = 18) presentation and compared with 16 age-matched healthy subjects (HS). We analyzed their awake EEG signals by Partial Directed Coherence and graph indexes.

RESULTS

Out-density and betweenness centrality values were different between groups. With respect to both JAE and HS, JME showed unbalanced out-density and out-strength in alpha and beta bands on central regions and reduced alpha out-strength from fronto-polar to occipital regions, correlating with photosensitivity. With respect to HS, JAE showed enhanced alpha out-density and out-strength on fronto-polar regions. In gamma band, JAE showed reduced Global/Local Efficiency and Clustering Coefficient with respect to HS, while JME showed more scattered values.

CONCLUSIONS

Our data suggest that regional network changes in alpha and beta bands underlie the different presentation distinguishing JME and JAE resulting in motor vs non-motor seizures characterizing these two syndromes. Conversely, impaired gamma-activity within the network seems to be a non-local marker of defective inhibition.

Brainwaves beyond diagnosis: Wider applications of electroencephalography in idiopathic generalized epilepsy

Electroencephalography (EEG) has long been used as a versatile and noninvasive diagnostic tool in epilepsy. With the advent of digital EEG, more advanced applications of EEG have emerged. Compared with technologically advanced practice in focal epilepsies, the utilization of EEG in idiopathic generalized epilepsy (IGE) has been lagging, often restricted to a simple diagnostic tool. In this narrative review, we provide an overview of broader applications of EEG beyond this narrow scope, discussing how the current clinical and research applications of EEG may potentially be extended to IGE. The current literature, although limited, suggests that EEG can be used in syndromic classification, guiding antiseizure medication therapy, predicting prognosis, unraveling biorhythms, and investigating functional brain connectivity of IGE. We emphasize the need for longer recordings, particularly 24-h ambulatory EEG, to capture discharges reflecting circadian and sleep-wake cycle-associated variations for wider EEG applications in IGE. Finally, we highlight the challenges and limitations of the current body of literature and suggest future directions to encourage and enhance more extensive applications of this potent tool.

Functional network topology in drug resistant and well-controlled idiopathic generalized epilepsy: a resting state functional MRI study

Despite an increasing number of drug treatment options for people with idiopathic generalized epilepsy (IGE), drug resistance remains a significant issue and the mechanisms underlying it remain poorly understood. Previous studies have largely focused on potential cellular or genetic explanations for drug resistance. However, epilepsy is understood to be a network disorder and there is a growing body of literature suggesting altered topology of large-scale resting networks in people with epilepsy compared with controls. We hypothesize that network alterations may also play a role in seizure control. The aim of this study was to compare resting state functional network structure between well-controlled IGE (WC-IGE), drug resistant IGE (DR-IGE) and healthy controls. Thirty-three participants with IGE (10 with WC-IGE and 23 with DR-IGE) and 34 controls were included. Resting state functional MRI networks were constructed using the Functional Connectivity Toolbox (CONN). Global graph theoretic network measures of average node strength (an equivalent measure to mean degree in a network that is fully connected), node strength distribution variance, characteristic path length, average clustering coefficient, small-world index and average betweenness centrality were computed. Graphs were constructed separately for positively weighted connections and for absolute values. Individual nodal values of strength and betweenness centrality were also measured and ‘hub nodes’ were compared between groups. Outcome measures were assessed across the three groups and between both groups with IGE and controls. The IGE group as a whole had a higher average node strength, characteristic path length and average betweenness centrality. There were no clear differences between groups according to seizure control. Outcome metrics were sensitive to whether negatively correlated connections were included in network construction. There were no clear differences in the location of ‘hub nodes’ between groups. The results suggest that, irrespective of seizure control, IGE interictal network topology is more regular and has a higher global connectivity compared to controls, with no alteration in hub node locations. These alterations may produce a resting state network that is more vulnerable to transitioning to the seizure state. It is possible that the lack of apparent influence of seizure control on network topology is limited by challenges in classifying drug response. It is also demonstrated that network topological features are influenced by the sign of connectivity weights and therefore future methodological work is warranted to account for anticorrelations in graph theoretic studies.

Characterizing the electrophysiological abnormalities in visually reviewed normal EEGs of drug-resistant focal epilepsy patients

Routine scalp EEG is essential in the clinical diagnosis and management of epilepsy. However, a normal scalp EEG (based on expert visual review) recorded from a patient with epilepsy can cause delays in diagnosis and clinical care delivery. Here, we investigated whether normal EEGs might contain subtle electrophysiological clues of epilepsy. Specifically, we investigated (i) whether there are indicators of abnormal brain electrophysiology in normal EEGs of epilepsy patients, and (ii) whether such abnormalities are modulated by the side of the brain generating seizures in focal epilepsy. We analysed awake scalp EEG recordings of age-matched groups of 144 healthy individuals and 48 individuals with drug-resistant focal epilepsy who had normal scalp EEGs. After preprocessing, using a bipolar montage of eight channels, we extracted the fraction of spectral power in the alpha band (8-13 Hz) relative to a wide band of 0.5-40 Hz within 10-s windows. We analysed the extracted features for (i) the extent to which people with drug-resistant focal epilepsy differed from healthy subjects, and (ii) whether differences within the drug-resistant focal epilepsy patients were related to the hemisphere generating seizures. We then used those differences to classify whether an EEG is likely to have been recorded from a person with drug-resistant focal epilepsy, and if so, the epileptogenic hemisphere. Furthermore, we tested the significance of these differences while controlling for confounders, such as acquisition system, age and medications. We found that the fraction of alpha power is generally reduced (i) in drug-resistant focal epilepsy compared to healthy controls, and (ii) in right-handed drug-resistant focal epilepsy subjects with left hemispheric seizures compared to those with right hemispheric seizures, and that the differences are most prominent in the frontal and temporal regions. The fraction of alpha power yielded area under curve values of 0.83 in distinguishing drug-resistant focal epilepsy from healthy and 0.77 in identifying the epileptic hemisphere in drug-resistant focal epilepsy patients. Furthermore, our results suggest that the differences in alpha power are greater when compared with differences attributable to acquisition system differences, age and medications. Our findings support that EEG-based measures of normal brain function, such as the normalized spectral power of alpha activity, may help identify patients with epilepsy even when an EEG does not contain any epileptiform activity, recorded seizures or other abnormalities. Although alpha abnormalities are unlikely to be disease-specific, we propose that such abnormalities may provide a higher pre-test probability for epilepsy when an individual being screened for epilepsy has a normal EEG on visual assessment.