Interictal regional delta slowing is an EEG marker of epileptic network in temporal lobe epilepsy

Gamma amplitude-envelope correlations are strongly elevated within hyperexcitable networks in focal epilepsy

Methods to quantify cortical hyperexcitability are of enormous interest for mapping epileptic networks in patients with focal epilepsy. We hypothesize that, in the resting state, cortical hyperexcitability increases firing-rate correlations between neuronal populations within seizure onset zones (SOZs). This hypothesis predicts that in the gamma frequency band (40-200 Hz), amplitude envelope correlations (AECs), a relatively straightforward measure of functional connectivity, should be elevated within SOZs compared to other areas. To test this prediction, we analyzed archived samples of interictal electrocorticographic (ECoG) signals recorded from patients who became seizure-free after surgery targeting SOZs identified by multiday intracranial recordings. We show that in the gamma band, AECs between nodes within SOZs are markedly elevated relative to those elsewhere. AEC-based node strength, eigencentrality, and clustering coefficient are also robustly increased within the SOZ with maxima in the low-gamma band (permutation test Z-scores > 8) and yield moderate discriminability of the SOZ using ROC analysis (maximal mean AUC ~ 0.73). By contrast to AECs, phase locking values (PLVs), a measure of narrow-band phase coupling across sites, and PLV-based graph metrics discriminate the seizure onset nodes weakly. Our results suggest that gamma band AECs may provide a clinically useful marker of cortical hyperexcitability in focal epilepsy.

Excitation/Inhibition balance relates to cognitive function and gene expression in temporal lobe epilepsy: a high density EEG assessment with aperiodic exponent

Patients with epilepsy are characterized by a dysregulation of excitation/inhibition balance (E/I). The assessment of E/I may inform clinicians during the diagnosis and therapy management, even though it is rarely performed. An accessible measure of the E/I of the brain represents a clinically relevant feature. Here, we exploited the exponent of the aperiodic component of the power spectrum of the electroencephalography (EEG) signal, as a non-invasive and cost-effective proxy of the E/I balance. We recorded resting-state activity with high-density EEG from 67 patients with temporal lobe epilepsy and 35 controls. We extracted the exponent of the aperiodic fit of the power spectrum from source-reconstructed EEG and tested differences between patients with epilepsy and controls. Spearman's correlation was performed between the exponent and clinical variables (age of onset, epilepsy duration and neuropsychology) and cortical expression of epilepsy-related genes derived from the Allen Human Brain Atlas. Patients with temporal lobe epilepsy showed a significantly larger exponent, corresponding to inhibition-directed E/I balance, in bilateral frontal and temporal regions. Lower E/I in the left entorhinal and bilateral dorsolateral prefrontal cortices corresponded to a lower performance of short-term verbal memory. Limited to patients with temporal lobe epilepsy, we detected a significant correlation between the exponent and the cortical expression of GABRA1, GRIN2A, GABRD, GABRG2, KCNA2 and PDYN genes. EEG aperiodic exponent maps the E/I balance non-invasively in patients with epilepsy and reveals a close relationship between altered E/I patterns, cognition and genetics.

Clinical characteristics and surgical outcomes of epilepsy associated with temporal encephalocele: A systematic review

Temporal encephaloceles (TE) are an under-identified, potentially intervenable cause of epilepsy. This systematic review consolidates the current data to identify the major clinical, neuroimaging, and EEG features and surgical outcomes of epilepsy associated with TE. Literature searches were carried out using MEDLINE, Embase, PsycINFO, Scopus, and Cochrane Library databases from inception to December 7, 2023. Studies were included if they described clinical, neuroimaging, EEG, or surgical data in ≥5 patients with TE and epilepsy. Of 562 studies identified in the search, 24 met the eligibility criteria, reporting 423 unique patients with both epilepsy and TE. Compared to epilepsy patients without TE, those with TE had a higher mean age of seizure onset and were less likely to have a history of febrile seizures. Seizure semiologies were variable, but primarily mirrored temporal lobe onset patterns. Epilepsy patients with TE had a higher likelihood of having clinical or radiographic features of idiopathic intracranial hypertension (IIH) than those without. Brain MRI may show ipsilateral mesial temporal sclerosis (16 %). CT scans of the skull base usually revealed bony defects near the TE (90 %). Brain PET scans primarily showed ipsilateral temporal lobe hypometabolism (80 %), mostly in the anterior temporal lobe (67 %). Scalp EEG mostly lateralized ipsilateral to the implicated TE (92 % seizure onset) and localized to the temporal lobe (96 %). Intracranial EEG revealed seizure onset near the TE (11 of 12 cases including TE-adjacent electrodes) with variable timing of spread to the ipsilateral hippocampus. After surgical treatment of the TE, the rate of Engel I or ILAE 1 outcomes at one year was 75 % for lesionectomy, 85 % for anterior temporal lobectomy (ATL), and 80 % for ATL with amygdalohippocampectomy. Further studies are needed to better elucidate the relationship between IIH, TE, and epilepsy, improve the identification of TE, and optimize surgical interventions.

Impaired brain-heart axis in focal epilepsy: Alterations in information flow and implications for seizure dynamics

This study delves into functional brain-heart interplay (BHI) dynamics during interictal periods before and after seizure events in focal epilepsy. Our analysis focuses on elucidating the causal interaction between cortical and autonomic nervous system (ANS) oscillations, employing electroencephalography and heart rate variability series. The dataset for this investigation comprises 47 seizure events from 14 independent subjects, obtained from the publicly available Siena Dataset. Our findings reveal an impaired brain-heart axis especially in the heart-to-brain functional direction. This is particularly evident in bottom-up oscillations originating from sympathovagal activity during the transition between preictal and postictal periods. These results indicate a pivotal role of the ANS in epilepsy dynamics. Notably, the brain-to-heart information flow targeting cardiac oscillations in the low-frequency band does not display significant changes. However, there are noteworthy changes in cortical oscillations, primarily originating in central regions, influencing heartbeat oscillations in the high-frequency band. Our study conceptualizes seizures as a state of hyperexcitability and a network disease affecting both cortical and peripheral neural dynamics. Our results pave the way for a deeper understanding of BHI in epilepsy, which holds promise for the development of advanced diagnostic and therapeutic approaches also based on bodily neural activity for individuals living with epilepsy.

Valine and Inflammation Drive Epilepsy in a Mouse Model of ECHS1 Deficiency

ECHS1 Deficiency (ECHS1D) is a rare and devastating pediatric disease that currently has no defined treatments. This disorder results from missense loss-of-function mutations in the ECHS1 gene that result in severe developmental delays, encephalopathy, hypotonia, and early death. ECHS1 enzymatic activity is necessary for the beta-oxidation of fatty acids and the oxidation of branched-chain amino acids within the inner mitochondrial matrix. The pathogenesis of disease remains unknown, however it is hypothesized that disease is driven by an accumulation of toxic metabolites from impaired valine oxidation. To expand our knowledge on disease mechanisms, a novel mouse model of ECHS1D was generated that possesses a disease-associated knock-in (KI) allele and a knock-out (KO) allele. To investigate the behavioral phenotype, a battery of testing was performed at multiple time points, which included assessments of learning, motor function, endurance, sensory responses, and anxiety. Neurological abnormalities were assessed using wireless telemetry EEG recordings, pentylenetetrazol (PTZ) seizure induction, and immunohistochemistry. Metabolic perturbations were measured within the liver, serum, and brain using mass spectrometry and magnetic resonance spectroscopy. To test disease mechanisms, mice were subjected to disease pathway stressors and then survival, body weight gain, and epilepsy were assessed. Mice containing KI/KI or KI/KO alleles were viable with normal development and survival, and the presence of KI and KO alleles resulted in a significant reduction in ECHS1 protein. ECHS1D mice displayed reduced exercise capacity and pain sensation. EEG analysis revealed increased slow wave power that was associated with perturbations in sleep. ECHS1D mice had significantly increased epileptiform EEG discharges, and were sensitive to seizure induction, which resulted in death of 60% of ECHS1D mice. Under basal conditions, brain structure was grossly normal, although histological analysis revealed increased microglial activation in aged ECHS1D mice. Increased dietary valine only affected ECHS1D mice, which significantly exacerbated seizure susceptibility and resulted in death. Lastly, acute inflammatory challenge drove regression and early lethality in ECHS1D mice. In conclusion, we developed a novel model of ECHS1D that may be used to further knowledge on disease mechanisms and to develop therapeutics. Our data suggests altered metabolic signaling and inflammation may contribute to epilepsy in ECHS1D, and these alterations may be attributed to impaired valine metabolism.

Effect of the closed-loop hippocampal low-frequency stimulation on seizure severity, learning, and memory in pilocarpine epilepsy rat model

AIMS

In this study, the anticonvulsant action of closed-loop, low-frequency deep brain stimulation (DBS) was investigated. In addition, the changes in brain rhythms and functional connectivity of the hippocampus and prefrontal cortex were evaluated.

METHODS

Epilepsy was induced by pilocarpine in male Wistar rats. After the chronic phase, a tripolar electrode was implanted in the right ventral hippocampus and a monopolar electrode in medial prefrontal cortex (mPFC). Subjects' spontaneous seizure behaviors were observed in continuous video recording, while the local field potentials (LFPs) were recorded simultaneously. In addition, spatial memory was evaluated by the Barnes maze test.

RESULTS

Applying hippocampal DBS, immediately after seizure detection in epileptic animals, reduced their seizure severity and duration, and improved their performance in Barnes maze test. DBS reduced the increment in power of delta, theta, and gamma waves in pre-ictal, ictal, and post-ictal periods. Meanwhile, DBS increased the post-ictal-to-pre-ictal ratio of theta band. DBS decreased delta and increased theta coherences, and also increased the post-ictal-to-pre-ictal ratio of coherence. In addition, DBS increased the hippocampal-mPFC coupling in pre-ictal period and decreased the coupling in the ictal and post-ictal periods.

CONCLUSION

Applying closed-loop, low-frequency DBS at seizure onset reduced seizure severity and improved memory. In addition, the changes in power, coherence, and coupling of the LFP oscillations in the hippocampus and mPFC demonstrate low-frequency DBS efficacy as an antiepileptic treatment, returning LFPs to a seemingly non-seizure state in subjects that received DBS.

Network analysis of preictal iEEG reveals changes in network structure preceding seizure onset

Seizures likely result from aberrant network activity and synchronization. Changes in brain network connectivity may underlie seizure onset. We used a novel method of rapid network model estimation from intracranial electroencephalography (iEEG) data to characterize pre-ictal changes in network structure prior to seizure onset. We analyzed iEEG data from 20 patients from the iEEG.org database. Using 10 s epochs sliding by 1 s intervals, a multiple input, single output (MISO) state space model was estimated for each output channel and time point with all other channels as inputs, generating sequential directed network graphs of channel connectivity. These networks were assessed using degree and betweenness centrality. Both degree and betweenness increased at seizure onset zone (SOZ) channels 37.0 ± 2.8 s before seizure onset. Degree rose in all channels 8.2 ± 2.2 s prior to seizure onset, with increasing connections between the SOZ and surrounding channels. Interictal networks showed low and stable connectivity. A novel MISO model-based network estimation method identified changes in brain network structure just prior to seizure onset. Increased connectivity was initially isolated within the SOZ and spread to non-SOZ channels before electrographic seizure onset. Such models could help confirm localization of SOZ regions.

Epileptic Seizure Detection Using Geometric Features Extracted from SODP Shape of EEG Signals and AsyLnCPSO-GA

Epilepsy is a neurological disorder that is characterized by transient and unexpected electrical disturbance of the brain. Seizure detection by electroencephalogram (EEG) is associated with the primary interest of the evaluation and auxiliary diagnosis of epileptic patients. The aim of this study is to establish a hybrid model with improved particle swarm optimization (PSO) and a genetic algorithm (GA) to determine the optimal combination of features for epileptic seizure detection. First, the second-order difference plot (SODP) method was applied, and ten geometric features of epileptic EEG signals were derived in each frequency band (δ, θ, α and β), forming a high-dimensional feature vector. Secondly, an optimization algorithm, AsyLnCPSO-GA, combining a modified PSO with asynchronous learning factor (AsyLnCPSO) and the genetic algorithm (GA) was proposed for feature selection. Finally, the feature combinations were fed to a naïve Bayesian classifier for epileptic seizure and seizure-free identification. The method proposed in this paper achieved 95.35% classification accuracy with a tenfold cross-validation strategy when the interfrequency bands were crossed, serving as an effective method for epilepsy detection, which could help clinicians to expeditiously diagnose epilepsy based on SODP analysis and an optimization algorithm for feature selection.

The longitudinal evolution of cerebral blood flow in children with tuberous sclerosis assessed by arterial spin labeling magnetic resonance imaging may be related to cognitive performance

OBJECTIVE

To study longitudinal changes in tuber and whole-brain perfusion in children with tuberous sclerosis complex (TSC) using arterial spin labeling (ASL) perfusion MRI and correlate them with pathological EEG slow wave activity and neurodevelopmental outcomes.

METHODS

Retrospective longitudinal cohort study of 13 children with TSC, 3 to 6 serial ASL-MRI scans between 2 months and 7 years of age (53 scans in total), and an EEG examination performed within 2 months of the last MRI. Tuber cerebral blood flow (CBF) values were calculated in tuber segmentation masks, and tuber:cortical CBF ratios were used to study tuber perfusion. Logistic regression analysis was performed to identify which initial tuber characteristics (CBF value, volume, location) in the first MRI predicted tubers subsequently associated with EEG slow waves. Whole-brain and lobar CBF values were extracted for all patient scans and age-matched controls. CBF ratios were compared in patients and controls to study longitudinal changes in whole-brain CBF.

RESULTS

Perfusion was reduced in tubers associated with EEG slow waves compared with other tubers. Low tuber CBF values around 6 months of age and large tuber volumes were predictive of tubers subsequently associated with EEG slow waves. Patients with severe developmental delay had more severe whole-brain hypoperfusion than those with no/mild delay, which became apparent after 2 years of age and were not associated with a higher tuber load.

CONCLUSIONS

Dynamic changes in tuber and brain perfusion occur over time. Perfusion is significantly reduced in tubers associated with EEG slow waves. Whole-brain perfusion is significantly reduced in patients with severe delay.

KEY POINTS

• Tubers associated with EEG slow wave activity were significantly more hypoperfused than other tubers, especially after 1 year of age. • Larger and more hypoperfused tubers at 6 months of age were more likely to subsequently be associated with pathological EEG slow wave activity. • Patients with severe developmental delay had more extensive and severe global hypoperfusion than those without developmental delay.

Electrophysiological Evidence of Local Sleep During Yoga Nidra Practice

Background and Objectives

Yoga nidra is a technique sages use to self-induce sleep. Classically, sleep is characterized by three cardinal electrophysiological features, namely, electroencephalography (EEG), electromyography (EMG), and electrooculography (EOG). As the literature on electrophysiological characterization of Yoga nidra is lacking, it is not known whether it is a sleep or awake state. The objective of the study was to electrophysiologically characterize yoga nidra practice.

Materials and Methods

Thirty subjects underwent five initial supervised yoga nidra sessions and then continued practice on their own. The subjects completed their sleep diaries for 2 weeks before and during the intervention. The electrophysiological characterization was done after 2 weeks of yoga nidra practice using 19 EEG channels polysomnography for pre-yoga nidra, yoga nidra practice and post-yoga nidra. Polysomnographic data were scored for sleep-wake stages as per standard criteria. Power spectral density (PSD) was calculated from various frequency bands in different time bins. EEG data were grouped by areas, namely, central, frontal, prefrontal, parietal, temporal, and occipital in time bins. Sleep diary parameters were also compared for pre-post-yoga nidra training.

Results

After 2 weeks of yoga nidra practice, awake was scored throughout the session (n = 26). PSD results (mean difference in dB between different time bins; P value) showed significant changes. When compared to pre-yoga nidra, there was an increase in delta power in the central area (1.953; P = 0.033) and a decrease in the prefrontal area (2.713; P = 0.041) during yoga nidra. Sleep diary showed improvement in sleep duration (P = 0.0001), efficiency (P = 0.0005), quality (P = 0.0005), and total wake duration (P = 0.00005) after 2 weeks of practice.

Interpretations and Conclusions

Yoga nidra practice in novices is electrophysiologically an awake state with signs of slow waves locally, often referred to as local sleep.

Clinical Trial

Clinical Trial Registry of India, http://www.ctri.nic.in/Clinicaltrials/pmaindet2.php? trialid = 6253, 2013/05/003682.

Seizure classification with selected frequency bands and EEG montages: a Natural Language Processing approach

Individualized treatment is crucial for epileptic patients with different types of seizures. The differences among patients impact the drug choice as well as the surgery procedure. With the advance in machine learning, automatic seizure detection can ease the manual time-consuming and labor-intensive procedure for diagnose seizure in the clinical setting. In this paper, we present an electroencephalography (EEG) frequency bands (sub-bands) and montages selection (sub-zones) method for classifier training that exploits Natural Language Processing from individual patients' clinical report. The proposed approach is targeting for individualized treatment. We integrated the prior knowledge from patient's reports into the classifier-building process, mimicking the authentic thinking process of experienced neurologist's when diagnosing seizure using EEG. The keywords from clinical documents are mapped to the EEG data in terms of frequency bands and scalp EEG electrodes. The data of experiments are from the Temple University Hospital EEG seizure corpus, and the dataset is divided based on each group of patients with same seizure type and same recording electrode references. The classifier includes Random Forest, Support Vector Machine and Multi-Layer Perceptron. The classification performance indicates that competitive results can be achieve with a small portion of EEG the data. Using the sub-zones selection for Generalized Seizures (GNSZ) on all three electrodes, data are reduced by nearly 50% while the performance metrics remain at the same level with the whole frequency and zones. Moreover, when selecting by sub-zones and sub-bands together for GNSZ with Linked Ears reference, the data range reduced to 0.3% of whole range, and the performance deviates less than 3% from the results with whole range of data. Results show that using proposed approach may lead to more efficient implementations of the seizure classifier to be executed on power-efficient devices for long lasting real-time seizures detection.

Bumetanide Effects on Resting-State EEG in Tuberous Sclerosis Complex in Relation to Clinical Outcome: An Open-Label Study

Neuronal excitation-inhibition (E/I) imbalances are considered an important pathophysiological mechanism in neurodevelopmental disorders. Preclinical studies on tuberous sclerosis complex (TSC), suggest that altered chloride homeostasis may impair GABAergic inhibition and thereby E/I-balance regulation. Correction of chloride homeostasis may thus constitute a treatment target to alleviate behavioral symptoms. Recently, we showed that bumetanide-a chloride-regulating agent-improved behavioral symptoms in the open-label study Bumetanide to Ameliorate Tuberous Sclerosis Complex Hyperexcitable Behaviors trial (BATSCH trial; Eudra-CT: 2016-002408-13). Here, we present resting-state EEG as secondary analysis of BATSCH to investigate associations between EEG measures sensitive to network-level changes in E/I balance and clinical response to bumetanide. EEGs of 10 participants with TSC (aged 8-21 years) were available. Spectral power, long-range temporal correlations (LRTC), and functional E/I ratio (fE/I) in the alpha-frequency band were compared before and after 91 days of treatment. Pre-treatment measures were compared against 29 typically developing children (TDC). EEG measures were correlated with the Aberrant Behavioral Checklist-Irritability subscale (ABC-I), the Social Responsiveness Scale-2 (SRS-2), and the Repetitive Behavior Scale-Revised (RBS-R). At baseline, TSC showed lower alpha-band absolute power and fE/I than TDC. Absolute power increased through bumetanide treatment, which showed a moderate, albeit non-significant, correlation with improvement in RBS-R. Interestingly, correlations between baseline EEG measures and clinical outcomes suggest that most responsiveness might be expected in children with network characteristics around the E/I balance point. In sum, E/I imbalances pointing toward an inhibition-dominated network are present in TSC. We established neurophysiological effects of bumetanide although with an inconclusive relationship with clinical improvement. Nonetheless, our results further indicate that baseline network characteristics might influence treatment response. These findings highlight the possible utility of E/I-sensitive EEG measures to accompany new treatment interventions for TSC.

Clinical Trial Registration

EU Clinical Trial Register, EudraCT 2016-002408-13 (www.clinicaltrialsregister.eu/ctr-search/trial/2016-002408-13/NL). Registered 25 July 2016.

Electroclinical Mismatch During EEG Acquisition: What It Might Mean, What We Might Need to Do

An electroclinical mismatch is present if the electroencephalogram (EEG) shows evidence of moderate to severe diffuse encephalopathy but the patient’s mental status is only mildly altered. We describe five cases in which seizure or status epilepticus was suspected due to electroclinical mismatch. In all five cases, EEG was ordered to rule out nonconvulsive status epilepticus as the cause of the altered mental status. EEG initially showed generalized delta activity (GDA), with variable degrees of rhythmicity, with or without superimposed theta activity, with or without sporadic epileptiform discharges. During EEG acquisition, all patients followed commands and answered questions. The mental status change was limited to mild inattention and temporal disorientation. Benzodiazepine challenge was performed by administering lorazepam 2-mg IV. Within 10 minutes of injection, GDA started to break up and subsequently disappeared. EEG showed prominent sleep spindles in three patients and background changes, indicating drowsiness in two patients. The assessment of clinical response to lorazepam was confounded by sleepiness in all patients. Serial EEG recording or continuous EEG monitoring revealed reemergence of GDA, at times appearing more rhythmic than the GDA in the baseline study. All patients received nonsedating antiseizure drugs. GDA completely resolved and mental status normalized two to five days after starting antiseizure medication. In cases of electroclinical mismatch, the absence of clear-cut epileptiform discharges does not exclude the possibility that cortical hyperexcitability is contributing to the encephalopathic process. A positive response to benzodiazepine challenge suggests the presence of cortical hyperexcitability and the need to start, or increase the dosage of, antiseizure drugs.

Interictal epileptiform discharges in focal epilepsy are preceded by increase in low-frequency oscillations

OBJECTIVE

Interictal epileptiform discharges (IEDs) constitute a diagnostic signature of epilepsy. These events reflect epileptogenic hypersynchronization. Previous studies indicated that IEDs arise from slow neuronal activation accompanied by metabolic and hemodynamic changes. These might induce cortical inhibition followed hypersynchronization at IED onset. As cortical inhibition is mediated by low-frequency oscillations, we aimed to analyze the role of low-frequency oscillations prior the IED using magnetencephalography (MEG).

METHODS

Low-frequency (1-8 Hz) oscillations pre-IED ([-1000 milliseconds (ms), IED onset]) were analyzed using MEG in 14 focal epilepsy patients (median age = 23 years, range = 7-46 age). Occurrence of local pre-IED oscillations was analyzed using Beamformer Dynamical Imaging of Coherent Sources (DICS) and event-related desynchronization/synchronization (ERD-ERS) maps constructed using cluster-based permutation tests. The development of pre-IED oscillations was characterized using Hilbert transformation.

RESULTS

All patients exhibited statistically significant increase in delta (1-4 Hz) and/or theta (4-8 Hz) oscillations pre-IED compared to baseline [-2000 ms, -1000 ms]. Furthermore, all patients exhibited low-frequency power increase up to IED onset.

CONCLUSIONS

We demonstrated consistently occurring, low-frequency oscillations prior to IED onset.

SIGNIFICANCE

As low-frequency activity mediates cortical inhibition, our study demonstrates that a focal inhibition precedes hypersynchronization at IED onset.

Probabilistic comparison of gray and white matter coverage between depth and surface intracranial electrodes in epilepsy

In this study, we quantified the coverage of gray and white matter during intracranial electroencephalography in a cohort of epilepsy patients with surface and depth electrodes. We included 65 patients with strip electrodes (n = 12), strip and grid electrodes (n = 24), strip, grid, and depth electrodes (n = 7), or depth electrodes only (n = 22). Patient-specific imaging was used to generate probabilistic gray and white matter maps and atlas segmentations. Gray and white matter coverage was quantified using spherical volumes centered on electrode centroids, with radii ranging from 1 to 15 mm, along with detailed finite element models of local electric fields. Gray matter coverage was highly dependent on the chosen radius of influence (RoI). Using a 2.5 mm RoI, depth electrodes covered more gray matter than surface electrodes; however, surface electrodes covered more gray matter at RoI larger than 4 mm. White matter coverage and amygdala and hippocampal coverage was greatest for depth electrodes at all RoIs. This study provides the first probabilistic analysis to quantify coverage for different intracranial recording configurations. Depth electrodes offer increased coverage of gray matter over other recording strategies if the desired signals are local, while subdural grids and strips sample more gray matter if the desired signals are diffuse.

Paroxysmal slow wave events predict epilepsy following a first seizure

Objective

Management of a patient presenting with a first seizure depends on the risk of additional seizures. In clinical practice, the recurrence risk is estimated by the treating physician using the neurological examination, brain imaging, a thorough history for risk factors, and routine scalp electroencephalogram (EEG) to detect abnormal epileptiform activity. The decision to use antiseizure medication can be challenging when objective findings are missing. There is a need for new biomarkers to better diagnose epilepsy following a first seizure. Recently, an EEG‐based novel analytical method was reported to detect paroxysmal slowing in the cortical network of patients with epilepsy. The aim of our study is to test this method's sensitivity and specificity to predict epilepsy following a first seizure.

Methods

We analyzed interictal EEGs of 70 patients admitted to the emergency department of a tertiary referral center after a first seizure. Clinical data from a follow‐up period of at least 18 months were available. EEGs of 30 healthy controls were also analyzed and included. For each EEG, we applied an automated algorithm to detect paroxysmal slow wave events (PSWEs).

Results

Of patients presenting with a first seizure, 40% had at least one additional recurring seizure and were diagnosed with epilepsy. Sixty percent did not report additional seizures. A significantly higher occurrence of PSWEs was detected in the first interictal EEG test of those patients who were eventually diagnosed with epilepsy. Conducting the EEG test within 72 h after the first seizure significantly increased the likelihood of detecting PSWEs and the predictive value for epilepsy up to 82%.

Significance

The quantification of PSWEs by an automated algorithm can predict epilepsy and help the neurologist in evaluating a patient with a first seizure.

Practical Fundamentals of Clinical MEG Interpretation in Epilepsy

Magnetoencephalography (MEG) is a neurophysiologic test that offers a functional localization of epileptic sources in patients considered for epilepsy surgery. The understanding of clinical MEG concepts, and the interpretation of these clinical studies, are very involving processes that demand both clinical and procedural expertise. One of the major obstacles in acquiring necessary proficiency is the scarcity of fundamental clinical literature. To fill this knowledge gap, this review aims to explain the basic practical concepts of clinical MEG relevant to epilepsy with an emphasis on single equivalent dipole (sECD), which is one the most clinically validated and ubiquitously used source localization method, and illustrate and explain the regional topology and source dynamics relevant for clinical interpretation of MEG-EEG.

Seizure Susceptibility Prediction in Uncontrolled Epilepsy

Epileptic seizure forecasting, combined with the delivery of preventative therapies, holds the potential to greatly improve the quality of life for epilepsy patients and their caregivers. Forecasting seizures could prevent some potentially catastrophic consequences such as injury and death in addition to several potential clinical benefits it may provide for patient care in hospitals. The challenge of seizure forecasting lies within the seemingly unpredictable transitions of brain dynamics into the ictal state. The main body of computational research on determining seizure risk has been focused solely on prediction algorithms, which involves a challenging issue of balancing sensitivity and false alarms. There have been some studies on identifying potential biomarkers for seizure forecasting; however, the questions of “What are the true biomarkers for seizure prediction” or even “Is there a valid biomarker for seizure prediction?” are yet to be fully answered. In this paper, we introduce a tool to facilitate the exploration of the potential biomarkers. We confirm using our tool that interictal slowing activities are a promising biomarker for epileptic seizure susceptibility prediction.

High-Frequency Hubs of the Ictal Cross-Frequency Coupling Network Predict Surgical Outcome in Epilepsy Patients

Seizure generation is thought to be a process driven by epileptogenic networks; thus, network analysis tools can help determine the efficacy of epilepsy treatment. Studies have suggested that low-frequency (LF) to high-frequency (HF) cross-frequency coupling (CFC) is a useful biomarker for localizing epileptogenic tissues. However, it remains unclear whether the LF or HF coordinated CFC network hubs are more critical in determining the treatment outcome. We hypothesize that HF hubs are primarily responsible for seizure dynamics. Stereo-electroencephalography (SEEG) recordings of 36 seizures from 16 intractable epilepsy patients were analyzed. We propose a new approach to model the temporal-spatial-spectral dynamics of CFC networks. Graph measures are then used to characterize the HF and LF hubs. In the patient group with Engel Class (EC) I outcome, the strength of HF hubs was significantly higher inside the resected regions during the early and middle stages of seizure, while such a significant difference was not observed in the EC III group and only for the early stage in the EC II group. For the LF hubs, a significant difference was identified at the late stage and only in the EC I group. Our findings suggest that HF hubs increase at early and middle stages of the ictal interval while LF hubs increase activity at the late stages. In addition, HF hubs can predict treatment outcomes more precisely, compared to the LF hubs of the CFC network. The proposed method promises to identify more accurate targets for surgical interventions or neuromodulation therapies.

How cerebral cortex protects itself from interictal spikes: The alpha/beta inhibition mechanism

Interactions between interictal epileptiform discharges (IEDs) and distant cortical regions subserve potential effects on cognition of patients with focal epilepsy. We hypothesize that "healthy" brain areas at a distance from the epileptic focus may respond to the interference of IEDs by generating inhibitory alpha and beta oscillations. We predict that more prominent alpha-beta oscillations can be found in patients with less impaired neurocognitive profile. We performed a source imaging magnetoencephalography study, including 41 focal epilepsy patients: 21 with frontal lobe epilepsy (FLE) and 20 with mesial temporal lobe epilepsy. We investigated the effect of anterior (i.e., frontal and temporal) IEDs on the oscillatory pattern over posterior head regions. We compared cortical oscillations (5-80 Hz) temporally linked to 3,749 IEDs (1,945 frontal and 1,803 temporal) versus an equal number of IED-free segments. We correlated results from IED triggered oscillations to global neurocognitive performance. Only frontal IEDs triggered alpha-beta oscillations over posterior head regions. IEDs with higher amplitude triggered alpha-beta oscillations of higher magnitude. The intensity of posterior head region alpha-beta oscillations significantly correlated with a better neuropsychological profile. Our study demonstrated that cerebral cortex protects itself from IEDs with generation of inhibitory alpha-beta oscillations at distant cortical regions. The association of more prominent oscillations with a better cognitive status suggests that this mechanism might play a role in determining the cognitive resilience in patients with FLE.

Surgical Outcomes and EEG Prognostic Factors After Stereotactic Laser Amygdalohippocampectomy for Mesial Temporal Lobe Epilepsy

Objective: To assess the seizure outcomes of stereotactic laser amygdalohippocampectomy (SLAH) in consecutive patients with mesial temporal lobe epilepsy (mTLE) in a single center and identify scalp EEG and imaging factors in the presurgical evaluation that correlate with post-surgical seizure recurrence.

Methods: We retrospectively reviewed the medical and EEG records of 30 patients with drug-resistant mTLE who underwent SLAH and had at least 1 year of follow-up. Surgical outcomes were classified using the Engel scale. Univariate hazard ratios were used to evaluate the risk factors associated with seizure recurrence after SLAH.

Results: The overall Engel class I outcome after SLAH was 13/30 (43%), with a mean postoperative follow-up of 48.9 ± 17.6 months. Scalp EEG findings of interictal regional slow activity (IRSA) on the side of surgery (HR = 4.05, p = 0.005) and non-lateralizing or contra-lateralizing seizure onset (HR = 4.31, p = 0.006) were negatively correlated with postsurgical seizure freedom. Scalp EEG with either one of the above features strongly predicted seizure recurrence after surgery (HR = 7.13, p < 0.001) with 100% sensitivity and 71% specificity.

Significance: Understanding the factors associated with good or poor surgical outcomes can help choose the best candidates for SLAH. Of the variables assessed, scalp EEG findings were the most clearly associated with seizure outcomes after SLAH.

Normal Variants in Magnetoencephalography

Normal variants, although not occurring frequently, may appear similar to epileptic activity. Misinterpretation may lead to false diagnoses. In the context of presurgical evaluation, normal variants may lead to mislocalizations with severe impact on the viability and success of surgical therapy. While the different variants are well known in EEG, little has been published in regard to their appearance in magnetoencephalography. Furthermore, there are some magnetoencephalography normal variants that have no counterparts in EEG. This article reviews benign epileptiform variants and provides examples in EEG and magnetoencephalography. In addition, the potential of oscillatory configurations in different frequency bands to appear as epileptic activity is discussed.

Discrimination of Tourette Syndrome Based on the Spatial Patterns of the Resting-State EEG Network

Tourette syndrome (TS) is a neuropsychiatric disorder with childhood onset characterized by chronic motor and vocal tics; however, the current diagnosis of TS patients is subjective, as it is mainly assessed based on the parents' description alongside specific evaluations. The early and accurate diagnosis of TS based on its potential symptoms in children would be of benefit in their future therapy, but reliable diagnoses are difficult due to the lack of objective knowledge of the etiology and pathogenesis of TS. In this study, resting-state electroencephalograms were first collected from 36 patients and 21 healthy controls (HCs); the corresponding resting-state functional networks were then constructed, and the potential differences in network topology between the two groups were extracted by using the topology of the spatial pattern of the network (SPN). Compared to the HCs, the TS patients exhibited decreased frontotemporal/occipital/parietal connectivity. When classifying the two groups, compared to the network properties, the derived SPN features achieved a much higher accuracy of 92.31%. The intrinsic long-range connectivity between the frontal and the temporal/occipital/parietal lobes was damaged in the patient group, and this dysfunctional network pattern might serve as a reliable biomarker to differentiate TS patients from HCs as well as to assess the severity of tic symptoms.

Transitions between neocortical seizure and non-seizure-like states and their association with presynaptic glutamate release

The transition between seizure and non-seizure states in neocortical epileptic networks is governed by distinct underlying dynamical processes. Based on the gamma distribution of seizure and inter-seizure durations, over time, seizures are highly likely to self-terminate; whereas, inter-seizure durations have a low chance of transitioning back into a seizure state. Yet, the chance of a state transition could be formed by multiple overlapping, unknown synaptic mechanisms. To identify the relationship between the underlying synaptic mechanisms and the chance of seizure-state transitions, we analyzed the skewed histograms of seizure durations in human intracranial EEG and seizure-like events (SLEs) in local field potential activity from mouse neocortical slices, using an objective method for seizure state classification. While seizures and SLE durations were demonstrated to have a unimodal distribution (gamma distribution shape parameter >1), suggesting a high likelihood of terminating, inter-SLE intervals were shown to have an asymptotic exponential distribution (gamma distribution shape parameter <1), suggesting lower probability of cessation. Then, to test cellular mechanisms for these distributions, we studied the modulation of synaptic neurotransmission during, and between, the in vitro SLEs. Using simultaneous local field potential and whole-cell voltage clamp recordings, we found a suppression of presynaptic glutamate release at SLE termination, as demonstrated by electrically- and optogenetically-evoked excitatory postsynaptic currents (EPSCs), and focal hypertonic sucrose application. Adenosine A1 receptor blockade interfered with the suppression of this release, changing the inter-SLE shape parameter from asymptotic exponential to unimodal, altering the chance of state transition occurrence with time. These findings reveal a critical role for presynaptic glutamate release in determining the chance of neocortical seizure state transitions.

Ictal-interictal continuum: a review of recent advancements

Continuous electroencephalogram (cEEG) has become an indispensable technique in the management of critically ill patients for early detection and treatment of non-convulsive seizures (NCS) and non-convulsive status epilepticus (NCSE). It has also brought about a renaissance in a wide range of rhythmic and periodic patterns with heterogeneous frequency and morphology. These patterns share the rhythmic and sharp appearances of electrographic seizures, but often lack the necessary frequency, spatiotemporal evolution and clinical accompaniments to meet the definitive criteria for ictal patterns. They may be associated with cerebral metabolic crisis and neuronal injury, therefore not clearly interictal either, but lie along an intervening spectrum referred to as ictal-interictal continuum (IIC). Generally speaking, rhythmic and periodic patterns are categorized as interictal patterns when occurring at a rate of <1Hz, and are categorized as NCS and NCSE when occurring at a rate of >2.5 Hz with spatiotemporal evolution. As such, IIC commonly includes the rhythmic and periodic patterns occurring at a rate of 1–2.5 Hz without spatiotemporal evolution and clinical correlates. Currently there are no evidence-based guidelines on when and if to treat patients with IIC patterns, and particularly how aggressively to treat, presenting a challenging electrophysiological and clinical conundrum. In practice, a diagnostic trial with preferably a non-sedative anti-seizure medication (ASM) can be considered with the end point being both clinical and electrographic improvement. When available and necessary, correlation of IIC with biomarkers of neuronal injury, such as neuronal specific enolase (NSE), neuroimaging, depth electrode recording, cerebral microdialysis and oxygen measurement, can be assessed for the consideration of ASM treatment. Here we review the recent advancements in their clinical significance, risk stratification and treatment algorithm.

Measurement of excitation-inhibition ratio in autism spectrum disorder using critical brain dynamics

Balance between excitation (E) and inhibition (I) is a key principle for neuronal network organization and information processing. Consistent with this notion, excitation-inhibition imbalances are considered a pathophysiological mechanism in many brain disorders including autism spectrum disorder (ASD). However, methods to measure E/I ratios in human brain networks are lacking. Here, we present a method to quantify a functional E/I ratio (fE/I) from neuronal oscillations, and validate it in healthy subjects and children with ASD. We define structural E/I ratio in an in silico neuronal network, investigate how it relates to power and long-range temporal correlations (LRTC) of the network's activity, and use these relationships to design the fE/I algorithm. Application of this algorithm to the EEGs of healthy adults showed that fE/I is balanced at the population level and is decreased through GABAergic enforcement. In children with ASD, we observed larger fE/I variability and stronger LRTC compared to typically developing children (TDC). Interestingly, visual grading for EEG abnormalities that are thought to reflect E/I imbalances revealed elevated fE/I and LRTC in ASD children with normal EEG compared to TDC or ASD with abnormal EEG. We speculate that our approach will help understand physiological heterogeneity also in other brain disorders.

Multiple Oscillatory Push-Pull Antagonisms Constrain Seizure Propagation

OBJECTIVE

Drug-resistant focal epilepsy is widely recognized as a network disease in which epileptic seizure propagation is likely coordinated by different neuronal oscillations such as low-frequency activity (LFA), high-frequency activity (HFA), or low-to-high cross-frequency coupling. However, the mechanism by which different oscillatory networks constrain the propagation of focal seizures remains unclear.

METHODS

We studied focal epilepsy patients with invasive electrocorticography (ECoG) recordings and compared multilayer directional network interactions between focal seizures either with or without secondary generalization. Within-frequency and cross-frequency directional connectivity were estimated by an adaptive directed transfer function and cross-frequency directionality, respectively.

RESULTS

In the within-frequency epileptic network, we found that the seizure onset zone (SOZ) always sent stronger information flow to the surrounding regions, and secondary generalization was accompanied by weaker information flow in the LFA from the surrounding regions to SOZ. In the cross-frequency epileptic network, secondary generalization was associated with either decreased information flow from surrounding regions' HFA to SOZ's LFA or increased information flow from SOZ's LFA to surrounding regions' HFA.

INTERPRETATION

Our results suggest that the secondary generalization of focal seizures is regulated by numerous within- and cross-frequency push-pull dynamics, potentially reflecting impaired excitation-inhibition interactions of the epileptic network. ANN NEUROL 2019;86:683-694.

Association Between Quantitative Electroencephalogram Frequency Composition and Post-Surgical Evolution in Pharmacoresistant Temporal Lobe Epilepsy Patients

The purpose of this paper is to estimate the association between quantitative electroencephalogram frequency composition (QEEGC) and post-surgical evolution in patients with pharmacoresistant temporal lobe epilepsy (TLE) and to evaluate the predictive value of QEEGC before and after surgery. A prospective, longitudinal study was made at International Neurological Restoration Center, Havana, Cuba. Twenty-nine patients with TLE submitted to epilepsy surgery were evaluated before surgery, and six months and two years after. They were classified as unsatisfactory and satisfactory post-surgical clinical evolution using the Modified Engels Scale. Eighty-seven electroencephalograms with quantitative narrow- and broad-band measures were analyzed. A Mann Whitney test (p > 0.05) showed that QEEGC before surgery was similar between groups independently of two years post-surgical evolution. A Mann Whitney test (p ˂ 0.05) showed that subjects with two years satisfactory post-surgical evolution had greater alpha power compared to subjects with two years unsatisfactory post-surgical evolution that showed greater theta power. A Wilcoxon test (p ˂ 0.05) showed that alpha and theta power increased for two groups from pre-surgical state to post-surgical state. Logit regression (p ˂ 0.05) showed that six months after surgery, quantitative electroencephalogram frequency value with the greatest power at occipital regions shows predictive value for two years evolution. QEEGC can be a tool to predict the outcome of epilepsy surgery.

The Hippocampus and Cortex Together Generate the Scalp EEG Ictal Discharge in Temporal Lobe Epilepsy

PURPOSE

The scalp EEG ictal discharge in temporal lobe epilepsy is reportedly visible only after the intracranial discharge becomes well synchronized and present over 10 to 30 cm of cortex. We investigated the role of the hippocampal formation in the generation of the scalp EEG ictal discharge.

METHODS

Intracranial EEG video monitors were recorded using simultaneous scalp, stereotaxic depth, and subdural strip electrodes in 19 subjects with temporal lobe epilepsy. The location, frequency, morphology, and timing of the initial ictal discharge, and subsequent ictal patterns, were examined in hippocampal formation, medial paleocortex, and lateral temporal neocortex electrocorticographic and scalp temporal EEG recordings.

RESULTS

In every subject, a scalp ictal discharge was visible only after the intracranial ictal discharge had spread to involve the whole temporal lobe (hippocampal formation, medial paleocortex, and lateral temporal neocortex). Beta/gamma frequency and decremental electrocorticographic ictal discharges were never visualized in the EEG. The scalp EEG ictal discharge frequency was 2.4 to 10 Hz and appeared a median of 18 seconds after a faster frequency electrocorticographic initial ictal discharge, once the intracranial discharge slowed to an alpha, theta, or delta frequency.

CONCLUSIONS

In temporal lobe epilepsy, an ictal pattern is not readily visible in the scalp EEG until the intracranial ictal discharge is ≤10 Hz and has propagated from its site of onset to involve the hippocampus, medial paleocortex, and lateral temporal neocortex.

Link Prediction Investigation of Dynamic Information Flow in Epilepsy

As a brain disorder, epilepsy is characterized with abnormal hypersynchronous neural firings. It is known that seizures initiate and propagate in different brain regions. Long-term intracranial multichannel electroencephalography (EEG) reflects broadband ictal activity under seizure occurrence. Network-based techniques are efficient in discovering brain dynamics and offering finger-print features for specific individuals. In this study, we adopt link prediction for proposing a novel workflow aiming to quantify seizure dynamics and uncover pathological mechanisms of epilepsy. A dataset of EEG signals was enrolled that recorded from 8 patients with 3 different types of pharmocoresistant focal epilepsy. Weighted networks are obtained from phase locking value (PLV) in subband EEG oscillations. Common neighbor (CN), resource allocation (RA), Adamic-Adar (AA), and Sorenson algorithms are brought in for link prediction performance comparison. Results demonstrate that RA outperforms its rivals. Similarity, matrix was produced from the RA technique performing on EEG networks later. Nodes are gathered to form sequences by selecting the ones with the highest similarity. It is demonstrated that variations are in accordance with seizure attack in node sequences of gamma band EEG oscillations. What is more, variations in node sequences monitor the total seizure journey including its initiation and termination.

Identifying Phase-Amplitude Coupling in Cyclic Alternating Pattern using Masking Signals

Judiciously classifying phase-A subtypes in cyclic alternating pattern (CAP) is critical for investigating sleep dynamics. Phase-amplitude coupling (PAC), one of the representative forms of neural rhythmic interaction, is defined as the amplitude of high-frequency activities modulated by the phase of low-frequency oscillations. To examine PACs under more or less synchronized conditions, we propose a nonlinear approach, named the masking phase-amplitude coupling (MPAC), to quantify physiological interactions between high (α/lowβ) and low (δ) frequency bands. The results reveal that the coupling intensity is generally the highest in subtype A1 and lowest in A3. MPACs among various physiological conditions/disorders (p < 0.0001) and sleep stages (p < 0.0001 except S4) are tested. MPACs are found significantly stronger in light sleep than deep sleep (p < 0.0001). Physiological conditions/disorders show similar order in MPACs. Phase-amplitude dependence between δ and α/lowβ oscillations are examined as well. δ phase tent to phase-locked to α/lowβ amplitude in subtype A1 more than the rest. These results suggest that an elevated δ-α/lowβ MPACs can reflect some synchronization in CAP. Therefore, MPAC can be a potential tool to investigate neural interactions between different time scales, and δ-α/lowβ MPAC can serve as a feasible biomarker for sleep microstructure.

Usefulness of EEG for the differential diagnosis of possible transient ischemic attack

OBJECTIVE

EEG value in possible transient ischemic attacks (TIA) is unknown. We aim to quantify focal slow wave activity (FSWA) and epileptiform activity (EA) frequency in possible TIA, and to analyse its contribution to the final diagnosis of seizures and/or definitive TIA.

METHODS

Prospective longitudinal study of possible TIA patients evaluated at a tertiary centre during 36 months and with 1-3 months follow-up. EEG was performed as soon as possible (early EEG) and one month later (late EEG). A stroke neurologist established final diagnosis after reassessing all clinical and diagnostic tests.

RESULTS

80 patients underwent an early EEG (45.8 h after possible TIA): 52 had FSWA and 6 of them also EA. Early FSWA was associated with epileptic seizure or definitive TIA final diagnosis (p = .041). Patients with these diagnoses had more frequently early FSWA (19/23; 82.6%) than EA (6/23; 26.1%). 6/13 (46.2%) patients with epileptic seizure final diagnosis had EA.In the late EEG, 43 (58.1%) patients demonstrated persistent FSWA and 3 of them also EA. Persistent FSWA in the late EEG was more frequent in seizures than in TIA patients (91.7% vs. 45.5%). FSWA disappearance was associated with acute vascular lesion on neuroimage.

CONCLUSIONS

FSWA was the commonest EEG abnormality found in the early EEG of patients with possible TIA, but did not distinguish between TIA and seizure patients. In patients with seizures, FSWA was more common than EA and its presence in the late EEG was more likely in patients with epileptic seizures than with TIA.

SIGNIFICANCE

The majority of possible TIA patients with the final diagnosis of epileptic seizures do not have EA in the early or late EEG.

Slow Activity in Focal Epilepsy During Sleep and Wakefulness

INTRODUCTION

We aimed to test differences between healthy subjects and patients with respect to slow wave activity during wakefulness and sleep.

METHODS

Fifteen patients affected by nonlesional focal epilepsy originating within temporal areas and fourteen matched controls underwent a 24-hour EEG recording. We studied the EEG power spectral density during wakefulness and sleep in delta (1-4 Hz), theta (5-7 Hz), alpha (8-11 Hz), sigma (12-15 Hz), and beta (16-20 Hz) bands.

RESULTS

During sleep, patients with focal epilepsy showed higher power from delta to beta frequency bands compared with controls. The effect was widespread for alpha band and above, while localized over the affected hemisphere for delta (sleep cycle 1, P = .006; sleep cycle 2, P = .008; sleep cycle 3, P = .017). The analysis of interhemispheric differences showed that the only frequency band stronger over the affected regions was the delta band during the first 2 sleep cycles (sleep cycle 1, P = .014; sleep cycle 2, P = .002). During wakefulness, patients showed higher delta/theta activity over the affected regions compared with controls.

CONCLUSIONS

Patients with focal epilepsy showed a pattern of power increases characterized by a selective slow wave activity enhancement over the epileptic regions during daytime and sleep. This phenomenon was stronger and asymmetric during the first sleep cycles.

Interictal network synchrony and local heterogeneity predict epilepsy surgery outcome among pediatric patients

OBJECTIVE

Epilepsy is a disorder of aberrant cortical networks. Researchers have proposed that characterizing presurgical network connectivity may improve the surgical management of intractable seizures, but few studies have rigorously examined the relationship between network activity and surgical outcome. In this study, we assessed whether local and global measures of network activity differentiated patients with favorable (seizure-free) versus unfavorable (seizure-persistent) surgical outcomes.

METHODS

Seventeen pediatric intracranial electroencephalography (IEEG) patients were retrospectively examined. For each patient, 1,200 random interictal epochs of 1-s duration were analyzed. Functional connectivity networks were constructed using an amplitude-based correlation technique (Spearman correlation). Global network synchrony was computed as the average pairwise connectivity strength. Local signal heterogeneity was defined for each channel as the variability of EEG amplitude (root mean square) and absolute delta power (μV² /Hz) across epochs. A support vector machine learning algorithm used global and local measures to classify patients by surgical outcome. Classification was assessed using the Leave-One-Out (LOO) permutation test.

RESULTS

Global synchrony was increased in the seizure-persistent group compared to seizure-free patients (Student's t-test, p = 0.006). Seizure-onset zone (SOZ) electrodes exhibited increased signal heterogeneity compared to non-SOZ electrodes, primarily in seizure-persistent patients. Global synchrony and local heterogeneity measures were used to accurately classify 16 (94.1%) of 17 patients by surgical outcome (LOO test, iterations = 10,000, p < 0.001).

SIGNIFICANCE

Measures of global network synchrony and local signal heterogeneity represent promising biomarkers for assessing patient candidacy in pediatric epilepsy surgery.

Temporally correlated fluctuations drive epileptiform dynamics

Macroscopic models of brain networks typically incorporate assumptions regarding the characteristics of afferent noise, which is used to represent input from distal brain regions or ongoing fluctuations in non-modelled parts of the brain. Such inputs are often modelled by Gaussian white noise which has a flat power spectrum. In contrast, macroscopic fluctuations in the brain typically follow a 1/fb spectrum. It is therefore important to understand the effect on brain dynamics of deviations from the assumption of white noise. In particular, we wish to understand the role that noise might play in eliciting aberrant rhythms in the epileptic brain. To address this question we study the response of a neural mass model to driving by stochastic, temporally correlated input. We characterise the model in terms of whether it generates "healthy" or "epileptiform" dynamics and observe which of these dynamics predominate under different choices of temporal correlation and amplitude of an Ornstein-Uhlenbeck process. We find that certain temporal correlations are prone to eliciting epileptiform dynamics, and that these correlations produce noise with maximal power in the δ and θ bands. Crucially, these are rhythms that are found to be enhanced prior to seizures in humans and animal models of epilepsy. In order to understand why these rhythms can generate epileptiform dynamics, we analyse the response of the model to sinusoidal driving and explain how the bifurcation structure of the model gives rise to these findings. Our results provide insight into how ongoing fluctuations in brain dynamics can facilitate the onset and propagation of epileptiform rhythms in brain networks. Furthermore, we highlight the need to combine large-scale models with noise of a variety of different types in order to understand brain (dys-)function.

EEG Abnormalities Are Associated With Poorer Depressive Symptom Outcomes With Escitalopram and Venlafaxine-XR, but Not Sertraline: Results From the Multicenter Randomized iSPOT-D Study

Rationale Limited research is available on electrophysiological abnormalities such as epileptiform EEG or EEG slowing in depression and its association with antidepressant treatment response. Objectives We investigated the association between EEG abnormalities and antidepressant treatment response in the international Study to Predict Optimized Treatment in Depression (iSPOT-D). Methods Of 1008 participants with major depressive disorder randomized to escitalopram, sertraline, or venlafaxine-XR, 622 completed 8 weeks of treatment per protocol. The study also recruited 336 healthy controls. Treatment response was established after 8 weeks using the 17-item Hamilton Rating Scale for Depression (HRSD₁₇). The resting-state EEG was assessed at baseline with eyes closed. EEG abnormalities including epileptiform activity, EEG slowing, and alpha peak frequency (APF) were scored for all subjects, blind to treatment outcome. Results Patients and controls did not differ in the occurrence of EEG abnormalities. Furthermore, in the per protocol sample the occurrence of epileptiform EEG and EEG slowing (as a combined marker) were associated with a reduced likelihood of responding to escitalopram (P = .019; odds ratio [OR] = 3.56) and venlafaxine-XR (P = .043; OR = 2.76), but not sertraline (OR = 0.73). The response rates for this "any EEG abnormality" groups versus the "no-abnormality" group were 33% and 64% for escitalopram and 41% and 66% for venlafaxine-XR, respectively. A slow APF was associated with treatment response only in the sertraline group (P = .21; d = .027). Conclusions EEG abnormalities are associated with nonresponse to escitalopram and venlafaxine-XR, but not sertraline, whereas a slow APF is associated to response for sertraline only.

TIRDA Originating From Lateral Temporal Cortex in a Patient With mTLE Is Not Related to Hippocampal Activity

Electrophysiological studies have suggested that temporal intermittent rhythmic delta activity (TIRDA) has a localizing value similar to interictal spikes in patients with temporal lobe epilepsy and is associated with a favorable outcome after temporal lobectomy. However, it remains controversial whether TIRDA is an EEG marker for mesial or lateral temporal epileptogenesis. We simultaneously recorded scalp EEG and stereoencephalography in a patient with mesial temporal lobe epilepsy during epilepsy presurgical evaluation. Seizure onset was localized to the hippocampus. However, TIRDA originated from the lateral temporal cortex, and rhythmic delta activity was not observed concomitantly in the hippocampus. In addition, TIRDA was not associated with repetitive interictal spikes or subclinical seizures in the hippocampus as previously speculated. This case suggests that TIRDA can be an EEG marker that is independent of hippocampal activity and can represent temporal neocortical epileptogenesis.

Phase-Amplitude Coupling Is Elevated in Deep Sleep and in the Onset Zone of Focal Epileptic Seizures

The interactions between different EEG frequency bands have been widely investigated in normal and pathologic brain activity. Phase-amplitude coupling (PAC) is one of the important forms of this interaction where the amplitude of higher frequency oscillations is modulated by the phase of lower frequency activity. Here, we studied the dynamic variations of PAC of high (gamma and ripple) and low (delta, theta, alpha, and beta) frequency bands in patients with focal epilepsy in different sleep stages during the interictal period, in an attempt to see if coupling is different in more or less epileptogenic regions. Sharp activities were excluded to avoid their effect on the PAC. The results revealed that the coupling intensity was generally the highest in stage N3 of sleep and the lowest in rapid eye movement sleep. We also compared the coupling strength in different regions [seizure onset zone (SOZ), exclusively irritative zone, and normal zone]. PAC between high and low frequency rhythms was found to be significantly stronger in the SOZ compared to normal regions. Also, the coupling was generally more elevated in spiking channels outside the SOZ than in normal regions. We also examined how the power in the delta band correlates to the PAC, and found a mild but statistically significant correlation between slower background activity in epileptic channels and the elevated coupling in these channels. The results suggest that an elevated PAC may reflect some fundamental abnormality, even after exclusion of sharp activities and even in the interictal period. PAC may therefore contribute to understanding the underlying dynamics of epileptogenic brain regions.

The role of delta-modulated high frequency oscillations in seizure state classification

High frequency oscillations (HFOs), which collectively refer to ripples (80-200 Hz) and fast ripples (>200 Hz), have been implicated as key players in epileptogenesis. However, their presence alone is not in and of itself indicative of a pathological brain state. Rather, spatial origins as well as coexistence with other neural rhythms are essential components in defining pathological HFOs. This study investigates how the phase of the delta rhythm (0.5-4 Hz) modulates the amplitude of HFOs during a seizure episode. Seven seizures recorded from three patients presenting with intractable temporal lobe epilepsy were obtained via intracranial electroencephalography (iEEG) from a 64-electrode grid. Delta modulation of the HFO rhythms was found to emerge at seizure onset and termination regardless of the dynamics present within the seizure episode itself. Moreover, the differences between delta modulating the ripple or fast ripple may be due to the sleep stage of the patient when the seizures were being recorded. Further studies exploring how this modulation changes in space across the grid may also highlight additional properties of this phenomenon. Its temporal pattern suggests that it is a potential iEEG-based biomarker for seizure state classification.

Unbalanced Peptidergic Inhibition in Superficial Neocortex Underlies Spike and Wave Seizure Activity

Slow spike and wave discharges (0.5-4 Hz) are a feature of many epilepsies. They are linked to pathology of the thalamocortical axis and a thalamic mechanism has been elegantly described. Here we present evidence for a separate generator in local circuits of associational areas of neocortex manifest from a background, sleep-associated delta rhythm in rat. Loss of tonic neuromodulatory excitation, mediated by nicotinic acetylcholine or serotonin (5HT3A) receptors, of 5HT3-immunopositive interneurons caused an increase in amplitude and slowing of the delta rhythm until each period became the "wave" component of the spike and wave discharge. As with the normal delta rhythm, the wave of a spike and wave discharge originated in cortical layer 5. In contrast, the "spike" component of the spike and wave discharge originated from a relative failure of fast inhibition in layers 2/3-switching pyramidal cell action potential outputs from single, sparse spiking during delta rhythms to brief, intense burst spiking, phase-locked to the field spike. The mechanisms underlying this loss of superficial layer fast inhibition, and a concomitant increase in slow inhibition, appeared to be precipitated by a loss of neuropeptide Y (NPY)-mediated local circuit inhibition and a subsequent increase in vasoactive intestinal peptide (VIP)-mediated disinhibition. Blockade of NPY Y1 receptors was sufficient to generate spike and wave discharges, whereas blockade of VIP receptors almost completely abolished this form of epileptiform activity. These data suggest that aberrant, activity-dependent neuropeptide corelease can have catastrophic effects on neocortical dynamics.

Delta rhythm in wakefulness: evidence from intracranial recordings in human beings

A widely accepted view is that wakefulness is a state in which the entire cortical mantle is persistently activated, and therefore desynchronized. Consequently, the EEG is dominated by low-amplitude, high-frequency fluctuations. This view is currently under revision because the 1-4 Hz delta rhythm is often evident during "quiet" wakefulness in rodents and nonhuman primates. Here we used intracranial EEG recordings to assess the occurrence of delta rhythm in 18 awake human beings. Our recordings reveal rhythmic delta during wakefulness at 10% of all recording sites. Delta rhythm could be observed in a single cortical lobe or in multiple lobes. Sites with high delta could flip between high and low delta power or could be in a persistently high delta state. Finally, these sites were rarely identified as the sites of seizure onset. Thus rhythmic delta can dominate the background operation and activity of some neocortical circuits in awake human beings.

Increased Intrinsic Connectivity of the Default Mode Network in Temporal Lobe Epilepsy: Evidence from Resting-State MEG Recordings

The electrophysiological signature of resting state oscillatory functional connectivity within the default mode network (DMN) during spike-free periods in temporal lobe epilepsy (TLE) remains unclear. Using magnetoencephalographic (MEG) recordings, this study investigated how the connectivity within the DMN was altered in TLE, and we examined the effect of lateralized TLE on functional connectivity. Sixteen medically intractable TLE patients and 22 controls participated in this study. Whole-scalp 306-channel MEG epochs without interictal spikes generated from both MEG and EEG data were analyzed using a minimum norm estimate (MNE) and source-based imaginary coherence analysis. With this processing, we obtained the cortical activation and functional connectivity within the DMN. The functional connectivity was increased between DMN and the right medial temporal (MT) region at the delta band and between DMN and the bilateral anterior cingulate cortex (ACC) regions at the theta band. The functional change was associated with the lateralization of TLE. The right TLE showed enhanced DMN connectivity with the right MT while the left TLE demonstrated increased DMN connectivity with the bilateral MT. There was no lateralization effect of TLE upon the DMN connectivity with ACC. These findings suggest that the resting-state functional connectivity within the DMN is reinforced in temporal lobe epilepsy during spike-free periods. Future studies are needed to examine if the altered functional connectivity can be used as a biomarker for treatment responses, cognitive dysfunction and prognosis in patients with TLE.

Defining regions of interest using cross-frequency coupling in extratemporal lobe epilepsy patients

OBJECTIVE

Clinicians identify seizure onset zones (SOZs) for resection in an attempt to localize the epileptogenic zone (EZ), which is the cortical tissue that is indispensible for seizure generation. An automated system is proposed to objectively localize this EZ by identifying regions of interest (ROIs).

METHODS

Intracranial electroencephalogram recordings were obtained from seven patients presenting with extratemporal lobe epilepsy and the interaction between neuronal rhythms in the form of phase-amplitude coupling was investigated. Modulation of the amplitude of high frequency oscillations (HFOs) by the phase of low frequency oscillations was measured by computing the modulation index (MI). Delta- (0.5-4 Hz) and theta- (4-8 Hz) modulation of HFOs (30-450 Hz) were examined across the channels of a 64-electrode subdural grid. Surrogate analysis was performed and false discovery rates were computed to determine the significance of the modulation observed. Mean MI values were subjected to eigenvalue decomposition (EVD) and channels defining the ROIs were selected based on the components of the eigenvector corresponding to the largest eigenvalue. ROIs were compared to the SOZs identified by two independent neurologists. Global coherence values were also computed.

MAIN RESULTS

MI was found to capture the seizure in time for six of seven patients and identified ROIs in all seven. Patients were found to have a poorer post-surgical outcome when the number of EVD-selected channels that were not resected increased. Moreover, in patients who experienced a seizure-free outcome (i.e., Engel Class I) all EVD-selected channels were found to be within the resected tissue or immediately adjacent to it. In these Engel Class I patients, delta-modulated HFOs were found to identify more of the channels in the resected tissue compared to theta-modulated HFOs. However, for the Engel Class IV patient, the delta-modulated HFOs did not identify any of the channels in the resected tissue suggesting that the resected tissue was not appropriate, which was also suggested by the Engel Class IV outcome. A sensitivity of 75.4% and a false positive rate of 15.6% were achieved using delta-modulated HFOs in an Engel Class I patient.

SIGNIFICANCE

LFO-modulated HFOs can be used to identify ROIs in extratemporal lobe patients. Moreover, delta-modulated HFOs may provide more accurate localization of the EZ. These ROIs may result in better surgical outcomes when used to compliment the SOZs identified by clinicians for resection.

Interictal regional delta slowing in cerebral sinus vein thrombosis

The electroencephalographic finding of regional delta activity should alert to the possibility of an underlying structural abnormality of the brain as a cause. A 5-year-old boy, who presented with severe headache and focal seizures, had normal neurological examination and brain CT findings. The initial electroencephalograph showed focal delta activity. An emergent brain MRI disclosed a thrombosis of the left sigmoid sinus and jugular vein, but no parenchymal lesions. The regional delta activity can presumably serve as a marker for brain tissue damage in cerebral sinus vein thrombosis, and sometimes, even to add information to that gained from imaging studies.

What is the importance of abnormal "background" activity in seizure generation?

Investigations of interictal epileptiform spikes and seizures have played a central role in the study of epilepsy. The background EEG activity, however, has received less attention. In this chapter we discuss the characteristic features of the background activity of the brain when individuals are at rest and awake (resting wake) and during sleep. The characteristic rhythms of the background EEG are presented, and the presence of 1/f (β) behavior of the EEG power spectral density is discussed and its possible origin and functional significance. The interictal EEG findings of focal epilepsy and the impact of interictal epileptiform spikes on cognition are also discussed.

Local MEG networks: the missing link between protein expression and epilepsy in glioma patients?

Connectivity and network analysis in neuroscience has been applied to multiple spatial scales, but the links between these different scales have rarely been investigated. In tumor-related epilepsy, altered network topology is related to behavior, but the molecular basis of these observations is unknown. We elucidate the associations between microscopic features of brain tumors, local network topology, and functional patient status. We hypothesize that expression of proteins related to tumor-related epilepsy is directly correlated with network characteristics of the tumor area. Glioma patients underwent magnetoencephalography, and functional network topology of the tumor area was used to predict tissue protein expression patterns of tumor tissue collected during neurosurgery. Protein expression and network topology were interdependent; in particular between-module connectivity was selectively associated with two epilepsy-related proteins. Total number of seizures was related to both the role of the tumor area in the functional network and to protein expression. Importantly, classification of protein expression was predicted by between-module connectivity with up to 100% accuracy. Thus, network topology may serve as an intermediate level between molecular features of tumor tissue and symptomatology in brain tumor patients, and can potentially be used as a non-invasive marker for microscopic tissue characteristics.

Widespread grey matter changes and hemodynamic correlates to interictal epileptiform discharges in pharmacoresistant mesial temporal epilepsy

Focal onset epilepsies most often occur in the temporal lobes. To improve diagnosis and therapy of patients suffering from pharmacoresistant temporal lobe epilepsy it is highly important to better understand the underlying functional and structural networks. In mesial temporal lobe epilepsy (MTLE) widespread functional networks are involved in seizure generation and propagation. In this study we have analyzed the spatial distribution of hemodynamic correlates (HC) to interictal epileptiform discharges on simultaneous EEG/fMRI recordings and relative grey matter volume (rGMV) reductions in 10 patients with MTLE. HC occurred beyond the seizure onset zone in the hippocampus, in the ipsilateral insular/operculum, temporo-polar and lateral neocortex, cerebellum, along the central sulcus and bilaterally in the cingulate gyrus. rGMV reductions were detected in the middle temporal gyrus, inferior temporal gyrus and uncus to the hippocampus, the insula, the posterior cingulate and the anterior lobe of the cerebellum. Overlaps between HC and decreased rGMV were detected along the mesolimbic network ipsilateral to the seizure onset zone. We conclude that interictal epileptic activity in MTLE induces widespread metabolic changes in functional networks involved in MTLE seizure activity. These functional networks are spatially overlapping with areas that show a reduction in relative grey matter volumes.