Scalp EEG Research Articles

Focal epilepsy: EEG interictal patterns and etiology correlation (S35.009)

<h3>Objective:</h3> To evaluate the relationship between certain scalp interictal epileptiform discharges (IEDs) and the underlying seizure pathology. <h3>Background:</h3> Ictal characteristic patterns in focal epilepsy have been observed to be highly associated with certain pathologies and identifying such patterns can act as a predictor for possible underling etiology and to guide further investigations in work up of epilepsy. <h3>Design/Methods:</h3> Retrospective analysis of the interictal, ictal, and postictal epileptiform discharges recorded from scalp EEG based on frequency, amplitude, distribution, and morphology. <h3>Results:</h3> Independent or asynchronous bursts of IEDs in the form of spike trains consisting of monomorphic alpha frequencies with crescendo amplitudes, at times preceded or followed by 2–3 Hz slow waves, were associated with FCD. IEDs of spike trains with crescendo amplitudes and frequencies 5–6 Hz small spikes followed by 6–8 Hz higher amplitudes spikes with sharp peaks per second, then followed by 1–3 Hz slow waves, and at times showed broad field to the contralateral side were associated with heterotopia. An isolated spike-slow wave discharge confined to the temporal electrodes without broad field ipsilaterally were associated with MTS of possible dentate and proper hippocampus origin. IEDs of spike-slow wave discharges with broad field to the ipsilateral side were associated with MTS of possible hippocampal and para-hippocampal origin. Irregular 1–2 Hz spikes/sharp waves-slow waves and appearing as irregular “M shape” correlated with neocortical gliosis. Spike/sharp-slow wave complexes with broad field ipsilaterally and contralaterally while sustaining higher amplitudes ipsilaterally, might indicate kindling. <h3>Conclusions:</h3> Scalp EEG provides information about different epileptic parameters. IEDs are heterogeneous in terms of pattern and underlying mechanisms. EEG is not only a method to prove the presence of an epileptic focus, but can also correlate with the underlying etiology, and can be a useful tool to guide medical and surgical interventions. <b>Disclosure:</b> Dr. Lagnf has nothing to disclose. Dr. Souidan has nothing to disclose. Dr. Alsherbini has received personal compensation for serving as an employee of Detroit Medical Center. Prof. ANDRADE MACHADO has nothing to disclose. Dr. Elsayed has nothing to disclose.

Ictal Single Photon Emission Computed Tomography (SPECT) In Patients with Focal Aware Seizure without Ictal Electroencephalographic Changes: A Single Center Experience (P2-1.012)

<h3>Objective:</h3> N/A <h3>Background:</h3> There is currently limited literature on the localization value of ictal SPECT tracer injection during patient-reported typical focal aware seizures (auras) that lack electrographic changes. One study reported that ictal SPECT studies in isolated auras lack reliability, with concordant localization with the seizure onset zone obtained in 5%, and lateralization in 35% of cases. In another study, localizing information of self-injection of ictal SPECT was better in three patients and obviated the need for intracranial monitoring in one patient. We present a study in which tracer injection was administered for patient-reported typical focal aware seizures, where the subsequent analysis of ictal and interictal SPECT helped inform further management. <h3>Design/Methods:</h3> We performed a retrospective review of cases who underwent ictal SPECT between 2020 to 2022 at University of Kentucky hospital. Cases with radiotracer injection during patient-reported auras lacking electrographic changes were identified. Timing of injection from onset of reported aura, hyperperfusion changes on ictal SPECT, and the outcome of imaging on further management of refractory epilepsy were noted. <h3>Results:</h3> 20 patients underwent ictal SPECT, of which 7 met our inclusion criteria. None of the 7 cases had electrographic correlate with aura on scalp EEG. Time of tracer injection ranged from 1–17 seconds following onset of aura. The ictal SPECT changes correctly localized the seizure onset zone in five patients. This was followed by intracranial monitoring and subsequent temporal lobectomy in three patients, interventricular ablation of epileptogenic focus in one patient, and Responsive Neurostimulation implant in one patient. One patient was managed medically, and one is ongoing pre-surgical evaluation. <h3>Conclusions:</h3> Ictal SPECT with radiotracer injection during typical focal aware seizures (patient reported auras) without ictal electrographic changes is helpful in the localization of the seizure onset zone. Additional studies with larger sample size and outcome information are needed to assess accuracy of localization. <b>Disclosure:</b> Dr. Pervin has nothing to disclose. Dr. Khan has received personal compensation for serving as an employee of University of Kentucky. Dr. Clay has nothing to disclose. Riham El Khouli has nothing to disclose. Dr. Mirza has nothing to disclose. Dr. Mathias has received research support from My Epilepsy Society Non Profit Organization.

Ictal Thalamic Recordings in Patients with Deep Brain Stimulation Targeting Thalamic NucleiIdentify whether ictal thalamic EEG signals could be derived from ambulatory recordings in patients with pharmacoresistant epilepsy treated with deep brain stimulation (DBS) targeting either bilateral anterior thalamic nuclei (ATN) or centromedian nuclei (CMN). (P5-1.008)

<h3>Objective:</h3> Identify whether ictal thalamic EEG signals could be derived from ambulatory recordings in patients with pharmacoresistant epilepsy treated with deep brain stimulation (DBS) targeting bilateral anterior thalamic nuclei (ATN) or centromedian nuclei (CMN). <h3>Background:</h3> The DBS system is one modality that can target thalamic nuclei for neuromodulation. The most recently available model features the capability to record live streamed intracranial EEG (local field potentials, or LFP). <h3>Design/Methods:</h3> We selected four patients who had habitual electroclinical and electrographic seizures during live streaming (based on the simultaneous scalp EEG). All the devices were Percept^™ PC (by Medtronic^®), utilizing the BrainSense^® feature to record bipolar LFP signals between two contacts of each of the bilateral four-contact leads. LFP time series were extracted using MATLAB and exported as EEG for review and visualization. <h3>Results:</h3> Three patients were adults (mean age 43, median age 37), and one was a child (10 years). Two had generalized forms of epilepsy (acquired Lennox-Gastaut Syndrome due to diffuse bilateral polymicrogyria; generalized non-lesional epilepsy characterized by absence and myoclonic seizures). One patient had unilateral hemispheric epilepsy due to dual pathology (adult-onset Rasmussen’s encephalitis and focal cortical dysplasia) and prior frontal topectomy. The last patient presented with non-lesional multifocal epilepsy and a history of left parietal topectomy. The DBS for two types of generalized epilepsy targeted bilateral CMN, while the other two targeted bilateral ATN. Generalized ictal patterns involving the four contacts of both thalamic nuclei have been identified as ictal patterns. In contrast, in hemispheric or multifocal epilepsies, only unilateral thalamic nuclei exhibited ictal EEG changes. Ictal EEG alterations consisted of a combination of rhythmic activity and sharp waves or rhythmic spiking activity. Our figures depict the ictal thalamic recordings with the corresponding ictal thalamic waves. <h3>Conclusions:</h3> Our observations call for a more systematic investigation of ictal thalamic signature in various types of epilepsy. <b>Disclosure:</b> Miss Saab has nothing to disclose. Ms. Jiang has nothing to disclose. The institution of Dr. Quraishi has received research support from NeuroPace. The institution of Dr. Quraishi has received research support from Biogen. The institution of Dr. Quraishi has received research support from Praxis Precision Medicines. Dr. Herlopian has nothing to disclose.

Heading perception from optic flow occurs at both perceptual representation and working memory stages with EEG evidence

Psychophysical studies have demonstrated that heading perception from optic flow occurs in perceptual and post-perceptual stages. The post-perception stage is a complex concept, containing working memory. The current study examined whether working memory was involved in heading perception from optic flow by asking participants to conduct a heading perception task and recording their scalp EEG. On each trial, an optic flow display was presented, followed by a blank display. Participants were then asked to report their perceived heading. We know that participants would tend to automatically forget previous headings when they learned that previously presented headings were unrelated to the current heading perception to save cognitive resources. As a result, we could not decode previous headings from the EEG data of current trials. More importantly, if we successfully decoded previous headings when the blank display (optic flow) was presented, then working memory (perceptual representation stage) was involved in heading perception. Our results showed that the decoding accuracy was significantly higher than the chance level when the optic flow and blank displays were presented. Therefore, the current study provided electrophysiological evidence that heading perception from optic flow occurred in the perceptual representation and working memory stages, against the previous perceptual claim.

Spike-microstates correlate with interictal epileptogenic discharges: a marker for hidden epileptic activity.

Objectively estimating disease severity and treatment success is a main problem in outpatient managing of epilepsy. Self-reported seizures diaries are well-known to underestimate the actual seizure count, and repeated EEGs might not show interictal epileptiform discharges (IEDs), although patients suffer from seizures. In this prospective study, we investigate the potential of microstate analysis to monitor epilepsy patients independently of their IED count. From our databank of candidates for epilepsy surgery, we included 18 patients who underwent controlled resting EEG sessions (with eyes closed, 30 min), at around the same time of the day, during at least four days (range: 4-8 days; mean: 5). Nine patients with temporal foci, six with extratemporal foci, and three with generalized epilepsy were included. Each patient's IEDs were marked and the topographic voltage maps of the IED peaks were averaged, and an individual average spike topography (AST) was created. The AST was then backfitted to each timepoint of the whole EEG resulting in the Spike-Microstate (SMS). The presence of the SMS in the residual EEG outside of the short IEDs epochs was determined for each recording session in each patient and correlated with the occurrence of the IEDs across all recording session, as well as with the drug charge of each day. Overall, SMS was much more represented in the routine EEG than the IEDs: they were identified 262 times more often than IEDs. The SMS time coverage correlated significantly with the IED occurrence rate (rho = 0.56; P < 0.001). If only patients with focal epilepsy were considered, this correlation was even higher rho = 0.69 (P < 0.001). Drug charge per day did not correlate with SMS. In this proof-of-concept study, the time coverage of SMS correlated strongly with the occurrence rate of the IEDs, they can be retrieved in the scalp EEG at a much higher occurrence rate. We conclude that SMS, once obtained for a given patient, are a more abundant marker of hidden epileptic activity than IEDs, in particular in focal epilepsy, and can be used also in absence of IEDs. Future larger studies are needed to verify its potential as monitoring tool and to determine cut-off values when drug protection becomes imperfect.

Epileptic prediction using spatiotemporal information combined with optimal features strategy on EEG.

Epilepsy is the second most common brain neurological disease after stroke, which has the characteristics of sudden and recurrence. Seizure prediction is seriously important for improving the quality of patients' lives. From the perspective of multiple dimensions including time-frequency, entropy and brain network, this paper proposed a novel approach by constructing the optimal spatiotemporal feature set to predict seizures. Based on strong independence and large information capabilities, the two-dimensional feature screening algorithm is performed to eliminate unnecessary redundant features. In order to verify the effectiveness of the optimal feature set, support vector machine (SVM) was used to classify the preictal and interictal states on both the Kaggle intracranial EEG and CHB-MIT scalp EEG dataset. This model achieved an average accuracy of 98.01%, AUC of 0.96, F-Score of 98.3% and FPR of 0.0383/h on the Kaggle dataset; On the CHB-MIT dataset, the average accuracy, AUC, F-score and FPR were 95.93%, 0.92, 94.97% and 0.0473/h, respectively. Further ablation experiments have confirmed that the temporal and spatial features fusion has better performance than the individual temporal or spatial features. Compared to the state-of-the-art methods, our approach outperforms most of these existing techniques. The results show that our approach can effectively extract the spatiotemporal information of epileptic EEG signals to predict epileptic seizures with high performance.

Alpha Oscillations During Effortful Continuous Speech: From Scalp EEG to Ear-EEG.

The purpose of this study was to investigate alpha power as an objective measure of effortful listening in continuous speech with scalp and ear-EEG. Scalp and ear-EEG were recorded simultaneously during presentation of a 33-s news clip in the presence of 16-talker babble noise. Four different signal-to-noise ratios (SNRs) were used to manipulate task demand. The effects of changes in SNR were investigated on alpha event-related synchronization (ERS) and desynchronization (ERD). Alpha activity was extracted from scalp EEG using different referencing methods (common average and symmetrical bi-polar) in different regions of the brain (parietal and temporal) and ear-EEG. Alpha ERS decreased with decreasing SNR (i.e., increasing task demand) in both scalp and ear-EEG. Alpha ERS was also positively correlated to behavioural performance which was based on the questions regarding the contents of the speech. Alpha ERS/ERD is better suited to track performance of a continuous speech than listening effort. EEG alpha power in continuous speech may indicate of how well the speech was perceived and it can be measured with both scalp and Ear-EEG.

LightSeizureNet: A Lightweight Deep Learning Model for Real-Time Epileptic Seizure Detection.

The monitoring of epilepsy patients in non-hospital environment is highly desirable, where ultra-low power wearable seizure detection devices are essential in such a system. The state-of-the-art epileptic seizure detection algorithms targeting such devices either rely on manual feature extractions, which can be biased due to the experience of experts, or deep neural networks, which suffer from high computation complexity. In this paper, we propose a lightweight deep learning model, LightSeizureNet (LSN), for real-time epileptic seizure detection based on raw EEG data in ultra-low power wearable seizure detection devices. The proposed LSN model includes a patient-independent version and a patient-specific version, both of which avoids manual feature extractions and high computation complexity, while maintaining good classification accuracy. Dilated one-dimensional (1D) convolution, global average pooling, and kernel-wise pruning are adopted to compress the LSN model. The proposed models are evaluated on the CHB-MIT scalp EEG database. The patient-independent LSN model achieves 97.09% accuracy with 6.2M MACs, while the patient-specific LSN model achieves 99.77% accuracy with 3.7 M MACs, which are competitive compared to the state of the art in terms of accuracy and complexity. Furthermore, the proposed model is highly interpretable, which is missing in many previous works. By using a uniform approach to explore the interpretability of the proposed model, fine-grained information such as the activated brain region and the frequency of brainwave during seizures is obtained for clinical diagnosis.

469 Electroencephalographic Correlate of Sensory Over-Responsivity in Adults with Chronic Tic Disorders

OBJECTIVES/GOALS: To identify an electroencephalographic (EEG) signature of SOR in adults with TS METHODS/STUDY POPULATION: We will recruit 60 adults with CTD and 60 sex- and age-matched healthy controls to complete scales assessing severity of SOR (Sensory Gating Inventory, SGI), tics, and psychiatric symptoms. Subjects will then be monitored on dense-array scalp EEG during sequential auditory and tactile sensory gating paradigms, as such paradigms have been shown to correlate with self-report measures of SOR in other populations. Single-trial EEG data will be segmented into 100-ms epochs and spectrally deconvoluted into standard frequency bands (delta, theta, alpha, beta, gamma) for pre-defined regions of interest. We will conduct between-group contrasts (Wilcoxon rank-sum) of band-specific sensory gating indices and within-group correlations (Spearman rank correlations) between sensory gating indices and SGI scores. RESULTS/ANTICIPATED RESULTS: We hypothesize that, relative to controls, adults with CTD exhibit impaired sensory gating and that extent of impairment correlates with severity of SOR. 14 adults with CTD (9 men, 5 women) and 16 controls (10 men, 6 women) have completed the protocol to date. Within this sample, adults with CTD showed significantly reduced sensory gating compared to controls in frontal (CTD median 0.12 dB (interquartile range -0.15–0.70 dB); control -0.37 dB (-0.80–-0.13 dB); p = 0.01) and parietal (CTD 0.17 dB (-0.08–0.50 dB); control -0.20 dB (-0.43–0.10 dB); p = 0.01) gamma band during the 100-200 ms epoch in the tactile paradigm. No significant between-group differences were evident for the auditory paradigm. Among adults with CTD, multiple sensory gating indices significantly correlated with SGI scores. Enrollment continues. DISCUSSION/SIGNIFICANCE: Results aim to clarify the extent of sensory gating impairment in TS and identify a clinical correlate of neurophysiologic dysfunction in the disorder. Such knowledge has direct implications for identification of candidate neurophysiologic biomarkers, an express goal of the National Institutes of Health.

EEG decoding of finger numeral configurations with machine learning

In this study, we used multivariate decoding methods to study processing differences between canonical (montring and count) and noncanonical finger numeral configurations (FNCs). While previous research investigated these processing differences using behavioral and event-related potentials (ERP) methods, conventional univariate ERP analyses focus on specific time intervals and electrode sites and fail to capture broader scalp distribution and EEG frequency patterns. To address this issue a supervised learning classifier—support vector machines (SVM)—was used to decode ERP scalp distributions and alpha-band power for montring, counting, and noncanonical FNCs (for integers 1 to 4). The SVM was used to test whether the numerical information presented in FNCs can be decoded from the EEG data. Differences in the magnitude and timing of accuracy rates were used to compare the three types of FNCs. Overall, the algorithm was able to predict numerical information presented in FNCs beyond the random chance level accuracy, with higher rates for ERP scalp distributions than alpha-power. Montring had lower peak accuracy compared to counting and noncanonical configurations, likely due to automaticity in processing montring configurations leading to less distinct scalp distributions for the four numerical magnitudes (1 to 4). Paralleling the response time data, the peak decoding accuracy time for montring was earlier for montring (472 ms), compared to counting (577 ms) and noncanonical FNCs (604 ms). The results provide support for montring configurations being processed automatically, somewhat similar to number symbols, and provide additional insights for processing differences across different forms of FNCs. This study also highlights the strengths of decoding methods in EEG/ERP research on numerical cognition.

Diagnostic and therapeutic approaches in refractory insular epilepsy.

Due to heterogenous seizure semiology and poor contribution of scalp electroencephalography (EEG) signals, insular epilepsy requires use of the appropriate diagnostic tools for its diagnosis and characterization. The deep location of the insula also presents surgical challenges. The aim of this article is to review the current diagnostic and therapeutic tools and their contribution to the management of insular epilepsy. Magnetic resonance imaging (MRI), isotopic imaging, neurophysiological imaging, and genetic testing should be used and interpretated with caution. Isotopic imaging and scalp EEG have demonstrated a lower value in epilepsy from insular compared to temporal origin, which increases the interest of functional MRI and magnetoencephalography. Intracranial recording with stereo-electroencephalography (SEEG) is often required. The insular cortex, being highly connected and deeply located under highly functional areas, is difficult to reach, and its ablative surgery raises functional issues. Tailored resection based on SEEG or alternative curative treatments, such as radiofrequency thermocoagulation, laser interstitial thermal therapy, or stereotactic radiosurgery, have produced encouraging results. The management of insular epilepsy has benefited from major advances in the last years. Perspectives for diagnostic and therapeutic procedures will contribute to better management of this complex form of epilepsy.

Scalp ripple rates for rapid epilepsy differentiation and seizure activity assessment: Applicability and influential factors.

We aim to determine whether automatically detected ripple rate (ADRR) of 10-min scalp electroencephalography (EEG) during slow-wave sleep can be a useful tool for rapid epilepsy differentiation and seizure activity assessment, and we analyze the clinical factors that may affect the scalp ripple rates. We retrospectively included 336 patients who underwent long-term video-EEG with a sampling rate ≥1000 Hz, and three groups were established based on their final clinical diagnosis (non-epilepsy; non-active epilepsy [epilepsy being seizure-free for at least 1 year]; and active epilepsy [epilepsy with one or more seizures in the past year]). ADRRs between groups were compared and diagnostic thresholds set according to the maximum of Youden index with the receiver-operating characteristic curve. The 336 patients comprised 49 non-epilepsy and 287 epilepsy patients (95 non-active epilepsy and 192 active epilepsy). The median ADRR of the epilepsy group was significantly greater than in the non-epilepsy group, with a diagnostic threshold of 4.25 /min (specificity 89.8%, sensitivity 47.74%, p<.001). Following stratification by age, the area under the curve was greatest in the 0-20 year subgroup, threshold 4.10 /min (specificity 100%, sensitivity 52.47%, p<.001). Regarding distinguishing active epilepsy from non-active epilepsy patients, the area under the curve was also greatest in patients 0-20 years of age, threshold 13.05/min (specificity 98.36%, sensitivity 35.64%, p<.001). Following stratification by epilepsy type, the diagnostic efficiency was best in children with developmental and epileptic encephalopathies/epileptic encephalopathies (DEEs/EEs) (threshold 5.20/min, specificity 100%, sensitivity 100%) and self-limited focal epilepsies (SeLFEs) (threshold 5.45/min, specificity 80%, sensitivity 100%). Multivariate analysis revealed that the influential factors of ADRRs were age, depth of epileptogenic lesion, and seizure frequency. ADRR of scalp EEG can be a rapid and specific method to differentiate epilepsy and evaluate seizure activity. This method is especially suitable for young patients.

Intracerebral Correlates of Scalp EEG Ictal Discharges Based on Simultaneous Stereo-EEG Recordings.

It remains unknown to what extent ictal scalp EEG can accurately predict the localization of the intracerebral seizure onset in presurgical evaluation of drug-resistant epilepsies. In this study, we aimed to define homogeneous ictal scalp EEG profiles (based on their first ictal abnormality) and assess their localizing value using simultaneously recorded scalp EEG and stereo-EEG. We retrospectively included consecutive patients with drug-resistant focal epilepsy who had simultaneous stereo-EEG and scalp EEG recordings of at least 1 seizure in the epileptology unit in Nancy, France. We analyzed 1 seizure per patient and used hierarchical cluster analysis to group similar seizure profiles on scalp EEG and then performed a descriptive analysis of their intracerebral correlates. We enrolled 129 patients in this study. The hierarchical cluster analysis showed 6 profiles on scalp EEG first modification. None were specific to a single intracerebral localization. The "normal EEG" and "blurred EEG" clusters (early muscle artifacts) comprised only 5 patients each and corresponded to no preferential intracerebral localization. The "temporal discharge" cluster (n = 46) was characterized by theta or delta discharges on ipsilateral anterior temporal scalp electrodes and corresponded to a preferential mesial temporal intracerebral localization. The "posterior discharge" cluster (n = 42) was characterized by posterior ipsilateral or contralateral rhythmic alpha discharges or slow waves on scalp and corresponded to a preferential temporal localization. However, this profile was the statistically most frequent scalp EEG correlate of occipital and parietal seizures. The "diffuse suppression" cluster (n = 9) was characterized by a bilateral and diffuse background activity suppression on scalp and corresponded to mesial, and particularly insulo-opercular, localization. Finally, the "frontal discharge" cluster (n = 22) was characterized by bilateral frontal rhythmic fast activity or preictal spike on scalp and corresponded to preferential ventrodorsal frontal intracerebral localizations. The hierarchical cluster analysis identified 6 seizure profiles regarding the first abnormality on scalp EEG. None of them were specific of a single intracerebral localization. Nevertheless, the strong relationships between the "temporal," "frontal," "diffuse suppression," and "posterior" profiles and intracerebral discharge localizations may contribute to hierarchize hypotheses derived from ictal scalp EEG analysis regarding intracerebral seizure onset.

Predicting epileptic seizures based on EEG signals using spatial depth features of a 3D-2D hybrid CNN.

Epilepsy is a recurrent chronic brain disease that affects nearly 75 million people around the world. Therefore, the ability to reliably predict epileptic seizures would be instrumental for implementing interventions to reduce brain injury and improve patients' quality of life. In addition to classical machine learning algorithms and feature engineering methods, the use of electroencephalography (EEG) to predict seizures has gradually become a mainstream trend. Here, we propose a patient-specific method to predict epileptic seizures based on EEG data acquired using spatial depth features of a three-dimensional-two-dimensional hybrid convolutional neural network (3D-2D HyCNN) model. This method facilitates the acquisition of abundant and reliable deep features from multi-channel EEG signals. We first developed a reliable data preprocessing method to reconstruct time-series EEG signals into 3D feature images. Then, the 3D-2D HyCNN model was used to extract correlation features between multiple channels of EEG signals, which are automatically exploited by the network to improve seizure prediction. The method achieved accuracy of 98.43% and 93.11%, sensitivity of 98.58% and 90.98%, and specificity of 96.86% and 92.39% on the CHB-MIT Scalp EEG dataset and the American Epilepsy Society Epilepsy Prediction Challenge dataset, respectively. The results revealed that the new algorithm is reliable. Graphical Abstract A new patient-specific epilepsy prediction approach.

Magnetoencephalographic spikes with small spikes on simultaneous electroencephalography have high spatial clustering in temporal lobe epilepsy

ObjectiveTo classify magnetoencephalographic (MEG) spikes according to the findings of simultaneous scalp electroencephalography (EEG) to study dipole estimation in patients with temporal lobe epilepsy. MethodsWe analyzed MEG and simultaneous scalp EEG in 27 patients with intractable temporal lobe epilepsy. We classified MEG spikes into three groups (H-EM-spikes, L-EM-spikes, M-spikes) based on the amplitude of simultaneous EEG (50 μV or higher, lower than 50 μV, no spike morphology on EEG, respectively). We calculated parameters of the dipoles, such as goodness of fit (GOF), current moment, and location. ResultsWe detected 707 MEG spikes, consisting of 175 H-EM-spikes, 245 L-EM-spikes, and 287 M-spikes. Dipoles of H-EM-spikes showed the highest current moment among the three spike groups. Dipoles of L-EM-spikes showed the highest GOF, a moderate current moment, the highest density to cluster, and the highest proportion of being located in the temporal lobe among the three groups. Dipoles of M-spikes showed the lowest GOF and current moment among the three groups. ConclusionsThe characteristics of the dipoles of the MEG spikes differ depending on the simultaneous scalp EEG findings, though most of the MEG spikes were located in the temporal lobe. MEG spikes with concurrent small spikes on simultaneous scalp EEG may have higher spatial clustering in temporal lobe epilepsy.

Electrocorticographic Activation Patterns of Electroencephalographic Microstates.

Electroencephalography (EEG) microstates are short successive periods of stable scalp field potentials representing spontaneous activation of brain resting-state networks. EEG microstates are assumed to mediate local activity patterns. To test this hypothesis, we correlated momentary global EEG microstate dynamics with the local temporo-spectral evolution of electrocorticography (ECoG) and stereotactic EEG (SEEG) depth electrode recordings. We hypothesized that these correlations involve the gamma band. We also hypothesized that the anatomical locations of these correlations would converge with those of previous studies using either combined functional magnetic resonance imaging (fMRI)-EEG or EEG source localization. We analyzed resting-state data (5min) of simultaneous noninvasive scalp EEG and invasive ECoG and SEEG recordings of two participants. Data were recorded during the presurgical evaluation of pharmacoresistant epilepsy using subdural and intracranial electrodes. After standard preprocessing, we fitted a set of normative microstate template maps to the scalp EEG data. Using covariance mapping with EEG microstate timelines and ECoG/SEEG temporo-spectral evolutions as inputs, we identified systematic changes in the activation of ECoG/SEEG local field potentials in different frequency bands (theta, alpha, beta, and high-gamma) based on the presence of particular microstate classes. We found significant covariation of ECoG/SEEG spectral amplitudes with microstate timelines in all four frequency bands (p = 0.001, permutation test). The covariance patterns of the ECoG/SEEG electrodes during the different microstates of both participants were similar. To our knowledge, this is the first study to demonstrate distinct activation/deactivation patterns of frequency-domain ECoG local field potentials associated with simultaneous EEG microstates.

Posterior Cingulate Epilepsy: A Systematic Review.

In this review, authors discuss epilepsy originating from posterior cingulate regions, a challenging entity to diagnose and most likely underrecognized. A systematic review of posterior middle and posterior cingulate epilepsy cases was conducted to present a summary of current knowledge about this localization-based type of epilepsy. The literature search identified 32 articles, for a total of 69 patients (34 with posterior middle cingulate epilepsy [pMCE] and 35 with posterior cingulate epilepsy [PCE]). Most patients were children and young adults with drug-resistant lesional epilepsy with high seizure burden. In both groups, most patients reported auras, mainly sensory, but various types were reported, including autonomic, behavioral, and cognitive manifestations. Most pMCE and PCE showed motor manifestations (mainly respectively asymmetric tonic posturing and automotor features). Impaired awareness during seizures was more frequently reported in PCE than in pMCE. As for ictal scalp EEG, epileptogenic abnormalities were poorly lateralized and did not localize the seizure onset zone. An ictal temporal involvement was only observed in PCE. Interictal EEG findings were nonspecific. As for other presurgical noninvasive investigations, data are limited, and no studies have adequately assessed their value. Partly explained by our inclusion criteria, most patients underwent a surgical procedure (either lesionectomy or resection of epileptogenic zone as defined by intracranial EEG study results), which overall yielded good outcomes.

Decoding human spontaneous spiking activity in medial temporal lobe from scalp EEG

Linking scalp electroencephalography (EEG) signals and spontaneous firing activity from deep nuclei in humans is not trivial. To examine this, we analyzed simultaneous recordings of scalp EEG and unit activity in deeply located sites recorded overnight from patients undergoing pre-surgical invasive monitoring. We focused on modeling the within-subject average unit activity of two medial temporal lobe areas: amygdala and hippocampus. Linear regression model correlates the units' average firing activity to spectral features extracted from the EEG during wakefulness or non-REM sleep. We show that changes in mean firing activity in both areas and states can be estimated from EEG (Pearson r>0.2, p≪0.001). Region specificity was shown with respect to other areas. Both short- and long-term fluctuations in firing rates contributed to the model accuracy. This demonstrates that scalp EEG frequency modulations can predict changes in neuronal firing rates, opening a new horizon for non-invasive neurological and psychiatric interventions.

Development of Expert-Level Classification of Seizures and Rhythmic and Periodic Patterns During EEG Interpretation.

Seizures (SZs) and other SZ-like patterns of brain activity can harm the brain and contribute to in-hospital death, particularly when prolonged. However, experts qualified to interpret EEG data are scarce. Prior attempts to automate this task have been limited by small or inadequately labeled samples and have not convincingly demonstrated generalizable expert-level performance. There exists a critical unmet need for an automated method to classify SZs and other SZ-like events with expert-level reliability. This study was conducted to develop and validate a computer algorithm that matches the reliability and accuracy of experts in identifying SZs and SZ-like events, known as "ictal-interictal-injury continuum" (IIIC) patterns on EEG, including SZs, lateralized and generalized periodic discharges (LPD, GPD), and lateralized and generalized rhythmic delta activity (LRDA, GRDA), and in differentiating these patterns from non-IIIC patterns. We used 6,095 scalp EEGs from 2,711 patients with and without IIIC events to train a deep neural network, SPaRCNet, to perform IIIC event classification. Independent training and test data sets were generated from 50,697 EEG segments, independently annotated by 20 fellowship-trained neurophysiologists. We assessed whether SPaRCNet performs at or above the sensitivity, specificity, precision, and calibration of fellowship-trained neurophysiologists for identifying IIIC events. Statistical performance was assessed by the calibration index and by the percentage of experts whose operating points were below the model's receiver operating characteristic curves (ROCs) and precision recall curves (PRCs) for the 6 pattern classes. SPaRCNet matches or exceeds most experts in classifying IIIC events based on both calibration and discrimination metrics. For SZ, LPD, GPD, LRDA, GRDA, and "other" classes, SPaRCNet exceeds the following percentages of 20 experts-ROC: 45%, 20%, 50%, 75%, 55%, and 40%; PRC: 50%, 35%, 50%, 90%, 70%, and 45%; and calibration: 95%, 100%, 95%, 100%, 100%, and 80%, respectively. SPaRCNet is the first algorithm to match expert performance in detecting SZs and other SZ-like events in a representative sample of EEGs. With further development, SPaRCNet may thus be a valuable tool for an expedited review of EEGs. This study provides Class II evidence that among patients with epilepsy or critical illness undergoing EEG monitoring, SPaRCNet can differentiate (IIIC) patterns from non-IIIC events and expert neurophysiologists.

Intraarterial encephalography from an acutely implanted aneurysm embolization device in awake humans.

Endovascular electroencephalography (evEEG) uses the cerebrovascular system to record electrical activity from adjacent neural structures. The safety, feasibility, and efficacy of using the Woven EndoBridge Aneurysm Embolization System (WEB) for evEEG has not been investigated. Seventeen participants undergoing awake WEB endovascular treatment of unruptured cerebral aneurysms were included. After WEB deployment and before detachment, its distal deployment wire was connected to an EEG receiver, and participants performed a decision-making task for 10 minutes. WEB and scalp recordings were captured. All patients underwent successful embolization and evEEG with no complications. Event-related potentials were detected on scalp EEG in 9/17 (53%) patients. Of these 9 patients, a task-related low-gamma (30-70 Hz) response on WEB channels was captured in 8/9 (89%) cases. In these 8 patients, the WEB was deployed in 2 middle cerebral arteries, 3 anterior communicating arteries, the terminal internal carotid artery, and 2 basilar tip aneurysms. Electrocardiogram artifact on WEB channels was present in 12/17 cases. The WEB implanted within cerebral aneurysms of awake patients is capable of capturing task-specific brain electrical activities. Future studies are warranted to establish the efficacy of and support for evEEG as a tool for brain recording, brain stimulation, and brain-machine interface applications.