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Abstract Background Ictal brain perfusion SPECT provides higher sensitivity for the identification of the epileptic seizure onset zone (SOZ) than interictal SPECT. However, ictal SPECT is demanding due to the unpredictable waiting period for the next seizure to allow for ictal tracer injection. Thus, starting with an interictal scan and skipping the ictal scan if the interictal scan provides a SOZ candidate with high confidence could be an efficient approach. The current study estimated the rate of high-confidence SOZ candidates and the false lateralization rate among them for interictal and ictal SPECT. Methods 177 patients (48% females, median age 38y, interquartile range 27–48y) with ictal and interictal SPECT acquired with 99mTc-HMPAO (n = 141) or -ECD (n = 36) were included retrospectively. The vast majority of the patients was suspected to have temporal lobe epilepsy. Visual interpretation of the SPECT data was performed independently by 3 readers in 3 settings: “interictal only” (interictal SPECT and statistical hypoperfusion map), “ictal only” (ictal SPECT and hyperperfusion map), and “full” setting (side-by-side interpretation of ictal and interictal SPECT including statistical maps and SISCOM analysis). The readers lateralized the SOZ (right, left, none) and characterized their confidence using a 5-score. A case was considered "lateralizing with high confidence” if all readers lateralized to the same hemisphere with at least 4 of 5 confidence points. Lateralization of the SOZ in the “full” setting was used as reference standard. Results The proportion of “lateralizing with high confidence” cases was 4.5/31.6/38.4% in the “interictal only”/“ictal only”/“full” setting. One (12.5%) of the 8 cases that were “lateralizing with high confidence” in the “interictal only” setting lateralized to the wrong hemisphere. Among the 56 cases that were “lateralizing with high confidence” in the “ictal only” setting, 54 (96.4%) were also lateralizing in the “full” setting, all to the same hemisphere. Conclusions Starting brain perfusion SPECT in the presurgical evaluation of epilepsy with an interictal scan to skip the ictal scan in case of a high-confidence interictal SOZ candidate is not a useful approach. In contrast, starting with an ictal scan to skip the interictal scan in case of a high-confidence ictal SOZ candidate can be recommended.

Background: Ictal brain perfusion SPECT provides higher sensitivity for the identification of the epileptic seizure onset zone (SOZ) than interictal SPECT. However, ictal SPECT is demanding due to the unpredictable waiting period for the next seizure to allow for ictal tracer injection. Thus, starting with an interictal scan and skipping the ictal scan if the interictal scan provides a SOZ candidate with high confidence could be an efficient approach. The current study estimated the rate of high-confidence SOZ candidates and the false lateralization rate among them for interictal and ictal SPECT. Methods: 177 patients (48% females, median age 38y, interquartile range 27–48y) with ictal and interictal SPECT acquired with 99mTc-HMPAO (n = 141) or -ECD (n = 36) were included retrospectively. The vast majority of the patients was suspected to have temporal lobe epilepsy. Visual interpretation of the SPECT data was performed independently by 3 readers in 3 settings: "interictal only" (interictal SPECT and statistical hypoperfusion map), "ictal only" (ictal SPECT and hyperperfusion map), and "full" setting (side-by-side interpretation of ictal and interictal SPECT including statistical maps and SISCOM analysis). The readers lateralized the SOZ (right, left, none) and characterized their confidence using a 5-score. A case was considered "lateralizing with high confidence" if all readers lateralized to the same hemisphere with at least 4 of 5 confidence points. Lateralization of the SOZ in the "full" setting was used as reference standard. Results: The proportion of "lateralizing with high confidence" cases was 4.5/31.6/38.4% in the "interictal only"/"ictal only"/"full" setting. One (12.5%) of the 8 cases that were "lateralizing with high confidence" in the "interictal only" setting lateralized to the wrong hemisphere. Among the 56 cases that were "lateralizing with high confidence" in the "ictal only" setting, 54 (96.4%) were also lateralizing in the "full" setting, all to the same hemisphere. Conclusions: Starting brain perfusion SPECT in the presurgical evaluation of epilepsy with an interictal scan to skip the ictal scan in case of a high-confidence interictal SOZ candidate is not a useful approach. In contrast, starting with an ictal scan to skip the interictal scan in case of a high-confidence ictal SOZ candidate can be recommended. [ABSTRACT FROM AUTHOR]

Introduction. Epilepsy is a disease characterized by abnormal paroxysmal bioelectrical activity in the brain cortex and subcortical structures. Seizures per se change brain metabolism in epileptic focus and in distal parts of the brain. However, interictal phenomena can also affect functional connectivity (FC) and brain metabolism in other parts of the brain. Aim of study. We hypothesised that epilepsy affects functional connectivity not only among cortical, but also between subcortical, structures of the brain in a resting state condition. Clinical rationale for study. Investigating functional connectivity in patients with epilepsy could provide insights into the underlying pathophysiological mechanisms. Better understanding may lead to more effective treatment strategies. Material and methods. Functional connectivity was analysed in 35 patients with epilepsy and in 28 healthy volunteers. The group of patients was divided into generalised and focal epilepsy (temporal and extratemporal subgroups). Each patient and healthy volunteer underwent an fMRI resting-state session. During the study, EEG signals were simultaneously recorded with fMRI to facilitate the subsequent detection of potential interictal epileptiform discharges (IEDs). Their potential impact on BOLD signals was mitigated through linear regression. The data was processed and correlation coefficients (FC values) between the BOLD signal from selected structures of the central nervous system were determined and compared between study groups. The results were presented as significant differences in correlation coefficients between brain/subcortical structures in the epilepsy and control groups. Results. Lower FC values for the epilepsy group compared to the control group were shown for connections related to the MPFC, hippocampus, thalamus, amygdala, and the parahippocampal gyrus. Conclusions. Epilepsy alters the functional connectivity of resting state subcortical networks. Patterns of pathological changes of FC differ between epilepsy subtypes, with predominant lower FC between the hippocampus, parahippocampal gyrus, amygdala and thalamus in patients with epilepsy. Clinical implications. This study suggests that epilepsy affects subcortical structures. Identifying distinct patterns of altered FC in epilepsy subtypes may help to tailor treatment strategies. Changes in FC detected by fMRI may precede clinical symptoms, aiding in the early diagnosis of cognitive and emotional disorders in focal epilepsy. [ABSTRACT FROM AUTHOR]

Background: Patients with vestibular migraine (VM) exhibit higher levels of central sensitization and share similar disorder characteristics with migraine with vestibular symptoms (MwVS), except in terms of disability. These patients experience fluctuating mechanical pain thresholds and persistent vestibular symptoms even without a migraine attack. Objective: This study aimed to investigate whether interictal allodynia or hyperalgesia can differentiate between VM, MwVS, and migraine only. Methods: We conducted a cross-sectional study of patients with episodic migraine aged between 18 and 65 years, categorized into three groups. A questionnaire was used to collect and compare demographic and clinical variables. Interictal widespread pressure hyperalgesia (IWPH) was evaluated using the Manual Tender Point Survey. Patients with tender point counts ≥7 were classified as having IWPH. Results: The study included 163 patients: 31 with VM, 54 with MwVS, and 78 with migraine without vestibular symptoms (migraine only). We found that aura (p = 0.042, odds ratio 3.50, 95% confidence interval 1.26-10.4), tender point count (p < 0.001, d = 0.889, median difference = 2), and IWPH (p = 0.002, odds ratio 5.3, 95% confidence interval 1.80-17.2) were significantly associated with VM compared to MwVS. Aura and IWPH were significantly associated with VM. However, there were no significant associations observed for interictal allodynia or hyperalgesia between the other two groups. Conclusion: IWPH and aura are associated with VM, indicating their potential roles in its pathogenesis. These findings may contribute to the differential diagnosis and management of migraine, potentially leading to targeted treatment strategies. [ABSTRACT FROM AUTHOR]

The small molecule calcitonin gene–related peptide receptor antagonists (gepants) are the only drug class with medicines indicated for both the acute and preventive treatment of migraine. Given this dual capacity to both treat and prevent, along with their favorable tolerability profiles and lack of an association with medication‐overuse headache, headache specialists have begun to use gepants in ways that transcend the traditional categories of acute and preventive treatment. One approach, called situational prevention, directs patients to treat during the interictal phase, before symptoms develop, in situations of increased risk for migraine attacks. Herein, we present three patients to illustrate scenarios of gepant use for situational prevention. In each case, a gepant was started in anticipation of a period of increased headache probability (vulnerability) and continued for a duration of 1 day to 5 consecutive days. Although this approach may expose patients to medication when headache may not have developed, the tolerability and safety profile and preventive effect of gepants may represent a feasible approach for some patients. Situational prevention is an emerging strategy for managing migraine before symptoms develop in individuals who can identify periods when the probability of headache is high. This paper is intended to increase awareness of this strategy and stimulate future randomized, placebo‐controlled trials to rigorously assess this strategy. Plain Language Summary: A new class of migraine medicines, the gepants, does not cause medication‐overuse headache; these drugs can be taken in periods of increased headache risk, even before symptoms begin, to keep headaches from coming on. This new way of treating migraine is called "situational prevention." Experience in clinical practice suggests that treating migraine patients without current symptoms during times of increased headache risk, for example in connection with the menstrual cycle, travel, stress, or relaxation after stress, may be good times for situational prevention, but studies are needed to confirm when this strategy is most likely to be beneficial. [ABSTRACT FROM AUTHOR]

Background: Déjà vu, French for “already seen,” is a phenomenon most people will experience at least once in their lifetime. Emerging evidence suggests that déjà vu occurs in healthy individuals (as “non-ictal déjà vu”) and in epilepsy patients during seizures (as “ictal déjà vu”) and between seizures (as “interictal déjà vu”). Although the ILAE has recognized déjà vu as a feature of epileptic seizures, it is notably absent from the ICD-11. A lack of evidence-based research may account for this omission. To our knowledge, this study represents the first systematic review and meta-analysis on déjà vu experiences. Through detailed examinations of non-ictal, interictal and ictal déjà vu, we seek to highlight possible clinical implications. Rethinking the status quo of ictal déjà vu could potentially lead to earlier interventions and improve outcomes for epilepsy patients. Methods: This study was registered in PROSPERO (ID: CRD42023394239) on 5 February 2023. Systematic searches were conducted across four databases: EMBASE, MEDLINE, PsycINFO, and PubMed, from inception to 1 February 2023, limited to English language and human participants. Studies were included/ excluded based on predefined criteria. Data was extracted according to the PICO framework and synthesized through a thematic approach. Meta-analyses were performed to estimate prevalence’s of the phenomena. Study quality, heterogeneity, and publication bias were assessed. Results: Database searching identified 1,677 records, of which 46 studies were included. Meta-analyses of prevalence showed that non-ictal déjà vu was experienced by 0.74 (95% CI [0.67, 0.79], p  <  0.001) of healthy individuals, whereas interictal déjà vu was experienced by 0.62 (95% CI [0.48, 0.75], p  =  0.0 9 9 ) and ictal déjà vu by 0.22 (95% CI [0.15, 0.32], p  =  0.001) of epilepsy patients. Examinations of phenomenological (sex, age, frequency, duration, emotional valence, and dissociative symptoms) and neuroscientific (brain structures and functions) data revealed significant variations between non-ictal, interictal and ictal déjà vu on several domains. Conclusion: This systematic review and meta-analysis do not support the notion that non-ictal, interictal and ictal déjà vu are homogenous experiences. Instead, it provides insight into ictal déjà vu as a symptom of epilepsy that should be considered included in future revisions of the ICD-11. [ABSTRACT FROM AUTHOR]

Epileptic seizures, unpredictable in nature and potentially dangerous during activities like driving, pose significant risks to individual and public safety. Traditional diagnostic methods, which involve labour-intensive manual feature extraction from Electroencephalography (EEG) data, are being supplanted by automated deep learning frameworks. This paper introduces an automated epileptic seizure detection framework utilizing deep learning to bypass manual feature extraction. Our framework incorporates detailed preprocessing techniques: normalization via L2 normalization, filtering with an 80 Hz and 0,5 Hz Butterworth low-pass and high-pass filter, and a 50 Hz IIR Notch filter, channel selection based on standard deviation calculations and Mutual Information algorithm, and frequency domain transformation using FFT or STFT with Hann windows and 50% hop. We evaluated on two datasets: the first comprising 4 canines and 8 patients with 2.299 ictal, 23.445 interictal, and 32.915 test data, 400-5000Hz sampling rate across 16-72 channels; the second dataset, intended for testing, 733 icatal, 4.314 interictal, and 1908 test data, each 10 minutes long, recorded at 400Hz across 16 channels. Three deep learning architectures were assessed: CNN, LSTM, and a hybrid CNN-LSTM model-stems from their demonstrated efficacy in handling the complex nature of EEG data. Each model offers unique strengths, with the CNN excelling in spatial feature extraction, LSTM in temporal dynamics, and the hybrid model combining these advantages. The CNN model, comprising 31 layers, yielded highest accuracy, achieving 91% on the first dataset (precision 92%, recall 91%, F1-score 91%) and 82% on the second dataset using a 30-second threshold. This threshold was chosen for its clinical relevance. The research advances epileptic seizure detection using deep learning, indicating a promising direction for future medical technology. Future work will focus on expanding dataset diversity and refining methodologies to build upon these foundational results. [ABSTRACT FROM AUTHOR]

BackgroundDéjà vu, French for “already seen,” is a phenomenon most people will experience at least once in their lifetime. Emerging evidence suggests that déjà vu occurs in healthy individuals (as “non-ictal déjà vu”) and in epilepsy patients during seizures (as “ictal déjà vu”) and between seizures (as “interictal déjà vu”). Although the ILAE has recognized déjà vu as a feature of epileptic seizures, it is notably absent from the ICD-11. A lack of evidence-based research may account for this omission. To our knowledge, this study represents the first systematic review and meta-analysis on déjà vu experiences. Through detailed examinations of non-ictal, interictal and ictal déjà vu, we seek to highlight possible clinical implications. Rethinking the status quo of ictal déjà vu could potentially lead to earlier interventions and improve outcomes for epilepsy patients.MethodsThis study was registered in PROSPERO (ID: CRD42023394239) on 5 February 2023. Systematic searches were conducted across four databases: EMBASE, MEDLINE, PsycINFO, and PubMed, from inception to 1 February 2023, limited to English language and human participants. Studies were included/excluded based on predefined criteria. Data was extracted according to the PICO framework and synthesized through a thematic approach. Meta-analyses were performed to estimate prevalence’s of the phenomena. Study quality, heterogeneity, and publication bias were assessed.ResultsDatabase searching identified 1,677 records, of which 46 studies were included. Meta-analyses of prevalence showed that non-ictal déjà vu was experienced by 0.74 (95% CI [0.67, 0.79], p

Epilepsy is a common neurological disease characterized by seizures. A person with a seizure onset can lose consciousness which in turn can lead to fatal accidents. Electroencephalogram (EEG) is a recording of the electrical signals from the brain which is used to analyse the epileptic seizures. Physical visual examination of the EEG by trained neurologists is subjective and highly difficult due to the non-linear complex nature of the EEG. This opens a window for automatic detection of epileptic seizures using machine learning methods. In this work, we have used a standard database that consists of five different sets of EEG data including the epileptic EEG. Using this data, we have devised a novel 22 possible clinically significant cases with the combination of binary and multi class type of classification problem to automatically classify epileptic EEG. As the EEG is non-linear, we have devised 11 statistically significant non-linear entropy features to extract from this database. These features are fed to 10 different classifiers of various types for each of the 22 clinically significant cases and their classification accuracy is reported for 10-fold cross validation. Random Forest and Optimized Forest classifiers reported accuracies above 90% for all 22 cases considered in this study. Such vast possible clinically significant 22 cases from the combination of the data from the database considered has not been in the literature with the best of the knowledge of the authors. Comparing with the literature, several studies have presented one or few combinations of these 22 cases in this work. In comparison to similar works, the accuracies obtained by the classifiers were highly competitive. In addition, a novel integrated epilepsy detection index named EpilepIndex (IED) is able to differentiate between epileptic EEG and a normal EEG with 100% accuracy.

Background: Longitudinal EEG recorded by implanted devices is critical for understanding and managing epilepsy. Recent research reports patient-specific, multi-day cycles in device-detected epileptiform events that coincide with increased likelihood of clinical seizures. Understanding these cycles could elucidate mechanisms generating seizures and advance drug and neurostimulation therapies. Objective/hypothesis: We hypothesize that seizure-correlated cycles are present in background neural activity, independent of interictal epileptiform spikes, and that neurostimulation may temporarily interrupt these cycles. Methods: We analyzed regularly-recorded seizure-free data epochs from 20 patients implanted with a responsive neurostimulation (RNS) device for at least 1.5 years, to explore the relationship between cycles in device-detected interictal epileptiform activity (dIEA), clinician-validated interictal spikes, background EEG features, and neurostimulation. Results: Background EEG features tracked the cycle phase of dIEA in all patients (AUC: 0.63 [0.56–0.67]) with a greater effect size compared to clinically annotated spike rate alone (AUC: 0.55 [0.53–0.61], p

Sleep and wake are defined through physiological and behavioral criteria and can be typically separated into non‐rapid eye movement (NREM) sleep stages N1, N2, and N3, rapid eye movement (REM) sleep, and wake. Sleep and wake states are not homogenous in time. Their properties vary during the night and day cycle. Given that brain activity changes as a function of NREM, REM, and wake during the night and day cycle, are seizures more likely to occur during NREM, REM, or wake at a specific time? More generally, what is the relationship between sleep–wake cycles and epilepsy? We will review specific examples from clinical data and results from experimental models, focusing on the diversity and heterogeneity of these relationships. We will use a top‐down approach, starting with the general architecture of sleep, followed by oscillatory activities, and ending with ionic correlates selected for illustrative purposes, with respect to seizures and interictal spikes. The picture that emerges is that of complexity; sleep disruption and pathological epileptic activities emerge from reorganized circuits. That different circuit alterations can occur across patients and models may explain why sleep alterations and the timing of seizures during the sleep–wake cycle are patient‐specific. [ABSTRACT FROM AUTHOR]

Automatic seizure onset zone (SOZ) localization using interictal electrocorticogram (ECoG) improves the diagnosis and treatment of patients with medically refractory epilepsy. This study aimed to investigate the characteristics of phase-amplitude coupling (PAC) extracted from interictal ECoG and the feasibility of PAC serving as a promising biomarker for SOZ identification. We employed the mean vector length modulation index approach on the 20-s ECoG window to calculate PAC features between low-frequency rhythms (0.5–24 Hz) and high frequency oscillations (HFOs) (80–560 Hz). We used statistical measures to test the significant difference in PAC between the SOZ and non-seizure onset zone (NSOZ). To overcome the drawback of handcraft feature engineering, we established novel machine learning models to learn automatically the characteristics of the obtained PAC features and classify them to identify the SOZ. Besides, to handle imbalanced dataset classification, we introduced novel feature-wise/class-wise re-weighting strategies in conjunction with classifiers. In addition, we proposed a time-series nest cross-validation to provide more accurate and unbiased evaluations for this model. Seven patients with focal cortical dysplasia were included in this study. The experiment results not only showed that a significant coupling at band pairs of slow waves and HFOs exists in the SOZ when compared with the NSOZ, but also indicated the effectiveness of the PAC features and the proposed models in achieving better classification performance. [ABSTRACT FROM AUTHOR]

Longitudinal EEG recorded by implanted devices is critical for understanding and managing epilepsy. Recent research reports patient-specific, multi-day cycles in device-detected epileptiform events that coincide with increased likelihood of clinical seizures. Understanding these cycles could elucidate mechanisms generating seizures and advance drug and neurostimulation therapies. We hypothesize that seizure-correlated cycles are present in background neural activity, independent of interictal epileptiform spikes, and that neurostimulation may temporarily interrupt these cycles. We analyzed regularly-recorded seizure-free data epochs from 20 patients implanted with a responsive neurostimulation (RNS) device for at least 1.5 years, to explore the relationship between cycles in device-detected interictal epileptiform activity (dIEA), clinician-validated interictal spikes, background EEG features, and neurostimulation. Background EEG features tracked the cycle phase of dIEA in all patients (AUC: 0.63 [0.56–0.67]) with a greater effect size compared to clinically annotated spike rate alone (AUC: 0.55 [0.53–0.61], p < 0.01). After accounting for circadian variation and spike rate, we observed significant population trends in elevated theta and beta band power and theta and alpha connectivity features at the cycle peaks (sign test, p < 0.05). In the period directly after stimulation we observe a decreased association between cycle phase and EEG features compared to background recordings (AUC: 0.58 [0.55–0.64]). Our findings suggest that seizure-correlated dIEA cycles are not solely due to epileptiform discharges but are associated with background measures of brain state; and that neurostimulation may temporarily interrupt these cycles. These results may help elucidate mechanisms underlying seizure generation, provide new biomarkers for seizure risk, and facilitate monitoring, treating, and managing epilepsy with implantable devices. • Background EEG features track multidien cycles in RNS dIEA. • dIEA linked EEG features are patient-specific and may differ with cortical structure. • Responsive neurostimulation suppresses EEG-dIEA coupling. [ABSTRACT FROM AUTHOR]

A typical absence seizure is a generalized epileptic event characterized by a sudden, brief alteration of consciousness that serves as a hallmark for various generalized epilepsy syndromes. Distinguishing between similar interictal and ictal electroencephalographic (EEG) epileptiformpatterns poses a challenge. However, quantitative EEG, particularly spectral analysis focused on EEG rhythms, shows potential for differentiation. This study was designed to investigate discernible differences in EEG spectral dynamics and entropy patterns during the pre-ictal and post-ictal periods compared to the interictal state. We analyzed 20 EEG ictal patterns from 11 patients with confirmed typical absence seizures, and assessed recordings made during the preictal, post-ictal, and interictal intervals. Power spectral density (PSD) was used for the quantitative analysis that focused on the delta, theta, alpha, and beta bands. In addition, we measured EEG signal regularity using approximate (ApEn) and multi-scale sample entropy (MSE). Findings demonstrate a significant increase in delta and theta power in the pre-ictal and post-ictal intervals compared to the interictal interval, especially in the posterior brain region. We also observed a notable decrease in entropy in the pre-ictal and post-ictal intervals, with a more pronounced effect in anterior brain regions. These results provide valuable information that can potentially aid in differentiating epileptiform patterns in typical absence seizures. The implications of our findings are promising for precision medicine approaches to epilepsy diagnoses and patient management. In conclusion, our quantitative analysis of EEG data suggests that PSD and entropy measures hold promise as potential biomarkers for distinguishing ictal from interictal epileptiform patterns in patients with confirmed or suspected typical absence seizures. [ABSTRACT FROM AUTHOR]

A typical absence seizure is a generalized epileptic event characterized by a sudden, brief alteration of consciousness that serves as a hallmark for various generalized epilepsy syndromes. Distinguishing between similar interictal and ictal electroencephalographic (EEG) epileptiform patterns poses a challenge. However, quantitative EEG, particularly spectral analysis focused on EEG rhythms, shows potential for differentiation. This study was designed to investigate discernible differences in EEG spectral dynamics and entropy patterns during the pre-ictal and post-ictal periods compared to the interictal state. We analyzed 20 EEG ictal patterns from 11 patients with confirmed typical absence seizures, and assessed recordings made during the pre-ictal, post-ictal, and interictal intervals. Power spectral density (PSD) was used for the quantitative analysis that focused on the delta, theta, alpha, and beta bands. In addition, we measured EEG signal regularity using approximate (ApEn) and multi-scale sample entropy (MSE). Findings demonstrate a significant increase in delta and theta power in the pre-ictal and post-ictal intervals compared to the interictal interval, especially in the posterior brain region. We also observed a notable decrease in entropy in the pre-ictal and post-ictal intervals, with a more pronounced effect in anterior brain regions. These results provide valuable information that can potentially aid in differentiating epileptiform patterns in typical absence seizures. The implications of our findings are promising for precision medicine approaches to epilepsy diagnoses and patient management. In conclusion, our quantitative analysis of EEG data suggests that PSD and entropy measures hold promise as potential biomarkers for distinguishing ictal from interictal epileptiform patterns in patients with confirmed or suspected typical absence seizures.

Electroencephalogram (EEG) have been widely used for the diagnosis of neurological diseases like epilepsy and sleep disorders. Support vector machines (SVMs) are widely used classifiers for the classification of EEG signals due to their better generalization performance. However, SVM suffers due to high computational complexity. To reduce the computations, twin support vector machines (TWSVM) solved smaller size quadratic optimization problems. To enhance the performance of the SVM and TWSVM models, prior information known as universum data has been incorporated in the universum SVM (USVM) and universum twin (UTSVM) models. Both SVM and UTSVM employ hinge loss which results in sensitivity to noise and instability. To overcome these issues and incorporate the prior information of the EEG signals, we propose a novel universum twin support vector machine with pinball loss function (Pin-UTSVM) for the classification of EEG signals. The proposed Pin-UTSVM is more stable for resampling and is noise insensitive. Furthermore, the computational complexity of proposed Pin-UTSVM model is similar to the standard UTSVM model. In the proposed approach, we used the interictal EEG signal as the universum data. Numerical experiments at varying level of noise show that the proposed Pin-UTSVM is more robust to noise compared to standard models. To show the efficiency of the proposed Pin-UTSVM model, we used multiple feature extraction techniques for the classification of the EEG signal. Experimental results reveal that the proposed Pin-UTSVM model is performing better compared to the existing models. Moreover, statistical tests show that the proposed Pin-UTSVM model is significantly better in comparison with the existing baseline models. [ABSTRACT FROM AUTHOR]

Objective: Effective surgical treatment of drug‐resistant epilepsy depends on accurate localization of the epileptogenic zone (EZ). High‐frequency oscillations (HFOs) are potential biomarkers of the EZ. Previous research has shown that HFOs often occur within submillimeter areas of brain tissue and that the coarse spatial sampling of clinical intracranial electrode arrays may limit the accurate capture of HFO activity. In this study, we sought to characterize microscale HFO activity captured on thin, flexible microelectrocorticographic (μECoG) arrays, which provide high spatial resolution over large cortical surface areas. Methods: We used novel liquid crystal polymer thin‐film μECoG arrays (.76–1.72‐mm intercontact spacing) to capture HFOs in eight intraoperative recordings from seven patients with epilepsy. We identified ripple (80–250 Hz) and fast ripple (250–600 Hz) HFOs using a common energy thresholding detection algorithm along with two stages of artifact rejection. We visualized microscale subregions of HFO activity using spatial maps of HFO rate, signal‐to‐noise ratio, and mean peak frequency. We quantified the spatial extent of HFO events by measuring covariance between detected HFOs and surrounding activity. We also compared HFO detection rates on microcontacts to simulated macrocontacts by spatially averaging data. Results: We found visually delineable subregions of elevated HFO activity within each μECoG recording. Forty‐seven percent of HFOs occurred on single 200‐μm‐diameter recording contacts, with minimal high‐frequency activity on surrounding contacts. Other HFO events occurred across multiple contacts simultaneously, with covarying activity most often limited to a.95‐mm radius. Through spatial averaging, we estimated that macrocontacts with 2–3‐mm diameter would only capture 44% of the HFOs detected in our μECoG recordings. Significance: These results demonstrate that thin‐film microcontact surface arrays with both highresolution and large coverage accurately capture microscale HFO activity and may improve the utility of HFOs to localize the EZ for treatment of drug‐resistant epilepsy. [ABSTRACT FROM AUTHOR]

Purpose: The electrophysiological characterization of resting state oscillatory functional connectivity within the default mode network (DMN) during interictal periods in childhood absence epilepsy (CAE) remains unclear. Using magnetoencephalographic (MEG) recordings, this study investigated how the connectivity within the DMN was altered in CAE. Methods: Using a cross-sectional design, we analyzed MEG data from 33 children newly diagnosed with CAE and 26 controls matched for age and sex. The spectral power and functional connectivity of the DMN were estimated using minimum norm estimation combined with the Welch technique and corrected amplitude envelope correlation. Results: Default mode network showed stronger activation in the delta band during the ictal period, however, the relative spectral power in other bands was significantly lower than that in the interictal period (pcorrected < 0.05 for DMN regions, except bilateral medial frontal cortex, left medial temporal lobe, left posterior cingulate cortex in the theta band, and the bilateral precuneus in the alpha band). It should be noted that the significant power peak in the alpha band was lost compared with the interictal data. Compared with controls, the interictal relative spectral power of DMN regions (except bilateral precuneus) in CAE patients was significantly increased in the delta band (pcorrected < 0.01), whereas the values of all DMN regions in the beta-gamma 2 band were significantly decreased (pcorrected < 0.01). In the higher frequency band (alpha-gamma1), especially in the beta and gamma1 band, the ictal node strength of DMN regions except the left precuneus was significantly higher than that in the interictal periods (pcorrected < 0.01), and the node strength of the right inferior parietal lobe increased most significantly in the beta band (Ictal: 3.8712 vs. Interictal: 0.7503, pcorrected < 0.01). Compared with the controls, the interictal node strength of DMN increased in all frequency bands, especially the right medial frontal cortex in the beta band (Controls: 0.1510 vs. Interictal: 3.527, pcorrected < 0.01). Comparing relative node strength between groups, the right precuneus in CAE children decreased significantly (b: Controls: 0.1009 vs. Interictal: 0.0475; g 1: Controls:0.1149 vs. Interictal:0.0587, pcorrected < 0.01) such that it was no longer the central hub. Conclusion: These findings indicated DMN abnormalities in CAE patients, even in interictal periods without interictal epileptic discharges. Abnormal functional connectivity in CAE may reflect abnormal anatomo-functional architectural integration in DMN, as a result of cognitive mental impairment and unconsciousness during absence seizure. 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 CAE patients. [ABSTRACT FROM AUTHOR]

Background: Migraine has a largely unexplained connection with sleep and is possibly related to a dysfunction of thalamocortical systems and cortical inhibition. In this study we investigate the effect of insufficient sleep on cortical sensorimotor processing in migraine. Methods: We recorded electroencephalography during a sensorimotor task from 46 interictal migraineurs and 28 controls after two nights of eight-hour habitual sleep and after two nights of four-hour restricted sleep. We compared changes in beta oscillations of the sensorimotor cortex after the two sleep conditions between migraineurs, controls and subgroups differentiating migraine subjects usually having attacks starting during sleep and not during sleep. We included preictal and postictal recordings in a secondary analysis of temporal changes in relation to attacks. Results: Interictally, we discovered lower beta synchronisation after sleep restriction in sleep related migraine compared to non-sleep related migraine (p=0.006) and controls (p=0.01). No differences were seen between controls and the total migraine group in the interictal phase. After migraine attacks, we observed lower beta synchronisation (p<0.001) and higher beta desynchronisation (p=0.002) after sleep restriction closer to the end of the attack compared to later after the attack. Conclusion: The subgroup with sleep related migraine had lower sensorimotor beta synchronisation after sleep restriction, possibly related to dysfunctional GABAergic inhibitory systems. Sufficient sleep during or immediately after migraine attacks may be of importance for maintaining normal cortical excitability. [ABSTRACT FROM AUTHOR]

Abstract Objective Focal cortical dysplasia (FCD) represents an essential cause of drug‐resistant epilepsy with surgery as an effective treatment option. This study aimed to identify the important predictors of favorable surgical outcomes and the impact of the interictal scalp electroencephalogram (EEG) patterns in predicting postsurgical seizure outcomes. Methods We retrospectively evaluated 210 consecutive patients between 2015 and 2019. They were diagnosed with FCD by pathology, underwent resection, and had at least one year of postsurgical follow‐up. Predictors of seizure freedom were analyzed. Results Based on the information at the latest follow‐up, seizure outcome was classified as Engel Class I (seizure‐free) in 81.4% and Engel Class II‐IV (non‐seizure‐free) in 18.6% of patients. There were 43, 105, and 62 cases of FCD type I, type II, and type III, respectively. The interictal EEG showed a repetitive discharge pattern (REDP) in 87 (41.4%) patients, polyspike discharge pattern (PDP) in 41 (19.5%), and the coexistence of REDP and PDP in the same location in 32 (15.2%) patients. The analyzed patterns in order of frequency were repetitive discharges lasting 5 seconds or more (32.4%); polyspikes (16.7%); RED type 1 (11.4%); continuous epileptiform discharges occupying >80% of the recording (11.4%); RED type 2 (6.2%); brushes (3.3%); focal, fast, continuous spikes (2.4%); focal fast rhythmic epileptiform discharges (1.43%); and frequent rhythmic bursting epileptiform activity (1.4%). The coexistence of REDP and PDP in the same location on scalp EEG and complete resection of the assumed epileptogenic zone (EZ) was independently associated with favorable postsurgical prognosis. Significance Resective epilepsy surgery for intractable epilepsy caused by FCD has favorable outcomes. Interictal scalp EEG patterns were revealed to be predictive of excellent surgical outcomes and may help clinical decision‐making and enable better presurgical evaluation.

The detection of epileptic seizures is key for neurologists to initiate the right treatment at the earliest. However, the traditional methods are dependent on manual diagnosis which are error-prone. Hence, the current article presents an automated machine learning-based approach with competing results to detect seizures and its types using traditional features. Towards this goal, the present paper explores the performance of two fractal parameters namely Higuchi and, Kat'z fractal dimension, along with power spectral density and spectral entropy to detect EEG signals with seizure and non-seizure characteristics. To validate the performance via cross-validation technique, the features are extracted and experimented from three publically available databases such as Bern-Barcelona (focal, non-focal), Khas (preictal, interictal, ictal), and Bonn. Thereafter evaluated with different machine learning algorithms like Random Forest classifier, Ada Boost classifier, Gradient Boosting classifier, Extra Tree classifier, SVM classifier, and Naive Bayes classifier. The experiments showed 100% F1 score and accuracy in classifying focal and non-focal signals for random forest and extra tree classifier methods. To the best of our knowledge, this is the first paper reporting this score for the Bern-Barcelona dataset. The same features are also experimented for the detection of interictal, ictal and preictal scenarios. In the process, 100% accuracy is obtained in classifying interictal and ictal EEG signals, and an accuracy of 94% is attained in classifying interictal-preictal signals. This score is 14% higher than the current state-of-the-art methods using the same database. The features are also used in the Bonn database, where the proposed approach gave an accuracy of 100% same as the state-of-the-art methods. • Obtained competing evaluation score relative to SOTA methods using ML methods. • Competing score reported for interictal-preictal based classification of EEG data. • EEG classification is performed in the sensing time domain itself. • The proposed approach is validated using three benchmark datasets. • The performance is evaluated using cross-validation technique. [ABSTRACT FROM AUTHOR]

Interictal electroencephalogram (EEG) patterns, including high-frequency oscillations (HFOs), interictal spikes (ISs), and slow wave activities (SWAs), are defined as specific oscillations between seizure events. These interictal oscillations reflect specific dynamic changes in network excitability and play various roles in epilepsy. In this review, we briefly describe the electrographic characteristics of HFOs, ISs, and SWAs in the interictal state, and discuss the underlying cellular and network mechanisms. We also summarize representative evidence from experimental and clinical epilepsy to address their critical roles in ictogenesis and epileptogenesis, indicating their potential as electrophysiological biomarkers of epilepsy. Importantly, we put forwards some perspectives for further research in the field.

While a significant proportion of the population suffer from migraine, the existing research literature does not provide a clear indication as to whether migraineurs experience objective cognitive deficits outside of acute migraine attacks. This meta-analysis was conducted to investigate which cognitive domains if any were affected by migraine, by synthesising the existing research quantitatively. The meta-analysis was prospectively registered with the PROSPERO International prospective register of systematic reviews (registration no.: CRD42019134138). A search of the electronic databases PubMed, Ovid MEDLINE, and PsycINFO was conducted for journal articles published between January 1980 and January 2020. Seventeen studies met the inclusion criteria, allowing for the calculation of pooled effect sizes between migraineurs (with and without aura) and healthy controls in the several cognitive domains. During the interictal period, migraineurs demonstrated a moderate, negative effect on complex attention immediate and delayed memory, spatial cognition, and executive functioning. This effect was not attributable to migraine history, attack frequency, or participant age. However, the lack of performance validity testing, and limited data on mood symptomatology and migraine medication use in the included studies may be confounds potentially overestimating the magnitude of effect. Comparison with a clinical control group, which may have accounted for some these extraneous variables, was unable to be conducted. Recommendations for comprehensive future neuropsychological research are provided. [ABSTRACT FROM AUTHOR]

Periventricular nodular heterotopia (PNH) is a malformation of cortical development that frequently causes drug-resistant epilepsy. The epileptogenicity of ectopic neurons in PNH as well as their role in generating interictal and ictal activity is still a matter of debate. We report the first in vivo microelectrode recording of heterotopic neurons in humans. Highly consistent interictal patterns (IPs) were identified within the nodules: (1) Periodic Discharges PLUS Fast activity (PD+F), (2) Sporadic discharges PLUS Fast activity (SD+F), and (3) epileptic spikes (ES). Neuronal firing rates were significantly modulated during all IPs, suggesting that multiple IPs were generated by the same local neuronal populations. Furthermore, firing rates closely followed IP morphologies. Among the different IPs, the SD+F pattern was found only in the three nodules that were actively involved in seizure generation but was never observed in the nodule that did not take part in ictal discharges. On the contrary, PD+F and ES were identified in all nodules. Units that were modulated during the IPs were also found to participate in seizures, increasing their firing rate at seizure onset and maintaining an elevated rate during the seizures. Together, nodules in PNH are highly epileptogenic and show several IPs that provide promising pathognomonic signatures of PNH. Furthermore, our results show that PNH nodules may well initiate seizures. [ABSTRACT FROM AUTHOR]

Over 15 million epilepsy patients worldwide have drug-resistant epilepsy. Successful surgery is a standard of care treatment but can only be achieved through complete resection or disconnection of the epileptogenic zone, the brain region(s) where seizures originate. Surgical success rates vary between 20% and 80%, because no clinically validated biological markers of the epileptogenic zone exist. Localizing the epileptogenic zone is a costly and time-consuming process, which often requires days to weeks of intracranial EEG (iEEG) monitoring. Clinicians visually inspect iEEG data to identify abnormal activity on individual channels occurring immediately before seizures or spikes that occur interictally (i.e. between seizures). In the end, the clinical standard mainly relies on a small proportion of the iEEG data captured to assist in epileptogenic zone localization (minutes of seizure data versus days of recordings), missing opportunities to leverage these largely ignored interictal data to better diagnose and treat patients. IEEG offers a unique opportunity to observe epileptic cortical network dynamics but waiting for seizures increases patient risks associated with invasive monitoring. In this study, we aimed to leverage interictal iEEG data by developing a new network-based interictal iEEG marker of the epileptogenic zone. We hypothesized that when a patient is not clinically seizing, it is because the epileptogenic zone is inhibited by other regions. We developed an algorithm that identifies two groups of nodes from the interictal iEEG network: those that are continuously inhibiting a set of neighbouring nodes ('sources') and the inhibited nodes themselves ('sinks'). Specifically, patient-specific dynamical network models were estimated from minutes of iEEG and their connectivity properties revealed top sources and sinks in the network, with each node being quantified by source-sink metrics. We validated the algorithm in a retrospective analysis of 65 patients. The source-sink metrics identified epileptogenic regions with 73% accuracy and clinicians agreed with the algorithm in 93% of seizure-free patients. The algorithm was further validated by using the metrics of the annotated epileptogenic zone to predict surgical outcomes. The source-sink metrics predicted outcomes with an accuracy of 79% compared to an accuracy of 43% for clinicians' predictions (surgical success rate of this dataset). In failed outcomes, we identified brain regions with high metrics that were untreated. When compared with high frequency oscillations, the most commonly proposed interictal iEEG feature for epileptogenic zone localization, source-sink metrics outperformed in predictive power (by a factor of 1.2), suggesting they may be an interictal iEEG fingerprint of the epileptogenic zone. [ABSTRACT FROM AUTHOR]

Objective: Accurate seizure prediction could prove critical for improving patient safety and quality of life in drug-resistant epilepsy. While deep learning-based approaches have shown promising performance using scalp electroencephalogram (EEG) signals, the incomplete understanding and variability of the preictal state imposes challenges in identifying the optimal preictal period (OPP) for labeling the EEG segments. This study introduces novel measures to capture model behavior under different preictal definitions and proposes a data-driven methodology to identify the OPP. &#xD;&#xD;Approach: We employed a competent subject-specific CNN-Transformer model (Area Under the Curve [AUC] of 99.35\% and F1-score of 97.46\%) to accurately detect preictal EEG segments using the open-access CHB-MIT dataset. To capture the temporal dynamics of the model's predictions, we fitted a sigmoidal curve to the model outputs obtained from uninterrupted multi-hour EEG recordings prior to seizure onset. From this fitted curve, we derived key performance measures reflecting the timing of predictions, including classifier convergence, average error, output stability, and the transition between interictal and preictal states. These measures were then combined to synthesize the Continuous Input-Output Performance Ratio (CIOPR), a novel metric designed to suggest the OPP for each patient.&#xD;&#xD;Significance: The newly developed metrics demonstrate that varying the preictal period significantly (p<0.001) impacts the timing of predictions in ways not captured by conventional accuracy-related metrics. Understanding this impact is essential for developing intelligent systems tailored to individual patient needs and for underlining practical limitations in detecting the preictal period in real-world clinical applications., (Creative Commons Attribution license.)

Periventricular nodular heterotopia (PNH) is a malformation of cortical development that frequently causes drug-resistant epilepsy. The epileptogenicity of ectopic neurons in PNH as well as their role in generating interictal and ictal activity is still a matter of debate. We report the first in vivo microelectrode recording of heterotopic neurons in humans. Highly consistent interictal patterns (IPs) were identified within the nodules: (1) Periodic Discharges PLUS Fast activity (PD+F), (2) Sporadic discharges PLUS Fast activity (SD+F), and (3) epileptic spikes (ES). Neuronal firing rates were significantly modulated during all IPs, suggesting that multiple IPs were generated by the same local neuronal populations. Furthermore, firing rates closely followed IP morphologies. Among the different IPs, the SD+F pattern was found only in the three nodules that were actively involved in seizure generation but was never observed in the nodule that did not take part in ictal discharges. On the contrary, PD+F and ES were identified in all nodules. Units that were modulated during the IPs were also found to participate in seizures, increasing their firing rate at seizure onset and maintaining an elevated rate during the seizures. Together, nodules in PNH are highly epileptogenic and show several IPs that provide promising pathognomonic signatures of PNH. Furthermore, our results show that PNH nodules may well initiate seizures.

Objective: Genetic generalized epilepsy (GGE) is characterized by aberrant neuronal dynamics and subtle structural alterations. We evaluated whether a combination of magnetic and electrical neuronal signals and cortical thickness would provide complementary information about network pathology in GGE. We also investigated whether these imaging phenotypes were present in healthy siblings of the patients to test for genetic influence. Methods: In this cross‐sectional study, we analyzed 5 min of resting state data acquired using electroencephalography (EEG) and magnetoencephalography (MEG) in patients, their siblings, and controls, matched for age and sex. We computed source‐reconstructed power and connectivity in six frequency bands (1–40 Hz) and cortical thickness (derived from magnetic resonance imaging). Group differences were assessed using permutation analysis of linear models for each modality separately and jointly for all modalities using a nonparametric combination. Results: Patients with GGE (n = 23) had higher power than controls (n = 35) in all frequencies, with a more posterior focus in MEG than EEG. Connectivity was also increased, particularly in frontotemporal and central regions in theta (strongest in EEG) and low beta frequencies (strongest in MEG), which was eminent in the joint EEG/MEG analysis. EEG showed weaker connectivity differences in higher frequencies, possibly related to drug effects. The inclusion of cortical thickness reinforced group differences in connectivity and power. Siblings (n = 18) had functional and structural patterns intermediate between those of patients and controls. Significance: EEG detected increased connectivity and power in GGE similar to MEG, but with different spectral sensitivity, highlighting the importance of theta and beta oscillations. Cortical thickness reductions in GGE corresponded to functional imaging patterns. Our multimodal approach extends the understanding of the resting state in GGE and points to genetic underpinnings of the imaging markers studied, providing new insights into the causes and consequences of epilepsy. [ABSTRACT FROM AUTHOR]

Human epilepsy patients suffer from spontaneous seizures, which originate in brain regions that also subserve normal function. Prior studies demonstrate focal, neocortical epilepsy is associated with dysfunction, several hours before seizures. How does the epileptic network perpetuate dysfunction during baseline periods? To address this question, we developed an unsupervised machine learning technique to disentangle patterns of functional interactions between brain regions, or subgraphs, from dynamic functional networks constructed from approximately 100 h of intracranial recordings in each of 22 neocortical epilepsy patients. Using this approach, we found: (1) subgraphs from ictal (seizure) and interictal (baseline) epochs are topologically similar, (2) interictal subgraph topology and dynamics can predict brain regions that generate seizures, and (3) subgraphs undergo slower and more coordinated fluctuations during ictal epochs compared to interictal epochs. Our observations suggest that seizures mark a critical shift away from interictal states that is driven by changes in the dynamical expression of strongly interacting components of the epileptic network.

Objective: Focal cortical dysplasia (FCD) represents an essential cause of drug‐resistant epilepsy with surgery as an effective treatment option. This study aimed to identify the important predictors of favorable surgical outcomes and the impact of the interictal scalp electroencephalogram (EEG) patterns in predicting postsurgical seizure outcomes. Methods: We retrospectively evaluated 210 consecutive patients between 2015 and 2019. They were diagnosed with FCD by pathology, underwent resection, and had at least one year of postsurgical follow‐up. Predictors of seizure freedom were analyzed. Results: Based on the information at the latest follow‐up, seizure outcome was classified as Engel Class I (seizure‐free) in 81.4% and Engel Class II‐IV (non‐seizure‐free) in 18.6% of patients. There were 43, 105, and 62 cases of FCD type I, type II, and type III, respectively. The interictal EEG showed a repetitive discharge pattern (REDP) in 87 (41.4%) patients, polyspike discharge pattern (PDP) in 41 (19.5%), and the coexistence of REDP and PDP in the same location in 32 (15.2%) patients. The analyzed patterns in order of frequency were repetitive discharges lasting 5 seconds or more (32.4%); polyspikes (16.7%); RED type 1 (11.4%); continuous epileptiform discharges occupying >80% of the recording (11.4%); RED type 2 (6.2%); brushes (3.3%); focal, fast, continuous spikes (2.4%); focal fast rhythmic epileptiform discharges (1.43%); and frequent rhythmic bursting epileptiform activity (1.4%). The coexistence of REDP and PDP in the same location on scalp EEG and complete resection of the assumed epileptogenic zone (EZ) was independently associated with favorable postsurgical prognosis. Significance: Resective epilepsy surgery for intractable epilepsy caused by FCD has favorable outcomes. Interictal scalp EEG patterns were revealed to be predictive of excellent surgical outcomes and may help clinical decision‐making and enable better presurgical evaluation. [ABSTRACT FROM AUTHOR]

Context: Electrophysiological and hemodynamic data can be integrated to accurately identify the generators of abnormal electrical activity in drug-resistant focal epilepsy. Arterial Spin Labeling (ASL), a magnetic resonance imaging (MRI) technique for quantitative noninvasive measurement of cerebral blood flow (CBF), can provide a direct measure of variations in cerebral perfusion associated with the epileptogenic zone. Aims: 1. To evaluate usefulness of ASL for detecting interictal temporal hypoperfusion to localize the epileptogenic zone in patients of drug resistant mesial temporal lobe epilepsy (MTLE). 2. Correlation of localization of epileptogenic zone on ASL MR perfusion with structural MRI and EEG. Methods and Materials: 30 patients with MTLE and10 age and gender matched normal controls were studied. All patients underwent ictal video EEG monitoring non-invasively, MR imaging with epilepsy protocol and pseudocontinuous ASL (PCASL) perfusion study. Relative CBF (rCBF) values in bilateral mesial temporal lobes were measured utilizing quantitative analysis of perfusion images. A perfusion asymmetry index (AI) was calculated for each region. Results: In patients, ipsilateral mesial temporal rCBF was significantly decreased compared with contralateral mesial temporal rCBF (p = 0.021). Mesial temporal blood flow was more asymmetric in patients than in normal control participants (p = 0.000). Clear perfusion asymmetry on PCASL-MRI was identified despite normal structural-MRI in 5 cases, agreeing with EEG laterality. Conclusions: Pseudo-continuous ASL offers a promising approach to detect interictal hypoperfusion in TLE and as a clinical alternative to SPECT and PET due to non-invasiveness and easy accessibility. Incorporation of ASL into routine pre-surgical evaluation protocols can help to localize epileptogenic zone in surgical candidates.

Simple Summary: Mesial temporal lobe epilepsy (MTLE) is the most common partial complex epilepsy in adults and the most often refractory to medications. Electrical deep brain stimulation (DBS) has proved effective in controlling seizures in animal models and in drug-refractory MTLE patients. However, there is still no unifying framework for DBS parameters, which are obtained by trial-and-error and based on arbitrary, fixed stimulation frequencies rather than on physiologically relevant patterns. Interictal activity may present the key to devising personalized and physiologically relevant DBS strategies. Interictal activity occurs between seizures and is a hallmark of the hyperexcitability of the epileptic brain; depending on the underlying mechanisms and site of origin, it may promote seizures (pro-ictogenic) or dampen them (anti-ictogenic). In this work, we address the possibility of controlling seizure activity by means of electrical stimulation fashioned as a surrogate interictal pattern known to be anti-ictogenic. We show that this approach can effectively control seizure activity while delivering fewer electrical pulses than fixed-frequency stimulation. Thus, mimicking the temporal dynamics of an anti-ictogenic interictal pattern may represent a straightforward, personalized and more efficient DBS strategy to ameliorate drug-refractory epilepsy. Our work heralds a paradigm shift toward physiologically meaningful rather than arbitrary DBS parameters. Mesial temporal lobe epilepsy (MTLE) is the most common partial complex epilepsy in adults and the most unresponsive to medications. Electrical deep brain stimulation (DBS) of the hippocampus has proved effective in controlling seizures in epileptic rodents and in drug-refractory MTLE patients. However, current DBS paradigms implement arbitrary fixed-frequency or patterned stimuli, disregarding the temporal profile of brain electrical activity. The latter, herein included hippocampal spontaneous firing, has been shown to follow lognormal temporal dynamics. Here, we present a novel paradigm to devise DBS protocols based on stimulation patterns fashioned as a surrogate brain signal. We focus on the interictal activity originating in the hippocampal subfield CA3, which has been shown to be anti-ictogenic. Using 4-aminopyridine-treated hippocampus-cortex slices coupled to microelectrode array, we pursue three specific aims: (1) address whether lognormal temporal dynamics can describe the CA3-driven interictal pattern, (2) explore the possibility of restoring the non-seizing state by mimicking the temporal dynamics of this anti-ictogenic pattern with electrical stimulation, and (3) compare the performance of the CA3-surrogate against periodic stimulation. We show that the CA3-driven interictal activity follows lognormal temporal dynamics. Further, electrical stimulation fashioned as a surrogate interictal pattern exhibits similar efficacy but uses less pulses than periodic stimulation. Our results support the possibility of mimicking the temporal dynamics of relevant brain signals as a straightforward DBS strategy to ameliorate drug-refractory epilepsy. Further, they herald a paradigm shift in neuromodulation, wherein a compromised brain signal can be recreated by the appropriate stimuli distribution to bypass trial-and-error studies and attain physiologically meaningful DBS operating modes. [ABSTRACT FROM AUTHOR]

Objective: The integration of high‐frequency oscillations (HFOs; ripples [80–250 Hz], fast ripples [250–500 Hz]) in epilepsy evaluation is hampered by physiological HFOs, which cannot be reliably differentiated from pathological HFOs. We evaluated whether defining abnormal HFO rates by statistical comparison to region‐specific physiological HFO rates observed in the healthy brain improves identification of the epileptic focus and surgical outcome prediction. Methods: We detected HFOs in 151 consecutive patients who underwent stereo‐electroencephalography and subsequent resective epilepsy surgery at two tertiary epilepsy centers. We compared how HFOs identified the resection cavity and predicted seizure‐free outcome using two thresholds from the literature (HFO rate > 1/min; 50% of the total number of a patient's HFOs) and three thresholds based on normative rates from the Montreal Neurological Institute Open iEEG Atlas (https://mni‐open‐ieegatlas.research.mcgill.ca/): global Atlas threshold, regional Atlas threshold, and regional + 10% threshold after regional Atlas correction. Results: Using ripples, the regional + 10% threshold performed best for focus identification (77.3% accuracy, 27% sensitivity, 97.1% specificity, 80.6% positive predictive value [PPV], 78.2% negative predictive value [NPV]) and outcome prediction (69.5% accuracy, 58.6% sensitivity, 76.3% specificity, 60.7% PPV, 74.7% NPV). This was an improvement for focus identification (+1.1% accuracy, +17.0% PPV; p <.001) and outcome prediction (+12.0% sensitivity, +1.0% PPV; p =.05) compared to the 50% threshold. The improvement was particularly marked for foci in cortex, where physiological ripples are frequent (outcome: +35.3% sensitivity, +5.3% PPV; p =.014). In these cases, the regional + 10% threshold outperformed fast ripple rate > 1/min (+3.6% accuracy, +26.5% sensitivity, +21.6% PPV; p <.001) and seizure onset zone (+13.5% accuracy, +29.4% sensitivity, +17.0% PPV; p <.05–.01) for outcome prediction. Normalization did not improve the performance of fast ripples. Significance: Defining abnormal HFO rates by statistical comparison to rates in healthy tissue overcomes an important weakness in the clinical use of ripples. It improves focus identification and outcome prediction compared to standard HFO measures, increasing their clinical applicability. [ABSTRACT FROM AUTHOR]

Objective: To evaluate a novel analysis method (SAMepi) in the localization of interictal epileptiform magnetoencephalographic (MEG) activity in parietal lobe epilepsy (PLE) patients in comparison with equivalent current dipole (ECD) analysis. Methods: We analyzed the preoperative interictal MEG of 17 operated PLE patients utilizing visual analysis and: (1) ECD with a spherical conductor model; (2) ECD with a boundary element method (BEM) conductor model; and (3) SAMepi – a kurtosis beamformer method. Localization results were compared between the three methods, to the location of the resection and to the clinical outcome. Results: Fourteen patients had an epileptiform finding in the visual analysis; SAMepi detected spikes in 11 of them. A unifocal finding in both the ECD and in the SAMepi analysis was associated with a better chance of seizure-freedom (p = 0.02). There was no significant difference in the distances from the unifocal MEG localizations to the nearest border of the resection between the different analysis methods. Conclusions: Localizations of unifocal interictal spikes detected by SAMepi did not significantly differ from the conventional ECD localizations. Significance: SAMepi – a novel semiautomatic analysis method – is useful in localizing interictal epileptiform MEG activity in the presurgical evaluation of parietal lobe epilepsy patients.

Background: Patients with vestibular migraine (VM) exhibit higher levels of central sensitization and share similar disorder characteristics with migraine with vestibular symptoms (MwVS), except in terms of disability. These patients experience fluctuating mechanical pain thresholds and persistent vestibular symptoms even without a migraine attack., Objective: This study aimed to investigate whether interictal allodynia or hyperalgesia can differentiate between VM, MwVS, and migraine only., Methods: We conducted a cross-sectional study of patients with episodic migraine aged between 18 and 65 years, categorized into three groups. A questionnaire was used to collect and compare demographic and clinical variables. Interictal widespread pressure hyperalgesia (IWPH) was evaluated using the Manual Tender Point Survey. Patients with tender point counts ≥7 were classified as having IWPH., Results: The study included 163 patients: 31 with VM, 54 with MwVS, and 78 with migraine without vestibular symptoms (migraine only). We found that aura ( p = 0.042, odds ratio 3.50, 95% confidence interval 1.26-10.4), tender point count (p < 0.001, d = 0.889, median difference = 2), and IWPH ( p = 0.002, odds ratio 5.3, 95% confidence interval 1.80-17.2) were significantly associated with VM compared to MwVS. Aura and IWPH were significantly associated with VM. However, there were no significant associations observed for interictal allodynia or hyperalgesia between the other two groups., Conclusion: IWPH and aura are associated with VM, indicating their potential roles in its pathogenesis. These findings may contribute to the differential diagnosis and management of migraine, potentially leading to targeted treatment strategies., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Toriyama, Hanaoka and Horiuchi.)

Objective: Accurate estimation of the epileptogenic zone (EZ) is essential for favorable outcomes in epilepsy surgery. Conventional ictal electrocorticography (ECoG) onset is generally used to detect the EZ but is insufficient in achieving seizure-free outcomes. By contrast, high-frequency oscillations (HFOs) could be useful markers of the EZ. Hence, we aimed to detect the EZ using interictal spikes and investigated whether the onset area of interictal spike-related HFOs was within the EZ. Methods: The EZ is considered to be included in the resection area among patients with seizure-free outcomes after surgery. Using a complex demodulation technique, we developed a method to determine the onset channels of interictal spike-related ripples (HFOs of 80–200 Hz) and investigated whether they are within the resection area. Results: We retrospectively examined 12 serial patients who achieved seizure-free status after focal resection surgery. Using the method that we developed, we determined the onset channels of interictal spike-related ripples and found that for all 12 patients, they were among the resection channels. The onset frequencies of ripples were in the range of 80–150 Hz. However, the ictal onset channels (evaluated based on ictal ECoG patterns) and ripple onset channels coincided in only 3 of 12 patients. Conclusions: Determining the onset area of interictal spike-related ripples could facilitate EZ estimation. This simple method that utilizes interictal ECoG may aid in preoperative evaluation and improve epilepsy surgery outcomes. [ABSTRACT FROM AUTHOR]

Objective: Accurate estimation of the epileptogenic zone (EZ) is essential for favorable outcomes in epilepsy surgery. Conventional ictal electrocorticography (ECoG) onset is generally used to detect the EZ but is insufficient in achieving seizure-free outcomes. By contrast, high-frequency oscillations (HFOs) could be useful markers of the EZ. Hence, we aimed to detect the EZ using interictal spikes and investigated whether the onset area of interictal spike-related HFOs was within the EZ.Methods: The EZ is considered to be included in the resection area among patients with seizure-free outcomes after surgery. Using a complex demodulation technique, we developed a method to determine the onset channels of interictal spike-related ripples (HFOs of 80–200 Hz) and investigated whether they are within the resection area.Results: We retrospectively examined 12 serial patients who achieved seizure-free status after focal resection surgery. Using the method that we developed, we determined the onset channels of interictal spike-related ripples and found that for all 12 patients, they were among the resection channels. The onset frequencies of ripples were in the range of 80–150 Hz. However, the ictal onset channels (evaluated based on ictal ECoG patterns) and ripple onset channels coincided in only 3 of 12 patients.Conclusions: Determining the onset area of interictal spike-related ripples could facilitate EZ estimation. This simple method that utilizes interictal ECoG may aid in preoperative evaluation and improve epilepsy surgery outcomes.

Context: Electrophysiological and hemodynamic data can be integrated to accurately identify the generators of abnormal electrical activity in drug-resistant focal epilepsy. Arterial Spin Labeling (ASL), a magnetic resonance imaging (MRI) technique for quantitative noninvasive measurement of cerebral blood flow (CBF), can provide a direct measure of variations in cerebral perfusion associated with the epileptogenic zone. Aims: 1. To evaluate usefulness of ASL for detecting interictal temporal hypoperfusion to localize the epileptogenic zone in patients of drug resistant mesial temporal lobe epilepsy (MTLE). 2. Correlation of localization of epileptogenic zone on ASL MR perfusion with structural MRI and EEG. Methods and Materials: 30 patients with MTLE and10 age and gender matched normal controls were studied. All patients underwent ictal video EEG monitoring non-invasively, MR imaging with epilepsy protocol and pseudocontinuous ASL (PCASL) perfusion study. Relative CBF (rCBF) values in bilateral mesial temporal lobes were measured utilizing quantitative analysis of perfusion images. A perfusion asymmetry index (AI) was calculated for each region. Results: In patients, ipsilateral mesial temporal rCBF was significantly decreased compared with contralateral mesial temporal rCBF (p = 0.021). Mesial temporal blood flow was more asymmetric in patients than in normal control participants (p = 0.000). Clear perfusion asymmetry on PCASL-MRI was identified despite normal structural-MRI in 5 cases, agreeing with EEG laterality. Conclusions: Pseudo-continuous ASL offers a promising approach to detect interictal hypoperfusion in TLE and as a clinical alternative to SPECT and PET due to non-invasiveness and easy accessibility. Incorporation of ASL into routine pre-surgical evaluation protocols can help to localize epileptogenic zone in surgical candidates. [ABSTRACT FROM AUTHOR]

Oscillations represent basic operational modes of the human brain. They reflect local field potential activity generated by the laminar arrangement of cell-type specific microcircuits interacting brain-wide under the influence of neuromodulators, endogenous processes and cognitive demands. Under neuropathological conditions, the spatiotemporal structure of physiological brain oscillations is disrupted as recorded by electroencephalography and event-relate potentials. Such rhythmopathies can be used to track microcircuit alterations leading not only to transient pathological activities such as interictal discharges and seizures but also to a range of cognitive co-morbidities. Here we review how basic oscillatory modes induced in human brain slices prepared after surgical treatment can help us to understand basic aspects of brain function and dysfunction. We propose to overcome the traditional view of examining human brain slices merely as generators of epileptiform activities and to integrate them in a more physiologically-oriented oscillatory framework to better understand mechanisms of the diseased human brain.

Objective: The aim of the current study was to investigate the rate and clinical significance of bitemporal interictal epileptiform discharges (IEDs) in a large cohort of patients with temporal lobe epilepsy (TLE). Methods: The data used in this study were collected at the Epilepsy Care Unit, Namazi Hospital, Shiraz University of Medical sciences, Shiraz, Iran, from 2008 to 2020. Inclusion criteria were a confirmed diagnosis of TLE based on the clinical grounds (history and the described seizure semiology) and a 2‐hour interictal video‐electroencephalography (EEG) monitoring. The EEG recording of each patient included both sleep (about 90 minutes) and wakefulness (about 30 minutes). Results: 532 patients were included in this study [420 patients (79%) had unilateral IEDs, and 112 patients (21%) had bilateral IEDs]. Patients with bilateral IEDs less often had auras with their seizures and had higher frequencies of seizures (as a trend for focal to bilateral tonic‐clonic seizures and significantly in focal seizures with impaired awareness) compared with those who had unilateral IEDs. Patients with bilateral epileptiform discharges showed a trend to experiencing ictal injury more frequently. Brain MRI findings were different between these two groups (p = 0.0001). Conclusion: It is important to recognize that a patient with TLE has unilateral vs. bilateral IEDs. Bilateral IEDs in a patient with TLE may suggest a more severe disease (with a higher risk for ictal injuries and other significant consequences of frequent seizures). It may also suggest a somewhat different etiology. [ABSTRACT FROM AUTHOR]