Epilepsy Subjects Research Articles

Defining of the nasal anthropometry on facial computed tomography in subjects with epilepsy and healthy subjects.

Epilepsy is a frequent chronic and genetic brain disease. In diagnose of genetic and craniofacial disease, the face is one of the gold standard phenotypic features. This study was carried out to determine the angle and linear measurements of face region in Turkish healthy and epilepsy subjects, and to apply a feature selection method to identify the most important attributes that affect epilepsy decision. The retrospective and observational study was conducted with 120 subjects with epilepsy aged between 18 to 60 years (56 males; 64 females) and 60 healthy subjects aged between 18 and 55 years (29 males, 31 females). Pyramidal angle performed from 3 reference points, nasal bone length, the width of piriform aperture, nasofrontal angle, nasolabial angle, distance between glabella and nasion were measured on computed tomography. Also, we used supervised machine learning to learn classification models to detect epilepsy as our dataset has class label where 1 means epilepsy, 2 means healthy. The well-known classification model learning algorithms implemented in Weka (with version 3.8.6) machine learning toolkit were applied. All parameters excluding nasal bone length were higher in epilepsy patients than in healthy subjects. Also, there was a significant difference in nasal pyramidal angle nasal bone, nasal pyramidal angle nasal tip, piriform aperture, and nasal bone lengths between epilepsy and healthy subjects. However, age related changes for healthy subjects were no seen in healthy subjects. In epilepsy subjects there was a significant difference in 6 measurements all nasal pyramidal angles, piriform aperture width, nasofrontal angle, and nasolabial angle. Gender related changes were found in only nasal pyramidal angle nasal root and nasal bone of healthy subjects, in nasal pyramidal angle nasal bone, nasal bone length, nasofrontal angle of epilepsy subjects. We can say epilepsy may affect the some facial parameters and these, although anthropometric measurements are affected by age and gender parameters. Comprehensive knowledge of this region's normal references ranges is essential for planning, proper selection of silicone implants or osteotomy determining the limitations of the surgical field and minimizing the risk of complication and performing aesthetic facial surgery or rhinoplasty in epilepsy patients.

Retrospective Review of Treatment Outcomes in Epilepsy Patients: Seizure Control and Medication Side Effects

ABSTRACT Background: Epilepsy management requires balancing seizure control with minimizing medication side effects. This retrospective review examines treatment outcomes in epilepsy patients, focusing on seizure control and medication-related adverse effects. Methods: Medical records of epilepsy subjects treated tertiary care center between 2017 and 2022 were retrospectively reviewed. Data on demographic characteristics, seizure control, medication regimen, and side effects were collected and analyzed. Results: A total of 200 subjects were included. Sixty percent of subjects achieved total seizure independence, 30% reduced seizure frequency, and 10% did not improve or deteriorate. The kind of antiepileptic medicine provided substantially affects prescription side effects (P < 0.05), with drug X causing more dizziness than drug Y (P = 0.02). Elderly individuals were more likely to suffer weariness than younger ones (P = 0.03). Conclusion: This review provides valuable insights into epilepsy treatment outcomes, emphasizing the need for personalized management strategies to optimize seizure control while minimizing medication-related adverse effects.

Diagnostic biomarker discovery from brain EEG data using LSTM, reservoir-SNN, and NeuCube methods in a pilot study comparing epilepsy and migraine

The study introduces a new online spike encoding algorithm for spiking neural networks (SNN) and suggests new methods for learning and identifying diagnostic biomarkers using three prominent deep learning neural network models: deep BiLSTM, reservoir SNN, and NeuCube. EEG data from datasets related to epilepsy, migraine, and healthy subjects are employed. Results reveal that BiLSTM hidden neurons capture biological significance, while reservoir SNN activities and NeuCube spiking dynamics identify EEG channels as diagnostic biomarkers. BiLSTM and reservoir SNN achieve 90 and 85% classification accuracy, while NeuCube achieves 97%, all methods pinpointing potential biomarkers like T6, F7, C4, and F8. The research bears implications for refining online EEG classification, analysis, and early brain state diagnosis, enhancing AI models with interpretability and discovery. The proposed techniques hold promise for streamlined brain-computer interfaces and clinical applications, representing a significant advancement in pattern discovery across the three most popular neural network methods for addressing a crucial problem. Further research is planned to study how early can these diagnostic biomarkers predict an onset of brain states.

Prospective Long-term Seizure Outcomes in Medically-resistant Focal Epilepsy Subjects with Neuromodulation Devices (P3-1.009)

Prospective Long-term Seizure Outcomes in Medically-resistant Focal Epilepsy Subjects with Neuromodulation Devices (P3-1.009)

243 Intracranial Theta-burst Stimulation Modulates Cortical Excitability in a Dose and Location-dependent Fashion

INTRODUCTION: Direct electrical stimulation is a powerful therapeutic approach to treating a wide range of brain disorders. In particular, theta-burst stimulation (TBS) which delivers electrical pulses in rhythmic bursts of 3-8 Hz to mimic endogenous brain rhythms, has been increasingly used to improve cognitive processes and relieve symptoms of depression. However, how TBS alters underlying neural activity is poorly understood. METHODS: In nine neurosurgical epilepsy subjects undergoing intracranial monitoring, we applied direct cortical TBS at varying stimulation amplitudes and locations (prefrontal, temporal, parietal). We obtained single-pulse corticocortical evoked potentials (CCEPs) prior to stimulation to map functional connectivity. Intracranial EEG (iEEG) was recorded from non-stimulated electrodes before, during and after TBS. RESULTS: We found that TBS evoked consistent responses after each burst. These responses were observed in regions with high amplitude CCEPs and resting spontaneous delta (1-4 Hz) phase-locking to the stimulation site, consistent with our hypothesis that the underlying functional brain architecture guides information flow after stimulation. Furthermore, we observed changes in cortical excitability over time as measured by changes in amplitude of the TBS evoked responses both within stimulation burst trains and across burst trains. The degree of change in cortical excitability was modulated by anatomical location, proximity of the stimulating electrode to white matter, and current amplitude. CONCLUSIONS: These results indicate that direct cortical TBS produces neural effects that can be measured over time and correlate with baseline biophysical parameters. Thus, personalizing stimulation parameters might be critical to predictably maximize our ability to alter disease-relevant brain networks.

Exercise-induced central and peripheral sympathetic activity in a community-based group of epilepsy patients differ from healthy controls

Ictal and interictal activity within the autonomic nervous system is characterized by a sympathetic overshoot in people with epilepsy. This autonomic dysfunction is assumed to be driven by alterations in the central autonomic network. In this study, exercise-induced changes of the interrelation of central and peripheral autonomic activity in patients with epilepsy was assessed. 21 patients with epilepsy (16 seizure-free), and 21 healthy matched controls performed an exhaustive bicycle ergometer test. Immediately before and after the exercise test, resting state electroencephalography measurements (Brain Products GmbH, 128-channel actiCHamp) of 5 min were carried out to investigate functional connectivity assessed by phase locking value in source space for whole brain, central autonomic network and visual network. Additionally, 1-lead ECG (Brain products GmbH) was performed to analyze parasympathetic (root mean square of successive differences (RMSSD) of the heart rate variability) and sympathetic activity (electrodermal activity (meanEDA)). MeanEDA increased (p < 0.001) and RMSSD decreased (p < 0.001) from pre to post-exercise in both groups. Correlation coefficients of meanEDA and central autonomic network functional connectivity differed significantly between the groups (p = 0.004) after exercise. Both patients with epilepsy and normal control subjects revealed the expected physiological peripheral autonomic responses to acute exhaustive exercise, but alterations of the correlation between central autonomic and peripheral sympathetic activity may indicate a different sympathetic reactivity after exercise in patients with epilepsy. The clinical relevance of this finding and its modulators (seizures, anti-seizure medication, etc.) still needs to be elucidated.

Magnetoencephalography Atlas Viewer for Dipole Localization and Viewing.

Magnetoencephalography (MEG) is a noninvasive neuroimaging technique widely recognized for epilepsy and tumor mapping. MEG clinical reporting requires a multidisciplinary team, including expert input regarding each dipole's anatomic localization. Here, we introduce a novel tool, the "Magnetoencephalography Atlas Viewer" (MAV), which streamlines this anatomical analysis. The MAV normalizes the patient's Magnetic Resonance Imaging (MRI) to the Montreal Neurological Institute (MNI) space, reverse-normalizes MNI atlases to the native MRI, identifies MEG dipole files, and matches dipoles' coordinates to their spatial location in atlas files. It offers a user-friendly and interactive graphical user interface (GUI) for displaying individual dipoles, groups, coordinates, anatomical labels, and a tri-planar MRI view of the patient with dipole overlays. It evaluated over 273 dipoles obtained in clinical epilepsy subjects. Consensus-based ground truth was established by three neuroradiologists, with a minimum agreement threshold of two. The concordance between the ground truth and MAV labeling ranged from 79% to 84%, depending on the normalization method. Higher concordance rates were observed in subjects with minimal or no structural abnormalities on the MRI, ranging from 80% to 90%. The MAV provides a straightforward MEG dipole anatomic localization method, allowing a nonspecialist to prepopulate a report, thereby facilitating and reducing the time of clinical reporting.

Optimization of epilepsy detection method based on dynamic EEG channel screening

To decrease the interference in the process of epileptic feature extraction caused by insufficient detection capability in partial channels of focal epilepsy, this paper proposes a novel epilepsy detection method based on dynamic electroencephalogram (EEG) channel screening. This method not only extracts more effective epilepsy features but also finds common features among different epilepsy subjects, providing an effective approach and theoretical support for across-subject epilepsy detection in clinical scenarios.Firstly, we use the Refine Composite Multiscale Dispersion Entropy (RCMDE) to measure the complexity of EEG signals between normal and seizure states and realize the dynamic EEG channel screening among different subjects, which can enhance the capability of feature extraction and the robustness of epilepsy detection. Subsequently, we discover common epilepsy features in 3–15 Hz among different subjects by the screened EEG channels. By this finding, we construct the Residual Convolutional Long Short-Term Memory (ResCon-LSTM) neural network to accomplish across-subject epilepsy detection.The experiment results on the CHB-MIT dataset indicate that the highest accuracy of epilepsy detection in the single-subject experiment is 98.523 %, improved by 5.298 % compared with non-channel screening. In the across-subject experiment, the average accuracy is 96.596 %. Therefore, this method could be effectively applied to different subjects by dynamically screening optimal channels and keep a good detection performance.

Clinicoradiological Profile of Incomplete Hippocampal Inversion Diagnosed on MR Neuroimaging.

Incomplete hippocampal inversion (IHI) is a developmental failure of normal hippocampal inversion. Previous studies have described IHI in epilepsy and non-epilepsy subjects. IHI has also been reported with malformations of cortical development (MCDs) and corpus callosal agenesis that have association with neuropsychiatric disorders such as autism spectrum disorder (ASD). This study aims to describe the clinical profile of magnetic resonance imaging (MRI)-diagnosed IHI. We studied patients with IHI who were identified after a retrospective review of the MRI archives of the past 3 years. The MRI findings of partial and total IHI were included. The clinical profiles associated with IHI were classified into epilepsy and non-epilepsy categories. A retrospective review of MRI done over 3 years revealed 54 cases of IHI (32 left-sided, 20 bilateral, and 2 isolated right-sided), and out of 74 IHI, 59 were of total type and 15 partial. Thirty-six subjects (61.1%) had epilepsy (9 with neurodevelopmental problems), 17 subjects (31.5%) had ASD, and 4 subjects (7.4%) had only neurodevelopmental disorders. MCDs were seen in 7 (12.9%): polymicrogyria (4), periventricular heterotopia (2), and pachygyria (1). Hippocampal volume loss was seen in 10, and contralateral mesial temporal sclerosis was seen in 2 patients. Hippocampal inversion has been reported in MRI scans of patients with epilepsy, ASD, MCDs, and many other related disorders. Further studies are required to know its occurrence among patients who get MRI scans due to many other disorders such as headaches, psychiatric disorders, minor hear trauma, and perinatal insults. If possible, studies among normal populations also need to be done.

Automated recognition of epilepsy from EEG signals using a combining space–time algorithm of CNN-LSTM

Intelligent recognition methods for classifying non-stationary and non-invasive epileptic diagnoses are essential tools in neurological research. Electroencephalogram (EEG) signals exhibit better temporal characteristics in the detection of epilepsy compared to radiation medical images like computed tomography (CT) and magnetic resonance imaging (MRI), as they provide real-time insights into the disease’ condition. While classical machine learning methods have been used for epilepsy EEG classification, they still often require manual parameter adjustments. Previous studies primarily focused on binary epilepsy recognition (epilepsy vs. healthy subjects) rather than as ternary status recognition (continuous epilepsy vs. intermittent epilepsy vs. healthy subjects). In this study, we propose a novel deep learning method that combines a convolution neural network (CNN) with a long short-term memory (LSTM) network for multi-class classification including both binary and ternary tasks, using a publicly available benchmark database on epilepsy EEGs. The hybrid CNN-LSTM automatically acquires knowledge without the need for extra pre-processing or manual intervention. Besides, the joint network method benefits from memory function and stronger feature extraction ability. Our proposed hybrid CNN-LSTM achieves state-of-the-art performance in ternary classification, outperforming classical machine learning and the latest deep learning models. For the three-class classification, in the method achieves an accuracy, specificity, sensitivity, and ROC of 98%, 97.4, 98.3% and 96.8%, respectively. In binary classification, the method achieves better results, with ACC of 100%, 100%, and 99.8%, respectively. Our dual stream spatiotemporal hybrid network demonstrates superior performance compared to other methods. Notably, it eliminates the need for manual operations, making it more efficient for doctors to diagnose during the clinical process and alleviating the workload of neurologists.

Individuals with chronic epilepsy have elevated P-wave heterogeneity comparable to patients with atrial fibrillation.

Identification of epilepsy patients with elevated risk for atrial fibrillation (AF) is critical given the heightened morbidity and premature mortality associated with this arrhythmia. Epilepsy is a worldwide health problem affecting nearly 3.4 million people in the United States alone. The potential for increased risk for AF in patients with epilepsy is not well appreciated, despite recent evidence from a national survey of 1.4 million hospitalizations indicating that AF is the most common arrhythmia in people with epilepsy. We analyzed inter-lead heterogeneity of P-wave morphology, a marker reflecting arrhythmogenic nonuniformities of activation/conduction in atrial tissue. The study groups consisted of 96 patients with epilepsy and 44 consecutive patients with AF in sinus rhythm before clinically indicated ablation. Individuals without cardiovascular or neurological conditions (n = 77) were also assessed. We calculated P-wave heterogeneity (PWH) by second central moment analysis of simultaneous beats from leads II, III, and aVR ("atrial dedicated leads") from standard 12-lead electrocardiography (ECG) recordings from admission day to the epilepsy monitoring unit (EMU). Female patients composed 62.5%, 59.6%, and 57.1% of the epilepsy, AF, and control subjects, respectively. The AF cohort was older (66 ± 1.1 years) than the epilepsy group (44 ± 1.8 years, p < .001). The level of PWH was greater in the epilepsy group than in the control group (67 ± 2.6 vs. 57 ± 2.5 μV, p = .046) and reached levels observed in AF patients (67 ± 2.6 vs. 68 ± 4.9 μV, p = .99). In multiple linear regression analysis, PWH levels in individuals with epilepsy were mainly correlated with the PR interval and could be related to sympathetic tone. Epilepsy remained associated with PWH after adjustments for cardiac risk factors, age, and sex. Patients with chronic epilepsy have increased PWH comparable to levels observed in patients with AF, while being ~20 years younger, suggesting an acceleration in structural change and/or cardiac electrical instability. These observations are consistent with emerging evidence of an "epileptic heart" condition.

Dyadic boundary points based empirical wavelet transform for the elimination of eye movement and eye blink-based ocular artifacts from EEG signals

The movement of eyeballs and eye blinks produces ocular artifacts in the electroencephalogram (EEG) signal during recording. It is necessary to filter out these artifacts from the EEG signal for different biomedical applications. The state-of-the-art (SOTA) methods have less denoising performance to filter ocular artifacts from EEG signals. To overcome this, we have proposed a novel filter-bank-based hybrid approach in this paper to eliminate ocular artifacts from EEG signals. The optimal filter-bank based on the dyadic boundary points based empirical wavelet transform (DBPEWT) is introduced for the multiscale decomposition of EEG signal into various sub-band (SB) signals. The Savitzky–Golay-driven total variation (SGDTV) filter is formulated and applied to the contaminated EEG’s low-frequency range sub-band or first sub-band signal to obtain the filtered sub-band signal. The filtered EEG signal is calculated based on the linear combination of the filtered first sub-band signal evaluated from the SGDTV stage with other sub-band signals of the contaminated EEG signal. The filtering performance of the proposed approach is assessed using the mean absolute error (MAE) in the power spectrum (PS) for θ, α, and β-bands of contaminated and filtered EEG signals. The suggested approach is evaluated using the eye-blink, eye-movement-based ocular artifact-contaminated EEG signals from two public databases. Furthermore, the denoising performance of the proposed filter-bank approach is evaluated using the wearable EEG sensor signals from healthy and epilepsy subjects while performing physical activities such as sitting, walking, stairs, and running, respectively. The results reveal that the proposed approach has obtained the average MAE-PS values for (θ, α, and β) bands as (0.156, 0.308, and 0.201), and (0.081, 0.089, and 0.088), respectively for the removal of eye-blink and eye-movement artifacts from EEG signals. The suggested approach has obtained the average MAE-PS values for (θ, α, and β-bands) as (4.555±0.203, 0.057±0.026, 0.062±0.005), and (4.085±0.086, 0.068±0.043, 0.048±0.006) for healthy and epilepsy subjects during running activity to eliminate artifacts from the wearable sensor-based EEG signals. The denoising performance of the proposed approach has been compared with existing methods to remove eye-movement and eye-blink artifacts from EEG signals.

Single-Cell RNA and TCR Sequencing Reveals Novel Immune Cell Networks in Poorly Controlled Epilepsy (S35.001)

<h3>Objective:</h3> To perform a deep dissection of the role and regulation of innate and adaptive immunity in poorly controlled (PC) and well controlled (WC) focal epilepsy. <h3>Background:</h3> The role of the immune system in the pathophysiology of epilepsy has been underappreciated. There is mounting evidence that surgical epilepsy samples contain infiltrating immune cells. However, their phenotype and the mechanism of their recruitment are not well understood. <h3>Design/Methods:</h3> Individuals with focal epilepsy were prospectively recruited from Brigham and Women’s Hospital outpatient epilepsy clinics. Epilepsy subjects were divided into a WC or PC group based on whether they had seizures within the 6 months prior to study enrollment. Age-and sex-matched healthy controls were also included. Using peripheral blood mononuclear cells (PBMCs), we performed a detailed analysis of ~84,000 PBMCs using single-cell RNA and T-cell receptor sequencing (scRNA/TCR-seq). <h3>Results:</h3> A total of 107 subjects were recruited including 87 epilepsy subjects and 20 healthy controls. 49.5% of epilepsy subjects had a seizure within 6 months of recruitment, and 52% had an MRI abnormality. scRNA/TCR-seq data highlighted differences in the composition of adaptive and innate immune cells in well-controlled and poorly-controlled subjects. In particular, memory CD4+ and CD8+ T-cells as well as NK T-cells that express cytotoxic cytokines such as granzyme and perforin, constitute a higher proportion of T-cells in PC epilepsy subjects, whereas CD14+ and CD16+ monocytes, and B memory cells show depletion. Moreover, ligand/receptor network analyses using the CellChat computational tool identified several statistically significant intercellular interactions such as the Selectin cell adhesion pathway that is involved in the recruitment of leukocytes to the site of injury in the brain. <h3>Conclusions:</h3> We identified disrupted adaptive and innate immune homeostasis in poorly controlled focal epilepsy. Network analyses revealed ligand/receptor pairs with well-known roles in lymphocyte trafficking that may be targeted for immunomodulatory interventions to treat epilepsy. <b>Disclosure:</b> Dr. Sarkis has nothing to disclose. Ms. Amer has nothing to disclose. Mr. Mares has nothing to disclose. Mr. Hobson has nothing to disclose. The institution of Vilas Menon has received research support from Chan Zuckerberg Initiative. The institution of Vilas Menon has received research support from National Institute of Aging. Vilas Menon has received personal compensation in the range of $0-$499 for serving as a Grant Reviewer with National Institutes of Health. Dr. Pennell has received personal compensation in the range of $0-$499 for serving as an officer or member of the Board of Directors for American Epilepsy Society. The institution of Dr. Pennell has received research support from NIH. The institution of an immediate family member of Dr. Pennell has received research support from Department of Defense. The institution of an immediate family member of Dr. Pennell has received research support from Environmental Protection Agency. The institution of an immediate family member of Dr. Pennell has received research support from NIH. The institution of an immediate family member of Dr. Pennell has received research support from Advanced Energy Consortium. Dr. Pennell has received publishing royalties from a publication relating to health care. Dr. Elyaman has nothing to disclose.

Detecting Cortical Thickness Changes in Epileptogenic Lesions Using Machine Learning.

Epilepsy is a neurological disorder characterized by abnormal brain activity. Epileptic patients suffer from unpredictable seizures, which may cause a loss of awareness. Seizures are considered drug resistant if treatment does not affect success. This leads practitioners to calculate the cortical thickness to measure the distance between the brain's white and grey matter surfaces at various locations to perform a surgical intervention. In this study, we introduce using machine learning as an approach to classify extracted measurements from T1-weighted magnetic resonance imaging. Data were collected from the epilepsy unit at King Abdulaziz University Hospital. We applied two trials to classify the extracted measurements from T1-weighted MRI for drug-resistant epilepsy and healthy control subjects. The preprocessing sequence on T1-weighted MRI images was performed using C++ through BrainSuite's pipeline. The first trial was performed on seven different combinations of four commonly selected measurements. The best performance was achieved in Exp6 and Exp7, with 80.00% accuracy, 83.00% recall score, and 83.88% precision. It is noticeable that grey matter volume and white matter volume measurements are more significant than the cortical thickness measurement. The second trial applied four different machine learning classifiers after applying 10-fold cross-validation and principal component analysis on all extracted measurements as in the first trial based on the mentioned previous works. The K-nearest neighbours model outperformed the other machine learning classifiers with 97.11% accuracy, 75.00% recall score, and 75.00% precision.

The study of rs324420 (C385A) polymorphism of the FAAH gene of the endocannabinoid system in patients with epilepsy and ADHD

The endocannabinoid (eCB) system regulates many physiological functions in the central nervous system. Fatty acid amide hydrolase (FAAH) is an essential enzyme in the eCB system, degrading anandamide. Single nucleotide polymorphism (SNP) rs324420 is a common genetic polymorphism of the FAAH gene and has been associated with susceptibility to neurological conditions. This study examined whether the SNP rs324420 (C385A) is associated with epilepsy and attention deficit hyperactivity disorder (ADHD). This study consists of two case-control parts. The first part comprises 250 epilepsy subjects and 250 healthy individuals as controls. The second one comprises 157 cases with ADHD and 136 healthy individuals as controls. Genotyping was carried out using polymerase chain reaction and restriction fragment length polymorphism (PCR-RFLP) technique. Interestingly, the FAAH C384A genotype (OR 1.755, 95 % CI 1.124–2.742, p = 0.013) and allele (OR 1.462, 95 % CI 1.006–2.124, p = 0.046) distribution showed an association with generalized epilepsy. On the other hand, this SNP was not associated with the risk of ADHD. To our knowledge, there was no study on the association between rs324420 (C385A) polymorphism and the risks of ADHD or epilepsy. This study provided the first evidence of an association between generalized epilepsy and rs324420 (C385A) of FAAH. Larger sample sizes and functional studies are warranted to explore the clinical utility of FAAH genotyping as a possible marker for increased generalized epilepsy risk.

Seizure epilepsy genesis and epileptogenic nodes in epileptic patients: A procedure

In this study we attempted to design, develop and substantiate a modern contemporary biomarker for epileptic (epilepsy) subjects (patients) neuronal-instability. Initial study is done on 91 subjects through the application of neuronal-unpredictability and/or variability of the marked e-SoZ as a metric to envisage and foresee the epileptic operational (surgical) outcome. the neural-instability predict (42/45) subjects unsuccess with surgery, by a total accuracy of 75% (predictive) when matched with subjective-clinicians accuracy at 49%(results-effective). We differentiate instable zonal areas (zones) which were not diagnosed in unsuccessful cases (i.e., unsuccessful outcomes). While compared with EEG features, the neural-variability outpaced in prognosis strength and, also construal, which support that neuronal delicacy as a bio-marker for the electro encephalography e-SoZ.

Entropy Measures of Electroencephalograms towards the Diagnosis of Psychogenic Non-Epileptic Seizures.

Psychogenic non-epileptic seizures (PNES) may resemble epileptic seizures but are not caused by epileptic activity. However, the analysis of electroencephalogram (EEG) signals with entropy algorithms could help identify patterns that differentiate PNES and epilepsy. Furthermore, the use of machine learning could reduce the current diagnosis costs by automating classification. The current study extracted the approximate sample, spectral, singular value decomposition, and Renyi entropies from interictal EEGs and electrocardiograms (ECG)s of 48 PNES and 29 epilepsy subjects in the broad, delta, theta, alpha, beta, and gamma frequency bands. Each feature-band pair was classified by a support vector machine (SVM), k-nearest neighbour (kNN), random forest (RF), and gradient boosting machine (GBM). In most cases, the broad band returned higher accuracy, gamma returned the lowest, and combining the six bands together improved classifier performance. The Renyi entropy was the best feature and returned high accuracy in every band. The highest balanced accuracy, 95.03%, was obtained by the kNN with Renyi entropy and combining all bands except broad. This analysis showed that entropy measures can differentiate between interictal PNES and epilepsy with high accuracy, and improved performances indicate that combining bands is an effective improvement for diagnosing PNES from EEGs and ECGs.

Patterned Hippocampal Stimulation Facilitates Memory in Patients With a History of Head Impact and/or Brain Injury.

Rationale: Deep brain stimulation (DBS) of the hippocampus is proposed for enhancement of memory impaired by injury or disease. Many pre-clinical DBS paradigms can be addressed in epilepsy patients undergoing intracranial monitoring for seizure localization, since they already have electrodes implanted in brain areas of interest. Even though epilepsy is usually not a memory disorder targeted by DBS, the studies can nevertheless model other memory-impacting disorders, such as Traumatic Brain Injury (TBI). Methods: Human patients undergoing Phase II invasive monitoring for intractable epilepsy were implanted with depth electrodes capable of recording neurophysiological signals. Subjects performed a delayed-match-to-sample (DMS) memory task while hippocampal ensembles from CA1 and CA3 cell layers were recorded to estimate a multi-input, multi-output (MIMO) model of CA3-to-CA1 neural encoding and a memory decoding model (MDM) to decode memory information from CA3 and CA1 neuronal signals. After model estimation, subjects again performed the DMS task while either MIMO-based or MDM-based patterned stimulation was delivered to CA1 electrode sites during the encoding phase of the DMS trials. Each subject was sorted (post hoc) by prior experience of repeated and/or mild-to-moderate brain injury (RMBI), TBI, or no history (control) and scored for percentage successful delayed recognition (DR) recall on stimulated vs. non-stimulated DMS trials. The subject’s medical history was unknown to the experimenters until after individual subject memory retention results were scored. Results: When examined compared to control subjects, both TBI and RMBI subjects showed increased memory retention in response to both MIMO and MDM-based hippocampal stimulation. Furthermore, effects of stimulation were also greater in subjects who were evaluated as having pre-existing mild-to-moderate memory impairment. Conclusion: These results show that hippocampal stimulation for memory facilitation was more beneficial for subjects who had previously suffered a brain injury (other than epilepsy), compared to control (epilepsy) subjects who had not suffered a brain injury. This study demonstrates that the epilepsy/intracranial recording model can be extended to test the ability of DBS to restore memory function in subjects who previously suffered a brain injury other than epilepsy, and support further investigation into the beneficial effect of DBS in TBI patients.

Phase-amplitude coupling between low-frequency scalp EEG and high-frequency intracranial EEG during working memory task

Objective. Revealing the relationship between simultaneous scalp electroencephalography (EEG) and intracranial electroencephalography (iEEG) is of great importance for both neuroscientific research and translational applications. However, whether prominent iEEG features in the high-gamma band can be reflected by scalp EEG is largely unknown. To address this, we investigated the phase-amplitude coupling (PAC) phenomenon between the low-frequency band of scalp EEG and the high-gamma band of iEEG. Approach. We analyzed a simultaneous iEEG and scalp EEG dataset acquired under a verbal working memory paradigm from nine epilepsy subjects. The PAC values between pairs of scalp EEG channel and identified iEEG channel were explored. After identifying the frequency combinations and electrode locations that generated the most significant PAC values, we compared the PAC values of different task periods (encoding, maintenance, and retrieval) and memory loads. Main results. We demonstrated that the amplitude of high-gamma activities in the entorhinal cortex, hippocampus, and amygdala was correlated to the delta or theta phase at scalp locations such as Cz and Pz. In particular, the frequency bin that generated the maximum PAC value centered at 3.16–3.84 Hz for the phase and 50–85 Hz for the amplitude. Moreover, our results showed that PAC values for the retrieval period were significantly higher than those of the encoding and maintenance periods, and the PAC was also influenced by the memory load. Significance. This is the first human simultaneous iEEG and scalp EEG study demonstrating that the amplitude of iEEG high-gamma components is associated with the phase of low-frequency components in scalp EEG. These findings enhance our understanding of multiscale neural interactions during working memory, and meanwhile, provide a new perspective to estimate intracranial high-frequency features with non-invasive neural recordings.

Neuroanatomic Markers of Posttraumatic Epilepsy Based on MR Imaging and Machine Learning.

Although posttraumatic epilepsy is a common complication of traumatic brain injury, the relationship between these conditions is unclear and early posttraumatic epilepsy detection and prevention remain major unmet clinical challenges. This study aimed to identify imaging biomarkers that predict posttraumatic epilepsy among survivors of traumatic brain injury on the basis of an MR imaging data set. We performed tensor-based morphometry to analyze brain-shape changes associated with traumatic brain injury and to derive imaging features for statistical group comparison. Additionally, machine learning was used to identify structural anomalies associated with brain lesions. Automatically generated brain lesion maps were used to identify brain regions where lesion load may indicate an increased incidence of posttraumatic epilepsy. We used 138 non-posttraumatic epilepsy subjects for training the machine learning method. Validation of lesion delineation was performed on 15 subjects. Group analysis of the relationship between traumatic brain injury and posttraumatic epilepsy was performed on an independent set of 74 subjects (37 subjects with and 37 randomly selected subjects without epilepsy). We observed significant F-statistics related to tensor-based morphometry analysis at voxels close to the pial surface, which may indicate group differences in the locations of edema, hematoma, or hemorrhage. The results of the F-test on lesion data showed significant differences between groups in both the left and right temporal lobes. We also saw significant differences in the right occipital lobe and cerebellum. Statistical analysis suggests that lesions in the temporal lobes, cerebellum, and the right occipital lobe are associated with an increased posttraumatic epilepsy incidence.