Recognition of interictal and ictal discharges on EEG. Focal vs generalized epilepsy

EEG in focal and generalized epilepsies: Pearls and perils

Correctly diagnosing and classifying seizures and epilepsies is vital to ensure a tailored approach to patients with epilepsy. The ILAE seizure classification consists of two main groups: focal and generalized. Establishing if a seizure is focal or generalized is essential to classify the epilepsy type and the epilepsy syndrome, providing more personalized treatment and counseling about prognosis. EEG is one of the most essential tools for this classification process and further localization of the epileptogenic focus. However, some EEG findings are misleading and may postpone the correct diagnosis and proper treatment. Knowing the most common EEG pitfalls in focal and generalized epilepsies is valuable for clinical practice, avoiding misinterpretations. Some atypical features can be challenging in focal epilepsies, such as secondary bilateral synchrony, focal epileptiform activity induced by hyperventilation and photic stimulation, and non-focal slowing. On the other hand, more than 60 % of persons with idiopathic generalized epilepsies have at least one type of atypical abnormality. In this manuscript, we describe and illustrate some of the most common EEG findings that can make even experienced epileptologists question not only where the epileptogenic focus is but also if the patient has focal or generalized epilepsy. This review summarizes the perils and provide some pearls to assist EEG readers.

Unraveling the Neural Circuits: Techniques, Opportunities and Challenges in Epilepsy Research

Epilepsy, a prevalent neurological disorder characterized by high morbidity, frequent recurrence, and potential drug resistance, profoundly affects millions of people globally. Understanding the microscopic mechanisms underlying seizures is crucial for effective epilepsy treatment, and a thorough understanding of the intricate neural circuits underlying epilepsy is vital for the development of targeted therapies and the enhancement of clinical outcomes. This review begins with an exploration of the historical evolution of techniques used in studying neural circuits related to epilepsy. It then provides an extensive overview of diverse techniques employed in this domain, discussing their fundamental principles, strengths, limitations, as well as their application. Additionally, the synthesis of multiple techniques to unveil the complexity of neural circuits is summarized. Finally, this review also presents targeted drug therapies associated with epileptic neural circuits. By providing a critical assessment of methodologies used in the study of epileptic neural circuits, this review seeks to enhance the understanding of these techniques, stimulate innovative approaches for unraveling epilepsy's complexities, and ultimately facilitate improved treatment and clinical translation for epilepsy.

The seizure classification of focal epilepsy based on the network motif analysis

Due to the complexity of focal epilepsy and its risk for transiting to the generalized epilepsy, the development of reliable classification methods to accurately predict and classify focal and generalized seizures is critical for the clinical management of patients with epilepsy. In order to holistically understand the seizure propagation behavior of focal epilepsy, we propose a three-node motif reduced network by respectively simplifying the focal region, surrounding healthy region and their critical regions as the single node. Because three-node motif can richly characterize information evolutions, the motif analysis method could comprehensively investigate the seizure behavior of focal epilepsy. Firstly, we define a new seizure propagation marker value to capture the seizure onsets and intensity. Based on the three-node motif analysis, it is shown that the focal seizure and spreading can be categorized as inhibitory seizure, focal seizure, focal-critical seizure and generalized seizures, respectively. The four types of seizures correspond to specific modal types respectively, reflecting the strong correlation between seizure behavior and information flow evolution. In addition, it is found that the intensity difference of outflow and inflow information from the critical node (connection heterogeneity) and the excitability of the critical node significantly affected the distribution and transition of the four seizure types. In particular, the method of local linear stability analysis also verifies the effectiveness of four types of seizures classification. In sum, this paper computationally confirms the complex dynamic behavior of focal seizures, and the study of criticality is helpful to propose novel seizure control strategies.

Juvenile idiopathic epilepsy in Egyptian Arabian foals, a potential animal model of self-limited epilepsy in children

BACKGROUND

Juvenile idiopathic epilepsy (JIE) is categorized as a generalized epilepsy. Epilepsy classification entails electrocortical characterization and localization of epileptic discharges (ED) using electroencephalography (EEG).

HYPOTHESIS/OBJECTIVES

Characterize epilepsy in Egyptian Arabian foals with JIE using EEG.

ANIMALS

Sixty-nine foals (JIE, 48; controls, 21).

METHODS

Retrospective study. Inclusion criteria consisted of Egyptian Arabian foals: (1) JIE group diagnosed based on witnessed or recorded seizures, and neurological and EEG findings, and (2) control group of healthy nonepileptic age-matched foals. Clinical data were obtained in 48 foals. Electroencephalography with photic stimulation was performed under standing sedation in 37 JIE foals and 21 controls.

RESULTS

Abnormalities on EEG were found in 95% of epileptic foals (35 of 37) and in 3 of 21 control asymptomatic foals with affected siblings. Focal ED were detected predominantly in the central vertex with diffusion into the centroparietal or frontocentral regions (n = 35). Generalization of ED occurred in 14 JIE foals. Epileptic discharges commonly were seen during wakefulness (n = 27/37 JIE foals) and sedated sleep (n = 35/37 JIE foals; 3/21 controls). Photic stimulation triggered focal central ED in 15 of 21 JIE foals.

CONCLUSIONS AND CLINICAL IMPORTANCE

Juvenile idiopathic epilepsy has a focal onset of ED at the central vertex with spread resulting in clinical generalized tonic-clonic seizures with facial motor activity and loss of consciousness. Electroencephalography with photic stimulation contributes to accurate phenotyping of epilepsy. Foals with this benign self-limiting disorder might serve as a naturally occurring animal model for self-limited epilepsy in children.

Nonconvulsive Status Epilepticus With Generalized Spike-and-Wave Discharges: Pathophysiological and Nosological Considerations

Absence status epilepticus (ASE) is the most common type of status epilepticus in patients with idiopathic generalized epilepsy (IGE). Like absence seizure, ASE is characterized by generalized spike-and-wave discharges (GSWDs) on the electroencephalogram (EEG). Once considered specific for IGE, GSWDs have increasingly been observed in other forms of epilepsy, as well as in patients with no prior epilepsy. Here, we report three patients with different types of nonconvulsive status epilepticus (NCSE) in which the EEG correlate was GSWDs: a 44-year-old woman with juvenile absence epilepsy who manifested ASE, a 73-year-old woman with anoxic brain injury complicated by NCSE with well-formed GSWDs (as seen in IGE and de novo ASE), and a 41-year-old woman with frontal lobe epilepsy who developed focal NCSE with impaired consciousness. Evidently, patients with generalized epilepsy, focal epilepsy, and no prior epilepsy can all manifest NCSE with similar electroclinical characteristics, i.e., GSWDs and impaired consciousness. This observation adds to the existing evidence that seizures, whether classified as focal or generalized, often involve focal and generalized elements in their pathophysiology. To fully understand seizure pathophysiology, we must steer away from the focal-versus-generalized paradigm that has dominated the nosology of seizures and epilepsy for a very long time.

The Application of Deep Learning to Electroencephalograms, Magnetic Resonance Imaging, and Implants for the Detection of Epileptic Seizures: A Narrative Review

Epilepsy is a neurological disorder characterized by recurrent seizures affecting millions worldwide. Medically intractable seizures in epilepsy patients are not only detrimental to the quality of life but also pose a significant threat to their safety. Outcomes of epilepsy therapy can be improved by early detection and intervention during the interictal window period. Electroencephalography is the primary diagnostic tool for epilepsy, but accurate interpretation of seizure activity is challenging and highly time-consuming. Machine learning (ML) and deep learning (DL) algorithms enable us to analyze complex EEG data, which can not only help us diagnose but also locate epileptogenic zones and predict medical and surgical treatment outcomes. DL models such as convolutional neural networks (CNNs), inspired by visual processing, can be used to classify EEG activity. By applying preprocessing techniques, signal quality can be enhanced by denoising and artifact removal. DL can also be incorporated into the analysis of magnetic resonance imaging (MRI) data, which can help in the localization of epileptogenic zones in the brain. Proper detection of these zones can help in good neurosurgical outcomes. Recent advancements in DL have facilitated the implementation of these systems in neural implants and wearable devices, allowing for real-time seizure detection. This has the potential to transform the management of drug-refractory epilepsy. This review explores the application of ML and DL techniques to Electroencephalograms (EEGs), MRI, and wearable devices for epileptic seizure detection. This review briefly explains the fundamentals of both artificial intelligence (AI) and DL, highlighting these systems' potential advantages and undeniable limitations.

Dynamics of AMPA receptors regulate epileptogenesis in patients with epilepsy

The excitatory glutamate α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptors (AMPARs) contribute to epileptogenesis. Thirty patients with epilepsy and 31 healthy controls are scanned using positron emission tomography with our recently developed radiotracer for AMPARs, [¹¹C]K-2, which measures the density of cell-surface AMPARs. In patients with focal-onset seizures, an increase in AMPAR trafficking augments the amplitude of abnormal gamma activity detected by electroencephalography. In contrast, patients with generalized-onset seizures exhibit a decrease in AMPARs coupled with increased amplitude of abnormal gamma activity. Patients with epilepsy had reduced AMPAR levels compared with healthy controls, and AMPARs are reduced in larger areas of the cortex in patients with generalized-onset seizures compared with those with focal-onset seizures. Thus, epileptic brain function can be regulated by the enhanced trafficking of AMPAR due to Hebbian plasticity with increased simultaneous neuronal firing and compensational downregulation of cell-surface AMPARs by the synaptic scaling.

Altered spontaneous brain activity as a potential imaging biomarker for generalized and focal to bilateral tonic-clonic seizures: A resting-state fMRI study

OBJECTIVE

We aimed to determine whether alterations in spontaneous regional brain activity in those with generalized tonic-clonic seizures (GTCS) and focal to bilateral tonic-clonic seizures (FBTCS) and explore whether the alterations could be used as biomarkers to classify disease subtypes through support vector machine analysis (SVM).

METHODS

The fractional amplitude of low-frequency fluctuations (fALFF) and regional homogeneity (ReHo) from resting-state functional magnetic resonance imaging (rs-fMRI) data were extracted from 57 patients with GTCS, 35 patients with FBTCS, and 50 age-matched and sex-matched normal controls (NCs) using the DPARSF 5.0 toolbox. Between-group comparisons were adjusted for covariates (age, sex, and equipment). Correlation analyses between imaging biomarkers and the frequency or duration of seizure activity were calculated using partial correlations. The differential imaging indicators, age, and sex were considered as the discriminative features in the SVM to evaluate classification performance.

RESULTS

The patients with GTCS showed lower fALFF values (voxel p < 0.001, cluster p < 0.05, Gaussian random field corrected, GRF corrected) in the right postcentral gyrus and precentral gyrus and lower ReHo values (GRF corrected) in the middle temporal gyrus than the NCs. The patients with FBTCS showed higher fALFF (GRF corrected) values in the right postcentral and precentral gyrus and higher ReHo (GRF corrected) values in the right postcentral gyrus. Both fALFF (GRF corrected) and ReHo (GRF corrected) values were lower in the right postcentral gyrus and precentral gyrus in the GTCS group than in the FBTCS group. In patients with FBTCS, fALFF values in the right postcentral and precentral gyrus were positively correlated with duration (r = 0.655, p = 0.008, Bonferroni corrected) in the low-duration group, and ReHo values in the right postcentral gyrus were positively correlated with frequency (r = 0.486, p = 0.022, uncorrected) in the low-frequency group. SVM results showed receiver operating characteristic curves of 0.89, 0.87, and 0.76 for the classification between GTCS and NC, between FBTCS and NC, and GTCS and FBTCS, respectively.

SIGNIFICANCE

This study detected alterations in fALFF and ReHo in the postcentral gyrus and precentral gyrus in patients with GTCS and FBTCS, which might contribute to understanding the pathogenesis, disease classification, and clinical targeted therapy.

A Data-Driven Adaptive Emotion Recognition Model for College Students Using an Improved Multifeature Deep Neural Network Technology

With the increasing pressure on college students in terms of study, work, emotion, and life, the emotional changes of college students are becoming more and more obvious. For college student management workers, if they can accurately grasp the emotional state of each college student in all aspects of the whole process, it will be of great help to student management work. The traditional way to understand students’ emotions at a certain stage is mostly through chats, questionnaires, and other methods. However, data collection in this way is time-consuming and labor-intensive, and the authenticity of the collected data cannot be guaranteed because students will lie out of impatience or unwillingness to reveal their true emotions. In order to explore an accurate and efficient emotion recognition method for college students, more objective physiological data are used for emotion recognition research. Since emotion is generated by the central nervous system of the human brain, EEG signals directly reflect the electrophysiological activity of the brain. Therefore, in the field of emotion recognition based on physiological signals, EEG signals are favored due to their ability to intuitively respond to emotions. Therefore, a deep neural network (DNN) is used to classify the collected emotional EEG data and obtain the emotional state of college students according to the classification results. Considering that different features can represent different information of the original data, in order to express the original EEG data information as comprehensively as possible, various features of the EEG are first extracted. Second, feature fusion is performed on multiple features using the autosklearn model integration technique. Third, the fused features are input to the DNN, resulting in the final classification result. The experimental results show that the method has certain advantages in public datasets, and the accuracy of emotion recognition exceeds 88%. This proves the used emotion recognition is feasible to be applied in real life.