Pathophysiology, Diagnosis, Prognosis, and Prevention of Poststroke Epilepsy: Clinical and Research Implications

Poststroke epilepsy (PSE) is associated with higher mortality and poor functional and cognitive outcomes in patients with stroke. With the remarkable development of acute stroke treatment, there is a growing number of survivors with PSE. Although approximately 10% of patients with stroke develop PSE, given the significant burden of stroke worldwide, PSE is a significant problem in stroke survivors. Therefore, the attention of health policymakers and significant funding are required to promote PSE prevention research. The current PSE definition includes unprovoked seizures occurring more than 7 days after stroke onset, given the high recurrence risks of seizures. However, the pathologic cascade of stroke is not uniform, indicating the need for a tissue-based approach rather than a time-based one to distinguish early seizures from late seizures. EEG is a commonly used tool in the diagnostic work-up of PSE. EEG findings during the acute phase of stroke can potentially stratify the risk of subsequent seizures and predict the development of poststroke epileptogenesis. Recent reports suggest that cortical superficial siderosis, which may be involved in epileptogenesis, is a promising marker for PSE. By incorporating such markers, future risk-scoring models could guide treatment strategies, particularly for the primary prophylaxis of PSE. To date, drugs that prevent poststroke epileptogenesis are lacking. The primary challenge involves the substantial cost burden due to the difficulty of reliably enrolling patients who develop PSE. There is, therefore, a critical need to determine reliable biomarkers for PSE. The goal is to be able to use them for trial enrichment and as a surrogate outcome measure for epileptogenesis. Moreover, seizure prophylaxis is essential to prevent functional and cognitive decline in stroke survivors. Further elucidation of factors that contribute to poststroke epileptogenesis is eagerly awaited. Meanwhile, the regimen of antiseizure medications should be based on individual cardiovascular risk, psychosomatic comorbidities, and concomitant medications. This review summarizes the current understanding of poststroke epileptogenesis, its risks, prognostic models, prophylaxis, and strategies for secondary prevention of seizures and suggests strategies to advance research on PSE.

Dynamical graph neural network with attention mechanism for epilepsy detection using single channel EEG

Epilepsy is a chronic brain disease, and identifying seizures based on electroencephalogram (EEG) signals would be conducive to implement interventions to help patients reduce impairment and improve quality of life. In this paper, we propose a classification algorithm to apply dynamical graph neural network with attention mechanism to single channel EEG signals. Empirical mode decomposition (EMD) are adopted to construct graphs and the optimal adjacency matrix is obtained by model optimization. A multilayer dynamic graph neural network with attention mechanism is proposed to learn more discriminative graph features. The MLP-pooling structure is proposed to fuse graph features. We performed 12 classification tasks on the epileptic EEG database of the University of Bonn, and experimental results showed that using 25 runs of ten-fold cross-validation produced the best classification results with an average of 99.83[Formula: see text] accuracy, 99.91[Formula: see text] specificity, 99.78[Formula: see text] sensitivity, 99.87[Formula: see text] precision, and 99.47[Formula: see text] [Formula: see text] score for the 12 classification tasks.

Microvascular changes associated with epilepsy: A narrative review

The blood-brain barrier (BBB) is dysfunctional in temporal lobe epilepsy (TLE). In this regard, microvascular changes are likely present. The aim of this review is to provide an overview of the current knowledge on microvascular changes in epilepsy, and includes clinical and preclinical evidence of seizure induced angiogenesis, barriergenesis and microcirculatory alterations. Anatomical studies show increased microvascular density in the hippocampus, amygdala, and neocortex accompanied by BBB leakage in various rodent epilepsy models. In human TLE, a decrease in afferent vessels, morphologically abnormal vessels, and an increase in endothelial basement membranes have been observed. Both clinical and experimental evidence suggests that basement membrane changes, such as string vessels and protrusions, indicate and visualize a misbalance between endothelial cell proliferation and barriergenesis. Vascular endothelial growth factor (VEGF) appears to play a crucial role. Following an altered vascular anatomy, its physiological functioning is affected as expressed by neurovascular decoupling that subsequently leads to hypoperfusion, disrupted parenchymal homeostasis and potentially to seizures". Thus, epilepsy might be a condition characterized by disturbed cerebral microvasculature in which VEGF plays a pivotal role. Additional physiological data from patients is however required to validate findings from models and histological studies on patient biopsies.

Automatic epileptic seizure detection via Stein kernel-based sparse representation

Epileptic seizure detection is of great significance in the diagnosis of epilepsy and relieving the heavy workload of visual inspection of electroencephalogram (EEG) recordings. This paper presents a novel method for seizure detection using the Stein kernel-based sparse representation (SR) for EEG recordings. Different from the traditional SR scheme that works with vector data in Euclidean space, the Stein kernel-based SR framework is constructed for seizure detection in the space of the symmetric positive definite (SPD) matrices, which form a Riemannian manifold. Due to the non-Euclidean geometry of the Riemannian manifold, the Stein kernel on the manifold permits the embedding of the manifold in a high-dimensional reproducing kernel Hilbert space (RKHS) to perform SR. In the Stein kernel-based SR framework, EEG samples are described by SPD matrices in the form of covariance descriptors (CovDs). Then, a test EEG sample is sparsely represented on the training set, and the test sample is classified as a member of the class, which leads to the minimum reconstructed residual. Finally, by using three widely used EEG datasets to evaluate the detection performance of the proposed method, the experimental results demonstrate that it achieves good classification accuracy on each dataset. Furthermore, the fast computational speed of the Stein kernel-based SR also meets the basic requirements for real-time seizure detection.

Experimental Therapeutic Strategies in Epilepsies Using Anti-Seizure Medications

Epilepsies are among the most common neurological problems. The disease burden in patients with epilepsy is significantly high, and epilepsy has a huge negative impact on patients' quality of life with epilepsy and their families. Anti-seizure medications are the mainstay treatment in patients with epilepsy, and around 70% of patients will ultimately control with a combination of at least two appropriately selected anti-seizure medications. However, in one-third of patients, seizures are resistant to drugs, and other measures will be needed. The primary goal in using experimental therapeutic medication strategies in patients with epilepsy is to prevent recurrent seizures and reduce the rate of traumatic events that may occur during seizures. So far, various treatments using medications have been offered for patients with epilepsies, which have been classified according to the type of epilepsy, the effectiveness of the medications, and the adverse effects. Medications such as Levetiracetam, valproic acid, and lamotrigine are at the forefront of these patients' treatment. Epilepsy surgery, neuro-stimulation, and the ketogenic diet are the main measures in patients with medication-resistant epilepsies. In this paper, we will review the therapeutic approach using anti-seizure medications in patients with epilepsy. However, it should be noted that some of these patients still do not respond to existing treatments; therefore, the limited ability of current therapies has fueled research efforts for the development of novel treatment strategies. Thus, it seems that in addition to surgical measures, we should look for more specific agents that have less adverse events and have a greater effect in stopping seizures.

Compromised Dynamic Cerebral Autoregulation in Patients with Epilepsy

Objective

The aim of this study is to analyze dynamic cerebral autoregulation (dCA) in patients with epilepsy.

Methods

One hundred patients with epilepsy and 100 age- and sex-matched healthy controls were recruited. Noninvasive continuous cerebral blood flow velocity of the bilateral middle artery and arterial blood pressure were recorded. Transfer function analyses were used to analyze the autoregulatory parameters (phase difference and gain).

Results

The overall phase difference of patients with epilepsy was significantly lower than that of the healthy control group (p = 0.046). Furthermore, patients with interictal slow wave had significant lower phase difference than the slow-wave-free patients (p = 0.012). There was no difference in overall phase between focal discharges and multifocal discharges in patients with epilepsy. Simultaneously, there was no difference in mean phase between the affected and unaffected hemispheres in patients with unilateral discharges. In particular, interictal slow wave was an independent factor that influenced phase difference in patients with epilepsy (p = 0.016).

Conclusions

Our study documented that dCA is impaired in patients with epilepsy, especially in those with interictal slow wave. The impairment of dCA occurs irrespective of the discharge location and type. Interictal slow wave is an independent factor to predict impaired dCA in patients with epilepsy.

Clinical Trial Identifier

This trial is registered with NCT02775682.

Post-stroke epilepsy

Post-stroke epilepsy (PSE) is a common complication after stroke, yet treatment options remain limited. While many physicians prescribe antiepileptic drugs (AED) for secondary prevention of PSE, it is unclear which treatments are most effective in the prevention of recurrence of symptoms, or whether such therapy is needed for primary prevention. This review discusses the current understanding of epidemiology, diagnoses, mechanisms, risk factors, and treatments of PSE.