Update on the Neuroimaging and Electroencephalographic Biomarkers of Epileptogenesis: A Literature Review

Understanding epileptogenesis from molecules to network alteration

Epilepsy is characterized by recurrent seizures. Following an initial insult, a latent period precedes the onset of spontaneous seizures, a process referred to as epileptogenesis. This period plays a critical role in halting the progression toward epilepsy before the onset of abnormal molecular and network alterations. In this study, the fundamental concepts of epileptogenesis as well as the associated molecular and cellular targets are reviewed.

The Neurovascular Unit Dysfunction in the Molecular Mechanisms of Epileptogenesis and Targeted Therapy

Epilepsy is a multifaceted neurological syndrome characterized by recurrent, spontaneous, and synchronous seizures. The pathogenesis of epilepsy, known as epileptogenesis, involves intricate changes in neurons, neuroglia, and endothelium, leading to structural and functional disorders within neurovascular units and culminating in the development of spontaneous epilepsy. Although current research on epilepsy treatments primarily centers around anti-seizure drugs, it is imperative to seek effective interventions capable of disrupting epileptogenesis. To this end, a comprehensive exploration of the changes and the molecular mechanisms underlying epileptogenesis holds the promise of identifying vital biomarkers for accurate diagnosis and potential therapeutic targets. Emphasizing early diagnosis and timely intervention is paramount, as it stands to significantly improve patient prognosis and alleviate the socioeconomic burden. In this review, we highlight the changes and molecular mechanisms of the neurovascular unit in epileptogenesis and provide a theoretical basis for identifying biomarkers and drug targets.

Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist

PURPOSE OF REVIEW

Machine Learning (ML) and Artificial Intelligence (AI) are data-driven techniques to translate raw data into applicable and interpretable insights that can assist in clinical decision making. Some of these tools have extremely promising initial results, earning both great excitement and creating hype. This non-technical article reviews recent developments in ML/AI in epilepsy to assist the current practicing epileptologist in understanding both the benefits and limitations of integrating ML/AI tools into their clinical practice.

RECENT FINDINGS

ML/AI tools have been developed to assist clinicians in almost every clinical decision including (1) predicting future epilepsy in people at risk, (2) detecting and monitoring for seizures, (3) differentiating epilepsy from mimics, (4) using data to improve neuroanatomic localization and lateralization, and (5) tracking and predicting response to medical and surgical treatments. We also discuss practical, ethical, and equity considerations in the development and application of ML/AI tools including chatbots based on Large Language Models (e.g., ChatGPT). ML/AI tools will change how clinical medicine is practiced, but, with rare exceptions, the transferability to other centers, effectiveness, and safety of these approaches have not yet been established rigorously. In the future, ML/AI will not replace epileptologists, but epileptologists with ML/AI will replace epileptologists without ML/AI.

Altered neuromagnetic activity in default mode network in childhood absence epilepsy

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 (β: Controls: 0.1009 vs. Interictal: 0.0475; γ 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.

Role of Stat3 in NLRP3/caspase-1-mediated hippocampal neuronal pyroptosis in epileptic mice

Epilepsy, a fairly common neurological disorder, is linked to various sequelae and greatly impairs the quality of life. Meanwhile, there is evidence to suggest an association between pyroptosis and epilepsy. Accordingly, the current study sought to determine the role of signal transduction activator of transcription 3 (Stat3) in pyroptosis in epileptic mice. Firstly, epileptic mouse models were induced by lithium chloride, atropine, and pilocarpine, and HT22 cells were treated with lipopolysaccharide (LPS) to establish hippocampal neuronal inflammation models. Subsequently, Stat3, NOD-like receptor protein 3 (NLRP3), cleaved-caspase-1, gasdermin D (GSDMD)-N, activated Stat3 (p-Stat3), and H3K9Ac levels were detected in the mouse hippocampus and HT22 cells. Morris water maze test was further performed to detect changes in the learning and memory abilities of epileptic mice, and hematoxylin-eosin staining and Nissl staining were conducted to detect the pathological injury. HT22 cell proliferation and apoptosis were also detected using a cell counting kit-8 assay and flow cytometry. An enzyme-linked immunosorbent assay was adopted to detect Interleukin (IL)-1β and IL-18 concentrations in the mouse hippocampus and HT22 cells. Furthermore, the enrichment of H3K9Ac and p-Stat3 in the NLRP3 promoter region was detected with the help of a chromatin immunoprecipitation assay. The obtained findings revealed that Stat3 was highly-expressed in the hippocampus of epileptic mice and LPS-treated HT22 cells. Meanwhile, Stat3 silencing brought about improvements in the learning and memory abilities of the mice, in addition to alleviation of hippocampal neuronal damage and pyroptosis-related factors in hippocampal tissue and HT22 cells. We also observed that Stat3 bound to the NLRP3 promoter to promote H3K9 acetylation and NLRP3 transcription. Moreover, increasing H3K9Ac in cells annulled the inhibition of silencing Stat3 on neuronal pyroptosis. To conclude, our findings revealed that Stat3 bound to the NLRP3 promoter to augment H3K9 acetylation, NLRP3 transcription, and NLRP3/caspase-1-mediated neuronal pyroptosis, resulting in aggravation of neuronal damage in epileptic mice. This article is protected by copyright. All rights reserved.