Early brain biomarkers of post-traumatic seizures: initial report of the multicentre epilepsy bioinformatics study for antiepileptogenic therapy (EpiBioS4Rx) prospective study

The predicative value of early quantitative electroencephalograph in epilepsy after severe traumatic brain injury in children

Objective

To explore whether early quantitative electroencephalograph (EEG) can predict the development of epilepsy in pediatric patients with severe traumatic brain injury (TBI).

Methods

A total of 78 children with severe TBI who were admitted to our hospital were divided into post-traumatic epilepsy (PTE) and non-PTE groups according to whether or not they developed PTE. EEGs of frontal, central and parietal lobes were recorded at the time of their admission. The power values of each frequency band, odds ratio and peak envelope power values of each brain region were statistically analyzed. In addition, the patients were followed up for two years, and the occurrence of PTE was documented.

Results

During the follow-up period, PTE occurred in 8 patients. Analysis of EEG signals across different brain regions (frontal, central, and parietal lobes) revealed significant differences between the PTE and non-PTE groups. Patients with PTE exhibited significantly higher δ and θ power values (P < 0.01), lower α/θ ratios (P < 0.01), and elevated θ/β, (δ + θ)/(α + β), and peak envelope power (P < 0.01) compared to those in the non-PTE group.

Conclusion

In children with severe TBI, the parameter characterization of early quantitative EEG has potential application in predicting PTE.

Advancing post-traumatic seizure classification and biomarker identification: Information decomposition based multimodal fusion and explainable machine learning with missing neuroimaging data

A late post-traumatic seizure (LPTS), a consequence of traumatic brain injury (TBI), can potentially evolve into a lifelong condition known as post-traumatic epilepsy (PTE). Presently, the mechanism that triggers epileptogenesis in TBI patients remains elusive, inspiring the epilepsy community to devise ways to predict which TBI patients will develop PTE and to identify potential biomarkers. In response to this need, our study collected comprehensive, longitudinal multimodal data from 48 TBI patients across multiple participating institutions. A supervised binary classification task was created, contrasting data from LPTS patients with those without LPTS. To accommodate missing modalities in some subjects, we took a two-pronged approach. Firstly, we extended a graphical model-based Bayesian estimator to directly classify subjects with incomplete modality. Secondly, we explored conventional imputation techniques. The imputed multimodal information was then combined, following several fusion and dimensionality reduction techniques found in the literature, and subsequently fitted to a kernel- or a tree-based classifier. For this fusion, we proposed two new algorithms: recursive elimination of correlated components (RECC) that filters information based on the correlation between the already selected features, and information decomposition and selective fusion (IDSF), which effectively recombines information from decomposed multimodal features. Our cross-validation findings showed that the proposed IDSF algorithm delivers superior performance based on the area under the curve (AUC) score. Ultimately, after rigorous statistical comparisons and interpretable machine learning examination using Shapley values of the most frequently selected features, we recommend the two following magnetic resonance imaging (MRI) abnormalities as potential biomarkers: the left anterior limb of internal capsule in diffusion MRI (dMRI), and the right middle temporal gyrus in functional MRI (fMRI).

Prognostic Implications of Early Prediction in Posttraumatic Epilepsy

Posttraumatic epilepsy (PTE) is a complication of traumatic brain injury that can increase morbidity, but predicting which patients may develop PTE remains a challenge. Much work has been done to identify a variety of risk factors and biomarkers, or a combination thereof, for patients at highest risk of PTE. However, several issues have hampered progress toward fully adapted PTE models. Such issues include the need for models that are well-validated, cost-effective, and account for competing outcomes like death. Additionally, while an accurate PTE prediction model can provide quantitative prognostic information, how such information is communicated to inform shared decision-making and treatment strategies requires consideration of an individual patient's clinical trajectory and unique values, especially given the current absence of direct anti-epileptogenic treatments. Future work exploring approaches integrating individualized communication of prediction model results are needed.

Insights into epileptogenesis from post-traumatic epilepsy

Post-traumatic epilepsy (PTE) accounts for 5% of all epilepsies. The incidence of PTE after traumatic brain injury (TBI) depends on the severity of injury, approaching one in three in groups with the most severe injuries. The repeated seizures that characterize PTE impair neurological recovery and increase the risk of poor outcomes after TBI. Given this high risk of recurrent seizures and the relatively short latency period for their development after injury, PTE serves as a model disease to understand human epileptogenesis and trial novel anti-epileptogenic therapies. Epileptogenesis is the process whereby previously normal brain tissue becomes prone to recurrent abnormal electrical activity, ultimately resulting in seizures. In this Review, we describe the clinical course of PTE and highlight promising research into epileptogenesis and treatment using animal models of PTE. Clinical, imaging, EEG and fluid biomarkers are being developed to aid the identification of patients at high risk of PTE who might benefit from anti-epileptogenic therapies. Studies in preclinical models of PTE have identified tractable pathways and novel therapeutic strategies that can potentially prevent epilepsy, which remain to be validated in humans. In addition to improving outcomes after TBI, advances in PTE research are likely to provide therapeutic insights that are relevant to all epilepsies.

Quantitative epileptiform burden and electroencephalography background features predict post-traumatic epilepsy

BACKGROUND

Post-traumatic epilepsy (PTE) is a severe complication of traumatic brain injury (TBI). Electroencephalography aids early post-traumatic seizure diagnosis, but its optimal utility for PTE prediction remains unknown. We aim to evaluate the contribution of quantitative electroencephalograms to predict first-year PTE (PTE1).

METHODS

We performed a multicentre, retrospective case-control study of patients with TBI. 63 PTE1 patients were matched with 63 non-PTE1 patients by admission Glasgow Coma Scale score, age and sex. We evaluated the association of quantitative electroencephalography features with PTE1 using logistic regressions and examined their predictive value relative to TBI mechanism and CT abnormalities.

RESULTS

In the matched cohort (n=126), greater epileptiform burden, suppression burden and beta variability were associated with 4.6 times higher PTE1 risk based on multivariable logistic regression analysis (area under the receiver operating characteristic curve, AUC (95% CI) 0.69 (0.60 to 0.78)). Among 116 (92%) patients with available CT reports, adding quantitative electroencephalography features to a combined mechanism and CT model improved performance (AUC (95% CI), 0.71 (0.61 to 0.80) vs 0.61 (0.51 to 0.72)).

CONCLUSIONS

Epileptiform and spectral characteristics enhance covariates identified on TBI admission and CT abnormalities in PTE1 prediction. Future trials should incorporate quantitative electroencephalography features to validate this enhancement of PTE risk stratification models.

Exploring the pathogenesis linking traumatic brain injury and epilepsy via bioinformatic analyses

Traumatic brain injury (TBI) is a serious disease that could increase the risk of epilepsy. The purpose of this article is to explore the common molecular mechanism in TBI and epilepsy with the aim of providing a theoretical basis for the prevention and treatment of post-traumatic epilepsy (PTE). Two datasets of TBI and epilepsy in the Gene Expression Omnibus (GEO) database were downloaded. Functional enrichment analysis, protein-protein interaction (PPI) network construction, and hub gene identification were performed based on the cross-talk genes of aforementioned two diseases. Another dataset was used to validate these hub genes. Moreover, the abundance of infiltrating immune cells was evaluated through Immune Cell Abundance Identifier (ImmuCellAI). The common microRNAs (miRNAs) between TBI and epilepsy were acquired via the Human microRNA Disease Database (HMDD). The overlapped genes in cross-talk genes and target genes predicted through the TargetScan were obtained to construct the common miRNAs-mRNAs network. A total of 106 cross-talk genes were screened out, including 37 upregulated and 69 downregulated genes. Through the enrichment analyses, we showed that the terms about cytokine and immunity were enriched many times, particularly interferon gamma signaling pathway. Four critical hub genes were screened out for co-expression analysis. The miRNA-mRNA network revealed that three miRNAs may affect the shared interferon-induced genes, which might have essential roles in PTE. Our study showed the potential role of interferon gamma signaling pathway in pathogenesis of PTE, which may provide a promising target for future therapeutic interventions.

Acute Hippocampal Damage as a Prognostic Biomarker for Cognitive Decline but Not for Epileptogenesis after Experimental Traumatic Brain Injury

It is necessary to develop reliable biomarkers for epileptogenesis and cognitive impairment after traumatic brain injury when searching for novel antiepileptogenic and cognition-enhancing treatments. We hypothesized that a multiparametric magnetic resonance imaging (MRI) analysis along the septotemporal hippocampal axis could predict the development of post-traumatic epilepsy and cognitive impairment. We performed quantitative T2 and T2* MRIs at 2, 7 and 21 days, and diffusion tensor imaging at 7 and 21 days after lateral fluid-percussion injury in male rats. Morris water maze tests conducted between 35-39 days post-injury were used to diagnose cognitive impairment. One-month-long continuous video-electroencephalography monitoring during the 6th post-injury month was used to diagnose epilepsy. Single-parameter and regularized multiple linear regression models were able to differentiate between sham-operated and brain-injured rats. In the ipsilateral hippocampus, differentiation between the groups was achieved at most septotemporal locations (cross-validated area under the receiver operating characteristic curve (AUC) 1.0, 95% confidence interval 1.0-1.0). In the contralateral hippocampus, the highest differentiation was evident in the septal pole (AUC 0.92, 95% confidence interval 0.82-0.97). Logistic regression analysis of parameters imaged at 3.4 mm from the contralateral hippocampus's temporal end differentiated between the cognitively impaired rats and normal rats (AUC 0.72, 95% confidence interval 0.55-0.84). Neither single nor multiparametric approaches could identify the rats that would develop post-traumatic epilepsy. Multiparametric MRI analysis of the hippocampus can be used to identify cognitive impairment after an experimental traumatic brain injury. This information can be used to select subjects for preclinical trials of cognition-improving interventions.

Role of TREM2 in the Development of Neurodegenerative Diseases After Traumatic Brain Injury

Traumatic brain injury (TBI) has been found as the primary cause of morbidity and disability worldwide, which has posed a significant social and economic burden. The first stage of TBI produces brain edema, axonal damage, and hypoxia, thus having an effect on the blood-brain barrier function, promoting inflammatory responses, and increasing oxidative stress. Patients with TBI are more likely to develop post-traumatic epilepsy, behavioral issues, as well as mental illnesses. The long-term effects arising from TBI have aroused rising attention over the past few years. Microglia in the brain can express the triggering receptor expressed on myeloid cells 2 (TREM2), which is a single transmembrane receptor pertaining to the immunoglobulin superfamily. The receptor has been correlated with a number of neurodegenerative disorders, including Alzheimer's disease, Parkinson's disease, and other relevant diseases. In this review, it is demonstrated that TREM2 is promising to serve as a neuroprotective factor for neurodegenerative disorders following TBI by modulating the function of microglial cells. Accordingly, it has potential avenues for TREM2-related therapies to improve long-term recovery after TBI.

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

Epilepsy is one of the most common debilitating neurological disorders that lead to severe socio-cognitive dysfunction. While there are currently more than 30 antiseizure medications available for the treatment and prevention of seizures, none address the prevention of epileptogenesis that leading to the development of epilepsy following a potential brain insult. Hence, there is a growing need for the identification of accurate biomarkers of epileptogenesis that enable the prediction of epilepsy following a known brain insult. Although recent studies using various neuroimages and electroencephalography have found promising biomarkers of epileptogenesis, their utility needs to be further validated in larger clinical trials. In this literature review, we searched the Medline, Pubmed, and Embase databases using the following search algorithm: "epileptogenesis" and "biomarker" and "EEG" or "electroencephalography" or "neuroimaging" limited to publications in English. We presented a comprehensive overview of recent innovations in the role of neuroimaging and EEG in identifying reliable biomarkers of epileptogenesis.

Antiepileptogenesis and disease modification: Clinical and regulatory issues

This is a summary report of clinical and regulatory issues discussed at the 2018 NINDS workshop, entitled “Accelerating Therapies for Antiepileptogenesis and Disease Modification.” The intent of the workshop was to optimize and accelerate development of therapies for antiepileptogenesis (AEG) and disease modification in the epilepsies. The working group discussed nomenclature for antiepileptogenic therapies, subdividing them into “antiepileptogenic therapies” and “disease modifying therapies,” both of which are urgently needed. We use the example of traumatic brain injury to explain issues and complexities in designing a trial for disease‐preventing antiepileptogenic therapies, including identifying timing of intervention, selecting the appropriate dose, and the need for biomarkers. We discuss the recent trials of vigabatrin to prevent onset and modify epilepsy outcome in children with tuberous sclerosis (Epistop and PreVeNT). We describe a potential approach to a disease modification trial in adults, using patients with temporal lobe epilepsy. Finally, we discuss regulatory hurdles for antiepileptogenesis and disease‐modifying trials.

Post-Traumatic Seizures: A Deep-Dive Into Pathogenesis

Post-traumatic seizures (PTS) have become an emerging challenge for neurologists worldwide with the rise of brain injuries. Trauma can lead to various outcomes, ranging from naive spasms to debilitating post-traumatic epilepsy (PTE). In this article, we will explore the pathogenesis of convulsions following a concussion. We will look at multiple studies to explain the various structural, metabolic, and inflammatory changes leading to seizures. Additionally, we will explore the association between severity and location of injury and PTE. PTE's pathophysiology is not entirely implicit, and we are still in the dark as to which anti-epileptic drugs will be useful in circumventing these attacks. The purpose of this narrative review is to explain the post-traumatic brain changes in detail so that such attacks can be either thwarted or treated more resourcefully in the future.

Multiplex Networks to Characterize Seizure Development in Traumatic Brain Injury Patients

Traumatic brain injury (TBI) may cause secondary debilitating problems, such as post-traumatic epilepsy (PTE), which occurs with unprovoked recurrent seizures, months or even years after TBI. Currently, the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) has been enrolling moderate-severe TBI patients with the goal to identify biomarkers of epileptogenesis that may help to prevent seizure occurrence and better understand the mechanism underlying PTE. In this work, we used a novel complex network approach based on segmenting T1-weighted Magnetic Resonance Imaging (MRI) scans in patches of the same dimension (network nodes) and measured pairwise patch similarities using Pearson's correlation (network connections). This network model allowed us to obtain a series of single and multiplex network metrics to comprehensively analyze the different interactions between brain components and capture structural MRI alterations related to seizure development. We used these complex network features to train a Random Forest (RF) classifier and predict, with an accuracy of 70 and a 95% confidence interval of [67, 73%], which subjects from EpiBioS4Rx have had at least one seizure after a TBI. This complex network approach also allowed the identification of the most informative scales and brain areas for the discrimination between the two clinical groups: seizure-free and seizure-affected subjects, demonstrating to be a promising pilot study which, in the future, may serve to identify and validate biomarkers of PTE.