Imaging biomarkers of posttraumatic epileptogenesis

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.

Reduced total number of enlarged perivascular spaces in post-traumatic epilepsy patients with unilateral lesions - a feasibility study

BACKGROUND

We investigated the value of automated enlarged perivascular spaces (ePVS) quantification to distinguish chronic traumatic brain injury (TBI) patients with post-traumatic epilepsy (PTE+) from chronic TBI patients without PTE (PTE-) in a feasibility study.

METHODS

Patients with and without PTE were recruited and underwent an MRI post-TBI. Multimodal auto identification of ePVS algorithm was applied to T1-weighted MRIs to segment ePVS. The total number of ePVS was calculated and corrected for white matter volume, and an asymmetry index (AI) derived.

RESULTS

PTE was diagnosed in 7 out of the 99 participants (male=69) after a median time of less than one year since injury (range 10-22). Brain lesions were observed in all 7 PTE+ cases (unilateral=4, 57%; bilateral=3, 43%) as compared to 40 PTE- cases (total 44%; unilateral=17, 42%; bilateral=23, 58%). There was a significant difference between PTE+ (M=1.21e-4, IQR [8.89e-5]) and PTE- cases (M=2.79e-4, IQR [6.25e-5]) in total corrected numbers of ePVS in patients with unilateral lesions (p=0.024). No differences in AI, trauma severity and lesion volume were seen between groups.

CONCLUSION

This study has shown that automated quantification of ePVS is feasible and provided initial evidence that individuals with PTE with unilateral lesions may have fewer ePVS compared to TBI patients without epilepsy. Further studies with larger sample sizes should be conducted to determine the value of ePVS quantification as a PTE-biomarker.

Functional connectivity alterations in traumatic brain injury patients with late seizures

PTE is a neurological disorder characterized by recurrent and spontaneous epileptic seizures. PTE is a major public health problem occurring in 2-50% of TBI patients. Identifying PTE biomarkers is crucial for the development of effective treatments. Functional neuroimaging studies in patients with epilepsy and in epileptic rodents have observed that abnormal functional brain activity plays a role in the development of epilepsy. Network representations of complex systems ease quantitative analysis of heterogeneous interactions within a unified mathematical framework. In this work, graph theory was used to study resting state functional magnetic resonance imaging (rs-fMRI) and reveal functional connectivity abnormalities that are associated with seizure development in traumatic brain injury (TBI) patients. We examined rs-fMRI of 75 TBI patients from Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) which aims to identify validated Post-traumatic epilepsy (PTE) biomarkers and antiepileptogenic therapies using multimodal and longitudinal data acquired from 14 international sites. The dataset includes 28 subjects who had at least one late seizure after TBI and 47 subjects who had no seizures within 2 years post-injury. Each subject's neural functional network was investigated by computing the correlation between the low frequency time series of 116 regions of interest (ROIs). Each subject's functional organization was represented as a network consisting of nodes, brain regions, and edges that show the relationship between the nodes. Then, several graph measures concerning the integration and the segregation of the functional brain networks were extracted in order to highlight changes in functional connectivity between the two TBI groups. Results showed that the late seizure-affected group had a compromised balance between integration and segregation and presents functional networks that are hyperconnected, hyperintegrated but at the same time hyposegregated compared with seizure-free patients. Moreover, TBI subjects who developed late seizures had more low betweenness hubs.

Manual lesion segmentations for traumatic brain injury characterization

Traumatic brain injury (TBI) often results in heterogenous lesions that can be visualized through various neuroimaging techniques, such as magnetic resonance imaging (MRI). However, injury burden varies greatly between patients and structural deformations often impact usability of available analytic algorithms. Therefore, it is difficult to segment lesions automatically and accurately in TBI cohorts. Mislabeled lesions will ultimately lead to inaccurate findings regarding imaging biomarkers. Therefore, manual segmentation is currently considered the gold standard as this produces more accurate masks than existing automated algorithms. These masks can provide important lesion phenotype data including location, volume, and intensity, among others. There has been a recent push to investigate the correlation between these characteristics and the onset of post traumatic epilepsy (PTE), a disabling consequence of TBI. One motivation of the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) is to identify reliable imaging biomarkers of PTE. Here, we report the protocol and importance of our manual segmentation process in patients with moderate-severe TBI enrolled in EpiBioS4Rx. Through these methods, we have generated a dataset of 127 validated lesion segmentation masks for TBI patients. These ground-truths can be used for robust PTE biomarker analyses, including optimization of multimodal MRI analysis via inclusion of lesioned tissue labels. Moreover, our protocol allows for analysis of the refinement process. Though tedious, the methods reported in this work are necessary to create reliable data for effective training of future machine-learning based lesion segmentation methods in TBI patients and subsequent PTE analyses.

Biomarkers: Promising and valuable tools towards diagnosis, prognosis and treatment of Covid-19 and other diseases

The use of biomarkers as early warning systems in the evaluation of disease risk has increased markedly in the last decade. Biomarkers are indicators of typical biological processes, pathogenic processes, or pharmacological reactions to therapy. The application and identification of biomarkers in the medical and clinical fields have an enormous impact on society. In this review, we discuss the history, various definitions, classifications, characteristics, and discovery of biomarkers. Furthermore, the potential application of biomarkers in the diagnosis, prognosis, and treatment of various diseases over the last decade are reviewed. The present review aims to inspire readers to explore new avenues in biomarker research and development.

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.

Post-Traumatic Epilepsy and Comorbidities: Advanced Models, Molecular Mechanisms, Biomarkers, and Novel Therapeutic Interventions

Post-traumatic epilepsy (PTE) is one of the most devastating long-term, network consequences of traumatic brain injury (TBI). There is currently no approved treatment that can prevent onset of spontaneous seizures associated with brain injury, and many cases of PTE are refractory to antiseizure medications. Post-traumatic epileptogenesis is an enduring process by which a normal brain exhibits hypersynchronous excitability after a head injury incident. Understanding the neural networks and molecular pathologies involved in epileptogenesis are key to preventing its development or modifying disease progression. In this article, we describe a critical appraisal of the current state of PTE research with an emphasis on experimental models, molecular mechanisms of post-traumatic epileptogenesis, potential biomarkers, and the burden of PTE-associated comorbidities. The goal of epilepsy research is to identify new therapeutic strategies that can prevent PTE development or interrupt the epileptogenic process and relieve associated neuropsychiatric comorbidities. Therefore, we also describe current preclinical and clinical data on the treatment of PTE sequelae. Differences in injury patterns, latency period, and biomarkers are outlined in the context of animal model validation, pathophysiology, seizure frequency, and behavior. Improving TBI recovery and preventing seizure onset are complex and challenging tasks; however, much progress has been made within this decade demonstrating disease modifying, anti-inflammatory, and neuroprotective strategies, suggesting this goal is pragmatic. Our understanding of PTE is continuously evolving, and improved preclinical models allow for accelerated testing of critically needed novel therapeutic interventions in military and civilian persons at high risk for PTE and its devastating comorbidities.

Diffusion Tensor Orientation as a Microstructural MRI Marker of Mossy Fiber Sprouting After TBI in Rats

Diffusion tensor imaging (DTI) metrics are highly sensitive to microstructural brain alterations and are potentially useful imaging biomarkers for underlying neuropathologic changes after experimental and human traumatic brain injury (TBI). As potential imaging biomarkers require direct correlation with neuropathologic alterations for validation and interpretation, this study systematically examined neuropathologic abnormalities underlying alterations in DTI metrics in the hippocampus and cortex following controlled cortical impact (CCI) in rats. Ex vivo DTI metrics were directly compared with a comprehensive histologic battery for neurodegeneration, microgliosis, astrocytosis, and mossy fiber sprouting by Timm histochemistry at carefully matched locations immediately, 48 hours, and 4 weeks after injury. DTI abnormalities corresponded to spatially overlapping but temporally distinct neuropathologic alterations representing an aggregate measure of dynamic tissue damage and reorganization. Prominent DTI alterations of were observed for both the immediate and acute intervals after injury and associated with neurodegeneration and inflammation. In the chronic period, diffusion tensor orientation in the hilus of the dentate gyrus became prominently abnormal and was identified as a reliable structural biomarker for mossy fiber sprouting after CCI in rats, suggesting potential application as a biomarker to follow secondary progression in experimental and human TBI.

Distribution and volume analysis of early hemorrhagic contusions by MRI after traumatic brain injury: a preliminary report of the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx)

Traumatic brain injury (TBI) can produce heterogeneous injury patterns including a variety of hemorrhagic and non-hemorrhagic lesions. The impact of lesion size, location, and interaction between total number and location of contusions may influence the occurrence of seizures after TBI. We report our methodologic approach to this question in this preliminary report of the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx). We describe lesion identification and segmentation of hemorrhagic contusions by early posttraumatic magnetic resonance imaging (MRI). We describe the preliminary methods of manual lesion segmentation in an initial cohort of 32 TBI patients from the EpiBioS4Rx cohort and the preliminary association of hemorrhagic contusion and edema location and volume to seizure incidence.

Automatic Detection of EEG Epileptiform Abnormalities in Traumatic Brain Injury using Deep Learning

Traumatic brain injury (TBI) is a sudden injury that causes damage to the brain. TBI can have wide-ranging physical, psychological, and cognitive effects. TBI outcomes include acute injuries, such as contusion or hematoma, as well as chronic sequelae that emerge days to years later, including cognitive decline and seizures. Some TBI patients develop posttraumatic epilepsy (PTE), or recurrent and unprovoked seizures following TBI. In recent years, significant efforts have been made to identify biomarkers of epileptogenesis, the process by which a normal brain becomes capable of generating seizures. These biomarkers would allow for a higher standard of care by identifying patients at risk of developing PTE as candidates for antiepileptogenic interventions. In this paper, we use deep neural network architectures to automatically detect potential biomarkers of PTE from electroencephalogram (EEG) data collected between post-injury day 1-7 from patients with moderate-to-severe TBI. Continuous EEG is often part of multimodal monitoring for TBI patients in intensive care units. Clinicians review EEG to identify the presence of epileptiform abnormalities (EAs), such as seizures, periodic discharges, and abnormal rhythmic delta activity, which are potential biomarkers of epileptogenesis. We show that a recurrent neural network trained with continuous EEG data can be used to identify EAs with the highest accuracy of 80.78%, paving the way for robust, automated detection of epileptiform activity in TBI patients.

Lesion Normalization and Supervised Learning in Post-traumatic Seizure Classification with Diffusion MRI

Traumatic brain injury (TBI) is a serious condition, potentially causing seizures and other lifelong disabilities. Patients who experience at least one seizure one week after TBI (late seizure) are at high risk for lifelong complications of TBI, such as post-traumatic epilepsy (PTE). Identifying which TBI patients are at risk of developing seizures remains a challenge. Although magnetic resonance imaging (MRI) methods that probe structural and functional alterations after TBI are promising for biomarker detection, physical deformations following moderate-severe TBI present problems for standard processing of neuroimaging data, complicating the search for biomarkers. In this work, we consider a prediction task to identify which TBI patients will develop late seizures, using fractional anisotropy (FA) features from white matter tracts in diffusion-weighted MRI (dMRI). To understand how best to account for brain lesions and deformations, four preprocessing strategies are applied to dMRI, including the novel application of a lesion normalization technique to dMRI. The pipeline involving the lesion normalization technique provides the best prediction performance, with a mean accuracy of 0.819 and a mean area under the curve of 0.785. Finally, following statistical analyses of selected features, we recommend the dMRI alterations of a certain white matter tract as a potential biomarker.

Biomarkers for posttraumatic epilepsy

A biomarker is a characteristic that can be objectively measured as an indicator of normal biologic processes, pathogenic processes, or responses to an exposure or intervention, including therapeutic interventions. Biomarker modalities include molecular, histologic, radiographic, or physiologic characteristics. To improve the understanding and use of biomarker terminology in biomedical research, clinical practice, and medical product development, the Food and Drug Administration (FDA)-National Institutes of Health (NIH) Joint Leadership Council developed the BEST Resource (Biomarkers, EndpointS, and other Tools). The seven BEST biomarker categories include the following: (a) susceptibility/risk biomarkers, (b) diagnostic biomarkers, (c) monitoring biomarkers, (d) prognostic biomarkers, (e) predictive biomarkers, (f) pharmacodynamic/response biomarkers, and (g) safety biomarkers. We hypothesize some potential overlap between the reported biomarkers of traumatic brain injury (TBI), epilepsy, and posttraumatic epilepsy (PTE). Here, we tested this hypothesis by reviewing studies focusing on biomarker discovery for posttraumatic epileptogenesis and epilepsy. The biomarker modalities reviewed here include plasma/serum and cerebrospinal fluid molecular biomarkers, imaging biomarkers, and electrophysiologic biomarkers. Most of the reported biomarkers have an area under the receiver operating characteristic curve greater than 0.800, suggesting both high sensitivity and high specificity. Our results revealed little overlap in the biomarker candidates between TBI, epilepsy, and PTE. In addition to using single parameters as biomarkers, machine learning approaches have highlighted the potential for utilizing patterns of markers as biomarkers. Although published data suggest the possibility of identifying biomarkers for PTE, we are still in the early phase of the development curve. Many of the seven biomarker categories lack PTE-related biomarkers. Thus, further exploration using proper, statistically powered, and standardized study designs with validation cohorts, and by developing and applying novel analytical methods, is needed for PTE biomarker discovery.

Acute thalamic damage as a prognostic biomarker for post-traumatic epileptogenesis

OBJECTIVE

To identify magnetic resonance imaging (MRI) biomarkers for post-traumatic epilepsy.

METHODS

The EPITARGET (Targets and biomarkers for antiepileptogenesis, epitarget.eu) animal cohort completing T₂ relaxation and diffusion tensor MRI follow-up and 1-month-long video-electroencephalography monitoring included 98 male Sprague-Dawley rats with traumatic brain injury and 18 controls. T₂ imaging was performed on day (D) 2, D7, and D21 and diffusion tensor imaging (DTI) on D7 and D21 using a 7-Tesla Bruker PharmaScan MRI scanner. The mean and standard deviation (SD) of the T₂ relaxation rate, multiple diffusivity measures, and diffusion anisotropy at each time-point within the ventroposterolateral and ventroposteromedial thalamus were used as predictor variables in multi-variable logistic regression models to distinguish rats with and without epilepsy.

RESULTS

Twenty-nine percent (28/98) of the rats with traumatic brain injury (TBI) developed epilepsy. The best-performing logistic regression model utilized the D2 and D7 T₂ relaxation time as well as the D7 diffusion tensor data. The model distinguished rats with and without epilepsy (Bonferroni-corrected p-value < .001) with a cross-validated concordance statistic of 0.74 (95% confidence interval [CI] 0.60-0.84). In a cross-validated classification test, the model exhibited 54% sensitivity and 91% specificity, enriching the epilepsy rate within the study population from the expected 29% to 71%. A model using the D2 T₂ data only resulted in a 73% enriched epilepsy rate (regression p-value .007, cross-validated concordance 0.70, 95% CI 0.56-0.80, sensitivity 29%, specificity 96%).

SIGNIFICANCE

An MRI parameter set reporting on acute and subacute neuropathologic changes common to experimental and human TBI presents a diagnostic biomarker for post-traumatic epileptogenesis. Significant enrichment of the study population was achieved even when using a single time-point measurement, producing an expected epilepsy rate of 73%.

New approaches for developing multi-targeted drug combinations for disease modification of complex brain disorders. Does epilepsy prevention become a realistic goal?

Over decades, the prevailing standard in drug discovery was the concept of designing highly selective compounds that act on individual drug targets. However, more recently, multi-target and combinatorial drug therapies have become an important treatment modality in complex diseases, including neurodegenerative diseases such as Alzheimer's and Parkinson's disease. The development of such network-based approaches is facilitated by the significant advance in our understanding of the pathophysiological processes in these and other complex brain diseases and the adoption of modern computational approaches in drug discovery and repurposing. However, although drug combination therapy has become an effective means for the symptomatic treatment of many complex diseases, the holy grail of identifying clinically effective disease-modifying treatments for neurodegenerative and other brain diseases remains elusive. Thus, despite extensive research, there remains an urgent need for novel treatments that will modify the progression of the disease or prevent its development in patients at risk. Here we discuss recent approaches with a focus on multi-targeted drug combinations for prevention or modification of epilepsy. Over the last ~10 years, several novel promising multi-targeted therapeutic approaches have been identified in animal models. We envision that synergistic combinations of repurposed drugs as presented in this review will be demonstrated to prevent epilepsy in patients at risk within the next 5-10 years.

Progenitor cell therapy for acquired pediatric nervous system injury: Traumatic brain injury and acquired sensorineural hearing loss

While cell therapies hold remarkable promise for replacing injured cells and repairing damaged tissues, cell replacement is not the only means by which these therapies can achieve therapeutic effect. For example, recent publications show that treatment with varieties of adult, multipotent stem cells can improve outcomes in patients with neurological conditions such as traumatic brain injury and hearing loss without directly replacing damaged or lost cells. As the immune system plays a central role in injury response and tissue repair, we here suggest that multipotent stem cell therapies achieve therapeutic effect by altering the immune response to injury, thereby limiting damage due to inflammation and possibly promoting repair. These findings argue for a broader understanding of the mechanisms by which cell therapies can benefit patients.

Is Spreading Depolarization a Risk Factor for Late Epilepsy? A Prospective Study in Patients with Traumatic Brain Injury and Malignant Ischemic Stroke Undergoing Decompressive Craniectomy

OBJECTIVE

Spreading depolarizations (SDs) have been described in patients with ischemic and haemorrhagic stroke, traumatic brain injury, and migraine with aura, among other conditions. The exact pathophysiological mechanism of SDs is not yet fully established. Our aim in this study was to evaluate the relationship between the electrocorticography (ECoG) findings of SDs and/or epileptiform activity and subsequent epilepsy and electroclinical outcome.

METHODS

This was a prospective observational study of 39 adults, 17 with malignant middle cerebral artery infarction (MMCAI) and 22 with traumatic brain injury, who underwent decompressive craniectomy and multimodal neuromonitoring including ECoG in penumbral tissue. Serial electroencephalography (EEG) recordings were obtained for all surviving patients. Functional disability at 6 and 12 months after injury were assessed using the Barthel, modified Rankin (mRS), and Extended Glasgow Outcome (GOS-E) scales.

RESULTS

SDs were recorded in 58.9% of patients, being more common-particularly those of isoelectric type-in patients with MMCAI (p < 0.04). At follow-up, 74.7% of patients had epileptiform abnormalities on EEG and/or seizures. A significant correlation was observed between the degree of preserved brain activity on EEG and disability severity (R [mRS]: + 0.7, R [GOS-E, Barthel]: - 0.6, p < 0.001), and between the presence of multifocal epileptiform abnormalities on EEG and more severe disability on the GOS-E at 6 months (R: - 0.3, p = 0.03) and 12 months (R: - 0.3, p = 0.05). Patients with more SDs and higher depression ratios scored worse on the GOS-E (R: - 0.4 at 6 and 12 months) and Barthel (R: - 0.4 at 6 and 12 months) disability scales (p < 0.05). The number of SDs (p = 0.064) and the depression ratio (p = 0.1) on ECoG did not show a statistically significant correlation with late epilepsy.

CONCLUSIONS

SDs are common in the cortex of ischemic or traumatic penumbra. Our study suggests an association between the presence of SDs in the acute phase and worse long-term outcome, although no association with subsequent epilepsy was found. More comprehensive studies, involving ECoG and EEG could help determine their association with epileptogenesis.

Genome-wide microRNA profiling of plasma from three different animal models identifies biomarkers of temporal lobe epilepsy

Epilepsy diagnosis is complex, requires a team of specialists and relies on in-depth patient and family history, MRI-imaging and EEG monitoring. There is therefore an unmet clinical need for a non-invasive, molecular-based, biomarker to either predict the development of epilepsy or diagnose a patient with epilepsy who may not have had a witnessed seizure. Recent studies have demonstrated a role for microRNAs in the pathogenesis of epilepsy. MicroRNAs are short non-coding RNA molecules which negatively regulate gene expression, exerting profound influence on target pathways and cellular processes. The presence of microRNAs in biofluids, ease of detection, resistance to degradation and functional role in epilepsy render them excellent candidate biomarkers. Here we performed the first multi-model, genome-wide profiling of plasma microRNAs during epileptogenesis and in chronic temporal lobe epilepsy animals. From video-EEG monitored rats and mice we serially sampled blood samples and identified a set of dysregulated microRNAs comprising increased miR-93-5p, miR-142-5p, miR-182-5p, miR-199a-3p and decreased miR-574-3p during one or both phases. Validation studies found miR-93-5p, miR-199a-3p and miR-574-3p were also dysregulated in plasma from patients with intractable temporal lobe epilepsy. Treatment of mice with common anti-epileptic drugs did not alter the expression levels of any of the five miRNAs identified, however administration of an anti-epileptogenic microRNA treatment prevented dysregulation of several of these miRNAs. The miRNAs were detected within the Argonuate2-RISC complex from both neurons and microglia indicating these miRNA biomarker candidates can likely be traced back to specific brain cell types. The current studies identify additional circulating microRNA biomarkers of experimental and human epilepsy which may support diagnosis of temporal lobe epilepsy via a quick, cost-effective rapid molecular-based test.