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%.

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.

Reorganization of Thalamic Inputs to Lesioned Cortex Following Experimental Traumatic Brain Injury.

Traumatic brain injury (TBI) disrupts thalamic and cortical integrity. The effect of post-injury reorganization and plasticity in thalamocortical pathways on the functional outcome remains unclear. We evaluated whether TBI causes structural changes in the thalamocortical axonal projection terminals in the primary somatosensory cortex (S1) that lead to hyperexcitability. TBI was induced in adult male Sprague Dawley rats with lateral fluid-percussion injury. A virus carrying the fluorescent-tagged opsin channel rhodopsin 2 transgene was injected into the ventroposterior thalamus. We then traced the thalamocortical pathways and analyzed the reorganization of their axonal terminals in S1. Next, we optogenetically stimulated the thalamocortical relays from the ventral posterior lateral and medial nuclei to assess the post-TBI functionality of the pathway. Immunohistochemical analysis revealed that TBI did not alter the spatial distribution or lamina-specific targeting of projection terminals in S1. TBI reduced the axon terminal density in the motor cortex by 44% and in S1 by 30%. A nematic tensor-based analysis revealed that in control rats, the axon terminals in layer V were orientated perpendicular to the pial surface (60.3°). In TBI rats their orientation was more parallel to the pial surface (5.43°, difference between the groups p < 0.05). Moreover, the level of anisotropy of the axon terminals was high in controls (0.063) compared with TBI rats (0.045, p < 0.05). Optical stimulation of the sensory thalamus increased alpha activity in electroencephalography by 312% in controls (p > 0.05) and 237% (p > 0.05) in TBI rats compared with the baseline. However, only TBI rats showed increased beta activity (33%) with harmonics at 5 Hz. Our findings indicate that TBI induces reorganization of thalamocortical axonal terminals in the perilesional cortex, which alters responses to thalamic stimulation.

Plasma miR-9-3p and miR-136-3p as Potential Novel Diagnostic Biomarkers for Experimental and Human Mild Traumatic Brain Injury.

Noninvasive, affordable circulating biomarkers for difficult-to-diagnose mild traumatic brain injury (mTBI) are an unmet medical need. Although blood microRNA (miRNA) levels are reportedly altered after traumatic brain injury (TBI), their diagnostic potential for mTBI remains inconclusive. We hypothesized that acutely altered plasma miRNAs could serve as diagnostic biomarkers both in the lateral fluid percussion injury (FPI) model and clinical mTBI. We performed plasma small RNA-sequencing from adult male Sprague-Dawley rats (n = 31) at 2 days post-TBI, followed by polymerase chain reaction (PCR)-based validation of selected candidates. miR-9a-3p, miR-136-3p, and miR-434-3p were identified as the most promising candidates at 2 days after lateral FPI. Digital droplet PCR (ddPCR) revealed 4.2-, 2.8-, and 4.6-fold elevations in miR-9a-3p, miR-136-3p, and miR-434-3p levels (p < 0.01 for all), respectively, distinguishing rats with mTBI from naïve rats with 100% sensitivity and specificity. DdPCR further identified a subpopulation of mTBI patients with plasma miR-9-3p (n = 7/15) and miR-136-3p (n = 5/15) levels higher than one standard deviation above the control mean at <2 days postinjury. In sTBI patients, plasma miR-9-3p levels were 6.5- and 9.2-fold in comparison to the mTBI and control groups, respectively. Thus, plasma miR-9-3p and miR-136-3p were identified as promising biomarker candidates for mTBI requiring further evaluation in a larger patient population.

Postinjury weight rather than cognitive or behavioral impairment predicts development of posttraumatic epilepsy after lateral fluid-percussion injury in rats.

OBJECTIVE: To identify postinjury physiologic, behavioral, and cognitive biomarkers for posttraumatic epilepsy to enrich study populations for long-term antiepileptogenesis studies. METHODS: The EPITARGET cohort with behavioral follow-up and 1-month 24/7 video-electroencephalography (vEEG) monitoring included 115 adult male Sprague-Dawley rats with lateral fluid-percussion-induced traumatic brain injury (TBI), 23 sham-operated controls, and 13 naive rats. Animals underwent assessment of somatomotor performance (composite neuroscore), anxiety-like behavior (elevated plus maze, open field), spatial memory (Morris water maze), and depression-like behavior (Porsolt forced swim, sucrose preference). Impact force, postimpact apnea time, postimpact seizure-like behavior, and body weight were monitored. RESULTS: TBI rats were impaired in the composite neuroscore (P < .001) on days (D) 2-14 and in the spatial memory test (P < .001) on D35-39 post-TBI. Differences in the elevated plus-maze (D28 and D126) and in the open field (D29 and D127) between TBI rats and controls were meager. No differences were observed in the Porsolt forced swim and sucrose preference tests as compared with sham-operated controls. Epilepsy developed in 27% of rats by the end of the sixth month. None of the behavioral or cognitive outcome measures discriminated rats with or without epilepsy. The receiver-operating characteristic analysis indicated that a decrease in body weight between D0 and D4 differentiated TBI rats with epilepsy from TBI rats without epilepsy (48% sensitivity, 83% specificity, area under the curve [AUC] 0.679, confidence interval [CI] 95% 0.56-0.80, P < .01). A 16% body weight decrease during D0-D4 could be used as a biomarker to enrich the study population from 27% (observed) to 50%. SIGNIFICANCE: Single behavioral and cognitive outcome measures showed no power as prognostic/diagnostic biomarkers for posttraumatic epilepsy. A reduction in body weight during the first postinjury week showed some prognostic value for posttraumatic epileptogenesis and could serve as a subacute measure for selectively enriching the study population for long-term preclinical biomarker and therapy discovery studies of posttraumatic epileptogenesis.

Epilepsy biomarkers - Toward etiology and pathology specificity.

A biomarker is a characteristic that is 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. In 2015, the FDA-NIH Joint Leadership Council developed the BEST Resource (Biomarkers, EndpointS, and other Tools) to improve the understanding and use of biomarker terminology in biomedical research, clinical practice, and medical product development. The BEST biomarker categories include: (a) susceptibility/risk biomarkers, (b) diagnostic biomarkers, (c) monitoring biomarkers, (d) prognostic biomarkers, (e) predictive biomarkers, (f) pharmacodynamic/response biomarkers, and (g) safety biomarkers. Here we review 30 epilepsy biomarker studies that have identified (a) diagnostic biomarkers for epilepsy, epileptogenesis, epileptogenicity, drug-refractoriness, and status epilepticus - some of the epileptogenesis and epileptogenicity biomarkers can also be considered prognostic biomarkers for the development of epilepsy in subjects with a given brain insult, (b) predictive biomarkers for epilepsy surgery outcome, and (c) a response biomarker for therapy outcome. The biomarker modalities include plasma/serum/exosomal and cerebrospinal fluid molecular biomarkers, brain tissue molecular biomarkers, imaging biomarkers, electrophysiologic biomarkers, and behavioral/cognitive biomarkers. Both single and combinatory biomarkers have been described. Most of the reviewed biomarkers have an area under the curve >0.800 in receiver operating characteristics analysis, suggesting high sensitivity and specificity. As discussed in this review, we are in the early phase of the learning curve in epilepsy biomarker discovery. Many of the seven biomarker categories lack epilepsy-related biomarkers. There is a need for epilepsy biomarker discovery using proper, statistically powered study designs with validation cohorts, and the development and use of novel analytical methods. A strategic roadmap to discuss the research priorities in epilepsy biomarker discovery, regulatory issues, and optimization of the use of resources, similar to those devised in the cancer and Alzheimer's disease research areas, is also needed.

Big data sharing and analysis to advance research in post-traumatic epilepsy.

We describe the infrastructure and functionality for a centralized preclinical and clinical data repository and analytic platform to support importing heterogeneous multi-modal data, automatically and manually linking data across modalities and sites, and searching content. We have developed and applied innovative image and electrophysiology processing methods to identify candidate biomarkers from MRI, EEG, and multi-modal data. Based on heterogeneous biomarkers, we present novel analytic tools designed to study epileptogenesis in animal model and human with the goal of tracking the probability of developing epilepsy over time.

Early Increase in Cortical T2 Relaxation Is a Prognostic Biomarker for the Evolution of Severe Cortical Damage, but Not for Epileptogenesis, after Experimental Traumatic Brain Injury.

Prognostic biomarkers for post-injury outcome are necessary for the development of neuroprotective and antiepileptogenic treatments for traumatic brain injury (TBI). We hypothesized that T2 relaxation magnetic resonance imaging (MRI) predicts the progression of perilesional cortical pathology and epileptogenesis. The EPITARGET animal cohort used for MRI analysis included 120 adult male Sprague-Dawley rats with TBI induced by lateral fluid-percussion injury and 24 sham-operated controls. T2 MRI was performed at days 2, 7, and 21 post-TBI. The lesioned cortex was outlined, and the T2 value of each imaging voxel within the lesion area was scored using a five-grade pathology classification. Analysis of 1-month video-electroencephalography recordings initiated 5 months post-TBI indicated that 27% (31 of 114) of the animals with TBI developed epilepsy. Multiple linear regression analysis indicated that T2-based classification of lesion volume at day 2 and day 7 post-TBI explained the necrotic lesion volume with greatly increased T2 (>102 ms) at day 21 post-TBI (F(13,103) = 52.5; p < 0.001; R2 = 0.87; adjusted R2 = 0.85). The volume of moderately increased (78-102 ms) T2 at day 7 post-TBI predicted the evolution of large (>12 mm3) cortical lesions (area under the curve, 0.92; p < 0.001; cutoff, 1.9 mm3; false positive rate, 0.10; true positive rate, 0.62). Logistic regression analysis, however, showed that the different severities of T2 lesion volumes at days 2, 7, and 21 post-TBI did not explain the development of epilepsy (χ2(18,95) = 18.4; p = 0.427). In addition, the location of the T2 abnormality within the cortex did not correlate with epileptogenesis. A single measurement of T2 relaxation MRI in the acute post-TBI phase is useful for identifying post-TBI subjects at highest risk of developing large cortical lesions, and thus, in the greatest need of neuroprotective therapies after TBI, but not the development of post-traumatic epilepsy.

Acute Non-Convulsive Status Epilepticus after Experimental Traumatic Brain Injury in Rats.

Severe traumatic brain injury (TBI) induces seizures or status epilepticus (SE) in 20-30% of patients during the acute phase. We hypothesized that severe TBI induced with lateral fluid-percussion injury (FPI) triggers post-impact SE. Adult Sprague-Dawley male rats were anesthetized with isoflurane and randomized into the sham-operated experimental control or lateral FPI-induced severe TBI groups. Electrodes were implanted right after impact or sham-operation, then video-electroencephalogram (EEG) monitoring was started. In addition, video-EEG was recorded from naïve rats. During the first 72 h post-TBI, injured rats had seizures that were intermingled with other epileptiform EEG patterns typical to non-convulsive SE, including occipital intermittent rhythmic delta activity, lateralized or generalized periodic discharges, spike-and-wave complexes, poly-spikes, poly-spike-and-wave complexes, generalized continuous spiking, burst suppression, or suppression. Almost all (98%) of the electrographic seizures were recorded during 0-72 h post-TBI (23.2 ± 17.4 seizures/rat). Mean latency from the impact to the first electrographic seizure was 18.4 ± 15.1 h. Mean seizure duration was 86 ± 57 sec. Analysis of high-resolution videos indicated that only 41% of electrographic seizures associated with behavioral abnormalities, which were typically subtle (Racine scale 1-2). Fifty-nine percent of electrographic seizures did not show any behavioral manifestations. In most of the rats, epileptiform EEG patterns began to decay spontaneously on Days 5-6 after TBI. Interestingly, also a few sham-operated and naïve rats had post-operation seizures, which were not associated with EEG background patterns typical to non-convulsive SE seen in TBI rats. To summarize, our data show that lateral FPI-induced TBI results in non-convulsive SE with subtle behavioral manifestations; this explains why it has remained undiagnosed until now. The lateral FPI model provides a novel platform for assessing the mechanisms of acute symptomatic non-convulsive SE and for testing treatments to prevent post-injury SE in a clinically relevant context.

Informatics tools to assess the success of procedural harmonization in preclinical multicenter biomarker discovery study on post-traumatic epileptogenesis.

The Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) is a National Institutes for Neurological Diseases and Stoke funded Centers-Without-Walls international multidisciplinary study aimed at preventing epileptogenesis. The preclinical biomarker discovery in EpiBios4Rx applies a multicenter study design to allow the number of animals that are required for adequate statistical power for the analysis to be studied in an efficient manner. Further, the use of multiple centers mimics the clinical trial situation, and therefore potentially the chance of successful clinical translation of the outcomes of the study. Its successful implementation requires harmonization of procedures and data analyses between the three contributing centers in Finland, Australia, and USA. The objective of the present analysis was to develop metrics for analysis of the success of harmonization of procedures to guide further data analyses and plan the future multicenter preclinical studies. The interim analysis of data is based on the analysis of data from 212 rats with lateral fluid-percussion injury or sham-operation included in the biomarker discovery by April 30, 2018. The details of protocols, including production of injury, post-injury follow-up, blood sampling, electroencephalogram recording, and magnetic resonance imaging have been presented in the accompanying manuscripts in this Supplement. Implementation of protocols in EpiBios4Rx project participant centers was visualized in 2D using t-distributed stochastic neighborhood embedding (t-SNE). The protocols applied to each rat were presented as feature vectors of procedure related variables (e.g., impact pressure, anesthesia time). The total number of protocol features linked to each rat was 112. The missing data was accounted in visualization by utilizing imputation and adding the number of missing values as a third dimension to 2D t-SNE plot, resulting in a 3D overview of protocol data. Intraclass correlation coefficient (ICC) using Euclidean distances and area under receiver operating characteristic curve (AUC) of k-nearest neighbor classifier (KNN) were utilized to quantify the degree of clustering by center. Both subsets of data with incomplete protocol vectors omitted and missing protocol data imputed were assessed. Our data show that a visible clustering by center was observed in all t-SNE plots, except for day 7 neuroscores. Both ICC and AUC indicated clustering by center in all protocol variable subsets, excluding unimputed day 7 neuroscores (ICC 0.04 and AUC 0.6). ICC for imputed set of all protocol related variables was 0.1 and KNN AUC 0.92. In conclusion, both ICC and AUC indicated differences in protocol between EpiBios4Rx participating centers, which needs to be taken into account in data analysis. Importantly, the majority of observed differences are recoverable as they relate to insufficient updates in record keeping. While AUC score of KNN is a more sensitive measure for protocol harmonization than ICC for data that displays complex splintered clustering, ICC and AUC provide complementary measures to assess the degree of procedural harmonization. This experience should be helpful for other groups planning such multicenter post-traumatic epileptogenesis studies in the future.

Algorithm for automatic detection of spontaneous seizures in rats with post-traumatic epilepsy.

BACKGROUND: Labor intensive electroencephalogram (EEG) analysis is a major bottleneck to identifying anti-epileptogenic treatments in experimental models of post-traumatic epilepsy. We aimed to develop an algorithm for automated seizure detection in experimental post-traumatic epilepsy. NEW METHOD: Continuous (24/7) 1-month-long video-EEG monitoring with three epidural screw electrodes was started 154 d after lateral fluid-percussion induced traumatic brain injury (TBI; n = 97) or sham-injury (n = 29) in adult male Sprague-Dawley rats. First, an experienced researcher screened a total of 90,720 h of digitized recordings on a computer screen to annotate the occurrence of spontaneous seizures. The same files were then analyzed using an algorithm in Spike2 (ver.9), which searching for temporally linked power peaks (14-42 Hz) in all three EEG channels, and then positive events were marked as a probable seizures. Finally, an experienced researcher confirmed all seizure candidates visually on the computer screen. RESULTS: Visual analysis identified 197 seizures in 29 rats. Automatic detection identified 4346 seizure candidates in 109 rats, of which 202 in the same 29 rats were true positives, resulting in a false positive rate of 0.046/h or 1.10/d. The algorithm demonstrated 5% specificity and 100% sensitivity. The algorithm analyzed 1-month 3-channel EEG in 7 cohorts in 2 h, whereas analysis by an experienced technician took ∼500 h. COMPARISON WITH EXISTING METHODS: The algorithm had 100% sensitivity. It performed slightly better and was substantially faster than investigator-performed visual analysis. CONCLUSIONS: We present a novel seizure detection algorithm for automated detection of seizures in a rat model of post-traumatic epilepsy.

Common data elements and data management: Remedy to cure underpowered preclinical studies.

Lack of translation of data obtained in preclinical trials to clinic has kindled researchers to develop new methodologies to increase the power and reproducibility of preclinical studies. One approach relates to harmonization of data collection and analysis, and has been used for a long time in clinical studies testing anti-seizure drugs. EPITARGET is a European Union FP7-funded research consortium composed of 18 partners from 9 countries. Its main research objective is to identify biomarkers and develop treatments for epileptogenesis. As the first step of harmonization of procedures between laboratories, EPITARGET established working groups for designing project-tailored common data elements (CDEs) and case report forms (CRFs) to be used in data collection and analysis. Eight major modules of CRFs were developed, presenting >1000 data points for each animal. EPITARGET presents the first single-project effort for harmonization of preclinical data collection and analysis in epilepsy research. EPITARGET is also anticipating the future challenges and requirements in a larger-scale preclinical harmonization of epilepsy studies, including training, data management expertise, cost, location, data safety and continuity of data repositories during and after funding period, and incentives motivating for the use of CDEs.