RT Journal Article
SR Electronic
T1 Electrocorticographic Dynamics as a Novel Biomarker in Five Models of Epileptogenesis
JF The Journal of Neuroscience
JO J. Neurosci.
FD Society for Neuroscience
SP 4450
OP 4461
DO 10.1523/JNEUROSCI.2446-16.2017
VO 37
IS 17
A1 Dan Z. Milikovsky
A1 Itai Weissberg
A1 Lyn Kamintsky
A1 Kristina Lippmann
A1 Osnat Schefenbauer
A1 Federica Frigerio
A1 Massimo Rizzi
A1 Liron Sheintuch
A1 Daniel Zelig
A1 Jonathan Ofer
A1 Annamaria Vezzani
A1 Alon Friedman
YR 2017
UL http://www.jneurosci.org/content/37/17/4450.abstract
AB Postinjury epilepsy (PIE) is a devastating sequela of various brain insults. While recent studies offer novel insights into the mechanisms underlying epileptogenesis and discover potential preventive treatments, the lack of PIE biomarkers hinders the clinical implementation of such treatments. Here we explored the biomarker potential of different electrographic features in five models of PIE. Electrocorticographic or intrahippocampal recordings of epileptogenesis (from the insult to the first spontaneous seizure) from two laboratories were analyzed in three mouse and two rat PIE models. Time, frequency, and fractal and nonlinear properties of the signals were examined, in addition to the daily rate of epileptiform spikes, the relative power of five frequency bands (theta, alpha, beta, low gamma, and high gamma) and the dynamics of these features over time. During the latent pre-seizure period, epileptiform spikes were more frequent in epileptic compared with nonepileptic rodents; however, this feature showed limited predictive power due to high inter- and intra-animal variability. While nondynamic rhythmic representation failed to predict epilepsy, the dynamics of the theta band were found to predict PIE with a sensitivity and specificity of >90%. Moreover, theta dynamics were found to be inversely correlated with the latency period (and thus predict the onset of seizures) and with the power change of the high-gamma rhythm. In addition, changes in theta band power during epileptogenesis were associated with altered locomotor activity and distorted circadian rhythm. These results suggest that changes in theta band during the epileptogenic period may serve as a diagnostic biomarker for epileptogenesis, able to predict the future onset of spontaneous seizures.SIGNIFICANCE STATEMENT Postinjury epilepsy is an unpreventable and devastating disorder that develops following brain injuries, such as traumatic brain injury and stroke, and is often associated with neuropsychiatric comorbidities. As PIE affects as many as 20% of brain-injured patients, reliable biomarkers are imperative before any preclinical therapeutics can find clinical translation. We demonstrate the capacity to predict the epileptic outcome in five different models of PIE, highlighting theta rhythm dynamics as a promising biomarker for epilepsy. Our findings prompt the exploration of theta dynamics (using repeated electroencephalographic recordings) as an epilepsy biomarker in brain injury patients.