Brief activation of GABAergic interneurons initiates the transition to ictal events through post-inhibitory rebound excitation.

Activation of γ-aminobutyric acid (GABAA) receptors have been associated with the onset of epileptiform events. To investigate if a causal relationship exists between GABAA receptor activation and ictal event onset, we activated inhibitory GABAergic networks in the superficial layer (2/3) of the somatosensory cortex during hyperexcitable conditions using optogenetic techniques in mice expressing channelrhodopsin-2 in all GABAergic interneurons. We found that a brief 30ms light pulse reliably triggered either an interictal-like event (IIE) or ictal-like ("ictal") event in the in vitro cortical 4-Aminopyridine (4-AP) slice model. The link between light pulse and epileptiform event onset was lost following blockade of GABAA receptors with bicuculline methiodide. Additionally, recording the chronological sequence of events following a light pulse in a variety of configurations (whole-cell, gramicidin-perforated patch, and multi-electrode array) demonstrated an initial hyperpolarization followed by post-inhibitory rebound spiking and a subsequent slow depolarization at the transition to epileptiform activity. Furthermore, the light-triggered ictal events were independent of the duration or intensity of the initiating light pulse, suggesting an underlying regenerative mechanism. Moreover, we demonstrated that brief GABAA receptor activation can initiate ictal events in the in vivo 4-AP mouse model, in another common in vitro model of epileptiform activity, and in neocortical tissue resected from epilepsy patients. Our findings reveal that the synchronous activation of GABAergic interneurons is a robust trigger for ictal event onset in hyperexcitable cortical networks.

Conundrums of high-frequency oscillations (80-800 Hz) in the epileptic brain.

Pathological high-frequency oscillations (HFOs) (80-800 Hz) are considered biomarkers of epileptogenic tissue, but the underlying complex neuronal events are not well understood. Here, we identify and discuss several outstanding issues or conundrums in regards to the recording, analysis, and interpretation of HFOs in the epileptic brain to critically highlight what is known and what is not about these enigmatic events. High-frequency oscillations reflect a range of neuronal processes contributing to overlapping frequencies from the lower 80 Hz to the very fast spectral frequency bands. Given their complex neuronal nature, HFOs are extremely sensitive to recording conditions and analytical approaches. We provide a list of recommendations that could help to obtain comparable HFO signals in clinical and basic epilepsy research. Adopting basic standards will facilitate data sharing and interpretation that collectively will aid in understanding the role of HFOs in health and disease for translational purpose.

Identification of brain regions of interest for epilepsy surgery planning using support vector machines.

In patients with intractable epilepsy, surgical resection is a promising treatment; however, post surgical seizure freedom is contingent upon accurate identification of the seizure onset zone (SOZ). Identification of the SOZ in extratemporal epilepsy requires invasive intracranial EEG (iEEG) recordings as well as resource intensive and subjective analysis by epileptologists. Expert inspection yields inconsistent localization of the SOZ which leads to comparatively poor post surgical outcomes for patients. This study employs recordings from 6 patients undergoing resection surgery in order to develop an automated and scalable system for identifying regions of interest (ROIs). Leveraging machine learning techniques and features used for seizure detection, a classification system was trained and tested on patients with Engel class I to class IV outcomes, demonstrating superior performance in the class I patients. Further, classification using features based upon both high frequency and low frequency oscillations was best able to identify channels suited for resection. This study demonstrates a novel approach to ROI identification and provides a path for developing tools to improve outcomes in epilepsy surgery.

Classification of Multiple Seizure-Like States in Three Different Rodent Models of Epileptogenesis.

Epilepsy is a dynamical disease and its effects are evident in over fifty million people worldwide. This study focused on objective classification of the multiple states involved in the brain's epileptiform activity. Four datasets from three different rodent hippocampal preparations were explored, wherein seizure-like-events (SLE) were induced by the perfusion of a low - Mg(2+) /high-K(+) solution or 4-Aminopyridine. Local field potentials were recorded from CA3 pyramidal neurons and interneurons and modeled as Markov processes. Specifically, hidden Markov models (HMM) were used to determine the nature of the states present. Properties of the Hilbert transform were used to construct the feature spaces for HMM training. By sequentially applying the HMM training algorithm, multiple states were identified both in episodes of SLE and nonSLE activity. Specifically, preSLE and postSLE states were differentiated and multiple inner SLE states were identified. This was accomplished using features extracted from the lower frequencies (1-4 Hz, 4-8 Hz) alongside those of both the low- (40-100 Hz) and high-gamma (100-200 Hz) of the recorded electrical activity. The learning paradigm of this HMM-based system eliminates the inherent bias associated with other learning algorithms that depend on predetermined state segmentation and renders it an appropriate candidate for SLE classification.