Gamma-aminobutyric acid (GABA) is a major inhibitory neurotransmitter that is essential for normal brain function. It is involved in multiple neuronal activities, including plasticity, information processing, and network synchronization. Abnormal GABA levels result in severe brain disorders and therefore GABA has been the target of a wide range of drug therapeutics. GABA being non-electroactive is challenging to detect in real-time. To date, GABA is detected mainly via microdialysis with a high-performance liquid chromatography (HPLC) system that employs electrochemical (EC) and spectroscopic methodology. However, these systems are bulky and unsuitable for real-time continuous monitoring. As opposed to microdialysis, biosensors are easy to miniaturize and are highly suitable for in vivo studies; they selectively oxidize GABA into a secondary electroactive product (usually hydrogen peroxide, H₂O₂) in the presence of enzymes, which is then detected by amperometry. Unfortunately, this method requires a rather cumbersome process with prereactors and relies on externally applied reagents. Here, we report the design and implementation of a GABA microarray probe that operates on a newly conceived principle. It consists of two microbiosensors, one for glutamate (Glu) and one for GABA detection, modified with glutamate oxidase and GABASE enzymes, respectively. By simultaneously measuring and subtracting the H₂O₂ oxidation currents generated from these microbiosensors, GABA and Glu can be detected continuously in real-time in vitro and ex vivo and without the addition of any externally applied reagents. The detection of GABA by this probe is based upon the in-situ generation of α-ketoglutarate from the Glu oxidation that takes place at the Glu microbiosensor. A GABA sensitivity of 36 ± 2.5 pA μM^-1cm^-2, which is 26-fold higher than reported in the literature, and a limit of detection of 2 ± 0.12 μM were achieved in an in vitro setting. The GABA probe was successfully tested in an adult rat brain slice preparation. These results demonstrate that the developed GABA probe constitutes a novel and powerful neuroscientific tool that could be employed in the future for in vivo longitudinal studies of the combined role of GABA and Glu (a major excitatory neurotransmitter) signaling in brain disorders, such as epilepsy and traumatic brain injury, as well as in preclinical trials of potential therapeutic agents for the treatment of these disorders.

Goal: Accurate determination of the epileptogenic focus is of paramount diagnostic and therapeutic importance in epilepsy. The current gold standard for focus localization is from ictal (seizure) onset and thus requires the occurrence and recording of multiple typical seizures of a patient. Localization of the focus from seizure-free (interictal) periods remains a challenging problem, especially in the absence of interictal epileptiform activity. Methods: By exploring the concept of effective inflow, we developed a focus localization algorithm (FLA) based on directed connectivity between brain sites. Subsequently, using the measure of generalized partial directed coherence over a broad frequency band in FLA for the analysis of interictal periods from long-term (days) intracranial electroencephalographic signals, we identified the brain region that is the most frequent receiver of maximal effective inflow from other brain regions. Results: In six out of nine patients with temporal lobe epilepsy, the thus identified brain region was a statistically significant outlier (p <; 0.01) and coincided with the clinically assessed epileptogenic focus. In the remaining three patients, the clinically assessed focus still exhibited the highest inflow, but it was not deemed an outlier (p > 0.01). Conclusions: These findings suggest that the epileptogenic focus is a region of intense influence from other regions interictally, possibly as a mechanism to keep it under control in seizure-free periods. Significance: The developed framework is expected to assist with the accurate epileptogenic focus localization, reduce hospital stay and healthcare cost, and provide guidance to treatment of epilepsy via resective surgery or neuromodulation.

Sudden unexpected death in epilepsy (SUDEP) is the leading cause of epilepsy-related mortality and its pathophysiological mechanisms remain unknown. Goal: We set to record and analyze for the first time concurrent electroencephalographic (EEG), electrocardiographic (ECG), and unrestrained whole-body plethysmographic (Pleth) signals from control (WT - wild type) and SUDEP-prone mice (KO- knockout Kcna1 animal model). Methods: Employing multivariate autoregressive models (MVAR) we measured all tri-organ effective directional interactions by the generalized partial directed coherence (GPDC) in the frequency domain over time (hours). Results: When compared to the control (WT) animals, the SUDEP-prone (KO) animals exhibited (p <; 0.001) reduced afferent and efferent interactions between the heart and the brain over the full frequency spectrum (0-200Hz), enhanced efferent interactions from the brain to the lungs and from the heart to the lungs at high (>90 Hz) frequencies (especially during periods with seizure activity), and decreased feedback from the lungs to the brain at low (<; 40 Hz) frequencies. Conclusions: These results show that impairment in the afferent and efferent pathways in the holistic neuro-cardio-respiratory network could lead to SUDEP, and effective connectivity measures and their dynamics could serve as novel biomarkers of susceptibility to SUDEP and seizures respectively.

The sudden and spontaneous occurrence of epileptic seizures can impose a significant burden on patients with epilepsy. If seizure onset can be prospectively predicted, it could greatly improve the life of patients with epilepsy and also open new therapeutic avenues for epilepsy treatment. However, discovering effective predictive patterns from massive brainwave signals is still a challenging problem. The prediction of epileptic seizures is still in its early stage. Most existing studies actually investigated the predictability of seizures offline instead of a truly prospective online prediction, and also the high inter-individual variability was not fully considered in prediction. In this study, we propose a novel adaptive pattern learning framework with a new online feature extraction approach to achieve personalized online prospective seizure prediction. In particular, a two-level online feature extraction approach is applied to monitor intracranial electroencephalogram (EEG) signals and construct a pattern library incrementally. Three prediction rules were developed and evaluated based on the continuously-updated patient-specific pattern library for each patient, including the adaptive probabilistic prediction (APP), adaptive linear-discriminant-analysis-based prediction (ALP), and adaptive Naive Bayes-based prediction (ANBP). The proposed online pattern learning and prediction system achieved impressive prediction results for 10 patients with epilepsy using long-term EEG recordings. The best testing prediction accuracy averaged over the 10 patients were 79, 78, and 82 percent for the APP, ALP, and ANBP prediction scheme, respectively. The experimental results confirmed that the proposed adaptive prediction framework offers a promising practical tool to solve the challenging seizure prediction problem.

Accurate epileptogenic focus localization is required prior to surgical resection of brain tissue for treatment of patients with antiepileptic drug resistant (intractable) epilepsy. This clinical need is only partially fulfilled through a subjective, and at times inconclusive, evaluation of the recorded electroence-phalogram (EEG) at seizures’ onset (the so-called gold standard for focus localization in epilepsy). We herein present a novel method of multivariate analysis of the EEG that appears to be very promising for an objective and robust localization of the epileptogenic focus at seizures’ onset. Using the measure of Generalized Partial Directed Coherence (GPDC), combined with surrogate data analysis, we first estimated from multichannel intracranial EEG the statistically significant causal interactions between brain regions at the onset of 92 clinical seizures from 9 patients with temporal lobe intractable epilepsy. From the networks that were formed based on the thus derived interactions, a set of centrality metrics was estimated per network node (brain site). Brain sites located anatomically within the epileptogenic focus were shown to be associated with greater inward centrality values than non-focal brain regions at high frequencies (.. band), and particular inward centrality metrics accurately localized the focus in all 9 patients. In addition to focus localization from seizure (ictal) onset, the developed novel framework for analysis of EEG could be employed to identify the changes of the focal network over time, peri-ictally and interictally, and thus shed light onto the dynamics of ictogenesis, which could then have a significant impact on automated prediction and closed-loop control of seizures by neuromodulation.

In this case study, we present evidence of resetting of brain dynamics following convulsive status epilepticus (SE) that was treated successfully with anti-epileptic medications (AEDs). The measure of effective inflow (EI), a novel measure of network connectivity, was applied to the recorded intracranial multi-channel SEEG before, during and after SE. Results from this analysis indicate trends of progressive reduction of EI over hours up to the onset of SE, mainly at sites of the epileptogenic focus with reversal of those trends upon successful treatment of SE by AEDs. The proposed analytical framework is promising for elucidation of the pathology of neuronal network dynamics that could lead to SE, evaluation of the efficacy of SE treatment strategies, as well as the development of biomarkers for susceptibility to SE.