Novel noninvasive identification of patient-specific epileptic networks in focal epilepsies: Linking single-photon emission computed tomography perfusion during seizures with resting-state magnetoencephalography dynamics.

Single-photon emission computed tomography (SPECT) during seizures and magnetoencephalography (MEG) during the interictal state are noninvasive modalities employed in the localization of the epileptogenic zone in patients with drug-resistant focal epilepsy (DRFE). The present study aims to investigate whether there exists a preferentially high MEG functional connectivity (FC) among those regions of the brain that exhibit hyperperfusion or hypoperfusion during seizures. We studied MEG and SPECT data in 30 consecutive DRFE patients who had resective epilepsy surgery. We parcellated each ictal perfusion map into 200 regions of interest (ROIs) and generated ROI time series using source modeling of MEG data. FC between ROIs was quantified using coherence and phase-locking value. We defined a generalized linear model to relate the connectivity of each ROI, ictal perfusion z score, and distance between ROIs. We compared the coefficients relating perfusion z score to FC of each ROI and estimated the connectivity within and between resected and unresected ROIs. We found that perfusion z scores were strongly correlated with the FC of hyper-, and separately, hypoperfused ROIs across patients. High interictal connectivity was observed between hyperperfused brain regions inside and outside the resected area. High connectivity was also observed between regions of ictal hypoperfusion. Importantly, the ictally hypoperfused regions had a low interictal connectivity to regions that became hyperperfused during seizures. We conclude that brain regions exhibiting hyperperfusion during seizures highlight a preferentially connected interictal network, whereas regions of ictal hypoperfusion highlight a separate, discrete and interconnected, interictal network.

Neurovascular networks in epilepsy: Correlating ictal blood perfusion with intracranial electrophysiology.

Perfusion patterns observed in Subtraction Ictal SPECT Co-registered to MRI (SISCOM) assist in focus localization and surgical planning for patients with medically intractable focal epilepsy. While the localizing value of SISCOM has been widely investigated, its relationship to the underlying electrophysiology has not been extensively studied and is therefore not well understood. In the present study, we set to investigate this relationship in a cohort of 70 consecutive patients who underwent ictal and interictal SPECT studies and subsequent stereo-electroencephalography (SEEG) monitoring for localization of the epileptogenic focus and surgical intervention. Seizures recorded during SEEG evaluation (SEEG seizures) were matched to semiologically-similar seizures during the preoperative ictal SPECT evaluation (SPECT seizures) by comparing the semiological changes in the course of each seizure. The spectral changes of the ictal SEEG with respect to interictal ones over 7 traditional frequency bands (0.1 to 150Hz) were analyzed at each SEEG site. Neurovascular (SEEG/SPECT) relations were assessed by comparing the estimated spectral power density changes of the SEEG at each site with the perfusion changes (SISCOM z-scores) estimated from the acquired SISCOM map at that site. Across patients, a significant correlation (p<0.05) was observed between spectral changes during the SEEG seizure and SISCOM perfusion z-scores. Brain sites with high perfusion z-score exhibited higher increased SEEG power in theta to ripple frequency bands with concurrent suppression in delta and theta frequency bands compared to regions with lower perfusion z-score. The dynamics of the correlation of SISCOM perfusion and SEEG spectral power from ictal onset to seizure end and immediate postictal period were also derived. Forty-six (46) of the 70 patients underwent resective epilepsy surgery. SISCOM z-score and power increase in beta to ripple frequency bands were significantly higher in resected than non-resected sites in the patients who were seizure-free following surgery. This study provides for the first time concrete evidence that both hyper-perfusion and hypo-perfusion patterns observed in SISCOM maps have strong electrophysiological underpinnings, and that integration of the information from SISCOM and SEEG can shed light on the location and dynamics of the underlying epileptic brain networks, and thus advance our anatomo-electro-clinical understanding and approaches to targeted diagnostic and therapeutic interventions.

Scn2a deletion improves survival and brain-heart dynamics in the Kcna1-null mouse model of sudden unexpected death in epilepsy (SUDEP).

People with epilepsy have greatly increased probability of premature mortality due to sudden unexpected death in epilepsy (SUDEP). Identifying which patients are most at risk of SUDEP is hindered by a complex genetic etiology, incomplete understanding of the underlying pathophysiology and lack of prognostic biomarkers. Here we evaluated heterozygous Scn2a gene deletion (Scn2a+/-) as a protective genetic modifier in the Kcna1 knockout mouse (Kcna1-/-) model of SUDEP, while searching for biomarkers of SUDEP risk embedded in electroencephalography (EEG) and electrocardiography (ECG) recordings. The human epilepsy gene Kcna1 encodes voltage-gated Kv1.1 potassium channels that act to dampen neuronal excitability whereas Scn2a encodes voltage-gated Nav1.2 sodium channels important for action potential initiation and conduction. SUDEP-prone Kcna1-/- mice with partial genetic ablation of Nav1.2 channels (i.e. Scn2a+/-; Kcna1-/-) exhibited a two-fold increase in survival. Classical analysis of EEG and ECG recordings separately showed significantly decreased seizure durations in Scn2a+/-; Kcna1-/- mice compared with Kcna1-/- mice, without substantial modification of cardiac abnormalities. Novel analysis of the EEG and ECG together revealed a significant reduction in EEG-ECG association in Kcna1-/- mice compared with wild types, which was partially restored in Scn2a+/-; Kcna1-/- mice. The degree of EEG-ECG association was also proportional to the survival rate of mice across genotypes. These results show that Scn2a gene deletion acts as protective genetic modifier of SUDEP and suggest measures of brain-heart association as potential indices of SUDEP susceptibility.

Linear and Nonlinear Directed Connectivity Analysis of the Cardio-Respiratory System in Type 1 Diabetes.

In this study, we explored the possibility of developing non-invasive biomarkers for patients with type 1 diabetes (T1D) by quantifying the directional couplings between the cardiac, vascular, and respiratory systems, treating them as interconnected nodes in a network configuration. Towards this goal, we employed a linear directional connectivity measure, the directed transfer function (DTF), estimated by a linear multivariate autoregressive modelling of ECG, respiratory and skin perfusion signals, and a nonlinear method, the dynamical Bayesian inference (DBI) analysis of bivariate phase interactions. The physiological data were recorded concurrently for a relatively short time period (5 min) from 10 healthy control subjects and 10 T1D patients. We found that, in both control and T1D subjects, breathing had greater influence on the heart and perfusion with respect to the opposite coupling direction and that, by both employed methods of analysis, the causal influence of breathing on the heart was significantly decreased (p < 0.05) in T1D patients compared to the control group. These preliminary results, although obtained from a limited number of subjects, provide a strong indication for the usefulness of a network-based multi-modal analysis for the development of biomarkers of T1D-related complications from short-duration data, as well as their potential in the exploration of the pathophysiological mechanisms that underlie this devastating and very widespread disease.

Directed Connectivity Analysis of the Neuro-Cardio- and Respiratory Systems Reveals Novel Biomarkers of Susceptibility to SUDEP.

Goal: Sudden unexpected death in epilepsy (SUDEP) is the leading cause of epilepsy-related mortality and its pathophysiological mechanisms remain unknown. 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). 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). 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. 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.

Acute modulation of the limbic network with low and high-frequency stimulation of the human fornix.

Targeted stimulation of white matter has opened newer perspectives in the field of neuromodulation, towards an attempt to improve memory or as a therapy for epilepsy. Stimulation of the fornix, being a part of the Papez circuit, is likely to modulate the limbic network excitability. However, the stimulation-frequency dependent variability in network excitability is unknown. In the case study, which involved stereo electroencephalographic (SEEG) recording of field potentials in a 48-year old left-handed woman with suspected temporal lobe epilepsy, we demonstrated the network effects of acute low (1 and 10 Hz) and high (50 Hz) frequency electrical stimulation of fornix. Mapping the short-latency evoked responses to forniceal stimulation confirmed the SEEG target localization within the Papez circuit. Low and high-frequency stimulation of the fornix produced opposite effects in the post-stimuli excitability, with the latter causing increased excitability in the limbic network that culminated in a clinical seizure. A distinct spectral peak around 8 Hz confirmed that sensing field potentials from the forniceal white matter is feasible. This is the first case study that provided an insight into how the temporal patterning of forniceal stimulation altered the downstream limbic network excitability.

Spectral organization of focal seizures within the thalamotemporal network.

OBJECTIVE: To investigate dynamic changes in neural activity between the anterior nucleus of the thalamus (ANT) and the seizure onset zone (SOZ) in patients with drug-resistant temporal lobe epilepsy (TLE) based on anatomic location, seizure subtype, and state of vigilance (SOV). METHODS: Eleven patients undergoing stereoelectroencephalography for seizure localization were recruited prospectively for local field potential (LFP) recording directly from the ANT. The SOZ was identified using line length and epileptogenicity index. Changes in power spectral density (PSD) were compared between the two anatomic sites as seizures (N = 53) transitioned from interictal baseline to the posttermination stage. RESULTS: At baseline, the thalamic LFPs were significantly lower and distinct from the SOZ with the presence of higher power in the fast ripple band (P < 0.001). Temporal changes in ictal power of neural activity within ANT mimic those of the SOZ, are increased significantly at seizure onset (P < 0.05), and are distinct for seizures that impaired awareness or that secondarily generalized (P < 0.05). The onset of seizure was preceded by a decrease in the mean power spectral density (PSD) in ANT and SOZ (P < 0.05). Neural activity correlated with different states of vigilance at seizure onset within the ANT but not in the SOZ (P = 0.005). INTERPRETATION: The ANT can be recruited at the onset of mesial temporal lobe seizures, and the recruitment pattern differs with seizure subtypes. Furthermore, changes in neural dynamics precede seizure onset and are widespread to involve temporo-thalamic regions, thereby providing an opportunity to intervene early with closed-loop DBS.

Connectivity and Centrality Characteristics of the Epileptogenic Focus Using Directed Network Analysis.

Accurate epileptogenic focus localization is required prior to surgical resection of brain tissue for the treatment of patients with antiepileptic drug-resistant (intractable) epilepsy. This clinical need is only partially fulfilled through a subjective, and at times inconclusive, the evaluation of the recorded electroencephalogram (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, 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 nine 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 ( γ band), and particular inward centrality metrics accurately localized the focus in all nine 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.

A Novel Microbiosensor Microarray for Continuous ex Vivo Monitoring of Gamma-Aminobutyric Acid in Real-Time.

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.

Predictability analysis for an automated seizure prediction algorithm.

Epileptic seizures of mesial temporal origin are preceded by changes in signal properties detectable in the intracranial EEG. A series of computer algorithms designed to detect the changes in spatiotemporal dynamics of the EEG signals and to warn of impending seizures have been developed. In this study, we evaluated the performance of a novel adaptive threshold seizure warning algorithm (ATSWA), which detects the convergence in Short-Term Maximum Lyapunov Exponent (STLmax) values among critical intracranial EEG electrode sites, as a function of different seizure warning horizons (SWHs). The ATSWA algorithm was compared to two statistical based naïve prediction algorithms (periodic and random) that do not employ EEG information. For comparison purposes, three performance indices "area above ROC curve" (AAC), "predictability power" (PP) and "fraction of time under false warnings" (FTF) were defined and the effect of SWHs on these indices was evaluated. The results demonstrate that this EEG based seizure warning method performed significantly better (P < 0.05) than both naïve prediction schemes. Our results also show that the performance indexes are dependent on the length of the SWH. These results suggest that the EEG based analysis has the potential to be a useful tool for seizure warning.

A novel spatiotemporal analysis of peri-ictal spiking to probe the relation of spikes and seizures in epilepsy.

The relation between epileptic spikes and seizures is an important but still unresolved question in epilepsy research. Preclinical and clinical studies have produced inconclusive results on the causality or even on the existence of such a relation. We set to investigate this relation taking in consideration seizure severity and spatial extent of spike rate. We developed a novel automated spike detection algorithm based on morphological filtering techniques and then tested the hypothesis that there is a pre-ictal increase and post-ictal decrease of the spatial extent of spike rate. Peri-ictal (around seizures) spikes were detected from intracranial EEG recordings in 5 patients with temporal lobe epilepsy. The 94 recorded seizures were classified into two classes, based on the percentage of brain sites having higher or lower rate of spikes in the pre-ictal compared to post-ictal periods, with a classification accuracy of 87.4%. This seizure classification showed that seizures with increased pre-ictal spike rate and spatial extent compared to the post-ictal period were mostly (83%) clinical seizures, whereas no such statistically significant (α = 0.05) increase was observed peri-ictally in 93% of sub-clinical seizures. These consistent across patients results show the existence of a causal relation between spikes and clinical seizures, and imply resetting of the preceding spiking process by clinical seizures.

Interactive rehabilitation and dynamical analysis of scalp EEG.

Electroencephalography (EEG) has been used for decades to measure the brain's electrical activity. Planning and performing a complex movement (e.g., reaching and grasping) requires the coordination of muscles by electrical activity that can be recorded with scalp EEG from relevant regions of the cortex. Prior studies, utilizing motion capture and kinematic measures, have shown that an augmented reality feedback system for rehabilitation of stroke patients can help patients develop new motor plans and perform reaching tasks more accurately. Historically, traditional signal analysis techniques have been utilized to quantify changes in EEG when subjects perform common, simple movements. These techniques have included measures of event-related potentials in the time and frequency domains (e.g., energy and coherence measures). In this study, a more advanced, nonlinear, analysis technique, mutual information (MI), is applied to the EEG to capture the dynamics of functional connections between brain sites. In particular, the cortical activity that results from the planning and execution of novel reach trajectories by normal subjects in an augmented reality system was quantified by using statistically significant MI interactions between brain sites over time. The results show that, during the preparation for as well as the execution of a reach, the functional connectivity of the brain changes in a consistent manner over time, in terms of both the number and strength of cortical connections. A similar analysis of EEG from stroke patients may provide new insights into the functional deficiencies developed in the brain after stroke, and contribute to evaluation, and possibly the design, of novel therapeutic schemes within the framework of rehabilitation and BMI (brain machine interface).

Optimisation and data mining techniques for the screening of epileptic patients.

Identifying abnormalities or anomalies by visual inspection on neurophysiologic signals such as ElectroEncephaloGrams (EEGs), is extremely challenging. We propose a novel Multi-Dimensional Time Series (MDTS) classification technique, called Connectivity Support Vector Machines (C-SVMs) that integrates brain connectivity network with SVMs. To alter noise in EEG data, Independent Component Analysis based on the Unbiased Quasi Newton Method was applied. C-SVM achieved 94.8% accuracy classifying subjects compared to 69.4% accuracy with standard SVMs. It suggests that C-SVM can be a rapid, yet accurate, technique for online differentiation between epileptic and normal subjects. It may solve other classification MDTS problems too.

An investigation of EEG dynamics in an animal model of temporal lobe epilepsy using the maximum Lyapunov exponent.

Analysis of intracranial electroencephalographic (iEEG) recordings in patients with temporal lobe epilepsy (TLE) has revealed characteristic dynamical features that distinguish the interictal, ictal, and postictal states and inter-state transitions. Experimental investigations into the mechanisms underlying these observations require the use of an animal model. A rat TLE model was used to test for differences in iEEG dynamics between well-defined states and to test specific hypotheses: 1) the short-term maximum Lyapunov exponent (STL(max)), a measure of signal order, is lowest and closest in value among cortical sites during the ictal state, and highest and most divergent during the postictal state; 2) STL(max) values estimated from the stimulated hippocampus are the lowest among all cortical sites; and 3) the transition from the interictal to ictal state is associated with a convergence in STL(max) values among cortical sites. iEEGs were recorded from bilateral frontal cortices and hippocampi. STL(max) and T-index (a measure of convergence/divergence of STL(max) between recorded brain areas) were compared among the four different periods. Statistical tests (ANOVA and multiple comparisons) revealed that ictal STL(max) was lower (p<0.05) than other periods, STL(max) values corresponding to the stimulated hippocampus were lower than those estimated from other cortical regions, and T-index values were highest during the postictal period and lowest during the ictal period. Also, the T-index values corresponding to the preictal period were lower than those during the interictal period (p<0.05). These results indicate that a rat TLE model demonstrates several important dynamical signal characteristics similar to those found in human TLE and support future use of the model to study epileptic state transitions.