Multiple oscillatory states in models of collective neuronal dynamics.

In our previous studies, we showed that the both realistic and analytical computational models of neural dynamics can display multiple sustained states (attractors) for the same values of model parameters. Some of these states can represent normal activity while other, of oscillatory nature, may represent epileptic types of activity. We also showed that a simplified, analytical model can mimic this type of behavior and can be used instead of the realistic model for large scale simulations. The primary objective of the present work is to further explore the phenomenon of multiple stable states, co-existing in the same operational model, or phase space, in systems consisting of large number of interconnected basic units. As a second goal, we aim to specify the optimal method for state control of the system based on inducing state transitions using appropriate external stimulus. We use here interconnected model units that represent the behavior of neuronal populations as an effective dynamic system. The model unit is an analytical model (S. Kalitzin et al., Epilepsy Behav. 22 (2011) S102-S109) and does not correspond directly to realistic neuronal processes (excitatory-inhibitory synaptic interactions, action potential generation). For certain parameter choices however it displays bistable dynamics imitating the behavior of realistic neural mass models. To analyze the collective behavior of the system we applied phase synchronization analysis (PSA), principal component analysis (PCA) and stability analysis using Lyapunov exponent (LE) estimation. We obtained a large variety of stable states with different dynamic characteristics, oscillatory modes and phase relations between the units. These states can be initiated by appropriate initial conditions; transitions between them can be induced stochastically by fluctuating variables (noise) or by specific inputs. We propose a method for optimal reactive control, allowing forced transitions from one state (attractor) into another.

Quantification of spontaneous and evoked HFO's in SEEG recording and prospective for pre-surgical diagnostics. Case study.

High frequency oscillations (HFO) in stereo electroencephalographic (SEEG) signals have been recently the focus of attention as biomarkers that can have potential predictive power for the spatial location and possibly the timing of the onset of epileptic seizures. In this work we present a case study where we compare two quantitative paradigms for automated detection of biomarkers, one based on spontaneous SEEG recordings of HFOs and the other using activity induced by direct electrical stimulation (relative Phase Clustering Index algorithm). We compare the performance of these automated methods with manually detected HFO ripples by a trained EEG analyst and explore their potential diagnostic relevance. Intracranial recordings from patients undergoing pre-surgical evaluation are processed with a combination of morphological filtering and the analysis of the auto-correlation function. The results were compared to those obtained by visual inspection and to results from an active paradigm involving stimulation with 20 Hz trains of biphasic pulses. The quantity of HFOs, estimated automatically, or "rippleness", was found to correspond to the findings of a trained EEG analyst. The relative phase clustering index (rPCI) obtained using periodic stimulation appeared to be associated with the closeness to the seizure onset zone (SOZ) detected from ictal epochs. The HFO estimates were also indicative for the SOZ but with less specificity.

Electroencephalographic events prior to epileptic major motor seizures.

RATIONALE: The goal of this study is to evaluate the electroencephalographic (EEG) events, prior to clonic phases of epileptic motor seizures. Analyzing video sequences we were able to detect these special phases of motor seizures, by image features. This can be used for an early detection and alerting for these events. In the study we analyzed 42 seizures. Based on collected data we compare the quantitative results from video detection of seizures with the features computed from EEG scalp recordings from about 3 minutes prior to the seizure. We analyze the non-stationary frequency spectrum of the EEG recordings and match it against our automated video detection output in order to investigate possible precursory EEG events. METHODS: Video recordings are analyzed by applying optical flow theory, reconstruction of geometrical flow invariants, low and high pass filtering, and suitable normalizations. EEG recordings are processed with use of a Gabor wavelet technique. Comparison is achieved by means of analysis of the cross-correlation function between the derivatives of the Gabor amplitudes and the measure of "seizureness" produced by our video detection algorithm. RESULTS: In the present study certain ranges of EEG frequencies were found, where electro-graphical events precede clonic phases of clinical motor seizures from 2-8 up to 30-40 seconds. These results could be used for construction of new generation of methods for automated motor seizure detection.

Computational model prospective on the observation of proictal states in epileptic neuronal systems.

Epilepsy is a pathological condition of the human central nervous system in which normal brain functions are impaired by unexpected transitions to states called seizures. We developed a lumped neuronal model that has the property of switching between two states as a result of intrinsic or extrinsic perturbations, such as noisy fluctuations. In one version of the model, seizure risk is controlled by a single connectivity parameter representing excitatory couplings between two model lumps. We show that this risk can be reconstructed from calculation of the cross-covariance between the activities of the two neural populations during the nonictal phase. In a second simulation sequence, we use a system of 10 interconnected lumps with randomly generated connectivity matrices. We show again that the tendency to develop seizures can be inferred from the cross-covariances calculated during the nonictal states. Our conclusion is that the risk of epileptic transitions in biological systems can be objectively quantified. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.