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Stiliyan Kalitzin

Publications and source records attributed to Stiliyan Kalitzin.

8 recordsLinked to original sources
PubMed2006

Dynamics of epileptic phenomena determined from statistics of ictal transitions.

In this paper, we investigate the dynamical scenarios of transitions between normal and paroxysmal state in epilepsy. We assume that some epileptic neural network are bistable i.e., they feature two operational states, ictal and interictal that co-exist. The transitions between these two states may occur according to a Poisson process, a random walk process or as a result of deterministic time-dependent mechanisms. We analyze data from animal models of absence epilepsy, human epilepsies and in vitro models. The distributions of durations of ictal and interictal epochs are fitted with a gamma distribution. On the basis of qualitative features of the fits, we identify the dynamical processes that may have generated the underlying data. The analysis showed that the following hold. 1) The dynamics of ictal epochs differ from those of interictal states. 2) Seizure initiation can be accounted for by a random walk process while seizure termination is often mediated by deterministic mechanisms. 3) In certain cases, the transitions between ictal and interictal states can be modeled by a Poisson process operating in a bistable network. These results imply that exact prediction of seizure occurrence is not possible but termination of an ictal state by appropriate counter stimulation might be feasible.

Adolescent↗
PubMed2005

Charting epilepsy by searching for intelligence in network space with the help of evolving autonomous agents.

The problem of demarcating neural network space is formidable. A simple fully connected recurrent network of five units (binary activations, synaptic weight resolution of 10) has 3.2 *10(26) possible initial states. The problem increases drastically with scaling. Here we consider three complementary approaches to help direct the exploration to distinguish epileptic from healthy networks. [1] First, we perform a gross mapping of the space of five-unit continuous recurrent networks using randomized weights and initial activations. The majority of weight patterns (>70%) were found to result in neural assemblies exhibiting periodic limit-cycle oscillatory behavior. [2] Next we examine the activation space of non-periodic networks demonstrating that the emergence of paroxysmal activity does not require changes in connectivity. [3] The next challenge is to focus the search of network space to identify networks with more complex dynamics. Here we rely on a major available indicator critical to clinical assessment but largely ignored by epilepsy modelers, namely: behavioral states. To this end, we connected the above network layout to an external robot in which interactive states were evolved. The first random generation showed a distribution in line with approach [1]. That is, the predominate phenotypes were fixed-point or oscillatory with seizure-like motor output. As evolution progressed the profile changed markedly. Within 20 generations the entire population was able to navigate a simple environment with all individuals exhibiting multiply-stable behaviors with no cases of default locked limit-cycle oscillatory motor behavior. The resultant population may thus afford us a view of the architectural principles demarcating healthy biological networks from the pathological. The approach has an advantage over other epilepsy modeling techniques in providing a way to clarify whether observed dynamics or suggested therapies are pointing to computational viability or dead space.

Action Potentials↗
PubMed2005

Synchrony dynamics in monkey V1 predict success in visual detection.

Behavioral measures such as expectancy and attention have been associated with the strength of synchronous neural activity. On this basis, it is hypothesized that synchronous activity affects our ability to detect and recognize visual objects. To investigate the role of synchronous activity in visual perception, we studied the magnitude and precision of correlated activity, before and after stimulus presentation within the visual cortex (V1), in relation to a monkey's performance in a figure-ground discrimination task. We show that during the period of stimulus presentation a transition in synchronized activity occurs that is characterized by a reduction of the correlation peak height and width. Before stimulus onset, broad peak correlations are observed that change towards thin peak correlations after stimulus onset, due to a specific decrease of low-frequency components. The magnitude of the transition in correlated activity is larger, i.e. a stronger desynchronization occurs, when the animal perceives the stimulus correctly than when the animal fails to detect the stimulus. These results therefore show that a transition in synchronous firing is important for the detection of sensory stimuli. We hypothesize that the transition in synchrony reflects a change from loose and global neuronal interactions towards a finer temporal and spatial scale of neuronal interactions, and that such a change in neuronal interactions is required for figure-ground discrimination.

Animals↗
PubMed2005

Phase clustering of high frequency EEG: MEG components.

The study of phase consistency of high frequency EEG/MEG components can reveal properties of neuronal networks that are informative about their excitability state. The clue is that these properties are easier to put in evidence when the response of the neuronal networks is evoked by an adequate stimulation paradigm. The latter may be considered a probe of neuronal excitability state capable of revealing hidden information contained in the phase structure of neuronal activities. In this context the high frequency band components appear to be the most reactive signals.

Action Potentials↗
PubMed2005

Epileptic transitions: model predictions and experimental validation.

The essence of epilepsy is that a patient displays (long) periods of normal EEG activity (i.e., nonepileptiform) intermingled occasionally with epileptiform paroxysmal activity. The mechanisms of transition between these two types of activity are not well understood. To provide more insight into the dynamics of the neuronal networks leading to seizure generation, the authors developed a computational model of thalamocortical circuits based on relevant patho(physiologic) data. The model exhibits bistability, i.e., it features two operational states, ictal and interictal, that coexist. The transitions between these two states occur according to a Poisson process. An alternative scenario for transitions can be a random walk of network parameters that ultimately leads to a paroxysmal discharge. Predictions of bistable computational model with experimental results from different types of epilepsy are compared.

Adolescent↗
PubMed2002

Correlation-based alignment of multichannel signals and application to paroxysmal events.

We propose a new group-theoretical approach to the problem of alignment of time events in multichannel signal recordings. Such an alignment is an essential phase in the classification of transients in electroencephalogram/magnetoencephalogram (MEG) signals. A common reference frame is reconstructed applying a time translation transformation based on delayed mutual correlation functions of the individual events. The method is applied to MEG data sets recorded from epileptic patients showing paroxysmal interictal discharges.

Algorithms↗
PubMed2002

Enhancement of phase clustering in the EEG/MEG gamma frequency band anticipates transitions to paroxysmal epileptiform activity in epileptic patients with known visual sensitivity.

A new analytical method for quantifying brain activity from magnetoelectroencephalogram (MEG) and electroencephalogram (EEG) recordings during periodic light stimulation is proposed. It consists in estimating the phase clustering of harmonically related frequency components of a subject's MEG/EEG responses evoked by the light stimulation. The method was developed to test the hypothesis that changes in the dynamics of brain systems in the course of intermittent photic stimulation (IPS) may precede the transition to seizure activity in photosensitive patients. We assumed that such changes would be reflected in the phase of harmonic components of the evoked responses. Thus, we determined the phase clustering for different harmonic components of these MEG/EEG signals. We found that the patients who develop epileptiform discharges during IPS present an enhancement of the phase clustering index at the gamma frequency band, compared with that at the driving frequency. We introduce a quantity--relative phase clustering index (rPCI)--by means of which this enhancement can be quantified. We argue that this quantity reflects the degree of excitability of the underlying dynamical system and it can indicate presence of nonlinear dynamics. rPCI can be applied to detect transitions to epileptic seizure activity in patients with known sensitivity to IPS.

Brain Mapping↗