Initiation of voluntary movements at free will and ongoing 0.1-Hz BOLD oscillations in the insula-a pilot study.

Recently we hypothesized that the intention to initiate a voluntary movement at free will may be related to the dynamics of hemodynamic variables, which may be supported by the intertwining of networks for the timing of voluntary movements and the control of cardiovascular variables in the insula. In the present study voluntary movements of 3 healthy subjects were analyzed using fMRI scans at 1.83-s intervals along with the time course of slow hemodynamic changes in sensorimotor networks. For the analyses of BOLD time courses the Wavelet transform coherence (WTC) and calculation of phase-locking values were used. Analyzed was the frequency band between 0.07 and 0.13 Hz in the supplementary motor area (SMA) and insula, two widely separated regions co-active in motor behavior. BOLD signals displayed slow fluctuations, concentrated around 0.1 Hz whereby the intrinsic oscillations in the insula preceded those in the SMA by 0.5-1 s. These preliminary results suggest that slow hemodynamic changes in SMA and insula may condition the initiation of a voluntary movement at free will.

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.

Reactive control of epileptiform discharges in realistic computational neuronal models with bistability.

We aim to derive fully autonomous seizure suppression paradigms based on reactive control of neuronal dynamics. A previously derived computational model of seizure generation describing collective degrees of freedom and featuring bistable dynamics is used. A novel technique for real-time control of epileptogenicity is introduced. The reactive control reduces practically all seizures in the model. The study indicates which parameters provide the maximal seizure reduction with minimal intervention. An adaptive scheme is proposed that optimizes the stimulation parameters in nonstationary situations.

High-frequency oscillations - where we are and where we need to go.

High-frequency oscillations (HFOs) are EEG field potentials with frequencies higher than 30 Hz; commonly the frequency band between 30 and 70 Hz is denominated the gamma band, but with the discovery of activities at frequencies higher than 70 Hz a variety of terms have been proposed to describe the latter (Gotman and Crone, 2011). In general we may consider that the term HFO encompasses activities from 30 to 600 Hz. The best practice is to indicate always explicitly the frequency range of the HFOs in any specific study. There are numerous types of HFOs: those in normal brain appear to facilitate synchronization and information transfer necessary for cognitive processes and memory, while a particular class of HFOs in the brain of animals and people with epilepsy appears to reflect fundamental mechanisms of epileptic phenomena and could serve as biomarkers of epileptogenesis and epileptogenicity in abnormal conditions such as epilepsy. A better understanding of the significance of HFOs depends on a deeper analysis of the mechanisms of generation of different kinds of HFOs, that typically are at the crossroads between intrinsic membrane properties and neuronal interactions, both chemical and electrical. There is still a lack of understanding of how specific information is carried by HFOs and can be operational in normal cognitive processes such as in working and long-term memory and abnormal conditions such as epilepsy. The complexity of these processes makes the development of relevant computational models of dynamical neuronal networks most compelling.

Active paradigms of seizure anticipation: computer model evidence for necessity of stimulation.

It has been shown that the analysis of electroencephalographic (EEG) signals submitted to an appropriate external stimulation (active paradigm) is efficient with respect to anticipating epileptic seizures [S. Kalitzin, Clin. Neurophysiol. 116, 718 (2005)]. To better understand how an active paradigm is able to detect properties of EEG signals by means of which proictal states can be identified, we performed a simulation study using a computational model of seizure generation of a hippocampal network. Applying the active stimulation methodology, we investigated (i) how changes in model parameters that lead to a transition from the normal ongoing EEG to an ictal pattern are reflected in the properties of the simulated EEG output signals and (ii) how the evolution of neuronal excitability towards seizures can be reconstructed from EEG data using an active paradigm, rather than passively, using only ongoing EEG signals. The simulations indicate that a stimulation paradigm combined with appropriate analytical tools, as proposed here, may yield information about the change in excitability that precedes the transition to a seizure. Such information is apparently not fully reflected in the ongoing EEG activity. These findings give strong support to the development and application of active paradigms with the aim of predicting the occurrence of a transition to an epileptic seizure.

Non-provocative diagnostics of photosensitivity using visual evoked potentials.

OBJECTIVE: Photosensitive epilepsy (PSE) is the most common form of reflex epilepsy. Usually, to find out whether a patient is sensitive, he/she is stimulated visually with, e.g. a stroboscopic light stimulus at variable frequency and intensity until a photo paroxysmal response (PPR) occurs. The research described in this work aims to find whether photosensitivity can be detected without provoking a PPR. METHODS: Twenty-two subjects, 15 with known photosensitivity, were stimulated with visual stimuli that did not provoke a PPR. Using an "evoked response representation", 18 features were analytically derived from EEG signals. Single- and multi-feature classification paradigms were applied to extract those features that separate best subjects with PSE from controls. RESULTS: Two variables in the "evoked response representation", a frequency term and a goodness of fit term to a particular template, appeared to be best suited to make a prediction about the photosensitivity of a subject. CONCLUSIONS: Evoked responses appear to carry information about potential PSE. SIGNIFICANCE: This result can be useful for screening patients for photosensitivity and it may also help to assess in a quantitative way the effectiveness of medical therapy.

Recommendations regarding the requirements and applications for long-term recordings in epilepsy.

The purpose of this paper is to update the state of knowledge with respect to long-term monitoring (LTM) in epilepsy and to formulate recommendations regarding the application of LTM in clinical practice. LTM is an established technique in use both in a hospital setting and, increasingly, in an ambulatory and more recently in a community-based setting. There has been sufficient evidence to substantiate the claim that LTM is of crucial importance in documenting electroclinical correlations both in epilepsy and in paroxysmally occurring behavioral changes often mistaken for epilepsy. Internationally recognized neurophysiological equipment standards, data acquisition and data transfer protocols and widely accepted safety standards have made widespread access to LTM facilities in epilepsy possible. Recommendations on efficient and effective use of resources as well as regarding training and competencies for personnel involved in LTM in epilepsy have been formulated. The DMC Neurophysiology Subcommittee of the ILAE recommends use of hospital-based LTM in the documentation of seizures including its application for assessing seizure type and frequency, in the evaluation of status epilepticus, in noninvasive and invasive video/EEG investigations for epilepsy surgery and for the differential diagnosis between epilepsy and paroxysmally occurring nonepileptic conditions, in children and in adults. Ambulatory outpatient and community-based LTM may be used as a substitute for inpatient LTM in cases where the latter is not cost-effective or feasible or when activation procedures aimed at increasing seizure yield are not indicated. However, outpatient ambulatory monitoring may be less informative than is inpatient monitoring in some cases because: (1) reduction of medication to provoke seizures may not be safe as an outpatient; (2) faulty electrode contacts cannot quickly be noticed and repaired; (3) the patient may move out of video surveillance; and (4) duration of ambulatory monitoring can be limited by technical constraints.

What does an epileptiform spike look like in MEG? Comparison between coincident EEG and MEG spikes.

Recent investigations suggest that there are differences between the characteristics of EEG and MEG epileptiform spikes. The authors performed an objective characterization of the morphology of epileptiform spikes recorded simultaneously in both EEG and MEG to determine whether they present the same morphologic characteristics. Based on a stepwise approach, the authors performed a computer analysis of EEG and MEG of a set of coincident epileptiform transients selected by a senior clinical neurophysiologist in recordings of three patients with drug-resistant epilepsy. A computer-based algorithm was applied to extract parameters that could be used to describe quantitatively the morphology of the transients, followed by a statistical comparison over the extracted metrics of the EEG and MEG waveforms. EEG and MEG coincident events were statistically different with respect to several morphologic characteristics, such as duration, sharpness, and shape. The differences found appear to be a consequence of MEG signals not being influenced by volume propagation through the tissues with different conductivities that surround the brain, compared with EEG, and of the different orientation of the underlying dipolar sources. The results indicate that visual inspection of MEG spikes and automatic spike-detector algorithms should use criteria adapted to the specific characteristics of the MEG, and not simply those used on conventional EEG.

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.