Estimation of the spatiotemporal structure of event-related desynchronization and synchronization in magnetoencephalography.

We present a comprehensive methodology for identifying cerebral areas involved in event-related changes of electromagnetic activity of the human brain, and also for tracing the temporal evolution of this activity. Information from pre- and peristimulus time intervals--in terms of event-related synchronization (ERS) and desynchronization (ERD) of the magnetoencephalographic (MEG) signal--was directly incorporated in the relevant test statistics. For the individual steps of the analysis, we used particular estimations of the time-frequency distribution of the energy along with particular error control methods, that is, short-time Fourier transform and false-discovery rate at the sensor level and multitapers and familywise error rate at the source level. This procedure was applied to two types of group-level tests, a within-condition test and a between-conditions test. The performance of the proposed methodology is assessed by (1) analyzing the event-related brain activity from two experimental conditions of an auditory MEG experiment--passive listening to a sequence of frequency-modulated sweeps and their active categorization with respect to the direction of frequency modulation, and (2) comparing the findings with those obtained with a widely used cluster-based analysis.

Electrical brain-stimulation paradigm for estimating the seizure onset site and the time to ictal transition in temporal lobe epilepsy.

OBJECTIVE: To explore and validate a novel stimulation and analysis paradigm proposed to monitor spatial distribution and temporal changes of the excitability state in patients with temporal lobe epilepsy (TLE). METHODS: We use intermittent pulse stimulation in the frequency range 10-20Hz. A quantitative measure of spectral phase de-modulation, the relative phase clustering index (rPCI) was applied to the evoked EEG signals, measured from electrodes implanted in the hippocampal formation. RESULTS: We found that in the interictal periods, high values of rPCI recorded from specific sites were correlated with the most probable seizure onset sites (SOS). Furthermore we found that high values of rPCI from certain locations correlated with shorter time intervals to the next seizure. CONCLUSIONS: Our clinical findings indicate that although the precise moment of ictal transitions is in general unpredictable, it may be possible to estimate the probability of occurrence of some epileptic seizures. SIGNIFICANCE: The use of the rPCI for probabilistic forecasting of upcoming epileptic seizures is warranted. rPCI measurements may be used to guide interventions with the aim of modifying local tissue excitability that ultimately might prevent ictal transitions.

Dynamics of non-convulsive epileptic phenomena modeled by a bistable neuronal network.

It is currently believed that the mechanisms underlying spindle oscillations are related to those that generate spike and wave (SW) discharges. The mechanisms of transition between these two types of activity, however, are not well understood. In order to provide more insight into the dynamics of the neuronal networks leading to seizure generation in a rat experimental model of absence epilepsy we developed a computational model of thalamo-cortical circuits based on relevant (patho)physiological data. The model is constructed at the macroscopic level since this approach allows to investigate dynamical properties of the system and the role played by different mechanisms in the process of seizure generation, both at short and long time scales. The main results are the following: (i) SW discharges represent dynamical bifurcations that occur in a bistable neuronal network; (ii) the durations of paroxysmal and normal epochs have exponential distributions, indicating that transitions between these two stable states occur randomly over time with constant probabilities; (iii) the probabilistic nature of the onset of paroxysmal activity implies that it is not possible to predict its occurrence; (iv) the bistable nature of the dynamical system allows that an ictal state may be aborted by a single counter-stimulus.

Computational model of thalamo-cortical networks: dynamical control of alpha rhythms in relation to focal attention.

EEG/MEG rhythmic activities such as alpha rhythms, of the visual or of the somato-sensory cortex, are commonly modulated as subjects perform certain tasks or react to specific stimuli. In general, these activities change depending on extrinsic or intrinsic events. A decrease of the amplitude of alpha rhythmic activity occurring after a given event, which manifests as a decrease of a spectral peak, is called event-related desynchronization (ERD), whereas the inverse is called event-related synchronization (ERS), since it is assumed that the power of a spectral peak is related to the degree of synchrony of the underlying oscillating neuronal populations. An intriguing observation in this respect [Pfurtscheller and Neuper, Neurosci. Lett. 174 (1994) 93-96] was that ERD of alpha rhythms recorded over the central areas was accompanied by ERS, within the same frequency band, recorded over neighboring areas. In case the event was a hand movement, ERD was recorded over the scalp overlying the hand cortical area, whereas ERS was concomitantly recorded over the midline, whereas if the movement was of the foot the opposite was found. We called this phenomenon 'focal ERD/surround ERS'. The question of how this phenomenon may be generated was approached by means of a computational model of thalamo-cortical networks, that incorporates basic properties of neurons and synaptic interactions. These simulation studies revealed that this antagonistic ERD/ERS phenomenon depends on the functional interaction between the populations of thalamo-cortical cells (TCR) and reticular nucleus cells (RE) and on how this interaction is modulated by cholinergic inputs. An essential feature of this interaction is the existence of cross-talk between different sectors of RE that correspond to distinct sensory modules (e.g. hand, foot). These observations led us to formulate the hypothesis that this basic neurophysiological mechanism can account for the general observation that enhanced attention given to a certain stimulus (the focus) is coupled to inhibition of attention to other stimuli (the surround).

Magnetic source imaging in fixation-off sensitivity: relationship with alpha rhythm.

A patient in whom a variety of abnormal EEG findings can be elicited by elimination of central vision and fixation demonstrates fixation-off sensitivity. The underlying mechanisms of fixation-off sensitivity and its relationship with alpha rhythm remain unclear. To obtain a better understanding of this issue, we used a whole-head magnetoencephalograph to study an epileptic child with fixation-off sensitivity resulting in a 3-Hz, large-amplitude oscillation (300 microV) over the occipital regions on the EEG. Magnetic source localization revealed alpha activity around the calcarine fissure and surrounding parieto-occipital areas. Magnetic sources of abnormalities relating to fixation-off sensitivity, however, usually were located deeper in the brain, suggesting more extensively distributed sources, with involvement of the cingulate gyrus and the basomesial occipitotemporal region. Distributions of the sources of both types of activities show independent clusters but also an appreciable domain of overlap. Our findings indicate that abnormalities related to fixation-off sensitivity can emerge in thalamocortical networks, with larger and more anterior cortical distribution than those that generate alpha rhythm. Transition in the type of oscillation appears not only to depend on a change in cellular dynamics but also to be reflected in a different spatial distribution of the underlying neuronal networks.

Dynamics of the human alpha rhythm: evidence for non-linearity?

OBJECT: For a better understanding of the physiological mechanisms responsible for alpha rhythms it is important to know whether non-linear processes play a role in their generation. We used non-linear forecasting in combination with surrogate data testing to investigate the prevalence and nature of alpha rhythm non-linearity, based on EEG recordings from humans. We interpreted these findings using computer simulations of the alpha rhythm model of Lopes da Silva et al. (1974). METHODS: EEGs were recorded at 02 and O1 in 60 healthy subjects (30 males; 30 females; age: 49.28 years; range 11-84) during a resting eyes-closed state. Four artefact-free epochs (2.5 s; sample frequency 200 Hz) from each subject were tested for non-linearity using a non-linear prediction statistic and phase-randomized surrogate data. A similar type of analysis was done on the output of the alpha model for different values of input. RESULTS: In the 480 (60 subjects, 2 derivations, 4 blocks) epochs studied, the null hypothesis that the alpha rhythms can result from linearly filtered noise, could be rejected in 6 cases (1.25%). The alpha model showed a bifurcation from a point attractor to a limit cycle at an input pulse density of 615 pps. Non-linearity could only be detected in the model output close to and beyond this bifurcation point. The sources of the non-linearity are the sigmoidal relationships between average membrane potential and output pulse density of the various cells of the neuronal populations. CONCLUSION: The alpha rhythm is a heterogeneous entity dynamically: 98.75% of the epochs (type I alpha) cannot be distinguished from filtered noise. Apparently, during these epochs the activity of the brain has such a high complexity that it cannot be distinguished from a random process. In 1.25% of the epochs (type II alpha) non-linearity was found which may be explained by dynamics in the vicinity of a bifurcation to a limit cycle. There is thus experimental evidence from the point of view of dynamics for the existence of the two types of alpha rhythm and the bifurcation predicted by the model.