Imaging brain synchrony at high spatio-temporal resolution: application to MEG signals during absence seizures

Abstract

Imaging the dynamics of distributed phase synchrony across brain signals is of crucial importance for the study of large-scale interactions in the brain, and requires combining at the same time, wide coverage of the brain with high spatial and temporal resolution. Electro- and magneto-encephalography (EEG–MEG), which provide full head coverage measurements of the human brain activity, can potentially satisfy those needs. Nevertheless, EEG–MEG signals reflects the integration of separately generated brain processes on the scalp that typically overlap in space and give rise to spurious phase-locking between their signals. Moreover, current phase synchronization measures do not have a sufficient time resolution to capture very brief periods of phase locking between brain signals, because of their dependence on a window of time integration. We present here a new, non-invasive technique for characterizing the phase synchronization between brain regions at high spatial and temporal resolution. An efficient inverse problem algorithm was used to estimate, from the MEG signals and with the help of the anatomical MRI, the corresponding intracranial brain sources on the cortical surface. The synchronization analysis was then directly performed on the cortex by the characterization of common instantaneous frequencies between groups of cortical sources which preserve a fine temporal resolution. The proposed method was illustrated by its application to MEG data recorded during absence seizures in two epileptic patients. The technique visualizes local and short-lasting synchronization patterns leading to the seizure, thus providing new potential for understanding non-invasively the origin of epileptic discharges.

Introduction

Normal cognitive operations require the rapid integration of activities in numerous functional areas widely distributed in the brain and in constant interaction with each other [1], [2], [3], [4]. Phase-locking synchrony is an important candidate for such large-scale integration, mediated by neuronal groups that oscillate in specific frequency bands and enter into precise phase-locking over a limited period of time [4]. Large-scale cortical synchronization has been demonstrated in numerous electro- and magneto-encephalographic (EEG–MEG) studies [5], [6]. EEG–MEG are particularly well suited for this type of exploration, as they provide full head coverage with a time resolution in the msec range. The quantification of synchrony between EEG–MEG signals is therefore of crucial importance for the study of these large-scale interactions. Measures of covariance or coherence have been extensively used to quantify brain synchronization [7], [8]. These tools are however highly dependent on the stationarity of the measured signal, and do not disentangle amplitudes and phases while evaluating their interrelations. In order to overcome these limitations, and motivated by previous theoretical works on chaotic systems [9], [10], methods of “phase” synchronization analysis were introduced in the neuroscience community by Tass et al. [11] and Lachaux et al. [12]. Phase synchrony measures the temporal adjustments of a pair of signals, within a given frequency band, whereas the amplitudes can remain uncorrelated. The local stability of the phase adjustment within the band is then characterized over a time window by means of various statistical parameters [13]. The relevant theoretical formalism was analysed in detail by Bruns [14]. Other techniques, such as adaptive phase estimation by ARMA modelling [15], have also been proposed in this context.

Although phase synchronization measures have been shown to be very useful for understanding large-scale dynamical properties of cognitive processes [5], [6], [16] or neurological disorders such as Parkinson's disease [11] or epilepsy [17], [18], [19], [20], phase synchrony still faces two problems to be solved before becoming a robust method in brain imaging: (1) EEG–MEG signals do have sufficient time resolution to capture the macroscopic dynamics of brain activation and synchronization. Nevertheless, the projections on the scalp of separately generated brain processes typically overlap both in time and space, becoming inextricably mixed in recordings from scalp electrodes. This lack of a good spatial resolution of EEG/MEG may cause two separate electrodes to record from overlapping neural populations, which may lead to false detection of synchrony between electrodes not due to a coupling between brain structures but to volume conduction [21], [22]. (2) It has been emphasized that classical phase synchronization methods may be insensitive to very brief periods of synchronization because of their use of a time integration window including several cycles of oscillation [23], [24]. Otherwise, signals can be phase locked whereas their frequency of synchronization vary rapidly according to time, so that time integration within a given frequency band can mask events of synchronization [25].

The technique presented in this paper intends to overcome these limitations, and is based on a new combination of two recent developments: (1) Instead of proceeding to a temporal analysis in the sensor space, we estimated the cortical currents by resolving the inverse problem, and then performed a synchronization analysis in the source space [6], [22]. An efficient source estimation algorithm was applied to the observed MEG signals to estimate intracranial brain sources with a high spatial resolution [26]. We used the inverse problem minimum norm method that constrains the locations of the sources on the cortical mantle on the basis of the anatomical MRI. (2) We then performed a distributed synchronization analysis directly on the cortical data using the method introduced in Rudrauf et al. [25]. This method is based on the classical equivalence between phase locking and frequency locking for narrow band signals. High-resolution time-frequency decompositions are used in order to reveal the instantaneous frequencies of possibly distinct spectral components [27], [28]. Synchronized groups of cortical sources are identified as groups showing short periods of common instantaneous frequencies. Our procedure results in a spatiotemporal mapping on the structural MRI of the ongoing dynamics of synchronization among cortical sources.

We evaluate this new technique using MEG data recorded during absence seizures in two epileptic patients. Absence seizures are the most characteristic expression of non-convulsive generalized epilepsy. They consist of a sudden arrest of ongoing behaviour and impairment of consciousness associated with abrupt occurrence of bilateral, synchronous and three-per-second spike-and-wave discharges (SWD) in EEG signals over wide cortical areas [29]. Despite evidence that absence seizures imply a corticothalamic network, the mechanisms responsible for the initiation and generalization of the discharges is still not understood. The application of the proposed technique to the corresponding MEG data revealed cortical short-lasting and spatially restricted synchronization patterns leading to the seizures, thus providing helpful information for understanding, with non-invasive methods, the origin of epileptic discharges associated with absence epilepsy.