Imaging brain synchrony at high spatio-temporal resolution: application to MEG signals during absence seizures
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.
Section snippets
MEG source estimation
The temporal resolution of the MEG/EEG (on the order of a millisecond) allows one to measure in real-time the electromagnetic field on the scalp generated by underlying cortical neurons. The MEG recordings are totally non-invasive, but access only remote measures of brain activity and therefore offer a very limited spatial resolution. To overcome this limitation, the MEG/EEG inverse problem aims to estimate the current density of clusters of cortical neurons at the origin of the magnetic fields
Detecting synchronization by common instantaneous frequencies
In the classical sense of periodic, self-sustained oscillators, synchronization is usually defined as locking (entrainment) of the phases Δφ=nφ1−mφ2=const (a), where n and m are integers, and φ1,φ2 are the phases of the two oscillators. Condition (a) is valid for quasi-periodic oscillators only. The amplitudes of phase synchronized oscillations can be quite different and do not even need to be related [9]. For periodic oscillators, the condition of phase locking (a) is equivalent to the notion
Discussion
We have presented a new, non-invasive method for imaging at high temporal and spatial resolution, the dynamics of synchronization over the human cerebral cortex. Instead of analyzing synchronization in the sensor space, which mixes signals originating from separately generated brain processes, we first estimated cortical current densities using an inverse procedure, and then performed a distributed synchronization analysis in the sources’ space. The anatomies of the head and brain were used to
Acknowledgements
We are grateful to Richard ROBERTSON, Michel BESSERVE and Sylvain BAILLET for their suggestions and criticisms. FA is supported by a grant from the french Délégation Générale pour l’Armement.
References (43)
- et al.
Waves of consciousness: ongoing cortical patterns during binocular rivalry
Neuroimage
(2004) - et al.
Lateral coherence of the electrocorticogram: a new measure of brain synchrony
Electroenceph. Clin. Neurophysiol.
(1989) - et al.
Comparison of Hilbert transform and wavelet methods for the analysis of neuronal synchrony
J. Neurosci. Methods
(2001) - et al.
Hilbert- and wavelet-based signal analysis: are they really different approaches?
J. Neurosci. Methods
(2004) - et al.
Adaptive phase estimation and its application in EEG analysis of word processing
J. Neurosci. Methods
(1999) - et al.
Mean phase coherence as a measure for phase synchronization and its application to the EEG of epilepsy patients
Physica D
(2000) - et al.
Preictal state identification by synchronization changes in long-term intracranial EEG recordings
Clin Neurophysiol
(2005) - et al.
Spectral spatiotemporal imaging of cortical oscillations and interactions in the human brain
Neuroimage
(2004) - et al.
The use of anatomical constraints with MEG beamformers
NeuroImage
(2003) - et al.
Exploring the nonlinear dynamics of the brain
J. Physiol. Paris
(2003)
Time-frequency analysis of chaotic systems
Physica D
Synchronous activation in multiple cortical regions: a mechanism for recall
Seminars in Neurosciences
Transient phase-locking and dynamic correlations: are they the same thing?
Human Brain Mapping
Consciousness and complexity
Science
The brainweb: phase synchronization and large-scale integration
Nat. Rev. Neurosci.
Perception's shadow: long-distance synchronization of human brain activity
Nature
Episodic multiregional cortical coherence at multiple frequencies during visual task performance
Nature
Phase synchronization of chaotic oscillators
Phys. Rev. Lett.
Synchronization: A Universal Concept in Nonlinear Sciences
Detection of n:m phase locking from noisy data: application to magnetoencephalography
Phys. Rev. Lett.
Measuring phase-synchrony in brain signal
Human Brain Mapping
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