Synchronization Tomography: A Method for Three-Dimensional Localization of Phase Synchronized Neuronal Populations in the Human Brain using Magnetoencephalography

Abstract

We present a noninvasive technique which allows the anatomical localization of phase synchronized neuronal populations in the human brain with magnetoencephalography. We study phase synchronization between the reconstructed current source density (CSD) of different brain areas as well as between the CSD and muscular activity. We asked four subjects to tap their fingers in synchrony with a rhythmic tone, and to continue tapping at the same rate after the tone was switched off. The phase synchronization behavior of brain areas relevant for movement coordination, inner voice, and time estimation changes drastically when the transition to internal pacing occurs, while their averaged amplitudes remain unchanged. Information of this kind cannot be derived with standard neuroimaging techniques like functional magnetic resonance imaging or positron emission tomography. [5,6] systems. In animals, phase synchronization has been demonstrated to be a fundamental mechanism for motor control [7]. Active neuronal populations in the brain generate currents which, in turn, produce a magnetic field that can noninvasively be measured by means of magnetoencephalography (MEG) with a time resolution in the millisecond range. Phase synchronization can be detected in noisy, nonstationary MEG signals [5]. However, the spatial resolution of this approach has so far been severely limited, since MEG sensors measure the magnetic field which may originate from different brain areas. To study normal brain function as well as pathological synchronization (e.g., in Parkinson's disease and epilepsy) a correct anatomical localization of synchronization processes is crucial. We here present a novel method which allows a reliable 3D localization of phase synchronization in the human brain with MEG.We first reconstructed the cerebral current source density jx; t, which generates the measured magnetic field, in each volume element (voxel) for all times t with magnetic field tomography [8]. x denotes the spatial coordinates representing the center of a voxel. We then analyzed phase synchronization voxel by voxel: To detect cerebro-muscular synchronization (CMS) we determined phase synchronization between the muscular activity recorded with electromyography (EMG) and jx; t in each of the voxels representing the brain. To detect cerebrocerebral synchronization (CCS) we determined phase synchronization of jx; t in all pairs of voxels.We applied our approach to study internal rhythm generation in humans. The latter is essential for performing rhythmic movements without external stimulus, e.g., during locomotion or skilled actions like playing muscial instruments. We performed a paced finger tapping (PFT) experiment [9], where subjects are first asked to tap with their index finger in synchrony with a periodic train of tones (external pacing). After discontinuing the tones, the subjects then have to continue the tapping at the same pace (internal pacing), by generating the rhythm alone.Behavioral studies of movement timing in PFT studies revealed an inte...

Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer's disease

“…Two approaches have been submitted to overcome this problem. First, one may study interdependencies between time series of reconstructed sources rather than signals of recording electrodes or sensors (Gross et al, 2001;David et al, 2002;Tass et al, 2003;Amor et al, 2005;Hadjipapas et al, 2005;Lehmann et al, 2006). While this approach has certainly the added benefit of dealing with interactions between anatomically well-defined brain regions, a major pitfall is the absence of a unique definition of the corresponding source space.…”

Parametric analysis of oscillatory activity as measured with EEG/MEG

“…A further difference is that the window of the MWT is Gaussian, whereas the STFT and the bandpass filter can be applied with arbitrary forms. The bandpass filter does not need to conform to a windowed FIR filter [see, e.g., Tass et al, 2003]. In theory, free choice of the bandpass filter gives more latitude to the frequency response.…”

Neural Computation and the Computational Theory of Cognition

“…Even if such absolute time determinations were possible, the inherent noisiness in neural firing times would result in a varying degree of synchrony over time. Synchrony between spikes has been studied intensely by the nonlinear dynamics community over the past few decades in the context of the synchronization of coupled oscillators (Pikovsky, Rosenblum, & Kurths, 2003); it may play a role in normal brain functions such as attention (Fries, Reynolds, Rorie, & Desimone, 2001;Fries, Womelsdorf, Oostenveld, & Desimone, 2008;Roy, Steinmetz, Hsiao, Johnson, & Niebur, 2007;Steinmetz et al, 2000); it has been applied to the analysis of neural pathologies such as epilepsy (Uhlhaas & Singer, 2006;Wong, Traub, & Miles, 1986) and Parkinson's disease (Tass et al, 2003). But the interesting question about neural synchrony is not whether, in an absolute sense, there is synchrony in a neural process, but rather how much synchrony there is.…”