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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...

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The magnetic field induced by a current dipole situated in a realistic volume conductor cannot be computed exactly. Here, we derive approximate analytical solutions based on the fact that in magnetoencephalography the deviation of the volume conductor ͑i.e., the head͒ from a spherical approximation is small. We present an explicit integral form which allows to calculate the nth order Taylor expansion of the magnetic field with respect to this deviation from the corresponding solution of the electric problem of order nϪ1. Especially, for a first order solution of the magnetic problem only the well-known electric solution for a spherical volume conductor is needed. The evaluation of this integral by a series of spherical harmonics results in a fast algorithm for the computation of the external magnetic field which is an excellent approximation of the true field for smooth volume conductor deformations of realistic magnitude. Since the approximation of the magnetic field is exactly curl-free it is equally good for all components. We estimate the performance for a realistic magnitude of deformations by comparing the results to the exact solution for a prolate spheroid. We found a relevant improvement over corresponding solutions given by the boundary element method for superficial sources while the performance is in the same order for deep sources.

We apply a quantitative method for the identification of asymmetric relations between weakly interacting self-sustained oscillators to the study of rhythmic neural electrical activity. We begin by testing the method on biophysically motivated neural oscillator models considering first two diffusively coupled Hindmarsh-Rose oscillators, and then two ensembles of globally coupled neurons interacting through their mean fields. Next, we consider the more complex case of interactions among several oscillatory units. The method is further applied to the analysis of the control of externally vs internally paced movements in humans. A pilot study in one healthy subject reveals that asymmetry of interactions between different brain areas may strongly change with the transition from external to internal pacing, while the degree of synchronization hardly changes. Furthermore, our preliminary results highlight the important role of the secondary auditory cortex in internal rhythm generation. at University of Washington Libraries on March 15, 2015 http://ptps.oxfordjournals.org/ Downloaded from