Jens Haueisen

Involuntary Motor Activity in Pianists Evoked by Music Perception

Pianists often report that pure listening to a well-trained piece of music can involuntarily trigger the respective finger movements. We designed a magnetoencephalography (MEG) experiment to compare the motor activation in pianists and nonpianists while listening to piano pieces. For pianists, we found a statistically significant increase of activity above the region of the contralateral motor cortex. Brain surface current density (BSCD) reconstructions revealed a spatial dissociation of this activity between notes preferably played by the thumb and the little finger according to the motor homunculus. Hence, we could demonstrate that pianists, when listening to well-trained piano music, exhibit involuntary motor activity involving the contralateral primary motor cortex (M1).

Influence of tissue resistivities on neuromagnetic fields and electric potentials studied with a finite element model of the head

Modeling in magnetoencephalography (MEG) and electroencephalography (EEG) requires knowledge of the in vivo tissue resistivities of the head. The aim of this paper is to examine the influence of tissue resistivity changes on the neuromagnetic field and the electric scalp potential. A high-resolution finite element method (FEM) model (452162 elements, 2-mm resolution) of the human head with 13 different tissue types is employed for this purpose. Our main finding was that the magnetic fields are sensitive to changes in the tissue resistivity in the vicinity of the source. In comparison, the electric surface potentials are sensitive to changes in the tissue resistivity in the vicinity of the source and in the vicinity of the position of the electrodes. The magnitude (strength) of magnetic fields and electric surface potentials is strongly influenced by tissue resistivity changes, while the topography is not as strongly influenced. Therefore, an accurate modeling of magnetic field and electric potential strength requires accurate knowledge of tissue resistivities, while for source localization procedures this knowledge might not be a necessity.

The Influence of Brain Tissue Anisotropy on Human EEG and MEG

The influence of gray and white matter tissue anisotropy on the human electroencephalogram (EEG) and magnetoencephalogram (MEG) was examined with a high resolution finite element model of the head of an adult male subject. The conductivity tensor data for gray and white matter were estimated from magnetic resonance diffusion tensor imaging. Simulations were carried out with single dipoles or small extended sources in the cortical gray matter. The inclusion of anisotropic volume conduction in the brain was found to have a minor influence on the topology of EEG and MEG (and hence source localization). We found a major influence on the amplitude of EEG and MEG (and hence source strength estimation) due to the change in conductivity and the inclusion of anisotropy. We expect that inclusion of tissue anisotropy information will improve source estimation procedures.

Dipole models for the EEG and MEG

The current dipole is a widely used source model in forward and inverse electroencephalography and magnetoencephalography applications. Analytic solutions to the governing field equations have been developed for several approximations of the human head using ideal dipoles as the source model. Numeric approaches such as the finite-element and finite-difference methods have become popular because they allow the use of anatomically realistic head models and the increased computational power that they require has become readily available. Although numeric methods can represent more realistic domains, the sources in such models are an approximation of the ideal dipole. In this paper, we examine several methods for representing dipole sources in finite-element models and compare the resulting surface potentials and external magnetic field with those obtained from analytic solutions using ideal dipoles.

Influence of anisotropic electrical conductivity in white matter tissue on the EEG/MEG forward and inverse solution. A high-resolution whole head simulation study.

To investigate the influence of anisotropic electrical conductivity in white matter on the forward and inverse solution in electroencephalography (EEG) and magnetoencephalography (MEG) numerical simulation studies were performed. A high-resolution (1 mm3 isotropic) finite element model of a human head was implemented to study the sensitivity of EEG and MEG source localization. In vivo information on the anisotropy was obtained from magnetic resonance diffusion tensor imaging and included into the model, whereas both a direct transformation and a direct transformation with volume normalization were used to obtain conductivity tensors. Additionally, fixed artificial anisotropy ratios were also used, while considering only the orientation information from DTI, to generate conductivity tensors. Analysis was performed using over 25,000 single dipolar sources covering the full neocortex. Major findings of the study include that EEG is more sensitive to anisotropic conductivities in white matter compared to MEG. Especially with the inverse analysis, we found that sources placed deep in sulci are located more laterally if anisotropic conductivity of white matter tissue is neglected. Overall, the single-source localization errors resulting from a neglect of anisotropy were found to be smaller compared to errors associated with other modeling errors, like misclassified tissue or the use of nonrealistic head models. In contrast to the small localization error we observed significant changes in magnitude and orientation. The latter is important since dipole orientation might be more important than absolute dipole localization in assigning, e.g., epileptic activity to the wall of the affected brain sulcal area. If high-resolution finite element models are used to perform source localization in EEG and MEG experiments and the quality of the measured data permits localization accuracy of 1 mm and below, the influence of anisotropic compartments has to be taken into account.

Influence of head models on EEG simulations and inverse source localizations

BackgroundThe structure of the anatomical surfaces, e.g., CSF and gray and white matter, could severely influence the flow of volume currents in a head model. This, in turn, will also influence the scalp potentials and the inverse source localizations. This was examined in detail with four different human head models.MethodsFour finite element head models constructed from segmented MR images of an adult male subject were used for this study. These models were: (1) Model 1: full model with eleven tissues that included detailed structure of the scalp, hard and soft skull bone, CSF, gray and white matter and other prominent tissues, (2) the Model 2 was derived from the Model 1 in which the conductivity of gray matter was set equal to the white matter, i.e., a ten tissue-type model, (3) the Model 3 was derived from the Model 1 in which the conductivities of gray matter and CSF were set equal to the white matter, i.e., a nine tissue-type model, (4) the Model 4 consisted of scalp, hard skull bone, CSF, gray and white matter, i.e., a five tissue-type model. How model complexity influences the EEG source localizations was also studied with the above four finite element models of the head. The lead fields and scalp potentials due to dipolar sources in the motor cortex were computed for all four models. The inverse source localizations were performed with an exhaustive search pattern in the motor cortex area. The inverse analysis was performed by adding uncorrelated Gaussian noise to the scalp potentials to achieve a signal to noise ratio (SNR) of -10 to 30 dB. The Model 1 was used as a reference model.ResultsThe reference model, as expected, performed the best. The Model 3, which did not have the CSF layer, performed the worst. The mean source localization errors (MLEs) of the Model 3 were larger than the Model 1 or 2. The scalp potentials were also most affected by the lack of CSF geometry in the Model 3. The MLEs for the Model 4 were also larger than the Model 1 and 2. The Model 4 and the Model 3 had similar MLEs in the SNR range of -10 dB to 0 dB. However, in the SNR range of 5 dB to 30 dB, the Model 4 has lower MLEs as compared with the Model 3.DiscussionThese results indicate that the complexity of head models strongly influences the scalp potentials and the inverse source localizations. A more complex head model performs better in inverse source localizations as compared to a model with lesser tissue surfaces. The CSF layer plays an important role in modifying the scalp potentials and also influences the inverse source localizations. In summary, for best results one needs to have highly heterogeneous models of the head for accurate simulations of scalp potentials and for inverse source localizations.

Perception of phrase structure in music

Neither music nor spoken language form uniform auditory streams, rather, they are structured into phrases. For the perception of such structures, the detection of phrase boundaries is crucial. We discovered electroencephalography (EEG) and magnetoencephalography (MEG) correlates for the perception of phrase boundaries in music. In EEG, this process was marked by a positive wave approximately between 500 and 600 ms after the offset of a phrase boundary with a centroparietal maximum. In MEG, we found major activity in an even broader time window (400–700 ms). Source localization revealed that likely candidates for the generation of the observed effects are structures in the limbic system, including anterior and posterior cingulate as well as posterior mediotemporal cortex. The timing and topography of the EEG effect bear some resemblance to a positive shift (closure positive shift, CPS) found for prosodic phrase boundaries during speech perception in an earlier study, suggesting that the underlying processes might be related. Because the brain structures, which possibly underlie the observed effects, are known to be involved in memory and attention processes, we suggest that the CPS may not reflect the detection of the phrase boundary as such, but those memory and attention related processes that are necessary to guide the attention focus from one phrase to the next, thereby closing the former and opening up the next phrase. Hum Brain Mapp 24:259–273, 2005.

Time-Frequency Mixed-Norm Estimates: Sparse M/EEG imaging with non-stationary source activations

Magnetoencephalography (MEG) and electroencephalography (EEG) allow functional brain imaging with high temporal resolution. While solving the inverse problem independently at every time point can give an image of the active brain at every millisecond, such a procedure does not capitalize on the temporal dynamics of the signal. Linear inverse methods (minimum-norm, dSPM, sLORETA, beamformers) typically assume that the signal is stationary: regularization parameter and data covariance are independent of time and the time varying signal-to-noise ratio (SNR). Other recently proposed non-linear inverse solvers promoting focal activations estimate the sources in both space and time while also assuming stationary sources during a time interval. However such a hypothesis holds only for short time intervals. To overcome this limitation, we propose time-frequency mixed-norm estimates (TF-MxNE), which use time-frequency analysis to regularize the ill-posed inverse problem. This method makes use of structured sparse priors defined in the time-frequency domain, offering more accurate estimates by capturing the non-stationary and transient nature of brain signals. State-of-the-art convex optimization procedures based on proximal operators are employed, allowing the derivation of a fast estimation algorithm. The accuracy of the TF-MxNE is compared with recently proposed inverse solvers with help of simulations and by analyzing publicly available MEG datasets.

On the influence of volume currents and extended sources on neuromagnetic fields: A simulation study

The influence of volume currents on the magnetic field is an important question in magnetoencephalography since the spherical volume conductor is still widely used for source localization. In theory, the magnetic field of a radial dipole in a homogeneous sphere is zero. In realistic models of the head, the field is suppressed when compared with a tangential dipole. To determine the influence of the volume currents, this suppression ratio (magnetic field of the radial dipole divided by the field of the tangential dipole) needs to be quantified. Large-scale finite element method models of the human head and the rabbit head were constructed and the suppression ratio was computed. The computed suppression value of 0.28 in the rabbit head was similar to the previously measured experimental value. In the human head, an average suppression ratio of 0.19±0.07 was found for different regions and depths in the gray matter. It was found that the computed magnetic field of radial sources varied significantly with the conductivities of the surrounding tissues where the dipole was located. We also modeled the magnetic field of an epileptic interictal spike in a finite element model of the rabbit head with a single dipole and with extended sources of varying length (1–8 mm). The extended source models developed were based on invasive measurements of an interictal spike within the rabbit brain. The field patterns of the small (1–2 mm) extended sources were similar to a single dipolar source and begin to deviate significantly from a dipolar field for the larger extended sources (6–8 mm).

Role of Soft Bone, CSF and Gray Matter in EEG Simulations

Effects of soft skull bone, cerebrospinal fluid (CSF) and gray matter on scalp potentials were examined with highly heterogeneous finite element models of an adult male subject. These models were constructed from segmented T1 weighted magnetic resonance images. Models had voxel resolutions of 1x1x3.2 mm with a total of about 1.5 million voxels. The scalp potentials, due to a dipolar source in the motor cortex area, were computed with an adaptive finite element solver. It was found that the scalp potentials were significantly affected by the soft bone, CSF and gray matter tissue boundaries in the models.