Alpha power during task performance predicts individual language comprehension.

Alpha power attenuation during cognitive task performing has been suggested to reflect a process of release of inhibition, increase of excitability, and thereby benefit the improvement of performance. Here, we hypothesized that changes in individual alpha power during the execution of a complex language comprehension task may correlate with the individual performance in that task. We tested this using magnetoencephalography (MEG) recorded during comprehension of German sentences of different syntactic complexity. Results showed that neither the frequency nor the power of the spontaneous oscillatory activity at rest were associated with the individual performance. However, during the execution of a sentences processing task, the individual alpha power attenuation did correlate with individual language comprehension performance. Source reconstruction localized these effects in left temporal-parietal brain regions known to be associated with language processing and their right-hemisphere homologues. Our results support the notion that in-task attenuation of individual alpha power is related to the essential mechanisms of the underlying cognitive processes, rather than merely to general phenomena like attention or vigilance.

Zoomed MRI Guided by Combined EEG/MEG Source Analysis: A Multimodal Approach for Optimizing Presurgical Epilepsy Work-up and its Application in a Multi-focal Epilepsy Patient Case Study.

In recent years, the use of source analysis based on electroencephalography (EEG) and magnetoencephalography (MEG) has gained considerable attention in presurgical epilepsy diagnosis. However, in many cases the source analysis alone is not used to tailor surgery unless the findings are confirmed by lesions, such as, e.g., cortical malformations in MRI. For many patients, the histology of tissue resected from MRI negative epilepsy shows small lesions, which indicates the need for more sensitive MR sequences. In this paper, we describe a technique to maximize the synergy between combined EEG/MEG (EMEG) source analysis and high resolution MRI. The procedure has three main steps: (1) construction of a detailed and calibrated finite element head model that considers the variation of individual skull conductivities and white matter anisotropy, (2) EMEG source analysis performed on averaged interictal epileptic discharges (IED), (3) high resolution (0.5 mm) zoomed MR imaging, limited to small areas centered at the EMEG source locations. The proposed new diagnosis procedure was then applied in a particularly challenging case of an epilepsy patient: EMEG analysis at the peak of the IED coincided with a right frontal focal cortical dysplasia (FCD), which had been detected at standard 1 mm resolution MRI. Of higher interest, zoomed MR imaging (applying parallel transmission, 'ZOOMit') guided by EMEG at the spike onset revealed a second, fairly subtle, FCD in the left fronto-central region. The evaluation revealed that this second FCD, which had not been detectable with standard 1 mm resolution, was the trigger of the seizures.

Influences of skull segmentation inaccuracies on EEG source analysis.

The low-conducting human skull is known to have an especially large influence on electroencephalography (EEG) source analysis. Because of difficulties segmenting the complex skull geometry out of magnetic resonance images, volume conductor models for EEG source analysis might contain inaccuracies and simplifications regarding the geometry of the skull. The computer simulation study presented here investigated the influences of a variety of skull geometry deficiencies on EEG forward simulations and source reconstruction from EEG data. Reference EEG data was simulated in a detailed and anatomically plausible reference model. Test models were derived from the reference model representing a variety of skull geometry inaccuracies and simplifications. These included erroneous skull holes, local errors in skull thickness, modeling cavities as bone, downward extension of the model and simplifying the inferior skull or the inferior skull and scalp as layers of constant thickness. The reference EEG data was compared to forward simulations in the test models, and source reconstruction in the test models was performed on the simulated reference data. The finite element method with high-resolution meshes was employed for all forward simulations. It was found that large skull geometry inaccuracies close to the source space, for example, when cutting the model directly below the skull, led to errors of 20mm and more for extended source space regions. Local defects, for example, erroneous skull holes, caused non-negligible errors only in the vicinity of the defect. The study design allowed a comparison of influence size, and guidelines for modeling the skull geometry were concluded.

Overlap of musical and linguistic syntax processing: intracranial ERP evidence.

The present study investigated the co-localization of musical and linguistic syntax processing in the human brain. EEGs were recorded from subdural electrodes placed on the left and right perisylvian cortex. The neural generators of the early potentials elicited by syntactic errors in music and language were localized by means of distributed source modeling and compared within subjects. The combined results indicated a partial overlap of the sources within the bilateral superior temporal gyrus, and, to a lesser extent, in the left inferior frontal gyrus, qualifying these areas as shared anatomic substrates of early syntactic error detection in music and language.

Quantifying brain connectivity: a comparative tractography study.

In this paper, we compare a representative selection of current state-of-the-art algorithms in diffusion-weighted magnetic resonance imaging (dwMRI) tractography, and propose a novel way to quantitatively define the connectivity between brain regions. As criterion for the comparison, we quantify the connectivity computed with the different methods. We provide initial results using diffusion tensor, spherical deconvolution, ball-and-stick model, and persistent angular structure (PAS) along with deterministic and probabilistic tractography algorithms on a human DWI dataset. The connectivity is presented for a representative selection of regions in the brain in matrices and connectograms. Our results show that fiber crossing models are able to reveal connections between more brain areas than the simple tensor model. Probabilistic approaches show in average more connected regions but lower connectivity values than deterministic methods.

Connectivity-Based Parcellation of Broca's Area.

It is generally agreed that the cerebral cortex can be segregated into structurally and functionally distinct areas. Anatomical subdivision of Broca's area has been achieved using different microanatomical criteria, such as cytoarchitecture and distribution of neuroreceptors. However, brain function also strongly depends upon anatomical connectivity, which therefore forms a sensible criterion for the functio-anatomical segregation of cortical areas. Diffusion-weighted magnetic resonance (MR) imaging offers the opportunity to apply this criterion in the individual living subject. Probabilistic tractographic methods provide excellent means to extract the connectivity signatures from diffusion-weighting MR data sets. The correlations among these signatures may then be used by an automatic clustering method to identify cortical regions with mutually distinct and internally coherent connectivity. We made use of this principle to parcellate Broca's area. As it turned out, 3 subregions are discernible that were identified as putative Brodmann area (BA) 44, BA45, and the deep frontal operculum. These results are discussed in the light of previous evidence from other methods in both human and nonhuman primates. We conclude that plausible results can be achieved by the proposed technique, which cannot be obtained by any other method in vivo. For the first time, there is a possibility to investigate the anatomical subdivision of Broca's area noninvasively in the individual living human subject.

Rapidly induced changes in neuromagnetic fields following repetitive hand movements.

Sensory feedback plays a major role in movement execution and motor learning, particularly in motor rehabilitation. Whilst elaborating therapeutic strategies, it is of interest to visualize the effect of a therapeutic intervention at the moment of its application. We analyzed the effect of repeated execution of a simple extension and flexion movement of the wrist on the sensorimotor cortex of seven healthy subjects using magnetoencephalography. Spatial filtering based on current dipoles was used to quantify the strength of cortical activation. Our results showed an increase of cortical activation reflecting activity of efferent neurons, whereas the activity of proprioceptive afferent neurons was not affected. Since only efferent activity increased, it is suggested that this reflects phenomena of long-term potentiation.

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).

Tangential derivative mapping of axial MEG applied to event-related desynchronization research.

OBJECTIVES: A problem with the topographic mapping of MEG data recorded with axial gradiometers is that field extrema are measured at sensors located at either side of a neuronal generator instead of at sensors directly above the source. This is problematic for the computation of event-related desynchronization (ERD) on MEG data, since ERD relies on a correspondence between the signal maximum and the location of the neuronal generator. METHODS: We present a new method based on computing spatial derivatives of the MEG data. The limitations of this method were investigated by means of forward simulations, and the method was applied to a 150-channel MEG dataset. RESULTS: The simulations showed that the method has some limitations. (1) Fewer channels reduce accuracy and amplitude. (2) It is less suitable for deep or very extended sources. (3) Multiple sources can only be distinguished if they are not too close to each other. Applying the method in the calculation of ERD on experimental data led to a considerable improvement of the ERD maps. CONCLUSIONS: The proposed method offers a significant advantage over raw MEG signals, both for the topographic mapping of MEG and for the analysis of rhythmic MEG activity by means of ERD.

Processing of syntactic information monitored by brain surface current density mapping based on MEG.

The cortical network subserving language processing is likely to exhibit a high spatial and temporal complexity. Studies using brain imaging methods, like fMRI or PET, succeeded in identifying a number of brain structures that seem to contribute to the processing of syntactic structures, while their dynamic interaction remains unclear due to the low temporal resolution of the methods. On the other hand, ERP studies have revealed a great deal of the temporal dimension of language processing without being able to provide more than very coarse information on the localisation of the underlying generators. MEG has a temporal resolution similar to EEG combined with a better spatial resolution. In this paper, Brain Surface Current Density (BSCD) mapping in a standard brain model was used to identify statistically significant differences between the activity of certain brain regions due to syntactically correct and incorrect auditory language input. The results show that the activity in the first 600 ms after violation onset is mainly concentrated in the temporal cortex and the adjacent frontal and parietal areas of both hemispheres. The statistical analysis reveals significantly different activity mainly in both frontal and temporal cortices. For longer latencies above 250 ms, the differential activity is more prominent in the right hemisphere. These findings confirm other recent results that suggest right hemisphere involvement in auditory language processing. One interpretation might be that right hemisphere regions play an important role in repair and re-analysis processes in order to free the specialised left hemisphere language areas for processing further input.

Determining the number of independent sources of the EEG: a simulation study on information criteria.

The separation of signal and noise is an important problem in the analysis of EEG and MEG data. Furthermore, many source localisation strategies need the number of independent signal components as input parameter (e.g., dipole fit, multiple signal classification). Information criteria offer a relatively objective way to separate the space spanned by the principal components of the data covariance matrix into a signal and a noise part. Eighteen such criteria were extensively tested by simulations. They differ with respect to the statistical model of the data, the assumptions on the noise, and the correction term. In the simulations, different dipole sources were used to generate EEG, which was then distorted by Gaussian correlated or uncorrelated noise. The noise level, the accuracy of the noise covariance matrix used by the criteria, the numbers of channels and time samples, and the stochastic or deterministic nature of the source waveforms were varied. The performance of the criteria was very variable. For each criterion, limits for the noise level and the relative inaccuracy of the noise covariance matrix could be established. Taking more channels or time steps did increase the criteria's ability to tolerate noise, but at the same time, made them more vulnerable to inaccuracies in the (estimated) noise covariance matrices. Out of the eighteen criteria investigated, we recommend two criteria that are best suited for the cases of (1) high noise and accurate covariances and (2) low noise and less accurate covariances.