MEG Node Degree Differences in Patients with Focal Epilepsy vs. Controls-Influence of Experimental Conditions.

Drug-resistant epilepsy can be most limiting for patients, and surgery represents a viable therapy option. With the growing research on the human connectome and the evidence of epilepsy being a network disorder, connectivity analysis may be able to contribute to our understanding of epilepsy and may be potentially developed into clinical applications. In this magnetoencephalographic study, we determined the whole-brain node degree of connectivity levels in patients and controls. Resting-state activity was measured at five frequency bands in 15 healthy controls and 15 patients with focal epilepsy of different etiologies. The whole-brain all-to-all imaginary part of coherence in source space was then calculated. Node degree was determined and parcellated and was used for further statistical evaluation. In comparison to controls, we found a significantly higher overall node degree in patients with lesional and non-lesional epilepsy. Furthermore, we examined the conditions of high/reduced vigilance and open/closed eyes in controls, to analyze whether patient node degree levels can be achieved. We evaluated intraclass-correlation statistics (ICC) to evaluate the reproducibility. Connectivity and specifically node degree analysis could present new tools for one of the most common neurological diseases, with potential applications in epilepsy diagnostics.

External noise removed from magnetoencephalographic signal using independent component analyses of reference channels.

BACKGROUND: Many magnetoencephalographs (MEG) contain, in addition to data channels, a set of reference channels positioned relatively far from the head that provide information on magnetic fields not originating from the brain. This information is used to subtract sources of non-neural origin, with either geometrical or least mean squares (LMS) methods. LMS methods in particular tend to be biased toward more constant noise sources and are often unable to remove intermittent noise. NEW METHOD: To better identify and eliminate external magnetic noise, we propose performing ICA directly on the MEG reference channels. This in most cases produces several components which are clear summaries of external noise sources with distinct spatio-temporal patterns. We present two algorithms for identifying and removing such noise components from the data which can in many cases significantly improve data quality. RESULTS: We performed simulations using forward models that contained both brain sources and external noise sources. First, traditional LMS-based methods were applied. While this removed a large amount of noise, a significant portion still remained. In many cases, this portion could be removed using the proposed technique, with little to no false positives. COMPARISON WITH EXISTING METHOD(S): The proposed method removes significant amounts of noise to which existing LMS-based methods tend to be insensitive. CONCLUSIONS: The proposed method complements and extends traditional reference based noise correction with little extra computational cost and low chances of false positives. Any MEG system with reference channels could profit from its use, particularly in labs with intermittent noise sources.

Neurophysiological evidence for rapid processing of verbal and gestural information in understanding communicative actions.

During everyday social interaction, gestures are a fundamental part of human communication. The communicative pragmatic role of hand gestures and their interaction with spoken language has been documented at the earliest stage of language development, in which two types of indexical gestures are most prominent: the pointing gesture for directing attention to objects and the give-me gesture for making requests. Here we study, in adult human participants, the neurophysiological signatures of gestural-linguistic acts of communicating the pragmatic intentions of naming and requesting by simultaneously presenting written words and gestures. Already at ~150 ms, brain responses diverged between naming and request actions expressed by word-gesture combination, whereas the same gestures presented in isolation elicited their earliest neurophysiological dissociations significantly later (at ~210 ms). There was an early enhancement of request-evoked brain activity as compared with naming, which was due to sources in the frontocentral cortex, consistent with access to action knowledge in request understanding. In addition, an enhanced N400-like response indicated late semantic integration of gesture-language interaction. The present study demonstrates that word-gesture combinations used to express communicative pragmatic intentions speed up the brain correlates of comprehension processes - compared with gesture-only understanding - thereby calling into question current serial linguistic models viewing pragmatic function decoding at the end of a language comprehension cascade. Instead, information about the social-interactive role of communicative acts is processed instantaneously.