Neural interactions and dynamics
Our group is interested in understanding functional implications of large-scale spatio-temporal neuronal complexity in the human brain. The main emphasis is on the investigation of neuronal dynamics unfolding at different temporal and spatial scales as well on the interactions between local and global neuronal processes. This particularly relates to the research question of how resting state dynamics and instantaneous neuronal states shape performance in different sensory, motor and cognitive tasks. In explaining large-scale brain organization, we favor ideas describing neuronal activity in terms of the critical dynamics and in terms of neuronal interactions on the basis of linear and non-linear coupling. We study these aspects of neuronal activity in healthy subjects and in patients with neurological (e.g., Stroke and Parkinson's Disease) and psychiatric disorders (e.g., Schizophrenia, Depression). In addition, we develop novel multivariate approaches for the advanced analysis of multichannel EEG/MEG data and develop further multimodal techniques (e.g., a combination of EEG and TMS).
Anyone studying non-invasively neuronal oscillations in the human brain notices their ever-changing amplitude fluctuations both at rest and during different cognitive and motor tasks. We have previously shown that these amplitude fluctuations have 1/f spectrum indicating a presence of long-range temporal correlations (LRTC) in EEG/MEG. These findings along with empirical and modeling research in other groups are consistent with the idea that the neuronal networks might operate at the critical state, which might be considered as a delicate balance between excitation and inhibition. Our research has shown that such dynamics indeed can be beneficial for the task performance. Moreover, we demonstrated that LRTC appear to be a sensitive indicator of abnormal neuronal dynamics in different psychiatric (Schizophrenia, Depression) and neurological diseases (Parkinson’s disease, essential tremor).
Cross-frequency neuronal interactions
Conventionally neuronal interactions in EEG/MEG research are studied within the same frequency range, e.g. for alpha, beta or gamma oscillations. However, many areas in the brain operate at distinct and different from each other frequency ranges. This naturally indicates that there might exist cross-frequency neuronal interactions allowing a propagation of information in the distributed neuronal networks producing oscillations with different frequencies. We investigate such cross-frequency coupling at rest and during different tasks.
Cortico-muscular coherence (CMC) is a unique neurophysiological indicator allowing non-invasive investigation of a coupling between cortical and spinal cord neuronal activity. It is dependent on both afferent and efferent neuronal processes and is sensitive to different task conditions. We investigate how both phase and amplitude of neuronal oscillations, involved in CMC, relate to the movement performance and to clinical conditions leading to changes in the motor control, e.g. in stroke. In addition we develop novel methods for the optimal detection of cortico-muscular interactions.
Both Transcranial Magnetic Stimulation (TMS) and EEG have been used extensively for the investigation of different motor and cognitive brain function. TMS is particularly useful in investigation of the motor cortex due to a possibility to obtain peripheral motor evoked potentials (MEP). However, the effects of the stimulation in other cortical areas rely primarily on the investigation of hit rates or reaction times as no MEPs are evoked. In order to investigate effect of TMS on cortical processing, we combine TMS with EEG – the latter providing a direct way to estimate reactivity and connectivity of the stimulated areas. In this research TMS-evoked EEG responses serve as a probe of the current cortical state and might allow investigation of the processes not directly accessible to either TMS or EEG when used separately.
Development of novel multivariate techniques for the investigation of temporal and spatial aspects of neuronal activity
Traditionally EEG/MEG activity is studied in sensor space with all the concomitant negative consequences relating to the pitfalls in the interpretation of the neuronal data due to the excessive effects of volume conduction and low signal-to-noise ratio. We advocate the use of different multivariate linear decomposition techniques for the extraction of oscillatory components optimized for maximal SNR of neuronal oscillations, and for revealing linear and non-linear interactions between oscillations.