MEG

Magnetoencephalography at the CCNi

Research Areas

 

How are the ~1011 neurons in the brain organized structurally and functionally to endow us with our remarkable information processing capabilities? What are the mechanisms balancing segregation and integration, excitation and inhibition? How does the brain achieve its highly efficient communication between different neural entities on different spatial scales? These fundamental and exciting questions are at the very heart of my research in the field of Neuroscience that is best summarized in the title of our recent article “Normal and Pathological Oscillatory Communication in the Brain” (Schnitzler & Gross, 2005).

My own contribution to this particular field is the development, validation and application of methods for non-invasive connectivity studies with MEG/EEG with the aim of better understanding large-scale brain activity and communication.

Previous work

During my PhD I have been working on multivariate time series analysis, particularly on linear and non-linear signal transformations, spatial filters, principal and independent component analysis in the context of electrophysiological data analysis and source localization.

After my PhD I extended this work in collaboration with people at the Helsinki University of Technology to develop the first method for non-invasive tomographic connectivity analysis with MEG/EEG (Dynamic Imaging of Coherent Sources (DICS) (Gross et al., 2001)). DICS allows a visualization of communication processes between brain and motor units (cerebro-muscular communication) and between brain areas (cerebro-cerebral communication). My first applications focussed on cerebro-muscular communication. During isometric muscle contraction oscillatory cerebro-muscular communication is evident as coherence between muscle activity (as measured with electromyography) and MEG-signals above the contralateral sensorimotor cortex. This indicates a rhythmic modulation of motor unit firing. DICS localizes strongest coherence in the individual anatomical MRI images in primary motor cortex. Even more interesting, we used DICS to identify large-scale brain networks associated with regular velocity changes during slow finger movements (Gross et al. 2002).

Altogether, large-scale brain networks were studied with DICS in a number of physiological and pathological movement conditions (see publication list). These studies, supported by third-party funding, demonstrated that physiological movement control is associated with oscillatory communication in large-scale networks. Furthermore, different movement disorders seem to be associated with specific pathological alterations in large-scale communication.

Recently, DICS was adapted for networks showing transient synchronization and was successfully applied to an attentional paradigm (Gross et al. 2004). The study demonstrated the functional relevance of large-scale neural synchronization by showing that behavioural performance (being able to correctly report a target) depends on a specific pattern of synchronization and desynchronization.

Current and future work

Currently my methodological developments (funded by the VolkswagenStiftung) aim at identifying statistically validated, task-dependent large-scale networks building on my previous experience with DICS. Further developments deal with the incorporation of hierarchical clustering and multidimensional scaling, system identification methods, autoregressive models and randomization procedures.

Concerning the design of experiments, controlled perturbations of neural systems (while monitoring the behavioural output) are important to learn about the functional relevance of large-scale activation and communication. Controlled perturbations may be achieved e.g. by TMS, pharmacological interventions or appropriately designed stimuli.

Non-invasive functional connectivity analysis should further be complemented with anatomical connectivity information (e.g. from DTI).

A separate field of interest is the design of computational models of large-scale brain activation and communication. These models are very valuable since their output can be compared with experimental data. They allow easy variations of parameters and help to understand the mechanisms giving rise to experimentally observed phenomena.

Knowledge obtained from invasive (animal) studies is extremely important to guide the design of these models and to better understand the mechanisms underlying neural oscillations and interneuronal communication.

In summary, my research aims at unravelling some of the basic rules governing interneuronal communication (at different spatial scales) and focuses on the following interconnected neuroscientific aspects:

The Mechanisms of neural communication

  • Filtering and resonance

Filtering and resonance phenomena describe the dependence of neuronal activity on the frequency content of the input and are implemented at the level of synapses, single neurons and neuronal populations. They result in the transmission of selected aspects of information, enhance the sensitivity to a certain input frequency and are subject to plastic changes.

  • Structural connectivity

In general, neurons in the brain are tightly interconnected. The abundance of short-range connections is the structural basis for the emergence of functional local networks. A small percentage of long-range connections greatly reduces the minimal pathlength between any two neurons. This structural connectivity pattern is optimal for selectively biasing local and global network communication. Changing communication demands might lead to changes in anatomical connectivity.

  • Spatio-temporal pattern of excitation and inhibition

In general, inhibition is important for the emergence of oscillations in networks of principal cells and interneurons. The output of neurons with a balanced excitatory and inhibitory input is particularly sensitive to correlated input. Inhibitory interneurons also provide a possible mechanism for synchronization of the LFP oscillations of remote neural populations.

Physiological and pathological relevance of synchronization

Although neural oscillations are prominent features in electrophysiological recordings their functional role is still unclear. I am interested in further exploring this aspect e.g. by focusing on information processing in primary sensory areas. In addition, our studies of pathological synchronization in movement disorders demonstrated the feasibility and importance of connectivity analysis using patient data.

In addition, I am interested in exploring the role of synchronization in bottom-up and top-down processes (see Gross et al. 2004)

Models of large-scale brain dynamics

Information from various measurement modalities as well as anatomical connectivity data should be combined with knowledge of biophysical properties of large-scale brain areas to design models of large-scale brain dynamics. It seems that (subcortico-cortical and cortico-cortical) loops will play a major role in these models. Comparison of model output with experimental data and systematic variations of model parameters will likely yield further insights into basic mechanisms of brain dynamics.

Research Overview