Studying the brain as a network using MEG: practical considerations

Proposal for Satellite Workshop at Biomag 2012

August 25, 2012


Ole Jensen (OJ), Donders Institute for Brain, Cognition and Behavior. Radboud University. Nijmegen, The Netherlands 
Sarang Dalal (SD), University of Konstanz, Germany 
Johanna Zumer (JZ), Donders Institute for Brain, Cognition and Behavior. Radboud University. Nijmegen, The Netherlands

Contact person:
Ole Jensen; ; Phone: +31 24 3610884


It is becoming increasingly important to study the working brain as network both in cognitive and clinical neuroscience. MEG provides an excellent opportunity to study the functional interactions between various brain regions. There are now multiple approaches and tools available for doing this. The aim of the proposed workshop is to elucidate practical approaches for studying brain connectivity using MEG. This will be done by a set of presentations in which basics of connectivity and source space analysis are introduced. Then follows presentations by various toolbox developers. The toolbox presenters will be asked to 1) outline conceptually the types of connectivity approaches their toolboxes allow for and 2) describe how it practically can be done. Finally we will discuss how various connectivity measures can be tested using standardized data sets. 

Target audience:
The workshop is targeted towards the advanced clinical or cognitive MEG user with an interest in embracing state-of-the-art methods for functional connectivity. 

Preliminary program (responsible organizer in parentheses) 

The toolbox presenters will be asked to provide a title, abstract (200 words), weblink and a reading list (1-5 papers). According to this feedback we will rearrange and group the toolbox presentations according to topics (e.g., coherence, causality measures, etc.). The slides will be made available to the audience. We are currently in the process of contacting the presenters and hope to have the speakers confirmed by Dec 1. 

09:30-10:00 The basics of connectivity analysis 101 (coherence, imaginary coherence and Granger)
    Jan-Mathijs Schoffelen   (JZ, invited)

10:00-10:30 Source-space analysis 101 (beamformer and ICA)
    Matt Brookes (JZ, invited)

10:30-11:00 Coffee break

11:00-11:30 SPM/DCM 
    Vladimir Litvak (JZ, confirmed for 25th)

11:30-12:00 eConnectome
    Laura Astolfi (SD, confirmed for 25th)

12:00-13:30 Lunch

13:30-14:00 Nutmeg 
    Adrian Guggisberg (SD,  confirmed for 25th)

14:00-14:30 FieldTrip
    Robert Oostenveld (JZ, confirmed for 25th)

14:30-15:00 The SIFT/EEGlab toolbox 
    Tim Mullen/ (OJ, confirmed for 25th)

15:00-15:30 Coffee break

15:30-16:00 BrainStorm
    Sylvain Baillet or François Tadel (SD, confirmed for 25th)

16:00-16:30 MNE Suite 
    Matti Hamalainen (OJ, confirmed for 25th)

16:30-17:00 MEG SIM - datasets for testing analysis 
    Cheryl Aine (OJ, confirmed)

Estimated attendance (max 180):

Method of promotion:
mailing lists

Registration fee:
support would be needed for coffee 


Vladimir Litvak:

Dynamic Causal Modelling for M/EEG as implemented in SPM8

Dynamic Causal Modelling (DCM) is a framework bringing together data analysis and neural modelling. In DCM measured data are explained by a network model consisting of a few sources, which are dynamically coupled. This network model is fitted to the data using a Bayesian inversion scheme. Practically this means that what is being optimized is not only the goodness of data fit but also the deviation of model parameters from their expected prior values (e.g. physiologically meaningful range). Model inversion provides two main results. The model evidence is a single number specific to the combination of data and model which can be used to compare models and test specific hypotheses. The posterior density on model parameters can be used to make inferences about connections between sources or their condition-specific modulation under the model selected.

For M/EEG data, DCM can be a powerful technique for inferring (neuronal) parameters not observable with M/EEG directly. Specifically, one is not limited to questions about source strength, but can test hypotheses about connections between sources in a network. In the recent years, several variants of DCM for M/EEG have been developed for modelling evoked responses, steady state power and cross-spectra, induced responses and phase coupling.

Sylvain Baillet:

(Brain)Storming through Connectivity Metrics 

 Academic software applications for MEG and EEG data analysis have grown considerably over the past decade, reaching to an increasing number of users and achieving higher degrees of sophistication (Baillet et al., 2011). Brainstorm is a software project that is entering its second decade of development and distribution, with an open-source and free-of-charge policy (Tadel et al., 2011, and<>). Brainstorm's website statistics indicate than more 4000 users have downloaded the software code or executable packages (which does not require a Matlab license), with 500 registered active users, accessing the software updates on a regular basis. Brainstorm's user community is growing rapidly and features a great variety of research areas in MEG and EEG. We believe Brainstorm is serving well its users, in terms of facility and convenience of utilization and improvements in productivity and reproducibility of their research output. To date, more than 90 journal articles feature results obtained using Brainstorm. The next big step in Brainstorm's development is the introduction of efficiently-coded and practical metrics of connectivity. In collaboration with the Neuroimaging Group at the University of Southern California (Richard M. Leahy), the software now features a toolkit specifically for connectivity analysis of MEG/EEG source time series. This latter benefits from a user-friendly integration in Brainstorm's graphical user interface, and can be accessed through Matlab scripts, like any other process featured in the software (Brainstorm has a unique graphical scripting tool to design analysis pipelines efficiently). We will feature this new extension to Brainstorm by reviewing its integration within the application and providing examples from experimental data. 

Baillet, S.; Friston, K. & Oostenveld, R. (2011) Academic software applications for electromagnetic brain mapping using MEG and EEG. Comput Intell Neurosci, 972050 

 Tadel, F.; Baillet, S.; Mosher, J. C.; Pantazis, D. & Leahy, R. M. (2011) Brainstorm: a user-friendly application for MEG/EEG analysis. Comput Intell Neurosci, 879716