Causality analysis between brain areas based on multivariate autoregressive models of MEG sensor data

In recent years, significant efforts have been aimed towards the development of methods for identifying causality in information flow between brain areas from MEG/ EEG data. As such multichannel time-series correspond to inherently multivariate processes, bivariate causality measures such as the Granger Causality Test can only be inconsistently applied. Two recent methods, the Directed Transfer Function (DTF) and the Partial Directed Coherence (PDC) [1], have been widely used in intra-brain connectivity studies. These methods study the frequency characteristics of multivariate autoregressive models (MVAR) built on time-series of activated brain sources. These time-series are reconstructed from MEG/EEG sensor data through spatial filters derived commonly by beamforming algorithms [2]. The large number of potential activation sources produced by such algorithms corresponds to a large number of activation time series, too many for the derivation of robust multivariate autoregressive models. Typical methods of overcoming this obstacle make a priori assumptions about certain brain locations of potential activity or eliminate areas with low neural activity index (NAI). This is a significant drawback, as neglected areas or areas with low NAI might contain important functional connectivity paths and causality information. In the main part of this work, we investigate the derivation of the multivariate autoregressive model directly on MEG sensor data and subsequently its projection in the source space within the brain through the spatial filters. Here, the modeling process is performed on the sensor space which has moderate dimensionality as compared to the high-dimensional source space. This leads to greater model robustness as well as significantly reduced from Eighteenth Annual Computational Neuroscience Meeting: CNS*2009 Berlin, Germany. 18–23 July 2009