A Methodology for Dynamic Functional Connectivity

Classical measures of functional connectivity assume that the stationarity of the time courses and the time-invariance of functional connectivity under investigation. These assumptions may not be valid in the real cases. Also, they are bivariate measures and may not provide the directional information flow between brain units. A new approach is proposed to tackle these problems. A statistics reasoning shows that the short-length time course is more likely to be stationary than the long-length time course. Thus, the entire time course under investigation is divided into short segments with the proper length. Magnitude squared coherence (in spectrum domain) is computed to assess functional connectivity on these segments, hence, provides a dynamic measure of functional connectivity. The averaged magnitude squared coherence over the segments gives an overall measure of functional connectivity. This approach has been applied to several neuroimaging data analysis. The results and the interpretations / predictions are in good agreement. Mutual coherence (in time domain) is computed to assess functional connectivity, hence, provides an insight on directional information flow. By using grid computing, this approach will be extended from the bivariate to the multivariate.