University of Wisconsin–Madison

Effective Connectivity

Connectivity analyses of functional imaging such as correlation/coherence analysis, independent component analysis (ICA), granger causality, structural equation modeling (SEM) and dynamic causal modeling (DCM) can allow researchers to understand how connections between regions are utilized during particular tasks and how they may be affected by disease or damage to a network (de Marco, et al., 2009a; de Marco, et al., 2009b; Friston and Buchel, 2003; Friston, et al., 2003; Calhoun, et al., 2009; Roebroeck, et al., 2005). These multivariate techniques extend univariate analyses (e.g. general linear model) by allowing one to analyze several brain regions simultaneously. Depending on the algorithm used, these approaches can detect fluctuations common to multiple brain regions or identify directed influences of one brain region over another.

Our lab focuses primarily on DCM and closely related methods. DCM can provide additional insights into how various neuronal structures communicate and how this communication may change depending on the tasks being processed (Friston, et al., 2003). DCM allows three types of influential interactions: interconnections, driving inputs, and modulatory effects. The interconnections describe how separate brain regions influence each other directly. Driving inputs allow tasks to effect activity within brain regions. Finally, modulatory effects allow tasks to effect interconnections.  Shown below is a Figure from our paper, Wildenberg JC, Meyerand ME, Danilov YP, Tyler ME, Kaczmarek KA.(2013) Brain  Connect. 3(1):87-97. PCM3621359 which shows the relevant effective connectivity relationships between different brain regions involved in visual processing of balance information in patients with balance dysfunction vs. healthy subjects. Our current research involves modifications to DCM to allow more complicated and anatomically realistic models.

effective_connectivity

References

  1. Calhoun, V.D., Liu, J., Adali, T., 2009. A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data. Neuroimage. 45, S163-72.
  2. de Marco, G., Devauchelle, B., Berquin, P., 2009a. Brain functional modeling, what do we measure with fMRI data? Neurosci Res. 64, 12-19.
  3. de Marco, G., Vrignaud, P., Destrieux, C., de Marco, D., Testelin, S., Devauchelle, B., Berquin, P., 2009b. Principle of structural equation modeling for exploring functional interactivity within a putative network of interconnected brain areas. Magn Reson Imaging. 27, 1-12.
  4. Friston, K.J., Buchel, C., 2003. Functional connectivity. Human Brain Function.
  5. Friston, K.J., Harrison, L., Penny, W., 2003. Dynamic causal modelling. Neuroimage. 19, 1273-1302.
  6. Roebroeck, A., Formisano, E., Goebel, R., 2005. Mapping directed influence over the brain using Granger causality and fMRI. Neuroimage. 25, 230-242.