New statistical methods for studying neural connectivity show tremendous promise for understanding diseased brain states. An example of such a technique is dynamic causal modeling, which, broadly, attempts to create realistic models of the interaction between neural activity in different brain regions.
Note that effective connectivity implies some degree of causation of neural activity from one brain region to another, whereas functional connectivity only implies a statistical association of activity between regions.
A key physiological component of Parkinson’s that their model captured was the oscillations at beta frequencies (13-30 Hz) in regions such as the subthalamic nucleus. These oscillations are associated with motor impairments, including bradykinesia and rigidity.
The authors used a biologically-motivated generative Bayesian model to fit simultaneous local field potential recordings from key brain regions. Here are their results for the circuit connection strengths:
As you can see above, there is consistent increase in connection strength between the cortex and the subthalamic nucleus in Parkinson’s model animals, while there is a consistent decrease in connection strength between the subthalamic nucleus and the external globus pallidus.
Subsequent analyses showed that the connection from the cortex to the subthalamic nucleus had one of the largest contributions to the beta oscillations seen in the Parkinson’s model rats. This result helps to confirm the importance of this connection pathway in Parkinson’s pathophysiology.
The authors speculate that connections in which there are apparent differences between healthy and diseased brains may represent viable targets for therapeutic strategies. If so, this paper would provide an example of how emerging statistical techniques for studying connectivity can offer downstream clinical benefits.
Moran RJ, Mallet N, Litvak V, Dolan RJ, Magill PJ, et al. (2011) Alterations in Brain Connectivity Underlying Beta Oscillations in Parkinsonism. PLoS Comput Biol 7(8): e1002124. doi:10.1371/journal.pcbi.1002124