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+ New preprint!
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+ Model connectivity: leveraging the power of encoding models to overcome
+ the limitations of functional connectivity
+ (Meschke et al., in review).
+ Functional connectivity (FC) is the most popular method for recovering
+ functional networks of brain areas with fMRI. However, because FC is
+ defined as temporal correlations in brain activity, FC networks are
+ inevitably confounded by noise and their function cannot be determined
+ directly from FC. To overcome these limitations, we have developed model
+ connectivity (MC). MC is defined as similarities in encoding model weights,
+ which quantify reliable functional activity in terms of interpretable
+ stimulus- or task-related features. In this paper we compare these two
+ methods directly in a language comprehension dataset. We confirm the
+ confounds of FC, and we show that MC does not suffer from these confounds.
+ MC recovers more spatially localized networks and it reveals their
+ functional assignment. MC is powerful tool for recovering the functional
+ networks that support complex cognitive processes.
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