It follows that while functional connectivity investigations themselves require much less elaborate modeling and a priori assumptions than most approaches to effective connectivity analyses, they at the same time are much more susceptible to biological and technical confounds that may influence the noise spectrum of the data and induce spurious correlations that may be mistaken as functional interactions. If their influence is not removed from the data or accommodated in the analysis, spurious correlations will arise. A similar consideration also holds for structured noise or confounds, such as motion or physiological effects ( Birn, 2012 Duncan & Northoff, 2013). Thus, functional connectivity may be observed even between regions that are not functionally interacting with each other due to effects of the experimental setup. Even if both would be implemented in completely segregated streams, this scenario would lead to correlated activity changes in higher sensory areas and motor regions, that is, functional connectivity between them. An example of such situation would be the feedforward of stimulus-driven activity in early sensory areas that is forwarded to parietal sensory areas for perceptual analysis and, in parallel, to premotor cortex for response preparation. Therefore, functional connectivity may be driven by an external source inducing concurrent activity in both areas. Furthermore, it is also possible that a third area induces correlated activation between regions that actually do not have any form of direct interaction. Strong functional connectivity may hence be observed even if structural connections are weak or absent, although in most cases, these two aspects of brain connectivity show at least some level of convergence ( Eickhoff et al., 2010). In such cases, activity in one area may represent the ultimate drive of activity in the other even in the absence of a structural connection, that is, fibers running between the two areas. Such relay processes could moreover be transmitted through cascades of several intermediates or via corticalвЂ“subcortical loops involving, for example, the basal ganglia or the cerebellum. Correlated activity in two regions may, for example, be mediated via additional structures relaying information from the first area to the second.

As always with correlations, however, this does not imply any causal relationship or even any sort of direct connection between these two regions. As just noted, two regions show functional connectivity, if increased activity in one region is associated above chance with activity in another. The first is the important caveat that functional connectivity per se is purely correlative in nature. This definition, however, already clearly reveals two key aspects that need to be considered when dealing with functional connectivity analyses.

By relying very little on a priori assumptions, functional connectivity analysis thus rather reflects a straightforward, observational measure of functional relationships. In particular, it concurs with the intuitive notion that when two things happen together, these two things should be related to each other. In contrast to effective connectivity analyses, which rest on numerous assumptions regarding both the underlying neurobiology and the model chosen to estimate it, functional connectivity represents a much more direct approach to the analysis of functional networks. The notion behind this connectivity approach is that areas are presumed to be coupled or are components of the same network if their functional behavior is consistently correlated with each other. That is, two regions are considered to show functional connectivity if there is a statistical relationship between the measures of activity recorded for them.

MГјller, in Brain Mapping, 2015 Functional Connectivity: Definition and Conceptual ImplicationsДЇunctional connectivity is defined as the temporal coincidence of spatially distant neurophysiological events ( Friston, 1994).

Functional region definition