Functional connectivity in the brain

A physical connection between two regions allows them to exchange signals, that much is clear. But there’s another kind of relationship that we need to entertain – what we call a functional connection. Let’s first consider an example unrelated to the brain, where in fact there aren’t any physical connections. Genes are segments of DNA that specify how individual proteins are put together, and a protein itself is made of a long chain of amino acids. Proteins have diverse functions, including carrying out chemical reactions, transporting substances, and serving as messengers between cells. We can think of genes that guide the building of proteins that have related functions (for example, acting as hormones in the body, such as insulin, estrogen, and testosterone) as “functionally connected.” The genes themselves aren’t physically connected but they are functionally related. Here, we’ll see how functional connectivity is a useful concept in the case of the brain.

At first glance, the notion of an architecture anchored on physical connections goes without saying. Region A influences region B because there’s a pathway from A to B. However, the distinction between anatomy and function becomes blurred very quickly. Connections are sometimes “modulatory,” in which case region A can influence the probability of responding at B, and at times “driving,” in which case they actually cause cells in B to fire. In many instances, the link between A and B is not direct but involves so-called interneurons: A projects first to an interneuron (often in area B itself) which then influences responses in other cells in B. The projections from interneurons to other cells in B can be excitatory or inhibitory, although they are often inhibitory. Of course, the strength of the fiber itself is critical. Furthermore, the presence of multiple feedforward and feedback pathways, as well as diffuse projections, further muddy the picture. Taken together, we see that connections between regions are not simply binary (they exist or not, as in a computer), and even a single weight value (say, a strength of 0.4 on a scale from 0 to 1) doesn’t capture the richness of the underlying information.

Functional connectivity thus answers the following question: how coordinated is the activity of two brain regions that may or may not be directly joined anatomically? (Figure 1). The basic idea is to gauge if different regions form a functional unit. What do we mean by “coordinated?” There are multiple ways to capture this concept, but the simplest is to ascertain how correlated the signals from regions A and B are, and the stronger their correlation, the higher the functional association, or functional connection. Correlation is an operation that is summarized by values from -1 to +1. When two signals are perfectly related (which is never the case with noisy biological measurements), their correlation is +1; when they are in perfect opposition to one another (one is high when the other is low, and vice versa), their correlation is -1; when they are unrelated to each other, their correlation is 0 (this means that information about one of the signals tells us nothing about the other one, and vice versa).

Figure 1. Functional connectivity measures the extent to which signals from two regions are in synchrony. Whether or not the regions are directed connected via an anatomical pathway is unimportant.

Let’s consider what I called the “two-step” property of the amygdala. Because this area is connected physically to approximately 40 percent of prefrontal subregions, it can influence a sizeable portion of this lobe in a direct manner, that is, via a single step (such as regions in the orbitofrontal cortex and the medial prefrontal cortex). But approximately 90 percent of prefrontal cortex can receive amygdala signals after a single additional connection within prefrontal cortex[1]. Thus, there are two-step pathways that join the amygdala with nearly all of prefrontal cortex. Consequently, the amygdala can engage in meaningful functional interactions with areas that are not supported by strong direct anatomical connections (such as the lateral prefrontal cortex), or even not connected at all.

The foregoing discussion is worth highlighting because it’s not how neuroscientists think typically. They tend to reason in a much more direct fashion, considering the influences of region A to be most applicable to the workings of regions B, to which it is directly connected – a type of connection called monosynaptic. To be sure, a circuit involving A –> X –> B is more indirect than A –> B, and if the intermediate pathway involving X is very weak, the impact of A on X may be negligible. But the point here is that this needn’t be the case, and we should not discard this form of communication simply because it’s indirect (recall the discussion about network efficiency above).

It’s natural to anticipate a functional association between brain regions that are directly connected. Yet, the relationship between structural and functional connectivity is not always a simple one, which shouldn’t be surprising because the mapping between structure and function in an object as interwoven as the brain is staggeringly complex. A vivid example of structure-function dissociation is illustrated by adults born without the corpus callosum, which contains massive bundles of axonal extensions joining the two hemispheres. Although starkly different structurally relative to controls, individuals without the callosum exhibit very similar patterns of functional connectivity compared to normal individuals[2]. Thus, largely normal coordinated activity emerges in brains with dramatically altered structural connectivity, providing a clear example of how functional organization is driven by factors that extend beyond direct pathways.

The upshot is that to understand how behavior is instantiated in the brain, in addition to working out anatomy, it is necessary to elucidate the functional relationships between areas. Importantly, anatomical architectural features support the efficient communication of information even when strong direct fibers aren’t present, and undergird functional interactions that vary based on a host of factors.

An experiment further illustrating the above issues studied monkeys with functional MRI during a “rest” condition, when the animal was not performing an explicit task[3]. They observed robust signal correlation (the signals went up and down together) between the amygdala and several regions that aren’t connected to it (as far as we know). They asked, too, whether functional connectivity is more related to direct (monosynaptic) pathways or connectivity via multiple steps (polysynaptic) by undertaking graph analysis. Are there efficient routes of travel between regions even when they aren’t directly connected? To address this question quantitatively, they estimated a graph measure called communicability (related to the concept of efficiency discussed previously), and found that amygdala functional connectivity was more closely related to their measure of communicability than what would be expected by only considering monosynaptic pathways. In other words, polysynaptic, multi-step routes should be acknowledged. In fact, their finding shows that to understand the relationship between signals in the amygdala and that of any other brain region, it’s important to consider all pathways that can bridge them.


[1] Averbeck and Seo (2008).

[2] Tyszka et al. (2011).

[3] Grayson et al. (2016).