What is Dynamic Functional Connectivity? Implications for Performance and More

One of the more recent discussions in neuroscience concerns dynamic functional connectivity. This phenomenon demonstrates the brain’s incredible adaptability and malleability, and it has some startling implications.

If you are interested in cutting edge discussion regarding the human brain, read on to get up to speed.

What is Dynamic Functional Connectivity?

Dynamic functional connectivity is the brain’s ability to form different networks of activation between different brain regions, or functional hubs.

You may have heard terms like “default mode network” or “salience network.” These are networks of brain regions that are in some way linked. Brain regions are considered “linked” either because they are anatomically connected (meaning that large numbers of neurons physically connect the two areas), or because they are functionally linked. Functional connectivity measures the tendency for correlated activation in two different areas. Two or more brain regions may not be physically linked, but if they are constantly used at the same time, we would consider them to be functionally linked. This can be measured with the use of an fMRI scanner.

See also: Manipulating the Brain’s Salience Network and How I Write 10,000+ Works a Day

For example, the salience network is a network of brain regions that activate to alert us of “salient” information (important information). This can then help us to switch our attention. The salience network can be detected using fMRI images (study).

The general idea is that the brain often uses different combinations of brain regions in conjunction and that these can reflect specific tasks.

But dynamic functional connectivity suggests that static brain scans may be missing the big picture. More important than the specific brain networks themselves, is the way the brain changes between these networks on the fly. Methods such as “sliding window” analysis analyze a series of scans in an fMRI session (the number of scans = the “length” of the window) before moving the window forward a few frames in time with some overlap. This allows researchers to see how patterns of activation change over time.

Implications of Dynamic Functional Connectivity

There has been some backlash to the idea of dynamic functional connectivity. Some argue that this is just the result of noise from fMRI scanners. fMRI scanners are limited by low temporal resolution (0.5Hz) and their indirect nature. However, evidence from other methods such as EEG and behavioral analysis seem to confirm the importance of DFC.

And to me, this only makes logical sense. No two cognitive tasks are identical, and they change dynamically with time. If I ask you to think of an elephant, then you will probably picture an elephant using your occipital lobe (visual cortex), referencing information from your hippocampus (memory). At the same time though, you might “feel” the texture of the elephant and thus activate areas relating to your sense of touch (somatosensory cortex). Your parietal lobe combines these senses (multisensory integration). But maybe you also rotate the elephant in your mind’s eye, requiring help from your premotor cortex’s built-in “physics engine.” Perhaps you replay a related memory. Maybe you trigger certain emotional responses.

You might, for a moment, also notice that you are sitting awkwardly.

Of course we should expect to see fluctuations in the brain regions involved during this process. And for a more complex task, such as performing math, we would expect this to be much greater.

But we would also expect to see certain combinations of brain regions used more often than others, especially as “neurons that fire together, wire together.”

Hierarchies of Connectivity

Indeed: this is what we see. Steady state functional connectivity exists between networks but these are less stable than anatomically connected brain regions. Moreover, there is a “hierarchy” of connectivity with some networks being far more consistent than others (in particular, the connection between bilaterally symmetric brain regions).

Remember, too, that measuring correlation does not establish causality. That is to say that functionally linked areas may even be anatomically linked via other brain regions, or might be innervated by the same, third brain region.

Dynamic Functional Connectivity and Human Performance

What’s interesting, is that “network behavior” between brain regions appears to predict performance in a wide range of tasks: particularly pertaining to vigilance and attention. Whereas previous studies looked at the magnitude of activation across linked brain regions, more recent research shows that the ability to maintain certain patterns of activation over time may even be a superior predictor of performance (study).

This also makes a lot of sense! My ability to concentrate on writing an essay for hours at a time requires me to continuously utilize a select number of brain regions, while ignoring the temptation to attend to other thoughts and feelings.

But before we get carried away, we should consider that this benefit may be task-specific. In other words: there will almost certainly be times where a more dynamic, shifting network would be more useful. This would represent a dynamic brain capable of utilizing multiple different brain areas to solve a given problem. Indeed, global brain connectivity may be one predictor of overall intelligence.

See also: The Neuroscience of Genius and Increasing Intelligence

Dynamic functional connectivity also appears to provide useful insight for a number of mental health disorders. In particular, unusual dynamic activity seems to predict schizophrenia (study). We might also expect to see similar patterns relating to creativity.

A subtle shift in our understanding of cognitive performance, perhaps. But one that may yet yield smarter approaches to training and enhancing attention and vigilance! Study of dynamic functional connectivity has only just begun, though, so watch this space.

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