Wheels are Turning: How Cyclicity Analysis is Changing the Way I Think About the Brain

Photo by David Matos on Unsplash

“Their brains lit up!” Most people will be familiar with the hot yellow and red blobs superimposed on beautifully high-resolution brain images to show brain activation. These images give the impression that the brain is switching modules on and off as needed to complete its arcane computations, but the typical blob is just a statistical contrast comparing groups or experimental conditions. In reality, the brain is brimming with activity everywhere, all the time. Even in the absence of any experimental task, there are dynamic fluctuations in metabolism all over the brain. Loads of research now supports the idea that these fluctuations are not just random noise, but rather that they carry information predictive of behavior and cognition.

The conventional way that neuroscientists have been thinking about these fluctuations is in terms of functional networks, which are composed of specialized nodes distributed throughout the brain. One of the more surprising findings that led to this network-architecture-thinking is that even during rest, when a participant is instructed to do nothing in particular, brain activity seems to be organized into networks. The time-course of activity of any small portion is highly correlated with the time-course of activity in other specific parts of the brain in very replicable patterns. For example, if you took a functional scan of your brain while laying in an MRI scanner for 10 minutes, I can predict with some confidence that the fluctuations in metabolism going on in your angular gyrus, way off towards the back and side of your brain, will be highly correlated with the fluctuations going on all the way on the other side of your brain, in your medial prefrontal cortex. These same networks that can be identified during rest also seem to show up during certain types of cognitive tasks. Scientists have mostly assumed that these networks of nodes in the brain have modular functions, and they use cognitive tasks to try to learn what those functions are.

The biggest technical limitation in the conventional analysis comes from the decision to correlate the full time series between two regions to establish their connectivity. This methodology has implications for how we interpret the signal. What the method misses are two critical…