Into the nitty-gritty: gradients in brain networks
The world of functional imaging research is on fire right now with connectivity studies. (See my post here for an introduction to the domain of functional connectivity as a tool for studying the brain.) Although we have miles to go before we sleep, the study of distributed networks in the human brain is the forefront right now in bridging the field of psychology with the discipline of neuroscience…a bridge which science will be trying to build in a comprehensive way for the foreseeable future.
The most recent work that I will be presenting at the Human Brain Mapping (HBM) conference in Quebec addresses the relationships between two of the major functional networks in the human brain. Namely, the default mode network, and the attention control system of networks.
It has been observed that these networks are anti-correlated. In other words, as one network increases its activity, it is accompanied by diminished activity in the opposing network. The “how” and the “why” behind this anti-correlated activity is an open question…the former being a physical question, and the latter a question pertaining to the psychology correspondent to the system.
The findings I will present next week at HBM principally address topics related to the former set of questions, i.e., how these networks are physically interacting with one another. There are also insights, though, that may be gleaned about the psychological phenomena that are emergent from the mechanical dynamics in the brain.
In short, each node in these networks has a distinct hub that is the hot-spot of anti-correlation between the networks, and there is a gradient of diminishing anti-correlations fanning out from each respective hub. This means that these hot-spot points in each node of the network are likely the signal sources for turning one network down when the other network becomes active, and that the on/off signals spread outward from these points to the rest of the network.
Interestingly, these gradients become sharper with age, meaning that the network boundaries and the anti-correlations between the networks become more pronounced along the course of brain development. This may ultimately implications for why focused attention on a task increases naturally with age, in addition to possibly providing insights into what can go wrong in the development of attention-switching in the brain.
It’s an exciting time to be researching the brain–stay tuned for more to come!
Figure details: The highlighted regions in panel A are the key regions in the default mode network (DMN). The color gradients represent the correlation of the DMN with the attention control network (ACN). As can be seen in the figure, each hub of the DMN has a gradient of connectivity to the ACN, with a core of minimal correlation that is the likely source of inhibition for the network. Panel B illustrates the same idea for the ACN relative to its correlation with the DMN.
Figure details: (Acronyms: DMN=default mode network; ACN=attention control network; ROI=region of interest.) For DMN ROIs, the more strongly connected an ROI was to the network, the more strongly its connectivity to the DMN increased with age (r = 0.57, p = 0.8 * 10-72) consistent with a “sharpening” of boundaries during development.
A) Scatter plot of DMN ROIs comparing mean correlation to DMN (x-axis) to change in DMN correlation with age.
B) Mean correlation of DMN ROIs to the DMN vs. change in correlation with age to the ACN.
C) Mean correlation of ACN ROIs vs. change in correlation to ACN with age. Insula and anterior cingulate ROIs are shown in red and blue, respectively.
D) Mean correlation of ACN ROIs to DMN vs. change in correlation with age to ACN.