A question that dominated my undergraduate education in philosophy and cognitive science motivated this paper: how modular is brain function?
The logic of the analysis is simple: if network a in the brain is engaged in cognition, does additional networks engaging in cognition increase the computational load of the network a? If not, brain function is likely modular. We find that, unless network a is the fronto-parietal network, a network comprised of connector nodes–nodes with diverse connections across the brain’s networks–the computational load does not increase when more networks engage in cognition. Thus, the brain is mostly modular, with select connector nodes that likely integrate across the brain.
This work combined a network model with an author topic model and 83 unique tasks from over 10,000 brain experiments. The network model allowed us to identify connector nodes, which we found do not function modularly, versus local nodes, which we found do function modularly. The author-topic model gave us an ontology of brain networks, with precise measurements of how engaged each network is in each cognitive task. Thus, we were able to correlate each node’s activity level with the number of networks engaged in each cognitive task.
What does this mean for neuroscience as a whole? We can still divide and conquer. Modular models of brain function are, more or less, accurate. The visual system does not, for the most part, care about the motor system. However, a complete and accurate model of brain function, including cognition that, for example, relies on both the visual and motor system, must account for the integrative functions of connector node brain regions. These aspects of brain function are necessarily global and holistic and require measuring and modeling the entire brain’s activity simultaneously, making these problems particularly well suited to MRI and network analyses.
Bertolero, Yeo, D’Esposito, the modular and integrative functional architecture of the human brain, Proceedings of the National Academy of Science (2015). http://www.pnas.org/content/112/49/E6798