Fast macroscale cortical dynamics shaped by long-range connections

Brisbane Convention & Exhibition Centre 

The cerebral cortex takes the form of a sheet of interconnected neurons that interact both through local intracortical connections, and through a complex network of long-range connections (LRCs) that facilitate rapid communication between distant neural populations. LRCs play a well-established functional role in supporting information processing across distributed neural systems, and underpin the prevalent conceptualization of the brain as a communication network (or connectome) of functionally specialized regions. However, recent results have challenged this network-based view, showing that key properties of resting-state fMRI dynamics can be accurately captured by simple geometric models that neglect the LRCs measured from connectomics^1,2. A key open question thus remains: if LRCs are crucial for cortical function, why do they appear to play a minimal role in capturing key dynamical properties of resting-state fMRI?
Here we address this question through a range of investigations using a novel mathematical model of cortical dynamics, in which neural populations interact both through a connectome of LRCs and through a sheet of local connections. For a large variety of connectome topologies, we demonstrate that, in spontaneous settings and on long timescales (as per resting-state fMRI data), simulated brain dynamics increasingly resemble that of a geometric model that excludes the connectome. Our results thus provide a plausible account for the role of LRCs in shaping cortical dynamics on different length and timescales, and explain why LRCs (which predominantly shape fast information processing of precise input stimuli) have a minimal role in shaping resting-state fMRI dynamics.