Fast macroscale cortical dynamics shaped by long-range connections
Presented During:
Saturday, June 28, 2025: 11:30 AM - 12:45 PM
Brisbane Convention & Exhibition Centre
Room:
Great Hall
Poster No:
1440
Submission Type:
Abstract Submission
Authors:
Rishi Maran1, Eli Müller2, Ben Fulcher3
Institutions:
1University of Sydney, Sydney, New South Wales, 2University of Sydney, Sydney, NSW, 3University of Sydney, Sydney, Australia
First Author:
Co-Author(s):
Introduction:
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 connectomics1,2. 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.
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.
Methods:
We develop a new model of cortical dynamics in which neural populations interact both by local connections and LRCs (Fig. 1). Local connections allow activity to propagate between any two points on the sheet as waves; whereas additional LRCs allow activity to be rapidly transmitted between specific distant regions on the sheet.
Results:
Our simulations establish a fundamental result that cortical dynamics in response to a focal stimulus can be significantly influenced by a LRC, under the necessary conditions that the dynamics can be resolved at millisecond timescales, and the stimulus is proximate to the LRC (Fig. 2). With reference to these conditions, we find that the functional contribution of a LRC to cortical dynamics diminishes, both: (i) when the dynamics is measured in spontaneous settings (relative to the response to a focal input stimulus); and (ii) when measured by fMRI, whose access to underlying cortical activity is restricted to its slow (long-timescale) dynamics. Importantly, our results demonstrate that the extent to which brain dynamics can be approximated by that of a geometric model varies with the experimental conditions under which the dynamics are generated and measured, and becomes sufficiently valid under the conditions of resting-state fMRI.
Conclusions:
We have developed a new model of macroscale cortical dynamics capable of exhibiting complex spatiotemporal dynamics that combine wave propagation (underpinned by local connectivity) and distal activations (underpinned by LRCs). Our model simulations support a novel account for why neural population-level dynamics are strongly shaped by LRCs when measured on short timescales in stimulus-response settings, but play a minimal role (and can be mostly neglected) on longer timescales (such as those accessible from fMRI). Our findings provide a compelling explanation for a major open problem in macroscale neuroscience, while providing a foundation on which future brain models can be developed and refined.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
fMRI Connectivity and Network Modeling 1
Task-Independent and Resting-State Analysis
Keywords:
Computational Neuroscience
FUNCTIONAL MRI
Modeling
Systems
1|2Indicates the priority used for review
Abstract Information
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2. Pang, J. C., Aquino, K. M., Oldehinkel, M., Robinson, P. A., Fulcher, B. D., Breakspear, M., & Fornito, A. (2023). Geometric constraints on human brain function. Nature, 618(7965), 566-574.