P079 Characterizing optimal communication in the human brain
Kayson Fakhar*1,2,Fatemeh Hadaeghi2, Caio Seguin3, Alessandra Griffa4, Shrey Dixit2,5, Kenza Fliou2,6, Arnaud Messé2, Gorka Zamora-López7,8, Bratislav Misic9, Claus Hilgetag2,10
1MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.
2Institute of Computational Neuroscience, University Medical Center Eppendorf-Hamburg, Hamburg University, Hamburg Center of Neuroscience, Germany.
3Department of Psychological and Brain Sciences, Indiana University, Bloomington, USA.
4Leenaards Memory Center, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Montpaisible 16, 1011 Lausanne, Switzerland
5Department of Psychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
6Sorbonne University, Paris, France.
7Center for Brain and Cognition, Pompeu Fabra University, Barcelona, Spain.
8Department of Information and Communication Technologies, Pompeu Fabra University, Barcelona, Spain.
9McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada.
10Department of Health Sciences, Boston University, Boston, MA, USA.
*Email: kayson.fakhar@mrc-cbu.cam.ac.uk
Introduction
Efficient communication is shown to be a key characteristic of the organization of the human brain(Chen et al., 2013). In fact, it was found to bias the wiring economy of the brain networks in its favour by allocating expensive long-range shortcuts among its hubs(van den Heuvel et al., 2012). However, communication efficiency is often defined through specific signalling models — such as routing along shortest paths, broadcasting via parallel pathways, or diffusive random-walk dynamics — that omit important biological aspects of brain dynamics, including conductance delays, oscillations, and inhibitory interactions(Seguin et al., 2023). As a result, a more general framework is needed to characterize optimal signal transmission within a given brain network and to assess whether actual brain communication is truly efficient.
Methods
Here, we introduce a model-agnostic framework based on multi-site virtual lesions in large-scale neural mass models. Our approach uses a game-theoretical perspective: each brain region seeks to maximize its influence over others, subject to constraints from underlying network structure and local dynamics. This perspective yields a mathematically rigorous definition of optimal communication given any model of local dynamics on any arbitrary network structure. We used a linear, nonlinear, and oscillatory neural mass model and compared the resulting optimal influence patterns with those derived from abstract models of signalling, i.e., routing, navigation, broadcasting, and diffusion.
Results
Our results are as follows: First, we found that the broadcasting regime has the closest resemblance to the optimal communication patterns derived from game theory. Second, although the underlying structural connection weight reliably predicts the efficiency of communication between regions, it fails to capture the true influence of weakly connected hub regions. In other words, hubs harness their rich connectivity to broadcast their signal over multiple pathways when they lack a reliable direct connection to their targets. Further comparisons with functional connectivity (fMRI-based correlations) and cortico-cortical evoked potentials reveal two additional insights: (i) functional connectivity is a poor indicator of actual information exchange; and (ii) brain communication is likely to take place close to optimal levels.
Discussion
Altogether, this work provides a rigorous, versatile framework for characterizing optimal brain communication, identifies the most influential regions in the network, and offers further evidence supporting efficient signalling in the brain.
Acknowledgements
This work is in part funded by the German Research Foundation (DFG)-SFB 936-178316478-A1; TRR169-A2; SPP 2041/GO 2888/2-2 and the Templeton World Charity Foundation, Inc. (funder DOI 501100011730) under grant TWCF-2022-30510.
References
Chen, Y., Wang, S., Hilgetag, C. C., & Zhou, C. (2013). Trade-off between multiple constraints enables simultaneous formation of modules and hubs in neural systems.PLoS Comput. Biol.,9(3), e1002937.
Seguin, C., Sporns, O., & Zalesky, A. (2023). Brain network communication: Concepts, models and applications.Nat. Rev. Neurosci.,24(9), 557–574.
van den Heuvel, M. P., Kahn, R. S., Goñi, J., & Sporns, O. (2012). High-cost, high-capacity backbone for global brain communication.Proc. Natl. Acad. Sci. U. S. A.,109(28), 11372–11377.
