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The physics of brain network structure, function and control

Nature Reviews Physics volume 1, pages 318–332 (2019)Cite this article

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Abstract

The brain is characterized by heterogeneous patterns of structural connections supporting unparalleled feats of cognition and a wide range of behaviours. New non-invasive imaging techniques now allow comprehensive mapping of these patterns. However, a fundamental challenge remains to understand how the brain’s structural wiring supports cognitive processes, with major implications for personalized mental health treatments. Here, we review recent efforts to meet this challenge, drawing on physics intuitions, models and theories, spanning the domains of statistical mechanics, information theory, dynamical systems and control. We first describe the organizing principles of brain network architecture instantiated in structural wiring under constraints of spatial embedding and energy minimization. We then survey models of brain network function that stipulate how neural activity propagates along structural connections. Finally, we discuss perturbative experiments and models for brain network control; these use the physics of signal transmission along structural connections to infer intrinsic control processes that support goal-directed behaviour and to inform stimulation-based therapies for neurological and psychiatric disease. Throughout, we highlight open questions that invite the creative efforts of pioneering physicists.

Key points

  • From the first measurement of the nerve impulse by Hermann von Helmholtz in 1849 to the cutting-edge superconducting devices used in magnetoencephalography, physics and neuroscience have always been inextricably linked.

  • Today, network neuroscience — the study of the brain as a complex web of interacting components — draws intuitions and techniques from nearly every branch of physics.

  • The architecture of structural connections between neurons or brain regions is constrained by requirements of energy minimization and efficient information transfer.

  • The materialization of long-range correlations and synchronization from the collective firing of individual neurons conjures notions of emergence and criticality from statistical mechanics.

  • Together, these investigations of brain network structure and function guide targeted treatments for cognitive disorders using theories of network control.

  • Now more than ever, understanding the complexities of the mind lies at the feet of curious and pioneering physicists.

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Fig. 1: Measuring and modelling brain network structure.
Fig. 2: Measuring and modelling brain network function.
Fig. 3: Controllability metrics provide summary statistics regarding the ease with which a given node can enact influence on the network.

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Acknowledgements

The authors are grateful to L. Papadopoulos, J. Z. Kim and V. Buch for helpful comments on an earlier version of this manuscript. The authors also thank A. E. Sizemore for artistic inspiration. D.S.B. and C.W.L. acknowledge support from the Penn NSF MRSEC Grant DMR-1720530, the John D. and Catherine T. MacArthur Foundation, the Alfred P. Sloan Foundation, the ISI Foundation, the Paul Allen Foundation, the Army Research Laboratory (W911NF-10-2-0022), the Army Research Office (Bassett-W911NF-14-1-0679, Grafton-W911NF-16-1-0474 and DCIST- W911NF-17-2-0181), the Office of Naval Research, the US National Institute of Mental Health (2-R01-DC-009209-11, R01-MH112847, R01-MH107235 and R21-M MH-106799), the US National Institute of Child Health and Human Development (1R01HD086888-01), the US National Institute of Neurological Disorders and Stroke (R01 NS099348) and the US National Science Foundation (BCS-1441502, BCS-1430087, NSF PHY-1554488 and BCS-1631550).

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Nature Reviews Physics thanks Y. He and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Authors and Affiliations

  1. Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, PA, USA

    Christopher W. Lynn & Danielle S. Bassett

  2. Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA, USA

    Danielle S. Bassett

  3. Department of Electrical & Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA, USA

    Danielle S. Bassett

  4. Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Danielle S. Bassett

  5. Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Danielle S. Bassett

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  1. Christopher W. Lynn
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All authors contributed to all aspects of manuscript preparation, revision and editing.

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Correspondence to Danielle S. Bassett.

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Lynn, C.W., Bassett, D.S. The physics of brain network structure, function and control. Nat Rev Phys 1, 318–332 (2019). https://doi.org/10.1038/s42254-019-0040-8

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