Local inhibitory plasticity tunes macroscopic brain dynamics and allows the emergence of functional brain networks.
Rich, spontaneous brain activity has been observed across a range of different temporal and spatial scales. These dynamics are thought to be important for efficient neural functioning. A range of experimental evidence suggests that these neural dynamics are maintained across a variety of different cognitive states, in response to alterations of the environment and to changes in brain configuration (e.g., across individuals, development and in many neurological disorders). This suggests that the brain has evolved mechanisms to maintain rich dynamics across a broad range of situations. Several mechanisms based around homeostatic plasticity have been proposed to explain how these dynamics emerge from networks of neurons at the microscopic scale. Here we explore how a homeostatic mechanism may operate at the macroscopic scale: in particular, focusing on how it interacts with the underlying structural network topology and how it gives rise to well-described functional connectivity networks. We use a simple mean-field model of the brain, constrained by empirical white matter structural connectivity where each region of the brain is simulated using a pool of excitatory and inhibitory neurons. We show, as with the microscopic work, that homeostatic plasticity regulates network activity and allows for the emergence of rich, spontaneous dynamics across a range of brain configurations, which otherwise show a very limited range of dynamic regimes. In addition, the simulated functional connectivity of the homeostatic model better resembles empirical functional connectivity network. To accomplish this, we show how the inhibitory weights adapt over time to capture important graph theoretic properties of the underlying structural network. Therefore, this work presents suggests how inhibitory homeostatic mechanisms facilitate stable macroscopic dynamics to emerge in the brain, aiding the formation of functional connectivity networks.
Computational, Cognitive, and Clinical Neuroimaging Laboratory, Division of Brain Sciences, Faculty of Medicine, Imperial College London, Hammersmith Hospital, Du Cane Road, London W12 0NN, UK; Centre for Neuroimaging Science, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, De Crespigny Park, London SE5 8AF, UK.
Peter J
PJ
Hellyer
Computational, Cognitive, and Clinical Neuroimaging Laboratory, Division of Brain Sciences, Faculty of Medicine, Imperial College London, Hammersmith Hospital, Du Cane Road, London W12 0NN, UK.
Barbara
B
Jachs
Department of Bioengineering, Imperial College London, Room B435, Bessemer Building, South Kensington Campus, SW7 2AZ, UK. Electronic address: c.clopath@imperial.ac.uk.
Claudia
C
Clopath
Computational, Cognitive, and Clinical Neuroimaging Laboratory, Division of Brain Sciences, Faculty of Medicine, Imperial College London, Hammersmith Hospital, Du Cane Road, London W12 0NN, UK. Electronic address: r.leech@imperial.ac.uk.
Robert
R
Leech
D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D006706:Homeostasis; D006801:Humans; D008959:Models, Neurological; D009433:Neural Inhibition; D016571:Neural Networks, Computer; D009434:Neural Pathways / Q000502:physiology; D009473:Neuronal Plasticity; D009474:Neurons / Q000502:physiology*; D066127:White Matter / Q000502:physiology
Homeostasis; Intrinsic connectivity networks; Neural dynamics; Plasticity