2021/02/28 by Teresa Chouzouris, Nicolas Roth, Caglar Cakan +1
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · Mathematics · Medicine · Neuroscience · Physics and Astronomy · #Advanced MRI Techniques and Applications #Advanced Neuroimaging Techniques and Applications #Applied mathematics #Artificial intelligence #Computer science #Connectome #Control (management) #Control theory (sociology) #Controllability #Controller (irrigation) #Functional Brain Connectivity Studies #Human Connectome Project #Mathematics #Network dynamics #Node (physics) #Nonlinear system #Physics #Synchronization (alternating current) #Topology (electrical circuits) #cs.SY #eess.SY #nlin.AO #q-bio.NC
paper · pdf · doi:10.1103/physreve.104.024213
published as Phys. Rev. E 104, 024213 (2021) · Main paper: 20 pages, 14 figures; supplemental material: 10 pages, 9 figures. For associated video files, see https://github.com/rederoth/nonlinearControlFHN-videos
openalex publication_date 2021/08/19 · arxiv created 2021/09/03 · arxiv updated 2021/09/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
We apply the framework of optimal nonlinear control to steer the dynamics of a whole-brain network of FitzHugh-Nagumo oscillators. Its nodes correspond to the cortical areas of an atlas-based segmentation of the human cerebral cortex, and the internode coupling strengths are derived from diffusion tensor imaging data of the connectome of the human brain. Nodes are coupled using an additive scheme without delays and are driven by background inputs with fixed mean and additive Gaussian noise. Optimal control inputs to nodes are determined by minimizing a cost functional that penalizes the deviations from a desired network dynamic, the control energy, and spatially nonsparse control inputs. Using the strength of the background input and the overall coupling strength as order parameters, the network's state-space decomposes into regions of low- and high-activity fixed points separated by a high-amplitude limit cycle, all of which qualitatively correspond to the states of an isolated network node. Along the borders, however, additional limit cycles, asynchronous states, and multistability can be observed. Optimal control is applied to several state-switching and network synchronization tasks, and the results are compared to controllability measures from linear control theory for the same connectome. We find that intuitions from the latter about the roles of nodes in steering the network dynamics, which are solely based on connectome features, do not generally carry over to nonlinear systems, as had been previously implied. Instead, the role of nodes under optimal nonlinear control critically depends on the specified task and the system's location in state space. Our results shed new light on the controllability of brain network states and may serve as an inspiration for the design of new paradigms for noninvasive brain stimulation.