Structural anomalies in brain networks induce dynamical pacemaker effects

Abstract

Dynamical effects on healthy brains and brains affected by tumor are investigated via numerical simulations. The brains are modeled as multilayer networks consisting of neuronal oscillators whose connectivities are extracted from Magnetic Resonance Imaging (MRI) data. The numerical results demonstrate that the healthy brain presents chimera-like states where regions with high white matter concentrations in the direction connecting the two hemispheres act as the coherent domain, while the rest of the brain presents incoherent oscillations. To the contrary, in brains with destructed structures, traveling waves are produced initiated at the region where the tumor is located. These areas act as the pacemaker of the waves sweeping across the brain. The numerical simulations are performed using two neuronal models: (a) the FitzHugh-Nagumo model and (b) the leaky integrate-and-fire model. Both models give consistent results regarding the chimeralike oscillations in healthy brains and the pacemaker effect in the tumorous brains. These results are considered a starting point for further investigation in the detection of tumors with small sizes before becoming discernible on MRI recordings as well as in tumor development and evolution.

In-phase and anti-phase bursting dynamics and synchronisation scenario in neural network by varying coupling phase

“…The two coupled HR neurons have been analysed under the influence of self, mixed and cross coupling of state variables [23,49]. The dynamical system is expressed by the equations of the form:…”

Alternate attractor chimeralike states on rings of chaotic Lorenz-type oscillators

“…For example, breathing chimeras [39], spiral-wave chimeras [40], multi-chimeras [41], amplitude-mediated chimeras [42], chimeralike states [43], phase-flip chimeras [44], traveling chimeras [45], self-propelled chimeras [46], as well as frequency chimeras [47], have been identified in different kinds of complex systems. These findings can give us rich clues in understanding the mechanism of some physiological phenomena such as the unihemispheric sleep of some marine mammals [48] and the first-night effect in human sleep [49], and guiding the treatments of tumors [50] and epilepsy [51].…”

Effect of cognitive training on brain dynamics

“…[26] A similar approach was used in the early diagnosis of brain tumors. [27] Antonopoulos et al verified that the brain evolves by maximizing its information flow capacity through HR neural networks. [28] Focusing on dynamic modeling based on brain networks, Santos et al discovered chimera-like states in cat brain networks with the HR model.…”