Towards dynamical network biomarkers in neuromodulation of episodic migraine

Abstract

Computational methods have complemented experimental and clinical neurosciences and led to improvements in our understanding of the nervous systems in health and disease. In parallel, neuromodulation in form of electric and magnetic stimulation is gaining increasing acceptance in chronic and intractable diseases. In this paper, we firstly explore the relevant state of the art in fusion of both developments towards translational computational neuroscience. Then, we propose a strategy to employ the new theoretical concept of dynamical network biomarkers (DNB) in episodic manifestations of chronic disorders. In particular, as a first example, we introduce the use of computational models in migraine and illustrate on the basis of this example the potential of DNB as early-warning signals for neuromodulation in episodic migraine.

Computational modeling of neurostimulation in brain diseases

“…The particular prediction in Dahlem et al (2015) is that such stimulation must be patient-specific due to the individual gyrification patterns of each patient. Furthermore, Dahlem et al (2013) also suggests the possibility of detecting impending CSDs early and respond with a control accordingly. Control points in such a scenario could be the CSD initiation site (if detectable); or subcortical structures involved in pain modulation and perception .…”

Synchronization in complex networks and its application – A survey of recent advances and challenges

“…From experiments, abnormal neural synchronization is found to be closely related to schizophrenia, epilepsy, autism, Alzheimer's disease, and Parkinson's disease. As illustrated in Palva et al (2010), Tang et al (2012cTang et al ( , 2012bTang et al ( , 2013a, Dahlem et al (2013), it remains of great significance to study the synchronization or controllability of a neuronal network, which can not only offer a deep understanding of intrinsic features of synchronization or control weighted and directed networks, but also be beneficial to supply some suggestions to avoid abnormal synchronization to suppress neural diseases such as schizophrenia, epilepsy, autism, Alzheimer's disease, and Parkinson's disease (Uhlhaas & Singer, 2006).…”

“…Synchronization of a number of coupled systems has been widely observed in numerous distinct scenarios such as neuroscience, systems biology, electrochemistry, earth science, social societies and engineering (Arenas, Guilera, Kurths, Moreno, & Zhou, 2008;Dahlem et al, 2013;Gao, Chen, & Lam, 2008;Gu, Pasqualetti, Cieslak, Grafton, & Bassett, 2014;Huang, Ho, & Lu, 2012;Jadbabaie, Lin, & Morse, 2003;Li, Ho, & Lu, 2013;Lu, Kurths, Cao, Mahdavi, & Huang, 2012;Maraun & Kurths, 2005;Pikovsky, Rosenblum, & Kurths, 2001;Ren & Beard, 2008;Saber & Murray, 2004;Wielanda, Sepulchre, & Allgöwer, 2011;ZamoraLópez, Zhou, & Kurths, 2010). The analysis of synchronization is strengthened due to the fact that natural systems, which we intend to understand and exploit, are often interacted closely from different perspectives, determining the complex dynamics of system's properties.…”

Synchronization of Complex Dynamical Networks with Dynamical Behavior Links

“…where 2 ∈  n×k is a known constant real matrix, > 0 and c > 0 are the node and coupling time-delays, respectively. The other parameters are defined in (1). The nonlinear function ∶  n →  q also satisfies Assumption 1.…”

“…s (t). We consider five numbers of identical Chua's circuits (32) to create a complex dynamical network as (1). As many real-world complex networks can be modeled by scale-free networks, a scale-free network with five nodes is considered where the coupling topology is randomly generated by a BA scale free model (Fig.…”