Character out-of mathematical relationships certainly node education, amplitude regarding local vibration and you can directionality of affairs

Character out-of mathematical relationships certainly node education, amplitude regarding local vibration and you can directionality of affairs

After that, the directionality between most of the local node character are measured utilising the directed phase lag directory (dPLI), and that works out the fresh stage lead and lag matchmaking between two oscillators (come across Content and techniques to possess intricate definition)

The new main aim of this research was to select a general relationships out of circle topology, regional node personality and you can directionality during the inhomogeneous networks. We proceeded by the design a straightforward combined Dating-Apps für iPhone oscillatory community model, playing with an effective Stuart-Landau design oscillator to help you depict the fresh sensory mass inhabitants craft at each node of system (get a hold of Materials and methods, and you can S1 Text having information). The newest Stuart-Landau model is the normal kind of this new Hopf bifurcation, meaning that simple fact is that best design trapping more attributes of the device near the bifurcation area [22–25]. The newest Hopf bifurcation appears extensively inside physical and toxins systems [24–33] that’s tend to accustomed investigation oscillatory choices and you can attention figure [twenty-five, twenty-seven, 30, 33–36].

We basic went 78 combined Stuart-Landau habits into a level-free model system [37, 38]-that is, a network having a qualification shipping following an electrical power legislation-where coupling fuel S between nodes is ranged as manage parameter. The pure regularity each and every node are randomly removed off good Gaussian shipping with the indicate within 10 Hz and you may important departure of 1 Hz, simulating the new leader data transfer (8-13Hz) regarding human EEG, and then we systematically ranged the fresh coupling fuel S off 0 in order to 50. I plus ranged the full time impede factor across the a broad assortment (dos

50ms), but this did not yield a qualitative difference in the simulation results as long as the delay was less than a quarter cycle (< 25 ms) of the given natural frequency (in this case, one cycle is about 100 ms since the frequency is around 10Hz). The simulation was run 1000 times for each parameter set.

I upcoming proceeded to identify new relationships ranging from network topology (node knowledge), node fictional character (amplitude) and you will directionality between node character (dPLI) (look for S1 Text message having over derivation)

dPLI between two nodes a and b, dPLIab, can be interpreted as the time average of the sign of phase difference . It will yield a positive/negative value if a is phase leading/lagging b, respectively. dPLI was used as a surrogate measure for directionality between coupled oscillators . Without any initial bias, if one node leads/lags in phase and therefore has a higher/lower dPLI value than another node, the biased phases reflect the directionality of interaction of coupled local dynamics. dPLI was chosen as the measure of analysis because its simplicity facilitated the analytic derivation of the relationship between topology and directionality. However, we note that we also reach qualitatively similar conclusions with our analysis of other frequently-used measures such as Granger causality (GC) and symbolic transfer entropy (STE) (see S1 Text and S1 Fig for the comparison) [39–41].

Fig 2A–2C demonstrates how the network topology is related to the amplitude and phase of local oscillators. Fig 2A shows the mean phase coherence (measure of how synchronized the oscillators are; see Materials and Methods for details) for two groups of nodes in the network: 1) hub nodes, here defined as nodes with a degree above the group standard deviation (green triangles, 8 out of 78 nodes); and 2) peripheral nodes, here defined as nodes with a degree of 1 (yellow circles, 33 out of 78 nodes). When the coupling strength S is large enough, we observed distinct patterns for each group. For example, at the coupling strength of S = 1.5, which represents a state in between the extremes of a fully desynchronized and a fully synchronized network (with the coherence value in the vicinity of 0.5), the amplitudes of node activity are plitudes, and peripheral nodes, with smaller amplitudes (Fig 2B). More strikingly, the phase lead/lag relationship is clearly differentiated between the hub and peripheral nodes: hub nodes phase lag with dPLI <0, while the peripheral nodes phase lead with dPLI >0 (Fig 2C). Fig 3 shows the simulation results in random and scale-free networks, which represent two extreme cases of inhomogeneous degree networks. This figure clearly demonstrates that larger degree nodes lag in phase with dPLI <0 and larger amplitude (see S2 Fig for various types of networks: scale free, random, hierarchical modular and two different human brain networks) even at the coupling strength S = 1.5, where the separation of activities between hub nodes and peripheral nodes just begins to emerge. To explain these simulation results, we utilized Ko et al.'s mean-field technique approach to derive the relationships for the coupled Stuart-Landau oscillators with inhomogeneous coupling strength, which in turn can be applied to inhomogeneous degree networks by interpreting inhomogeneous coupling strength as inhomogeneous degree for each oscillator .