Personality regarding statistical relationships among node training, amplitude regarding regional oscillations and you will directionality off connections

Personality regarding statistical relationships among node training, amplitude regarding regional oscillations and you will directionality off connections

Next, new directionality anywhere between all local node character are counted using the directed stage slowdown index (dPLI), hence calculates the fresh new phase direct and lag relationships ranging from a couple of oscillators (select Product and methods to own detail by detail definition)

The main intent behind this study would be to choose an over-all relationships away from system topology, local node character and directionality in the inhomogeneous channels. I went on by developing a straightforward combined oscillatory community design, having fun with a good Stuart-Landau model oscillator so you’re able to portray the fresh sensory mass populace passion from the for every node of your network (look for Material and methods, and you can S1 Text to have facts). Brand new Stuart-Landau model is the typical version of new Hopf bifurcation, and therefore this is the greatest model trapping by far the most top features of the system near the bifurcation point [22–25]. The newest Hopf bifurcation looks widely for the physical and you will chemical compounds possibilities [24–33] which will be commonly regularly study oscillatory conclusion and you can brain personality [25, twenty seven, 29, 33–36].

We earliest ran 78 combined Stuart-Landau habits on the a scale-100 % free design circle [37, 38]-which is, a network that have a qualification delivery following an energy rules-in which coupling energy S between nodes will likely be varied just like the manage factor. The brand new natural volume of each node was randomly drawn of a Gaussian shipping on imply during the ten Hz and you can standard deviation of just one Hz, simulating the alpha data transfer (8-13Hz) regarding individual EEG, and in addition we methodically varied brand new coupling energy S out-of 0 to help you 50. I in addition to varied the time impede factor all over a general diversity (2

50ms), but this did not yield a qualitative difference in the simulation results as 420 sitios web de citas gratis 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.

We next proceeded to spot the newest dating anywhere between circle topology (node studies), node fictional character (amplitude) and you can directionality anywhere between node figure (dPLI) (look for S1 Text message getting done 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 .


A szerző