In complex networks such as gene networks, traffic systems or brain circuits it is important to
understand how long it takes for the different parts of the network to effectively influence one …

… filtering normalized signals with a Morlet wavelet h(t,f M ) where f M is the filter frequency.
The filtered complex signal is given by y F (t,f M ) = y(t) * h(t,f M ) where h(t,f M ) = A exp(− t 2 /2σ …

… the probability of spurious synchronization and anatomically misplaced connections, for
both statistical analyses and graph visualization in this study, we removed parcels with f …

… The model comprised 2 “areas” that each contained 2 populations of weakly coupled
oscillators: one at LF and another at HF, so that f HF = 2 × f LF , ie, with the n:m ratio (hereafter …

… By repeating Step 3 for all window lengths of T defined in Step 2, F, a vector of F(t), t∈T,
was obtained. The DFA exponent is the slope of the trend line of F as a function of T on log-log …

… We next find the mixing function f mix between all involved nodes from A mix (Fig. 2C, and
illustrated geometrically in Fig. 2D) to compute the edge-to-edge adjacency in signal mixing. …

Empirical studies over the past two decades have provided support for the hypothesis that
schizophrenia is characterized by altered connectivity patterns in functional brain networks. …

… We set the prior distribution of c o e f f v a r d e l a y to 0.2 ± … For the “mixed delays” method,
we had the c o e f f b a l a n c e … We set the prior distribution of c o e f f b a l a n c e to 0.5 ± …

… where f is the frequency and γ is the scaling exponent. The shape of this “1/f ” distribution
might relate to properties of cognitive function. For example, it has been shown that the shape …

… significant local f low : f high CFC in p and significant interareal synchronization at f high ,
or significant local f low : f high CFC in q and significant interareal synchronization at f low . …