Non-linear effective connectivity can be explored by means of discrete dynamic Bayesian networks, which do not require a pre-definition of structure and do not make assumptions about the functional form of interactions between the nodes, i.e. whether they are stochastic, combinatorial or non-linear (Smith et al., 2002, 2006).
Non-linear functional connectivity can be established by means of mutual information analyses that (i) describe the amount of information in one area given the time series information in another area; and (ii) are sensitive to static non-linear dependencies (Roulston, 1999; David et al., 2004).
(2) Phase synchronization index (PSI) is a non-linear measure of connectivity.
The non-linear nature of connectivity in networks means that small differences in concurrency can create large differences in epidemic potential and thus tremendous opportunities for prevention.
The SL, like coherence, captures overall functional connectivity but additionally detects non-linear coupling, does not show spurious connectivity between bandpass filtered signals, is insensitive to fluctuations in power, and robust to non-stationarity of the EEG (Stam and van Dijk 2002).
Some recent studies of large-scale network models that combine accurate anatomical connectivity with non-linear dynamics have propelled us towards a better understanding of the relationship between structural and functional connectivity (Honey et al., 2007).
Although it might be tempting to translate d into proper neural activity for all subsequent analysis, doing so would merely introduce freely tunable parameters (e.g., in the usually non-linear neural response function or connectivity weights).
Among the novel insights we underline from this work are the possibilities that (i) selective mitophagy is not required for quality control because selective fusion is sufficient; (ii) increased connectivity may have non-linear effects on the diffusion rate of proteins; and (iii) fused networks can act to dampen biochemical fluctuations.
Small increases in p (i.e. small increases in network connectivity may lead to large, non-linear, increases in diffusion rate, especially near the percolating value pc.
Multi-variate approaches or non-linear techniques [52], [53] may yield different estimates of functional connectivity and distinct results for how similar functional connectivity is across timescales.
This is due to the temporal variability in landscape connectivity and sediment transport and the non-linear relationship between land use intensity and soil erosion.
