A Principled Basis for Understanding Local Dynamics
It is well known that functional dynamics occur around a static anatomical (white matter) skeleton (Honey et al., 2009). Computational models suggest that realistic network dynamics, and the influence of noise within them, cannot exist without a healthy, biologically plausible WM structure (Deco et al., 2009). However, it remains unknown to what extent the WM skeleton constrains functional dynamics/variability in humans, and across the lifespan. Conversely, although structural connections form the “core skeleton” of brain function, functional connections also exist broadly in absence of direct structural connections (Deco et al., 2009; Ghosh et al., 2008; Honey et al., 2007). Notably, a theoretically and computationally informed contributing factor to brain signal variability effects is that our neural system can explore a broad repertoire of brain states/networks from moment to moment when variability is present (Deco et al., 2009, 2011; Garrett et al., 2013, NBR). Accordingly, a healthy, developed brain (i.e., in young adults) may be more flexible in the face of changing systemic or environmental demands, as it can reconfigure efficiently as required. Assuming that node-based signal variability reflects a dynamic system that is constantly reconfiguring in strength and direction, it is clear that explicitly measuring network-level dynamics is required to determine the bounds of node-based dynamics. We are examining these issues in detail within the LNDG. A recent attempt to link node-wise temporal dynamics to network-level effects (Garrett et al., 2018, NeuroImage) revealed that the vast majority of local temporal variability may be a reflection of network communication/integration rather than local “noise.” Crucially, the thalamus provided a unique and fundamental variability-based signature of how the brain integrates across scales, providing a key anatomical target for future LNDG work probing the generation and mechanisms of temporal variability in the brain.