Functional connectivity dynamically evolves on multiple time-scales over a static structural connectome: Models and mechanisms

Abstract

Over the last decade, we have observed a revolution in brain structural and functional Connectomics. On one hand, we have an ever-more detailed characterization of the brain's white matter structural connectome. On the other, we have a repertoire of consistent functional networks that form and dissipate over time during rest. Despite the evident spatial similarities between structural and functional connectivity, understanding how different time-evolving functional networks spontaneously emerge from a single structural network requires analyzing the problem from the perspective of complex network dynamics and dynamical system's theory. In that direction, bottom-up computational models are useful tools to test theoretical scenarios and depict the mechanisms at the genesis of resting-state activity. Here, we provide an overview of the different mechanistic scenarios proposed over the last decade via computational models. Importantly, we highlight the need of incorporating additional model constraints considering the properties observed at finer temporal scales with MEG and the dynamical properties of FC in order to refresh the list of candidate scenarios.

Altered ability to access a clinically relevant control network in patients remitted from major depressive disorder

“…Kaiser et al () used a sliding‐window analysis, which has limitations related to window size, and Demirtaş et al () used instantaneous FC, more comparable to our study. Our approach of focusing on the dominant FC state has the advantage of being more robust to high‐frequency noise, as recurrences of the same pattern are more clearly detected (Cabral et al, ; Cabral, Vidaurre, et al, ). Merit for future studies lies in examining whether FC states are altered when MDD patients change from a remitted to a depressed state, and whether FC alterations predict short‐ and long‐term MDD recurrence.…”

“…However, the scarce research that has been conducted in remitted ‐MDD previously examined “static” FC, representing mean connectivity over a period of scanning. Instead, growing evidence shows that brain activity at rest is not stable during the scan, but slowly wanders through a repertoire of time‐varying, but reoccurring, states of coupling among brain regions (Cabral, Kringelbach, & Deco, ; Deco, Jirsa, & McIntosh, ; Hansen, Battaglia, Spiegler, Deco, & Jirsa, ). Dynamic‐FC (dFC) analysis allows characterizing these reoccurring FC states.…”

Sustained versus instantaneous connectivity differentiates cognitive functions of processing speed and episodic memory

“…Extending temporal connectivity analysis to the frequency domain has motivated descriptions of harmonic brain modes that vary by brain network (Atasoy, Deco, Kringelbach, & Pearson, 2017). Another approach has been to estimate static functional connectivity between regions and perform simulations of dynamical connectivity using in silico models to evaluate dynamical stability of networks, oscillators, meta-stable states, and temporal patterns of activity (Cabral, Kringelbach, & Deco, 2017; Deco, Jirsa, McIntosh, Sporns, & Kotter, 2009; Deco, Kringelbach, Jirsa, & Ritter, 2017; M. A. Ferguson & Anderson, 2011; Ponce-Alvarez et al, 2015).…”

Is chaos making a difference? Synchronization transitions in chaotic and nonchaotic neuronal networks

“…Our first finding is that isolated chaotic neurons in networks do not always make a 49 visible difference in process of network synchronization transitions. The heterogeneity of 50 firing rate and the types of firing patterns make a greater contribution to the steepness 51 of the synchronization transition curve.…”

“…These results suggest that 228 MLE at the network level (macroscopic chaos) can be a predictor of metastability, 236 Finally, we measured the ability of our network models to display multi-stable behavior 237 by characterizing their functional connectivity dynamics (FCD). This analysis is being 238 extensively applied to fMRI and M/EEG recordings [47][48][49] and is explained in Fig 6 239 and Methods. Briefly, the series is divided in overlapping time windows and for each The FCD matrices that we obtained showed distinctive patterns for the 244 unsynchronized and synchronized situations (Fig 7A).…”