Network analysis of whole-brain fMRI dynamics: A new framework based on dynamic communicability

Abstract

Neuroimaging techniques such as MRI have been widely used to explore the associations between brain areas. Structural connectivity (SC) captures the anatomical pathways across the brain and functional connectivity (FC) measures the correlation between the activity of brain regions. These connectivity measures have been much studied using network theory in order to uncover the distributed organization of brain structures, in particular FC for task-specific brain communication. However, the application of network theory to study FC matrices is often "static" despite the dynamic nature of time series obtained from fMRI. The present study aims to overcome this limitation by introducing a network-oriented analysis applied to whole-brain effective connectivity (EC) useful to interpret the brain dynamics. Technically, we tune a multivariate Ornstein-Uhlenbeck (MOU) process to reproduce the statistics of the whole-brain resting-state fMRI signals, which provides estimates for MOU-EC as well as input properties (similar to local excitabilities). The network analysis is then based on the Green function (or network impulse response) that describes the interactions between nodes across time for the estimated dynamics. This model-based approach provides time-dependent graph-like descriptor, named communicability, that characterize the roles that either nodes or connections play in the propagation of activity within the network. They can be used at both global and local levels, and also enables the comparison of estimates from real data with surrogates (e.g. random network or ring lattice). In contrast to classical graph approaches to study SC or FC, our framework stresses the importance of taking the temporal aspect of fMRI signals into account. Our results show a merging of functional communities over time (in which input properties play a role), moving from segregated to global integration of the network activity. Our formalism sets a solid ground for the analysis and interpretation of fMRI data, including task-evoked activity.

MOU-EC: model-based whole-brain effective connectivity to extract biomarkers for brain dynamics from fMRI data and study distributed cognition

“…It shows 645 a merging of almost all ROIs that eventually exchange flow in an even manner. This agrees with our previous results for resting state only and a different parcellation [67]. This speaks to a transition from segregated processing (within each diagonal block of left matrices) to a global integration.…”

“…Nonetheless, it can be argued that such "integration" measures somehow rely on the observed phenomena (in empirical or model activity) rather than their causes. Rather, dynamic models enable the application of graph measures to the underlying connectivity that generates the 545 dynamics, as was recently proposed [120,67].…”

“…Here we follow this novel direction with our adaptation of graph-like measures for the network dynamics themselves [68,67], whose main details are presented in the following; see also Appendix E for mathematical details. This section illustrates the following aspects of network-oriented analysis for MOU-EC:…”

“…It also highlights some advantages of machine learning over statistical testing in making use of the multivariate nature of connectivity measures, transposing and abstracting concepts presented in a recent study for subject identification [112]. The fourth section bridges with network analysis [68,67], interpreting 160 changes in MOU-EC connections in a collective and model-based manner. In particular, it adapts community detection to our dynamic model (Fig.…”

“…A special case of the dynamic flow -termed dynamic communicability-that only depends on the MOU-EC (and not Σ) can also be defined assuming that all ROIs have independent spontaneous activity with the same intensity; it corresponds to a diagonal and homogeneous matrix Σ. Like the flow, dynamic 575 communicability describes directional interactions, which differs from the symmetric interactions obtained from the direct application of graph theory to FC or SC [67]. It is worth stressing that the dynamic flow and communicability are graph-like measures that genuinely capture the properties of weighted networks, similar to the previously-proposed graph communicability [51,50].…”