Tracking Whole-Brain Connectivity Dynamics in the Resting State

Abstract

Spontaneous fluctuations are a hallmark of recordings of neural signals, emergent over time scales spanning milliseconds and tens of minutes. However, investigations of intrinsic brain organization based on resting-state functional magnetic resonance imaging have largely not taken into account the presence and potential of temporal variability, as most current approaches to examine functional connectivity (FC) implicitly assume that relationships are constant throughout the length of the recording. In this work, we describe an approach to assess whole-brain FC dynamics based on spatial independent component analysis, sliding time window correlation, and k-means clustering of windowed correlation matrices. The method is applied to resting-state data from a large sample (n = 405) of young adults. Our analysis of FC variability highlights particularly flexible connections between regions in lateral parietal and cingulate cortex, and argues against a labeling scheme where such regions are treated as separate and antagonistic entities. Additionally, clustering analysis reveals unanticipated FC states that in part diverge strongly from stationary connectivity patterns and challenge current descriptions of interactions between large-scale networks. Temporal trends in the occurrence of different FC states motivate theories regarding their functional roles and relationships with vigilance/arousal. Overall, we suggest that the study of time-varying aspects of FC can unveil flexibility in the functional coordination between different neural systems, and that the exploitation of these dynamics in further investigations may improve our understanding of behavioral shifts and adaptive processes.

A novel concurrent TMS‐fMRI method to reveal propagation patterns of prefrontal magnetic brain stimulation

“…Finally, functional brain connectivity shows substantial variability between participants, with strong variability observed in the prefrontal cortex (Mueller et al, 2013). Moreover, resting state functional connectivity studies show that brain functional connectivity shows substantial temporal variability, especially in the sgACC (among other regions) (Allen et al, 2014; Handwerker, Roopchansingh, Gonzalez‐Castillo, & Bandettini, 2012; Mueller et al, 2013). To summarize, the structural and functional organization of the brain together with the dynamic nature of functional connectivity could explain the variability in propagation patterns of TMS‐induced activity.…”

The effect of preprocessing in dynamic functional network connectivity used to classify mild traumatic brain injury

“…We choose four clusters based on the elbow criteria as described in Allen et al. (2012). Centroids of obtained clusters are depicted in Figure 3.…”

“…For some pipelines spikes were also processed using a regression, which simply set spike time courses to zero. In static connectivity it is possible to simply censure spiky time courses (Vergara et al., 2015), but dFNC requires the use of interpolation to avoid discontinuities in small time windows (Allen et al., 2012). Filtering was implemented using a fifth‐order Butterworth filter with bandwidth [0.01 0.15] Hz as it has been suggested in previous dFNC literature (Allen et al., 2011, 2012).…”

Time‐resolved detection of stimulus/task‐related networks, via clustering of transient intersubject synchronization

“…Sliding windows have been combined with various methods, with the aim of assessing whether a given index of connectivity changes over the duration of the experiment. These indexes may be derived simply by correlating the BOLD signal in two brain areas; or can include more complex measures such a inter‐regional correlation matrices (tracking the connectivity between a seed‐region and an extensive set of other regions) [e.g., Allen et al, 2014] and networks derived from spatial ICA [e.g., Kiviniemi et al, 2011; Morton and Hutchison, 2014; see also Chang and Glover, 2010; Grigg and Grady, 2010; Majeed et al, 2011, for other indexes of inter‐regional connectivity]. Even at rest, these approaches revealed dynamic reconfigurations of brain networks, reflecting both the fluctuation of high‐level processes (e.g.…”

“…Cluster analyses have been extensively used to analyze resting‐state data for brain parcellization [e.g., Craddock et al, 2012; Thirion et al, 2014] and to index connectivity at rest [Cordes et al 2002]. Moreover, time‐resolved applications of cluster analysis contributed to the identification of networks' dynamics at rest [Allen et al, 2014; Liu et al, 2013; Yaesoubi et al, 2014]. Using sliding‐windows and hierarchical clustering, Yang et al [2014] reported that the duration of specific “network states” correlated with individual neuropsychological scores, again indicating a possible relevance of networks' fluctuations at rest (see also above).…”