Time scale properties of task and resting-state functional connectivity: Detrended partial cross-correlation analysis

Abstract

Functional connectivity analysis is an essential tool for understanding brain function. Previous studies showed that brain regions are functionally connected through low-frequency signals both within the default mode network (DMN) and task networks. However, no studies have directly compared the time scale (frequency) properties of network connectivity during task versus rest, or examined how they relate to task performance. Here, using fMRI data collected from sixty-eight subjects at rest and during a stop signal task, we addressed this issue with a novel functional connectivity measure based on detrended partial cross-correlation analysis (DPCCA). DPCCA has the advantage of quantifying correlations between two variables in different time scales while controlling for the influence of other variables. The results showed that the time scales of within-network connectivity of the DMN and task networks are modulated in opposite directions across rest and task, with the time scale increased during rest vs. task in the DMN and vice versa in task networks. In regions of interest analysis, the within-network connectivity time scale of the pre-supplementary motor area - a medial prefrontal cortical structure of the task network and critical to proactive inhibitory control - correlated inversely with Barratt impulsivity and stop signal reaction time. Together, these findings demonstrate that time scale properties of brain networks may vary across mental states and provide evidence in support of a role of low frequency fluctuations of BOLD signals in behavioral control.

Keywords: DPCCA; Functional connectivity; Multiple time scales; Resting state; Stop-signal task; preSMA.

Figure 1

Illustration of a sample DPCCA profile. Given simulated BOLD time series, DPCCA coefficients were computed for different time scales ranging from 6 to 40 s (18 time points spaced by 2 s). Solid line represents the average across 100 simulated data sets; blue shade represents the standard deviation; and dashed lines are 95% confidence intervals (CI) estimated from the empirical null dataset. Peak time scale smax=10s. Details are available in (Ide et al., 2017).

Figure 2

(a) Yale functional brain atlas of 268 nodes covering the whole-brain (Shen et al., 2013). (b) Connectivity matrix of the 268 nodes, given by the DPCCA_max values thresholded at 90% percentile for rest and task. (c) Ventral precuneus connections as an example to illustrate network connectivity. In the circle plot, lines indicate the ‘networks’ to which precuneus nodes are connected.

Figure 3

Average time scales of the peak connectivity within each one of the eight functional networks (“In” network connectivity). (a) Map of s_max(In) during rest. (b) Map of s_max(In) during the stop signal task. vmPFC: ventromedial prefrontal cortex (DMN); PCC: posterior cingulate cortex (DMN); mSFG: medial superior frontal gyrus (MF network, including the presupplementary motor area), DLPFC: dorsolateral prefrontal cortex (FP network); and IPL: inferior parietal lobule (FP network).

Figure 4

Comparison of peak time scales of within network connectivity or s_max(In). (a) Average s_max for resting and task conditions within MF, FP and DMN. Blue and red shades represent the standard error of the mean for resting and task conditions, respectively. (b) Paired t-test (task vs. rest) results for each network, and the interaction effects (two-way ANOVA: task vs. rest × MF+FP vs. DMN).

Figure 5

Association between task performance and the connectivity of the pre-supplementary motor area (preSMA). (a) Cluster of the preSMA as identified from a recent work on proactive control and response inhibition (Hu et al., 2015). (b, c) Increased time scale of “within- preSMA” connectivity is associated with decreased impulsivity and improved inhibition performance (decreased SSRT).