Quasi-periodic patterns contribute to functional connectivity in the brain

Abstract

Functional connectivity is widely used to study the coordination of activity between brain regions over time. Functional connectivity in the default mode and task positive networks is particularly important for normal brain function. However, the processes that give rise to functional connectivity in the brain are not fully understood. It has been postulated that low-frequency neural activity plays a key role in establishing the functional architecture of the brain. Quasi-periodic patterns (QPPs) are a reliably observable form of low-frequency neural activity that involve the default mode and task positive networks. Here, QPPs from resting-state and working memory task-performing individuals were acquired. The spatiotemporal pattern, strength, and frequency of the QPPs between the two groups were compared and the contribution of QPPs to functional connectivity in the brain was measured. In task-performing individuals, the spatiotemporal pattern of the QPP changes, particularly in task-relevant regions, and the QPP tends to occur with greater strength and frequency. Differences in the QPPs between the two groups could partially account for the variance in functional connectivity between resting-state and task-performing individuals. The QPPs contribute strongly to connectivity in the default mode and task positive networks and to the strength of anti-correlation seen between the two networks. Many of the connections affected by QPPs are also disrupted during several neurological disorders. These findings contribute to understanding the dynamic neural processes that give rise to functional connectivity in the brain and how they may be disrupted during disease.

Keywords: Default mode network; Functional connectivity; Quasi-periodic patterns; Resting state; Task; Task positive network.

Figure 1:

Quasi-periodic patterns in resting-state and task-performing groups. (a) Top: Spatiotemporal pattern seen in the resting-state QPP. Only BOLD signal changes 1.5x the standard deviation from the mean are shown. Bottom-left: DMN and TPN timecourse during the resting-state QPP. Bottom-right: Maps of DMN and TPN acquired from resting-state individuals. (b) Top: Spatiotemporal pattern seen in the task-performing QPP. Only BOLD signal changes 1.5x the standard deviation from the mean are shown. Bottom-left: DMN and TPN timecourses during the task-performing QPP. Bottom-right: Maps of DMN and TPN acquired from task-performing individuals. (c) Top: Spatiotemporal differences between the resting-state and task-performing QPPs. Bottom: Regions in the DMN and TPN that showed strong similarity between groups (> 0.6 Pearson correlation, shown in red/yellow) and strong dissimilarity between groups (< −0.6 Pearson correlation, shown in blue/turquoise). A full list of these regions can be found in Supplementary Table 1.

Figure 2:

Strength and frequency of QPPs in resting-state and task-performing groups. (a) Example sliding correlation of the resting-state QPP with three concatenated scans from unique individuals during rest (left) and the same scans during task (right) before QPP regression (blue) and after QPP regression (red). (b) Example sliding correlation of the task-performing QPP with three concatenated scans from unique individuals during rest (left) and the same scans during task (right) before QPP regression (blue) and after QPP regression (red). (c) Mean correlation strength of peaks > 0.1 in the cumulative sliding correlation of the resting-state and task-performing QPPs with all resting-state scans (left) and all task-performing scans (right) before QPP regression (blue) and after QPP regression (red). (d) Mean time interval between peaks with correlation strength > 0.1 in the cumulative sliding correlation of the resting-state and task-performing QPPs with all resting-state scans (left) and all task-performing scans (right) before QPP regression (blue) and after QPP regression (red). (e) Histogram of the cumulative sliding correlation of the resting-state QPP with all resting-state scans (left) and all task-performing scans (right) before QPP regression (blue) and after QPP regression (red). (f) Histogram of the cumulative sliding correlation of the task-performing QPP with all resting-state scans (left) and all task-performing scans (right) before QPP regression (blue) and after QPP regression (red).

Figure 3:

Functional connectivity (FC) in 273 regions of interest. (a) Bottom-left: Mean FC in the resting-state group. Top-right: Mean FC in the task-performing group. (b) Bottom-left: Significant differences in FC between the resting-state and task-performing group (n = 17,156). Top-right: Significant differences in FC between the resting-state and task-performing group after regression of their native QPPs (n = 10,259). (c) Bottom-left: Significant differences in FC in the resting-state group after regression of the resting-state QPP (n = 8,662). Top-right: Significant differences in FC in the resting-state group after regression of the task-performing QPP (n = 188). (d) Bottom-left: Significant differences in FC in the task-performing group after regression of the resting-state QPP (n = 1,062).

Figure 4:

Significant functional connectivity changes in regions within the DMN and TPN after regression of QPPs. (a) Bottom-left: Significant differences in FC in the resting-state group after regression of the resting-state QPP. Top-right: Significant differences in FC in the resting-state group after regression of the task-performing QPP. (b) Bottom-left: Significant differences in FC in the task-performing group after regression of the resting-state QPP. Top-right: Significant differences in FC in the task-performing group after regression of the task-performing QPP.