Can apparent resting state connectivity arise from systemic fluctuations?

Abstract

It is widely accepted that the fluctuations in resting state blood oxygenation level dependent (BOLD) functional MRI (fMRI) reflect baseline neuronal activation through neurovascular coupling; this data is used to infer functional connectivity in the human brain during rest. Consistent activation patterns, i.e., resting state networks (RSN) are seen across groups, conditions, and even species. In this study, we show that some of these patterns can also be generated from the dynamic, systemic, non-neuronal physiological low frequency oscillations (sLFOs) in the BOLD signal alone. We have previously used multimodal imaging to demonstrate the wide presence of the same sLFOs in the brain (BOLD) and periphery with different time delays. This study shows that these sLFOs from BOLD signals alone can give rise to stable spatial patterns, which can be detected during resting state analyses. We generated synthetic resting state data for 11 subjects based only on subject-specific, dynamic sLFO information obtained from resting state data using concurrent peripheral optical imaging or a novel recursive procedure. We compared the results obtained by performing a group independent component analysis (ICA) on this synthetic data (i.e., the result from simulation) to the results obtained from analysis of the real data. ICA detected most of the eight well-known RSNs, including visual, motor, and default mode networks (DMNs), in both the real and the synthetic data sets. These findings suggest that RSNs may reflect, to some extent, vascular anatomy associated with systemic fluctuations, rather than neuronal connectivity.

Keywords: BOLD fMRI; cerebral blood flow; resting state networks; slow oscillations; systemic oscillations.

Figure 1

Synthetic data for a single “subject.” Two identical blocks were selected, as shown in (A), in which, artificial traveling waves (as shown in B) were used to replace the original BOLD signals. The red arrows indicate the direction of the traveling wave and synthetic data from three positions marked by circled numbers (1–3) were shown in (B). The remaining BOLD signal was also replaced by an identical synthetic data as (2) in (B).

Figure 2

Five ICs (A–E) resulting from ICA on a single “subject.” Each IC pattern is shown in three viewpoints (Sagittal, Coronal, and Axial). The locations of synthetic traveling waves are indicated by the black boxes in all three views. The color bar represents the z-value as result of ICA.

Figure 3

A delay map of the synthetic data of one subject derived with the recursive method (A) and its synthetic BOLD data (B,C). Three example voxels were selected from the delay map with their time delays (A) and the corresponding synthetic temporal traces were shown in (B). (C) The enlarged section of (B) with circles indicating the corresponding voxel of the marked trace. The delay map derived with the NIRS method of the same subject is shown as an inlet in (A).

Figure 4

A maximum z-statistic map (A) of the same subject as in Figure 3. The histogram of the maximum z-statistic map is shown in (B), in which, the black line (z = 6) indicates the threshold.

Figure 5

Eight groups of ICs resulting from group ICA on 11 subjects' real BOLD data (A), synthetic BOLD data derived with the recursive method (B), and synthetic BOLD data derived from NIRS data (C). The eight ICs were selected in each group result (real vs. synthetic) using RSNs templates (Beckmann et al., 2005) and the value in each IC shows the spatial correlation coefficient calculated between that IC and the corresponding RSN from the template. The ICs in the red block are the same IC.

Figure 6

Averaged default mode network detected by seed analysis from 11 subjects' real resting state data (A) and synthetic resting state data (B). Black circles indicate the seed location.

Figure 7

Normalized average temporal delays of each networks (networks 1–8 in the same order as in Figure 5) for each participant. Each color line represents one participant by connecting the average temporal delays of eight networks from this participant.