The power of spectral density analysis for mapping endogenous BOLD signal fluctuations

Abstract

FMRI has revealed the presence of correlated low-frequency cerebro-vascular oscillations within functional brain systems, which are thought to reflect an intrinsic feature of large-scale neural activity. The spatial correlations shown by these fluctuations has been their identifying feature, distinguishing them from fluctuations associated with other processes. Major analysis methods characterize these correlations, identifying networks and their interactions with various factors. However, other analysis approaches are required to fully characterize the regional signal dynamics contributing to these correlations between regions. In this study we show that analysis of the power spectral density (PSD) of regional signals can identify changes in oscillatory dynamics across conditions, and is able to characterize the nature and spatial extent of signal changes underlying changes in measures of connectivity. We analyzed spectral density changes in sessions consisting of both resting-state scans and scans recording 2 min blocks of continuous unilateral finger tapping and rest. We assessed the relationship of PSD and connectivity measures by additionally tracking correlations between selected motor regions. Spectral density gradually increased in gray and white matter during the experiment. Finger tapping produced widespread decreases in low-frequency spectral density. This change was symmetric across the cortex, and extended beyond both the lateralized task-related signal increases, and the established "resting-state" motor network. Correlations between motor regions also reduced with task performance. In conclusion, analysis of PSD is a sensitive method for detecting and characterizing BOLD signal oscillations that can enhance the analysis of network connectivity.

Figure 1

(a) Experimental sessions: the first and final 4‐min scan of each session were resting‐state scans with an acquisition rate of 1.4 Hz, and a reduced field of view (green box). The second through fifth scans recorded alternating 2 min periods of task performance and rest, at an acquisition rate of 0.5 Hz, with full brain coverage (white box). Task performance was randomized between two simple, untrained, left handed thumb‐to‐finger tapping sequences. (b) Spatial distribution of the average estimated spectral power at frequencies 0.03, 0.10, 0.23, and 0.35 Hz in a single slice from the first resting‐state scan [position shown in red in (a)]. (c) Regions showing significant group‐level differences in spectral density in the second resting‐state scan, compared to the first, at the frequencies shown. There were significant increases in average spectral density at higher frequencies across the cortex.

Figure 2

(a) Average spectral properties of the SMA, right and left motor cortices, and the left middle frontal gyrus for the two resting‐state scans. Dashed lines show 95% confidence intervals for the mean, determined by a jack‐knife procedure. (b) Group‐average spectrograms of the SMA ROI for the first and second resting‐state scans, using a 60‐s window.

Figure 3

(a) Regions showing changes in spectral power during task periods compared to the spectral power during the rest periods, at frequencies 0.03, 0.10, and 0.23 Hz. (b) Regions showing sustained increases in BOLD signal amplitude during the task periods, compared to the rest periods (activation).

Figure 4

(a) Time plots of average BOLD signal power in ROIs over the four task‐performance scans (scans 2–5). Task periods are shaded. Error bars show standard errors. (b) Time plots of changes in correlation between ROIs over the four task scans.