The spectral diversity of resting-state fluctuations in the human brain

Abstract

In order to assess whole-brain resting-state fluctuations at a wide range of frequencies, resting-state fMRI data of 20 healthy subjects were acquired using a multiband EPI sequence with a low TR (354 ms) and compared to 20 resting-state datasets from standard, high-TR (1800 ms) EPI scans. The spatial distribution of fluctuations in various frequency ranges are analyzed along with the spectra of the time-series in voxels from different regions of interest. Functional connectivity specific to different frequency ranges (<0.1 Hz; 0.1-0.25 Hz; 0.25-0.75 Hz; 0.75-1.4 Hz) was computed for both the low-TR and (for the two lower-frequency ranges) the high-TR datasets using bandpass filters. In the low-TR data, cortical regions exhibited highest contribution of low-frequency fluctuations and the most marked low-frequency peak in the spectrum, while the time courses in subcortical grey matter regions as well as the insula were strongly contaminated by high-frequency signals. White matter and CSF regions had highest contribution of high-frequency fluctuations and a mostly flat power spectrum. In the high-TR data, the basic patterns of the low-TR data can be recognized, but the high-frequency proportions of the signal fluctuations are folded into the low frequency range, thus obfuscating the low-frequency dynamics. Regions with higher proportion of high-frequency oscillations in the low-TR data showed flatter power spectra in the high-TR data due to aliasing of the high-frequency signal components, leading to loss of specificity in the signal from these regions in high-TR data. Functional connectivity analyses showed that there are correlations between resting-state signal fluctuations of distant brain regions even at high frequencies, which can be measured using low-TR fMRI. On the other hand, in the high-TR data, loss of specificity of measured fluctuations leads to lower sensitivity in detecting functional connectivity. This underlines the advantages of low-TR EPI sequences for resting-state and potentially also task-related fMRI experiments.

Figure 1. Fractional amplitudes of fluctuations.

Fractional amplitudes of fluctuations in four different frequency bands in the low-TR dataset (a), in two different frequency bands in the high-TR dataset (b), and in the two lower frequency bands combined in the low-TR dataset (c). All color bars are set to the same window width (0.15).

Figure 2. ROI power spectra.

Power spectra of the center voxel time courses of selected ROIs for the low-TR dataset (left) and the high-TR dataset (right). Vertical red lines indicate 0.1 Hz and 0.25 Hz, dotted grey horizontal lines mark the minimum power in each plot. (mPFC stands for medial prefrontal cortex, PCC for posterior cingulate cortex).

Figure 3. Basal ganglia functional connectivity.

Functional connectivity of the basal ganglia seed in the lowest frequency band (<0.1 Hz) in the low-TR (top) and high-TR (bottom) datasets. The colors represent z-transformed correlation coefficients.

Figure 4. White matter functional connectivity.

Functional connectivity of the white matter seed in the two lower frequency bands (<0.1 Hz and 0.1–0.25 Hz) in the low-TR (top) and high-TR (bottom) datasets. The colors represent z-transformed correlation coefficients.

Figure 5. Functional connectivity of cortical regions in higher frequency bands.

Each row shows the connectivity of the seed given on the left for the data band-passed to the frequency range noted there. The colors represent z-transformed correlation coefficients.