Functional integration between brain regions at rest occurs in multiple-frequency bands

Abstract

Studies of resting-state fMRI have shown that blood oxygen level dependent (BOLD) signals giving rise to temporal correlation across voxels (or regions) are dominated by low-frequency fluctuations in the range of ∼ 0.01-0.1 Hz. These low-frequency fluctuations have been further divided into multiple distinct frequency bands (slow-5 and -4) based on earlier neurophysiological studies, though low sampling frequency of fMRI (∼ 0.5 Hz) has substantially limited the exploration of other known frequency bands of neurophysiological origins (slow-3, -2, and -1). In this study, we used resting-state fMRI data acquired from 21 healthy subjects at a higher sampling frequency of 1.5 Hz to assess the presence of resting-state functional connectivity (RSFC) across multiple frequency bands: slow-5 to slow-1. The effect of different frequency bands on spatial extent and connectivity strength for known resting-state networks (RSNs) was also evaluated. RSNs were derived using independent component analysis and seed-based correlation. Commonly known RSNs, such as the default mode, the fronto-parietal, the dorsal attention, and the visual networks, were consistently observed at multiple frequency bands. Significant inter-hemispheric connectivity was observed between each seed and its contra lateral brain region across all frequency bands, though overall spatial extent of seed-based correlation maps decreased in slow-2 and slow-1 frequency bands. These results suggest that functional integration between brain regions at rest occurs over multiple frequency bands and RSFC is a multiband phenomenon. These results also suggest that further investigation of BOLD signal in multiple frequency bands for related cognitive processes should be undertaken.

Keywords: BOLD; ICA; RSFC; high frequency; multiband.

FIG. 1.

Group independent components derived using unFILT blood oxygen level dependent (BOLD) fMRI data (0.01–0.75 Hz) and corresponding power spectrum of group-level independent component time series from 13 resting-state networks/regions. (A–C) The visual cortex (VIS1–3), (D) the default mode network (DMN)1, (E) the left frontal parietal network (LFP), (F) the dorsal attention network (DAN), (G) the right frontal parietal network (RFP), (H) the DMN2, (I) the superior temporal gyrus (STG), (J) the salience network (SAL), (K) the precentral gyrus (PCG), and (L) the inferior frontal gyrus (IFG). Bar plot represents percentage of power explained by each frequency band (slow-1 to slow-5) to the total power for each network.

FIG. 2.

Group independent component analysis (ICA) maps derived from unFILT BOLD fMRI data and corresponding group ICA maps derived from different frequency bands (slow-1 to slow-5). (A–C) The visual cortex (VIS1–3), (D) the DMN1, (E) the LFP, (F) the DAN, (G) the RFP, (H) the DMN2, (I) the STG, (J) the SAL, (K) the PCG, and (L) the IFG.

FIG. 3.

Group-level seed-based correlation maps for each of the five specific frequency bands slow-5, slow-4, slow-3, slow-2, and slow-1 and for seven different seed regions, frontal eye field (FEF), inferior parietal sulcus (IPS), lateral parietal cortex (LPC), medial prefrontal cortex (MPF), middle temporal gyrus (MTG), posterior cingulate cortex (PCC), and PCG. Images show results of one-sample t-test (p<0.05, with FDR correction) overlaid on the surface map using BrainNet viewer.

FIG. 4.

Frequency-specific effects on spatial extent of group-level seed-based correlation maps (top panel), mean positive connectivity strength (middle panel), and mean negative connectivity strength (lower panel) for seven different seed regions, FEF, IPS, LPC, MPF, MTG, PCC, and PCG (one-sample t-test, p<0.05, with FDR correction).