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.

Implication of the Slow-5 Oscillations in the Disruption of the Default-Mode Network in Healthy Aging and Stroke

“…The reduction of slow-5 fALFF was likely not due to changes at the higher frequencies (0.073-0.192 Hz), as power in those frequencies is minimal. This was demonstrated in this study as well as in prior studies (Calhoun Zuo et al, 2010), Furthermore, low-TR fMRI (Kalcher et al, 2014) and MR-Encephalography (Lee et al, 2013) studies, techniques offering much improved temporal resolution, also confirmed the small contributions of high oscillations during the resting state.…”

Mapping the end-tidal CO2 response function in the resting-state BOLD fMRI signal: Spatial specificity, test–retest reliability and effect of fMRI sampling rate

“…Thus, in this work, we simply establish the feasibility of multiband imaging as a viable method to investigate rs-fMRI physiology. While slice-accelerated imaging offers alluring potential and its reliability in multiple applications has been tested (Kalcher et al, 2014;Xu et al, 2013) it will need to undergo further tests of time. Fig.…”

Synchronization, non-linear dynamics and low-frequency fluctuations: Analogy between spontaneous brain activity and networked single-transistor chaotic oscillators

“…The observed distributions of node degree and relative Fourier amplitude of low-frequency activity are in close agreement with existing reports considering the two parameters separately and are unlikely to be driven primarily by noise. [13][14][15][16]18,28,29,37,61 This spectral heterogeneity points to different dynamics of underlying neural activity. Even though BOLD signals are not well-suited for non-linear dynamical analysis due to short time-series length, undersampling, physiological noise, and confounds from neurovascular coupling, we attempted Figure 4(a) for network diagram).…”

“…[25][26][27] Recent increases in the sampling rate of functional MRI have made it possible to study inter-regional spectral differences, often represented with power-law frequency scaling coefficients or relative Fourier amplitudes of low frequencies (typically <0.1 Hz). 19,28,29 At the same time, the fact that realistic RSNs emerge even in highly simplified simulation scenarios, for example, from networked phase (Kuramoto) oscillators or discrete excitable units, raises the intriguing possibility of recapitulating some dynamical phenomena underlying brain function in other physical systems, where direct manipulation of connectivity is possible and causal relationships between connectivity and non-linear dynamics can be explored experimentally. [8][9][10]30 In particular, it has recently been shown that singletransistor oscillators can exhibit strikingly complex activity depending on an easily tunable control parameter (DC voltage source series resistance), oscillating periodically, chaotically, or close to criticality.…”