The Spectral Diversity of Resting-State Fluctuations in the Human Brain

• Published in: PloS one Vol. 9; no. 4; p. e93375
• Main Authors: Kalcher, Klaudius, Boubela, Roland N., Huf, Wolfgang, Bartova, Lucie, Kronnerwetter, Claudia, Derntl, Birgit, Pezawas, Lukas, Filzmoser, Peter, Nasel, Christian, Moser, Ewald
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.04.2014; Public Library of Science (PLoS)
• Subjects: Aliasing; Analysis; Bandpass filters; Biology and Life Sciences; Biomedical engineering; Brain; Brain - physiology; Brain mapping; Brain research; Cerebrospinal fluid; Correlation analysis; Cortex; Data processing; Datasets; Engineering; Fluctuations; Frequency ranges; Functional magnetic resonance imaging; Health physics; Hemodynamics; Humans; Magnetic Resonance Imaging; Medicine and Health Sciences; Neural networks; Oscillations; Pattern recognition; Physical Sciences; Physics; Physiology; Power spectra; Psychiatry; Psychotherapy; Rest - physiology; Sequences; Spatial distribution; Studies; Substantia alba; Substantia grisea; Austria
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0093375

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.