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PLoS One. 2014 Apr 11;9(4):e93375. doi: 10.1371/journal.pone.0093375. eCollection 2014.

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

Author information

1
Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria; MR Centre of Excellence, Medical University of Vienna, Vienna, Austria; Department of Statistics and Probability Theory, Vienna University of Technology, Vienna, Austria.
2
Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria.
3
MR Centre of Excellence, Medical University of Vienna, Vienna, Austria; Department of Radiodiagnostics and Nuclear Medicine, Medical University of Vienna, Vienna, Austria.
4
Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany.
5
Department of Statistics and Probability Theory, Vienna University of Technology, Vienna, Austria.
6
Department of Radiology, Tulln Hospital, Tulln, Austria.
7
Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria; MR Centre of Excellence, Medical University of Vienna, Vienna, Austria; Brain Behaviour Laboratory, Department of Psychiatry, University of Pennsylvania Medical Center, Philadelphia, Pennsylvania, United States of America.

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.

PMID:
24728207
PMCID:
PMC3984093
DOI:
10.1371/journal.pone.0093375
[Indexed for MEDLINE]
Free PMC Article
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