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

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 spati...

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Main Authors: Klaudius Kalcher, Roland N Boubela, Wolfgang Huf, Lucie Bartova, Claudia Kronnerwetter, Birgit Derntl, Lukas Pezawas, Peter Filzmoser, Christian Nasel, Ewald Moser
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2014-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC3984093?pdf=render
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spelling doaj-444bfa0dd72e4f69af1768796ef4e0402020-11-24T22:08:38ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-0194e9337510.1371/journal.pone.0093375The spectral diversity of resting-state fluctuations in the human brain.Klaudius KalcherRoland N BoubelaWolfgang HufLucie BartovaClaudia KronnerwetterBirgit DerntlLukas PezawasPeter FilzmoserChristian NaselEwald MoserIn 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.http://europepmc.org/articles/PMC3984093?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Klaudius Kalcher
Roland N Boubela
Wolfgang Huf
Lucie Bartova
Claudia Kronnerwetter
Birgit Derntl
Lukas Pezawas
Peter Filzmoser
Christian Nasel
Ewald Moser
spellingShingle Klaudius Kalcher
Roland N Boubela
Wolfgang Huf
Lucie Bartova
Claudia Kronnerwetter
Birgit Derntl
Lukas Pezawas
Peter Filzmoser
Christian Nasel
Ewald Moser
The spectral diversity of resting-state fluctuations in the human brain.
PLoS ONE
author_facet Klaudius Kalcher
Roland N Boubela
Wolfgang Huf
Lucie Bartova
Claudia Kronnerwetter
Birgit Derntl
Lukas Pezawas
Peter Filzmoser
Christian Nasel
Ewald Moser
author_sort Klaudius Kalcher
title The spectral diversity of resting-state fluctuations in the human brain.
title_short The spectral diversity of resting-state fluctuations in the human brain.
title_full The spectral diversity of resting-state fluctuations in the human brain.
title_fullStr The spectral diversity of resting-state fluctuations in the human brain.
title_full_unstemmed The spectral diversity of resting-state fluctuations in the human brain.
title_sort spectral diversity of resting-state fluctuations in the human brain.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2014-01-01
description 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.
url http://europepmc.org/articles/PMC3984093?pdf=render
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