Mapping transient hyperventilation induced alterations with estimates of the multi-scale dynamics of BOLD signal.
Temporal blood oxygen level dependent (BOLD) contrast signals in functional MRI during rest may be characterized by power spectral distribution (PSD) trends of the form 1/f α. Trends with 1/f characteristics comprise fractal properties with repeating oscillation patterns in multiple time s...
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doaj-97c804c8143643e484abe60f5094d9402020-11-24T23:12:53ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962009-07-01310.3389/neuro.11.018.2009521Mapping transient hyperventilation induced alterations with estimates of the multi-scale dynamics of BOLD signal.Vesa J Kiviniemi0Jukka Remes1Jukka Remes2Tuomo Starck3Juha Nikkinen4Marianne Haapea5Olli Silven6Osmo Tervonen7Oulu University HospitalOulu UniversityOulu University HospitalOulu University HospitalOulu University HospitalOulu University HospitalOulu UniversityOulu University HospitalTemporal blood oxygen level dependent (BOLD) contrast signals in functional MRI during rest may be characterized by power spectral distribution (PSD) trends of the form 1/f α. Trends with 1/f characteristics comprise fractal properties with repeating oscillation patterns in multiple time scales. Estimates of the fractal properties enable the quantification of phenomena that may otherwise be difficult to measure, such as transient, non-linear changes. In this study it was hypothesized that the fractal metrics of 1/f BOLD signal trends can map changes related to dynamic, multi-scale alterations in cerebral blood flow (CBF) after a transient hyperventilation challenge. Twenty-three normal adults were imaged in a resting-state before and after hyperventilation. Different variables (1/f trend constant α, fractal dimension Df, and, Hurst exponent H) characterizing the trends were measured from BOLD signals. The results show that fractal metrics of the BOLD signal follow the fractional Gaussian noise model, even during the dynamic CBF change that follows hyperventilation. The most dominant effect on the fractal metrics was detected in grey matter, in line with previous hyperventilation vaso-reactivity studies. The α was able to differentiate also blood vessels from grey matter changes. Df was most sensitive to grey matter. H correlated with default mode network areas before hyperventilation but this pattern vanished after hyperventilation due to a global increase in H. In the future, resting-state fMRI combined with fractal metrics of the BOLD signal may be used for analyzing multi-scale alterations of cerebral blood flow.http://journal.frontiersin.org/Journal/10.3389/neuro.11.018.2009/fullHyperventilationCBFresting-stateblood oxygen dependentfractal dimensionHurst exponent |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Vesa J Kiviniemi Jukka Remes Jukka Remes Tuomo Starck Juha Nikkinen Marianne Haapea Olli Silven Osmo Tervonen |
spellingShingle |
Vesa J Kiviniemi Jukka Remes Jukka Remes Tuomo Starck Juha Nikkinen Marianne Haapea Olli Silven Osmo Tervonen Mapping transient hyperventilation induced alterations with estimates of the multi-scale dynamics of BOLD signal. Frontiers in Neuroinformatics Hyperventilation CBF resting-state blood oxygen dependent fractal dimension Hurst exponent |
author_facet |
Vesa J Kiviniemi Jukka Remes Jukka Remes Tuomo Starck Juha Nikkinen Marianne Haapea Olli Silven Osmo Tervonen |
author_sort |
Vesa J Kiviniemi |
title |
Mapping transient hyperventilation induced alterations with estimates of the multi-scale dynamics of BOLD signal. |
title_short |
Mapping transient hyperventilation induced alterations with estimates of the multi-scale dynamics of BOLD signal. |
title_full |
Mapping transient hyperventilation induced alterations with estimates of the multi-scale dynamics of BOLD signal. |
title_fullStr |
Mapping transient hyperventilation induced alterations with estimates of the multi-scale dynamics of BOLD signal. |
title_full_unstemmed |
Mapping transient hyperventilation induced alterations with estimates of the multi-scale dynamics of BOLD signal. |
title_sort |
mapping transient hyperventilation induced alterations with estimates of the multi-scale dynamics of bold signal. |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Neuroinformatics |
issn |
1662-5196 |
publishDate |
2009-07-01 |
description |
Temporal blood oxygen level dependent (BOLD) contrast signals in functional MRI during rest may be characterized by power spectral distribution (PSD) trends of the form 1/f α. Trends with 1/f characteristics comprise fractal properties with repeating oscillation patterns in multiple time scales. Estimates of the fractal properties enable the quantification of phenomena that may otherwise be difficult to measure, such as transient, non-linear changes. In this study it was hypothesized that the fractal metrics of 1/f BOLD signal trends can map changes related to dynamic, multi-scale alterations in cerebral blood flow (CBF) after a transient hyperventilation challenge. Twenty-three normal adults were imaged in a resting-state before and after hyperventilation. Different variables (1/f trend constant α, fractal dimension Df, and, Hurst exponent H) characterizing the trends were measured from BOLD signals. The results show that fractal metrics of the BOLD signal follow the fractional Gaussian noise model, even during the dynamic CBF change that follows hyperventilation. The most dominant effect on the fractal metrics was detected in grey matter, in line with previous hyperventilation vaso-reactivity studies. The α was able to differentiate also blood vessels from grey matter changes. Df was most sensitive to grey matter. H correlated with default mode network areas before hyperventilation but this pattern vanished after hyperventilation due to a global increase in H. In the future, resting-state fMRI combined with fractal metrics of the BOLD signal may be used for analyzing multi-scale alterations of cerebral blood flow. |
topic |
Hyperventilation CBF resting-state blood oxygen dependent fractal dimension Hurst exponent |
url |
http://journal.frontiersin.org/Journal/10.3389/neuro.11.018.2009/full |
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