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Impact of Healthy Aging on Multifractal Hemodynamic Fluctuations in the Human Prefrontal Cortex
Fluctuations in resting-state cerebral hemodynamics show scale-free behavior over two distinct scaling ranges. Changes in such bimodal (multi) fractal pattern give insight to altered cerebrovascular or neural function. Our main goal was to assess the distribution of local scale-free properties chara...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6097581/ https://www.ncbi.nlm.nih.gov/pubmed/30147657 http://dx.doi.org/10.3389/fphys.2018.01072 |
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author | Mukli, Peter Nagy, Zoltan Racz, Frigyes S. Herman, Peter Eke, Andras |
author_facet | Mukli, Peter Nagy, Zoltan Racz, Frigyes S. Herman, Peter Eke, Andras |
author_sort | Mukli, Peter |
collection | PubMed |
description | Fluctuations in resting-state cerebral hemodynamics show scale-free behavior over two distinct scaling ranges. Changes in such bimodal (multi) fractal pattern give insight to altered cerebrovascular or neural function. Our main goal was to assess the distribution of local scale-free properties characterizing cerebral hemodynamics and to disentangle the influence of aging on these multifractal parameters. To this end, we obtained extended resting-state records (N = 2(14)) of oxyhemoglobin (HbO), deoxyhemoglobin (HbR) and total hemoglobin (HbT) concentration time series with continuous-wave near-infrared spectroscopy technology from the brain cortex. 52 healthy volunteers were enrolled in this study: 24 young (30.6 ± 8.2 years), and 28 elderly (60.5 ± 12.0 years) subjects. Using screening tests on power-law, multifractal noise, and shuffled data sets we evaluated the presence of true multifractal hemodynamics reflecting long-range correlation (LRC). Subsequently, scaling-range adaptive bimodal signal summation conversion (SSC) was performed based on standard deviation (σ) of signal windows across a range of temporal scales (s). Building on moments of different order (q) of the measure, σ(s), multifractal SSC yielded generalized Hurst exponent function, H(q), and singularity spectrum, D(h) separately for a fast and slow component (the latter dominating the highest temporal scales). Parameters were calculated reflecting the estimated measure at s = N (focus), degree of LRC [Hurst exponent, H(2) and maximal Hölder exponent, h(max)] and measuring strength of multifractality [full-width-half-maximum of D(h) and ΔH(15) = H(−15)−H(15)]. Correlation-based signal improvement (CBSI) enhanced our signal in terms of interpreting changes due to neural activity or local/systemic hemodynamic influences. We characterized the HbO-HbR relationship with the aid of fractal scale-wise correlation coefficient, r(σ)(s) and SSC-based multifractal covariance analysis. In the majority of subjects, cerebral hemodynamic fluctuations proved bimodal multifractal. In case of slow component of raw HbT, h(max), and Ĥ(2) were lower in the young group explained by a significantly increased r(σ)(s) among elderly at high temporal scales. Regarding the fast component of CBSI-pretreated HbT and that of HbO-HbR covariance, h(max), and focus were decreased in the elderly group. These observations suggest an attenuation of neurovascular coupling reflected by a decreased autocorrelation of the neuronal component concomitant with an accompanying increased autocorrelation of the non-neuronal component in the elderly group. |
format | Online Article Text |
id | pubmed-6097581 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-60975812018-08-24 Impact of Healthy Aging on Multifractal Hemodynamic Fluctuations in the Human Prefrontal Cortex Mukli, Peter Nagy, Zoltan Racz, Frigyes S. Herman, Peter Eke, Andras Front Physiol Physiology Fluctuations in resting-state cerebral hemodynamics show scale-free behavior over two distinct scaling ranges. Changes in such bimodal (multi) fractal pattern give insight to altered cerebrovascular or neural function. Our main goal was to assess the distribution of local scale-free properties characterizing cerebral hemodynamics and to disentangle the influence of aging on these multifractal parameters. To this end, we obtained extended resting-state records (N = 2(14)) of oxyhemoglobin (HbO), deoxyhemoglobin (HbR) and total hemoglobin (HbT) concentration time series with continuous-wave near-infrared spectroscopy technology from the brain cortex. 52 healthy volunteers were enrolled in this study: 24 young (30.6 ± 8.2 years), and 28 elderly (60.5 ± 12.0 years) subjects. Using screening tests on power-law, multifractal noise, and shuffled data sets we evaluated the presence of true multifractal hemodynamics reflecting long-range correlation (LRC). Subsequently, scaling-range adaptive bimodal signal summation conversion (SSC) was performed based on standard deviation (σ) of signal windows across a range of temporal scales (s). Building on moments of different order (q) of the measure, σ(s), multifractal SSC yielded generalized Hurst exponent function, H(q), and singularity spectrum, D(h) separately for a fast and slow component (the latter dominating the highest temporal scales). Parameters were calculated reflecting the estimated measure at s = N (focus), degree of LRC [Hurst exponent, H(2) and maximal Hölder exponent, h(max)] and measuring strength of multifractality [full-width-half-maximum of D(h) and ΔH(15) = H(−15)−H(15)]. Correlation-based signal improvement (CBSI) enhanced our signal in terms of interpreting changes due to neural activity or local/systemic hemodynamic influences. We characterized the HbO-HbR relationship with the aid of fractal scale-wise correlation coefficient, r(σ)(s) and SSC-based multifractal covariance analysis. In the majority of subjects, cerebral hemodynamic fluctuations proved bimodal multifractal. In case of slow component of raw HbT, h(max), and Ĥ(2) were lower in the young group explained by a significantly increased r(σ)(s) among elderly at high temporal scales. Regarding the fast component of CBSI-pretreated HbT and that of HbO-HbR covariance, h(max), and focus were decreased in the elderly group. These observations suggest an attenuation of neurovascular coupling reflected by a decreased autocorrelation of the neuronal component concomitant with an accompanying increased autocorrelation of the non-neuronal component in the elderly group. Frontiers Media S.A. 2018-08-10 /pmc/articles/PMC6097581/ /pubmed/30147657 http://dx.doi.org/10.3389/fphys.2018.01072 Text en Copyright © 2018 Mukli, Nagy, Racz, Herman and Eke. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Physiology Mukli, Peter Nagy, Zoltan Racz, Frigyes S. Herman, Peter Eke, Andras Impact of Healthy Aging on Multifractal Hemodynamic Fluctuations in the Human Prefrontal Cortex |
title | Impact of Healthy Aging on Multifractal Hemodynamic Fluctuations in the Human Prefrontal Cortex |
title_full | Impact of Healthy Aging on Multifractal Hemodynamic Fluctuations in the Human Prefrontal Cortex |
title_fullStr | Impact of Healthy Aging on Multifractal Hemodynamic Fluctuations in the Human Prefrontal Cortex |
title_full_unstemmed | Impact of Healthy Aging on Multifractal Hemodynamic Fluctuations in the Human Prefrontal Cortex |
title_short | Impact of Healthy Aging on Multifractal Hemodynamic Fluctuations in the Human Prefrontal Cortex |
title_sort | impact of healthy aging on multifractal hemodynamic fluctuations in the human prefrontal cortex |
topic | Physiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6097581/ https://www.ncbi.nlm.nih.gov/pubmed/30147657 http://dx.doi.org/10.3389/fphys.2018.01072 |
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