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Hurst Exponent Analysis of Resting-State fMRI Signal Complexity across the Adult Lifespan
Exploring functional information among various brain regions across time enables understanding of healthy aging process and holds great promise for age-related brain disease diagnosis. This paper proposed a method to explore fractal complexity of the resting-state functional magnetic resonance imagi...
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/PMC5801317/ https://www.ncbi.nlm.nih.gov/pubmed/29456489 http://dx.doi.org/10.3389/fnins.2018.00034 |
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author | Dong, Jianxin Jing, Bin Ma, Xiangyu Liu, Han Mo, Xiao Li, Haiyun |
author_facet | Dong, Jianxin Jing, Bin Ma, Xiangyu Liu, Han Mo, Xiao Li, Haiyun |
author_sort | Dong, Jianxin |
collection | PubMed |
description | Exploring functional information among various brain regions across time enables understanding of healthy aging process and holds great promise for age-related brain disease diagnosis. This paper proposed a method to explore fractal complexity of the resting-state functional magnetic resonance imaging (rs-fMRI) signal in the human brain across the adult lifespan using Hurst exponent (HE). We took advantage of the examined rs-fMRI data from 116 adults 19 to 85 years of age (44.3 ± 19.4 years, 49 females) from NKI/Rockland sample. Region-wise and voxel-wise analyses were performed to investigate the effects of age, gender, and their interaction on complexity. In region-wise analysis, we found that the healthy aging is accompanied by a loss of complexity in frontal and parietal lobe and increased complexity in insula, limbic, and temporal lobe. Meanwhile, differences in HE between genders were found to be significant in parietal lobe (p = 0.04, corrected). However, there was no interaction between gender and age. In voxel-wise analysis, the significant complexity decrease with aging was found in frontal and parietal lobe, and complexity increase was found in insula, limbic lobe, occipital lobe, and temporal lobe with aging. Meanwhile, differences in HE between genders were found to be significant in frontal, parietal, and limbic lobe. Furthermore, we found age and sex interaction in right parahippocampal gyrus (p = 0.04, corrected). Our findings reveal HE variations of the rs-fMRI signal across the human adult lifespan and show that HE may serve as a new parameter to assess healthy aging process. |
format | Online Article Text |
id | pubmed-5801317 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-58013172018-02-16 Hurst Exponent Analysis of Resting-State fMRI Signal Complexity across the Adult Lifespan Dong, Jianxin Jing, Bin Ma, Xiangyu Liu, Han Mo, Xiao Li, Haiyun Front Neurosci Neuroscience Exploring functional information among various brain regions across time enables understanding of healthy aging process and holds great promise for age-related brain disease diagnosis. This paper proposed a method to explore fractal complexity of the resting-state functional magnetic resonance imaging (rs-fMRI) signal in the human brain across the adult lifespan using Hurst exponent (HE). We took advantage of the examined rs-fMRI data from 116 adults 19 to 85 years of age (44.3 ± 19.4 years, 49 females) from NKI/Rockland sample. Region-wise and voxel-wise analyses were performed to investigate the effects of age, gender, and their interaction on complexity. In region-wise analysis, we found that the healthy aging is accompanied by a loss of complexity in frontal and parietal lobe and increased complexity in insula, limbic, and temporal lobe. Meanwhile, differences in HE between genders were found to be significant in parietal lobe (p = 0.04, corrected). However, there was no interaction between gender and age. In voxel-wise analysis, the significant complexity decrease with aging was found in frontal and parietal lobe, and complexity increase was found in insula, limbic lobe, occipital lobe, and temporal lobe with aging. Meanwhile, differences in HE between genders were found to be significant in frontal, parietal, and limbic lobe. Furthermore, we found age and sex interaction in right parahippocampal gyrus (p = 0.04, corrected). Our findings reveal HE variations of the rs-fMRI signal across the human adult lifespan and show that HE may serve as a new parameter to assess healthy aging process. Frontiers Media S.A. 2018-02-02 /pmc/articles/PMC5801317/ /pubmed/29456489 http://dx.doi.org/10.3389/fnins.2018.00034 Text en Copyright © 2018 Dong, Jing, Ma, Liu, Mo and Li. http://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 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 | Neuroscience Dong, Jianxin Jing, Bin Ma, Xiangyu Liu, Han Mo, Xiao Li, Haiyun Hurst Exponent Analysis of Resting-State fMRI Signal Complexity across the Adult Lifespan |
title | Hurst Exponent Analysis of Resting-State fMRI Signal Complexity across the Adult Lifespan |
title_full | Hurst Exponent Analysis of Resting-State fMRI Signal Complexity across the Adult Lifespan |
title_fullStr | Hurst Exponent Analysis of Resting-State fMRI Signal Complexity across the Adult Lifespan |
title_full_unstemmed | Hurst Exponent Analysis of Resting-State fMRI Signal Complexity across the Adult Lifespan |
title_short | Hurst Exponent Analysis of Resting-State fMRI Signal Complexity across the Adult Lifespan |
title_sort | hurst exponent analysis of resting-state fmri signal complexity across the adult lifespan |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5801317/ https://www.ncbi.nlm.nih.gov/pubmed/29456489 http://dx.doi.org/10.3389/fnins.2018.00034 |
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