Dynamic Complexity of Spontaneous BOLD Activity in Alzheimer’s Disease and Mild Cognitive Impairment Using Multiscale Entropy Analysis
Alzheimer’s disease (AD) is characterized by progressive deterioration of brain function among elderly people. Studies revealed aberrant correlations in spontaneous blood oxygen level-dependent (BOLD) signals in resting-state functional magnetic resonance imaging (rs-fMRI) over a wide range of tempo...
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| Format: | Article |
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Frontiers Media S.A.
2018-10-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fnins.2018.00677/full |
| _version_ | 1811222429614211072 |
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| author | Yan Niu Bin Wang Bin Wang Mengni Zhou Jiayue Xue Habib Shapour Rui Cao Xiaohong Cui Jinglong Wu Jinglong Wu Jie Xiang |
| author_facet | Yan Niu Bin Wang Bin Wang Mengni Zhou Jiayue Xue Habib Shapour Rui Cao Xiaohong Cui Jinglong Wu Jinglong Wu Jie Xiang |
| author_sort | Yan Niu |
| collection | DOAJ |
| description | Alzheimer’s disease (AD) is characterized by progressive deterioration of brain function among elderly people. Studies revealed aberrant correlations in spontaneous blood oxygen level-dependent (BOLD) signals in resting-state functional magnetic resonance imaging (rs-fMRI) over a wide range of temporal scales. However, the study of the temporal dynamics of BOLD signals in subjects with AD and mild cognitive impairment (MCI) remains largely unexplored. Multiscale entropy (MSE) analysis is a method for estimating the complexity of finite time series over multiple time scales. In this research, we applied MSE analysis to investigate the abnormal complexity of BOLD signals using the rs-fMRI data from the Alzheimer’s disease neuroimaging initiative (ADNI) database. There were 30 normal controls (NCs), 33 early MCI (EMCI), 32 late MCI (LMCI), and 29 AD patients. Following preprocessing of the BOLD signals, whole-brain MSE maps across six time scales were generated using the Complexity Toolbox. One-way analysis of variance (ANOVA) analysis on the MSE maps of four groups revealed significant differences in the thalamus, insula, lingual gyrus and inferior occipital gyrus, superior frontal gyrus and olfactory cortex, supramarginal gyrus, superior temporal gyrus, and middle temporal gyrus on multiple time scales. Compared with the NC group, MCI and AD patients had significant reductions in the complexity of BOLD signals and AD patients demonstrated lower complexity than that of the MCI subjects. Additionally, the complexity of BOLD signals from the regions of interest (ROIs) was found to be significantly associated with cognitive decline in patient groups on multiple time scales. Consequently, the complexity or MSE of BOLD signals may provide an imaging biomarker of cognitive impairments in MCI and AD. |
| first_indexed | 2024-04-12T08:16:42Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-04-12T08:16:42Z |
| publishDate | 2018-10-01 |
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| series | Frontiers in Neuroscience |
| spelling | doaj.art-7707197738854a8186df935a143f49b12022-12-22T03:40:47ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2018-10-011210.3389/fnins.2018.00677390360Dynamic Complexity of Spontaneous BOLD Activity in Alzheimer’s Disease and Mild Cognitive Impairment Using Multiscale Entropy AnalysisYan Niu0Bin Wang1Bin Wang2Mengni Zhou3Jiayue Xue4Habib Shapour5Rui Cao6Xiaohong Cui7Jinglong Wu8Jinglong Wu9Jie Xiang10College of Information and Computer, Taiyuan University of Technology, Taiyuan, ChinaCollege of Information and Computer, Taiyuan University of Technology, Taiyuan, ChinaDepartment of Radiology, First Hospital of Shanxi Medical University, Taiyuan, ChinaCollege of Information and Computer, Taiyuan University of Technology, Taiyuan, ChinaCollege of Information and Computer, Taiyuan University of Technology, Taiyuan, ChinaCollege of Information and Computer, Taiyuan University of Technology, Taiyuan, ChinaCollege of Information and Computer, Taiyuan University of Technology, Taiyuan, ChinaCollege of Information and Computer, Taiyuan University of Technology, Taiyuan, ChinaKey Laboratory of Biomimetic Robots and Systems, Ministry of Education, Beijing Institute of Technology, Beijing, ChinaGraduate School of Natural Science and Technology, Okayama University, Okayama, JapanCollege of Information and Computer, Taiyuan University of Technology, Taiyuan, ChinaAlzheimer’s disease (AD) is characterized by progressive deterioration of brain function among elderly people. Studies revealed aberrant correlations in spontaneous blood oxygen level-dependent (BOLD) signals in resting-state functional magnetic resonance imaging (rs-fMRI) over a wide range of temporal scales. However, the study of the temporal dynamics of BOLD signals in subjects with AD and mild cognitive impairment (MCI) remains largely unexplored. Multiscale entropy (MSE) analysis is a method for estimating the complexity of finite time series over multiple time scales. In this research, we applied MSE analysis to investigate the abnormal complexity of BOLD signals using the rs-fMRI data from the Alzheimer’s disease neuroimaging initiative (ADNI) database. There were 30 normal controls (NCs), 33 early MCI (EMCI), 32 late MCI (LMCI), and 29 AD patients. Following preprocessing of the BOLD signals, whole-brain MSE maps across six time scales were generated using the Complexity Toolbox. One-way analysis of variance (ANOVA) analysis on the MSE maps of four groups revealed significant differences in the thalamus, insula, lingual gyrus and inferior occipital gyrus, superior frontal gyrus and olfactory cortex, supramarginal gyrus, superior temporal gyrus, and middle temporal gyrus on multiple time scales. Compared with the NC group, MCI and AD patients had significant reductions in the complexity of BOLD signals and AD patients demonstrated lower complexity than that of the MCI subjects. Additionally, the complexity of BOLD signals from the regions of interest (ROIs) was found to be significantly associated with cognitive decline in patient groups on multiple time scales. Consequently, the complexity or MSE of BOLD signals may provide an imaging biomarker of cognitive impairments in MCI and AD.https://www.frontiersin.org/article/10.3389/fnins.2018.00677/fullmultiscale entropyAlzheimer’s diseasemild cognitive impairmentblood oxygen level-dependent signalsdynamic complexity |
| spellingShingle | Yan Niu Bin Wang Bin Wang Mengni Zhou Jiayue Xue Habib Shapour Rui Cao Xiaohong Cui Jinglong Wu Jinglong Wu Jie Xiang Dynamic Complexity of Spontaneous BOLD Activity in Alzheimer’s Disease and Mild Cognitive Impairment Using Multiscale Entropy Analysis Frontiers in Neuroscience multiscale entropy Alzheimer’s disease mild cognitive impairment blood oxygen level-dependent signals dynamic complexity |
| title | Dynamic Complexity of Spontaneous BOLD Activity in Alzheimer’s Disease and Mild Cognitive Impairment Using Multiscale Entropy Analysis |
| title_full | Dynamic Complexity of Spontaneous BOLD Activity in Alzheimer’s Disease and Mild Cognitive Impairment Using Multiscale Entropy Analysis |
| title_fullStr | Dynamic Complexity of Spontaneous BOLD Activity in Alzheimer’s Disease and Mild Cognitive Impairment Using Multiscale Entropy Analysis |
| title_full_unstemmed | Dynamic Complexity of Spontaneous BOLD Activity in Alzheimer’s Disease and Mild Cognitive Impairment Using Multiscale Entropy Analysis |
| title_short | Dynamic Complexity of Spontaneous BOLD Activity in Alzheimer’s Disease and Mild Cognitive Impairment Using Multiscale Entropy Analysis |
| title_sort | dynamic complexity of spontaneous bold activity in alzheimer s disease and mild cognitive impairment using multiscale entropy analysis |
| topic | multiscale entropy Alzheimer’s disease mild cognitive impairment blood oxygen level-dependent signals dynamic complexity |
| url | https://www.frontiersin.org/article/10.3389/fnins.2018.00677/full |
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