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Microcanonical and Canonical Ensembles for fMRI Brain Networks in Alzheimer’s Disease
This paper seeks to advance the state-of-the-art in analysing fMRI data to detect onset of Alzheimer’s disease and identify stages in the disease progression. We employ methods of network neuroscience to represent correlation across fMRI data arrays, and introduce novel techniques for network constr...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7916760/ https://www.ncbi.nlm.nih.gov/pubmed/33579012 http://dx.doi.org/10.3390/e23020216 |
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author | Wang, Jianjia Wu, Xichen Li, Mingrui |
author_facet | Wang, Jianjia Wu, Xichen Li, Mingrui |
author_sort | Wang, Jianjia |
collection | PubMed |
description | This paper seeks to advance the state-of-the-art in analysing fMRI data to detect onset of Alzheimer’s disease and identify stages in the disease progression. We employ methods of network neuroscience to represent correlation across fMRI data arrays, and introduce novel techniques for network construction and analysis. In network construction, we vary thresholds in establishing BOLD time series correlation between nodes, yielding variations in topological and other network characteristics. For network analysis, we employ methods developed for modelling statistical ensembles of virtual particles in thermal systems. The microcanonical ensemble and the canonical ensemble are analogous to two different fMRI network representations. In the former case, there is zero variance in the number of edges in each network, while in the latter case the set of networks have a variance in the number of edges. Ensemble methods describe the macroscopic properties of a network by considering the underlying microscopic characterisations which are in turn closely related to the degree configuration and network entropy. When applied to fMRI data in populations of Alzheimer’s patients and controls, our methods demonstrated levels of sensitivity adequate for clinical purposes in both identifying brain regions undergoing pathological changes and in revealing the dynamics of such changes. |
format | Online Article Text |
id | pubmed-7916760 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-79167602021-03-01 Microcanonical and Canonical Ensembles for fMRI Brain Networks in Alzheimer’s Disease Wang, Jianjia Wu, Xichen Li, Mingrui Entropy (Basel) Article This paper seeks to advance the state-of-the-art in analysing fMRI data to detect onset of Alzheimer’s disease and identify stages in the disease progression. We employ methods of network neuroscience to represent correlation across fMRI data arrays, and introduce novel techniques for network construction and analysis. In network construction, we vary thresholds in establishing BOLD time series correlation between nodes, yielding variations in topological and other network characteristics. For network analysis, we employ methods developed for modelling statistical ensembles of virtual particles in thermal systems. The microcanonical ensemble and the canonical ensemble are analogous to two different fMRI network representations. In the former case, there is zero variance in the number of edges in each network, while in the latter case the set of networks have a variance in the number of edges. Ensemble methods describe the macroscopic properties of a network by considering the underlying microscopic characterisations which are in turn closely related to the degree configuration and network entropy. When applied to fMRI data in populations of Alzheimer’s patients and controls, our methods demonstrated levels of sensitivity adequate for clinical purposes in both identifying brain regions undergoing pathological changes and in revealing the dynamics of such changes. MDPI 2021-02-10 /pmc/articles/PMC7916760/ /pubmed/33579012 http://dx.doi.org/10.3390/e23020216 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wang, Jianjia Wu, Xichen Li, Mingrui Microcanonical and Canonical Ensembles for fMRI Brain Networks in Alzheimer’s Disease |
title | Microcanonical and Canonical Ensembles for fMRI Brain Networks in Alzheimer’s Disease |
title_full | Microcanonical and Canonical Ensembles for fMRI Brain Networks in Alzheimer’s Disease |
title_fullStr | Microcanonical and Canonical Ensembles for fMRI Brain Networks in Alzheimer’s Disease |
title_full_unstemmed | Microcanonical and Canonical Ensembles for fMRI Brain Networks in Alzheimer’s Disease |
title_short | Microcanonical and Canonical Ensembles for fMRI Brain Networks in Alzheimer’s Disease |
title_sort | microcanonical and canonical ensembles for fmri brain networks in alzheimer’s disease |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7916760/ https://www.ncbi.nlm.nih.gov/pubmed/33579012 http://dx.doi.org/10.3390/e23020216 |
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