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Tracking the Progression and Influence of Beta-Amyloid Plaques Using Percolation Centrality and Collective Influence Algorithm: A Study Using PET Images
BACKGROUND: The study of brain networks, particularly the spread of disease, is made easier thanks to the network theory. The aberrant accumulation of beta-amyloid plaques and tau protein tangles in Alzheimer’s disease causes disruption in brain networks. The evaluation scores, such as the mini-ment...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10101156/ https://www.ncbi.nlm.nih.gov/pubmed/37064283 http://dx.doi.org/10.1177/09727531221117633 |
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author | Baboo, Gautam Kumar Prasad, Raghav Mahajan, Pranav Baths, Veeky |
author_facet | Baboo, Gautam Kumar Prasad, Raghav Mahajan, Pranav Baths, Veeky |
author_sort | Baboo, Gautam Kumar |
collection | PubMed |
description | BACKGROUND: The study of brain networks, particularly the spread of disease, is made easier thanks to the network theory. The aberrant accumulation of beta-amyloid plaques and tau protein tangles in Alzheimer’s disease causes disruption in brain networks. The evaluation scores, such as the mini-mental state examination (MMSE) and neuropsychiatric inventory questionnaire, which provide a clinical diagnosis, are affected by this build-up. PURPOSE: The percolation of beta-amyloid/tau tangles and their impact on cognitive tests are still unspecified. METHODS: Percolation centrality could be used to investigate beta-amyloid migration as a characteristic of positron emission tomography (PET)-image-based networks. The PET-image-based network was built utilizing a public database containing 551 scans published by the Alzheimer’s Disease Neuroimaging Initiative. Each image in the Julich atlas has 121 zones of interest, which are network nodes. Furthermore, the influential nodes for each scan are computed using the collective influence algorithm. RESULTS: For five nodal metrics, analysis of variance (ANOVA; P < .05) reveals the region of interest (ROI) in gray matter (GM) Broca’s area for Pittsburgh compound B (PiB) tracer type. The GM hippocampus area is significant for three nodal metrics in the case of florbetapir (AV45). Pairwise variance analysis of the clinical groups reveals five to twelve statistically significant ROIs for AV45 and PiB, respectively, that can distinguish between pairs of clinical situations. Based on multivariate linear regression, the MMSE is a trustworthy evaluation tool. CONCLUSION: Percolation values suggest that around 50 of the memory, visual-spatial skills, and language ROIs are critical to the percolation of beta-amyloids within the brain network when compared to the other extensively used nodal metrics. The anatomical areas rank higher with the advancement of the disease, according to the collective influence algorithm. |
format | Online Article Text |
id | pubmed-10101156 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-101011562023-04-14 Tracking the Progression and Influence of Beta-Amyloid Plaques Using Percolation Centrality and Collective Influence Algorithm: A Study Using PET Images Baboo, Gautam Kumar Prasad, Raghav Mahajan, Pranav Baths, Veeky Ann Neurosci Original Articles BACKGROUND: The study of brain networks, particularly the spread of disease, is made easier thanks to the network theory. The aberrant accumulation of beta-amyloid plaques and tau protein tangles in Alzheimer’s disease causes disruption in brain networks. The evaluation scores, such as the mini-mental state examination (MMSE) and neuropsychiatric inventory questionnaire, which provide a clinical diagnosis, are affected by this build-up. PURPOSE: The percolation of beta-amyloid/tau tangles and their impact on cognitive tests are still unspecified. METHODS: Percolation centrality could be used to investigate beta-amyloid migration as a characteristic of positron emission tomography (PET)-image-based networks. The PET-image-based network was built utilizing a public database containing 551 scans published by the Alzheimer’s Disease Neuroimaging Initiative. Each image in the Julich atlas has 121 zones of interest, which are network nodes. Furthermore, the influential nodes for each scan are computed using the collective influence algorithm. RESULTS: For five nodal metrics, analysis of variance (ANOVA; P < .05) reveals the region of interest (ROI) in gray matter (GM) Broca’s area for Pittsburgh compound B (PiB) tracer type. The GM hippocampus area is significant for three nodal metrics in the case of florbetapir (AV45). Pairwise variance analysis of the clinical groups reveals five to twelve statistically significant ROIs for AV45 and PiB, respectively, that can distinguish between pairs of clinical situations. Based on multivariate linear regression, the MMSE is a trustworthy evaluation tool. CONCLUSION: Percolation values suggest that around 50 of the memory, visual-spatial skills, and language ROIs are critical to the percolation of beta-amyloids within the brain network when compared to the other extensively used nodal metrics. The anatomical areas rank higher with the advancement of the disease, according to the collective influence algorithm. SAGE Publications 2022-08-22 2022-10 /pmc/articles/PMC10101156/ /pubmed/37064283 http://dx.doi.org/10.1177/09727531221117633 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-Commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Original Articles Baboo, Gautam Kumar Prasad, Raghav Mahajan, Pranav Baths, Veeky Tracking the Progression and Influence of Beta-Amyloid Plaques Using Percolation Centrality and Collective Influence Algorithm: A Study Using PET Images |
title | Tracking the Progression and Influence of Beta-Amyloid Plaques Using
Percolation Centrality and Collective Influence Algorithm: A Study Using PET
Images |
title_full | Tracking the Progression and Influence of Beta-Amyloid Plaques Using
Percolation Centrality and Collective Influence Algorithm: A Study Using PET
Images |
title_fullStr | Tracking the Progression and Influence of Beta-Amyloid Plaques Using
Percolation Centrality and Collective Influence Algorithm: A Study Using PET
Images |
title_full_unstemmed | Tracking the Progression and Influence of Beta-Amyloid Plaques Using
Percolation Centrality and Collective Influence Algorithm: A Study Using PET
Images |
title_short | Tracking the Progression and Influence of Beta-Amyloid Plaques Using
Percolation Centrality and Collective Influence Algorithm: A Study Using PET
Images |
title_sort | tracking the progression and influence of beta-amyloid plaques using
percolation centrality and collective influence algorithm: a study using pet
images |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10101156/ https://www.ncbi.nlm.nih.gov/pubmed/37064283 http://dx.doi.org/10.1177/09727531221117633 |
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