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Increased functional connectivity patterns in mild Alzheimer’s disease: A rsfMRI study

BACKGROUND: Alzheimer’s disease (AD) is the most common age-related neurodegenerative disorder. In view of our rapidly aging population, there is an urgent need to identify Alzheimer’s disease (AD) at an early stage. A potential way to do so is by assessing the functional connectivity (FC), i.e., th...

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Autores principales: Penalba-Sánchez, Lucía, Oliveira-Silva, Patrícia, Sumich, Alexander Luke, Cifre, Ignacio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9869068/
https://www.ncbi.nlm.nih.gov/pubmed/36698861
http://dx.doi.org/10.3389/fnagi.2022.1037347
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author Penalba-Sánchez, Lucía
Oliveira-Silva, Patrícia
Sumich, Alexander Luke
Cifre, Ignacio
author_facet Penalba-Sánchez, Lucía
Oliveira-Silva, Patrícia
Sumich, Alexander Luke
Cifre, Ignacio
author_sort Penalba-Sánchez, Lucía
collection PubMed
description BACKGROUND: Alzheimer’s disease (AD) is the most common age-related neurodegenerative disorder. In view of our rapidly aging population, there is an urgent need to identify Alzheimer’s disease (AD) at an early stage. A potential way to do so is by assessing the functional connectivity (FC), i.e., the statistical dependency between two or more brain regions, through novel analysis techniques. METHODS: In the present study, we assessed the static and dynamic FC using different approaches. A resting state (rs)fMRI dataset from the Alzheimer’s disease neuroimaging initiative (ADNI) was used (n = 128). The blood-oxygen-level-dependent (BOLD) signals from 116 regions of 4 groups of participants, i.e., healthy controls (HC; n = 35), early mild cognitive impairment (EMCI; n = 29), late mild cognitive impairment (LMCI; n = 30), and Alzheimer’s disease (AD; n = 34) were extracted and analyzed. FC and dynamic FC were extracted using Pearson’s correlation, sliding-windows correlation analysis (SWA), and the point process analysis (PPA). Additionally, graph theory measures to explore network segregation and integration were computed. RESULTS: Our results showed a longer characteristic path length and a decreased degree of EMCI in comparison to the other groups. Additionally, an increased FC in several regions in LMCI and AD in contrast to HC and EMCI was detected. These results suggest a maladaptive short-term mechanism to maintain cognition. CONCLUSION: The increased pattern of FC in several regions in LMCI and AD is observable in all the analyses; however, the PPA enabled us to reduce the computational demands and offered new specific dynamic FC findings.
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spelling pubmed-98690682023-01-24 Increased functional connectivity patterns in mild Alzheimer’s disease: A rsfMRI study Penalba-Sánchez, Lucía Oliveira-Silva, Patrícia Sumich, Alexander Luke Cifre, Ignacio Front Aging Neurosci Aging Neuroscience BACKGROUND: Alzheimer’s disease (AD) is the most common age-related neurodegenerative disorder. In view of our rapidly aging population, there is an urgent need to identify Alzheimer’s disease (AD) at an early stage. A potential way to do so is by assessing the functional connectivity (FC), i.e., the statistical dependency between two or more brain regions, through novel analysis techniques. METHODS: In the present study, we assessed the static and dynamic FC using different approaches. A resting state (rs)fMRI dataset from the Alzheimer’s disease neuroimaging initiative (ADNI) was used (n = 128). The blood-oxygen-level-dependent (BOLD) signals from 116 regions of 4 groups of participants, i.e., healthy controls (HC; n = 35), early mild cognitive impairment (EMCI; n = 29), late mild cognitive impairment (LMCI; n = 30), and Alzheimer’s disease (AD; n = 34) were extracted and analyzed. FC and dynamic FC were extracted using Pearson’s correlation, sliding-windows correlation analysis (SWA), and the point process analysis (PPA). Additionally, graph theory measures to explore network segregation and integration were computed. RESULTS: Our results showed a longer characteristic path length and a decreased degree of EMCI in comparison to the other groups. Additionally, an increased FC in several regions in LMCI and AD in contrast to HC and EMCI was detected. These results suggest a maladaptive short-term mechanism to maintain cognition. CONCLUSION: The increased pattern of FC in several regions in LMCI and AD is observable in all the analyses; however, the PPA enabled us to reduce the computational demands and offered new specific dynamic FC findings. Frontiers Media S.A. 2023-01-09 /pmc/articles/PMC9869068/ /pubmed/36698861 http://dx.doi.org/10.3389/fnagi.2022.1037347 Text en Copyright © 2023 Penalba-Sánchez, Oliveira-Silva, Sumich and Cifre. 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 Aging Neuroscience
Penalba-Sánchez, Lucía
Oliveira-Silva, Patrícia
Sumich, Alexander Luke
Cifre, Ignacio
Increased functional connectivity patterns in mild Alzheimer’s disease: A rsfMRI study
title Increased functional connectivity patterns in mild Alzheimer’s disease: A rsfMRI study
title_full Increased functional connectivity patterns in mild Alzheimer’s disease: A rsfMRI study
title_fullStr Increased functional connectivity patterns in mild Alzheimer’s disease: A rsfMRI study
title_full_unstemmed Increased functional connectivity patterns in mild Alzheimer’s disease: A rsfMRI study
title_short Increased functional connectivity patterns in mild Alzheimer’s disease: A rsfMRI study
title_sort increased functional connectivity patterns in mild alzheimer’s disease: a rsfmri study
topic Aging Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9869068/
https://www.ncbi.nlm.nih.gov/pubmed/36698861
http://dx.doi.org/10.3389/fnagi.2022.1037347
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