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Estimating effective connectivity in Alzheimer's disease progression: A dynamic causal modeling study
INTRODUCTION: Alzheimer's disease (AD) affects the whole brain from the cellular level to the entire brain network structure. The causal relationship among brain regions concerning the different AD stages is not yet investigated. This study used Dynamic Causal Modeling (DCM) method to assess ef...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9797690/ https://www.ncbi.nlm.nih.gov/pubmed/36590062 http://dx.doi.org/10.3389/fnhum.2022.1060936 |
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author | Huang, Jiali Jung, Jae-Yoon Nam, Chang S. |
author_facet | Huang, Jiali Jung, Jae-Yoon Nam, Chang S. |
author_sort | Huang, Jiali |
collection | PubMed |
description | INTRODUCTION: Alzheimer's disease (AD) affects the whole brain from the cellular level to the entire brain network structure. The causal relationship among brain regions concerning the different AD stages is not yet investigated. This study used Dynamic Causal Modeling (DCM) method to assess effective connectivity (EC) and investigate the changes that accompany AD progression. METHODS: We included the resting-state fMRI data of 34 AD patients, 31 late mild cognitive impairment (LMCI) patients, 34 early MCI (EMCI) patients, and 31 cognitive normal (CN) subjects selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The parametric Empirical Bayes (PEB) method was used to infer the effective connectivities and the corresponding probabilities. A linear regression analysis was carried out to test if the connection strengths could predict subjects' cognitive scores. RESULTS: The results showed that the connections reduced from full connection in the CN group to no connection in the AD group. Statistical analysis showed the connectivity strengths were lower for later-stage patients. Linear regression analysis showed that the connection strengths were partially predictive of the cognitive scores. DISCUSSION: Our results demonstrated the dwindling connectivity accompanying AD progression on causal relationships among brain regions and indicated the potential of EC as a loyal biomarker in AD progression. |
format | Online Article Text |
id | pubmed-9797690 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97976902022-12-30 Estimating effective connectivity in Alzheimer's disease progression: A dynamic causal modeling study Huang, Jiali Jung, Jae-Yoon Nam, Chang S. Front Hum Neurosci Human Neuroscience INTRODUCTION: Alzheimer's disease (AD) affects the whole brain from the cellular level to the entire brain network structure. The causal relationship among brain regions concerning the different AD stages is not yet investigated. This study used Dynamic Causal Modeling (DCM) method to assess effective connectivity (EC) and investigate the changes that accompany AD progression. METHODS: We included the resting-state fMRI data of 34 AD patients, 31 late mild cognitive impairment (LMCI) patients, 34 early MCI (EMCI) patients, and 31 cognitive normal (CN) subjects selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The parametric Empirical Bayes (PEB) method was used to infer the effective connectivities and the corresponding probabilities. A linear regression analysis was carried out to test if the connection strengths could predict subjects' cognitive scores. RESULTS: The results showed that the connections reduced from full connection in the CN group to no connection in the AD group. Statistical analysis showed the connectivity strengths were lower for later-stage patients. Linear regression analysis showed that the connection strengths were partially predictive of the cognitive scores. DISCUSSION: Our results demonstrated the dwindling connectivity accompanying AD progression on causal relationships among brain regions and indicated the potential of EC as a loyal biomarker in AD progression. Frontiers Media S.A. 2022-12-15 /pmc/articles/PMC9797690/ /pubmed/36590062 http://dx.doi.org/10.3389/fnhum.2022.1060936 Text en Copyright © 2022 Huang, Jung and Nam 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 | Human Neuroscience Huang, Jiali Jung, Jae-Yoon Nam, Chang S. Estimating effective connectivity in Alzheimer's disease progression: A dynamic causal modeling study |
title | Estimating effective connectivity in Alzheimer's disease progression: A dynamic causal modeling study |
title_full | Estimating effective connectivity in Alzheimer's disease progression: A dynamic causal modeling study |
title_fullStr | Estimating effective connectivity in Alzheimer's disease progression: A dynamic causal modeling study |
title_full_unstemmed | Estimating effective connectivity in Alzheimer's disease progression: A dynamic causal modeling study |
title_short | Estimating effective connectivity in Alzheimer's disease progression: A dynamic causal modeling study |
title_sort | estimating effective connectivity in alzheimer's disease progression: a dynamic causal modeling study |
topic | Human Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9797690/ https://www.ncbi.nlm.nih.gov/pubmed/36590062 http://dx.doi.org/10.3389/fnhum.2022.1060936 |
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