Cargando…

Deficit of Cross‐Frequency Integration in Mild Cognitive Impairment and Alzheimer's Disease: A Multilayer Network Approach

BACKGROUND: Studies at specific frequencies have shown abnormalities in brain functional networks among mild cognitive impairment (MCI) and Alzheimer's disease (AD) patients. Previous studies have failed to take into account the possibility that optimal cognitive integration requires interactio...

Descripción completa

Detalles Bibliográficos
Autores principales: Wang, Xiaoyue, Cui, Xiaohong, Ding, Congli, Li, Dandan, Cheng, Chen, Wang, Bin, Xiang, Jie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8247269/
https://www.ncbi.nlm.nih.gov/pubmed/33244827
http://dx.doi.org/10.1002/jmri.27453
_version_ 1783716485873532928
author Wang, Xiaoyue
Cui, Xiaohong
Ding, Congli
Li, Dandan
Cheng, Chen
Wang, Bin
Xiang, Jie
author_facet Wang, Xiaoyue
Cui, Xiaohong
Ding, Congli
Li, Dandan
Cheng, Chen
Wang, Bin
Xiang, Jie
author_sort Wang, Xiaoyue
collection PubMed
description BACKGROUND: Studies at specific frequencies have shown abnormalities in brain functional networks among mild cognitive impairment (MCI) and Alzheimer's disease (AD) patients. Previous studies have failed to take into account the possibility that optimal cognitive integration requires interactions between different frequency bands. PURPOSE: To study whether there is abnormal cross‐frequency integration in patients' brains during disease progression. STUDY TYPE: Retrospective. POPULATION: Forty‐six normal control (NC), 85 patients with MCI, and 31 patients with AD. FIELD STRENGTH/SEQUENCE: 3T. ASSESSMENT: Multilayer network models were constructed for NC, MCI, and AD, and multilayer participation coefficient (MPC) was used to study the changes of the interlayer relationship in the course of disease development. In addition, MPC and an overlapping degree were combined to classify nodes in the network, and the role of key nodes in the interlayer interaction was mainly observed. Finally, the correlation between multilayer network measures and cognitive function was investigated. STATISTICAL TESTS: Pearson chi‐squared two‐tailed test, one‐way analysis of variance (ANOVA), nonparametric Spearman correlation coefficient r, and the false discovery rate. RESULTS: The MPC of the network decreased significantly in MCI (P < 0.05) and AD (P < 0.05). The number of intralayer nodes increased significantly (MCI [P < 0.05], AD [P < 0.05]) and the number of interlayer nodes decreased significantly. Centrality loss between frequencies of a large number of hub nodes, among which the damaged hub nodes included the left hippocampus, left precuneus, right precuneus, left posterior cingulate gyrus, left precentral gyrus, right precentral gyrus, left medial superior frontal gyrus, and right postcentral gyrus. MPC was significantly associated with memory impairment in patients (AD [Spearman's r = 0.526, P < 0.05], MCI [Spearman's r = 0.229, P < 0.05]), and these related regions included damaged hub nodes in patients. DATA CONCLUSION: In the multilayer networks of patients, there was an obvious deficit in cross‐frequency integration and the hub nodes were preferentially damaged. Moreover, these vulnerable hubs are associated with patients' cognitive scores. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY STAGE: 3
format Online
Article
Text
id pubmed-8247269
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher John Wiley & Sons, Inc.
record_format MEDLINE/PubMed
spelling pubmed-82472692021-07-02 Deficit of Cross‐Frequency Integration in Mild Cognitive Impairment and Alzheimer's Disease: A Multilayer Network Approach Wang, Xiaoyue Cui, Xiaohong Ding, Congli Li, Dandan Cheng, Chen Wang, Bin Xiang, Jie J Magn Reson Imaging Original Research BACKGROUND: Studies at specific frequencies have shown abnormalities in brain functional networks among mild cognitive impairment (MCI) and Alzheimer's disease (AD) patients. Previous studies have failed to take into account the possibility that optimal cognitive integration requires interactions between different frequency bands. PURPOSE: To study whether there is abnormal cross‐frequency integration in patients' brains during disease progression. STUDY TYPE: Retrospective. POPULATION: Forty‐six normal control (NC), 85 patients with MCI, and 31 patients with AD. FIELD STRENGTH/SEQUENCE: 3T. ASSESSMENT: Multilayer network models were constructed for NC, MCI, and AD, and multilayer participation coefficient (MPC) was used to study the changes of the interlayer relationship in the course of disease development. In addition, MPC and an overlapping degree were combined to classify nodes in the network, and the role of key nodes in the interlayer interaction was mainly observed. Finally, the correlation between multilayer network measures and cognitive function was investigated. STATISTICAL TESTS: Pearson chi‐squared two‐tailed test, one‐way analysis of variance (ANOVA), nonparametric Spearman correlation coefficient r, and the false discovery rate. RESULTS: The MPC of the network decreased significantly in MCI (P < 0.05) and AD (P < 0.05). The number of intralayer nodes increased significantly (MCI [P < 0.05], AD [P < 0.05]) and the number of interlayer nodes decreased significantly. Centrality loss between frequencies of a large number of hub nodes, among which the damaged hub nodes included the left hippocampus, left precuneus, right precuneus, left posterior cingulate gyrus, left precentral gyrus, right precentral gyrus, left medial superior frontal gyrus, and right postcentral gyrus. MPC was significantly associated with memory impairment in patients (AD [Spearman's r = 0.526, P < 0.05], MCI [Spearman's r = 0.229, P < 0.05]), and these related regions included damaged hub nodes in patients. DATA CONCLUSION: In the multilayer networks of patients, there was an obvious deficit in cross‐frequency integration and the hub nodes were preferentially damaged. Moreover, these vulnerable hubs are associated with patients' cognitive scores. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY STAGE: 3 John Wiley & Sons, Inc. 2020-11-26 2021-05 /pmc/articles/PMC8247269/ /pubmed/33244827 http://dx.doi.org/10.1002/jmri.27453 Text en © 2020 The Authors. Journal of Magnetic Resonance Imaging published by Wiley Periodicals LLC. on behalf of International Society for Magnetic Resonance in Medicine. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Research
Wang, Xiaoyue
Cui, Xiaohong
Ding, Congli
Li, Dandan
Cheng, Chen
Wang, Bin
Xiang, Jie
Deficit of Cross‐Frequency Integration in Mild Cognitive Impairment and Alzheimer's Disease: A Multilayer Network Approach
title Deficit of Cross‐Frequency Integration in Mild Cognitive Impairment and Alzheimer's Disease: A Multilayer Network Approach
title_full Deficit of Cross‐Frequency Integration in Mild Cognitive Impairment and Alzheimer's Disease: A Multilayer Network Approach
title_fullStr Deficit of Cross‐Frequency Integration in Mild Cognitive Impairment and Alzheimer's Disease: A Multilayer Network Approach
title_full_unstemmed Deficit of Cross‐Frequency Integration in Mild Cognitive Impairment and Alzheimer's Disease: A Multilayer Network Approach
title_short Deficit of Cross‐Frequency Integration in Mild Cognitive Impairment and Alzheimer's Disease: A Multilayer Network Approach
title_sort deficit of cross‐frequency integration in mild cognitive impairment and alzheimer's disease: a multilayer network approach
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8247269/
https://www.ncbi.nlm.nih.gov/pubmed/33244827
http://dx.doi.org/10.1002/jmri.27453
work_keys_str_mv AT wangxiaoyue deficitofcrossfrequencyintegrationinmildcognitiveimpairmentandalzheimersdiseaseamultilayernetworkapproach
AT cuixiaohong deficitofcrossfrequencyintegrationinmildcognitiveimpairmentandalzheimersdiseaseamultilayernetworkapproach
AT dingcongli deficitofcrossfrequencyintegrationinmildcognitiveimpairmentandalzheimersdiseaseamultilayernetworkapproach
AT lidandan deficitofcrossfrequencyintegrationinmildcognitiveimpairmentandalzheimersdiseaseamultilayernetworkapproach
AT chengchen deficitofcrossfrequencyintegrationinmildcognitiveimpairmentandalzheimersdiseaseamultilayernetworkapproach
AT wangbin deficitofcrossfrequencyintegrationinmildcognitiveimpairmentandalzheimersdiseaseamultilayernetworkapproach
AT xiangjie deficitofcrossfrequencyintegrationinmildcognitiveimpairmentandalzheimersdiseaseamultilayernetworkapproach