Altered global signal topography in Alzheimer's disease.


Journal

EBioMedicine
ISSN: 2352-3964
Titre abrégé: EBioMedicine
Pays: Netherlands
ID NLM: 101647039

Informations de publication

Date de publication:
Mar 2023
Historique:
received: 07 06 2022
revised: 31 12 2022
accepted: 17 01 2023
pubmed: 10 2 2023
medline: 15 3 2023
entrez: 9 2 2023
Statut: ppublish

Résumé

Alzheimer's disease (AD) is a neurodegenerative disease associated with widespread disruptions in intrinsic local specialization and global integration in the functional system of the brain. These changes in integration may further disrupt the global signal (GS) distribution, which might represent the local relative contribution to global activity in functional magnetic resonance imaging (fMRI). fMRI scans from a discovery dataset (n = 809) and a validated dataset (n = 542) were used in the analysis. We investigated the alteration of GS topography using the GS correlation (GSCORR) in patients with mild cognitive impairment (MCI) and AD. The association between GS alterations and functional network properties was also investigated based on network theory. The underlying mechanism of GSCORR alterations was elucidated using imaging-transcriptomics. Significantly increased GS topography in the frontal lobe and decreased GS topography in the hippocampus, cingulate gyrus, caudate, and middle temporal gyrus were observed in patients with AD (P Our findings revealed significant changes in GS topography and its molecular basis, confirming the informative role of GS in AD and further contributing to the understanding of the relationship between global and local neuronal activities in patients with AD. Beijing Natural Science Funds for Distinguished Young Scholars, China; Fundamental Research Funds for the Central Universities, China; National Natural Science Foundation, China.

Sections du résumé

BACKGROUND BACKGROUND
Alzheimer's disease (AD) is a neurodegenerative disease associated with widespread disruptions in intrinsic local specialization and global integration in the functional system of the brain. These changes in integration may further disrupt the global signal (GS) distribution, which might represent the local relative contribution to global activity in functional magnetic resonance imaging (fMRI).
METHODS METHODS
fMRI scans from a discovery dataset (n = 809) and a validated dataset (n = 542) were used in the analysis. We investigated the alteration of GS topography using the GS correlation (GSCORR) in patients with mild cognitive impairment (MCI) and AD. The association between GS alterations and functional network properties was also investigated based on network theory. The underlying mechanism of GSCORR alterations was elucidated using imaging-transcriptomics.
FINDINGS RESULTS
Significantly increased GS topography in the frontal lobe and decreased GS topography in the hippocampus, cingulate gyrus, caudate, and middle temporal gyrus were observed in patients with AD (P
INTERPRETATION CONCLUSIONS
Our findings revealed significant changes in GS topography and its molecular basis, confirming the informative role of GS in AD and further contributing to the understanding of the relationship between global and local neuronal activities in patients with AD.
FUNDING BACKGROUND
Beijing Natural Science Funds for Distinguished Young Scholars, China; Fundamental Research Funds for the Central Universities, China; National Natural Science Foundation, China.

Identifiants

pubmed: 36758481
pii: S2352-3964(23)00020-8
doi: 10.1016/j.ebiom.2023.104455
pmc: PMC9941064
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

104455

Informations de copyright

Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of interests PW reports grants from the National Natural Science Foundation of China, during the conduct of the study; DW reports grants from the National Natural Science Foundation of China, during the conduct of the study; YH reports grants from the National Natural Science Foundation of China, during the conduct of the study; XZ reports grants from National Natural Science Foundation of China, during the conduct of the study; YL reports grants from Ministry of Education of the People's Republic of China, grants from Beijing Natural Science Funds, grants from National Natural Science Foundation of China, during the conduct of the study. The remaining authors reported no relevant conflicts.

Auteurs

Pindong Chen (P)

Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.

Kun Zhao (K)

School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China; Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical Engineering, Beihang University, Beijing, China.

Han Zhang (H)

School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.

Yongbin Wei (Y)

School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.

Pan Wang (P)

Department of Neurology, Tianjin Huanhu Hospital Tianjin University, Tianjin, China.

Dawei Wang (D)

Department of Radiology, Qilu Hospital of Shandong University, Ji'nan, China.

Chengyuan Song (C)

Department of Neurology, Qilu Hospital of Shandong University, Ji'nan, China.

Hongwei Yang (H)

Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China.

Zengqiang Zhang (Z)

Branch of Chinese PLA General Hospital, Sanya, China.

Hongxiang Yao (H)

Department of Radiology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China.

Yida Qu (Y)

Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.

Xiaopeng Kang (X)

Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.

Kai Du (K)

Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.

Lingzhong Fan (L)

Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.

Tong Han (T)

Department of Radiology, Tianjin Huanhu Hospital, Tianjin, China.

Chunshui Yu (C)

Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China.

Bo Zhou (B)

Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China.

Tianzi Jiang (T)

Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.

Yuying Zhou (Y)

Department of Neurology, Tianjin Huanhu Hospital Tianjin University, Tianjin, China.

Jie Lu (J)

Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China.

Ying Han (Y)

Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China; Beijing Institute of Geriatrics, Beijing, China; National Clinical Research Center for Geriatric Disorders, Beijing, China.

Xi Zhang (X)

Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China.

Bing Liu (B)

State Key Laboratory of Cognition Neuroscience & Learning, Beijing Normal University, Beijing, China.

Yong Liu (Y)

Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China. Electronic address: yongliu@bupt.edu.cn.

Articles similaires

[Redispensing of expensive oral anticancer medicines: a practical application].

Lisanne N van Merendonk, Kübra Akgöl, Bastiaan Nuijen
1.00
Humans Antineoplastic Agents Administration, Oral Drug Costs Counterfeit Drugs

Smoking Cessation and Incident Cardiovascular Disease.

Jun Hwan Cho, Seung Yong Shin, Hoseob Kim et al.
1.00
Humans Male Smoking Cessation Cardiovascular Diseases Female
Humans United States Aged Cross-Sectional Studies Medicare Part C
1.00
Humans Yoga Low Back Pain Female Male

Classifications MeSH