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Wang L, Hu W, Dong F, Sheng C, Wu J, Han Y, Jiang J, Weiner MW, Aisen P, Petersen R, Jack CR, Jagust W, Trojanowski JQ, Toga AW, Beckett L, Green RC, Saykin AJ, Morris J, Shaw LM, Khachaturian Z, Sorensen G, Kuller L, Raichle M, Paul S, Davies P, Fillit H, Hefti F, Holtzman D, Mesulam MM, Potter W, Snyder P, Schwartz A, Montine T, Thomas RG, Donohue M, Walter S, Gessert D, Sather T, Jiminez G, Harvey D, Bernstein M, Thompson P, Schuff N, Borowski B, Gunter J, Senjem M, Vemuri P, Jones D, Kantarci K, Ward C, Koeppe RA, Foster N, Reiman EM, Chen K, Mathis C, Landau S, Cairns NJ, Householder E, Taylor-Reinwald L, Lee V, Korecka M, Figurski M, Crawford K, Neu S, Foroud TM, Potkin SG, Shen L, Faber K, Kim S, Nho K, Thal L, Buckholtz N, Albert M, Frank R, Hsiao J, Kaye J, Quinn J, Lind B, Carter R, Dolen S, Schneider LS, Pawluczyk S, Beccera M, Teodoro L, Spann BM, Brewer J, Vanderswag H, Fleisher A, Heidebrink JL, Lord JL, Mason SS, Albers CS, Knopman D, Johnson K, Doody RS, Villanueva-Meyer J, Chowdhury M, Rountree S, Dang M, Stern Y, Honig LS, Bell KL, Ances B, Carroll M, Leon S, Mintun MA, Schneider S, Oliver A, Marson D, Griffith R, Clark D, Geldmacher D, Brockington J, Roberson E, Grossman H, Mitsis E, de Toledo-Morrell L, Shah RC, Duara R, Varon D, Greig MT, Roberts P, Onyike C, D'Agostino D, Kielb S, Galvin JE, Cerbone B, Michel CA, Rusinek H, de Leon MJ, Glodzik L, De Santi S, Doraiswamy PM, Petrella JR, Wong TZ, Arnold SE, Karlawish JH, Wolk D, Smith CD, Jicha G, Hardy P, Sinha P, Oates E, Conrad G, Lopez OL, Oakley M, Simpson DM, Porsteinsson AP, Goldstein BS, Martin K, Makino KM, Ismail MS, Brand C, Mulnard RA, Thai G, McAdams-Ortiz C, Womack K, Mathews D, Quiceno M, Diaz-Arrastia R, King R, Weiner M, Martin-Cook K, DeVous M, Levey AI, Lah JJ, Cellar JS, Burns JM, Anderson HS, Swerdlow RH, Apostolova L, Tingus K, Woo E, Silverman DHS, Lu PH, Bartzokis G, Graff-Radford NR, Parfitt F, Kendall T, Johnson H, Farlow MR, Hake AM, Matthews BR, Herring S, Hunt C, van Dyck CH, Carson RE, MacAvoy MG, Chertkow H, Bergman H, Hosein C, Hsiung GYR, Feldman H, Mudge B, Assaly M, Bernick C, Munic D, Kertesz A, Rogers J, Trost D, Kerwin D, Lipowski K, Wu CK, Johnson N, Sadowsky C, Martinez W, Villena T, Turner RS, Johnson K, Reynolds B, Sperling RA, Johnson KA, Marshall G, Frey M, Lane B, Rosen A, Tinklenberg J, Sabbagh MN, Belden CM, Jacobson SA, Sirrel SA, Kowall N, Killiany R, Budson AE, Norbash A, Johnson PL, Allard J, Lerner A, Ogrocki P, Hudson L, Fletcher E, Carmichae O, Olichney J, DeCarli C, Kittur S, Borrie M, Lee TY, Bartha R, Johnson S, Asthana S, Carlsson CM, Preda A, Nguyen D, Tariot P, Reeder S, Bates V, Capote H, Rainka M, Scharre DW, Kataki M, Adeli A, Zimmerman EA, Celmins D, Brown AD, Pearlson GD, Blank K, Anderson K, Santulli RB, Kitzmiller TJ, Schwartz ES, Sink KM, Williamson JD, Garg P, Watkins F, Ott BR, Querfurth H, Tremont G, Salloway S, Malloy P, Correia S, Rosen HJ, Miller BL, Mintzer J, Spicer K, Bachman D, Pasternak S, Rachinsky I, Drost D, Pomara N, Hernando R, Sarrael A, Schultz SK, Ponto LLB, Shim H, Smith KE, Relkin N, Chaing G, Raudin L, Smith A, Fargher K, Raj BA, Neylan T, Grafman J, Davis M, Morrison R, Hayes J, Finley S, Friedl K, Fleischman D, Arfanakis K, James O, Massoglia D, Fruehling JJ, Harding S, Peskind ER, Petrie EC, Li G, Yesavage JA, Taylor JL, Furst AJ. Dynamic proportional loss of functional connectivity revealed change of left superior frontal gyrus in subjective cognitive decline: an explanatory study based on Chinese and Western cohorts. GeroScience 2025:10.1007/s11357-025-01528-6. [PMID: 39888585 DOI: 10.1007/s11357-025-01528-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2024] [Accepted: 01/13/2025] [Indexed: 02/01/2025] Open
Abstract
Brain network dynamics have been extensively explored in patients with subjective cognitive decline (SCD). However, these studies are susceptible to individual differences, scanning parameters, and other confounding factors. Therefore, how to reveal subtle SCD-related subtle changes remains unclear. Cross-sectional and longitudinal resting-state functional magnetic resonance imaging data from both Chinese and Western populations were analyzed. We proposed a framework of dynamic proportional loss of functional connectivity (DPLFC). After its stability was validated, the optimal parameters were applied for the clinical diagnosis of SCD. DPLFC yielded a relatively high intraclass correlation coefficient. In particular, the DPLFC of the left superior frontal gyrus (SFG) progressively decreased along the Alzheimer's disease (AD) continuum. Compared with the traditional index, the DPLFC had better classification performance between cognitively normal controls and patients with SCD. Furthermore, DPLFC was related to Aβ deposition and scale scores. Patients with lower DPLFC values had a greater risk of cognitive decline. Decreased DPLFC in the left SFG may be a potential AD-related neuroimaging biomarker at an early stage.
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Affiliation(s)
| | - Wenjing Hu
- Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, 200444, China
| | - Fan Dong
- Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, 200444, China
| | - Can Sheng
- Department of Neurology, the Affiliated Hospital of Jining Medical University, Jining, 272000, China
| | - Jinglong Wu
- Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, 200444, China
- School of Medical Technology, Beijing Institute of Technology, Beijing, 100081, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China.
- National Clinical Research Center for Geriatric Disorders, Beijing, 100053, China.
- Hainan University, Haikou, 570228, China.
| | - Jiehui Jiang
- Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, 200444, China.
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Wang L, Xu H, Song Z, Wang H, Hu W, Gao Y, Zhang Z, Jiang J. fMRI signals in white matter rewire gray matter community organization. Neuroimage 2024; 297:120763. [PMID: 39084280 DOI: 10.1016/j.neuroimage.2024.120763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2024] [Revised: 07/17/2024] [Accepted: 07/29/2024] [Indexed: 08/02/2024] Open
Abstract
Human brain gray matter (GM) has usually been clustered into multiple functional networks. The white matter (WM) fiber bundles are known to interconnect these networks simultaneously, engaging in numerous cognitive functions. However, the exact interconnections between GM and WM are still unclear, whether functional signals in WM rewires GM community organization remains to be explored. In this study, we divided brain functional connections into three types by using edge-centric method, including intra-GM, intra-WM and GM-WM connections, and calculated the edge community evaluation indexes for quantifying GM community engagement. The results showed that the involvement of WM significantly enhanced community entropy in the heteromodal system, while the sensory-attention system remained barely changed. In addition, delta community entropy showed a significant correlation with clinical cognitive scale. Our results suggested that WM rewired GM community organization, enhancing the community engagement of brain regions in the heteromodal system. This involvement was observed to be disrupted in disease groups. Our study revealed that considering the functional signals of GM and WM simultaneously could better understand the brain's functional organization.
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Affiliation(s)
- Luyao Wang
- Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Huanyu Xu
- School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
| | - Ziyan Song
- Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Huanxin Wang
- Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Wenjing Hu
- Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Yiwen Gao
- Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Zhilin Zhang
- Research Center for Medical Artificial Intelligence, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China.
| | - Jiehui Jiang
- Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai 200444, China.
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Liu Z, Shi D, Cai Y, Li A, Lan G, Sun P, Liu L, Zhu Y, Yang J, Zhou Y, Guo L, Zhang L, Deng S, Chen S, Yu X, Chen X, Zhao R, Wang Q, Ran P, Xu L, Zhou L, Sun K, Wang X, Peng Q, Han Y, Guo T. Pathophysiology characterization of Alzheimer's disease in South China's aging population: for the Greater-Bay-Area Healthy Aging Brain Study (GHABS). Alzheimers Res Ther 2024; 16:84. [PMID: 38627753 PMCID: PMC11020808 DOI: 10.1186/s13195-024-01458-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Accepted: 04/12/2024] [Indexed: 04/19/2024]
Abstract
INTRODUCTION The Guangdong-Hong Kong-Macao Greater-Bay-Area of South China has an 86 million population and faces a significant challenge of Alzheimer's disease (AD). However, the characteristics and prevalence of AD in this area are still unclear due to the rarely available community-based neuroimaging AD cohort. METHODS Following the standard protocols of the Alzheimer's Disease Neuroimaging Initiative, the Greater-Bay-Area Healthy Aging Brain Study (GHABS) was initiated in 2021. GHABS participants completed clinical assessments, plasma biomarkers, genotyping, magnetic resonance imaging (MRI), β-amyloid (Aβ) positron emission tomography (PET) imaging, and tau PET imaging. The GHABS cohort focuses on pathophysiology characterization and early AD detection in the Guangdong-Hong Kong-Macao Greater Bay Area. In this study, we analyzed plasma Aβ42/Aβ40 (A), p-Tau181 (T), neurofilament light, and GFAP by Simoa in 470 Chinese older adults, and 301, 195, and 70 had MRI, Aβ PET, and tau PET, respectively. Plasma biomarkers, Aβ PET, tau PET, hippocampal volume, and temporal-metaROI cortical thickness were compared between normal control (NC), subjective cognitive decline (SCD), mild cognitive impairment (MCI), and dementia groups, controlling for age, sex, and APOE-ε4. The prevalence of plasma A/T profiles and Aβ PET positivity were also determined in different diagnostic groups. RESULTS The aims, study design, data collection, and potential applications of GHABS are summarized. SCD individuals had significantly higher plasma p-Tau181 and plasma GFAP than the NC individuals. MCI and dementia patients showed more abnormal changes in all the plasma and neuroimaging biomarkers than NC and SCD individuals. The frequencies of plasma A+/T+ (NC; 5.9%, SCD: 8.2%, MCI: 25.3%, dementia: 64.9%) and Aβ PET positivity (NC: 25.6%, SCD: 22.5%, MCI: 47.7%, dementia: 89.3%) were reported. DISCUSSION The GHABS cohort may provide helpful guidance toward designing standard AD community cohorts in South China. This study, for the first time, reported the pathophysiology characterization of plasma biomarkers, Aβ PET, tau PET, hippocampal atrophy, and AD-signature cortical thinning, as well as the prevalence of Aβ PET positivity in the Guangdong-Hong Kong-Macao Greater Bay Area of China. These findings provide novel insights into understanding the characteristics of abnormal AD pathological changes in South China's older population.
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Affiliation(s)
- Zhen Liu
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China
| | - Dai Shi
- Neurology Medicine Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, 518000, China
| | - Yue Cai
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China
| | - Anqi Li
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China
| | - Guoyu Lan
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China
| | - Pan Sun
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China
| | - Lin Liu
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China
| | - Yalin Zhu
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China
| | - Jie Yang
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China
| | - Yajing Zhou
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China
| | - Lizhi Guo
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China
| | - Laihong Zhang
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China
| | - Shuqing Deng
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China
| | - Shuda Chen
- Neurology Medicine Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, 518000, China
| | - Xianfeng Yu
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
| | - Xuhui Chen
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China
- Department of Neurology, Peking University Shenzhen Hospital, Shenzhen, 518000, China
| | - Ruiyue Zhao
- Department of Nuclear Medicine, The First Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510120, China
| | - Qingyong Wang
- Department of Neurology, University of Chinese Academy of Sciences-Shenzhen Hospital, Shenzhen, 518107, China
| | - Pengcheng Ran
- Department of Nuclear Medicine, Guangdong Hospital of Traditional Chinese Medicine, Guangzhou, 510120, China
| | - Linsen Xu
- Department of Medical Imaging, University of Chinese Academy of Sciences-Shenzhen Hospital, Shenzhen, 518106, China
| | - Liemin Zhou
- Neurology Medicine Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, 518000, China
| | - Kun Sun
- Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen, 518132, China
| | - Xinlu Wang
- Department of Nuclear Medicine, The First Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510120, China
| | - Qiyu Peng
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China
| | - Ying Han
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
- School of Biomedical Engineering, Hainan University, Haikou, 570228, China
- Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, 100053, China
- National Clinical Research Center for Geriatric Diseases, Beijing, 100053, China
| | - Tengfei Guo
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, No.5 Kelian Road, Shenzhen, 518132, China.
- Institute of Biomedical Engineering, Peking University Shenzhen Graduate School, Shenzhen, 518055, China.
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Cheng GR, Liu D, Huang LY, Han GB, Hu FF, Wu ZX, He XM, Huang YW, Yu YF, Xu L, Li JQ, Chen YS, Wei Z, Wu Q, Mei YF, Chen XX, Ou YM, Zhang JJ, Yang ML, Lian PF, Tan W, Xie XY, Zeng Y. Prevalence and risk factors for subjective cognitive decline and the correlation with objective cognition among community-dwelling older adults in China: Results from the Hubei memory and aging cohort study. Alzheimers Dement 2023; 19:5074-5085. [PMID: 37186161 DOI: 10.1002/alz.13047] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 02/18/2023] [Accepted: 02/23/2023] [Indexed: 05/17/2023]
Abstract
INTRODUCTION The prevalence and risk factors for subjective cognitive decline (SCD) and its correlation with objective cognition decline (OCD) among community-dwelling older adults is inconsistent. METHODS Older adults underwent neuropsychological and clinical evaluations to reach a consensus on diagnoses. RESULTS This study included 7486 adults without mild cognitive impairment and dementia (mean age: 71.35 years [standard deviation = 5.40]). The sex-, age-, and residence-adjusted SCD prevalence was 58.33% overall (95% confidence interval: 58.29% to 58.37%), with higher rates of 61.25% and 59.87% in rural and female subgroups, respectively. SCD global and OCD language, SCD memory and OCD global, SCD and OCD memory, and SCD and OCD language were negatively correlated in fully adjusted models. Seven health and lifestyle factors were associated with an increased risk for SCD. DISCUSSION SCD affected 58.33% of older adults and may indicate concurrent OCD, which should prompt the initiation of preventative intervention for dementia. HIGHLIGHTS SCD affects 58.33% of older adults in China. SCD may indicate concurrent objective cognitive decline. Difficulty finding words and memory impairments may indicate a risk for AD. The presence of SCD may prompt preventative treatment initiation of MCI or dementia. Social network factors may be initial targets for the early prevention of SCD.
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Affiliation(s)
- Gui-Rong Cheng
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
- School of Public Health, Wuhan University of Science and Technology, Wuhan, China
| | - Dan Liu
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
- School of Public Health, Wuhan University of Science and Technology, Wuhan, China
| | - Lin-Ya Huang
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Gang-Bin Han
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Fei-Fei Hu
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | | | - Xiao-Ming He
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Yu-Wei Huang
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Ya-Fu Yu
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Lang Xu
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
- School of Public Health, Wuhan University of Science and Technology, Wuhan, China
| | - Jin-Quan Li
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
- School of Public Health, Wuhan University of Science and Technology, Wuhan, China
| | - Yu-Shan Chen
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- School of Public Health, Wuhan University of Science and Technology, Wuhan, China
| | - Zhen Wei
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- School of Public Health, Wuhan University of Science and Technology, Wuhan, China
| | - Qiong Wu
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Yu-Fei Mei
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Xing-Xing Chen
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Yang-Ming Ou
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Jing-Jing Zhang
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Meng-Liu Yang
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Peng-Fei Lian
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Wei Tan
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Xin-Yan Xie
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
- School of Public Health, Wuhan University of Science and Technology, Wuhan, China
| | - Yan Zeng
- Brain Science and Advanced Technology Institute, Wuhan University of Science and Technology, Wuhan, China
- Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
- School of Public Health, Wuhan University of Science and Technology, Wuhan, China
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Peng S, Chen Y, Li J, Wang Y, Liu X, Wang Y, Gu S, Pei M, Zhang P. Correlation among sleep quality, physical frailty and cognitive function of the older adults in China: the mediating role. Front Public Health 2023; 11:1143033. [PMID: 37680277 PMCID: PMC10482234 DOI: 10.3389/fpubh.2023.1143033] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 08/04/2023] [Indexed: 09/09/2023] Open
Abstract
Objective To explore the correlation among sleep quality, physical frailty, and cognitive function in the older adults in community, and to explore the mediating role of sleep quality. Methods A total of 1,182 community-based older adults were investigated with frailty phenotype (FP), Pittsburgh sleep quality index (PISQI), Montreal cognitive assessment (MoCA) and self-made general information questionnaire. Results The incidence of physical frailty among the older adults in the community was 25.8% and the incidence of cognitive decline was 19.5%. Cognitive function was negatively correlated with physical frailty (r = -0.236, p < 0.01) and sleep quality (r = -0.558, p < 0.01). Sleep quality was positively correlated with physical frailty (r = 0.337, p < 0.01). Conclusion The physical frailty of the older adults has a direct prediction effect on cognitive function, and is regulated by the mediating role of sleep quality. Sleep quality partially mediates the relationship between cognitive dysfunction and physical frailty, which is a new insight into the study of cognition and physical frailty in the older adults. In the future, we can take measures to improve the sleep quality of the older adults, so as to reduce the occurrence of cognitive dysfunction and physical frailty of the older adults.
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Affiliation(s)
- Shuzhi Peng
- Graduate School of Nursing, Shanghai University of Medicine and Health Sciences, Shanghai, China
- Graduate School of Nursing, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yanping Chen
- Department of General Surgery, Funing People’s Hospital, Yancheng, Jiangsu, China
- School of Nursing, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Jie Li
- School of Public Health, Anhui Medical University, Hefei, Anhui, China
| | - Yan Wang
- Graduate School of Nursing, Huzhou University, Huzhou, Zhejiang, China
| | - Xingyue Liu
- Graduate School of Nursing, Shanghai University of Medicine and Health Sciences, Shanghai, China
- Graduate School of Nursing, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Ying Wang
- Graduate School of Nursing, Shanghai University of Medicine and Health Sciences, Shanghai, China
- Graduate School of Nursing, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Sainan Gu
- Graduate School of Nursing, Shanghai University of Medicine and Health Sciences, Shanghai, China
- Graduate School of Nursing, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Mengyun Pei
- Graduate School of Nursing, Shanghai University of Medicine and Health Sciences, Shanghai, China
- Graduate School of Nursing, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Peng Zhang
- School of Management, Hainan Medical University, Haikou, Hainan, China
- School of Clinical Medicine, Shanghai University of Medicine and Health Sciences, Shanghai, China
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Chen Q, Chen F, Long C, Zhu Y, Jiang Y, Zhu Z, Lu J, Zhang X, Nedelska Z, Hort J, Zhang B. Spatial navigation is associated with subcortical alterations and progression risk in subjective cognitive decline. Alzheimers Res Ther 2023; 15:86. [PMID: 37098612 PMCID: PMC10127414 DOI: 10.1186/s13195-023-01233-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 04/18/2023] [Indexed: 04/27/2023]
Abstract
BACKGROUND Subjective cognitive decline (SCD) may serve as a symptomatic indicator for preclinical Alzheimer's disease; however, SCD is a heterogeneous entity regarding clinical progression. We aimed to investigate whether spatial navigation could reveal subcortical structural alterations and the risk of progression to objective cognitive impairment in SCD individuals. METHODS One hundred and eighty participants were enrolled: those with SCD (n = 80), normal controls (NCs, n = 77), and mild cognitive impairment (MCI, n = 23). SCD participants were further divided into the SCD-good (G-SCD, n = 40) group and the SCD-bad (B-SCD, n = 40) group according to their spatial navigation performance. Volumes of subcortical structures were calculated and compared among the four groups, including basal forebrain, thalamus, caudate, putamen, pallidum, hippocampus, amygdala, and accumbens. Topological properties of the subcortical structural covariance network were also calculated. With an interval of 1.5 years ± 12 months of follow-up, the progression rate to MCI was compared between the G-SCD and B-SCD groups. RESULTS Volumes of the basal forebrain, the right hippocampus, and their respective subfields differed significantly among the four groups (p < 0.05, false discovery rate corrected). The B-SCD group showed lower volumes in the basal forebrain than the G-SCD group, especially in the Ch4p and Ch4a-i subfields. Furthermore, the structural covariance network of the basal forebrain and right hippocampal subfields showed that the B-SCD group had a larger Lambda than the G-SCD group, which suggested weakened network integration in the B-SCD group. At follow-up, the B-SCD group had a significantly higher conversion rate to MCI than the G-SCD group. CONCLUSION Compared to SCD participants with good spatial navigation performance, SCD participants with bad performance showed lower volumes in the basal forebrain, a reorganized structural covariance network of subcortical nuclei, and an increased risk of progression to MCI. Our findings indicated that spatial navigation may have great potential to identify SCD subjects at higher risk of clinical progression, which may contribute to making more precise clinical decisions for SCD individuals who seek medical help.
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Affiliation(s)
- Qian Chen
- Department of Radiology, Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Futao Chen
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Cong Long
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Yajing Zhu
- Department of Radiology, Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Yaoxian Jiang
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Zhengyang Zhu
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Jiaming Lu
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Xin Zhang
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Zuzana Nedelska
- Memory Clinic, Department of Neurology, 2nd Faculty of Medicine, Charles University, University Hospital Motol, Prague, Czechia
| | - Jakub Hort
- Memory Clinic, Department of Neurology, 2nd Faculty of Medicine, Charles University, University Hospital Motol, Prague, Czechia
| | - Bing Zhang
- Department of Radiology, Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, 210008, China.
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China.
- Medical Imaging Center, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China.
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China.
- Jiangsu Key Laboratory of Molecular Medicine, Nanjing, China.
- Institute of Brain Science, Nanjing University, Nanjing, China.
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Yang X, Zhou X, Xin F, Becker B, Linden D, Hernaus D. Age-dependent changes in the dynamic functional organization of the brain at rest: a cross-cultural replication approach. Cereb Cortex 2023; 33:6394-6406. [PMID: 36642496 PMCID: PMC10183740 DOI: 10.1093/cercor/bhac512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 12/02/2022] [Accepted: 12/03/2022] [Indexed: 01/17/2023] Open
Abstract
Age-associated changes in brain function play an important role in the development of neurodegenerative diseases. Although previous work has examined age-related changes in static functional connectivity, accumulating evidence suggests that advancing age is especially associated with alterations in the dynamic interactions and transitions between different brain states, which hitherto have received less attention. Conclusions of previous studies in this domain are moreover limited by suboptimal replicability of resting-state functional magnetic resonance imaging (fMRI) and culturally homogenous cohorts. Here, we investigate the robustness of age-associated changes in dynamic functional connectivity (dFC) by capitalizing on the availability of fMRI cohorts from two cultures (Western European and Chinese). In both the LEMON (Western European) and SALD (Chinese) cohorts, we consistently identify two distinct states: a more frequent segregated within-network connectivity state (state I) and a less frequent integrated between-network connectivity state (state II). Moreover, in both these cohorts, older (55-80 years) compared to younger participants (20-35 years) exhibited lower occurrence of and spent less time in state I. Older participants also tended to exhibit more transitions between networks and greater variance in global efficiency. Overall, our cross-cultural replication of age-associated changes in dFC metrics implies that advancing age is robustly associated with a reorganization of dynamic brain activation that favors the use of less functionally specific networks.
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Affiliation(s)
- Xi Yang
- Department of Psychiatry & Neuropsychology, School for Mental Health and NeuroScience MHeNS, Maastricht University, Minderbroedersberg 4-6, 6211 LK Maastricht, The Netherlands
| | - Xinqi Zhou
- Institute of Brain and Psychological Sciences, Sichuan Normal University, 610066 Chengdu, Sichuan, China
| | - Fei Xin
- School of Psychology, Shenzhen University, 518060 Shenzhen, Guangdong, China
| | - Benjamin Becker
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Xiyuan Ave, West Hi-Tech Zone, 611731 Chengdu, Sichuan, China
| | - David Linden
- Department of Psychiatry & Neuropsychology, School for Mental Health and NeuroScience MHeNS, Maastricht University, Minderbroedersberg 4-6, 6211 LK Maastricht, The Netherlands
| | - Dennis Hernaus
- Department of Psychiatry & Neuropsychology, School for Mental Health and NeuroScience MHeNS, Maastricht University, Minderbroedersberg 4-6, 6211 LK Maastricht, The Netherlands
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