1
|
Ficek-Tani B, Horien C, Ju S, Xu W, Li N, Lacadie C, Shen X, Scheinost D, Constable T, Fredericks C. Sex differences in default mode network connectivity in healthy aging adults. Cereb Cortex 2023; 33:6139-6151. [PMID: 36563018 PMCID: PMC10183749 DOI: 10.1093/cercor/bhac491] [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: 07/20/2022] [Revised: 11/18/2022] [Accepted: 11/20/2022] [Indexed: 12/24/2022] Open
Abstract
Women show an increased lifetime risk of Alzheimer's disease (AD) compared with men. Characteristic brain connectivity changes, particularly within the default mode network (DMN), have been associated with both symptomatic and preclinical AD, but the impact of sex on DMN function throughout aging is poorly understood. We investigated sex differences in DMN connectivity over the lifespan in 595 cognitively healthy participants from the Human Connectome Project-Aging cohort. We used the intrinsic connectivity distribution (a robust voxel-based metric of functional connectivity) and a seed connectivity approach to determine sex differences within the DMN and between the DMN and whole brain. Compared with men, women demonstrated higher connectivity with age in posterior DMN nodes and lower connectivity in the medial prefrontal cortex. Differences were most prominent in the decades surrounding menopause. Seed-based analysis revealed higher connectivity in women from the posterior cingulate to angular gyrus, which correlated with neuropsychological measures of declarative memory, and hippocampus. Taken together, we show significant sex differences in DMN subnetworks over the lifespan, including patterns in aging women that resemble changes previously seen in preclinical AD. These findings highlight the importance of considering sex in neuroimaging studies of aging and neurodegeneration.
Collapse
Affiliation(s)
- Bronte Ficek-Tani
- Department of Neurology, Yale School of Medicine, New Haven, CT 06520, United States
| | - Corey Horien
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06520, United States
| | - Suyeon Ju
- Department of Neurology, Yale School of Medicine, New Haven, CT 06520, United States
| | - Wanwan Xu
- Department of Biostatistics, Yale School of Medicine, New Haven, CT 06520, United States
| | - Nancy Li
- Department of Neurology, Yale School of Medicine, New Haven, CT 06520, United States
| | - Cheryl Lacadie
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, United States
| | - Xilin Shen
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, United States
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, United States
| | - Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, United States
| | - Carolyn Fredericks
- Department of Neurology, Yale School of Medicine, New Haven, CT 06520, United States
| |
Collapse
|
2
|
Bao H, Cao J, Chen M, Chen M, Chen W, Chen X, Chen Y, Chen Y, Chen Y, Chen Z, Chhetri JK, Ding Y, Feng J, Guo J, Guo M, He C, Jia Y, Jiang H, Jing Y, Li D, Li J, Li J, Liang Q, Liang R, Liu F, Liu X, Liu Z, Luo OJ, Lv J, Ma J, Mao K, Nie J, Qiao X, Sun X, Tang X, Wang J, Wang Q, Wang S, Wang X, Wang Y, Wang Y, Wu R, Xia K, Xiao FH, Xu L, Xu Y, Yan H, Yang L, Yang R, Yang Y, Ying Y, Zhang L, Zhang W, Zhang W, Zhang X, Zhang Z, Zhou M, Zhou R, Zhu Q, Zhu Z, Cao F, Cao Z, Chan P, Chen C, Chen G, Chen HZ, Chen J, Ci W, Ding BS, Ding Q, Gao F, Han JDJ, Huang K, Ju Z, Kong QP, Li J, Li J, Li X, Liu B, Liu F, Liu L, Liu Q, Liu Q, Liu X, Liu Y, Luo X, Ma S, Ma X, Mao Z, Nie J, Peng Y, Qu J, Ren J, Ren R, Song M, Songyang Z, Sun YE, Sun Y, Tian M, Wang S, Wang S, Wang X, Wang X, Wang YJ, Wang Y, Wong CCL, Xiang AP, Xiao Y, Xie Z, Xu D, Ye J, Yue R, Zhang C, Zhang H, Zhang L, Zhang W, Zhang Y, Zhang YW, Zhang Z, Zhao T, Zhao Y, Zhu D, Zou W, Pei G, Liu GH. Biomarkers of aging. SCIENCE CHINA. LIFE SCIENCES 2023; 66:893-1066. [PMID: 37076725 PMCID: PMC10115486 DOI: 10.1007/s11427-023-2305-0] [Citation(s) in RCA: 77] [Impact Index Per Article: 77.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 02/27/2023] [Indexed: 04/21/2023]
Abstract
Aging biomarkers are a combination of biological parameters to (i) assess age-related changes, (ii) track the physiological aging process, and (iii) predict the transition into a pathological status. Although a broad spectrum of aging biomarkers has been developed, their potential uses and limitations remain poorly characterized. An immediate goal of biomarkers is to help us answer the following three fundamental questions in aging research: How old are we? Why do we get old? And how can we age slower? This review aims to address this need. Here, we summarize our current knowledge of biomarkers developed for cellular, organ, and organismal levels of aging, comprising six pillars: physiological characteristics, medical imaging, histological features, cellular alterations, molecular changes, and secretory factors. To fulfill all these requisites, we propose that aging biomarkers should qualify for being specific, systemic, and clinically relevant.
Collapse
Affiliation(s)
- Hainan Bao
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
| | - Jiani Cao
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Mengting Chen
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, 410008, China
- Hunan Key Laboratory of Aging Biology, Xiangya Hospital, Central South University, Changsha, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Min Chen
- Clinic Center of Human Gene Research, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Clinical Research Center of Metabolic and Cardiovascular Disease, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Key Laboratory of Metabolic Abnormalities and Vascular Aging, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Wei Chen
- Stem Cell Translational Research Center, Tongji Hospital, Tongji University School of Medicine, Shanghai, 200065, China
| | - Xiao Chen
- Department of Nuclear Medicine, Daping Hospital, Third Military Medical University, Chongqing, 400042, China
| | - Yanhao Chen
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yu Chen
- Shanghai Key Laboratory of Maternal Fetal Medicine, Clinical and Translational Research Center of Shanghai First Maternity and Infant Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Yutian Chen
- The Department of Endovascular Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Zhiyang Chen
- Key Laboratory of Regenerative Medicine of Ministry of Education, Institute of Ageing and Regenerative Medicine, Jinan University, Guangzhou, 510632, China
| | - Jagadish K Chhetri
- National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
| | - Yingjie Ding
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Junlin Feng
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Jun Guo
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, 100730, China
| | - Mengmeng Guo
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China
| | - Chuting He
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Yujuan Jia
- Department of Neurology, First Affiliated Hospital, Shanxi Medical University, Taiyuan, 030001, China
| | - Haiping Jiang
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Ying Jing
- Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China
| | - Dingfeng Li
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230036, China
| | - Jiaming Li
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jingyi Li
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Qinhao Liang
- College of Life Sciences, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430072, China
| | - Rui Liang
- Research Institute of Transplant Medicine, Organ Transplant Center, NHC Key Laboratory for Critical Care Medicine, Tianjin First Central Hospital, Nankai University, Tianjin, 300384, China
| | - Feng Liu
- MOE Key Laboratory of Gene Function and Regulation, Guangzhou Key Laboratory of Healthy Aging Research, School of Life Sciences, Institute of Healthy Aging Research, Sun Yat-sen University, Guangzhou, 510275, China
| | - Xiaoqian Liu
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Zuojun Liu
- School of Life Sciences, Hainan University, Haikou, 570228, China
| | - Oscar Junhong Luo
- Department of Systems Biomedical Sciences, School of Medicine, Jinan University, Guangzhou, 510632, China
| | - Jianwei Lv
- School of Life Sciences, Xiamen University, Xiamen, 361102, China
| | - Jingyi Ma
- The State Key Laboratory of Organ Failure Research, National Clinical Research Center of Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Kehang Mao
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China
| | - Jiawei Nie
- Shanghai Institute of Hematology, State Key Laboratory for Medical Genomics, National Research Center for Translational Medicine (Shanghai), International Center for Aging and Cancer, Collaborative Innovation Center of Hematology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xinhua Qiao
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Xinpei Sun
- Peking University International Cancer Institute, Health Science Center, Peking University, Beijing, 100101, China
| | - Xiaoqiang Tang
- Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE, State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu, 610041, China
| | - Jianfang Wang
- Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Qiaoran Wang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Siyuan Wang
- Clinical Research Institute, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, 100730, China
| | - Xuan Wang
- Hepatobiliary and Pancreatic Center, Medical Research Center, Beijing Tsinghua Changgung Hospital, Beijing, 102218, China
| | - Yaning Wang
- Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
| | - Yuhan Wang
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Rimo Wu
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, China
| | - Kai Xia
- Center for Stem Cell Biologyand Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-sen University, Guangzhou, 510080, China
- National-Local Joint Engineering Research Center for Stem Cells and Regenerative Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
| | - Fu-Hui Xiao
- CAS Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China
- State Key Laboratory of Genetic Resources and Evolution, Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging Study, KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
| | - Lingyan Xu
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Yingying Xu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
| | - Haoteng Yan
- Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China
| | - Liang Yang
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou, 510530, China
| | - Ruici Yang
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yuanxin Yang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 201210, China
| | - Yilin Ying
- Department of Geriatrics, Medical Center on Aging of Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
- International Laboratory in Hematology and Cancer, Shanghai Jiao Tong University School of Medicine/Ruijin Hospital, Shanghai, 200025, China
| | - Le Zhang
- Gerontology Center of Hubei Province, Wuhan, 430000, China
- Institute of Gerontology, Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Weiwei Zhang
- Department of Cardiology, The Second Medical Centre, Chinese PLA General Hospital, National Clinical Research Center for Geriatric Diseases, Beijing, 100853, China
| | - Wenwan Zhang
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Xing Zhang
- Key Laboratory of Ministry of Education, School of Aerospace Medicine, Fourth Military Medical University, Xi'an, 710032, China
| | - Zhuo Zhang
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
- Research Unit of New Techniques for Live-cell Metabolic Imaging, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Min Zhou
- Department of Endocrinology, Endocrinology Research Center, Xiangya Hospital of Central South University, Changsha, 410008, China
| | - Rui Zhou
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Qingchen Zhu
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Zhengmao Zhu
- Department of Genetics and Cell Biology, College of Life Science, Nankai University, Tianjin, 300071, China
- Haihe Laboratory of Cell Ecosystem, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China
| | - Feng Cao
- Department of Cardiology, The Second Medical Centre, Chinese PLA General Hospital, National Clinical Research Center for Geriatric Diseases, Beijing, 100853, China.
| | - Zhongwei Cao
- State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu, 610041, China.
| | - Piu Chan
- National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
| | - Chang Chen
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Guobing Chen
- Department of Microbiology and Immunology, School of Medicine, Jinan University, Guangzhou, 510632, China.
- Guangdong-Hong Kong-Macau Great Bay Area Geroscience Joint Laboratory, Guangzhou, 510000, China.
| | - Hou-Zao Chen
- Department of Biochemistryand Molecular Biology, State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100005, China.
| | - Jun Chen
- Peking University Research Center on Aging, Beijing Key Laboratory of Protein Posttranslational Modifications and Cell Function, Department of Biochemistry and Molecular Biology, Department of Integration of Chinese and Western Medicine, School of Basic Medical Science, Peking University, Beijing, 100191, China.
| | - Weimin Ci
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
| | - Bi-Sen Ding
- State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu, 610041, China.
| | - Qiurong Ding
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Feng Gao
- Key Laboratory of Ministry of Education, School of Aerospace Medicine, Fourth Military Medical University, Xi'an, 710032, China.
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China.
| | - Kai Huang
- Clinic Center of Human Gene Research, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Hubei Clinical Research Center of Metabolic and Cardiovascular Disease, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Hubei Key Laboratory of Metabolic Abnormalities and Vascular Aging, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Department of Cardiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
| | - Zhenyu Ju
- Key Laboratory of Regenerative Medicine of Ministry of Education, Institute of Ageing and Regenerative Medicine, Jinan University, Guangzhou, 510632, China.
| | - Qing-Peng Kong
- CAS Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China.
- State Key Laboratory of Genetic Resources and Evolution, Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging Study, KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.
| | - Ji Li
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, 410008, China.
- Hunan Key Laboratory of Aging Biology, Xiangya Hospital, Central South University, Changsha, 410008, China.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China.
| | - Jian Li
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, 100730, China.
| | - Xin Li
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Baohua Liu
- School of Basic Medical Sciences, Shenzhen University Medical School, Shenzhen, 518060, China.
| | - Feng Liu
- Metabolic Syndrome Research Center, The Second Xiangya Hospital, Central South Unversity, Changsha, 410011, China.
| | - Lin Liu
- Department of Genetics and Cell Biology, College of Life Science, Nankai University, Tianjin, 300071, China.
- Haihe Laboratory of Cell Ecosystem, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China.
- Institute of Translational Medicine, Tianjin Union Medical Center, Nankai University, Tianjin, 300000, China.
- State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin, 300350, China.
| | - Qiang Liu
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230036, China.
| | - Qiang Liu
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, 300052, China.
- Tianjin Institute of Immunology, Tianjin Medical University, Tianjin, 300070, China.
| | - Xingguo Liu
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou, 510530, China.
| | - Yong Liu
- College of Life Sciences, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430072, China.
| | - Xianghang Luo
- Department of Endocrinology, Endocrinology Research Center, Xiangya Hospital of Central South University, Changsha, 410008, China.
| | - Shuai Ma
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Xinran Ma
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
| | - Zhiyong Mao
- Shanghai Key Laboratory of Maternal Fetal Medicine, Clinical and Translational Research Center of Shanghai First Maternity and Infant Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Jing Nie
- The State Key Laboratory of Organ Failure Research, National Clinical Research Center of Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
| | - Yaojin Peng
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Jing Qu
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Jie Ren
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Ruibao Ren
- Shanghai Institute of Hematology, State Key Laboratory for Medical Genomics, National Research Center for Translational Medicine (Shanghai), International Center for Aging and Cancer, Collaborative Innovation Center of Hematology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
- International Center for Aging and Cancer, Hainan Medical University, Haikou, 571199, China.
| | - Moshi Song
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Zhou Songyang
- MOE Key Laboratory of Gene Function and Regulation, Guangzhou Key Laboratory of Healthy Aging Research, School of Life Sciences, Institute of Healthy Aging Research, Sun Yat-sen University, Guangzhou, 510275, China.
- Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China.
| | - Yi Eve Sun
- Stem Cell Translational Research Center, Tongji Hospital, Tongji University School of Medicine, Shanghai, 200065, China.
| | - Yu Sun
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China.
- Department of Medicine and VAPSHCS, University of Washington, Seattle, WA, 98195, USA.
| | - Mei Tian
- Human Phenome Institute, Fudan University, Shanghai, 201203, China.
| | - Shusen Wang
- Research Institute of Transplant Medicine, Organ Transplant Center, NHC Key Laboratory for Critical Care Medicine, Tianjin First Central Hospital, Nankai University, Tianjin, 300384, China.
| | - Si Wang
- Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
- Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China.
| | - Xia Wang
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China.
| | - Xiaoning Wang
- Institute of Geriatrics, The second Medical Center, Beijing Key Laboratory of Aging and Geriatrics, National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100853, China.
| | - Yan-Jiang Wang
- Department of Neurology and Center for Clinical Neuroscience, Daping Hospital, Third Military Medical University, Chongqing, 400042, China.
| | - Yunfang Wang
- Hepatobiliary and Pancreatic Center, Medical Research Center, Beijing Tsinghua Changgung Hospital, Beijing, 102218, China.
| | - Catherine C L Wong
- Clinical Research Institute, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, 100730, China.
| | - Andy Peng Xiang
- Center for Stem Cell Biologyand Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-sen University, Guangzhou, 510080, China.
- National-Local Joint Engineering Research Center for Stem Cells and Regenerative Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
| | - Yichuan Xiao
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Zhengwei Xie
- Peking University International Cancer Institute, Health Science Center, Peking University, Beijing, 100101, China.
- Beijing & Qingdao Langu Pharmaceutical R&D Platform, Beijing Gigaceuticals Tech. Co. Ltd., Beijing, 100101, China.
| | - Daichao Xu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 201210, China.
| | - Jing Ye
- Department of Geriatrics, Medical Center on Aging of Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
- International Laboratory in Hematology and Cancer, Shanghai Jiao Tong University School of Medicine/Ruijin Hospital, Shanghai, 200025, China.
| | - Rui Yue
- Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Cuntai Zhang
- Gerontology Center of Hubei Province, Wuhan, 430000, China.
- Institute of Gerontology, Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
| | - Hongbo Zhang
- Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
| | - Liang Zhang
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Weiqi Zhang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Yong Zhang
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, China.
- The State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China.
| | - Yun-Wu Zhang
- Fujian Provincial Key Laboratory of Neurodegenerative Disease and Aging Research, Institute of Neuroscience, School of Medicine, Xiamen University, Xiamen, 361102, China.
| | - Zhuohua Zhang
- Key Laboratory of Molecular Precision Medicine of Hunan Province and Center for Medical Genetics, Institute of Molecular Precision Medicine, Xiangya Hospital, Central South University, Changsha, 410078, China.
- Department of Neurosciences, Hengyang Medical School, University of South China, Hengyang, 421001, China.
| | - Tongbiao Zhao
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Yuzheng Zhao
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China.
- Research Unit of New Techniques for Live-cell Metabolic Imaging, Chinese Academy of Medical Sciences, Beijing, 100730, China.
| | - Dahai Zhu
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, China.
- The State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China.
| | - Weiguo Zou
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Gang Pei
- Shanghai Key Laboratory of Signaling and Disease Research, Laboratory of Receptor-Based Biomedicine, The Collaborative Innovation Center for Brain Science, School of Life Sciences and Technology, Tongji University, Shanghai, 200070, China.
| | - Guang-Hui Liu
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China.
| |
Collapse
|
3
|
Huang J, Jung JY, Nam CS. Estimating effective connectivity in Alzheimer's disease progression: A dynamic causal modeling study. Front Hum Neurosci 2022; 16:1060936. [PMID: 36590062 PMCID: PMC9797690 DOI: 10.3389/fnhum.2022.1060936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 11/24/2022] [Indexed: 12/23/2022] Open
Abstract
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.
Collapse
Affiliation(s)
- Jiali Huang
- Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC, United States
| | - Jae-Yoon Jung
- Department of Industrial and Management Systems Engineering, Kyung Hee University, Yongin-si, South Korea,Department of Big Data Analytics, Kyung Hee University, Yongin-si, South Korea
| | - Chang S. Nam
- Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC, United States,Department of Industrial and Management Systems Engineering, Kyung Hee University, Yongin-si, South Korea,*Correspondence: Chang S. Nam
| |
Collapse
|
4
|
Mary A, Bastin C, Lina JM, Rauchs G. Editorial: The impact of age-related changes in brain network organization and sleep on memory. Front Aging Neurosci 2022; 14:1049278. [PMID: 36268189 PMCID: PMC9577460 DOI: 10.3389/fnagi.2022.1049278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 09/21/2022] [Indexed: 12/01/2022] Open
Affiliation(s)
- Alison Mary
- UR2NF—Neuropsychology and Functional Neuroimaging Research Unit at CRCN - Center for Research in Cognition and Neurosciences and UNI—ULB Neuroscience Institute, Université Libre de Bruxelles (ULB), Brussels, Belgium
- *Correspondence: Alison Mary
| | - Christine Bastin
- GIGA-Cyclotron Research Centre In Vivo Imaging & Psychology and Neuroscience of Cognition, University of Liège, Liège, Belgium
| | - Jean-Marc Lina
- Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal, Montreal, QC, Canada
- Department of Electrical Engineering, École de Technologie Supérieure, Montreal, QC, Canada
- Centre de Recherches Mathématiques, Université de Montréal, Montreal, QC, Canada
| | - Géraldine Rauchs
- Normandie Univ, University of Caen, Institut national de la santé et de la recherche médicale, U1237, Physiopathology and Imaging of Neurological Disorders (PhIND), GIP Cyceron, Institut Blood and Brain @ Caen-Normandie, Caen, France
| |
Collapse
|
5
|
Albertson AJ, Landsness EC, Tang MJ, Yan P, Miao H, Rosenthal ZP, Kim B, Culver JC, Bauer AQ, Lee JM. Normal aging in mice is associated with a global reduction in cortical spectral power and network-specific declines in functional connectivity. Neuroimage 2022; 257:119287. [PMID: 35594811 PMCID: PMC9627742 DOI: 10.1016/j.neuroimage.2022.119287] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 03/31/2022] [Accepted: 05/05/2022] [Indexed: 11/16/2022] Open
Abstract
Normal aging is associated with a variety of neurologic changes including declines in cognition, memory, and motor activity. These declines correlate with neuronal changes in synaptic structure and function. Degradation of brain network activity and connectivity represents a likely mediator of age-related functional deterioration resulting from these neuronal changes. Human studies have demonstrated both general decreases in spontaneous cortical activity and disruption of cortical networks with aging. Current techniques used to study cerebral network activity are hampered either by limited spatial resolution (e.g. electroencephalography, EEG) or limited temporal resolution (e.g., functional magnetic resonance imaging, fMRI). Here we utilize mesoscale imaging of neuronal activity in Thy1-GCaMP6f mice to characterize neuronal network changes in aging with high spatial resolution across a wide frequency range. We show that while evoked activity is unchanged with aging, spontaneous neuronal activity decreases across a wide frequency range (0.01–4 Hz) involving all regions of the cortex. In contrast to this global reduction in cortical power, we found that aging is associated with functional connectivity (FC) deterioration of select networks including somatomotor, cingulate, and retrosplenial nodes. These changes are corroborated by reductions in homotopic FC and node degree within somatomotor and visual cortices. Finally, we found that whole-cortex delta power and delta band node degree correlate with exploratory activity in young but not aged animals. Together these data suggest that aging is associated with global declines in spontaneous cortical activity and focal deterioration of network connectivity, and that these reductions may be associated with age-related behavioral declines.
Collapse
Affiliation(s)
- Asher J Albertson
- Department of Neurology, Washington University School of Medicine, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Eric C Landsness
- Department of Neurology, Washington University School of Medicine, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Michelle J Tang
- Duke University School of Medicine, DUMC 3878, Durham, NC 27710, USA
| | - Ping Yan
- Department of Neurology, Washington University School of Medicine, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Hanyang Miao
- Department of Neurology, Washington University School of Medicine, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Zachary P Rosenthal
- Medical Scientist Training Program, Washington University School of Medicine, 660 S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Byungchan Kim
- Boston University School of Medicine, 72 East Concord St., Boston, MA 02118, USA
| | - Joseph C Culver
- Department of Radiology, Washington University School of Medicine, 660 S. Euclid Ave, St. Louis, MO, 63110, USA; Department of Biomedical Engineering, Washington University, 1 Brookings Drive, St. Louis, MO, 63130, USA; Department of Physics, Washington University, 1 Brookings Drive, St. Louis, MO 63130, USA; Department of Electrical and Systems Engineering, Washington University, 1 Brookings Drive, St. Louis, MO 63130, USA
| | - Adam Q Bauer
- Department of Radiology, Washington University School of Medicine, 660 S. Euclid Ave, St. Louis, MO, 63110, USA; Department of Biomedical Engineering, Washington University, 1 Brookings Drive, St. Louis, MO, 63130, USA.
| | - Jin-Moo Lee
- Department of Neurology, Washington University School of Medicine, 660 S. Euclid Ave, St. Louis, MO, 63110, USA; Department of Radiology, Washington University School of Medicine, 660 S. Euclid Ave, St. Louis, MO, 63110, USA; Department of Biomedical Engineering, Washington University, 1 Brookings Drive, St. Louis, MO, 63130, USA.
| |
Collapse
|
6
|
A ketogenic intervention improves dorsal attention network functional and structural connectivity in mild cognitive impairment. Neurobiol Aging 2022; 115:77-87. [DOI: 10.1016/j.neurobiolaging.2022.04.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Revised: 03/21/2022] [Accepted: 04/04/2022] [Indexed: 12/14/2022]
|
7
|
Grober E, Lipton RB, Sperling RA, Papp KV, Johnson KA, Rentz DM, Veroff AE, Aisen PS, Ezzati A. Associations of Stages of Objective Memory Impairment With Amyloid PET and Structural MRI: The A4 Study. Neurology 2022; 98:e1327-e1336. [PMID: 35197359 PMCID: PMC8967421 DOI: 10.1212/wnl.0000000000200046] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 01/03/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES The goal of this work was to investigate the neuroimaging correlates of the Stages of Objective Memory Impairment (SOMI) system operationalized with the Free and Cued Selective Reminding Test (FCSRT), a widely used episodic memory measure. METHODS The FCSRT begins with a study phase in which items (e.g., grapes) are identified in response to unique semantic cues (e.g., fruit) that are used in the test phase to prompt recall of items not retrieved by free recall. There are 3 test trials of the 16 items (maximum 48). Data from 4,484 cognitively unimpaired participants from the Anti-Amyloid Treatment in Asymptomatic Alzheimer's (A4) study were used. All participants had amyloid PET imaging, and a subset of 1,262 β-amyloid (Aβ)-positive had structural MRIs. We compared the Aβ mean cortical standardized uptake value ratio (SUVR) and volumetric measures of hippocampus, parahippocampal gyrus, entorhinal cortex, and inferior temporal cortex between the 5 SOMI stages. RESULTS Participants had a mean age of 71.3 (SD 4.6) years; 40.6% were male; and 34.6% were APOE ε4 positive. Half had no memory impairment; the other half had retrieval deficits, storage limitations, or both. Analysis of covariance in the entire sample while controlling for age, sex, education, and APOE ε4 showed that individuals in higher SOMI stages had higher global amyloid SUVR (p < 0.001). Both SOMI-4 and -3 subgroups had higher amyloid SUVR than SOMI-0 and SOMI-1 subgroups. Individuals in higher SOMI stages had smaller hippocampal volume (p = 0.003), entorhinal cortex (p < 0.05), and inferior temporal lobes (p < 0.05), but there was no difference between parahippocampal gyrus volume of different SOMI stages. Pairwise comparison of SOMI subgroups showed that the SOMI-4, -3, and -2 subgroups had smaller hippocampal volume than the SOMI-0 and -1 subgroup. The SOMI-4 subgroup had significantly smaller entorhinal cortex and smaller inferior temporal lobe compared to all other groups. DISCUSSION Presence of Alzheimer disease pathology is closely related to memory impairment according to SOMI stages in the cognitively unimpaired sample of A4. Results from structural MRIs suggest that memory storage impairment (SOMI-3 and -4) is present when there is widespread medial temporal lobe atrophy. TRIAL REGISTRATION INFORMATION ClinicalTrials.gov identifier: NCT02008357. CLASSIFICATION OF EVIDENCE This study provides Class I evidence that, in normal older individuals, higher stages of memory impairment assessed with FCSRT were associated with higher amyloid imaging burden and lower volume of hippocampus, entorhinal cortex, and inferior temporal lobes.
Collapse
Affiliation(s)
- Ellen Grober
- From the Department of Neurology (E.G., R.B.L., A.E.V., A.E.), Albert Einstein College of Medicine, Bronx, NY; Harvard Aging Brain Study (R.A.S., K.V.P., K.A.J., D.M.R.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School; Center for Alzheimer Research and Treatment (R.A.S., K.V.P., K.A.J., D.M.R.), Department of Neurology, Brigham and Women's Hospital, Boston, MA; and Alzheimer's Therapeutic Research Institute (P.S.A.), University of Southern California, San Diego.
| | - Richard B Lipton
- From the Department of Neurology (E.G., R.B.L., A.E.V., A.E.), Albert Einstein College of Medicine, Bronx, NY; Harvard Aging Brain Study (R.A.S., K.V.P., K.A.J., D.M.R.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School; Center for Alzheimer Research and Treatment (R.A.S., K.V.P., K.A.J., D.M.R.), Department of Neurology, Brigham and Women's Hospital, Boston, MA; and Alzheimer's Therapeutic Research Institute (P.S.A.), University of Southern California, San Diego
| | - Reisa A Sperling
- From the Department of Neurology (E.G., R.B.L., A.E.V., A.E.), Albert Einstein College of Medicine, Bronx, NY; Harvard Aging Brain Study (R.A.S., K.V.P., K.A.J., D.M.R.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School; Center for Alzheimer Research and Treatment (R.A.S., K.V.P., K.A.J., D.M.R.), Department of Neurology, Brigham and Women's Hospital, Boston, MA; and Alzheimer's Therapeutic Research Institute (P.S.A.), University of Southern California, San Diego
| | - Kathryn V Papp
- From the Department of Neurology (E.G., R.B.L., A.E.V., A.E.), Albert Einstein College of Medicine, Bronx, NY; Harvard Aging Brain Study (R.A.S., K.V.P., K.A.J., D.M.R.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School; Center for Alzheimer Research and Treatment (R.A.S., K.V.P., K.A.J., D.M.R.), Department of Neurology, Brigham and Women's Hospital, Boston, MA; and Alzheimer's Therapeutic Research Institute (P.S.A.), University of Southern California, San Diego
| | - Keith A Johnson
- From the Department of Neurology (E.G., R.B.L., A.E.V., A.E.), Albert Einstein College of Medicine, Bronx, NY; Harvard Aging Brain Study (R.A.S., K.V.P., K.A.J., D.M.R.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School; Center for Alzheimer Research and Treatment (R.A.S., K.V.P., K.A.J., D.M.R.), Department of Neurology, Brigham and Women's Hospital, Boston, MA; and Alzheimer's Therapeutic Research Institute (P.S.A.), University of Southern California, San Diego
| | - Dorene M Rentz
- From the Department of Neurology (E.G., R.B.L., A.E.V., A.E.), Albert Einstein College of Medicine, Bronx, NY; Harvard Aging Brain Study (R.A.S., K.V.P., K.A.J., D.M.R.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School; Center for Alzheimer Research and Treatment (R.A.S., K.V.P., K.A.J., D.M.R.), Department of Neurology, Brigham and Women's Hospital, Boston, MA; and Alzheimer's Therapeutic Research Institute (P.S.A.), University of Southern California, San Diego
| | - Amy E Veroff
- From the Department of Neurology (E.G., R.B.L., A.E.V., A.E.), Albert Einstein College of Medicine, Bronx, NY; Harvard Aging Brain Study (R.A.S., K.V.P., K.A.J., D.M.R.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School; Center for Alzheimer Research and Treatment (R.A.S., K.V.P., K.A.J., D.M.R.), Department of Neurology, Brigham and Women's Hospital, Boston, MA; and Alzheimer's Therapeutic Research Institute (P.S.A.), University of Southern California, San Diego
| | - Paul S Aisen
- From the Department of Neurology (E.G., R.B.L., A.E.V., A.E.), Albert Einstein College of Medicine, Bronx, NY; Harvard Aging Brain Study (R.A.S., K.V.P., K.A.J., D.M.R.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School; Center for Alzheimer Research and Treatment (R.A.S., K.V.P., K.A.J., D.M.R.), Department of Neurology, Brigham and Women's Hospital, Boston, MA; and Alzheimer's Therapeutic Research Institute (P.S.A.), University of Southern California, San Diego
| | - Ali Ezzati
- From the Department of Neurology (E.G., R.B.L., A.E.V., A.E.), Albert Einstein College of Medicine, Bronx, NY; Harvard Aging Brain Study (R.A.S., K.V.P., K.A.J., D.M.R.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School; Center for Alzheimer Research and Treatment (R.A.S., K.V.P., K.A.J., D.M.R.), Department of Neurology, Brigham and Women's Hospital, Boston, MA; and Alzheimer's Therapeutic Research Institute (P.S.A.), University of Southern California, San Diego
| |
Collapse
|
8
|
Sevinc G, Rusche J, Wong B, Datta T, Kaufman R, Gutz SE, Schneider M, Todorova N, Gaser C, Thomalla G, Rentz D, Dickerson BD, Lazar SW. Mindfulness Training Improves Cognition and Strengthens Intrinsic Connectivity Between the Hippocampus and Posteromedial Cortex in Healthy Older Adults. Front Aging Neurosci 2021; 13:702796. [PMID: 34512305 PMCID: PMC8430251 DOI: 10.3389/fnagi.2021.702796] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 08/09/2021] [Indexed: 01/02/2023] Open
Abstract
Maintaining optimal cognitive functioning throughout the lifespan is a public health priority. Evaluation of cognitive outcomes following interventions to promote and preserve brain structure and function in older adults, and associated neural mechanisms, are therefore of critical importance. In this randomized controlled trial, we examined the behavioral and neural outcomes following mindfulness training (n = 72), compared to a cognitive fitness program (n = 74) in healthy, cognitively normal, older adults (65-80 years old). To assess cognitive functioning, we used the Preclinical Alzheimer Cognitive Composite (PACC), which combines measures of episodic memory, executive function, and global cognition. We hypothesized that mindfulness training would enhance cognition, increase intrinsic functional connectivity measured with magnetic resonance imaging (MRI) between the hippocampus and posteromedial cortex, as well as promote increased gray matter volume within those regions. Following the 8-week intervention, the mindfulness training group showed improved performance on the PACC, while the control group did not. Furthermore, following mindfulness training, greater improvement on the PACC was associated with a larger increase in intrinsic connectivity within the default mode network, particularly between the right hippocampus and posteromedial cortex and between the left hippocampus and lateral parietal cortex. The cognitive fitness training group did not show such effects. These findings demonstrate that mindfulness training improves cognitive performance in cognitively intact older individuals and strengthens connectivity within the default mode network, which is particularly vulnerable to aging affects. Clinical Trial Registration: [https://clinicaltrials.gov/ct2/show/NCT02628548], identifier [NCT02628548].
Collapse
Affiliation(s)
- Gunes Sevinc
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Johann Rusche
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- Kopf- und Neurozentrum, Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Bonnie Wong
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Tanya Datta
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Robert Kaufman
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Sarah E. Gutz
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- Program in Speech and Hearing Bioscience and Technology, Harvard Medical School, Boston, MA, United States
| | - Marissa Schneider
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Nevyana Todorova
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- Department of Behavioral Neuroscience, College of Science, Northeastern University, Boston, MA, United States
| | - Christian Gaser
- Department of Psychiatry and Neurology, Jena University Hospital, Jena, Germany
| | - Götz Thomalla
- Kopf- und Neurozentrum, Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Dorene Rentz
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Bradford D. Dickerson
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Sara W. Lazar
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| |
Collapse
|
9
|
Wales RM, Leung HC. The Effects of Amyloid and Tau on Functional Network Connectivity in Older Populations. Brain Connect 2021; 11:599-612. [PMID: 33813858 DOI: 10.1089/brain.2020.0902] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Background: Neuroimaging studies suggest that aged brains show altered connectivity within and across functional networks. Similar changes in functional network integrity are also linked to the accumulation of pathological proteins in the brain, such as amyloid-beta plaques and neurofibrillary tau tangles seen in Alzheimer's disease. However, less is known about the specific impacts of amyloid and tau on functional network connectivity in cognitively normal older adults who harbor these proteins. Methods: We briefly summarize recent neuroimaging studies of aging and then thoroughly review positron emission tomography and functional magnetic resonance imaging studies measuring the relationship between amyloid-tau pathology and functional connectivity in cognitively normal older individuals. Results: The literature overall suggests that amyloid-positive older individuals show minor cognitive dysfunction and aberrant default mode network connectivity compared with amyloid-negative individuals. Tau, however, is more closely associated with network hypoconnectivity and poorer cognition. Those with substantial amyloid and tau experience even greater cognitive decline compared with those with primarily amyloid or tau, suggesting a potential interaction. Multimodal neuroimaging studies suggest that older adults with pathological protein deposits show amyloid-related hyperconnectivity and tau-related hypoconnectivity in multiple functional networks, including the default mode and frontoparietal networks. Discussion: We propose an updated model considering the effects of amyloid and tau on functional connectivity in older individuals. Large, longitudinal neuroimaging studies with multiple levels of analysis are required to obtain a deeper understanding of the dynamic relationship between pathological protein accumulation and functional connectivity changes, as amyloid- and tau-induced connectivity alterations may have critical and time-varying effects on neurodegeneration and cognitive decline. Impact statement Amyloid and tau accumulation have been linked with altered functional connectivity in cognitively normal older adults. This review synthesized recent functional imaging literatures in a discussion of how amyloid and tau can interactively affect functional connectivity in nonlinear ways, which can explain previous conflicting findings. Changes in connectivity strength may depend on the accumulation of both amyloid and tau, and their integrative effects seem to have critical consequences on cognition. Elucidating the effects of these pathological proteins on brain functioning is paramount to understand the etiology of Alzheimer's disease and the aging process overall.
Collapse
Affiliation(s)
- Ryan Michael Wales
- Integrative Neuroscience Program, Department of Psychology, Stony Brook University, Stony Brook, New York, USA
| | - Hoi-Chung Leung
- Integrative Neuroscience Program, Department of Psychology, Stony Brook University, Stony Brook, New York, USA
| |
Collapse
|
10
|
Spallazzi M, Michelini G, Barocco F, Dieci F, Copelli S, Messa G, Scarlattei M, Pavesi G, Ruffini L, Caffarra P. The Role of Free and Cued Selective Reminding Test in Predicting [18F]Florbetaben PET Results in Mild Cognitive Impairment and Mild Dementia. J Alzheimers Dis 2021; 73:1647-1659. [PMID: 31958094 DOI: 10.3233/jad-190950] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
BACKGROUND Free and Cued Selective Reminding Test (FCSRT) is a reliable cognitive marker for Alzheimer's disease (AD), and the identification of neuropsychological tests sensitive to the early signs of AD pathology is crucial both in research and clinical practice. OBJECTIVE The study aimed to ascertain the ability of FCSRT in predicting the amyloid load as determined from amyloid PET imaging (Amy-PET) in patients with cognitive disorders. METHODS For our purpose, 79 patients (71 MCI, 8 mild dementia) underwent a complete workup for dementia, including the FCSRT assessment and a [18F]florbetaben PET scan. FCSRT subitem scores were used as predictors in different binomial regression models. RESULTS Immediate free recall and delayed free recall were the best predictors overall in the whole sample; whereas in patients <76 years, all models further improved with immediate total recall (ITR) and Index of Sensitivity of Cueing (ISC) resulting the most accurate in anticipating Amy-PET results, with a likelihood of being Amy-PET positive greater than 85% for ITR and ISC scores of less than 25 and 0.5, respectively. CONCLUSION FCSRT proved itself to be a valid tool in dementia diagnosis, also being able to correlate with amyloid pathology. The possibility to predict Amy-PET results through a simple and reliable neuropsychological test might be helpful for clinicians in the dementia field, adding value to a paper and pencil tool compared to most costly biomarkers.
Collapse
Affiliation(s)
- Marco Spallazzi
- Department of Medicine and Surgery, Unit of Neurology, Azienda Ospedaliero-Universitaria, Parma, Italy
| | - Giovanni Michelini
- Sigmund Freud University, Milano, Italy.,Department of Disability, Fondazione Istituto Ospedaliero di Sospiro - Onlus, Cremona, Italy
| | - Federica Barocco
- Alzheimer Center, FERB, Briolini Hospital, Gazzaniga, Bergamo, Italy
| | | | - Sandra Copelli
- Center for Cognitive Disorders, AUSL Parma, Parma, Italy
| | - Giovanni Messa
- Center for Cognitive Disorders, AUSL Parma, Parma, Italy
| | - Maura Scarlattei
- Department of Nuclear Medicine, Azienda Ospedaliero-Universitaria, Parma, Italy
| | - Giovanni Pavesi
- Department of Medicine and Surgery, Section of Neuroscience, Unit of Neurology, University of Parma, Parma, Italy
| | - Livia Ruffini
- Department of Nuclear Medicine, Azienda Ospedaliero-Universitaria, Parma, Italy
| | - Paolo Caffarra
- Department of Medicine and Surgery, Section of Neuroscience, Unit of Neurology, University of Parma, Parma, Italy
| |
Collapse
|
11
|
Coelho A, Fernandes HM, Magalhães R, Moreira PS, Marques P, Soares JM, Amorim L, Portugal‐Nunes C, Castanho T, Santos NC, Sousa N. Reorganization of brain structural networks in aging: A longitudinal study. J Neurosci Res 2021; 99:1354-1376. [PMID: 33527512 PMCID: PMC8248023 DOI: 10.1002/jnr.24795] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 12/31/2020] [Indexed: 12/12/2022]
Abstract
Normal aging is characterized by structural and functional changes in the brain contributing to cognitive decline. Structural connectivity (SC) describes the anatomical backbone linking distinct functional subunits of the brain and disruption of this communication is thought to be one of the potential contributors for the age-related deterioration observed in cognition. Several studies already explored brain network's reorganization during aging, but most focused on average connectivity of the whole-brain or in specific networks, such as the resting-state networks. Here, we aimed to characterize longitudinal changes of white matter (WM) structural brain networks, through the identification of sub-networks with significantly altered connectivity along time. Then, we tested associations between longitudinal changes in network connectivity and cognition. We also assessed longitudinal changes in topological properties of the networks. For this, older adults were evaluated at two timepoints, with a mean interval time of 52.8 months (SD = 7.24). WM structural networks were derived from diffusion magnetic resonance imaging, and cognitive status from neurocognitive testing. Our results show age-related changes in brain SC, characterized by both decreases and increases in connectivity weight. Interestingly, decreases occur in intra-hemispheric connections formed mainly by association fibers, while increases occur mostly in inter-hemispheric connections and involve association, commissural, and projection fibers, supporting the last-in-first-out hypothesis. Regarding topology, two hubs were lost, alongside with a decrease in connector-hub inter-modular connectivity, reflecting reduced integration. Simultaneously, there was an increase in the number of provincial hubs, suggesting increased segregation. Overall, these results confirm that aging triggers a reorganization of the brain structural network.
Collapse
Affiliation(s)
- Ana Coelho
- Life and Health Sciences Research Institute (ICVS), School of MedicineUniversity of MinhoBragaPortugal
- ICVS/3B’s, PT Government Associate LaboratoryBraga/GuimarãesPortugal
- Clinical Academic Center – BragaBragaPortugal
| | - Henrique M. Fernandes
- Center for Music in the Brain (MIB)Aarhus UniversityAarhusDenmark
- Department of PsychiatryUniversity of OxfordOxfordUK
| | - Ricardo Magalhães
- Life and Health Sciences Research Institute (ICVS), School of MedicineUniversity of MinhoBragaPortugal
- ICVS/3B’s, PT Government Associate LaboratoryBraga/GuimarãesPortugal
- Clinical Academic Center – BragaBragaPortugal
| | - Pedro S. Moreira
- Life and Health Sciences Research Institute (ICVS), School of MedicineUniversity of MinhoBragaPortugal
- ICVS/3B’s, PT Government Associate LaboratoryBraga/GuimarãesPortugal
- Clinical Academic Center – BragaBragaPortugal
| | - Paulo Marques
- Life and Health Sciences Research Institute (ICVS), School of MedicineUniversity of MinhoBragaPortugal
- ICVS/3B’s, PT Government Associate LaboratoryBraga/GuimarãesPortugal
- Clinical Academic Center – BragaBragaPortugal
| | - José M. Soares
- Life and Health Sciences Research Institute (ICVS), School of MedicineUniversity of MinhoBragaPortugal
- ICVS/3B’s, PT Government Associate LaboratoryBraga/GuimarãesPortugal
- Clinical Academic Center – BragaBragaPortugal
| | - Liliana Amorim
- Life and Health Sciences Research Institute (ICVS), School of MedicineUniversity of MinhoBragaPortugal
- ICVS/3B’s, PT Government Associate LaboratoryBraga/GuimarãesPortugal
- Clinical Academic Center – BragaBragaPortugal
| | - Carlos Portugal‐Nunes
- Life and Health Sciences Research Institute (ICVS), School of MedicineUniversity of MinhoBragaPortugal
- ICVS/3B’s, PT Government Associate LaboratoryBraga/GuimarãesPortugal
- Clinical Academic Center – BragaBragaPortugal
| | - Teresa Castanho
- Life and Health Sciences Research Institute (ICVS), School of MedicineUniversity of MinhoBragaPortugal
- ICVS/3B’s, PT Government Associate LaboratoryBraga/GuimarãesPortugal
- Clinical Academic Center – BragaBragaPortugal
| | - Nadine Correia Santos
- Life and Health Sciences Research Institute (ICVS), School of MedicineUniversity of MinhoBragaPortugal
- ICVS/3B’s, PT Government Associate LaboratoryBraga/GuimarãesPortugal
- Clinical Academic Center – BragaBragaPortugal
| | - Nuno Sousa
- Life and Health Sciences Research Institute (ICVS), School of MedicineUniversity of MinhoBragaPortugal
- ICVS/3B’s, PT Government Associate LaboratoryBraga/GuimarãesPortugal
- Clinical Academic Center – BragaBragaPortugal
| |
Collapse
|
12
|
Accelerated brain aging predicts impulsivity and symptom severity in depression. Neuropsychopharmacology 2021; 46:911-919. [PMID: 33495545 PMCID: PMC8115107 DOI: 10.1038/s41386-021-00967-x] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 01/08/2021] [Indexed: 01/30/2023]
Abstract
Multiple structural and functional neuroimaging measures vary over the course of the lifespan and can be used to predict chronological age. Accelerated brain aging, as quantified by deviations in the MRI-based predicted age with respect to chronological age, is associated with risk for neurodegenerative conditions, bipolar disorder, and mortality. Whether age-related changes in resting-state functional connectivity are accelerated in major depressive disorder (MDD) is unknown, and, if so, it is unclear if these changes contribute to specific cognitive weaknesses that often occur in MDD. Here, we delineated age-related functional connectivity changes in a large sample of normal control subjects and tested whether brain aging is accelerated in MDD. Furthermore, we tested whether accelerated brain aging predicts individual differences in cognitive function. We trained a support vector regression model predicting age using resting-state functional connectivity in 710 healthy adults aged 18-89. We applied this model trained on normal aging subjects to a sample of actively depressed MDD participants (n = 109). The difference between predicted brain age and chronological age was 2.11 years greater (p = 0.015) in MDD patients compared to control participants. An older MDD brain age was significantly associated with increased impulsivity and, in males, increased depressive severity. Unexpectedly, accelerated brain aging was also associated with increased placebo response in a sham-controlled trial of high-frequency repetitive transcranial magnetic stimulation targeting the dorsomedial prefrontal cortex. Our results indicate that MDD is associated with accelerated brain aging, and that accelerated aging is selectively associated with greater impulsivity and depression severity.
Collapse
|
13
|
Stefanovski L, Meier JM, Pai RK, Triebkorn P, Lett T, Martin L, Bülau K, Hofmann-Apitius M, Solodkin A, McIntosh AR, Ritter P. Bridging Scales in Alzheimer's Disease: Biological Framework for Brain Simulation With The Virtual Brain. Front Neuroinform 2021; 15:630172. [PMID: 33867964 PMCID: PMC8047422 DOI: 10.3389/fninf.2021.630172] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 03/08/2021] [Indexed: 12/18/2022] Open
Abstract
Despite the acceleration of knowledge and data accumulation in neuroscience over the last years, the highly prevalent neurodegenerative disease of AD remains a growing problem. Alzheimer's Disease (AD) is the most common cause of dementia and represents the most prevalent neurodegenerative disease. For AD, disease-modifying treatments are presently lacking, and the understanding of disease mechanisms continues to be incomplete. In the present review, we discuss candidate contributing factors leading to AD, and evaluate novel computational brain simulation methods to further disentangle their potential roles. We first present an overview of existing computational models for AD that aim to provide a mechanistic understanding of the disease. Next, we outline the potential to link molecular aspects of neurodegeneration in AD with large-scale brain network modeling using The Virtual Brain (www.thevirtualbrain.org), an open-source, multiscale, whole-brain simulation neuroinformatics platform. Finally, we discuss how this methodological approach may contribute to the understanding, improved diagnostics, and treatment optimization of AD.
Collapse
Affiliation(s)
- Leon Stefanovski
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
| | - Jil Mona Meier
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
| | - Roopa Kalsank Pai
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
| | - Paul Triebkorn
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
- Institut de Neurosciences des Systèmes, Aix Marseille Université, Marseille, France
| | - Tristram Lett
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
| | - Leon Martin
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
| | - Konstantin Bülau
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
| | - Martin Hofmann-Apitius
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Sankt Augustin, Germany
| | - Ana Solodkin
- Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, United States
| | | | - Petra Ritter
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
- Einstein Center for Neuroscience Berlin, Berlin, Germany
- Einstein Center Digital Future, Berlin, Germany
| |
Collapse
|
14
|
Edde M, Dilharreguy B, Theaud G, Chanraud S, Helmer C, Dartigues JF, Amieva H, Allard M, Descoteaux M, Catheline G. Age-related change in episodic memory: role of functional and structural connectivity between the ventral posterior cingulate and the parietal cortex. Brain Struct Funct 2020; 225:2203-2218. [PMID: 32728934 DOI: 10.1007/s00429-020-02121-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Accepted: 07/23/2020] [Indexed: 10/23/2022]
Abstract
While the neural correlates of age-related episodic memory decline have been extensively studied, the precise involvement of the Posterior Cingulate Cortex (PCC) and posterior parietal cortex (the precuneus and the angular gyrus), remains unclear. The present study examined functional and structural neural correlates of age-related episodic memory change assessed over 12 years in 120 older adults (range 76-90 years). Episodic memory performance was measured using the Free and Cued Selective Reminding Test (FCSRT); functional connectivity metrics were computed from resting-state fMRI images and structural connectivity metrics were assessed through microstructural properties of reconstructed tract using a native space pipeline. We found that FCSRT change was significantly associated with the functional connectivity between the ventral PCC and three parietal regions, the ventral superior, the inferior part of the precuneus, and the rostro dorsal part of the angular gyrus. This association was independent of hippocampal volume. In addition, we found the that change in FCSRT scores was associated with fractional anisotropy of the tract connecting the ventral PCC and the ventral superior part of the precuneus. Change in episodic memory in aging was therefore related to a combination of high functional connectivity and low structural connectivity between the ventral PCC and the ventral superior part of the precuneus.
Collapse
Affiliation(s)
- Manon Edde
- EPHE, PSL, 33000, Bordeaux, France. .,CNRS, INCIA, UMR 5287, 33000, Bordeaux, France. .,, Bât. 2A - 2ème Étage - Case 22, 146 Rue Léo Saignat, 33076, Bordeaux cedex, France.
| | | | - Guillaume Theaud
- Sherbrooke Connectivity Imaging Lab (SCIL), Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Sandra Chanraud
- EPHE, PSL, 33000, Bordeaux, France.,CNRS, INCIA, UMR 5287, 33000, Bordeaux, France.,INCIA, UMR 5287, Université de Bordeaux, 33000, Bordeaux, France
| | - Catherine Helmer
- Inserm, Bordeaux Population Health Research Center, UMR 1219, Université de Bordeaux, 33000, Bordeaux, France
| | - Jean-François Dartigues
- INCIA, UMR 5287, Université de Bordeaux, 33000, Bordeaux, France.,Inserm, Bordeaux Population Health Research Center, UMR 1219, Université de Bordeaux, 33000, Bordeaux, France.,CHU de Bordeaux, Bordeaux, France
| | - Hélène Amieva
- Inserm, Bordeaux Population Health Research Center, UMR 1219, Université de Bordeaux, 33000, Bordeaux, France
| | - Michèle Allard
- EPHE, PSL, 33000, Bordeaux, France.,CNRS, INCIA, UMR 5287, 33000, Bordeaux, France.,CHU de Bordeaux, Bordeaux, France
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab (SCIL), Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Gwénaëlle Catheline
- EPHE, PSL, 33000, Bordeaux, France.,CNRS, INCIA, UMR 5287, 33000, Bordeaux, France.,INCIA, UMR 5287, Université de Bordeaux, 33000, Bordeaux, France
| |
Collapse
|
15
|
Li Q, Dong C, Liu T, Chen X, Perry A, Jiang J, Cheng J, Niu H, Kochan NA, Brodaty H, Sachdev PS, Wen W. Longitudinal Changes in Whole-Brain Functional Connectivity Strength Patterns and the Relationship With the Global Cognitive Decline in Older Adults. Front Aging Neurosci 2020; 12:71. [PMID: 32256339 PMCID: PMC7090100 DOI: 10.3389/fnagi.2020.00071] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2019] [Accepted: 02/26/2020] [Indexed: 12/12/2022] Open
Abstract
Aging is associated with changes in brain functional patterns as well as cognition. The present research sought to investigate longitudinal changes in whole brain functional connectivity strength (FCS) and cognitive performance scores in very old cognitively unimpaired individuals. We studied 34 cognitively normal elderly individuals at both baseline and 4-year follow-up (baseline age = 78 ± 3.14 years) with resting-state functional magnetic resonance imaging (r-fMRI), structural MRI scans, and neuropsychological assessments conducted. Voxel-based whole brain FCS was calculated and we found that bilateral superior parietal and medial frontal regions showed decreased FCS, while the supplementary motor area (SMA) and insula showed increased FCS with age, along with a decrease in bilateral prefrontal cortical thickness. The changes of FCS in left precuneus were associated with an aging-related decline in global cognition. Taken together, our results suggest changes in FCS with aging with the precuneus as a hub and this may underlie changes in global cognition that accompany aging. These findings help better understand the normal aging mechanism.
Collapse
Affiliation(s)
- Qiongge Li
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Chao Dong
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Centre for Healthy Brain Aging, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - Tao Liu
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, China.,Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Xiaodan Chen
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Alistair Perry
- Max Planck UCL Centre for Computational Psychiatry and Aging Research, Berlin, Germany
| | - Jiyang Jiang
- Centre for Healthy Brain Aging, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - Jian Cheng
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, China
| | - Haijun Niu
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Nicole A Kochan
- Centre for Healthy Brain Aging, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia.,Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia
| | - Henry Brodaty
- Centre for Healthy Brain Aging, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia.,Dementia Centre for Research Collaboration, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - Perminder S Sachdev
- Centre for Healthy Brain Aging, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia.,Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia.,Dementia Centre for Research Collaboration, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - Wei Wen
- Centre for Healthy Brain Aging, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia.,Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia
| |
Collapse
|
16
|
Subramanian S, Rajamanickam K, Prakash JS, Ramachandran M. Study on structural atrophy changes and functional connectivity measures in Alzheimer's disease. J Med Imaging (Bellingham) 2020; 7:016002. [PMID: 32118092 DOI: 10.1117/1.jmi.7.1.016002] [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: 09/16/2019] [Accepted: 02/03/2020] [Indexed: 11/14/2022] Open
Abstract
Alzheimer's disease (AD) is characterized by the progressive accumulation of neurofibrillary tangles associated with amyloid plaques. We used 80 resting-state functional magnetic resonance imaging and 80 T 1 images acquired using MP-RAGE (magnetization-prepared rapid acquisition gradient echo) from Alzheimer's Disease Neuroimaging Initiative data to detect atrophy changes and functional connectivity patterns of the default mode networks (DMNs). The study subjects were classified into four groups (each with n = 20 ) based on their Mini-Mental State Examination (MMSE) score as follows: cognitively normal (CN), early mild cognitive impairment, late mild cognitive impairment, and AD. The resting-state functional connectivity of the DMN was examined between the groups using the CONN functional connectivity toolbox. Loss of gray matter in AD was observed. Atrophy measured by the volume of selected subcortical regions, using the Functional Magnetic Resonance Imaging of the Brain (FMRIB) Software Library's Integrated Registration and Segmentation Tool (FIRST), revealed significant volume loss in AD when compared to CN ( p < 0.05 ). DMNs were selected to assess functional connectivity. The negative connectivity of DMN increased in AD group compared to controls. Graph theory parameters, such as global and local efficiency, betweenness centrality, average path length, and cluster coefficient, were computed. Relatively higher correlation between MMSE and functional metrics ( r = 0.364 , p = 0.001 ) was observed as compared to atrophy measures ( r = 0.303 , p = 0.006 ). In addition, the receiver operating characteristic analysis showed large area under the curve ( A Z ) for functional parameters ( A Z > 0.9 ), compared to morphometric changes ( A Z < 0.8 ). In summary, it is observed that the functional connectivity measures may serve a better predictor in comparison to structural atrophy changes. We postulate that functional connectivity measures have the potential to evolve as a marker for the early detection of AD.
Collapse
Affiliation(s)
- Saraswathi Subramanian
- Chettinad Academy of Research and Education, Faculty of Allied Health Sciences, Kelambakkam, Chennai, Tamil Nadu, India
| | - Karunanithi Rajamanickam
- Chettinad Academy of Research and Education, Faculty of Allied Health Sciences, Kelambakkam, Chennai, Tamil Nadu, India
| | - Joy Sebastian Prakash
- Chettinad Academy of Research and Education, Faculty of Allied Health Sciences, Kelambakkam, Chennai, Tamil Nadu, India
| | - Murugesan Ramachandran
- Chettinad Academy of Research and Education, Faculty of Allied Health Sciences, Kelambakkam, Chennai, Tamil Nadu, India
| | | |
Collapse
|
17
|
Zhao X, Yao LI, Chen K, Li KE, Zhang J, Guo X. Changes in the Functional and Structural Default Mode Network across the Adult Lifespan Based on Partial Least Squares. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2019; 7:82256-82265. [PMID: 33224696 PMCID: PMC7677917 DOI: 10.1109/access.2019.2923274] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The default mode network (DMN) has been extensively investigated in the literature. However, previous studies have mainly focused on age-related changes in the DMN between old and young participants. Age-dependent changes in specific regions within the DMN have not been adequately explored across the entire adult lifespan. Thus, in the present study, we performed a seed partial least squares (PLS) analysis to investigate lifespan-wide changes in the regions of the functional and structural DMNs using resting-state functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (MRI) data from healthy subjects aged 16-85 years. The posterior cingulate area was selected as the seed region based on prior fMRI studies. The single-group functional connectivity analysis showed a stable connection between the seed and the posterior cingulate cortex (PCC), middle temporal gyrus (MTG) and inferior temporal gyrus (ITG); a decreased connection between the seed and the medial prefrontal cortex (MPFC), anterior cingulate cortex (ACC) and superior frontal gyrus (SFG); and an increased connection between the seed and the precuneus (PreC), inferior parietal lobule (IPL) and middle frontal gyrus (MFG) across the entire lifespan. In contrast, in the single-group structural covariance analysis, the covariance connections of the seed to the DMN regions demonstrated a stable covariance trend to the PCC, MTG, superior temporal gyrus (STG) and ITG; an inverted U-shaped covariance trend to the MPFC, ACC, SFG, MFG and inferior frontal gyrus (IFG); and a U-shaped covariance trend to the PreC with age. Full-group analyses found significant linear decreases in functional and structural DMN integrity. Our findings provide crucial information regarding the influence of age on the function and structure of the DMN and may contribute to the understanding of the underlying mechanism of age-related changes in the DMN over the lifespan.
Collapse
Affiliation(s)
- Xiaoyu Zhao
- College of Information Science and Technology, Beijing Normal University, Beijing, China
- College of Information Engineering, Ordos Institute of Technology, Ordos, China
| | - L I Yao
- College of Information Science and Technology, Beijing Normal University, Beijing, China
| | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, Arizona, USA
- Shanghai Green Valley Pharmaceutical Co Ltd, Shanghai, China
| | - K E Li
- Laboratory of Magnetic Resonance Imaging, Beijing 306 Hospital, Beijing, China
| | - Jiacai Zhang
- College of Information Science and Technology, Beijing Normal University, Beijing, China
- Beijing Advanced Innovation Center for Future Education, Beijing Normal University, Beijing, China
| | - Xiaojuan Guo
- College of Information Science and Technology, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| |
Collapse
|
18
|
Liu J, Tao J, Liu W, Huang J, Xue X, Li M, Yang M, Zhu J, Lang C, Park J, Tu Y, Wilson G, Chen L, Kong J. Different modulation effects of Tai Chi Chuan and Baduanjin on resting-state functional connectivity of the default mode network in older adults. Soc Cogn Affect Neurosci 2019; 14:217-224. [PMID: 30690554 PMCID: PMC6374601 DOI: 10.1093/scan/nsz001] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Revised: 11/14/2018] [Accepted: 01/12/2019] [Indexed: 02/06/2023] Open
Abstract
The default mode network (DMN) plays an important role in age-related cognitive decline. This study aims to explore the modulation effect of two mind-body interventions (Tai Chi Chuan and Baduanjin) on DMN in elderly individuals. Participants between 50 and 70 years old were recruited and randomized into a Tai Chi Chuan, Baduanjin or control group. The Wechsler Memory Scale-Chinese Revision and resting-state fMRI scans were administered at baseline and following 12 weeks of exercise. Seed-based resting-state functional connectivity (rsFC) was calculated. We found that (i) compared to the Baduanjin group, Tai Chi Chuan was significantly associated with increased rsFC between the medial prefrontal cortex (mPFC) and right putamen/caudate and (ii) compared to the control group, Tai Chi Chuan increased posterior cingulate cortex rsFC with the right putamen/caudate, while Baduanjin decreased rsFC between the mPFC and orbital prefrontal gyrus/putamen. Baseline mPFC rsFC with orbital prefrontal gyrus was negatively correlated with visual reproduction subscore. These results suggest that both Tai Chi Chuan and Baduanjin can modulate the DMN, but through different pathways. Elucidating the mechanisms underlying different mind-body interventions may shed light on the development of new methods to prevent age-related diseases as well as other disorders associated with disrupted DMN.
Collapse
Affiliation(s)
- Jiao Liu
- Fujian Rehabilitation Tech Co-innovation Center, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China
- Fujian Key Laboratory of Rehabilitation Technology, Fuzhou, Fujian, China
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Jing Tao
- Fujian Key Laboratory of Rehabilitation Technology, Fuzhou, Fujian, China
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China
| | - Weilin Liu
- Fujian Key Laboratory of Rehabilitation Technology, Fuzhou, Fujian, China
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China
| | - Jia Huang
- Fujian Key Laboratory of Rehabilitation Technology, Fuzhou, Fujian, China
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China
| | - Xiehua Xue
- Affiliated Rehabilitation Hospital, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China
| | - Ming Li
- Affiliated Rehabilitation Hospital, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China
| | - Mingge Yang
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China
| | - Jingfang Zhu
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China
| | - Courtney Lang
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Joel Park
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Yiheng Tu
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Georgia Wilson
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Lidian Chen
- Fujian Key Laboratory of Rehabilitation Technology, Fuzhou, Fujian, China
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China
| | - Jian Kong
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| |
Collapse
|
19
|
|
20
|
The Longitudinal Trajectory of Default Mode Network Connectivity in Healthy Older Adults Varies As a Function of Age and Is Associated with Changes in Episodic Memory and Processing Speed. J Neurosci 2018; 38:2809-2817. [PMID: 29440553 DOI: 10.1523/jneurosci.3067-17.2018] [Citation(s) in RCA: 139] [Impact Index Per Article: 23.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2017] [Revised: 02/01/2018] [Accepted: 02/02/2018] [Indexed: 12/12/2022] Open
Abstract
The default mode network (DMN) supports memory functioning and may be sensitive to preclinical Alzheimer's pathology. Little is known, however, about the longitudinal trajectory of this network's intrinsic functional connectivity (FC). In this study, we evaluated longitudinal FC in 111 cognitively normal older human adults (ages 49-87, 46 women/65 men), 92 of whom had at least three task-free fMRI scans (n = 353 total scans). Whole-brain FC and three DMN subnetworks were assessed: (1) within-DMN, (2) between anterior and posterior DMN, and (3) between medial temporal lobe network and posterior DMN. Linear mixed-effects models demonstrated significant baseline age × time interactions, indicating a nonlinear trajectory. There was a trend toward increasing FC between ages 50-66 and significantly accelerating declines after age 74. A similar interaction was observed for whole-brain FC. APOE status did not predict baseline connectivity or change in connectivity. After adjusting for network volume, changes in within-DMN connectivity were specifically associated with changes in episodic memory and processing speed but not working memory or executive functions. The relationship with processing speed was attenuated after covarying for white matter hyperintensities (WMH) and whole-brain FC, whereas within-DMN connectivity remained associated with memory above and beyond WMH and whole-brain FC. Whole-brain and DMN FC exhibit a nonlinear trajectory, with more rapid declines in older age and possibly increases in connectivity early in the aging process. Within-DMN connectivity is a marker of episodic memory performance even among cognitively healthy older adults.SIGNIFICANCE STATEMENT Default mode network and whole-brain connectivity, measured using task-free fMRI, changed nonlinearly as a function of age, with some suggestion of early increases in connectivity. For the first time, longitudinal changes in DMN connectivity were shown to correlate with changes in episodic memory, whereas volume changes in relevant brain regions did not. This relationship was not accounted for by white matter hyperintensities or mean whole-brain connectivity. Functional connectivity may be an early biomarker of changes in aging but should be used with caution given its nonmonotonic nature, which could complicate interpretation. Future studies investigating longitudinal network changes should consider whole-brain changes in connectivity.
Collapse
|
21
|
Abstract
This article presents a review of recent advances in neuroscience research in the specific area of brain connectivity as a potential biomarker of Alzheimer's disease with a focus on the application of graph theory. The review will begin with a brief overview of connectivity and graph theory. Then resent advances in connectivity as a biomarker for Alzheimer's disease will be presented and analyzed.
Collapse
Affiliation(s)
- Jon delEtoile
- 1 Biophysics Graduate Program, The Ohio State University, Columbus, OH, USA
| | - Hojjat Adeli
- 2 Departments of Biomedical Engineering, Biomedical Informatics, Neurological Surgery, and Neuroscience, and Biophysics Graduate Program, The Ohio State University, Columbus, OH, USA
| |
Collapse
|
22
|
Effects of aging on functional and structural brain connectivity. Neuroimage 2017; 160:32-40. [PMID: 28159687 DOI: 10.1016/j.neuroimage.2017.01.077] [Citation(s) in RCA: 336] [Impact Index Per Article: 48.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2016] [Revised: 01/20/2017] [Accepted: 01/31/2017] [Indexed: 12/13/2022] Open
Abstract
Over the past decade there has been an enormous rise in the application of functional and structural connectivity approaches to explore the brain's intrinsic organization in healthy and clinical populations. The notion underlying the application of these approaches to study aging is that subtle age-related disruption of the brain's regional integrity and information flow across the brain, are expressed by age-related differences in functional and structural connectivity. In this review I will discus recent advances in our understanding of how age affects our brain's intrinsic organization, and I will share my perspective on potential challenges and future directions of the field.
Collapse
|
23
|
Qiu Y, Liu S, Hilal S, Loke YM, Ikram MK, Xu X, Yeow Tan B, Venketasubramanian N, Chen CLH, Zhou J. Inter-hemispheric functional dysconnectivity mediates the association of corpus callosum degeneration with memory impairment in AD and amnestic MCI. Sci Rep 2016; 6:32573. [PMID: 27581062 PMCID: PMC5007647 DOI: 10.1038/srep32573] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2016] [Accepted: 08/10/2016] [Indexed: 12/21/2022] Open
Abstract
Evidences suggested that both corpus callosum (CC) degeneration and alternations of homotopic inter-hemispheric functional connectivity (FC) are present in Alzheimer's disease (AD). However, the associations between region-specific CC degeneration and homotopic inter-hemispheric FC and their relationships with memory deficits in AD remain uncharacterized. We hypothesized that selective CC degeneration is associated with memory impairment in AD and amnestic mild cognitive impairment (aMCI), which is mediated by homotopic inter-hemispheric functional dysconnectivity. Using structural magnetic resonance imaging (MRI) and task-free functional MRI, we assessed the CC volume and inter-hemispheric FC in 66 healthy controls, 41 aMCI and 41 AD. As expected, AD had CC degeneration and attenuated inter-hemispheric homotopic FC. Nevertheless, aMCI had relatively less severe CC degeneration (mainly in mid-anterior, central, and mid-posterior) and no reduction in inter-hemispheric homotopic FC. The degeneration of each CC sub-region was associated with specific inter-hemispheric homotopic functional disconnections in AD and aMCI. More importantly, impairment of inter-hemispheric homotopic FC partially mediated the association between CC (particularly the central and posterior parts) degeneration and memory deficit. Notably, these results remained after controlling for hippocampal volume. Our findings shed light on how CC degeneration and the related inter-hemispheric FC impact memory impairment in early stage of AD.
Collapse
Affiliation(s)
- Yingwei Qiu
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Graduate Medical School, Singapore
| | - Siwei Liu
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Graduate Medical School, Singapore
| | - Saima Hilal
- Department of Pharmacology, National University Health System, Clinical Research Centre, Singapore
- Memory Aging & Cognition Centre, National University Health System, Singapore
| | - Yng Miin Loke
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Graduate Medical School, Singapore
| | - Mohammad Kamran Ikram
- Memory Aging & Cognition Centre, National University Health System, Singapore
- Duke-NUS Graduate Medical School, National University of Singapore, Singapore
| | - Xin Xu
- Department of Pharmacology, National University Health System, Clinical Research Centre, Singapore
- Memory Aging & Cognition Centre, National University Health System, Singapore
| | | | | | - Christopher Li-Hsian Chen
- Department of Pharmacology, National University Health System, Clinical Research Centre, Singapore
- Memory Aging & Cognition Centre, National University Health System, Singapore
| | - Juan Zhou
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Graduate Medical School, Singapore
- Clinical Imaging Research Centre, the Agency for Science, Technology and Research and National University of Singapore, Singapore
| |
Collapse
|
24
|
Ng KK, Lo JC, Lim JK, Chee MW, Zhou J. Reduced functional segregation between the default mode network and the executive control network in healthy older adults: A longitudinal study. Neuroimage 2016; 133:321-330. [DOI: 10.1016/j.neuroimage.2016.03.029] [Citation(s) in RCA: 142] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2015] [Revised: 03/12/2016] [Accepted: 03/13/2016] [Indexed: 11/25/2022] Open
|