1
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Charisis S, Short MI, Bernal R, Kautz TF, Treviño HA, Mathews J, Dediós AGV, Muhammad JAS, Luckey AM, Aslam A, Himali JJ, Shipp EL, Habes M, Beiser AS, DeCarli C, Scarmeas N, Ramachandran VS, Seshadri S, Maillard P, Satizabal CL. Leptin bioavailability and markers of brain atrophy and vascular injury in the middle age. Alzheimers Dement 2024. [PMID: 39132759 DOI: 10.1002/alz.13879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 03/01/2024] [Accepted: 03/24/2024] [Indexed: 08/13/2024]
Abstract
INTRODUCTION We investigated the associations of leptin markers with cognitive function and magnetic resonance imaging (MRI) measures of brain atrophy and vascular injury in healthy middle-aged adults. METHODS We included 2262 cognitively healthy participants from the Framingham Heart Study with neuropsychological evaluation; of these, 2028 also had available brain MRI. Concentrations of leptin, soluble leptin receptor (sOB-R), and their ratio (free leptin index [FLI]), indicating leptin bioavailability, were measured using enzyme-linked immunosorbent assays. Cognitive and MRI measures were derived using standardized protocols. RESULTS Higher sOB-R was associated with lower fractional anisotropy (FA, β = -0.114 ± 0.02, p < 0.001), and higher free water (FW, β = 0.091 ± 0.022, p < 0.001) and peak-width skeletonized mean diffusivity (PSMD, β = 0.078 ± 0.021, p < 0.001). Correspondingly, higher FLI was associated with higher FA (β = 0.115 ± 0.027, p < 0.001) and lower FW (β = -0.096 ± 0.029, p = 0.001) and PSMD (β = -0.085 ± 0.028, p = 0.002). DISCUSSION Higher leptin bioavailability was associated with better white matter (WM) integrity in healthy middle-aged adults, supporting the putative neuroprotective role of leptin in late-life dementia risk. HIGHLIGHTS Higher leptin bioavailability was related to better preservation of white matter microstructure. Higher leptin bioavailability during midlife might confer protection against dementia. Potential benefits might be even stronger for individuals with visceral obesity. DTI measures might be sensitive surrogate markers of subclinical neuropathology.
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Affiliation(s)
- Sokratis Charisis
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
| | - Meghan I Short
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, Massachusetts, USA
| | - Rebecca Bernal
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
| | - Tiffany F Kautz
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
| | - Hector A Treviño
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
| | - Julia Mathews
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
| | - Angel Gabriel Velarde Dediós
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
| | - Jazmyn A S Muhammad
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
| | - Alison M Luckey
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
| | - Asra Aslam
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
| | - Jayandra J Himali
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
- The Framingham Heart Study, Framingham, Massachusetts, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
| | - Eric L Shipp
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
| | - Mohamad Habes
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
| | - Alexa S Beiser
- The Framingham Heart Study, Framingham, Massachusetts, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
| | - Charles DeCarli
- Department of Neurology, University of California, Davis, Sacramento, California, USA
| | - Nikolaos Scarmeas
- 1st Department of Neurology, National and Kapodistrian University of Athens, Athens, Greece
- Taub Institute for Research in Alzheimer's Disease and the Aging Brain, the Gertrude H. Sergievsky Center, Columbia University, New York, New York, USA
| | - Vasan S Ramachandran
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
- The Framingham Heart Study, Framingham, Massachusetts, USA
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
- The Framingham Heart Study, Framingham, Massachusetts, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
| | - Pauline Maillard
- Department of Neurology, University of California, Davis, Sacramento, California, USA
| | - Claudia L Satizabal
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
- The Framingham Heart Study, Framingham, Massachusetts, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
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Feng L, Ye Z, Du Z, Pan Y, Canida T, Ke H, Liu S, Chen S, Hong LE, Kochunov P, Chen J, Lei DK, Shenassa E, Ma T. Association between allostatic load and accelerated white matter brain aging: findings from the UK Biobank. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.26.24301793. [PMID: 38343822 PMCID: PMC10854327 DOI: 10.1101/2024.01.26.24301793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/19/2024]
Abstract
White matter (WM) brain age, a neuroimaging-derived biomarker indicating WM microstructural changes, helps predict dementia and neurodegenerative disorder risks. The cumulative effect of chronic stress on WM brain aging remains unknown. In this study, we assessed cumulative stress using a multi-system composite allostatic load (AL) index based on inflammatory, anthropometric, respiratory, lipidemia, and glucose metabolism measures, and investigated its association with WM brain age gap (BAG), computed from diffusion tensor imaging data using a machine learning model, among 22 951 European ancestries aged 40 to 69 (51.40% women) from UK Biobank. Linear regression, Mendelian randomization, along with inverse probability weighting and doubly robust methods, were used to evaluate the impact of AL on WM BAG adjusting for age, sex, socioeconomic, and lifestyle behaviors. We found increasing one AL score unit significantly increased WM BAG by 0.29 years in association analysis and by 0.33 years in Mendelian analysis. The age- and sex-stratified analysis showed consistent results among participants 45-54 and 55-64 years old, with no significant sex difference. This study demonstrated that higher chronic stress was significantly associated with accelerated brain aging, highlighting the importance of stress management in reducing dementia and neurodegenerative disease risks.
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Affiliation(s)
- Li Feng
- Department of Nutrition and Food Science, College of Agriculture & Natural Resources, University of Maryland, College Park, Maryland, United States of America
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, Maryland, United States of America
| | - Zhenyao Ye
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Zewen Du
- Department of Biostatistics, School of Global Public Health, New York University, New York, New York, United States of America
| | - Yezhi Pan
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Travis Canida
- Department of Mathematics, The college of Computer, Mathematical, and Natural Sciences, University of Maryland, College Park, Maryland, United States of America
| | - Hongjie Ke
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, Maryland, United States of America
| | - Song Liu
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China
| | - Shuo Chen
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - L. Elliot Hong
- Louis A. Faillace Department of Psychiatry & Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Peter Kochunov
- Louis A. Faillace Department of Psychiatry & Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Jie Chen
- Department of Health Policy and Management, School of Public Health, University of Maryland, College Park, Maryland, United States of America
| | - David K.Y. Lei
- Department of Nutrition and Food Science, College of Agriculture & Natural Resources, University of Maryland, College Park, Maryland, United States of America
| | - Edmond Shenassa
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, Maryland, United States of America
- Maternal & Child Health Program, School of Public Health, University of Maryland, College Park, Maryland, United States of America
- Department of Epidemiology, School of Public Health, Brown University, Rhode Island, United States of America
- Department of Epidemiology & Public Health, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Tianzhou Ma
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, Maryland, United States of America
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3
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Ahmadi K, Pereira JB, van Westen D, Pasternak O, Zhang F, Nilsson M, Stomrud E, Spotorno N, Hansson O. Fixel-Based Analysis Reveals Tau-Related White Matter Changes in Early Stages of Alzheimer's Disease. J Neurosci 2024; 44:e0538232024. [PMID: 38565289 PMCID: PMC11063818 DOI: 10.1523/jneurosci.0538-23.2024] [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: 03/24/2023] [Revised: 03/07/2024] [Accepted: 03/08/2024] [Indexed: 04/04/2024] Open
Abstract
Several studies have shown white matter (WM) abnormalities in Alzheimer's disease (AD) using diffusion tensor imaging (DTI). Nonetheless, robust characterization of WM changes has been challenging due to the methodological limitations of DTI. We applied fixel-based analyses (FBA) to examine microscopic differences in fiber density (FD) and macroscopic changes in fiber cross-section (FC) in early stages of AD (N = 393, 212 females). FBA was also compared with DTI, free-water corrected (FW)-DTI and diffusion kurtosis imaging (DKI). We further investigated the correlation of FBA and tensor-derived metrics with AD pathology and cognition. FBA metrics were decreased in the entire cingulum bundle, uncinate fasciculus and anterior thalamic radiations in Aβ-positive patients with mild cognitive impairment compared to control groups. Metrics derived from DKI, and FW-DTI showed similar alterations whereas WM degeneration detected by DTI was more widespread. Tau-PET uptake in medial temporal regions was only correlated with reduced FC mainly in the parahippocampal cingulum in Aβ-positive individuals. This tau-related WM alteration was also associated with impaired memory. Despite the spatially extensive between-group differences in DTI-metrics, the link between WM and tau aggregation was only revealed using FBA metrics implying high sensitivity but low specificity of DTI-based measures in identifying subtle tau-related WM degeneration. No relationship was found between amyloid load and any diffusion-MRI measures. Our results indicate that early tau-related WM alterations in AD are due to macrostructural changes specifically captured by FBA metrics. Thus, future studies assessing the effects of AD pathology in WM tracts should consider using FBA metrics.
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Affiliation(s)
- Khazar Ahmadi
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund 22362, Sweden
- Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum 44801, Germany
| | - Joana B Pereira
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund 22362, Sweden
- Division of Neuro, Department of Clinical Neurosciences, Karolinska Institutet, Stockholm 17176, Sweden
| | - Danielle van Westen
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund 22362, Sweden
- Diagnostic Radiology, Department of Clinical Sciences, Lund University, Lund 22185, Sweden
| | - Ofer Pasternak
- Departments of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115
- Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts 02114
| | - Fan Zhang
- Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115
| | - Markus Nilsson
- Diagnostic Radiology, Department of Clinical Sciences, Lund University, Lund 22185, Sweden
- Department of Medical Radiation Physics, Lund University, Lund 22185, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund 22362, Sweden
- Memory Clinic, Skåne University Hospital, Malmö 21428, Sweden
| | - Nicola Spotorno
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund 22362, Sweden
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund 22362, Sweden
- Memory Clinic, Skåne University Hospital, Malmö 21428, Sweden
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4
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Cogswell PM, Lundt ES, Therneau TM, Mester CT, Wiste HJ, Graff-Radford J, Schwarz CG, Senjem ML, Gunter JL, Reid RI, Przybelski SA, Knopman DS, Vemuri P, Petersen RC, Jack CR. Evidence against a temporal association between cerebrovascular disease and Alzheimer's disease imaging biomarkers. Nat Commun 2023; 14:3097. [PMID: 37248223 PMCID: PMC10226977 DOI: 10.1038/s41467-023-38878-8] [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: 08/30/2022] [Accepted: 05/15/2023] [Indexed: 05/31/2023] Open
Abstract
Whether a relationship exists between cerebrovascular disease and Alzheimer's disease has been a source of controversy. Evaluation of the temporal progression of imaging biomarkers of these disease processes may inform mechanistic associations. We investigate the relationship of disease trajectories of cerebrovascular disease (white matter hyperintensity, WMH, and fractional anisotropy, FA) and Alzheimer's disease (amyloid and tau PET) biomarkers in 2406 Mayo Clinic Study of Aging and Mayo Alzheimer's Disease Research Center participants using accelerated failure time models. The model assumes a common pattern of progression for each biomarker that is shifted earlier or later in time for each individual and represented by a per participant age adjustment. An individual's amyloid and tau PET adjustments show very weak temporal association with WMH and FA adjustments (R = -0.07 to 0.07); early/late amyloid or tau timing explains <1% of the variation in WMH and FA adjustment. Earlier onset of amyloid is associated with earlier onset of tau (R = 0.57, R2 = 32%). These findings support a strong mechanistic relationship between amyloid and tau aggregation, but not between WMH or FA and amyloid or tau PET.
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Affiliation(s)
- Petrice M Cogswell
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA.
| | - Emily S Lundt
- Department of Quantitative Health Sciences, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Terry M Therneau
- Department of Quantitative Health Sciences, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Carly T Mester
- Department of Quantitative Health Sciences, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Heather J Wiste
- Department of Quantitative Health Sciences, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | | | | | - Matthew L Senjem
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
- Department of Information Technology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Jeffrey L Gunter
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Robert I Reid
- Department of Information Technology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Scott A Przybelski
- Department of Quantitative Health Sciences, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - David S Knopman
- Department of Neurology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Prashanthi Vemuri
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Ronald C Petersen
- Department of Quantitative Health Sciences, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
- Department of Neurology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
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5
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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.
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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.
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6
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Chen Q, Abrigo J, Deng M, Shi L, Wang YX, Chu WCW. Diffusion Changes in Hippocampal Cingulum in Early Biologically Defined Alzheimer's Disease. J Alzheimers Dis 2023; 91:1007-1017. [PMID: 36530082 DOI: 10.3233/jad-220671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
BACKGROUND Diagnosis of Alzheimer's disease (AD) was recently shifted from clinical to biological construct to reflect underlying neuropathological status, where amyloid deposition designated patients to the Alzheimer's continuum, and additional tau positivity represented AD. OBJECTIVE To investigate white matter (WM) alteration in the brain of patients in the Alzheimer's continuum. METHODS A total of 236 subjects across the clinical and biological spectra of AD were included and stratified by normal/abnormal (-/+) amyloid (A) and tau (T) status based on positron emission tomography results, yielding five groups: A-T-cognitively normal (CN), A+T-CN, A+T+ CN, A+T+ mild cognitive impairment, and A+T+ AD. WM alteration was measured by diffusion tensor imaging (DTI). Group differences, correlation of DTI measures with amyloid and tau, and diagnostic performance of such measures were evaluated. RESULTS Compared with A-T-CN, widespread WM alteration was observed in the Alzheimer's continuum, including hippocampal cingulum (CGH), cingulum of the cingulate gyrus, and uncinate fasciculus. Diffusion changes measured by regional mean fractional anisotropy (FA) in the bilateral CGH were first detected in the A+T+ CN group and associated with tau burden in the Alzheimer's continuum (p < 0.001). For discrimination between A+T+ CN and A-T-CN groups, CGH FA achieved accuracy, sensitivity, and specificity of 74%, 58%, and 78% for right CGH and 57%, 83%, and 47% respectively for left CGH. CONCLUSION WM alteration is widespread in the Alzheimer's continuum. Diffusion alteration in CGH occurred early and was correlated with tau pathology, thus may be a promising biomarker in preclinical AD.
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Affiliation(s)
- Qianyun Chen
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Jill Abrigo
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Min Deng
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Lin Shi
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Yi-Xiang Wang
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Winnie Chiu Wing Chu
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
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7
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Hoffman LJ, Ngo CT, Canada KL, Pasternak O, Zhang F, Riggins T, Olson IR. The fornix supports episodic memory during childhood. Cereb Cortex 2022; 32:5388-5403. [PMID: 35169831 PMCID: PMC9712741 DOI: 10.1093/cercor/bhac022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 01/12/2022] [Accepted: 01/13/2022] [Indexed: 12/27/2022] Open
Abstract
Episodic memory relies on the coordination of widespread brain regions that reconstruct spatiotemporal details of an episode. These topologically dispersed brain regions can rapidly communicate through structural pathways. Research in animal and human lesion studies implicate the fornix-the major output pathway of the hippocampus-in supporting various aspects of episodic memory. Because episodic memory undergoes marked changes in early childhood, we tested the link between the fornix and episodic memory in an age window of robust memory development (ages 4-8 years). Children were tested on the stories subtest from the Children's Memory Scale, a temporal order memory task, and a source memory task. Fornix streamlines were reconstructed using probabilistic tractography to estimate fornix microstructure. In addition, we measured fornix macrostructure and computed free water. To assess selectivity of our findings, we also reconstructed the uncinate fasciculus. Findings show that children's memory increases from ages 4 to 8 and that fornix micro- and macrostructure increases between ages 4 and 8. Children's memory performance across nearly every memory task correlated with individual differences in fornix, but not uncinate fasciculus, white matter. These findings suggest that the fornix plays an important role in supporting the development of episodic memory, and potentially semantic memory, in early childhood.
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Affiliation(s)
- Linda J Hoffman
- Department of Psychology, Temple University, 1701 North 13th St., Philadelphia, PA 19122, USA
| | - Chi T Ngo
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany
| | - Kelsey L Canada
- Institute of Gerontology, Wayne State University, 87 East Ferry St., Detroit, MI 48202, USA
| | - Ofer Pasternak
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St., Boston, MA 02115, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St., Boston MA 02115, USA
| | - Fan Zhang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St., Boston MA 02115, USA
| | - Tracy Riggins
- Department of Psychology, University of Maryland, 4094 Campus Dr., College Park, MD, 20742, USA
| | - Ingrid R Olson
- Department of Psychology, Temple University, 1701 North 13th St., Philadelphia, PA 19122, USA
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8
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Vlegels N, Ossenkoppele R, van der Flier WM, Koek HL, Reijmer YD, Wisse LEM, Biessels GJ. Does Loss of Integrity of the Cingulum Bundle Link Amyloid-β Accumulation and Neurodegeneration in Alzheimer’s Disease? J Alzheimers Dis 2022; 89:39-49. [DOI: 10.3233/jad-220024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: Alzheimer’s disease is characterized by the accumulation of amyloid-β (Aβ) into plaques, aggregation of tau into neurofibrillary tangles, and neurodegenerative processes including atrophy. However, there is a poorly understood spatial discordance between initial Aβ deposition and local neurodegeneration. Objective: Here, we test the hypothesis that the cingulum bundle links Aβ deposition in the cingulate cortex to medial temporal lobe (MTL) atrophy. Methods: 21 participants with mild cognitive impairment (MCI) from the UMC Utrecht memory clinic (UMCU, discovery sample) and 37 participants with MCI from Alzheimer’s Disease Neuroimaging Initiative (ADNI, replication sample) with available Aβ-PET scan, T1-weighted and diffusion-weighted MRI were included. Aβ load of the cingulate cortex was measured by the standardized uptake value ratio (SUVR), white matter integrity of the cingulum bundle was assessed by mean diffusivity and atrophy of the MTL by normalized MTL volume. Relationships were tested with linear mixed models, to accommodate multiple measures for each participant. Results: We found at most a weak association between cingulate Aβ and MTL volume (added R2 <0.06), primarily for the posterior hippocampus. In neither sample, white matter integrity of the cingulum bundle was associated with cingulate Aβ or MTL volume (added R2 <0.01). Various sensitivity analyses (Aβ-positive individuals only, posterior cingulate SUVR, MTL sub region volume) provided similar results. Conclusion: These findings, consistent in two independent cohorts, do not support our hypothesis that loss of white matter integrity of the cingulum is a connecting factor between cingulate gyrus Aβ deposition and MTL atrophy.
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Affiliation(s)
- Naomi Vlegels
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Rik Ossenkoppele
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Clinical Memory Research Unit, Lund University, Lund, Sweden
| | - Wiesje M. van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Epidemiology and Data Science, VU University Medical Center, Amsterdam, The Netherlands
| | - Huiberdina L. Koek
- Department of Geriatrics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Yael D. Reijmer
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Laura EM Wisse
- Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - Geert Jan Biessels
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
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9
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Benitez A, Jensen JH, Thorn K, Dhiman S, Fountain-Zaragoza S, Rieter WJ, Spampinato MV, Hamlett ED, Nietert PJ, Falangola MDF, Helpern JA. Greater diffusion restriction in white matter in Preclinical Alzheimer's disease. Ann Neurol 2022; 91:864-877. [PMID: 35285067 DOI: 10.1002/ana.26353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 02/14/2022] [Accepted: 03/07/2022] [Indexed: 11/07/2022]
Abstract
OBJECTIVE The Alzheimer's Continuum is biologically defined by beta-amyloid deposition which, at the earliest stages, is superimposed upon white matter degeneration in aging. However, the extent to which these co-occurring changes are characterized is relatively under-explored. The goal of this study was to use Diffusional Kurtosis Imaging (DKI) and biophysical modeling to detect and describe amyloid-related white matter changes in preclinical Alzheimer's disease (AD). METHODS Cognitively unimpaired participants ages 45-85 completed brain MRI, amyloid PET (florbetapir), neuropsychological testing, and other clinical measures at baseline in a cohort study. We tested whether beta amyloid-negative (AB-) and -positive (AB+) participants differed on DKI-based conventional (i.e. Fractional Anisotropy [FA], Mean Diffusivity [MD], Mean Kurtosis [MK]) and modeling (i.e. Axonal Water Fraction [AWF], extra-axonal radial diffusivity [De,⊥ ]) metrics, and whether these metrics were associated with other biomarkers. RESULTS We found significantly greater diffusion restriction (higher FA/AWF, lower MD/ De,⊥ ) in white matter in AB+ than AB- (partial η2 = 0.08-0.19), more notably in the extra-axonal space within primarily late-myelinating tracts. Diffusion metrics predicted amyloid status incrementally over age (AUC=0.84) with modest yet selective associations, where AWF (a marker of axonal density) correlated with speed/executive functions and neurodegeneration, whereas De,⊥ (a marker of gliosis/myelin repair) correlated with amyloid deposition and white matter hyperintensity volume. INTERPRETATION These results support prior evidence of a non-monotonic change in diffusion behavior, where an early increase in diffusion restriction is hypothesized to reflect inflammation and myelin repair prior to an ensuing decrease in diffusion restriction, indicating glial and neuronal degeneration. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Andreana Benitez
- Department of Neurology, Medical University of South Carolina, Charleston, SC, USA
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC, USA
- Department of Radiology and Radiological Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - Jens H Jensen
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC, USA
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA
| | - Kathryn Thorn
- Department of Neurology, Medical University of South Carolina, Charleston, SC, USA
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA
| | - Siddhartha Dhiman
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA
| | - Stephanie Fountain-Zaragoza
- Department of Neurology, Medical University of South Carolina, Charleston, SC, USA
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC, USA
| | - William J Rieter
- Department of Radiology and Radiological Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - Maria Vittoria Spampinato
- Department of Radiology and Radiological Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - Eric D Hamlett
- Department of Pathology and Laboratory Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - Paul J Nietert
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - Maria de Fatima Falangola
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC, USA
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA
| | - Joseph A Helpern
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC, USA
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA
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10
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Seshadri S, Caunca MR, Rundek T. Vascular Dementia and Cognitive Impairment. Stroke 2022. [DOI: 10.1016/b978-0-323-69424-7.00018-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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11
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Yu M, Sporns O, Saykin AJ. The human connectome in Alzheimer disease - relationship to biomarkers and genetics. Nat Rev Neurol 2021; 17:545-563. [PMID: 34285392 PMCID: PMC8403643 DOI: 10.1038/s41582-021-00529-1] [Citation(s) in RCA: 87] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/10/2021] [Indexed: 02/06/2023]
Abstract
The pathology of Alzheimer disease (AD) damages structural and functional brain networks, resulting in cognitive impairment. The results of recent connectomics studies have now linked changes in structural and functional network organization in AD to the patterns of amyloid-β and tau accumulation and spread, providing insights into the neurobiological mechanisms of the disease. In addition, the detection of gene-related connectome changes might aid in the early diagnosis of AD and facilitate the development of personalized therapeutic strategies that are effective at earlier stages of the disease spectrum. In this article, we review studies of the associations between connectome changes and amyloid-β and tau pathologies as well as molecular genetics in different subtypes and stages of AD. We also highlight the utility of connectome-derived computational models for replicating empirical findings and for tracking and predicting the progression of biomarker-indicated AD pathophysiology.
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Affiliation(s)
- Meichen Yu
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana University Network Science Institute, Bloomington, IN, USA
| | - Olaf Sporns
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana University Network Science Institute, Bloomington, IN, USA
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Andrew J Saykin
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA.
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.
- Indiana University Network Science Institute, Bloomington, IN, USA.
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12
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Kara F, Belloy ME, Voncken R, Sarwari Z, Garima Y, Anckaerts C, Langbeen A, Leysen V, Shah D, Jacobs J, Hamaide J, Bols P, Van Audekerke J, Daans J, Guglielmetti C, Kantarci K, Prevot V, Roßner S, Ponsaerts P, Van der Linden A, Verhoye M. Long-term ovarian hormone deprivation alters functional connectivity, brain neurochemical profile and white matter integrity in the Tg2576 amyloid mouse model of Alzheimer's disease. Neurobiol Aging 2021; 102:139-150. [PMID: 33765427 PMCID: PMC8312737 DOI: 10.1016/j.neurobiolaging.2021.02.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 02/05/2021] [Accepted: 02/16/2021] [Indexed: 01/18/2023]
Abstract
Premenopausal bilateral ovariectomy is considered to be one of the risk factors of Alzheimer's disease (AD). However, the underlying mechanisms remain unclear. Here, we aimed to investigate long-term neurological consequences of ovariectomy in a rodent AD model, TG2576 (TG), and wild-type mice (WT) that underwent an ovariectomy or sham-operation, using in vivo MRI biomarkers. An increase in osmoregulation and energy metabolism biomarkers in the hypothalamus, a decrease in white matter integrity, and a decrease in the resting-state functional connectivity was observed in ovariectomized TG mice compared to sham-operated TG mice. In addition, we observed an increase in functional connectivity in ovariectomized WT mice compared to sham-operated WT mice. Furthermore, genotype (TG vs. WT) effects on imaging markers and GFAP immunoreactivity levels were observed, but there was no effect of interaction (Genotype × Surgery) on amyloid-beta-and GFAP immunoreactivity levels. Taken together, our results indicated that both genotype and ovariectomy alters imaging biomarkers associated with AD.
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Affiliation(s)
- Firat Kara
- Bio-imaging Lab- Member of INMIND consortium, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium.
| | - Michael E Belloy
- Bio-imaging Lab- Member of INMIND consortium, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Rick Voncken
- Bio-imaging Lab- Member of INMIND consortium, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Zahra Sarwari
- Bio-imaging Lab- Member of INMIND consortium, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Yadav Garima
- Bio-imaging Lab- Member of INMIND consortium, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Cynthia Anckaerts
- Bio-imaging Lab- Member of INMIND consortium, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - An Langbeen
- Veterinary Physiology and Biochemistry, Department of Veterinary Sciences, University of Antwerp, Antwerp, Belgium
| | - Valerie Leysen
- Univ. Lille, Inserm, CHU Lille, Development and Plasticity of the Neuroendocrine Brain, Lille Neurosciences and Cognition, UMR-S1172, DistalZ, Lille, France
| | - Disha Shah
- Bio-imaging Lab- Member of INMIND consortium, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Jules Jacobs
- University of Nijmegen, Nijmegen, the Netherlands
| | - Julie Hamaide
- Bio-imaging Lab- Member of INMIND consortium, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Peter Bols
- Veterinary Physiology and Biochemistry, Department of Veterinary Sciences, University of Antwerp, Antwerp, Belgium
| | - Johan Van Audekerke
- Bio-imaging Lab- Member of INMIND consortium, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Jasmijn Daans
- Laboratory of Experimental Hematology, Vaccine and Infectious Disease Institute (Vaxinfectio), University of Antwerp, Antwerp, Belgium
| | | | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Vincent Prevot
- Univ. Lille, Inserm, CHU Lille, Development and Plasticity of the Neuroendocrine Brain, Lille Neurosciences and Cognition, UMR-S1172, DistalZ, Lille, France
| | - Steffen Roßner
- Paul Flechsig Institute of Brain Research, Leipzig University, Leipzig, Germany
| | - Peter Ponsaerts
- Laboratory of Experimental Hematology, Vaccine and Infectious Disease Institute (Vaxinfectio), University of Antwerp, Antwerp, Belgium
| | - Annemie Van der Linden
- Bio-imaging Lab- Member of INMIND consortium, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Marleen Verhoye
- Bio-imaging Lab- Member of INMIND consortium, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
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13
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Wang YJ, Hu H, Yang YX, Zuo CT, Tan L, Yu JT. Regional Amyloid Accumulation and White Matter Integrity in Cognitively Normal Individuals. J Alzheimers Dis 2021; 74:1261-1270. [PMID: 32176644 DOI: 10.3233/jad-191350] [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: 11/15/2022]
Abstract
BACKGROUND Recent studies have shown that amyloid-β (Aβ) burden influenced white matter (WM) integrity before the onset of dementia. OBJECTIVE To assess whether the effects of Aβ burden on WM integrity in cognitively normal (CN) individuals were regionally specific. METHODS Our cohort consisted of 71 CNs from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database who underwent both AV45 amyloid-PET and diffusion tensor imaging. Standardized uptake value ratio (SUVR) was computed across four bilateral regions of interest (ROIs) corresponding to four stages of in vivo amyloid staging model (Amyloid stages I-IV). Linear regression models were conducted in entire CN group and between APOEɛ4 carriers and non-carriers. RESULTS Our results indicated that higher global Aβ-SUVR was associated with higher mean diffusivity (MD) in the entire CN group (p = 0.023), and with both higher MD (p = 0.015) and lower fractional anisotropy (FA) (p = 0.026) in APOEɛ4 carriers. Subregion analysis showed that higher Amyloid stage I-II Aβ-SUVRs were associated with higher MD (Stage-1: p = 0.030; Stage-2: p = 0.016) in the entire CN group, and with both higher MD (Stage-1: p = 0.004; Stage-2: p = 0.010) and lower FA (Stage-1: p = 0.022; Stage-2: p = 0.014) in APOEɛ4 carriers. No associations were found in APOEɛ4 non-carriers and in Amyloid stage III-IV ROIs. CONCLUSIONS Our results indicated that the effects of Aβ burden on WM integrity in CNs might be regionally specific, particularly in Amyloid stage I-II ROIs, and modulated by APOEɛ4 status.
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Affiliation(s)
- Ya-Juan Wang
- Department of Neurology, Qingdao Municipal Hospital, Dalian Medical University, China
| | - Hao Hu
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, China
| | - Yu-Xiang Yang
- Department of Neurology and Institute of Neurology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Chuan-Tao Zuo
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital, Dalian Medical University, China.,Department of Neurology, Qingdao Municipal Hospital, Qingdao University, China
| | - Jin-Tai Yu
- Department of Neurology and Institute of Neurology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
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14
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Lindberg O, Kern S, Skoog J, Machado A, Pereira JB, Sacuiu SF, Wahlund LO, Blennow K, Zetterberg H, Zettergren A, Westman E, Skoog I. Effects of amyloid pathology and the APOE ε4 allele on the association between cerebrospinal fluid Aβ38 and Aβ40 and brain morphology in cognitively normal 70-years-olds. Neurobiol Aging 2021; 101:1-12. [PMID: 33548794 DOI: 10.1016/j.neurobiolaging.2020.10.033] [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: 06/09/2020] [Revised: 10/26/2020] [Accepted: 10/29/2020] [Indexed: 11/25/2022]
Abstract
The association between cerebrospinal fluid (CSF) amyloid beta (Aβ) Aβ38 or Aβ40 and brain grey- and white matter integrity is poorly understood. We studied this in 213 cognitively normal 70-year-olds, and in subgroups defined by presence/absence of the APOE ε4 allele and Aβ pathology: Aβ-/APOE-, Aβ+/APOE-, Aβ-/APOE+ and Aβ+/APOE+. CSF Aβ was quantified using ELISA and genotyping for APOE was performed. Low CSF Aβ42 defined Aβ plaque pathology. Brain volumes were assessed using Freesurfer-5.3, and white matter integrity using tract-based statistics in FSL. Aβ38 and Aβ40 were positively correlated with cortical thickness, some subcortical volumes and white matter integrity in the total sample, and in 3 of the subgroups: Aβ-/APOE-, Aβ+/APOE- and Aβ-/APOE+. In Aβ+/APOE+ subjects, higher Aβ38 and Aβ40 were linked to reduced cortical thickness and subcortical volumes. We hypothesize that production of all Aβ species decrease in brain regions with atrophy. In Aβ+/APOE+, Aβ-dysregulation may be linked to cortical atrophy in which high Aβ levels is causing pathological changes in the gray matter of the brain.
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Affiliation(s)
- Olof Lindberg
- Division of Clinical Geriatrics, Department of Neurobiology, Center for Alzheimer Research, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
| | - Silke Kern
- Region Västra Götaland, Sahlgrenska University Hospital, Psychiatry Cognition and Old Age Psychiatry Clinic, Mölndal, Sweden; Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Mölndal, Sweden
| | - Johan Skoog
- Region Västra Götaland, Sahlgrenska University Hospital, Psychiatry Cognition and Old Age Psychiatry Clinic, Mölndal, Sweden; Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Mölndal, Sweden; Department of Psychology, University of Gothenburg, Gothenburg, Sweden
| | - Alejandra Machado
- Division of Clinical Geriatrics, Department of Neurobiology, Center for Alzheimer Research, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Joana B Pereira
- Division of Clinical Geriatrics, Department of Neurobiology, Center for Alzheimer Research, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Simona F Sacuiu
- Division of Clinical Geriatrics, Department of Neurobiology, Center for Alzheimer Research, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Region Västra Götaland, Sahlgrenska University Hospital, Psychiatry Cognition and Old Age Psychiatry Clinic, Mölndal, Sweden; Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Mölndal, Sweden
| | - Lars-Olof Wahlund
- Division of Clinical Geriatrics, Department of Neurobiology, Center for Alzheimer Research, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Kaj Blennow
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden; Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
| | - Henrik Zetterberg
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden; Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden; UK Dementia Research Institute at UCL, London, UK; Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
| | - Anna Zettergren
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Mölndal, Sweden
| | - Eric Westman
- Division of Clinical Geriatrics, Department of Neurobiology, Center for Alzheimer Research, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Ingmar Skoog
- Region Västra Götaland, Sahlgrenska University Hospital, Psychiatry Cognition and Old Age Psychiatry Clinic, Mölndal, Sweden; Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Mölndal, Sweden
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15
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Relationship between the disrupted topological efficiency of the structural brain connectome and glucose hypometabolism in normal aging. Neuroimage 2020; 226:117591. [PMID: 33248254 DOI: 10.1016/j.neuroimage.2020.117591] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 11/02/2020] [Accepted: 11/19/2020] [Indexed: 12/18/2022] Open
Abstract
Normal aging is accompanied by structural degeneration and glucose hypometabolism in the human brain. However, the relationship between structural network disconnections and hypometabolism in normal aging remains largely unknown. In the present study, by combining MRI and PET techniques, we investigated the metabolic mechanism of the structural brain connectome and its relationship with normal aging in a cross-sectional, community-based cohort of 42 cognitively normal elderly individuals aged 57-84 years. The structural connectome was constructed based on diffusion MRI tractography, and the network efficiency metrics were quantified using graph theory analyses. FDG-PET scanning was performed to evaluate the glucose metabolic level in the cortical regions of the individuals. The results of this study demonstrated that both network efficiency and cortical metabolism decrease with age (both p < 0.05). In the subregions of the bilateral thalamus, significant correlations between nodal efficiency and cortical metabolism could be observed across subjects. Individual-level analyses indicated that brain regions with higher nodal efficiency tend to exhibit higher metabolic levels, implying a tight coupling between nodal efficiency and glucose metabolism (r = 0.56, p = 1.15 × 10-21). Moreover, efficiency-metabolism coupling coefficient significantly increased with age (r = 0.44, p = 0.0046). Finally, the main findings were also reproducible in the ADNI dataset. Together, our results demonstrate a close coupling between structural brain connectivity and cortical metabolism in normal elderly individuals and provide new insight that improve the present understanding of the metabolic mechanisms of structural brain disconnections in normal aging.
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16
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Finsterwalder S, Vlegels N, Gesierich B, Caballero MÁA, Weaver NA, Franzmeier N, Georgakis MK, Konieczny MJ, Koek HL, Karch CM, Graff-Radford NR, Salloway S, Oh H, Allegri RF, Chhatwal JP, Jessen F, Düzel E, Dobisch L, Metzger C, Peters O, Incesoy EI, Priller J, Spruth EJ, Schneider A, Fließbach K, Buerger K, Janowitz D, Teipel SJ, Kilimann I, Laske C, Buchmann M, Heneka MT, Brosseron F, Spottke A, Roy N, Ertl-Wagner B, Scheffler K, Seo SW, Kim Y, Na DL, Kim HJ, Jang H, Ewers M, Levin J, Schmidt R, Pasternak O, Dichgans M, Biessels GJ, Duering M. Small vessel disease more than Alzheimer's disease determines diffusion MRI alterations in memory clinic patients. Alzheimers Dement 2020; 16:1504-1514. [PMID: 32808747 PMCID: PMC8102202 DOI: 10.1002/alz.12150] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 06/25/2020] [Accepted: 06/25/2020] [Indexed: 01/03/2023]
Abstract
INTRODUCTION Microstructural alterations as assessed by diffusion tensor imaging (DTI) are key findings in both Alzheimer's disease (AD) and small vessel disease (SVD). We determined the contribution of each of these conditions to diffusion alterations. METHODS We studied six samples (N = 365 participants) covering the spectrum of AD and SVD, including genetically defined samples. We calculated diffusion measures from DTI and free water imaging. Simple linear, multivariable random forest, and voxel-based regressions were used to evaluate associations between AD biomarkers (amyloid beta, tau), SVD imaging markers, and diffusion measures. RESULTS SVD markers were strongly associated with diffusion measures and showed a higher contribution than AD biomarkers in multivariable analysis across all memory clinic samples. Voxel-wise analyses between tau and diffusion measures were not significant. DISCUSSION In memory clinic patients, the effect of SVD on diffusion alterations largely exceeds the effect of AD, supporting the value of diffusion measures as markers of SVD.
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Affiliation(s)
- Sofia Finsterwalder
- Institute for Stroke and Dementia Research, University Hospital, LMU Munich, Munich, Germany
| | - Naomi Vlegels
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Benno Gesierich
- Institute for Stroke and Dementia Research, University Hospital, LMU Munich, Munich, Germany
| | - Miguel Á. Araque Caballero
- Institute for Stroke and Dementia Research, University Hospital, LMU Munich, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Nick A. Weaver
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Nicolai Franzmeier
- Institute for Stroke and Dementia Research, University Hospital, LMU Munich, Munich, Germany
| | - Marios K. Georgakis
- Institute for Stroke and Dementia Research, University Hospital, LMU Munich, Munich, Germany
| | - Marek J. Konieczny
- Institute for Stroke and Dementia Research, University Hospital, LMU Munich, Munich, Germany
| | - Huiberdina L. Koek
- Department of Geriatrics, University Medical Center Utrecht, Utrecht, Netherlands
| | | | - Celeste M. Karch
- Department of Psychiatry, Washington University in St Louis, St Louis, MO, USA
| | | | | | - Hwamee Oh
- Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Ricardo F. Allegri
- Department of Cognitive Neurology, FLENI Institute for Neurological Research, Buenos Aires, Argentina
| | | | | | - Frank Jessen
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Psychiatry, University of Cologne, Medical Faculty, Cologne, Germany
| | - Emrah Düzel
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany
| | - Laura Dobisch
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany
| | - Coraline Metzger
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany
- Department of Psychiatry and Psychotherapy, Otto-von-Guericke University, Magdeburg, Germany
| | - Oliver Peters
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Psychiatry and Psychotherapy, Berlin, Germany
| | - Enise I. Incesoy
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Psychiatry and Psychotherapy, Berlin, Germany
| | - Josef Priller
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Psychiatry and Psychotherapy, Berlin, Germany
| | - Eike J. Spruth
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Psychiatry and Psychotherapy, Berlin, Germany
| | - Anja Schneider
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Klaus Fließbach
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Katharina Buerger
- Institute for Stroke and Dementia Research, University Hospital, LMU Munich, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Daniel Janowitz
- Institute for Stroke and Dementia Research, University Hospital, LMU Munich, Munich, Germany
| | - Stefan J. Teipel
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
- Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Ingo Kilimann
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
- Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Christoph Laske
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
- Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Martina Buchmann
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
- Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Michael T. Heneka
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Frederic Brosseron
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Annika Spottke
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurology, University of Bonn, Bonn, Germany
| | - Nina Roy
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Birgit Ertl-Wagner
- Institute of Clinical Radiology, University Hospital, LMU Munich, Munich, Germany
- Department of Medical Imaging, University of Toronto, Toronto, Canada
| | - Klaus Scheffler
- Department for Biomedical Magnetic Resonance, University of Tübingen, Tübingen, Germany
| | | | | | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Korea
- Department of Clinical Research Design and Evaluation, Samsung Advanced Institute of Health Sciences and Technology, Sungkyunkwan University, Seoul, Korea
- Center for Imaging of Neurodegenerative Diseases, University of California, San Francisco
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Korea
| | - Yeshin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Korea
- Department of Neurology, Kangwon National University Hospital, Kangwon National University College of Medicine Chuncheon, Republic of Korea
| | - Duk L. Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Korea
- Department of Health Sciences and Technology, Samsung Advanced Institute of Health Sciences and Technology, Sungkyunkwan University, Seoul, Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Korea
| | - Michael Ewers
- Institute for Stroke and Dementia Research, University Hospital, LMU Munich, Munich, Germany
| | - Johannes Levin
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Department of Neurology, University Hospital, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Reinhold Schmidt
- Department of Neurology, Medical University of Graz, Graz, Austria
| | - Ofer Pasternak
- Department of Psychiatry and Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Martin Dichgans
- Institute for Stroke and Dementia Research, University Hospital, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Geert Jan Biessels
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Marco Duering
- Institute for Stroke and Dementia Research, University Hospital, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
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17
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Wang Z, Bai L, Liu Q, Wang S, Sun C, Zhang M, Zhang Y. Corpus callosum integrity loss predicts cognitive impairment in Leukoaraiosis. Ann Clin Transl Neurol 2020; 7:2409-2420. [PMID: 33119959 PMCID: PMC7732249 DOI: 10.1002/acn3.51231] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 09/24/2020] [Accepted: 10/06/2020] [Indexed: 12/29/2022] Open
Abstract
Objective To investigate regional white matter fibers loss in Leukoaraiosis (LA) and its relationship with cognitive impairments. Methods Fifty‐six participants with LA and 38 healthy controls underwent clinical evaluations and MR scans. Participants with LA were classified as cognitively normal (LA‐NC, n = 18), vascular cognitive impairment of none dementia (LA‐VCIND, n = 24), and vascular dementia (LA‐VaD, n = 14) by Mini‐Mental State Examination and Clinical Dementia Rating. Cognitive domains including visual‐spatial, naming, attention, language, abstraction, memory, and orientation were assessed. With the use of Tract‐based spatial statistics, mean fractional anisotropy (FA) of major white matter fiber tracts were compared between LA and controls and among LA groups with varying levels of cognitive impairments. Regression analyses were performed to evaluate relationships between FA values and cognitive performance. Results Participants showed significant FA reduction in the corpus callosum (CC), bilateral corona radiata, anterior limb of the internal capsule, external capsule, posterior thalamic radiation, and superior longitudinal fasciculus compared to controls and across LA groups. The LA‐VaD group showed consistent damage in the body and genu of CC compared to the LA‐NC and LA‐VCIND groups. A positive correlation between visual‐spatial and FA reduction in right anterior corona radiates in LA‐VCIND and body of CC in LA‐ VaD. Interpretation We found regional fiber loss in the CC across the cognitive spectrum in patients with LA and correlations between FA and visuospatial impairment in the anterior corona radiata in patients with LA‐VCIND and in the body of CC in patients with LA‐VaD.
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Affiliation(s)
- Zhuonan Wang
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.,Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Lijun Bai
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Qi Liu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Shan Wang
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Chuanzhu Sun
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Ming Zhang
- Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yumei Zhang
- Department of Rehabilitation, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
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18
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Luo C, Li M, Qin R, Chen H, Huang L, Yang D, Ye Q, Liu R, Xu Y, Zhao H, Bai F. Long Longitudinal Tract Lesion Contributes to the Progression of Alzheimer's Disease. Front Neurol 2020; 11:503235. [PMID: 33178095 PMCID: PMC7597387 DOI: 10.3389/fneur.2020.503235] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Accepted: 08/13/2020] [Indexed: 11/16/2022] Open
Abstract
Background: The degenerative pattern of white matter (WM) microstructures during Alzheimer's disease (AD) and its relationship with cognitive function have not yet been clarified. The present research aimed to explore the alterations of the WM microstructure and its impact on amnestic mild cognitive (aMCI) and AD patients. Mechanical learning methods were used to explore the validity of WM microstructure lesions on the classification in AD spectrum disease. Methods: Neuropsychological data and diffusion tensor imaging (DTI) images were collected from 28 AD subjects, 31 aMCI subjects, and 27 normal controls (NC). Tract-based spatial statistics (TBSS) were used to extract diffusion parameters in WM tracts. We performed ANOVA analysis to compare diffusion parameters and clinical features among the three groups. Partial correlation analysis was used to explore the relationship between diffusion metrics and cognitive functions controlling for age, gender, and years of education. Additionally, we performed the support vector machine (SVM) classification to determine the discriminative ability of DTI metrics in the differentiation of aMCI and AD patients from controls. Results: As compared to controls or aMCI patients, AD patients displayed widespread WM lesions, including in the inferior longitudinal fasciculus, inferior fronto-occipital fasciculi, and superior longitudinal fasciculus. Significant correlations between fractional anisotropy (FA), mean diffusivity (MD), and radial diffusion (RD) of the long longitudinal tract and memory deficits were found in aMCI and AD groups, respectively. Furthermore, through SVM classification, we found DTI indicators generated by FA and MD parameters can effectively distinguish AD patients from the control group with accuracy rates of up to 89 and 85%, respectively. Conclusion: The WM microstructure is extensively disrupted in AD patients, and the WM integrity of the long longitudinal tract is closely related to memory, which would hold potential value for monitoring the progression of AD. The method of classification based on SVM and WM damage features may be objectively helpful to the classification of AD diseases.
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Affiliation(s)
- Caimei Luo
- The State Key Laboratory of Pharmaceutical Biotechnology, Department of Neurology, Affiliated Drum Tower Hospital of Medical School, Institute of Brain Science, Nanjing University, Nanjing, China.,Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Mengchun Li
- The State Key Laboratory of Pharmaceutical Biotechnology, Department of Neurology, Affiliated Drum Tower Hospital of Medical School, Institute of Brain Science, Nanjing University, Nanjing, China.,Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Ruomeng Qin
- The State Key Laboratory of Pharmaceutical Biotechnology, Department of Neurology, Affiliated Drum Tower Hospital of Medical School, Institute of Brain Science, Nanjing University, Nanjing, China.,Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Haifeng Chen
- The State Key Laboratory of Pharmaceutical Biotechnology, Department of Neurology, Affiliated Drum Tower Hospital of Medical School, Institute of Brain Science, Nanjing University, Nanjing, China.,Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Lili Huang
- The State Key Laboratory of Pharmaceutical Biotechnology, Department of Neurology, Affiliated Drum Tower Hospital of Medical School, Institute of Brain Science, Nanjing University, Nanjing, China.,Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Dan Yang
- The State Key Laboratory of Pharmaceutical Biotechnology, Department of Neurology, Affiliated Drum Tower Hospital of Medical School, Institute of Brain Science, Nanjing University, Nanjing, China.,Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Qing Ye
- The State Key Laboratory of Pharmaceutical Biotechnology, Department of Neurology, Affiliated Drum Tower Hospital of Medical School, Institute of Brain Science, Nanjing University, Nanjing, China.,Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Renyuan Liu
- The State Key Laboratory of Pharmaceutical Biotechnology, Department of Neurology, Affiliated Drum Tower Hospital of Medical School, Institute of Brain Science, Nanjing University, Nanjing, China.,Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Yun Xu
- The State Key Laboratory of Pharmaceutical Biotechnology, Department of Neurology, Affiliated Drum Tower Hospital of Medical School, Institute of Brain Science, Nanjing University, Nanjing, China.,Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Hui Zhao
- The State Key Laboratory of Pharmaceutical Biotechnology, Department of Neurology, Affiliated Drum Tower Hospital of Medical School, Institute of Brain Science, Nanjing University, Nanjing, China.,Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Feng Bai
- The State Key Laboratory of Pharmaceutical Biotechnology, Department of Neurology, Affiliated Drum Tower Hospital of Medical School, Institute of Brain Science, Nanjing University, Nanjing, China.,Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
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19
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Falangola MF, Nie X, Ward R, McKinnon ET, Dhiman S, Nietert PJ, Helpern JA, Jensen JH. Diffusion MRI detects early brain microstructure abnormalities in 2-month-old 3×Tg-AD mice. NMR IN BIOMEDICINE 2020; 33:e4346. [PMID: 32557874 PMCID: PMC7683375 DOI: 10.1002/nbm.4346] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 05/08/2020] [Accepted: 05/20/2020] [Indexed: 06/11/2023]
Abstract
The 3×Tg-AD mouse is one of the most studied animal models of Alzheimer's disease (AD), and develops both amyloid beta deposits and neurofibrillary tangles in a temporal and spatial pattern that is similar to human AD pathology. Additionally, abnormal myelination patterns with changes in oligodendrocyte and myelin marker expression are reported to be an early pathological feature in this model. Only few diffusion MRI (dMRI) studies have investigated white matter abnormalities in 3×Tg-AD mice, with inconsistent results. Thus, the goal of this study was to investigate the sensitivity of dMRI to capture brain microstructural alterations in 2-month-old 3×Tg-AD mice. In the fimbria, the fractional anisotropy (FA), kurtosis fractional anisotropy (KFA), and radial kurtosis (K┴ ) were found to be significantly lower in 3×Tg-AD mice than in controls, while the mean diffusivity (MD) and radial diffusivity (D┴ ) were found to be elevated. In the fornix, K┴ was lower for 3×Tg-AD mice; in the dorsal hippocampus MD and D┴ were elevated, as were FA, MD, and D┴ in the ventral hippocampus. These results indicate, for the first time, dMRI changes associated with myelin abnormalities in young 3×Tg-AD mice, before they develop AD pathology. Morphological quantification of myelin basic protein immunoreactivity in the fimbria was significantly lower in the 3×Tg-AD mice compared with the age-matched controls. Our results demonstrate that dMRI is able to detect widespread, significant early brain morphological abnormalities in 2-month-old 3×Tg-AD mice.
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Affiliation(s)
- Maria Fatima Falangola
- Department of Neuroscience, Medical University of South Carolina, Charleston, South Carolina, US
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, South Carolina, US
| | - Xingju Nie
- Department of Neuroscience, Medical University of South Carolina, Charleston, South Carolina, US
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, South Carolina, US
| | - Ralph Ward
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, US
| | - Emilie T McKinnon
- Department of Neuroscience, Medical University of South Carolina, Charleston, South Carolina, US
- Department of Neurology, Medical University of South Carolina, Charleston, South Carolina, US
| | - Siddhartha Dhiman
- Department of Neuroscience, Medical University of South Carolina, Charleston, South Carolina, US
| | - Paul J Nietert
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, US
| | - Joseph A Helpern
- Department of Neuroscience, Medical University of South Carolina, Charleston, South Carolina, US
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, South Carolina, US
| | - Jens H Jensen
- Department of Neuroscience, Medical University of South Carolina, Charleston, South Carolina, US
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, South Carolina, US
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina, US
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20
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Harrison JR, Bhatia S, Tan ZX, Mirza-Davies A, Benkert H, Tax CMW, Jones DK. Imaging Alzheimer's genetic risk using diffusion MRI: A systematic review. Neuroimage Clin 2020; 27:102359. [PMID: 32758801 PMCID: PMC7399253 DOI: 10.1016/j.nicl.2020.102359] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Revised: 06/20/2020] [Accepted: 07/20/2020] [Indexed: 12/14/2022]
Abstract
Diffusion magnetic resonance imaging (dMRI) is an imaging technique which probes the random motion of water molecules in tissues and has been widely applied to investigate changes in white matter microstructure in Alzheimer's Disease. This paper aims to systematically review studies that examined the effect of Alzheimer's risk genes on white matter microstructure. We assimilated findings from 37 studies and reviewed their diffusion pre-processing and analysis methods. Most studies estimate the diffusion tensor (DT) and compare derived quantitative measures such as fractional anisotropy and mean diffusivity between groups. Those with increased AD genetic risk are associated with reduced anisotropy and increased diffusivity across the brain, most notably the temporal and frontal lobes, cingulum and corpus callosum. Structural abnormalities are most evident amongst those with established Alzheimer's Disease. Recent studies employ signal representations and analysis frameworks beyond DT MRI but show that dMRI overall lacks specificity to disease pathology. However, as the field advances, these techniques may prove useful in pre-symptomatic diagnosis or staging of Alzheimer's disease.
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Affiliation(s)
- Judith R Harrison
- Cardiff University Brain Research Imaging Centre (CUBRIC), Maindy Road, Cardiff CF24 4HQ, UK.
| | - Sanchita Bhatia
- Cardiff University School of Medicine, University Hospital of Wales, Heath Park, Cardiff CF14 4XN, UK
| | - Zhao Xuan Tan
- Cardiff University School of Medicine, University Hospital of Wales, Heath Park, Cardiff CF14 4XN, UK
| | - Anastasia Mirza-Davies
- Cardiff University School of Medicine, University Hospital of Wales, Heath Park, Cardiff CF14 4XN, UK
| | - Hannah Benkert
- Cardiff University School of Medicine, University Hospital of Wales, Heath Park, Cardiff CF14 4XN, UK
| | - Chantal M W Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), Maindy Road, Cardiff CF24 4HQ, UK
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), Maindy Road, Cardiff CF24 4HQ, UK; Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, VIC, Australia
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21
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Benear SL, Ngo CT, Olson IR. Dissecting the Fornix in Basic Memory Processes and Neuropsychiatric Disease: A Review. Brain Connect 2020; 10:331-354. [PMID: 32567331 DOI: 10.1089/brain.2020.0749] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background: The fornix is the primary axonal tract of the hippocampus, connecting it to modulatory subcortical structures. This review reveals that fornix damage causes cognitive deficits that closely mirror those resulting from hippocampal lesions. Methods: We reviewed the literature on the fornix, spanning non-human animal lesion research, clinical case studies of human patients with fornix damage, as well as diffusion-weighted imaging (DWI) work that evaluates fornix microstructure in vivo. Results: The fornix is essential for memory formation because it serves as the conduit for theta rhythms and acetylcholine, as well as providing mnemonic representations to deep brain structures that guide motivated behavior, such as when and where to eat. In rodents and non-human primates, fornix lesions lead to deficits in conditioning, reversal learning, and navigation. In humans, damage to the fornix manifests as anterograde amnesia. DWI research reveals that the fornix plays a key role in mild cognitive impairment and Alzheimer's Disease, and can potentially predict conversion from the former to the latter. Emerging DWI findings link perturbations in this structure to schizophrenia, mood disorders, and eating disorders. Cutting-edge research has investigated how deep brain stimulation of the fornix can potentially attenuate memory loss, control epileptic seizures, and even improve mood. Conclusions: The fornix is essential to a fully functioning memory system and is implicated in nearly all neurological functions that rely on the hippocampus. Future research needs to use optimized DWI methods to study the fornix in vivo, which we discuss, given the difficult nature of fornix reconstruction. Impact Statement The fornix is a white matter tract that connects the hippocampus to several subcortical brain regions and is pivotal for episodic memory functioning. Functionally, the fornix transmits essential neurotransmitters, as well as theta rhythms, to the hippocampus. In addition, it is the conduit by which memories guide decisions. The fornix is biomedically important because lesions to this tract result in irreversible anterograde amnesia. Research using in vivo imaging methods has linked fornix pathology to cognitive aging, mild cognitive impairment, psychosis, epilepsy, and, importantly, Alzheimer's Disease.
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Affiliation(s)
- Susan L Benear
- Department of Psychology, Temple University, Philadelphia, Pennsylvania, USA
| | - Chi T Ngo
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - Ingrid R Olson
- Department of Psychology, Temple University, Philadelphia, Pennsylvania, USA
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22
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Dong JW, Jelescu IO, Ades-Aron B, Novikov DS, Friedman K, Babb JS, Osorio RS, Galvin JE, Shepherd TM, Fieremans E. Diffusion MRI biomarkers of white matter microstructure vary nonmonotonically with increasing cerebral amyloid deposition. Neurobiol Aging 2020; 89:118-128. [PMID: 32111392 PMCID: PMC7314576 DOI: 10.1016/j.neurobiolaging.2020.01.009] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Revised: 12/14/2019] [Accepted: 01/14/2020] [Indexed: 01/27/2023]
Abstract
Beta amyloid (Aβ) accumulation is the earliest pathological marker of Alzheimer's disease (AD), but early AD pathology also affects white matter (WM) integrity. We performed a cross-sectional study including 44 subjects (23 healthy controls and 21 mild cognitive impairment or early AD patients) who underwent simultaneous PET-MR using 18F-Florbetapir, and were categorized into 3 groups based on Aβ burden: Aβ- [mean mSUVr ≤1.00], Aβi [1.00 < mSUVr <1.17], Aβ+ [mSUVr ≥1.17]. Intergroup comparisons of diffusion MRI metrics revealed significant differences across multiple WM tracts. Aβi group displayed more restricted diffusion (higher fractional anisotropy, radial kurtosis, axonal water fraction, and lower radial diffusivity) than both Aβ- and Aβ+ groups. This nonmonotonic trend was confirmed by significant continuous correlations between mSUVr and diffusion metrics going in opposite direction for 2 cohorts: pooled Aβ-/Aβi and pooled Aβi/Aβ+. The transient period of increased diffusion restriction may be due to inflammation that accompanies rising Aβ burden. In the later stages of Aβ accumulation, neurodegeneration is the predominant factor affecting diffusion.
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Affiliation(s)
- Jian W Dong
- Department of Radiology, New York University School of Medicine, New York, NY, USA; Department of Radiology, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ileana O Jelescu
- Department of Radiology, New York University School of Medicine, New York, NY, USA; Centre d'Imagerie Biomédicale, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Benjamin Ades-Aron
- Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Dmitry S Novikov
- Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Kent Friedman
- Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - James S Babb
- Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Ricardo S Osorio
- Center for Sleep and Brain Health, Department of Psychiatry, NYU Langone Medical Center, New York, NY, USA; Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - James E Galvin
- Comprehensive Center for Brain Health, Department of Neurology, University of Miami Miller School of Medicine, Boca-Raton, FL, USA
| | - Timothy M Shepherd
- Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Els Fieremans
- Department of Radiology, New York University School of Medicine, New York, NY, USA.
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23
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Chen HF, Huang LL, Li HY, Qian Y, Yang D, Qing Z, Luo CM, Li MC, Zhang B, Xu Y. Microstructural disruption of the right inferior fronto-occipital and inferior longitudinal fasciculus contributes to WMH-related cognitive impairment. CNS Neurosci Ther 2020; 26:576-588. [PMID: 31901155 PMCID: PMC7163793 DOI: 10.1111/cns.13283] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Revised: 12/07/2019] [Accepted: 12/12/2019] [Indexed: 01/03/2023] Open
Abstract
Aims White matter hyperintensity (WMH) is the most common neuroimaging manifestation of cerebral small vessel disease and is related to cognitive dysfunction or dementia. This study aimed to investigate the mechanism and effective indicators to predict WMH‐related cognitive impairment. Methods We recruited 22 healthy controls (HC), 25 cases of WMH with normal cognition (WMH‐NC), and 23 cases of WMH with mild cognitive impairment (WMH‐MCI). All individuals underwent diffusion tensor imaging (DTI) and a standardized neuropsychological assessment. Automated Fiber Quantification was used to extract altered DTI metrics between groups, and partial correlation was performed to assess the associations between WM integrity and cognitive performance. Furthermore, machine learning analyses were performed to determine underlying imaging markers of WMH‐related cognitive impairment. Results Our study found that mean diffusivity (MD) values of several fiber bundles including the bilateral anterior thalamic radiation (ATR), the left inferior fronto‐occipital fasciculus (IFOF), the right inferior longitudinal fasciculus (ILF), and the right superior longitudinal fasciculus (SLF) were negatively correlated with memory function, while that of the anterior component of the right IFOF and the posterior and intermediate component of the right ILF showed significant negative correlation with MMSE and episodic memory, respectively. Furthermore, machine learning analyses showed that the accuracy of recognizing WMH‐MCI patients from the WMH populations was up to 80.5% and the intermediate and posterior components of the right ILF and the anterior component of the right IFOF contribute the most. Conclusions Changes in the properties of DTI may be the potential mechanism of WMH‐related MCI, especially the right IFOF and the right ILF, which may become imaging markers for predicting WMH‐related cognitive dysfunction.
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Affiliation(s)
- Hai-Feng Chen
- Department of Neurology, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Drum Tower Hospital, Nanjing University, Nanjing, China.,Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Clinic Medical Center for Neurology, Nanjing, China
| | - Li-Li Huang
- Department of Neurology, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Drum Tower Hospital, Nanjing University, Nanjing, China.,Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Clinic Medical Center for Neurology, Nanjing, China
| | - Hui-Ya Li
- Department of Neurology, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Drum Tower Hospital, Nanjing University, Nanjing, China.,Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Clinic Medical Center for Neurology, Nanjing, China
| | - Yi Qian
- Department of Neurology, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Drum Tower Hospital, Nanjing University, Nanjing, China.,Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Clinic Medical Center for Neurology, Nanjing, China
| | - Dan Yang
- Department of Neurology, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Drum Tower Hospital, Nanjing University, Nanjing, China.,Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Clinic Medical Center for Neurology, Nanjing, China
| | - Zhao Qing
- Department of Radiology, Afliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Cai-Mei Luo
- Department of Neurology, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Drum Tower Hospital, Nanjing University, Nanjing, China.,Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Clinic Medical Center for Neurology, Nanjing, China
| | - Meng-Chun Li
- Department of Neurology, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Drum Tower Hospital, Nanjing University, Nanjing, China.,Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Clinic Medical Center for Neurology, Nanjing, China
| | - Bing Zhang
- Department of Radiology, Afliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Yun Xu
- Department of Neurology, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Drum Tower Hospital, Nanjing University, Nanjing, China.,Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China.,Nanjing Clinic Medical Center for Neurology, Nanjing, China
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24
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Haller S, Montandon ML, Rodriguez C, Garibotto V, Lilja J, Herrmann FR, Giannakopoulos P. Amyloid Load, Hippocampal Volume Loss, and Diffusion Tensor Imaging Changes in Early Phases of Brain Aging. Front Neurosci 2019; 13:1228. [PMID: 31803008 PMCID: PMC6872975 DOI: 10.3389/fnins.2019.01228] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 10/30/2019] [Indexed: 01/23/2023] Open
Abstract
Background and Purpose Amyloid imaging, gray matter (GM) morphometry and diffusion tensor imaging (DTI) have all been used as predictive biomarkers in dementia. Our objective was to define the imaging profile of healthy elderly controls as a function of their cognitive trajectories and explore whether amyloid burden and white matter (WM) microstructure changes are associated with subtle decrement of neuropsychological performances in old age. Materials and Methods We performed a 4.5-year longitudinal study in 133 elderly individuals who underwent cognitive testing at inclusion and follow-up, amyloid PET, MRI including DTI sequences at inclusion, and APOE epsilon 4 genotyping. All cases were assessed using a continuous cognitive score (CCS) taking into account the global evolution of neuropsychological performances. Data processing included region of interest analysis of amyloid PET analysis, GM densities and tract-based spatial statistics (TBSS)-DTI. Regression models were built to explore the association between the CCS and imaging parameters controlling for significant demographic and clinical covariates. Results Amyloid uptake was not related to the cognitive outcome. In contrast, GM densities in bilateral hippocampus were associated with worst CCS at follow-up. In addition, radial and axial diffusivities in left hippocampus were negatively associated with CCS. Amyloid load was associated with decreased VBM and increased radial and axial diffusivity in the same area. These associations persisted when adjusting for gender and APOE4 genotype. Importantly, they were absent in amygdala and neocortical areas studied. Conclusion The progressive decrement of neuropsychological performances in normal aging is associated with volume loss and WM microstructure changes in hippocampus long before the emergence of clinically overt symptoms. Higher amyloid load in hippocampus is compatible with cognitive preservation in cases with better preservation of GM densities and WM microstructure in this area.
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Affiliation(s)
- Sven Haller
- CIRD Centre d'Imagerie Rive Droite, Geneva, Switzerland.,Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden.,Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Marie-Louise Montandon
- Department of Rehabilitation and Geriatrics, Geneva University Hospitals, University of Geneva, Geneva, Switzerland.,Department of Psychiatry, University of Geneva, Geneva, Switzerland
| | - Cristelle Rodriguez
- Department of Psychiatry, University of Geneva, Geneva, Switzerland.,Division of Institutional Measures, Medical Direction, Geneva University Hospitals, Geneva, Switzerland
| | - Valentina Garibotto
- Division of Nuclear Medicine and Molecular Imaging, Department of Diagnostic, Geneva University Hospitals, Geneva, Switzerland
| | - Johan Lilja
- Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden.,Hermes Medical Solutions, Stockholm, Sweden
| | - François R Herrmann
- Department of Rehabilitation and Geriatrics, Geneva University Hospitals, University of Geneva, Geneva, Switzerland
| | - Panteleimon Giannakopoulos
- Department of Psychiatry, University of Geneva, Geneva, Switzerland.,Division of Institutional Measures, Medical Direction, Geneva University Hospitals, Geneva, Switzerland
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25
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Pereira JB, van Westen D, Stomrud E, Strandberg TO, Volpe G, Westman E, Hansson O. Abnormal Structural Brain Connectome in Individuals with Preclinical Alzheimer's Disease. Cereb Cortex 2019; 28:3638-3649. [PMID: 29028937 DOI: 10.1093/cercor/bhx236] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2017] [Indexed: 12/12/2022] Open
Abstract
Alzheimer's disease has a long preclinical phase during which amyloid pathology and neurodegeneration accumulate in the brain without producing overt cognitive deficits. It is currently unclear whether these early disease stages are associated with a progressive disruption in the communication between brain regions that subsequently leads to cognitive decline and dementia. In this study we assessed the organization of structural networks in cognitively normal (CN) individuals harboring amyloid pathology (A+N-), neurodegeneration (A-N+), or both (A+N+) from the prospective and longitudinal Swedish BioFINDER study. We combined graph theory with diffusion tensor imaging to investigate integration, segregation, and centrality measures in the brain connectome in the previous groups. At baseline, our findings revealed a disrupted network topology characterized by longer paths, lower efficiency, increased clustering and modularity in CN A-N+ and CN A+N+, but not in CN A+N-. After 2 years, CN A+N+ showed significant abnormalities in all global network measures, whereas CN A-N+ only showed abnormalities in the global efficiency. Network connectivity and organization were associated with memory in CN A+N+ individuals. Altogether, our findings suggest that amyloid pathology is not sufficient to disrupt structural network topology, whereas neurodegeneration is.
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Affiliation(s)
- Joana B Pereira
- Division of Clinical Geriatrics, Neurobiology, Care Sciences and Society Department, Karolinska Institutet, Stockholm, Sweden
| | - Danielle van Westen
- Department of Clinical Sciences Lund, Diagnostic radiology, Lund University, Lund, Sweden.,Imaging and Function, Skåne University Health Care, Lund, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden.,Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Tor Olof Strandberg
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden.,Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Giovanni Volpe
- Department of Physics, Göteborg University, Göteborg, Sweden
| | - Eric Westman
- Division of Clinical Geriatrics, Neurobiology, Care Sciences and Society Department, Karolinska Institutet, Stockholm, Sweden
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden.,Memory Clinic, Skåne University Hospital, Malmö, Sweden
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26
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Power MC, Su D, Wu A, Reid RI, Jack CR, Knopman DS, Coresh J, Huang J, Kantarci K, Sharrett AR, Gottesman RG, Griswold ME, Mosley TH. Association of white matter microstructural integrity with cognition and dementia. Neurobiol Aging 2019; 83:63-72. [PMID: 31585368 PMCID: PMC6914220 DOI: 10.1016/j.neurobiolaging.2019.08.021] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Revised: 08/07/2019] [Accepted: 08/21/2019] [Indexed: 10/26/2022]
Abstract
Late-life measures of white matter (WM) microstructural integrity may predict cognitive status, cognitive decline, and incident mild cognitive impairment (MCI) or dementia. We considered participants of the Atherosclerosis Risk in Communities study who underwent cognitive assessment and neuroimaging in 2011-2013 and were followed through 2016-2017 (n = 1775 for analyses of prevalent MCI and dementia, baseline cognitive performance, and longitudinal cognitive change and n = 889 for analyses of incident MCI, dementia, or death). Cross-sectionally, both overall WM fractional anisotropy and overall WM mean diffusivity were strongly associated with baseline cognitive performance and risk of prevalent MCI or dementia. Longitudinally, greater overall WM mean diffusivity was associated with accelerated cognitive decline, as well as incident MCI, incident dementia, and mortality, but WM fractional anisotropy was not robustly associated with cognitive change or incident cognitive impairment. Both cross-sectional and longitudinal associations were attenuated after additionally adjusting for likely downstream pathologic changes. Increased WM mean diffusivity may provide an early indication of dementia pathogenesis.
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Affiliation(s)
- Melinda C Power
- Department of Epidemiology and Biostatistics, Milken Institute School of Public Health, George Washington University, Washington, DC, USA.
| | - Dan Su
- Department of Data Science, JD Bower School of Population Health, University of Mississippi Medical Center, Jackson, MS, USA
| | - Aozhou Wu
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Robert I Reid
- Department of Information Technology, Mayo Clinic, Rochester, MN, USA
| | | | | | - Joe Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Juebin Huang
- Department of Neurology, University of Mississippi Medical Center, Jackson, MS, USA
| | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - A Richey Sharrett
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Rebecca G Gottesman
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Department of Neurology, Johns Hopkins University, Baltimore, MD, USA
| | - Mike E Griswold
- Department of Data Science, JD Bower School of Population Health, University of Mississippi Medical Center, Jackson, MS, USA
| | - Thomas H Mosley
- Department of Neurology, University of Mississippi Medical Center, Jackson, MS, USA; Department of Geriatrics, University of Mississippi Medical Center, Jackson, MS, USA
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27
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Cerebrospinal fluid and plasma neurofilament light relate to abnormal cognition. ALZHEIMER'S & DEMENTIA: DIAGNOSIS, ASSESSMENT & DISEASE MONITORING 2019; 11:700-709. [PMID: 31700989 PMCID: PMC6827361 DOI: 10.1016/j.dadm.2019.08.008] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
Introduction Neuroaxonal damage may contribute to cognitive changes preceding clinical dementia. Accessible biomarkers are critical for detecting such damage. Methods Plasma and cerebrospinal fluid (CSF) neurofilament light (NFL) were related to neuropsychological performance among Vanderbilt Memory & Aging Project participants (plasma n = 333, 73 ± 7 years; CSF n = 149, 72 ± 6 years) ranging from normal cognition (NC) to mild cognitive impairment (MCI). Models adjusted for age, sex, race/ethnicity, education, apolipoprotein E ε4 carriership, and Framingham Stroke Risk Profile. Results Plasma NFL was related to all domains (P values ≤ .008) except processing speed (P values ≥ .09). CSF NFL was related to memory and language (P values ≤ .04). Interactions with cognitive diagnosis revealed widespread plasma associations, particularly in MCI participants, which were further supported in head-to-head comparison models. Discussion Plasma and CSF NFL (reflecting neuroaxonal injury) relate to cognition among non-demented older adults albeit with small to medium effects. Plasma NFL shows particular promise as an accessible biomarker with relevance to cognition in MCI. Plasma and cerebrospinal fluid neurofilament light (NFL) were moderately correlated. Cerebrospinal fluid NFL was related to language and memory functions. Plasma NFL exhibited widespread cognitive associations. Plasma NFL associations were particularly robust in mild cognitive impairment.
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28
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Rabin JS, Perea RD, Buckley RF, Johnson KA, Sperling RA, Hedden T. Synergism between fornix microstructure and beta amyloid accelerates memory decline in clinically normal older adults. Neurobiol Aging 2019; 81:38-46. [PMID: 31207468 PMCID: PMC6732225 DOI: 10.1016/j.neurobiolaging.2019.05.005] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Revised: 05/07/2019] [Accepted: 05/09/2019] [Indexed: 01/09/2023]
Abstract
The fornix is the primary efferent white matter tract of the hippocampus and is implicated in episodic memory. In this study, we investigated whether baseline measures of altered fornix microstructure and elevated beta amyloid (Aβ) burden influence prospective cognitive decline. A secondary goal examined whether Aβ burden is negatively associated with fornix microstructure. 253 clinically normal older adults underwent diffusion-weighted imaging and Pittsburgh Compound B positron emission tomography at baseline. We applied a novel streamline tractography protocol to reconstruct a fornix bundle in native space. Cognition was measured annually in domains of episodic memory, executive function, and processing speed (median follow-up = 4.0 ± 1.4 years). After controlling for covariates, linear mixed-effects models demonstrated an interaction of fornix microstructure with Aβ burden on episodic memory, such that combined lower fornix microstructure and higher Aβ burden was associated with accelerated decline. By contrast, associations with executive function and processing speed were not significant. There was no cross-sectional association between Aβ burden and fornix microstructure. In conclusion, altered fornix microstructure may accelerate memory decline in preclinical Alzheimer's disease.
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Affiliation(s)
- Jennifer S Rabin
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Rodrigo D Perea
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Rachel F Buckley
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Florey Institutes of Neuroscience and Mental Health, Melbourne and Melbourne School of Psychological Science, University of Melbourne, Melbourne, Australia; Department of Neurology, Center for Alzheimer Research and Treatment, Brigham and Women's Hospital, Boston, MA, USA
| | - Keith A Johnson
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Neurology, Center for Alzheimer Research and Treatment, Brigham and Women's Hospital, Boston, MA, USA; Division of Nuclear Medicine and Molecular Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Reisa A Sperling
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Neurology, Center for Alzheimer Research and Treatment, Brigham and Women's Hospital, Boston, MA, USA
| | - Trey Hedden
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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29
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Ye C, Albert M, Brown T, Bilgel M, Hsu J, Ma T, Caffo B, Miller MI, Mori S, Oishi K. Extended multimodal whole-brain anatomical covariance analysis: detection of disrupted correlation networks related to amyloid deposition. Heliyon 2019; 5:e02074. [PMID: 31372540 PMCID: PMC6656959 DOI: 10.1016/j.heliyon.2019.e02074] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Revised: 04/22/2019] [Accepted: 07/08/2019] [Indexed: 01/27/2023] Open
Abstract
Background An anatomical covariance analysis (ACA) enables to elucidate inter-regional connections on a group basis, but little is known about the connections among white matter structures or among gray and white matter structures. Effect of including multiple magnetic resonance imaging (MRI) modalities into ACA framework in detecting white-to-white or gray-to-white connections is yet to be investigated. New method Proposed extended anatomical covariance analysis (eACA), analyzes correlations among gray and white matter structures (multi-structural) in various types of imaging modalities (T1-weighted images, T2 maps obtained from dual-echo sequences, and diffusion tensor images (DTI)). To demonstrate the capability to detect a disruption of the correlation network affected by pathology, we applied the eACA to two groups of cognitively-normal elderly individuals, one with (PiB+) and one without (PiB-) amyloid deposition in their brains. Results The volume of each anatomical structure was symmetric and functionally related structures formed a cluster. The pseudo-T2 value was highly homogeneous across the entire cortex in the PiB- group, while a number of physiological correlations were altered in the PiB + group. The DTI demonstrated unique correlation network among structures within the same phylogenetic portions of the brain that were altered in the PiB + group. Comparison with Existing Method The proposed eACA expands the concept of existing ACA to the connections among the white matter structures. The extension to other image modalities expands the way in which connectivity may be detected. Conclusion The eACA has potential to evaluate alterations of the anatomical network related to pathological processes.
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Affiliation(s)
- Chenfei Ye
- Department of Electronics and Information, Harbin Institute of Technology at Shenzhen, Shenzhen, Guangdong Province, China.,The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.,Peng Cheng Laboratory, Shenzhen, Guangdong, China
| | - Marilyn Albert
- Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.,The Johns Hopkins Alzheimer's Disease Research Center, Baltimore, MD, USA
| | - Timothy Brown
- Center for Imaging Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, Intramural Research Program, National Institute on Aging, Baltimore, MD, USA
| | - Johnny Hsu
- Peng Cheng Laboratory, Shenzhen, Guangdong, China
| | - Ting Ma
- Department of Electronics and Information, Harbin Institute of Technology at Shenzhen, Shenzhen, Guangdong Province, China.,Peng Cheng Laboratory, Shenzhen, Guangdong, China
| | - Brian Caffo
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA
| | - Michael I Miller
- Center for Imaging Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Susumu Mori
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Kenichi Oishi
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
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30
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Brown CA, Schmitt FA, Smith CD, Gold BT. Distinct patterns of default mode and executive control network circuitry contribute to present and future executive function in older adults. Neuroimage 2019; 195:320-332. [PMID: 30953834 PMCID: PMC6536351 DOI: 10.1016/j.neuroimage.2019.03.073] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Revised: 02/16/2019] [Accepted: 03/30/2019] [Indexed: 11/26/2022] Open
Abstract
Executive function (EF) performance in older adults has been linked with functional and structural profiles within the executive control network (ECN) and default mode network (DMN), white matter hyperintensities (WMH) burden and levels of Alzheimer's disease (AD) pathology. Here, we simultaneously explored the unique contributions of these factors to baseline and longitudinal EF performance in older adults. Thirty-two cognitively normal (CN) older adults underwent neuropsychological testing at baseline and annually for three years. Neuroimaging and AD pathology measures were collected at baseline. Separate linear regression models were used to determine which of these variables predicted composite EF scores at baseline and/or average annual change in composite ΔEF scores over the three-year follow-up period. Results demonstrated that low DMN deactivation, high ECN activation and WMH burden were the main predictors of EF scores at baseline. In contrast, poor DMN and ECN WM microstructure and higher AD pathology predicted greater annual decline in EF scores. Subsequent mediation analysis demonstrated that DMN WM microstructure uniquely mediated the relationship between AD pathology and ΔEF. These results suggest that functional activation patterns within the DMN and ECN and WMHs contribute to baseline EF while structural connectivity within these networks impact longitudinal EF performance in older adults.
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Affiliation(s)
- Christopher A Brown
- Department of Neuroscience, University of Kentucky, Lexington, KY, 40536, USA
| | - Frederick A Schmitt
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, 40536, USA; Department of Neurology, University of Kentucky, Lexington, KY, 40536, USA; Department of Psychiatry, University of Kentucky, Lexington, KY, 40536, USA
| | - Charles D Smith
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, 40536, USA; Department of Neurology, University of Kentucky, Lexington, KY, 40536, USA; Magnetic Resonance Imaging and Spectroscopy Center, University of Kentucky, Lexington, KY, 40536, USA
| | - Brian T Gold
- Department of Neuroscience, University of Kentucky, Lexington, KY, 40536, USA; Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, 40536, USA; Magnetic Resonance Imaging and Spectroscopy Center, University of Kentucky, Lexington, KY, 40536, USA.
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31
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Araque Caballero MÁ, Suárez-Calvet M, Duering M, Franzmeier N, Benzinger T, Fagan AM, Bateman RJ, Jack CR, Levin J, Dichgans M, Jucker M, Karch C, Masters CL, Morris JC, Weiner M, Rossor M, Fox NC, Lee JH, Salloway S, Danek A, Goate A, Yakushev I, Hassenstab J, Schofield PR, Haass C, Ewers M. White matter diffusion alterations precede symptom onset in autosomal dominant Alzheimer's disease. Brain 2019; 141:3065-3080. [PMID: 30239611 PMCID: PMC6158739 DOI: 10.1093/brain/awy229] [Citation(s) in RCA: 102] [Impact Index Per Article: 20.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2018] [Accepted: 07/20/2018] [Indexed: 12/30/2022] Open
Abstract
White matter alterations are present in the majority of patients with Alzheimer's disease type dementia. However, the spatiotemporal pattern of white matter changes preceding dementia symptoms in Alzheimer's disease remains unclear, largely due to the inherent diagnostic uncertainty in the preclinical phase and increased risk of confounding age-related vascular disease and stroke in late-onset Alzheimer's disease. In early-onset autosomal-dominantly inherited Alzheimer's disease, participants are destined to develop dementia, which provides the opportunity to assess brain changes years before the onset of symptoms, and in the absence of ageing-related vascular disease. Here, we assessed mean diffusivity alterations in the white matter in 64 mutation carriers compared to 45 non-carrier family non-carriers. Using tract-based spatial statistics, we mapped the interaction of mutation status by estimated years from symptom onset on mean diffusivity. For major atlas-derived fibre tracts, we determined the earliest time point at which abnormal mean diffusivity changes in the mutation carriers were detectable. Lastly, we assessed the association between mean diffusivity and cerebrospinal fluid biomarkers of amyloid, tau, phosphorylated-tau, and soluble TREM2, i.e. a marker of microglia activity. Results showed a significant interaction of mutations status by estimated years from symptom onset, i.e. a stronger increase of mean diffusivity, within the posterior parietal and medial frontal white matter in mutation carriers compared with non-carriers. The earliest increase of mean diffusivity was observed in the forceps major, forceps minor and long projecting fibres-many connecting default mode network regions-between 5 to 10 years before estimated symptom onset. Higher mean diffusivity in fibre tracts was associated with lower grey matter volume in the tracts' projection zones. Global mean diffusivity was correlated with lower cerebrospinal fluid levels of amyloid-β1-42 but higher levels of tau, phosphorylated-tau and soluble TREM2. Together, these results suggest that regionally selective white matter degeneration occurs years before the estimated symptom onset. Such white matter alterations are associated with primary Alzheimer's disease pathology and microglia activity in the brain.
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Affiliation(s)
- Miguel Ángel Araque Caballero
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
| | - Marc Suárez-Calvet
- German Center for Neurodegenerative Diseases (DZNE, Munich), Munich, Germany.,Biomedical Center, Biochemistry, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Marco Duering
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
| | - Nicolai Franzmeier
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
| | - Tammie Benzinger
- Department of Radiology, Washington University in St Louis, St Louis, MO, USA.,Knight Alzheimer's Disease Research Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Anne M Fagan
- Department of Radiology, Washington University in St Louis, St Louis, MO, USA.,Knight Alzheimer's Disease Research Center, Washington University in St. Louis, St. Louis, MO, USA.,Hope Center for Neurological Disorders, Washington University in St. Louis, St. Louis, MO, USA
| | - Randall J Bateman
- Department of Radiology, Washington University in St Louis, St Louis, MO, USA.,Knight Alzheimer's Disease Research Center, Washington University in St. Louis, St. Louis, MO, USA.,Hope Center for Neurological Disorders, Washington University in St. Louis, St. Louis, MO, USA
| | | | - Johannes Levin
- German Center for Neurodegenerative Diseases (DZNE, Munich), Munich, Germany.,Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Martin Dichgans
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany.,German Center for Neurodegenerative Diseases (DZNE, Munich), Munich, Germany.,Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Mathias Jucker
- Hertie Institute for Clinical Brain Research, Tübingen, Germany and German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
| | - Celeste Karch
- Knight Alzheimer's Disease Research Center, Washington University in St. Louis, St. Louis, MO, USA.,Hope Center for Neurological Disorders, Washington University in St. Louis, St. Louis, MO, USA.,Department of Psychiatry, Washington University in St Louis, St Louis, MO, USA
| | - Colin L Masters
- The Florey Institute, The University of Melbourne, Parkville, Victoria, Australia
| | - John C Morris
- Department of Radiology, Washington University in St Louis, St Louis, MO, USA.,Knight Alzheimer's Disease Research Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Michael Weiner
- University of California at San Francisco, San Francisco, CA94143, USA
| | - Martin Rossor
- Dementia Research Centre, University College London, Queen Square, London, UK
| | - Nick C Fox
- Dementia Research Centre, University College London, Queen Square, London, UK
| | - Jae-Hong Lee
- Department of Neurology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Stephen Salloway
- Department of Neurology, Warren Alpert Medical School of Brown University, Providence, Rhode Island, USA
| | - Adrian Danek
- German Center for Neurodegenerative Diseases (DZNE, Munich), Munich, Germany.,Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Alison Goate
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Ronald M. Loeb Center for Alzheimer's Disease, Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Igor Yakushev
- Department of Nuclear Medicine, Technical University of Munich, Munich, Germany
| | - Jason Hassenstab
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Peter R Schofield
- Neuroscience Research Australia, Barker Street Randwick, Sydney, Australia.,School of Medical Sciences, University of New South Wales, Sydney, Australia
| | - Christian Haass
- German Center for Neurodegenerative Diseases (DZNE, Munich), Munich, Germany.,Biomedical Center, Biochemistry, Ludwig-Maximilians-Universität München, Munich, Germany.,Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Michael Ewers
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
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32
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Sepehrband F, Cabeen RP, Choupan J, Barisano G, Law M, Toga AW. Perivascular space fluid contributes to diffusion tensor imaging changes in white matter. Neuroimage 2019; 197:243-254. [PMID: 31051291 DOI: 10.1016/j.neuroimage.2019.04.070] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Revised: 04/16/2019] [Accepted: 04/26/2019] [Indexed: 10/26/2022] Open
Abstract
Diffusion tensor imaging (DTI) has been extensively used to map changes in brain tissue related to neurological disorders. Among the most widespread DTI findings are increased mean diffusivity and decreased fractional anisotropy of white matter tissue in neurodegenerative diseases. Here we utilize multi-shell diffusion imaging to separate diffusion signal of the brain parenchyma from non-parenchymal fluid within the white matter. We show that unincorporated anisotropic water in perivascular space (PVS) significantly, and systematically, biases DTI measures, casting new light on the biological validity of many previously reported findings. Despite the challenge this poses for interpreting these past findings, our results suggest that multi-shell diffusion MRI provides a new opportunity for incorporating the PVS contribution, ultimately strengthening the clinical and scientific value of diffusion MRI.
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Affiliation(s)
- Farshid Sepehrband
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, USA.
| | - Ryan P Cabeen
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, USA
| | - Jeiran Choupan
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, USA; Department of Psychology, University of Southern California, Los Angeles, USA
| | - Giuseppe Barisano
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, USA; Neuroscience Graduate Program, University of Southern California, Los Angeles, USA
| | - Meng Law
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, USA; Radiology and Nuclear Medicine, Alfred Health, Melbourne, Australia
| | - Arthur W Toga
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, USA
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Nonparenchymal fluid is the source of increased mean diffusivity in preclinical Alzheimer's disease. ALZHEIMER'S & DEMENTIA: DIAGNOSIS, ASSESSMENT & DISEASE MONITORING 2019; 11:348-354. [PMID: 31049392 PMCID: PMC6479267 DOI: 10.1016/j.dadm.2019.03.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Introduction Although increased mean diffusivity of the white matter has been repeatedly linked to Alzheimer’s disease pathology, the underlying mechanism is not known. Methods Here, we used ADNI-3 multishell diffusion magnetic resonance imaging data to separate the diffusion signal of the parenchyma from less hindered fluid pools within the white matter such as perivascular space fluid and fluid-filled cavities. Results We found that the source of the pathological increase of the mean diffusivity is the increased nonparenchymal fluid, often found in lacunes and perivascular spaces. In this cohort, the cognitive decline was significantly associated with the fluid increase and not with the microstructural changes of the white matter parenchyma itself. The white matter fluid increase was dominantly observed in the sagittal stratum and anterior thalamic radiation. Discussion These findings are positive steps toward understanding the pathophysiology of white matter alteration and its role in the cognitive decline.
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Rabin JS, Perea RD, Buckley RF, Neal TE, Buckner RL, Johnson KA, Sperling RA, Hedden T. Global White Matter Diffusion Characteristics Predict Longitudinal Cognitive Change Independently of Amyloid Status in Clinically Normal Older Adults. Cereb Cortex 2019; 29:1251-1262. [PMID: 29425267 PMCID: PMC6499008 DOI: 10.1093/cercor/bhy031] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2017] [Revised: 01/08/2018] [Indexed: 02/07/2023] Open
Abstract
White matter degradation has been proposed as one possible explanation for age-related cognitive decline. In the present study, we examined 2 main questions: 1) Do diffusion characteristics predict longitudinal change in cognition independently or synergistically with amyloid status? 2) Are the effects of diffusion characteristics on longitudinal cognitive change tract-specific or global in nature? Cognitive domains of executive function, episodic memory, and processing speed were measured annually (mean follow-up = 3.93 ± 1.25 years). Diffusion tensor imaging and Pittsburgh Compound-B positron emission tomography were performed at baseline in 265 clinically normal older adults (aged 63-90). Tract-specific diffusion was measured as the mean fractional anisotropy (FA) for 9 major white matter tracts. Global diffusion was measured as the mean FA across the 9 white matter tracts. Linear mixed models demonstrated independent, rather than synergistic, effects of global FA and amyloid status on cognitive decline. After controlling for amyloid status, lower global FA was associated with worse longitudinal performance in episodic memory and processing speed, but not executive function. After accounting for global FA, none of the individual tracts predicted a significant change in cognitive performance. These findings suggest that global, rather than tract-specific, diffusion characteristics predict longitudinal cognitive decline independently of amyloid status.
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Affiliation(s)
- Jennifer S Rabin
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Rodrigo D Perea
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Rachel F Buckley
- Florey Institutes of Neuroscience and Mental Health, Melbourne and Melbourne School of Psychological Science, University of Melbourne, Melbourne, Australia
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Taylor E Neal
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Randy L Buckner
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Keith A Johnson
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Center for Alzheimer Research and Treatment, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Nuclear Medicine and Molecular Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Reisa A Sperling
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Center for Alzheimer Research and Treatment, Brigham and Women’s Hospital, Boston, MA, USA
| | - Trey Hedden
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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Schilling LP, Pascoal TA, Zimmer ER, Mathotaarachchi S, Shin M, de Mello Rieder CR, Gauthier S, Palmini A, Rosa-Neto P. Regional Amyloid-β Load and White Matter Abnormalities Contribute to Hypometabolism in Alzheimer's Dementia. Mol Neurobiol 2018; 56:4916-4924. [PMID: 30414086 DOI: 10.1007/s12035-018-1405-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2018] [Accepted: 10/22/2018] [Indexed: 12/18/2022]
Abstract
We investigated the association between amyloid-β deposition and white matter (WM) integrity as a determinant of brain glucose hypometabolism across the Alzheimer's disease (AD) spectrum. We assessed ninety-six subjects (27 cognitively normal, 49 mild cognitive impairment, and 20 AD dementia) who underwent [18F]FDG and [18F]Florbetapir positron emission tomography (PET) as well as magnetic resonance imaging (MRI) with diffusion tensor imaging. Among the regions with reduced fractional anisotropy (FA) in the AD group, we selected a voxel of interest in the angular bundle bilaterally for subsequent analyses. Using voxel-based interaction models at voxel level, we tested whether the regional hypometabolism is associated with FA in the angular bundle and regional amyloid-β deposition. In the AD patients, [18F]FDG hypometabolism in the striatum, mesiobasal temporal, orbitofrontal, precuneus, and cingulate cortices were associated with the interaction between high levels of [18F]Florbetapir standard uptake value ratios (SUVR) in these regions and low FA in the angular bundle. We found that the interaction between, rather than the independent effects of, high levels of amyloid-β deposition and WM integrity disruption determined limbic hypometabolism in patients with AD. This finding highlights a more integrative model for AD, where the interaction between partially independent processes determines the glucose hypometabolism.
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Affiliation(s)
- Lucas Porcello Schilling
- Translational Neuroimaging Laboratory (TNL), McGill Center for Studies in Aging (MCSA), Douglas Mental Health Research Institute, 6825, boul. LaSalle Blvd., Montréal, QC, H4H1R3, Canada.,Alzheimer's Disease Research Unit, MCSA, Douglas Mental Health Research Institute, Montréal, Canada.,Brain Institute of Rio Grande do Sul (BraIns), Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, Brazil
| | - Tharick A Pascoal
- Translational Neuroimaging Laboratory (TNL), McGill Center for Studies in Aging (MCSA), Douglas Mental Health Research Institute, 6825, boul. LaSalle Blvd., Montréal, QC, H4H1R3, Canada.,Alzheimer's Disease Research Unit, MCSA, Douglas Mental Health Research Institute, Montréal, Canada
| | - Eduardo R Zimmer
- Translational Neuroimaging Laboratory (TNL), McGill Center for Studies in Aging (MCSA), Douglas Mental Health Research Institute, 6825, boul. LaSalle Blvd., Montréal, QC, H4H1R3, Canada.,Alzheimer's Disease Research Unit, MCSA, Douglas Mental Health Research Institute, Montréal, Canada.,Brain Institute of Rio Grande do Sul (BraIns), Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, Brazil.,Department of Pharmacology, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil.,Graduate Program in Biological Science: Biochemistry, UFRGS, Porto Alegre, Brazil.,Graduate Program in Biological Sciences: Pharmacology and Therapeutics, UFRGS, Porto Alegre, Brazil
| | - Sulantha Mathotaarachchi
- Translational Neuroimaging Laboratory (TNL), McGill Center for Studies in Aging (MCSA), Douglas Mental Health Research Institute, 6825, boul. LaSalle Blvd., Montréal, QC, H4H1R3, Canada
| | - Monica Shin
- Translational Neuroimaging Laboratory (TNL), McGill Center for Studies in Aging (MCSA), Douglas Mental Health Research Institute, 6825, boul. LaSalle Blvd., Montréal, QC, H4H1R3, Canada
| | - Carlos Roberto de Mello Rieder
- Brain Institute of Rio Grande do Sul (BraIns), Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, Brazil
| | - Serge Gauthier
- Translational Neuroimaging Laboratory (TNL), McGill Center for Studies in Aging (MCSA), Douglas Mental Health Research Institute, 6825, boul. LaSalle Blvd., Montréal, QC, H4H1R3, Canada.,Alzheimer's Disease Research Unit, MCSA, Douglas Mental Health Research Institute, Montréal, Canada
| | - André Palmini
- Brain Institute of Rio Grande do Sul (BraIns), Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, Brazil
| | - Pedro Rosa-Neto
- Translational Neuroimaging Laboratory (TNL), McGill Center for Studies in Aging (MCSA), Douglas Mental Health Research Institute, 6825, boul. LaSalle Blvd., Montréal, QC, H4H1R3, Canada. .,Alzheimer's Disease Research Unit, MCSA, Douglas Mental Health Research Institute, Montréal, Canada.
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36
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Willey JZ, Moon YP, Dhamoon MS, Kulick ER, Bagci A, Alperin N, Cheung YK, Wright CB, Sacco RL, Elkind MSV. Regional Subclinical Cerebrovascular Disease Is Associated with Balance in an Elderly Multi-Ethnic Population. Neuroepidemiology 2018; 51:57-63. [PMID: 29953989 DOI: 10.1159/000490351] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2018] [Accepted: 05/22/2018] [Indexed: 11/19/2022] Open
Abstract
INTRODUCTION White matter hyperintensity volume (WMHV) and subclinical brain infarcts (SBI) are associated with impaired mobility, but less is known about the association of WMHV in specific brain regions. We hypothesized that anterior WMHV would be associated with lower scores on the Short Physical Performance Battery (SPPB), a well-validated mobility scale. METHODS The SPPB was measured a median of 5 years after enrollment into the Northern Manhattan MRI sub study. Volumetric distributions for WMHV in 14 brain regions as a proportion of total cranial volume were determined. Multi-variable linear regression was performed to examine the association of SBI and regional log-WMHV with the SPPB score. RESULTS Among 668 participants with SPPB measurements (mean 74 ± 9 years, 37% male and 70% Hispanic), the mean SPPB score was 8.2 ± 2.9. Total (beta = -0.3 per SD, p = 0.001), anterior periventricular (beta = -0.4 per SD, p = 0.001), parietal (beta = -0.2 per SD, p = 0.02) and frontal (beta = -0.3 per SD, p = 0.002) WMHVs were associated with SPPB; other WMHV and SBI were not associated with the SPPB. CONCLUSIONS WMHV, especially in the anterior -cerebral regions, is associated with a lower SPPB. Prevention of subclinical cerebrovascular disease is a potential target to prevent physical decline in the elderly.
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Affiliation(s)
- Joshua Z Willey
- Department of Neurology, Columbia University, New York, New York, USA
| | - Yeseon P Moon
- Department of Neurology, Columbia University, New York, New York, USA
| | - Mandip S Dhamoon
- Department of Neurology, Mount Sinai School of Medicine, New York, New York, USA
| | - Erin R Kulick
- Department of Epidemiology, Columbia University, New York, New York, USA
| | - Ahmet Bagci
- Evelyn McKnight Brain Institute, University of Miami, Miami, Florida, USA
| | - Noam Alperin
- Evelyn McKnight Brain Institute, University of Miami, Miami, Florida, USA
| | - Ying Kuen Cheung
- Department of Biostatistics, Columbia University, New York, New York, USA
| | | | - Ralph L Sacco
- Evelyn McKnight Brain Institute, University of Miami, Miami, Florida, USA.,Department of Neurology, University of Miami, Miami, Florida, USA
| | - Mitchell S V Elkind
- Department of Neurology, Columbia University, New York, New York, USA.,Department of Epidemiology, Columbia University, New York, New York, USA
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Jung WS, Um YH, Kang DW, Lee CU, Woo YS, Bahk WM, Lim HK. Diagnostic Validity of an Automated Probabilistic Tractography in Amnestic Mild Cognitive Impairment. CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE 2018; 16:144-152. [PMID: 29739127 PMCID: PMC5953013 DOI: 10.9758/cpn.2018.16.2.144] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2016] [Revised: 11/15/2016] [Accepted: 11/22/2016] [Indexed: 11/18/2022]
Abstract
Objective Although several prior works showed the white matter (WM) integrity changes in amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease, it is still unclear the diagnostic accuracy of the WM integrity measurements using diffusion tensor imaging (DTI) in discriminating aMCI from normal controls. The aim of this study is to explore diagnostic validity of whole brain automated probabilistic tractography in discriminating aMCI from normal controls. Methods One hundred-two subjects (50 aMCI and 52 normal controls) were included and underwent DTI scans. Whole brain WM tracts were reconstructed with automated probabilistic tractography. Fractional anisotropy (FA) and mean diffusivity (MD) values of the memory related WM tracts were measured and compared between the aMCI and the normal control groups. In addition, the diagnostic validities of these WM tracts were evaluated. Results Decreased FA and increased MD values of memory related WM tracts were observed in the aMCI group compared with the control group. Among FA and MD value of each tract, the FA value of left cingulum angular bundle showed the highest area under the curve (AUC) of 0.85 with a sensitivity of 88.2%, a specificity of 76.9% in differentiating MCI patients from control subjects. Furthermore, the combination FA values of WM integrity measures of memory related WM tracts showed AUC value of 0.98, a sensitivity of 96%, a specificity of 94.2%. Conclusion Our results with good diagnostic validity of WM integrity measurements suggest DTI might be promising neuroimaging tool for early detection of aMCI and AD patients.
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Affiliation(s)
- Won Sang Jung
- Department of Radiology, St. Vincent Hospital, Suwon, Korea
| | - Yoo Hyun Um
- Department of Psychiatry, St. Vincent Hospital, Suwon, Korea
| | - Dong Woo Kang
- Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Chang Uk Lee
- Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Young Sup Woo
- Department of Psychiatry, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Won-Myong Bahk
- Department of Psychiatry, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Hyun Kook Lim
- Department of Psychiatry, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
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Tardif CL, Devenyi GA, Amaral RSC, Pelleieux S, Poirier J, Rosa‐Neto P, Breitner J, Chakravarty MM. Regionally specific changes in the hippocampal circuitry accompany progression of cerebrospinal fluid biomarkers in preclinical Alzheimer's disease. Hum Brain Mapp 2018; 39:971-984. [PMID: 29164798 PMCID: PMC6866392 DOI: 10.1002/hbm.23897] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Revised: 11/10/2017] [Accepted: 11/13/2017] [Indexed: 01/18/2023] Open
Abstract
Neuropathological and in vivo brain imaging studies agree that the cornu ammonis 1 and subiculum subfields of the hippocampus are most vulnerable to atrophy in the prodromal phases of Alzheimer's disease (AD). However, there has been limited investigation of the structural integrity of the components of the hippocampal circuit, including subfields and extra-hippocampal white matter structure, in relation to the progression of well-accepted cerebrospinal fluid (CSF) biomarkers of AD, amyloid-β 1-42 (Aβ) and total-tau (tau). We investigated these relationships in 88 aging asymptomatic individuals with a parental or multiple-sibling familial history of AD. Apolipoprotein (APOE) ɛ4 risk allele carriers were identified, and all participants underwent cognitive testing, structural magnetic resonance imaging, and lumbar puncture for CSF assays of tau, phosphorylated-tau (p-tau) and Aβ. Individuals with a reduction in CSF Aβ levels (an indicator of amyloid accretion into neuritic plaques) as well as evident tau pathology (believed to be linked to neurodegeneration) exhibited lower subiculum volume, lower fornix microstructural integrity, and a trend towards lower cognitive score than individuals who showed only reduction in CSF Aβ. In contrast, persons with normal levels of tau showed an increase in structural MR markers in relation to declining levels of CSF Aβ. These results suggest that hippocampal subfield volume and extra-hippocampal white matter microstructure demonstrate a complex pattern where an initial volume increase is followed by decline among asymptomatic individuals who, in some instances, may be a decade or more away from onset of cognitive or functional impairment.
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Affiliation(s)
- Christine L. Tardif
- Cerebral Imaging Centre, Douglas Mental Health University InstituteVerdunQuebecCanada
- Montreal Neurological InstituteMontrealQuebecCanada
- Department of Biomedical EngineeringMcGill UniversityMontrealQuebecCanada
| | - Gabriel A. Devenyi
- Cerebral Imaging Centre, Douglas Mental Health University InstituteVerdunQuebecCanada
- Department of PsychiatryMcGill UniversityMontrealQuebecCanada
| | - Robert S. C. Amaral
- Cerebral Imaging Centre, Douglas Mental Health University InstituteVerdunQuebecCanada
| | - Sandra Pelleieux
- Centre for the Studies on the Prevention of AD, Douglas Mental Health University InstituteVerdunQuebecCanada
| | - Judes Poirier
- Centre for the Studies on the Prevention of AD, Douglas Mental Health University InstituteVerdunQuebecCanada
| | - Pedro Rosa‐Neto
- Montreal Neurological InstituteMontrealQuebecCanada
- McGill University, Research Centre for Studies in AgingMontreal QuebecCanada
| | | | - M. Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Mental Health University InstituteVerdunQuebecCanada
- Department of Biomedical EngineeringMcGill UniversityMontrealQuebecCanada
- Department of PsychiatryMcGill UniversityMontrealQuebecCanada
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Ramanan VK, Przybelski SA, Graff-Radford J, Castillo AM, Lowe VJ, Mielke MM, Roberts RO, Reid RI, Knopman DS, Jack CR, Petersen RC, Vemuri P. Statins and Brain Health: Alzheimer's Disease and Cerebrovascular Disease Biomarkers in Older Adults. J Alzheimers Dis 2018; 65:1345-1352. [PMID: 30149450 PMCID: PMC6260813 DOI: 10.3233/jad-180446] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
BACKGROUND Statins have been proposed to reduce the risk of Alzheimer's disease (AD). OBJECTIVE Assess whether long-term statin use was associated with neuroimaging biomarkers of aging and dementia. METHODS Methods: We analyzed neuroimaging biomarkers in 1,160 individuals aged 65+ from the Mayo Clinic Study of Aging, a population-based prospective longitudinal study of cognitive aging. RESULTS Statin-treated (5+ years of therapy) individuals had greater burden of mid-and late-life cardiovascular disease (p < 0.001) than statin-untreated (≤3 months) individuals. Lower fractional anisotropy in the genu of the corpus callosum, an early marker of cerebrovascular disease, was associated with long-term statin exposure (p < 0.035). No significant associations were identified between long-term statin exposure and cerebral amyloid or tau burden, AD pattern neurodegeneration, or white matter hyperintensity burden. CONCLUSIONS Long-term statin therapy was not associated with differences in AD biomarkers. Individuals with long-term statin exposure had worse white matter integrity in the genu of the corpus callosum, consistent with the coexistence of higher cerebrovascular risk factor burden in this group.
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Affiliation(s)
- Vijay K Ramanan
- Department of Neurology, Mayo Clinic-Rochester, Rochester, Minnesota, 55905, USA
| | - Scott A. Przybelski
- Department of Health Sciences Research, Mayo Clinic-Rochester, Rochester, Minnesota, 55905, USA
| | | | - Anna M. Castillo
- Department of Health Sciences Research, Mayo Clinic-Rochester, Rochester, Minnesota, 55905, USA
| | - Val J. Lowe
- Department of Radiology, Mayo Clinic-Rochester, Rochester, Minnesota, 55905, USA
| | - Michelle M. Mielke
- Department of Neurology, Mayo Clinic-Rochester, Rochester, Minnesota, 55905, USA
- Department of Health Sciences Research, Mayo Clinic-Rochester, Rochester, Minnesota, 55905, USA
| | - Rosebud O. Roberts
- Department of Neurology, Mayo Clinic-Rochester, Rochester, Minnesota, 55905, USA
- Department of Health Sciences Research, Mayo Clinic-Rochester, Rochester, Minnesota, 55905, USA
| | - Robert I. Reid
- Department of Information Technology, Mayo Clinic-Rochester, Rochester, Minnesota, 55905, USA
| | - David S. Knopman
- Department of Neurology, Mayo Clinic-Rochester, Rochester, Minnesota, 55905, USA
| | - Clifford R. Jack
- Department of Radiology, Mayo Clinic-Rochester, Rochester, Minnesota, 55905, USA
| | - Ronald C. Petersen
- Department of Neurology, Mayo Clinic-Rochester, Rochester, Minnesota, 55905, USA
- Department of Health Sciences Research, Mayo Clinic-Rochester, Rochester, Minnesota, 55905, USA
| | - Prashanthi Vemuri
- Department of Radiology, Mayo Clinic-Rochester, Rochester, Minnesota, 55905, USA
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Voevodskaya O, Pereira JB, Volpe G, Lindberg O, Stomrud E, van Westen D, Westman E, Hansson O. Altered structural network organization in cognitively normal individuals with amyloid pathology. Neurobiol Aging 2017; 64:15-24. [PMID: 29316528 DOI: 10.1016/j.neurobiolaging.2017.11.014] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2017] [Revised: 11/10/2017] [Accepted: 11/30/2017] [Indexed: 01/04/2023]
Abstract
Recent findings show that structural network topology is disrupted in Alzheimer's disease (AD), with changes occurring already at the prodromal disease stages. Amyloid accumulation, a hallmark of AD, begins several decades before symptom onset, and its effects on brain connectivity at the earliest disease stages are not fully known. We studied global and local network changes in a large cohort of cognitively healthy individuals (N = 299, Swedish BioFINDER study) with and without amyloid-β (Aβ) pathology (based on cerebrospinal fluid Aβ42/Aβ40 levels). Structural correlation matrices were constructed based on magnetic resonance imaging cortical thickness data. Despite the fact that no significant regional cortical atrophy was found in the Aβ-positive group, this group exhibited an altered global network organization, including decreased global efficiency and modularity. At the local level, Aβ-positive individuals displayed fewer and more disorganized modules as well as a loss of hubs. Our findings suggest that changes in network topology occur already at the presymptomatic (preclinical) stage of AD and may precede detectable cortical thinning.
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Affiliation(s)
- Olga Voevodskaya
- Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Stockholm, Sweden.
| | - Joana B Pereira
- Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Stockholm, Sweden
| | - Giovanni Volpe
- Department of Physics, University of Gothenburg, Gothenburg, Sweden
| | - Olof Lindberg
- Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Stockholm, Sweden
| | - Erik Stomrud
- Memory Clinic, Skåne University Hospital, Malmö, Sweden; Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Danielle van Westen
- Department of Clinical Sciences, Diagnostic radiology, Lund University, Lund, Sweden; Imaging and Function, Skåne University Health Care, Lund, Sweden
| | - Eric Westman
- Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Stockholm, Sweden
| | - Oskar Hansson
- Memory Clinic, Skåne University Hospital, Malmö, Sweden; Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
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Shahim P, Holleran L, Kim JH, Brody DL. Test-retest reliability of high spatial resolution diffusion tensor and diffusion kurtosis imaging. Sci Rep 2017; 7:11141. [PMID: 28894296 PMCID: PMC5593980 DOI: 10.1038/s41598-017-11747-3] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2017] [Accepted: 08/30/2017] [Indexed: 02/03/2023] Open
Abstract
We assessed the test-retest reliability of high spatial resolution diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI). Diffusion MRI was acquired using a Siemens 3 Tesla Prisma scanner with 80 mT/m gradients and a 32-channel head coil from each of 3 concussive traumatic brain injury (cTBI) patients and 4 controls twice 0 to 24 days apart. Coefficients of variation (CoV) for DTI parameters were calculated in each DTI Studio parcellated white matter tract at 1.25 mm and 1.75 mm isotropic voxel resolution, as well as DKI parameters at 1.75 mm isotropic. Overall, fractional anisotropy had the best reliability, with mean CoV at 5% for 1.25 mm and 3.5% for 1.75 mm isotropic voxels. Mean CoV for the other DTI metrics were <7.0% for both 1.25 and 1.75 mm isotropic voxels. The mean CoV was ≤4.5% across the DKI metrics. In the commonly injured orbitofrontal and temporal pole regions CoV was <3.5% for all parameters. Thus, with appropriate processing, high spatial resolution advanced diffusion MRI has good to excellent test-retest reproducibility in both human cTBI patients and controls. However, further technical improvements will be needed to reliably discern the most subtle diffusion abnormalities, especially at high spatial resolution.
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Affiliation(s)
- Pashtun Shahim
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA. .,Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, the Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden. .,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden.
| | - Laurena Holleran
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Joong H Kim
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - David L Brody
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA.,Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, Missouri, USA
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Tariq S, Barber PA. Dementia risk and prevention by targeting modifiable vascular risk factors. J Neurochem 2017; 144:565-581. [PMID: 28734089 DOI: 10.1111/jnc.14132] [Citation(s) in RCA: 59] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Revised: 06/23/2017] [Accepted: 07/15/2017] [Indexed: 01/04/2023]
Abstract
The incidence of dementia is expected to double in the next 20 years and will contribute to heavy social and economic burden. Dementia is caused by neuronal loss that leads to brain atrophy years before symptoms manifest. Currently, no cure exists and extensive efforts are being made to mitigate cognitive impairment in late life in order to reduce the burden on patients, caregivers, and society. The most common type of dementia, Alzheimer's disease (AD), and vascular dementia (VaD) often co-exists in the brain and shares common, modifiable risk factors, which are targeted in numerous secondary prevention trials. There is a growing need for non-pharmacological interventions and infrastructural support from governments to encourage psychosocial and behavioral interventions. Secondary prevention trials need to be redesigned based on the risk profile of individual subjects, which require the use of validated and standardized clinical, biological, and neuroimaging biomarkers. Multi-domain approaches have been proposed in high-risk populations that target optimal treatment; clinical trials need to recruit individuals at the highest risk of dementia before symptoms develop, thereby identifying an enriched disease group to test preventative and disease modifying strategies. The underlying aim should be to reduce microscopic brain tissue loss by modifying vascular and lifestyle risk factors over a relatively short period of time, thus optimizing the opportunity for preventing dementia in the future. Collaboration between international research groups is of key importance to the optimal use and allocation of existing resources, and the development of new techniques in preventing dementia. This article is part of the Special Issue "Vascular Dementia".
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Affiliation(s)
- Sana Tariq
- Seaman Family MR Center, Foothills Medical Centre, Calgary, AB, Canada.,Hotchkiss Brain Institute, Foothills Medical Center, Room 1A10 Health Research Innovation Center, Calgary, AB, Canada
| | - Philip A Barber
- Hotchkiss Brain Institute, Foothills Medical Center, Room 1A10 Health Research Innovation Center, Calgary, AB, Canada.,Calgary Stroke Program, Department of Clinical Neurosciences, Foothills Medical Centre, Calgary, AB, Canada
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Kantarci K, Murray ME, Schwarz CG, Reid RI, Przybelski SA, Lesnick T, Zuk SM, Raman MR, Senjem ML, Gunter JL, Boeve BF, Knopman DS, Parisi JE, Petersen RC, Jack CR, Dickson DW. White-matter integrity on DTI and the pathologic staging of Alzheimer's disease. Neurobiol Aging 2017; 56:172-179. [PMID: 28552181 DOI: 10.1016/j.neurobiolaging.2017.04.024] [Citation(s) in RCA: 133] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Revised: 04/07/2017] [Accepted: 04/25/2017] [Indexed: 11/16/2022]
Abstract
Pattern of diffusion tensor MRI (DTI) alterations were investigated in pathologically-staged Alzheimer's disease (AD) patients (n = 46). Patients with antemortem DTI studies and a range of AD pathology at autopsy were included. Patients with a high neurofibrillary tangle (NFT) stage (Braak IV-VI) had significantly elevated mean diffusivity (MD) in the crus of fornix and ventral cingulum tracts, precuneus, and entorhinal white matter on voxel-based analysis after adjusting for age and time from MRI to death (p < 0.001). Higher MD and lower fractional anisotropy in the ventral cingulum tract, entorhinal, and precuneus white matter was associated with higher Braak NFT stage and clinical disease severity. There were no MD and fractional anisotropy differences among the low (none and sparse) and high (moderate and frequent) β-amyloid neuritic plaque groups. The NFT pathology of AD is associated with DTI alterations involving the medial temporal limbic connections and medial parietal white matter. This pattern of diffusion abnormalities is also associated with clinical disease severity.
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Affiliation(s)
- Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, MN, USA.
| | - Melissa E Murray
- Department of Pathology and Laboratory Medicine, Mayo Clinic, Jacksonville, FL, USA
| | | | - Robert I Reid
- Department of Information Technology, Mayo Clinic, Rochester, MN, USA
| | | | - Timothy Lesnick
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Samantha M Zuk
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Mekala R Raman
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Matthew L Senjem
- Department of Information Technology, Mayo Clinic, Rochester, MN, USA
| | - Jeffrey L Gunter
- Department of Information Technology, Mayo Clinic, Rochester, MN, USA
| | | | | | - Joseph E Parisi
- Department of Pathology and Laboratory Medicine, Mayo Clinic, Rochester, MN, USA
| | | | | | - Dennis W Dickson
- Department of Pathology and Laboratory Medicine, Mayo Clinic, Jacksonville, FL, USA
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Gispert JD, Suárez-Calvet M, Monté GC, Tucholka A, Falcon C, Rojas S, Rami L, Sánchez-Valle R, Lladó A, Kleinberger G, Haass C, Molinuevo JL. Cerebrospinal fluid sTREM2 levels are associated with gray matter volume increases and reduced diffusivity in early Alzheimer's disease. Alzheimers Dement 2016; 12:1259-1272. [PMID: 27423963 DOI: 10.1016/j.jalz.2016.06.005] [Citation(s) in RCA: 84] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2016] [Revised: 04/28/2016] [Accepted: 06/09/2016] [Indexed: 01/12/2023]
Abstract
INTRODUCTION TREM2 is involved in the regulation of inflammatory response and phagocytosis. A soluble fragment (sTREM2) is often found abnormally increased in cerebrospinal fluid (CSF) in Alzheimer's disease (AD). METHODS One hundred fourteen participants (45 control, 19 preclinical, 27 mild cognitive impairment [MCI], and 23 AD) underwent CSF sTREM2 determination and magnetic resonance imaging (MRI). We studied the association between CSF sTREM2, gray matter volume, and water motion diffusivity and anisotropy across groups. RESULTS In MCI patients, a positive correlation between CSF sTREM2 and gray matter volume was found in the bilateral inferior and middle temporal cortices, precuneus, the supramarginal, and angular gyri, after controlling by age, sex, and p-tau. A negative correlation with mean diffusivity was detected in overlapping regions, among others. DISCUSSION In early AD, augmented CSF sTREM2 levels correspond with cerebral MRI features typical of brain swelling, supporting a role for TREM2 in the regulation of the neuroinflammatory response to early neurodegeneration.
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Affiliation(s)
- Juan Domingo Gispert
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Zaragoza, Spain; Pompeu Fabra University, Barcelona, Spain
| | - Marc Suárez-Calvet
- BioMedical Center (BMC), Biochemistry, Ludwig-Maximilians-University Munich, Munich, Germany; German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany
| | - Gemma C Monté
- Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital Clínic, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Alan Tucholka
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
| | - Carles Falcon
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Zaragoza, Spain
| | - Santiago Rojas
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain; Unit of human Anatomy and Embryology, Department of Morphological Sciences, Faculty of Medicine, Autonomous University of Barcelona, Cerdanyola del Vallès, Spain
| | - Lorena Rami
- Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital Clínic, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Raquel Sánchez-Valle
- Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital Clínic, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Albert Lladó
- Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital Clínic, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Gernot Kleinberger
- BioMedical Center (BMC), Biochemistry, Ludwig-Maximilians-University Munich, Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Christian Haass
- BioMedical Center (BMC), Biochemistry, Ludwig-Maximilians-University Munich, Munich, Germany; German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - José Luis Molinuevo
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain; Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital Clínic, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.
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Rieckmann A, Van Dijk KRA, Sperling RA, Johnson KA, Buckner RL, Hedden T. Accelerated decline in white matter integrity in clinically normal individuals at risk for Alzheimer's disease. Neurobiol Aging 2016; 42:177-88. [PMID: 27143434 PMCID: PMC4857135 DOI: 10.1016/j.neurobiolaging.2016.03.016] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2015] [Revised: 03/13/2016] [Accepted: 03/14/2016] [Indexed: 12/19/2022]
Abstract
Prior studies have identified white matter abnormalities in Alzheimer's disease (AD). Yet, cross-sectional studies in normal older individuals show little evidence for an association between markers of AD risk (APOE4 genotype and amyloid deposition), and white matter integrity. Here, 108 normal older adults (age, 66-87) with assessments of apolipoprotein e4 (APOE4) genotype and assessment of amyloid burden by positron emission tomography underwent diffusion tensor imaging scans for measuring white matter integrity at 2 time points, on average 2.6 years apart. Linear mixed-effects models showed that amyloid burden at baseline was associated with steeper decline in fractional anisotropy in the parahippocampal cingulum (p < 0.05). This association was not significant between baseline measures suggesting that longitudinal analyses can provide novel insights that are not detectable in cross-sectional designs. Amyloid-related changes in hippocampus volume did not explain the association between amyloid burden and change in fractional anisotropy. The results suggest that accumulation of cortical amyloid and white matter changes in parahippocampal cingulum are not independent processes in individuals at increased risk for AD.
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Affiliation(s)
- Anna Rieckmann
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiation Sciences, Diagnostic Radiology, Umeå University, Umeå, Sweden
| | - Koene R A Van Dijk
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Department of Psychology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Reisa A Sperling
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Keith A Johnson
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Randy L Buckner
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Department of Psychology and Center for Brain Science, Harvard University, Cambridge, MA, USA; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Trey Hedden
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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Strømland Ø, Jakubec M, Furse S, Halskau Ø. Detection of misfolded protein aggregates from a clinical perspective. J Clin Transl Res 2016; 2:11-26. [PMID: 30873457 PMCID: PMC6410640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2015] [Revised: 03/22/2016] [Accepted: 03/22/2016] [Indexed: 11/29/2022] Open
Abstract
Neurodegenerative Protein Misfolding Diseases (PMDs), such as Alzheimer's (AD), Parkinson's (PD) and prion diseases, are generally difficult to diagnose before irreversible damage to the central nervous system damage has occurred. Detection of the misfolded proteins that ultimately lead to these conditions offers a means for providing early detection and diagnosis of this class of disease. In this review, we discuss recent developments surrounding protein misfolding diseases with emphasis on the cytotoxic oligomers implicated in their aetiology. We also discuss the relationship of misfolded proteins with biological membranes. Finally, we discuss how far techniques for providing early diagnoses for PMDs have advanced and describe promising clinical approaches. We conclude that antibodies with specificity towards oligomeric species of AD and PD and lectins with specificity for particular glycosylation, show promise. However, it is not clear which approach may yield a reliable clinical test first. Relevance for patients: Individuals suffering from protein misfolding diseases will likely benefit form earlier, less- or even non-invasive diagnosis techniques. The current state and possible future directions for these are subject of this review.
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Affiliation(s)
- Øyvind Strømland
- Department of Molecular Biology, University of Bergen, Bergen, Norway
| | - Martin Jakubec
- Department of Molecular Biology, University of Bergen, Bergen, Norway
| | - Samuel Furse
- Department of Molecular Biology, University of Bergen, Bergen, Norway
| | - Øyvind Halskau
- Department of Molecular Biology, University of Bergen, Bergen, Norway
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Fletcher E, Villeneuve S, Maillard P, Harvey D, Reed B, Jagust W, DeCarli C. β-amyloid, hippocampal atrophy and their relation to longitudinal brain change in cognitively normal individuals. Neurobiol Aging 2016; 40:173-180. [PMID: 26973117 DOI: 10.1016/j.neurobiolaging.2016.01.133] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2015] [Revised: 01/25/2016] [Accepted: 01/30/2016] [Indexed: 12/26/2022]
Abstract
Recent literature has examined baseline hippocampal volume and extent of brain amyloidosis to test potential synergistic effects on worsening cognition and extent of brain atrophy. Use of hippocampal volume in prior studies was based on the notion that limbic circuit degeneration is an early manifestation of the Alzheimer's Disease (AD) pathophysiology. To clarify these interactions early in the AD process, we tested the effects of amyloid and baseline normalized hippocampal volume on longitudinal brain atrophy rates in a group of cognitively normal individuals. Results showed that the combination of elevated β-amyloid and baseline hippocampal atrophy is associated with increased rates specific to the limbic circuit and splenium. Importantly, this atrophy pattern emerged from a voxelwise analysis, corroborated by regression models over region of interests in native space. The results are broadly consistent with previous studies of the effects of amyloid and baseline hippocampal atrophy in normals, while pointing to accelerated atrophy of AD-vulnerable regions detectable at the preclinical stage.
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Affiliation(s)
- Evan Fletcher
- Department of Neurology, University of California at Davis, Davis, CA, USA.
| | - Sylvia Villeneuve
- Douglas Mental Health University Institute, McGill University, Montreal, Canada
| | - Pauline Maillard
- Department of Neurology, University of California at Davis, Davis, CA, USA
| | - Danielle Harvey
- Division of Biostatistics, School of Medicine, University of California at Davis, Davis, CA, USA
| | - Bruce Reed
- Department of Neurology, University of California at Davis, Davis, CA, USA
| | - William Jagust
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley CA, USA
| | - Charles DeCarli
- Department of Neurology, University of California at Davis, Davis, CA, USA
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Measuring Cortical Connectivity in Alzheimer's Disease as a Brain Neural Network Pathology: Toward Clinical Applications. J Int Neuropsychol Soc 2016; 22:138-63. [PMID: 26888613 DOI: 10.1017/s1355617715000995] [Citation(s) in RCA: 78] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
OBJECTIVES The objective was to review the literature on diffusion tensor imaging as well as resting-state functional magnetic resonance imaging and electroencephalography (EEG) to unveil neuroanatomical and neurophysiological substrates of Alzheimer's disease (AD) as a brain neural network pathology affecting structural and functional cortical connectivity underlying human cognition. METHODS We reviewed papers registered in PubMed and other scientific repositories on the use of these techniques in amnesic mild cognitive impairment (MCI) and clinically mild AD dementia patients compared to cognitively intact elderly individuals (Controls). RESULTS Hundreds of peer-reviewed (cross-sectional and longitudinal) papers have shown in patients with MCI and mild AD compared to Controls (1) impairment of callosal (splenium), thalamic, and anterior-posterior white matter bundles; (2) reduced correlation of resting state blood oxygen level-dependent activity across several intrinsic brain circuits including default mode and attention-related networks; and (3) abnormal power and functional coupling of resting state cortical EEG rhythms. Clinical applications of these measures are still limited. CONCLUSIONS Structural and functional (in vivo) cortical connectivity measures represent a reliable marker of cerebral reserve capacity and should be used to predict and monitor the evolution of AD and its relative impact on cognitive domains in pre-clinical, prodromal, and dementia stages of AD.
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Pelletier A, Barul C, Féart C, Helmer C, Bernard C, Periot O, Dilharreguy B, Dartigues J, Allard M, Barberger‐Gateau P, Catheline G, Samieri C. Mediterranean diet and preserved brain structural connectivity in older subjects. Alzheimers Dement 2015; 11:1023-31. [DOI: 10.1016/j.jalz.2015.06.1888] [Citation(s) in RCA: 83] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2014] [Revised: 02/27/2015] [Accepted: 06/02/2015] [Indexed: 12/11/2022]
Affiliation(s)
- Amandine Pelletier
- Univ. Bordeaux ISPED, Centre INSERM U897 F‐33076 Bordeaux France
- INSERM, ISPED Centre INSERM U897 F‐33076 Bordeaux France
| | - Christine Barul
- Univ. Bordeaux ISPED, Centre INSERM U897 F‐33076 Bordeaux France
- INSERM, ISPED Centre INSERM U897 F‐33076 Bordeaux France
| | - Catherine Féart
- Univ. Bordeaux ISPED, Centre INSERM U897 F‐33076 Bordeaux France
- INSERM, ISPED Centre INSERM U897 F‐33076 Bordeaux France
| | - Catherine Helmer
- Univ. Bordeaux ISPED, Centre INSERM U897 F‐33076 Bordeaux France
- INSERM, ISPED Centre INSERM U897 F‐33076 Bordeaux France
| | - Charlotte Bernard
- Univ. Bordeaux, INCIA, UMR 5287 F‐33076 Bordeaux France
- CNRS, INCIA, UMR 5287 F‐33076 Bordeaux France
| | - Olivier Periot
- Univ. Bordeaux, INCIA, UMR 5287 F‐33076 Bordeaux France
- CNRS, INCIA, UMR 5287 F‐33076 Bordeaux France
- CHU de Bordeaux F‐33076 Bordeaux France
| | - Bixente Dilharreguy
- Univ. Bordeaux, INCIA, UMR 5287 F‐33076 Bordeaux France
- CNRS, INCIA, UMR 5287 F‐33076 Bordeaux France
| | - Jean‐François Dartigues
- Univ. Bordeaux ISPED, Centre INSERM U897 F‐33076 Bordeaux France
- INSERM, ISPED Centre INSERM U897 F‐33076 Bordeaux France
| | - Michèle Allard
- Univ. Bordeaux, INCIA, UMR 5287 F‐33076 Bordeaux France
- CNRS, INCIA, UMR 5287 F‐33076 Bordeaux France
- CHU de Bordeaux F‐33076 Bordeaux France
- EPHE Laboratoire Neurobiologie Intégrative et Adaptative F‐33076 Bordeaux France
| | - Pascale Barberger‐Gateau
- Univ. Bordeaux ISPED, Centre INSERM U897 F‐33076 Bordeaux France
- INSERM, ISPED Centre INSERM U897 F‐33076 Bordeaux France
| | - Gwénaëlle Catheline
- Univ. Bordeaux, INCIA, UMR 5287 F‐33076 Bordeaux France
- CNRS, INCIA, UMR 5287 F‐33076 Bordeaux France
- EPHE Laboratoire Neurobiologie Intégrative et Adaptative F‐33076 Bordeaux France
| | - Cécilia Samieri
- Univ. Bordeaux ISPED, Centre INSERM U897 F‐33076 Bordeaux France
- INSERM, ISPED Centre INSERM U897 F‐33076 Bordeaux France
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Wolf D, Fischer FU, Scheurich A, Fellgiebel A. Non-Linear Association between Cerebral Amyloid Deposition and White Matter Microstructure in Cognitively Healthy Older Adults. J Alzheimers Dis 2015; 47:117-27. [DOI: 10.3233/jad-150049] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
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