The Brain Chart of Aging: Machine-learning analytics reveals links between brain aging, white matter disease, amyloid burden, and cognition in the iSTAGING consortium of 10,216 harmonized MR scans

被引:120
作者
Habes, Mohamad [1 ,2 ,3 ,4 ,5 ,26 ]
Pomponio, Raymond [1 ,26 ]
Shou, Haochang [1 ,6 ]
Doshi, Jimit [1 ,26 ]
Mamourian, Elizabeth [1 ,26 ]
Erus, Guray [1 ,26 ]
Nasrallah, Ilya [1 ,26 ]
Launer, Lenore J. [7 ]
Rashid, Tanweer [1 ,26 ]
Bilgel, Murat [8 ]
Fan, Yong [1 ,26 ]
Toledo, Jon B. [9 ,10 ]
Yaffe, Kristine [11 ,12 ,13 ]
Sotiras, Aristeidis [1 ,14 ]
Srinivasan, Dhivya [1 ,26 ]
Espeland, Mark [15 ]
Masters, Colin [16 ]
Maruff, Paul [16 ]
Fripp, Jurgen [17 ]
Volzk, Henry [18 ]
Johnson, Sterling C. [19 ]
Morris, John C. [20 ]
Albert, Marilyn S. [21 ]
Miller, Michael, I [22 ]
Bryan, R. Nick [23 ]
Grabe, Hans J. [24 ,25 ]
Resnick, Susan M. [8 ]
Wolk, David A. [1 ,2 ,3 ]
Davatzikos, Christos [1 ,26 ]
机构
[1] Univ Penn, Ctr Biomed Image Comp & Analyt, Philadelphia, PA 19104 USA
[2] Univ Penn, Dept Neurol, Philadelphia, PA 19104 USA
[3] Univ Penn, Penn Memory Ctr, Philadelphia, PA 19104 USA
[4] Univ Texas Hlth Sci Ctr San Antonio, Neuroimage Analyt Lab, Glenn Biggs Inst Neurodegenerat Disorders, San Antonio, TX 78229 USA
[5] Univ Texas Hlth Sci Ctr San Antonio, Biggs Inst Neuroimaging Core, Glenn Biggs Inst Neurodegenerat Disorders, San Antonio, TX 78229 USA
[6] Univ Penn, Dept Biostat Epidemiol & Informat, Philadelphia, PA 19104 USA
[7] NIA, Lab Epidemiol & Populat Sci, Bethesda, MD 20892 USA
[8] NIA, Lab Behav Neurosci, Bethesda, MD 20892 USA
[9] Univ Penn, Sch Med, Dept Pathol & Lab Med, Inst Aging,Ctr Neurodegenerat Dis Res, Philadelphia, PA 19104 USA
[10] Houston Methodist Hosp, Stanley Appel Dept Neurol, Houston, TX 77030 USA
[11] Univ Calif San Francisco, Dept Neurol, San Francisco, CA USA
[12] Univ Calif San Francisco, Dept Psychiat, San Francisco, CA USA
[13] Univ Calif San Francisco, Dept Epidemiol & Biostat, San Francisco, CA USA
[14] Washington Univ, Dept Radiol, St Louis, MO USA
[15] Wake Forest Sch Med, Dept Biostat & Data Sci, Winston Salem, NC 27101 USA
[16] Univ Melbourne, Florey Inst Neurosci & Mental Hlth, Melbourne, Vic, Australia
[17] Australian E Hlth Res Ctr CSIRO, CSIRO Hlth & Biosecu Ri Ty, Herston, Qld, Australia
[18] Ernst Moritz Arndt Univ Greifswald, Inst Community Med, Greifswald, Germany
[19] Univ Wisconsin, Wisconsin Alzheimers Inst, Sch Med & Publ Hlth, Madison, WI USA
[20] Washington Univ, Dept Neurol, St Louis, MO 63110 USA
[21] Johns Hopkins Univ, Sch Med, Dept Neurol, Baltimore, MD 21205 USA
[22] Johns Hopkins Univ, Dept Biomed Engn, Baltimore, MD USA
[23] Univ Texas Austin, Dept Diagnost Med, Austin, TX 78712 USA
[24] Ernst Moritz Arndt Univ Greifswald, Dept Psychiat & Psychotherapy, Greifswald, Germany
[25] German Ctr Neurodegenerat Dis DZNE, Greifswald, Germany
[26] Univ Penn, Dept Radiol, Philadelphia, PA 19104 USA
关键词
Alzheimer's disease pathology; beta-amyloid; brain aging; brain signatures; cognitive testing; Dementia; harmonized neuroimaging cohorts; Machine Learning; MRI; Neuroimaging; PET; preclinical Alzheimer's disease; small vessel ischemic disease; tau;
D O I
10.1002/alz.12178
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
IntroductionRelationships between brain atrophy patterns of typical aging and Alzheimer's disease (AD), white matter disease, cognition, and AD neuropathology were investigated via machine learning in a large harmonized magnetic resonance imaging database (11 studies; 10,216 subjects). Methods: Three brain signatures were calculated: Brain-age, AD-like neurodegeneration, and white matter hyperintensities (WMHs). Brain Charts measured and displayed the relationships of these signatures to cognition and molecular biomarkers of AD. Results: WMHs were associated with advanced brain aging, AD-like atrophy, poorer cognition, and AD neuropathology in mild cognitive impairment (MCI)/AD and cognitively normal (CN) subjects. High WMH volume was associated with brain aging and cognitive decline occurring in an approximate to 10-year period in CN subjects. WMHs were associated with doubling the likelihood of amyloid beta (A beta) positivity after age 65. Brain aging, AD-like atrophy, and WMHs were better predictors of cognition than chronological age in MCI/AD. Discussion: A Brain Chart quantifying brain-aging trajectories was established, enabling the systematic evaluation of individuals' brain-aging patterns relative to this large consortium.
引用
收藏
页码:89 / 102
页数:14
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