Mohamad Habes1,2,3,4, Raymond Pomponio1,4, Haochang Shou1,5, Jimit Doshi1,4, Elizabeth Mamourian1,4, Guray Erus1,4, Ilya Nasrallah1,4, Lenore J Launer6, Tanweer Rashid1,4, Murat Bilgel7, Yong Fan1,4, Jon B Toledo8,9, Kristine Yaffe10, Aristeidis Sotiras1,11, Dhivya Srinivasan1,4, Mark Espeland12, Colin Masters13, Paul Maruff13, Jurgen Fripp14, Henry Völzk15, Sterling C Johnson16, John C Morris17, Marilyn S Albert18, Michael I Miller19, R Nick Bryan20, Hans J Grabe21,22, Susan M Resnick7, David A Wolk1,2, Christos Davatzikos1,4. 1. Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA. 2. Department of Neurology and Penn Memory Center, University of Pennsylvania, Philadelphia, Pennsylvania, USA. 3. Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA. 4. Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA. 5. Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA. 6. Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Bethesda, Maryland, USA. 7. Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, Maryland, USA. 8. Department of Pathology and Laboratory Medicine, Institute on Aging, Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA. 9. Stanley Appel Department of Neurology, Houston Methodist Hospital, Houston, Texas, USA. 10. Departments of Neurology, Psychiatry and Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, USA. 11. Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA. 12. Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA. 13. Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia. 14. CSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO, Australia. 15. Institute for Community Medicine, University of Greifswald, Greifswald, Germany. 16. Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA. 17. Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA. 18. Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. 19. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA. 20. Department of Diagnostic Medicine, University of Texas, Austin, Texas, USA. 21. Department of Psychiatry and Psychotherapy, University of Greifswald, Germany. 22. German Center for Neurodegenerative Diseases (DZNE), Rostock, Greifswald, Germany.
Abstract
INTRODUCTION: Relationships 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 ≈10-year period in CN subjects. WMHs were associated with doubling the likelihood of amyloid beta (Aβ) 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.
INTRODUCTION: Relationships 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 ≈10-year period in CN subjects. WMHs were associated with doubling the likelihood of amyloid beta (Aβ) 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.
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