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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
Open accessAbstract
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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“Ageing is associated with a decrease in grey matter volume, which consists of neuronal cell bodies, dendrites, and synapses. The reduction in the grey matter volume is more pronounced in regions such as the hippocampus, which plays a critical role in memory and learning.162”
From Antiageing strategy for neurodegenerative diseases: from mechanisms to clinical advances (2025) · cites this paper as [162]
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N4ITK: Improved N3 Bias Correction
Tustison, N. J., Avants, B., Cook, P. A., Zheng, Y., Egan, A., Yushkevich, P. A., & Gee, J. C. (2010) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2010.2046908 · Cited by 6,119
Cardia: study design, recruitment, and some characteristics of the examined subjects
FRIEDMAN, G. D., Cutter, G., Donahue, R. P., Hughes, G. H., Hulley, S. B., Jacobs, D. R., Liu, K., & Savage, P. J. (1988) · Journal of Clinical Epidemiology · doi:10.1016/0895-4356(88)90080-7 · Cited by 1,689
Harmonization of cortical thickness measurements across scanners and sites
Fortin, J.-P., Cullen, N., Sheline, Y. I., Taylor, W. D., Aselcioglu, I., Cook, P. A., Adams, P., Cooper, C., Fava, M., McGrath, P. J., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.11.024 · Cited by 1,580
Memory and Executive Function in Aging and AD
Buckner, R. L. (2004) · Neuron · doi:10.1016/j.neuron.2004.09.006 · Cited by 1,508
Longitudinal Magnetic Resonance Imaging Studies of Older Adults: A Shrinking Brain
Resnick, S. M., Pham, D. L., Kraut, M. A., Zonderman, A. B., & Davatzikos, C. (2003) · The Journal of Neuroscience · doi:10.1523/jneurosci.23-08-03295.2003 · Cited by 1,425
Self-citationGeneralized additive models for medical research
Hastie, T. J., & Tibshirani, R. J. (1995) · Statistical Methods in Medical Research · doi:10.1177/096228029500400302 · Cited by 1,275
Building better biomarkers: brain models in translational neuroimaging
Woo, C.-W., Chang, L. J., Lindquist, M. A., & Wager, T. D. (2017) · Nature Neuroscience · doi:10.1038/nn.4478 · Cited by 1,056
Predicting Age Using Neuroimaging: Innovative Brain Ageing Biomarkers
Cole, J. H., & Franke, K. (2017) · Trends in Neurosciences · doi:10.1016/j.tins.2017.10.001 · Cited by 939
Midlife vascular risk factor exposure accelerates structural brain aging and cognitive decline
Debette, S., Seshadri, S., Beiser, A., Au, R., Himali, J. J., Palumbo, C., Wolf, P. A., & DeCarli, C. (2011) · Neurology · doi:10.1212/wnl.0b013e318227b227 · Cited by 772
Trajectories of brain aging in middle-aged and older adults: Regional and individual differences
Raz, N., Ghisletta, P., Rodrigue, K. M., Kennedy, K. M., & Lindenberger, U. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.03.020 · Cited by 626
Common brain disorders are associated with heritable patterns of apparent aging of the brain
Kaufmann, T., van der Meer, D., Doan, N. T., Schwarz, E., Lund, M. J., Agartz, I., Alnæs, D., Barch, D. M., Baur‐Streubel, R., Bertolino, A., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0471-7 · Cited by 575
Pomponio, R., Erus, G., Habes, M., Doshi, J. J., Srinivasan, D., Mamourian, E., Bashyam, V., Nasrallah, I. M., Satterthwaite, T. D., Yong Fan, A., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116450 · Cited by 561
Self-citationBrainAGE in Mild Cognitive Impaired Patients: Predicting the Conversion to Alzheimer’s Disease
Gaser, C., Franke, K., Klöppel, S., Koutsouleris, N., Sauer, H., & Initiative, A. D. N. (2013) · PLoS ONE · doi:10.1371/journal.pone.0067346 · Cited by 555
Lee, S., Viqar, F., Zimmerman, M. E., Narkhede, A., Tosto, G., Benzinger, T. L. S., Marcus, D. S., Fagan, A. M., Goate, A., Fox, N. C., et al. (2016) · Annals of Neurology · doi:10.1002/ana.24647 · Cited by 542
Self-citationWhite matter lesions impair frontal lobe function regardless of their location
Tullberg, M., Fletcher, E., DeCarli, C., Mungas, D., Reed, B. R., Harvey, D. J., Weiner, M. W., Chui, H. C., & Jagust, W. J. (2004) · Neurology · doi:10.1212/01.wnl.0000130530.55104.b5 · Cited by 530
Predicting brain-age from multimodal imaging data captures cognitive impairment
Liem, F., Varoquaux, G. P., Kynast, J., Beyer, F., Masouleh, S. K., Huntenburg, J. M., Lampe, L., Rahim, M., Abraham, A., Craddock, R. C., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.11.005 · Cited by 522
Brain Atrophy in Type 2 Diabetes
Moran, C., Phan, T. G., Chen, J., Blizzard, L., Beare, R. J., Venn, A., Münch, G. W., WOOD, A. G., Forbes, J. M., Greenaway, T. M., et al. (2013) · Diabetes Care · doi:10.2337/dc13-0143 · Cited by 521
Uncinate Fasciculus Findings in Schizophrenia: A Magnetic Resonance Diffusion Tensor Imaging Study
Kubicki, M. R., Westin, C., Maier, S. E., Frumin, M., Nestor, P. G., Salisbury, D. F., Kikinis, R., Jolesz, F. A., McCarley, R. W., & Shenton, M. E. (2002) · American Journal of Psychiatry · doi:10.1176/appi.ajp.159.5.813 · Cited by 454
White matter hyperintensities and imaging patterns of brain ageing in the general population
Habes, M., Erus, G., Toledo, J. B., Zhang, T., Bryan, R. N., Launer, L. J., Rosseel, Y., Janowitz, D., Doshi, J. J., der Auwera, S. V., et al. (2016) · Brain · doi:10.1093/brain/aww008 · Cited by 441
Self-citationAssociation of Intensive vs Standard Blood Pressure Control With Cerebral White Matter Lesions
Nasrallah, I. M., Pajewski, N. M., Auchus, A. P., Chelune, G. J., Cheung, A. K., Cleveland, M. L., Coker, L. H., Crowe, M. G., Cushman, W. C., Cutler, J. A., et al. (2019) · JAMA · doi:10.1001/jama.2019.10551 · Cited by 433
Self-citationDavatzikos, C., Xu, F., An, Y., Fan, Y., & Resnick, S. M. (2009) · Brain · doi:10.1093/brain/awp091 · Cited by 315
Self-citationThe Wisconsin Registry for Alzheimer's Prevention: A review of findings and current directions
Johnson, S. C., Koscik, R. L., Jonaitis, E. M., Clark, L. R., Mueller, K. D., Berman, S. E., Bendlin, B. B., Engelman, C. D., Okonkwo, O. C., Hogan, K. J., et al. (2017) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1016/j.dadm.2017.11.007 · Cited by 310
Self-citationDoshi, J., Erus, G., Ou, Y., Resnick, S. M., Gur, R. C., Gur, R. E., Satterthwaite, T. D., Furth, S., & Davatzikos, C. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.11.073 · Cited by 309
Self-citationDoshi, J. J., Erus, G., Ou, Y., Gaonkar, B., & Davatzikos, C. A. (2013) · Academic Radiology · doi:10.1016/j.acra.2013.09.010 · Cited by 258
Self-citationWhite matter hyperintensities: relationship to amyloid and tau burden
Graff-Radford, J., Arenaza-Urquijo, E. M., Knopman, D. S., Schwarz, C. G., Brown, R. D., Rabinstein, A. A., Gunter, J. L., Senjem, M. L., Przybelski, S. A., Lesnick, T., et al. (2019) · Brain · doi:10.1093/brain/awz162 · Cited by 253
Ihara, M., Polvikoski, T. M., Hall, R., Slade, J. Y., Perry, R. H., Oakley, A. E., Englund, E. M., O'Brien, J. T., Ince, P. G., & Kalaria, R. N. (2010) · Acta Neuropathologica · doi:10.1007/s00401-009-0635-8 · Cited by 251
Neuropathologic basis of white matter hyperintensity accumulation with advanced age
Erten‐Lyons, D., Woltjer, R. L., Kaye, J. A., Mattek, N., Dodge, H. H., Green, S., Tran, H. T. D., Howieson, D. B., Wild, K. V., & Silbert, L. C. (2013) · Neurology · doi:10.1212/wnl.0b013e3182a43e45 · Cited by 227
Advanced BrainAGE in older adults with type 2 diabetes mellitus
Franke, K., Gaser, C., Manor, B., & Novak, V. (2013) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2013.00090 · Cited by 221
Gray Matter Age Prediction as a Biomarker for Risk of Dementia
Wang, J. Z., Knol, M. J., Tiulpin, A., Dubost, F., de Bruijne, M., Vernooij, M. W., Adams, H. H. H., Ikram, M. A., Niessen, W. J., & Roshchupkin, G. V. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1902376116 · Cited by 216
Benavides‐Piccione, R., Fernaud-Espinosa, I., Robles, V., Yuste, R., & DeFelipe, J. (2012) · Cerebral Cortex · doi:10.1093/cercor/bhs154 · Cited by 180
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Habes, M., Grothe, M. J., Tunc, B., McMillan, C., Wolk, D. A., & Davatzikos, C. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.01.016 · Cited by 167
Self-citationAlbert, M., Soldan, A., Gottesman, R. F., McKhann, G. M., Sacktor, N. C., Farrington, L., Grega, M. A., Turner, R. S., Lu, Y., Li, S., et al. (2014) · Current Alzheimer Research · doi:10.2174/156720501108140910121920 · Cited by 158
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Dai, W., López, O. L., Carmichael, O. T., Becker, J. T., Kuller, L. H., & Gach, H. M. (2008) · Stroke · doi:10.1161/strokeaha.107.495457 · Cited by 154
Raz, N., Lindenberger, U., Ghisletta, P., Rodrigue, K. M., Kennedy, K. M., & Acker, J. D. (2007) · Cerebral Cortex · doi:10.1093/cercor/bhm108 · Cited by 149
Habes, M., Sotiras, A., Erus, G., Toledo, J. B., Janowitz, D., Wolk, D. A., Shou, H., Bryan, R. N., Doshi, J. J., Völzke, H., et al. (2018) · Neurology · doi:10.1212/wnl.0000000000006116 · Cited by 147
Self-citationHabes, M., Janowitz, D., Erus, G., Toledo, J. B., Resnick, S. M., Doshi, J. J., der Auwera, S. V., Wittfeld, K., Hegenscheid, K., Hosten, N., et al. (2016) · Translational Psychiatry · doi:10.1038/tp.2016.39 · Cited by 144
Self-citationDa, X., Toledo, J. B., Zee, J., Wolk, D. A., Xie, S. X., Ou, Y., Shacklett, A., Parmpi, P., Shaw, L. M. J., Trojanowski, J. Q., et al. (2013) · NeuroImage Clinical · doi:10.1016/j.nicl.2013.11.010 · Cited by 144
Self-citationDickerson, B. C., Wolk, D. A., & Initiative, T. A. D. N. (2010) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp.2009.199505 · Cited by 139
Self-citationDu, A., Schuff, N. W., Chao, L. L., Kornak, J., Ezekiel, F., Jagust, W. J., Kramer, J. H., Reed, B., Miller, B. L., Norman, D. M., et al. (2004) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2004.05.002 · Cited by 119
Eavani, H., Habes, M., Satterthwaite, T. D., An, Y., Hsieh, M.-K., Honnorat, N., Erus, G., Doshi, J. J., Ferrucci, L., Beason‐Held, L. L., et al. (2018) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2018.06.013 · Cited by 92
Self-citationDeepMRSeg: A convolutional deep neural network for anatomy and abnormality segmentation on MR imagesPreprint
Doshi, J. J., Erus, G., Habes, M., & Davatzikos, C. A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.02110 · Cited by 44
Self-citationBendayan, R., Piccinin, A. M., Hofer, S. M., Cadar, D., Johansson, B. E. A., & Terrera, G. M. (2017) · Journal of Aging Research · doi:10.1155/2017/6210105 · Cited by 24
Karolinska Schizophrenia Project (KaSP), Kaufmann, T., van der Meer, D., Doan, N. T., Schwarz, E., Lund, M. J., Agartz, I., Alnæs, D., Barch, D. M., Baur‐Streubel, R., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0553-6 · Cited by 8
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Orr, M., Gonzales, M., Garbarino, V., Kautz, T., Palavicini, J., Lopez-Cruzan, M., Dehkordi, S. K., Mathews, J., Zare, H., Xu, P., et al. (2023) · doi:10.21203/rs.3.rs-2809973/v1 · Cited by 319
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Jiang, Q., Liu, J., Huang, S., Wang, X.-Y., Chen, X., Liu, G.-H., Ye, K., Song, W., Masters, C. L., Wang, J., et al. (2025) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-025-02145-7 · Cited by 165
Self-citationWen, J., Fu, C. H. Y., Tosun, D., Veturi, Y., Yang, Z., Abdulkadir, A., Mamourian, E., Srinivasan, D., Skampardoni, I., Singh, A., et al. (2022) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2022.0020 · Cited by 142
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Metadata
- Authors
- Habes, M., Pomponio, R., Shou, H., Doshi, J., Mamourian, E., Erus, G., Nasrallah, I., Launer, L. J., Rashid, T., Bilgel, M., Fan, Y., Toledo, J. B., Yaffe, K., Sotiras, A., Srinivasan, D., Espeland, M., Masters, C., Maruff, P., Fripp, J., Völzk, H., Johnson, S. C., Morris, J. C., Albert, M. S., Miller, M. I., Bryan, R. N., Grabe, H. J., Resnick, S. M., Wolk, D. A., Davatzikos, C., & for the iSTAGING consortium, T. P. A. C.
- Affiliations
- The University of Texas at San Antonio Health Science Center (US)
- University of Pennsylvania (US)
- Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia Pennsylvania USA
- National Institutes of Health (US)
- National Institute on Aging (US)
- Houston Methodist (US)
- University of California, San Francisco (US)
- Washington University in St. Louis (US)
- Wake Forest University Health Sciences (US)
- The University of Melbourne (AU)
- CSIRO Health and Biosecurity Australian e‐Health Research Centre CSIRO Australia
- Universitätsmedizin Greifswald (DE)
- University of Wisconsin–Madison (US)
- Johns Hopkins University (US)
- The University of Texas at Austin (US)
- German Center for Neurodegenerative Diseases (DE)
- Corresponding author
- Habes, Mohamad
- ORCID
- Habes, Mohamad
- Pomponio, Raymond
- Shou, Haochang
- Doshi, Jimit
- Mamourian, Elizabeth
- Nasrallah, Ilya
- Launer, Lenore J.
- Rashid, Tanweer
- Bilgel, Murat
- Toledo, Jon B.
- Yaffe, Kristine
- Sotiras, Aristeidis
- Srinivasan, Dhivya
- Espeland, Mark
- Masters, Colin
- Maruff, Paul
- Johnson, Sterling C.
- Morris, John C.
- Albert, Marilyn S.
- Miller, Michael I.
- Bryan, R. Nick
- Grabe, Hans J.
- Resnick, Susan M.
- Wolk, David A.
- Davatzikos, Christos
- Venue
- Alzheimer's & Dementia
- Publisher
- Wiley
- Published
- 2020-09-13
- Volume
- 17
- Issue
- 1
- Pages
- 89-102
- ISSN
- 1552-5260 (ISSN-L)
- 1552-5279
- PMC
- PMC7923395
- OpenAlex
- W3084546953
- Cited by
- 173
- References
- 43
- Citations by year
- 2026: 23 · 2025: 28 · 2024: 35 · 2023: 27 · 2022: 39 · 2021: 19 · 2020: 2
- FWCI
- 15.87
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Alzheimer s & Dementia (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Minerva Access (University of Melbourne) (submittedVersion)
- DZNE Pub (submittedVersion)
- Funders
- National Institutes of Health (1s10od023495-01)
- National Institute on Aging (R01AG063887, P30AG049638, U19AG033655, RF1AG059869, RF1AG054409, P30AG066507, P30AG062715, U01AG068057)
- National Heart, Lung, and Blood Institute (HHSN271201600059C)
- National Institute on Drug Abuse (HHSN271201600059C)
- National Institute of Biomedical Imaging and Bioengineering (R01EB022573)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Clinical Neurology › Cerebrovascular and genetic disorders
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- brain aging, white matter hyperintensities
- MeSH terms
- Machine Learning, Adult, Aged, Aged, 80 and over, Aging, Atrophy, Brain, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Neuropsychological Tests, Biomarkers, Amyloid beta-Peptides, Disease Progression, Young Adult, Cerebral Small Vessel Diseases, Cognitive Dysfunction, White Matter
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