The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Longitudinal pattern of regional brain volume change differentiates normal aging from MCI
Open accessAbstract
BACKGROUND: Neuroimaging measures have potential as surrogate markers of disease through identification of consistent features that occur prior to clinical symptoms. Despite numerous investigations, especially in relation to the transition to clinical impairment, the regional pattern of brain changes in clinically normal older adults has not been established. We predict that the regions that show early pathologic changes in association with Alzheimer disease will show accelerated volume loss in mild cognitive impairment (MCI) compared to normal aging. METHODS: Through the Baltimore Longitudinal Study of Aging, we prospectively evaluated 138 nondemented individuals (age 64-86 years) annually for up to 10 consecutive years. Eighteen participants were diagnosed with MCI over the course of the study. Mixed-effects regression was used to compare regional brain volume trajectories of clinically normal individuals to those with MCI based on a total of 1,017 observations. RESULTS: All investigated volumes declined with normal aging (p < 0.05). Accelerated change with age was observed for ventricular CSF (vCSF), frontal gray matter, superior, middle, and medial frontal, and superior parietal regions (p < or = 0.04). The MCI group showed accelerated changes compared to normal controls in whole brain volume, vCSF, temporal gray matter, and orbitofrontal and temporal association cortices, including the hippocampus (p < or = 0.04). CONCLUSION: Although age-related regional volume loss is apparent and widespread in nondemented individuals, mild cognitive impairment is associated with a unique pattern of structural vulnerability reflected in differential volume loss in specific regions. Early identification of patterns of abnormality is of fundamental importance for detecting disease onset and tracking progression.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975) · Journal of Psychiatric Research · doi:10.1016/0022-3956(75)90026-6 · Cited by 88,282
A Voxel-Based Morphometric Study of Ageing in 465 Normal Adult Human Brains
Good, C. D., Johnsrude, I. S., Ashburner, J., Henson, R. N. A., Friston, K. J., & Frackowiak, R. S. J. (2001) · NeuroImage · doi:10.1006/nimg.2001.0786 · Cited by 4,561
Metabolic reduction in the posterior cingulate cortex in very early Alzheimer's disease
Minoshima, S., Giordani, B., Berent, S., Frey, K. A., Foster, N. L., & Kuhl, D. E. (1997) · Annals of Neurology · doi:10.1002/ana.410420114 · Cited by 1,802
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-citationTrajectories of brain loss in aging and the development of cognitive impairment
Carlson, N. E., Moore, M. M., Dame, A., Howieson, D., Silbert, L. C., Quinn, J. F., & Kaye, J. A. (2008) · Neurology · doi:10.1212/01.wnl.0000280577.43413.d9 · Cited by 144
Impact of Alzheimer's pathology on cognitive trajectories in nondemented elderly
Driscoll, I., Resnick, S. M., Troncoso, J. C., An, Y., O'Brien, R., & Zonderman, A. B. (2006) · Annals of Neurology · doi:10.1002/ana.21031 · Cited by 132
Self-citation
Cited by
The clinical use of structural MRI in Alzheimer disease
Frisoni, G. B., Fox, N. C., Jack, C. R., Scheltens, P., & Thompson, P. M. (2010) · Nature Reviews Neurology · doi:10.1038/nrneurol.2009.215 · Cited by 2,113
Miranda, M., Morici, J. F., Zanoni, M. B., & Bekinschtein, P. (2019) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2019.00363 · Cited by 1,416
Fjell, A. M., McEvoy, L., Holland, D., Dale, A. M., & Walhovd, K. B. (2014) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2014.02.004 · Cited by 799
One-Year Brain Atrophy Evident in Healthy Aging
Fjell, A. M., Walhovd, K. B., Fennema-Notestine, C., McEvoy, L. K., Hagler, D. J., Holland, D., Brewer, J. B., & Dale, A. M. (2009) · The Journal of Neuroscience · doi:10.1523/jneurosci.3252-09.2009 · Cited by 692
Brain-derived neurotrophic factor is associated with age-related decline in hippocampal volume.
Erickson, K. I., Prakash, R. S., Voss, M. W., Chaddock, L., Heo, S., McLaren, M., Pence, B. D., Martin, S. A., Vieira, V. J., Woods, J. A., et al. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.6251-09.2010 · Cited by 604
Storsve, A. B., Fjell, A. M., Tamnes, C. K., Westlye, L. T., Overbye, K., Aasland, H. W., & Walhovd, K. B. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.0391-14.2014 · Cited by 542
Spatial navigation deficits — overlooked cognitive marker for preclinical Alzheimer disease?
Coughlan, G., Laczó, J., Hort, J., Minihane, A.-M., & Hornberger, M. (2018) · Nature Reviews Neurology · doi:10.1038/s41582-018-0031-x · Cited by 500
Jack, C. R., Wiste, H. J., Vemuri, P., Weigand, S. D., Senjem, M. L., Zeng, G., Bernstein, M. A., Gunter, J. L., Pankratz, V. S., Aisen, P. S., et al. (2010) · Brain · doi:10.1093/brain/awq277 · Cited by 531
Sex differences in cognitive decline among US adults
Levine, D. A., Gross, A. L., Briceño, E. M., Tilton, N., Giordani, B. J., Sussman, J. B., Hayward, R. A., Burke, J. F., Hingtgen, S., Elkind, M. S. V., et al. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.0169 · Cited by 440
Brain morphology links systemic inflammation to cognitive function in midlife adults
Marsland, A. L., Gianaros, P. J., Kuan, D. C.-H., Sheu, L. K., Krajina, K., & Manuck, S. B. (2015) · Brain, Behavior, and Immunity · doi:10.1016/j.bbi.2015.03.015 · Cited by 321
BDNF mediates improvements in executive function following a 1-year exercise intervention.
Leckie, R. L., Oberlin, L. E., Voss, M. W., Prakash, R. S., Szabo-Reed, A., Chaddock-Heyman, L., Phillips, S. M., Gothe, N. P., Mailey, E., Vieira-Potter, V. J., et al. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00985 · Cited by 307
Changing perspectives regarding late-life dementia
Fotuhi, M., Hachinski, V., & Whitehouse, P. J. (2009) · Nature Reviews Neurology · doi:10.1038/nrneurol.2009.175 · Cited by 291
Bao, Y.-P., Han, Y., Ma, J., Wang, R.-J., Shi, L., Wang, T.-Y., He, J., Yue, J.-L., Shi, J., Tang, X.-D., et al. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.01.032 · Cited by 275
MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans
Mendrik, A. M., Vincken, K. L., Kuijf, H. J., Breeuwer, M., Bouvy, W. H., de Bresser, J., Alansary, A., de Bruijne, M., Carass, A., El-Baz, A., et al. (2015) · Computational Intelligence and Neuroscience · doi:10.1155/2015/813696 · Cited by 261
Aging‐dependent changes of microglial cells and their relevance for neurodegenerative disorders
Von Bernhardi, R., Tichauer, J. E., & Eugenín, J. (2010) · Journal of Neurochemistry · doi:10.1111/j.1471-4159.2009.06537.x · Cited by 258
Imaging Patterns of Brain Development and their Relationship to Cognition
Erus, G., Battapady, H., Satterthwaite, T. D., Hakonarson, H., Gur, R. E., Davatzikos, C., & Gur, R. C. (2015) · Cerebral Cortex · doi:10.1093/cercor/bht425 · Cited by 226
Self-citationPfefferbaum, A., Rohlfing, T., Rosenbloom, M. J., Chu, W., Colrain, I. M., & Sullivan, E. V. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.10.008 · Cited by 256
Amlien, I. K., & Fjell, A. M. (2014) · Neuroscience · doi:10.1016/j.neuroscience.2014.02.017 · Cited by 219
A culture-brain link: negative age stereotypes predict Alzheimer's disease biomarkers
Levy, B. R., Ferrucci, L., Zonderman, A. B., Slade, M. D., Troncoso, J., & Resnick, S. M. (2016) · Psychology and Aging · doi:10.1037/pag0000062 · Cited by 198
Self-citationA systematic review and meta-analysis of longitudinal hippocampal atrophy in healthy human ageing
Fraser, M. A., Shaw, M. E., & Cherbuin, N. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.03.035 · Cited by 175
Changes in Cortical Plasticity Across the Lifespan
Freitas, C., Perez, J., Knobel, M., Tormos, J. M., Oberman, L., Eldaief, M., Bashir, S., Vernet, M., Peña-Gómez, C., & Pascual-Leone, A. (2011) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2011.00005 · Cited by 172
Higher RBC EPA + DHA corresponds with larger total brain and hippocampal volumes: WHIMS-MRI Study
Pottala, J. V., Yaffe, K., Robinson, J. G., Espeland, M. A., Wallace, R., & Harris, W. S. (2014) · Neurology · doi:10.1212/wnl.0000000000000080 · Cited by 169
Moore, A. Z., Caturegli, G., Metter, E. J., Makrogiannis, S., Resnick, S. M., Harris, T. B., & Ferrucci, L. (2014) · Journal of the American Geriatrics Society · doi:10.1111/jgs.12653 · Cited by 144
Self-citationDriscoll, I., Troncoso, J. C., Rudow, G., Sojkova, J., Pletnikova, O., Zhou, Y., Kraut, M. A., Ferrucci, L., Mathis, C. A., Klunk, W. E., et al. (2012) · Acta Neuropathologica · doi:10.1007/s00401-012-1025-1 · Cited by 104
Self-citationNeuronal injury biomarkers and prognosis in ADNI subjects with normal cognition
Toledo, J. B., Weiner, M. W., Wolk, D. A., Da, X., Chen, K., Arnold, S. E., Jagust, W., Jack, C., Reiman, E. M., Davatzikos, C., et al. (2014) · Acta Neuropathologica Communications · doi:10.1186/2051-5960-2-26 · Cited by 89
Self-citationTian, Q., Mitchell, B. A., Zampino, M., Fishbein, K. W., Spencer, R. G., & Ferrucci, L. (2022) · Aging Cell · doi:10.1111/acel.13552 · Cited by 78
Frey, A., Homola, G. A., Henneges, C., Mühlbauer, L., Sell, R., Kraft, P., Franke, M., Morbach, C., Vogt, M., Müllges, W., et al. (2021) · European Heart Journal · doi:10.1093/eurheartj/ehab003 · Cited by 70
Müller-Oehring, E. M., Schulte, T., Rohlfing, T., Pfefferbaum, A., & Sullivan, E. V. (2013) · Neuropsychology · doi:10.1037/a0030921 · Cited by 56
Verfaillie, S. C. J., Slot, R. E., Tijms, B. M., Bouwman, F., Benedictus, M. R., Overbeek, J. M., Koene, T., Vrenken, H., Scheltens, P., Barkhof, F., et al. (2018) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2017.09.009 · Cited by 40
Identifying brain networks in synaptic density PET (11C-UCB-J) with independent component analysis
Fang, X. T., Toyonaga, T., Hillmer, A. T., Matuskey, D., Holmes, S. E., Radhakrishnan, R., Mecca, A. P., van Dyck, C. H., D'Souza, D. C., Esterlis, I., et al. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118167 · Cited by 30
Wang, Z., Williams, V. J., Stephens, K. A., Kim, C., Bai, L., Zhang, M., & Salat, D. H. (2020) · Human Brain Mapping · doi:10.1002/hbm.24871 · Cited by 28
Frye, B. M., Craft, S., Register, T. C., Kim, J., Whitlow, C. T., Barcus, R. A., Lockhart, S. N., Sai, K. K. S., & Shively, C. A. (2022) · Alzheimer's & Dementia: Translational Research & Clinical Interventions · doi:10.1002/trc2.12284 · Cited by 24
Zhang, Z., Lovato, J., Battapady, H., Davatzikos, C., Gerstein, H. C., Ismail-Beigi, F., Launer, L. J., Murray, A., Punthakee, Z., Tirado, A. A., et al. (2014) · Diabetes Care · doi:10.2337/dc14-0973 · Cited by 20
Self-citationTian, Y., Hornburg, K., Austin, W., Cook, J. J., Qi, Y., White, L. E., Killmar, J. T., Kaczorowski, C. C., Ashbrook, D. G., Johnson, G. A., et al. (2026) · Nature Neuroscience · doi:10.1038/s41593-025-02199-4 · Cited by 1
Metadata
- Authors
- Driscoll, I., Davatzikos, C., An, Y., Wu, X., Shen, D., Kraut, M., & Resnick, S. M.
- Venue
- Neurology
- Published
- 2009-06-02
- Volume
- 72
- Issue
- 22
- Pages
- 1906-1913
- ISSN
- 0028-3878 (ISSN-L)
- 1526-632X
- OpenAlex
- W2108150542
- Cited by
- 532
- References
- 6
- FWCI
- 17.54
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
