The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
Structural Imaging Measures of Brain Aging
Open accessAbstract
During the course of normal aging, biological changes occur in the brain that are associated with changes in cognitive ability. This review presents data from neuroimaging studies of primarily “normal” or healthy brain aging. As such, we focus on research in unimpaired or nondemented older adults, but also include findings from lifespan studies that include younger and middle aged individuals as well as from populations with prodromal or clinically symptomatic disease such as cerebrovascular or Alzheimer’s disease. This review predominantly addresses structural MRI biomarkers, such as volumetric or thickness measures from anatomical images, and measures of white matter injury and integrity respectively from FLAIR or DTI, and includes complementary data from PET and cognitive or clinical testing as appropriate. The findings reveal highly consistent age-related differences in brain structure, particularly frontal lobe and medial temporal regions that are also accompanied by age-related differences in frontal and medial temporal lobe mediated cognitive abilities. Newer findings also suggest that degeneration of specific white matter tracts such as those passing through the genu and splenium of the corpus callosum may also be related to age-related differences in cognitive performance. Interpretation of these findings, however, must be tempered by the fact that comorbid diseases such as cerebrovascular and Alzheimer’s disease also increase in prevalence with advancing age. As such, this review discusses challenges related to interpretation of current theories of cognitive aging in light of the common occurrence of these later-life diseases. Understanding the differences between “Normal” and “Healthy” brain aging and identifying potential modifiable risk factors for brain aging is critical to inform potential treatments to stall or reverse the effects of brain aging and possibly extend cognitive health for our aging society.
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
Amyloid oligomers exacerbate tau pathology in a mouse model of tauopathy
Selenica, M.-L. B., Brownlow, M., Jimenez, J. P., Lee, D. C., Pena, G., Dickey, C. A., Gordon, M. N., & Morgan, D. (2013) · Neurodegenerative Diseases · doi:10.1159/000337230 · Cited by 10,359
Prediction of coronary heart disease using risk factor categories
Wilson, P. W. F., D’Agostino, R. B., Levy, D., Belanger, A. M., Silbershatz, H., & Kannel, W. B. (1998) · Circulation · doi:10.1161/01.cir.97.18.1837 · Cited by 9,581
Sperling, R. A., Aisen, P. S., Beckett, L. A., Bennett, D. A., Craft, S., Fagan, A. M., Iwatsubo, T., Jack, C. R., Kaye, J., Montine, T. J., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.003 · Cited by 7,155
Dynamic mapping of human cortical development during childhood through early adulthood
Gogtay, N., Giedd, J. N., Lusk, L., Hayashi, K. M., Greenstein, D., Vaituzis, A. C., Nugent, T. F., Herman, D. H., Clasen, L. S., Toga, A. W., et al. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0402680101 · Cited by 5,604 · cited 1×
“One hypothesis suggests that, because these are the regions late to mature during development (Gogtay et al. 2004), they are more susceptible to later-life degeneration (Fjell et al. 2014a).”
BackgroundWardlaw, J. M., Smith, E. E., Biessels, G. J., Cordonnier, C., Fazekas, F., Frayne, R., Lindley, R. I., O'Brien, J. T., Barkhof, F., Benavente, O. R., et al. (2013) · The Lancet Neurology · doi:10.1016/s1474-4422(13)70124-8 · Cited by 5,544
Self-citationJack, C. R., Knopman, D. S., Jagust, W. J., Petersen, R. C., Weiner, M. W., Aisen, P. S., Shaw, L. M., Vemuri, P., Wiste, H. J., Weigand, S. D., et al. (2013) · The Lancet Neurology · doi:10.1016/s1474-4422(12)70291-0 · Cited by 4,396
The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods
Jack, C. R., Bernstein, M. A., Fox, N. C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P. J., L. Whitwell, J., Ward, C., et al. (2008) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.21049 · Cited by 4,347
The Nature of Recollection and Familiarity: A Review of 30 Years of Research
Yonelinas, A. P. (2002) · Journal of Memory and Language · doi:10.1006/jmla.2002.2864 · Cited by 3,840
Gorelick, P. B., Scuteri, A., Black, S. E., DeCarli, C., Greenberg, S. M., Iadecola, C., Launer, L. J., Laurent, S., Lopez, O. L., Nyenhuis, D., et al. (2011) · Stroke · doi:10.1161/str.0b013e3182299496 · Cited by 3,811
Self-citationGreicius, M. D., Srivastava, G., Reiss, A. L., & Menon, V. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0308627101 · Cited by 3,684
Cited by
Li, L. M., Uehara, K., & Hanakawa, T. (2015) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2015.00181 · Cited by 477
Brain aging mechanisms with mechanical manifestations
Blinkouskaya, Y., Caçoilo, A., Gollamudi, T., Jalalian, S., & Weickenmeier, J. (2021) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2021.111575 · Cited by 258
Weinstein, G., Zelber-Sagi, S., Preis, S. R., Beiser, A. S., DeCarli, C., Speliotes, E. K., Satizabal, C. L., Vasan, R. S., & Seshadri, S. (2018) · JAMA Neurology · doi:10.1001/jamaneurol.2017.3229 · Cited by 186
Self-citationRabinovici, G. D. (2019) · Continuum · doi:10.1212/con.0000000000000700 · Cited by 170
PET imaging of neural activity, β-amyloid, and tau in normal brain aging
Zhang, K., Mizuma, H., Zhang, X., Takahashi, K., Jin, C., Song, F., Gao, Y., Kanayama, Y., Wu, Y., Li, Y., et al. (2021) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-021-05230-5 · Cited by 49
Metadata
- Authors
- Lockhart, S. N., & DeCarli, C.
- Venue
- Neuropsychology Review
- Published
- 2014-08-22
- Volume
- 24
- Issue
- 3
- Pages
- 271-289
- ISSN
- 1040-7308 (ISSN-L)
- 1573-6660
- OpenAlex
- W2050683965
- Cited by
- 267
- References
- 53
- FWCI
- 10.56
- Type
- journal article
- Language
- en
- Open access
- green
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Structural Imaging Measures of Brain Aging. https://app.cochat.ai/papers/10.1007/s11065-014-9268-3
- The paper
- Lockhart, S. N., & DeCarli, C. (2014). Structural Imaging Measures of Brain Aging. Neuropsychology Review. https://doi.org/10.1007/s11065-014-9268-3
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.
