The DOI matches its Crossref record; 91 of the 91 references it cites were found in a registry or an index.
White matter hyperintensities and imaging patterns of brain ageing in the general population
Open accessAbstract
White matter hyperintensities are associated with increased risk of dementia and cognitive decline. The current study investigates the relationship between white matter hyperintensities burden and patterns of brain atrophy associated with brain ageing and Alzheimer's disease in a large populatison-based sample (n = 2367) encompassing a wide age range (20-90 years), from the Study of Health in Pomerania. We quantified white matter hyperintensities using automated segmentation and summarized atrophy patterns using machine learning methods resulting in two indices: the SPARE-BA index (capturing age-related brain atrophy), and the SPARE-AD index (previously developed to capture patterns of atrophy found in patients with Alzheimer's disease). A characteristic pattern of age-related accumulation of white matter hyperintensities in both periventricular and deep white matter areas was found. Individuals with high white matter hyperintensities burden showed significantly (P < 0.0001) lower SPARE-BA and higher SPARE-AD values compared to those with low white matter hyperintensities burden, indicating that the former had more patterns of atrophy in brain regions typically affected by ageing and Alzheimer's disease dementia. To investigate a possibly causal role of white matter hyperintensities, structural equation modelling was used to quantify the effect of Framingham cardiovascular disease risk score and white matter hyperintensities burden on SPARE-BA, revealing a statistically significant (P < 0.0001) causal relationship between them. Structural equation modelling showed that the age effect on SPARE-BA was mediated by white matter hyperintensities and cardiovascular risk score each explaining 10.4% and 21.6% of the variance, respectively. The direct age effect explained 70.2% of the SPARE-BA variance. Only white matter hyperintensities significantly mediated the age effect on SPARE-AD explaining 32.8% of the variance. The direct age effect explained 66.0% of the SPARE-AD variance. Multivariable regression showed significant relationship between white matter hyperintensities volume and hypertension (P = 0.001), diabetes mellitus (P = 0.023), smoking (P = 0.002) and education level (P = 0.003). The only significant association with cognitive tests was with the immediate recall of the California verbal and learning memory test. No significant association was present with the APOE genotype. These results support the hypothesis that white matter hyperintensities contribute to patterns of brain atrophy found in beyond-normal brain ageing in the general population. White matter hyperintensities also contribute to brain atrophy patterns in regions related to Alzheimer's disease dementia, in agreement with their known additive role to the likelihood of dementia. Preventive strategies reducing the odds to develop cardiovascular disease and white matter hyperintensities could decrease the incidence or delay the onset of dementia.
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
lavaan : An R Package for Structural Equation Modeling
Rosseel, Y. (2012) · Journal of Statistical Software · doi:10.18637/jss.v048.i02 · Cited by 27,955
Self-citationHirono, N., Mori, E., Ikejiri, Y., Imamura, T., Shimomura, T., Hashimoto, M., Yamashita, H., & Ikeda, M. (1997) · Brain and nerve · Cited by 21,012
The Nature of Statistical Learning Theory
Vapnik, V. N. (2000) · Springer eBooks · doi:10.1007/978-1-4757-3264-1 · Cited by 10,696
General Cardiovascular Risk Profile for Use in Primary Care: The Framingham Heart Study
D’Agostino, R. B., Vasan, R. S., Pencina, M. J., Wolf, P. A., Cobain, M., Massaro, J. M., & Kannel, W. B. (2008) · Circulation · doi:10.1161/circulationaha.107.699579 · Cited by 7,491
N4ITK: Improved N3 Bias Correction
Tustison, N. J., Avants, B. 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 6,073
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-citationSilent Brain Infarcts and the Risk of Dementia and Cognitive Decline
Vermeer, S. E., Prins, N. D., den Heijer, T., Hofman, A., Koudstaal, P. J., & Breteler, M. M. B. (2003) · New England Journal of Medicine · doi:10.1056/nejmoa022066 · Cited by 2,313
The Nature of Statistical Learning
Vapnik, V. N. (1995) · Medical Entomology and Zoology · Cited by 2,295
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
Mechanisms of sporadic cerebral small vessel disease: insights from neuroimaging
Wardlaw, J. M., Smith, C., & Dichgans, M. (2013) · The Lancet Neurology · doi:10.1016/s1474-4422(13)70060-7 · Cited by 1,657
Cited by
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
Oligodendrocytes in development, myelin generation and beyond
Kuhn, S., Gritti, L., Crooks, D., & Dombrowski, Y. (2019) · Cells · doi:10.3390/cells8111424 · Cited by 625
Rathore, S., Habes, M., Iftikhar, M. A., Shacklett, A., & Davatzikos, C. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.03.057 · Cited by 622
Self-citationOligodendrocyte Progenitor Cells Become Regionally Diverse and Heterogeneous with Age
Spitzer, S. O., Sitnikov, S., Kamen, Y., Evans, K. A., Kronenberg-Versteeg, D., Dietmann, S., de Faria, O., Agathou, S., & Káradóttir, R. T. (2019) · Neuron · doi:10.1016/j.neuron.2018.12.020 · Cited by 364
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
Metadata
- Authors
- Habes, M., Erus, G., Toledo, J. B., Zhang, T., Bryan, N., Launer, L. J., Rosseel, Y., Janowitz, D., Doshi, J., Van der Auwera, S., von Sarnowski, B., Hegenscheid, K., Hosten, N., Homuth, G., Völzke, H., Schminke, U., Hoffmann, W., Grabe, H. J., & Davatzikos, C.
- Venue
- Brain
- Publisher
- Oxford University Press (OUP)
- Published
- 2016-02-24
- Volume
- 139
- Issue
- 4
- Pages
- 1164-1179
- ISSN
- 0006-8950 (ISSN-L)
- 1460-2156
- OpenAlex
- W2300209210
- Cited by
- 422
- References
- 91
- FWCI
- 27.16
- 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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: White matter hyperintensities and imaging patterns of brain ageing in the general population. https://app.cochat.ai/papers/10.1093/brain/aww008
- The paper
- Habes, M., Erus, G., Toledo, J. B., Zhang, T., Bryan, N., Launer, L. J., Rosseel, Y., Janowitz, D., Doshi, J., Van der Auwera, S., von Sarnowski, B., Hegenscheid, K., Hosten, N., Homuth, G., Völzke, H., Schminke, U., Hoffmann, W., Grabe, H. J., & Davatzikos, C. (2016). White matter hyperintensities and imaging patterns of brain ageing in the general population. Brain. https://doi.org/10.1093/brain/aww008
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.