The DOI matches its Crossref record; 75 of the 75 references it cites were found in a registry or an index.
A large-scale comparison of cortical thickness and volume methods for measuring Alzheimer's disease severity
Open accessAbstract
Alzheimer's disease (AD) researchers commonly use MRI as a quantitative measure of disease severity. Historically, hippocampal volume has been favored. Recently, "AD signature" measurements of gray matter (GM) volumes or cortical thicknesses have gained attention. Here, we systematically evaluate multiple thickness- and volume-based candidate-methods side-by-side, built using the popular FreeSurfer, SPM, and ANTs packages, according to the following criteria: (a) ability to separate clinically normal individuals from those with AD; (b) (extent of) correlation with head size, a nuisance covariatel (c) reliability on repeated scans; and (d) correlation with Braak neurofibrillary tangle stage in a group with autopsy. We show that volume- and thickness-based measures generally perform similarly for separating clinically normal from AD populations, and in correlation with Braak neurofibrillary tangle stage at autopsy. Volume-based measures are generally more reliable than thickness measures. As expected, volume measures are highly correlated with head size, while thickness measures are generally not. Because approaches to statistically correcting volumes for head size vary and may be inadequate to deal with this underlying confound, and because our goal is to determine a measure which can be used to examine age and sex effects in a cohort across a large age range, we thus recommend thickness-based measures. Ultimately, based on these criteria and additional practical considerations of run-time and failure rates, we recommend an AD signature measure formed from a composite of thickness measurements in the entorhinal, fusiform, parahippocampal, mid-temporal, inferior-temporal, and angular gyrus ROIs using ANTs with input segmentations from SPM12.
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Cites
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Cited by
Jack, C. R., Wiste, H. J., Weigand, S. D., Therneau, T. M., Knopman, D. S., Lowe, V., Vemuri, P., Mielke, M. M., Roberts, R. O., Machulda, M. M., et al. (2017) · The Lancet Neurology · doi:10.1016/s1474-4422(17)30077-7 · Cited by 333
Self-citationCrous-Bou, M., Gascon, M., Gispert, J. D., Cirach, M., Sánchez-Benavides, G., Falcon, C., Arenaza-Urquijo, E. M., Gotsens, X., Fauria, K., Sunyer, J., et al. (2020) · Environment International · doi:10.1016/j.envint.2020.105546 · Cited by 145
Parker, T. D., Slattery, C. F., Zhang, J., Nicholas, J. M., Paterson, R. W., Foulkes, A. J. M., Malone, I. B., Thomas, D. L., Modat, M., Cash, D. M., et al. (2018) · Human Brain Mapping · doi:10.1002/hbm.24056 · Cited by 122
Predicting future rates of tau accumulation on PET
Jack, C. R., Wiste, H. J., Weigand, S. D., Therneau, T. M., Lowe, V. J., Knopman, D. S., Botha, H., Graff-Radford, J., Jones, D. T., Ferman, T. J., et al. (2020) · Brain · doi:10.1093/brain/awaa248 · Cited by 117
Self-citationPopuri, K., Ma, D., Wang, L., & Beg, M. F. (2020) · Human Brain Mapping · doi:10.1002/hbm.25115 · Cited by 96
Metadata
- Authors
- Schwarz, C. G., Gunter, J. L., Wiste, H. J., Przybelski, S. A., Weigand, S. D., Ward, C. P., Senjem, M. L., Vemuri, P., Murray, M. E., Dickson, D. W., Parisi, J. E., Kantarci, K., Weiner, M. W., Petersen, R. C., & Jack, C. R.
- Venue
- NeuroImage: Clinical
- Publisher
- Elsevier BV
- Published
- 2016
- Volume
- 11
- Pages
- 802-812
- ISSN
- 2213-1582 (ISSN-L)
- OpenAlex
- W2460653397
- Cited by
- 429
- References
- 75
- FWCI
- 14.75
- Type
- journal article
- Language
- en
- Open access
- gold
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- creativecommons.org/licenses/by-nc-nd/4.0/ (vor, 147 d)
- Funders
- The Alexander Family Professorship of Alzheimer's Disease Research, Mayo Clinic; the GHR Foundation (R01-AG040042, R01-AG011378, R00-AG37573, P50-AG16574/P1, R01-AG041851, U01-AG24904, U01-AG06786, P50-AG16574, R01-AG034676)
- Alzheimer's Disease Neuroimaging Initiative
- National Institutes of Health (U01 AG024904)
- DOD ADNI
- Department of Defense (W81XWH-12-2-0012)
- National Institute on Aging
- National Institute of Biomedical Imaging and Bioengineering
- AbbVie
- Alzheimer's Association
- Alzheimer's Drug Discovery Foundation
- Araclon Biotech
- BioClinica, Inc.
- Biogen
- Bristol-Myers Squibb Company
- CereSpir, Inc.
- Eisai Inc.
- Elan Pharmaceuticals, Inc.
- Eli Lilly and Company
- EuroImmun
- F. Hoffmann-La Roche Ltd.
- Genentech, Inc.
- Fujirebio
- GE Healthcare
- IXICO Ltd.
- Janssen Alzheimer Immunotherapy Research & Development, LLC.
- Johnson & Johnson Pharmaceutical Research & Development LLC.
- Lumosity
- Lundbeck
- Merck & Co., Inc.
- Meso Scale Diagnostics, LLC.
- NeuroRx Research
- Neurotrack Technologies
- Novartis Pharmaceuticals Corporation
- Pfizer Inc.
- Piramal Imaging; Servier
- Takeda Pharmaceutical Company
- Transition Therapeutics
- Canadian Institutes of Health Research
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- CoChat. (2026). Citation check: A large-scale comparison of cortical thickness and volume methods for measuring Alzheimer's disease severity. https://app.cochat.ai/papers/10.1016/j.nicl.2016.05.017
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- Schwarz, C. G., Gunter, J. L., Wiste, H. J., Przybelski, S. A., Weigand, S. D., Ward, C. P., Senjem, M. L., Vemuri, P., Murray, M. E., Dickson, D. W., Parisi, J. E., Kantarci, K., Weiner, M. W., Petersen, R. C., & Jack, C. R. (2016). A large-scale comparison of cortical thickness and volume methods for measuring Alzheimer's disease severity. NeuroImage: Clinical. https://doi.org/10.1016/j.nicl.2016.05.017
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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.
