Alzheimer Disease: Quantitative Structural Neuroimaging for Detection and Prediction of Clinical and Structural Changes in Mild Cognitive Impairment
Open accessAbstract
PURPOSE: To use structural magnetic resonance (MR) images to identify a pattern of regional atrophy characteristic of mild Alzheimer disease (AD) and to investigate whether presence of this pattern prospectively can aid prediction of 1-year clinical decline and increased structural loss in mild cognitive impairment (MCI). MATERIALS AND METHODS: The study was conducted with institutional review board approval and compliance with HIPAA regulations. Written informed consent was obtained from each participant. High-throughput volumetric segmentation and cortical surface reconstruction methods were applied to MR images from 84 subjects with mild AD, 175 with MCI, and 139 healthy control (HC) subjects. Stepwise linear discriminant analysis was used to identify regions that best can aid discrimination of HC subjects from subjects with AD. A classifier trained on data from HC subjects and those with AD was applied to data from subjects with MCI to determine whether presence of phenotypic AD atrophy at baseline was predictive of clinical decline and structural loss. RESULTS: Atrophy in mesial and lateral temporal, isthmus cingulate, and orbitofrontal areas aided discrimination of HC subjects from subjects with AD, with fully cross-validated sensitivity of 83% and specificity of 93%. Subjects with MCI who had phenotypic AD atrophy showed significantly greater 1-year clinical decline and structural loss than those who did not and were more likely to have progression to probable AD (annual progression rate of 29% for subjects with MCI who had AD atrophy vs 8% for those who did not). CONCLUSION: Semiautomated, individually specific quantitative MR imaging methods can be used to identify a pattern of regional atrophy in MCI that is predictive of clinical decline. Such information may aid in prediction of patient prognosis and increase the efficiency of clinical trials.
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A brain atrophy score derived using linear discrimination analysis from healthy controls and AD patient data was better for predicting one-year cognitive decline than atrophy in medial temporal structures alone.
““Brain atrophy score” was derived using the linear discrimination analysis from healthy controls and AD patient’s data, found to be better for predicting one-year cognitive decline than atrophy in medial temporal structures alone.”
From Recent Advancements in Pathogenesis, Diagnostics and Treatment of Alzheimer’s Disease (2020) · cites this paper as [89]
The brain atrophy score was ameliorated by adding MMSE scores and APOE ε4 allele number to the model.
“Later this brain atrophy score was ameliorated by Mini-Mental State Exam (MMSE) scores and APOE ε4 alleles number to the model [89].”
From Recent Advancements in Pathogenesis, Diagnostics and Treatment of Alzheimer’s Disease (2020) · cites this paper as [89]
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Reas, E. T., Hagler, D. J., White, N. S., Kuperman, J. M., Bartsch, H., Cross, K., Loi, R. Q., Balachandra, A. R., Meloy, M. J., Wierenga, C. E., et al. (2017) · Alzheimer's Research & Therapy · doi:10.1186/s13195-017-0281-7 · Cited by 35 · see citing works
Self-citationWang, 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 · see citing works
- Authors
- McEvoy, L. K., Fennema-Notestine, C., Roddey, J. C., Hagler, D. J., Holland, D., Karow, D. S., Pung, C. J., Brewer, J. B., & Dale, A. M.
- Affiliations
- University of California San Diego (US)
- Venue
- Radiology
- Published
- 2009-04
- Volume
- 251
- Issue
- 1
- Pages
- 195-205
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- PMC
- PMC2663582
- OpenAlex
- W2126927216
- Cited by
- 330
- References
- 66
- Citations by year
- 2026: 6 · 2025: 12 · 2024: 14 · 2023: 9 · 2022: 14 · 2021: 19 · 2020: 10 · 2019: 18 · 2018: 16 · 2017: 11 · 2016: 23 · 2015: 21 · 2014: 19 · 2013: 21 · 2012: 28
- FWCI
- 23.69
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Radiology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Foundation for the National Institutes of Health
- Alzheimer's Association
- Bristol-Myers Squibb
- Eli Lilly and Company
- Pfizer
- AstraZeneca
- GlaxoSmithKline
- Northern California Institute for Research and Education
- Eisai
- National Institutes of Health (5u01ag024904-10)
- U.S. Food and Drug Administration
- University of California, Los Angeles
- University of California, San Diego
- National Institute on Aging (U01-AG-024904, R01AG022381, K01AG029218, U19AG010483)
- National Institute of Biomedical Imaging and Bioengineering
- National Center for Research Resources (U24RR021382)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- Alzheimer's disease, mild cognitive impairment, regional brain atrophy, structural neuroimaging, 3D image segmentation, cortical surface reconstruction, linear discriminant analysis, conversion to dementia, temporal lobe atrophy, orbitofrontal cortex, prognosis
- MeSH terms
- Aged, Aged, 80 and over, Algorithms, Alzheimer Disease, Artificial Intelligence, Atrophy, Brain, Cognition Disorders, Female, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Male, Middle Aged, Pattern Recognition, Automated, Sensitivity and Specificity, Reproducibility of Results
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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.
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