Longitudinal progression of Alzheimer's-like patterns of atrophy in normal older adults: the SPARE-AD index
Open accessAbstract
A challenge in developing informative neuroimaging biomarkers for early diagnosis of Alzheimer's disease is the need to identify biomarkers that are evident before the onset of clinical symptoms, and which have sufficient sensitivity and specificity on an individual patient basis. Recent literature suggests that spatial patterns of brain atrophy discriminate amongst Alzheimer's disease, mild cognitive impairment (MCI) and cognitively normal (CN) older adults with high accuracy on an individual basis, thereby offering promise that subtle brain changes can be detected during prodromal Alzheimer's disease stages. Here, we investigate whether these spatial patterns of brain atrophy can be detected in CN and MCI individuals and whether they are associated with cognitive decline. Images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were used to construct a pattern classifier that recognizes spatial patterns of brain atrophy which best distinguish Alzheimer's disease patients from CN on an individual person basis. This classifier was subsequently applied to longitudinal magnetic resonance imaging scans of CN and MCI participants in the Baltimore Longitudinal Study of Aging (BLSA) neuroimaging study. The degree to which Alzheimer's disease-like patterns were present in CN and MCI subjects was evaluated longitudinally in relation to cognitive performance. The oldest BLSA CN individuals showed progressively increasing Alzheimer's disease-like patterns of atrophy, and individuals with these patterns had reduced cognitive performance. MCI was associated with steeper longitudinal increases of Alzheimer's disease-like patterns of atrophy, which separated them from CN (receiver operating characteristic area under the curve equal to 0.89). Our results suggest that imaging-based spatial patterns of brain atrophy of Alzheimer's disease, evaluated with sophisticated pattern analysis and recognition methods, may be useful in discriminating among CN individuals who are likely to be stable versus those who will show cognitive decline. Future prospective studies will elucidate the temporal dynamics of spatial atrophy patterns and the emergence of clinical symptoms.
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Structural MRI represents a relatively lower-cost alternative to spatially measure in vivo neurodegeneration, but it is substantially less specific to AD pathology compared to PET.
“Structural magnetic resonance imaging (MRI) represents a relatively lower‐cost alternative to spatially measure in vivo neurodegeneration, but it is substantially less specific to AD pathology compared to PET. 5 , 6 , 7 , 8”
From Machine learning prediction of tau‐PET in Alzheimer's disease using plasma, MRI, and clinical data (2025) · cites this paper as [7]
The Image acquisition section (page unavailable) says the study used structural MR images to measure regional patterns of brain atrophy, supporting the spatial-measurement part of the claim. The Introduction notes limits to the diagnostic value of simple brain-volume measures, but our copy does not say MRI costs less than PET or compare their specificity to AD pathology (searched: PET, cost, specificity). (LLM, conf=0.85)
Experimental — accuracy not yet measuredClassifiers using multimodal biological, imaging, and neuropsychological markers can separate AD patients from cognitively normal individuals accurately.
“Multi-modal biological, imaging, and neuropsychological markers have demonstrated promising performance for distinguishing Alzheimer’s disease (AD) patients from cognitively normal elders.”
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Self-citation
- Authors
- Davatzikos, C., Xu, F., An, Y., Fan, Y., & Resnick, S. M.
- Affiliations
- University of Pennsylvania (US)
- National Institute on Aging (US)
- Venue
- Brain
- Publisher
- Oxford University Press (OUP)
- Published
- 2009-05-04
- Volume
- 132
- Issue
- 8
- Pages
- 2026-2035
- ISSN
- 0006-8950 (ISSN-L)
- 1460-2156
- PMC
- PMC2714059
- OpenAlex
- W2152723280
- Cited by
- 315
- References
- 5
- Citations by year
- 2026: 17 · 2025: 17 · 2024: 17 · 2023: 22 · 2022: 23 · 2021: 23 · 2020: 16 · 2019: 13 · 2018: 15 · 2017: 17 · 2016: 16 · 2015: 13 · 2014: 20 · 2013: 22 · 2012: 16
- FWCI
- 12.95
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Brain (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (N01-AG-3-2124)
- National Institute on Aging (N01-AG-32124, Z01AG000191, R01 AG 14971, R01AG014971, U19AG010483)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Cancer-related cognitive impairment studies
- Keywords
- Alzheimer's disease, mild cognitive impairment, brain atrophy, neuroimaging biomarkers, early diagnosis, cognitive decline, cognitively normal older adults, longitudinal brain atrophy, pattern classification, prodromal Alzheimer's disease
- MeSH terms
- Aged, Aged, 80 and over, Aging, Alzheimer Disease, Atrophy, Brain, Cognition Disorders, Female, Humans, Image Interpretation, Computer-Assisted, Longitudinal Studies, Magnetic Resonance Imaging, Male, Middle Aged, Neuropsychological Tests, Prognosis, Disease Progression
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