Combining MR Imaging, Positron-Emission Tomography, and CSF Biomarkers in the Diagnosis and Prognosis of Alzheimer Disease
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BACKGROUND AND PURPOSE: Different biomarkers for AD may potentially be complementary in diagnosis and prognosis of AD. Our aim was to combine MR imaging, FDG-PET, and CSF biomarkers in the diagnostic classification and 2-year prognosis of MCI and AD, by examining the following: 1) which measures are most sensitive to diagnostic status, 2) to what extent the methods provide unique information in diagnostic classification, and 3) which measures are most predictive of clinical decline. MATERIALS AND METHODS: ADNI baseline MR imaging, FDG-PET, and CSF data from 42 controls, 73 patients with MCI, and 38 patients with AD; and 2-year clinical follow-up data for 36 controls, 51 patients with MCI, and 25 patients with AD were analyzed. The hippocampus and entorhinal, parahippocampal, retrosplenial, precuneus, inferior parietal, supramarginal, middle temporal, lateral, and medial orbitofrontal cortices were used as regions of interest. CSF variables included Abeta42, t-tau, p-tau, and ratios of t-tau/Abeta42 and p-tau/Abeta42. Regression analyses were performed to determine the sensitivity of measures to diagnostic status as well as 2-year change in CDR-SB, MMSE, and delayed logical memory in MCI. RESULTS: Hippocampal volume, retrosplenial thickness, and t-tau/Abeta42 uniquely predicted diagnostic group. Change in CDR-SB was best predicted by retrosplenial thickness; MMSE, by retrosplenial metabolism and thickness; and delayed logical memory, by hippocampal volume. CONCLUSIONS: All biomarkers were sensitive to the diagnostic group. Combining MR imaging morphometry and CSF biomarkers improved diagnostic classification (controls versus AD). MR imaging morphometry and PET were largely overlapping in value for discrimination. Baseline MR imaging and PET measures were more predictive of clinical change in MCI than were CSF measures.
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Walhovd et al. showed that good accuracy of classification of stable versus converting MCI subjects is obtained by morphometric MRI measures (hippocampal volume, entorhinal, and retrosplenial cortical thickness) in logistic regression analysis.
“Walhovd et al. 42 showed that good accuracy of classification of stable versus converting MCI subjects is obtained by morphometric MRI measures (hippocampal volume, entorhinal, and retrosplenial cortical thickness) in logistic regression analysis.”
From Early dementia diagnosis, MCI-to-dementia risk prediction, and the role of machine learning methods for feature extraction from integrated biomarkers, in particular for EEG signal analysis (2022) · cites this paper as [42]
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“By using a multi-modality regression approach to predicting cognitive decline in ADNI subjects, a very recent study found that a linear combination of MRI and PET-FDG was a better predictor of cognitive decline than CSF biomarkers (Walhovd et al., 2010), consistent with our best set of biomarkers.”
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- Authors
- Walhovd, K. B., Fjell, A. M., Brewer, J., McEvoy, L. K., Fennema-Notestine, C., Hagler, D. J., Jennings, R. G., Karow, D., Dale, A. M., & Initiative, T. A. D. N.
- Affiliations
- University of Oslo (NO)
- Oslo University Hospital (NO)
- Venue
- American Journal of Neuroradiology
- Published
- 2010-01-14
- Volume
- 31
- Issue
- 2
- Pages
- 347-354
- ISSN
- 0195-6108 (ISSN-L)
- 1936-959X
- PMC
- PMC2821467
- OpenAlex
- W2078563723
- Cited by
- 279
- References
- 8
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 9 · 2023: 8 · 2022: 7 · 2021: 10 · 2020: 16 · 2019: 11 · 2018: 16 · 2017: 12 · 2016: 16 · 2015: 21 · 2014: 24 · 2013: 34 · 2012: 18
- FWCI
- 26.46
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- American Journal of Neuroradiology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Foundation for the National Institutes of Health (U01AG024904, AG024904)
- Alzheimer's Association (U01 AG024904)
- Bristol-Myers Squibb (U01 AG024904, AG024904)
- Eli Lilly and Company (U01 AG024904, AG024904)
- Pfizer (AG024904, U01 AG024904)
- AstraZeneca
- GlaxoSmithKline
- Novartis Pharmaceuticals Corporation (U01 AG024904, AG024904)
- Northern California Institute for Research and Education (U01 AG024904)
- Elan (U01 AG024904)
- Eisai (U01 AG024904, AG024904)
- Norges Forskningsråd
- National Institutes of Health (5u01ag024904-10, U01-AG024904, AG024904)
- University of California, Los Angeles
- University of California, San Diego
- National Institute on Aging (U01-AG-024904, R01AG22381, R01AG031224, AG 024904, R01AG022381, K01AG029218, U19AG010483)
- National Center for Research Resources (R01AG22381, U24RR021382, U01 AG024904)
- 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, FDG-PET, cerebrospinal fluid biomarkers, hippocampal volume, retrosplenial cortex, diagnostic classification, multimodal biomarker integration, neuroimaging biomarkers
- MeSH terms
- Aged, Aged, 80 and over, Alzheimer Disease, Female, Follow-Up Studies, Hippocampus, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Peptide Fragments, Predictive Value of Tests, Prognosis, ROC Curve, Sensitivity and Specificity, Biomarkers, Logistic Models, Databases, Factual, Amyloid beta-Peptides, tau Proteins, Fluorodeoxyglucose F18, Positron-Emission Tomography
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