The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer's disease
Open accessAbstract
Imaging biomarkers for Alzheimer’s disease are desirable for improved diagnosis and monitoring, as well as drug discovery. Automated image-based classification of individual patients could provide valuable diagnostic support for clinicians, when considered alongside cognitive assessment scores. We investigate the value of combining cross-sectional and longitudinal multi-region FDG-PET information for classification, using clinical and imaging data from the Alzheimer’s Disease Neuroimaging Initiative. Whole-brain segmentations into 83 anatomically defined regions were automatically generated for baseline and 12-month FDG-PET images. Regional signal intensities were extracted at each timepoint, as well as changes in signal intensity over the follow-up period. Features were provided to a support vector machine classifier. By combining 12-month signal intensities and changes over 12 months, we achieve significantly increased classification performance compared with using any of the three feature sets independently. Based on this combined feature set, we report classification accuracies of 88% between patients with Alzheimer’s disease and elderly healthy controls, and 65% between patients with stable mild cognitive impairment and those who subsequently progressed to Alzheimer’s disease. We demonstrate that information extracted from serial FDG-PET through regional analysis can be used to achieve state-of-the-art classification of diagnostic groups in a realistic multi-centre setting. This finding may be usefully applied in the diagnosis of Alzheimer’s disease, predicting disease course in individuals with mild cognitive impairment, and in the selection of participants for clinical trials.
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Cites
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Nonrigid registration using free-form deformations: application to breast mr images
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The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods
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Discrimination between Alzheimer dementia and controls by automated analysis of multicenter FDG PET
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Cited by
Rathore, S., Habes, M., Iftikhar, M. A., Shacklett, A., & Davatzikos, C. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.03.057 · Cited by 622
Islam, J., & Zhang, Y. (2018) · Brain Informatics · doi:10.1186/s40708-018-0080-3 · Cited by 478
Weiner, M. W., Veitch, D. P., Aisen, P. S., Beckett, L. A., Cairns, N. J., Cedarbaum, J., Green, R. C., Harvey, D., Jack, C. R., Jagust, W., et al. (2015) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2014.11.001 · Cited by 348
Grueso, S., & Viejo-Sobera, R. (2021) · Alzheimer's Research & Therapy · doi:10.1186/s13195-021-00900-w · Cited by 303
Zhang, Y., Dong, Z., Phillips, P., Wang, S., Ji, G., Yang, J., & Yuan, T.-F. (2015) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2015.00066 · Cited by 279
Metadata
- Authors
- Gray, K. R., Wolz, R., Heckemann, R. A., Aljabar, P., Hammers, A., & Rueckert, D.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2012-03
- Volume
- 60
- Issue
- 1
- Pages
- 221-229
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2015259521
- Cited by
- 180
- References
- 11
- FWCI
- 6.15
- Type
- journal article
- Language
- en
- Open access
- green
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