Early identification of MCI converting to AD: a FDG PET study
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Mild cognitive impairment (MCI) is a transitional pathological stage between normal ageing (NA) and Alzheimer’s disease (AD). Although subjects with MCI show a decline at different rates, some individuals remain stable or even show an improvement in their cognitive level after some years. We assessed the accuracy of FDG PET in discriminating MCI patients who converted to AD from those who did not.
FDG PET was performed in 42 NA subjects, 27 MCI patients who had not converted to AD at 5 years (nc-MCI; mean follow-up time 7.5 ± 1.5 years), and 95 MCI patients who converted to AD within 5 years (MCI-AD; mean conversion time 1.8 ± 1.1 years). Relative FDG uptake values in 26 meta-volumes of interest were submitted to ANCOVA and support vector machine analyses to evaluate regional differences and discrimination accuracy.
The MCI-AD group showed significantly lower FDG uptake values in the temporoparietal cortex than the other two groups. FDG uptake values in the nc-MCI group were similar to those in the NA group. Support vector machine analysis discriminated nc-MCI from MCI-AD patients with an accuracy of 89% (AUC 0.91), correctly detecting 93% of the nc-MCI patients.
In MCI patients not converting to AD within a minimum follow-up time of 5 years and MCI patients converting within 5 years, baseline FDG PET and volume-based analysis identified those who converted with an accuracy of 89%. However, further analysis is needed in patients with amnestic MCI who convert to a dementia other than AD.
KeywordsAlzheimer’s disease Mild cognitive impairment Conversion to AD Positron emission tomography Support vector machine Volume of interest analysis
The authors thank Ms. Katja Gasperini for helping with the English editing.
Compliance with ethical standards
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Conflicts of interest
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the principles of the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards
- 8.Arbizu J, Prieto E, Martinez-Lage P, Marti-Climent JM, Garcia-Granero M, Lamet I, et al. Automated analysis of FDG PET as a tool for single-subject probabilistic prediction and detection of Alzheimer’s disease dementia. Eur J Nucl Med Mol Imaging. 2013;40:1394–405. doi: 10.1007/s00259-013-2458-z.CrossRefPubMedGoogle Scholar
- 9.Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehericy S, Habert MO, et al. Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 2011;56:766–81. doi: 10.1016/j.neuroimage.2010.06.013.CrossRefPubMedGoogle Scholar
- 10.Pagani M, De Carli F, Morbelli S, Oberg J, Chincarini A, Frisoni GB, et al. Volume of interest-based [18F]fluorodeoxyglucose PET discriminates MCI converting to Alzheimer’s disease from healthy controls. A European Alzheimer’s Disease Consortium (EADC) study. Neuroimage Clin. 2015;7:34–42. doi: 10.1016/j.nicl.2014.11.007.CrossRefPubMedGoogle Scholar
- 15.Shaffer JL, Petrella JR, Sheldon FC, Choudhury KR, Calhoun VD, Coleman RE, et al. Predicting cognitive decline in subjects at risk for Alzheimer disease by using combined cerebrospinal fluid, MR imaging, and PET biomarkers. Radiology. 2013;266:583–91. doi: 10.1148/radiol.12120010.CrossRefPubMedPubMedCentralGoogle Scholar
- 17.Young J, Modat M, Cardoso MJ, Mendelson A, Cash D, Ourselin S, et al. Accurate multimodal probabilistic prediction of conversion to Alzheimer’s disease in patients with mild cognitive impairment. Neuroimage Clin. 2013;2:735–45. doi: 10.1016/j.nicl.2013.05.004.CrossRefPubMedPubMedCentralGoogle Scholar
- 27.Pagani M, Giuliani A, Ӧberg J, De Carli F, Morbelli S, Girtler N, et al. Progressive disintegration of brain networking from normal aging to Alzheimer’s Disease. Analysis of Independent Components of 18F-FDG PET Data. J Nucl Med. 2017;58:1132–1139. doi: 10.2967/jnumed.116.184309.
- 29.Gorelick PB, Scuteri A, Black SE, Decarli C, Greenberg SM, Iadecola C, et al. Vascular contributions to cognitive impairment and dementia: a statement for healthcare professionals from the American Heart Association/American Stroke Association. Stroke. 2011;42:2672–713. doi: 10.1161/STR.0b013e3182299496.CrossRefPubMedPubMedCentralGoogle Scholar
- 35.Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage. 2002;15:273–89. doi: 10.1006/nimg.2001.0978.CrossRefPubMedGoogle Scholar
- 45.Sanchez-Catasus CA, Stormezand GN, van Laar PJ, De Deyn PP, Sanchez MA, Dierckx RA. FDG-PET for prediction of AD dementia in mild cognitive impairment. A review of the state of the art with particular emphasis on the comparison with other neuroimaging modalities (MRI and perfusion SPECT). Curr Alzheimer Res. 2017;14:127–42.CrossRefPubMedGoogle Scholar
- 50.Teipel SJ, Kurth J, Krause B, Grothe MJ; Alzheimer’s Disease Neuroimaging Initiative. The relative importance of imaging markers for the prediction of Alzheimer’s disease dementia in mild cognitive impairment – beyond classical regression. Neuroimage Clin. 2015;8:583–93. doi: 10.1016/j.nicl.2015.05.006.CrossRefPubMedPubMedCentralGoogle Scholar
- 53.Chen K, Langbaum JB, Fleisher AS, Ayutyanont N, Reschke C, Lee W, et al. Twelve-month metabolic declines in probable Alzheimer’s disease and amnestic mild cognitive impairment assessed using an empirically pre-defined statistical region-of-interest: findings from the Alzheimer’s Disease Neuroimaging Initiative. Neuroimage. 2010;51:654–64. doi: 10.1016/j.neuroimage.2010.02.064.CrossRefPubMedPubMedCentralGoogle Scholar
- 55.Garibotto V, Herholz K, Boccardi M, Picco A, Varrone A, Nordberg A, et al. Clinical validity of brain fluorodeoxyglucose positron emission tomography as a biomarker for Alzheimer’s disease in the context of a structured 5-phase development framework. Neurobiol Aging. 2017;52:183–195. doi: 10.1016/j.neurobiolaging.2016.03.033 CrossRefPubMedGoogle Scholar