Joint Assessment of Quantitative ¹⁸F-Florbetapir and ¹⁸F-FDG Regional Uptake Using Baseline Data from the ADNI

Article type: Research Article

Authors: Ben Bouallègue, Fayçal^{a; b; c; *} | Mariano-Goulart, Denis^{a; c} | Payoux, Pierre^{b; d} | for the Alzheimer’s Disease Neuroimaging Initiative (ADNI)¹

Affiliations: [a] Department of Nuclear Medicine, Montpellier University Hospital, Montpellier, France | [b] Department of Nuclear Medicine, Purpan University Hospital, Toulouse, France | [c] PhyMedExp, INSERM, CNRS, Montpellier University, Montpellier Cedex, France | [d] ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, Toulouse, France

Correspondence: [*] Correspondence to: Fayçal Ben Bouallègue, Lapeyronie University Hospital, Nuclear Medicine Department, Avenue du Doyen Giraud, 34295 Montpellier Cedex 5, France. Tel.: +33 467338598; Fax: +33 467338465; E-mail: [email protected].

Note: [1] Data used in preparation of this paper were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu/). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this paper. A complete listing of ADNI investigators can be found at http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

Abstract: Joint analysis of amyloid and metabolic PET patterns across healthy, mild cognitive impairment (MCI), and Alzheimer’s disease (AD) subjects was performed using baseline 18F-florbetapir and 18F-FDG PET of 684 subjects from the ADNI (251 normal, 204 stable MCI, 85 AD converters, and 144 AD). Correlation between regional amyloid and metabolic uptake was measured and predictive value of PET profile regarding AD conversion in cognitively impaired subjects was assessed using survival analysis and support vector machine classification (SVM). The highest correlations were found in the temporal cortex, precuneus, and posterior cingulum. With respect to normal controls, amyloid load increase was diffuse and early in MCI subjects, whereas metabolism decrease occurred later and predominated in temporo-parietal, precuneus, and cingulate cortices. Five-year AD conversion rates in cognitively impaired subjects were 5%, 22%, 42%, and 78% in amyloid-/FDG-, amyloid-/FDG+, amyloid+/FDG-, and amyloid+/FDG+ subjects respectively (mean follow-up 37±14 months). Using SVM, the combination of ADAS-cog score, amyloid PET, and FDG PET yielded better performance in predicting AD conversion (77% accuracy; 58% positive predictive value; 88% negative predictive value) than ADAS-cog (72%; 52%; 86%), amyloid PET (72%; 52%; 87%), and FDG PET (67%; 47%; 84%). This study attests the complementary value of amyloid and FDG PET in MCI assessment and the efficiency of combined cognitive, amyloid, and metabolic scores to predict AD conversion.

Keywords: Alzheimer’s disease, Alzheimer’s Disease Neuroimaging Initiative, amyloid PET, FDG PET, mild cognitive impairment

DOI: 10.3233/JAD-170833

Journal: Journal of Alzheimer's Disease, vol. 62, no. 1, pp. 399-408, 2018