β-amyloid PET harmonisation across longitudinal studies: Application to AIBL, ADNI and OASIS3

Pierrick Bourgeat; Vincent Doré; Samantha C Burnham; Tammie Benzinger; Duygu Tosun; Shenpeng Li; Manu Goyal; Pamela LaMontagne; Liang Jin; Christopher C Rowe; Michael W Weiner; John C Morris; Colin L Masters; Jurgen Fripp; Victor L Villemagne; Alzheimer's Disease Neuroimaging Initiative, OASIS3, and the AIBL research group

doi:10.1016/j.neuroimage.2022.119527

β-amyloid PET harmonisation across longitudinal studies: Application to AIBL, ADNI and OASIS3

Neuroimage. 2022 Nov 15:262:119527. doi: 10.1016/j.neuroimage.2022.119527. Epub 2022 Jul 30.

Authors

Pierrick Bourgeat¹, Vincent Doré², Samantha C Burnham³, Tammie Benzinger⁴, Duygu Tosun⁵, Shenpeng Li³, Manu Goyal⁶, Pamela LaMontagne⁶, Liang Jin⁷, Christopher C Rowe⁸, Michael W Weiner⁵, John C Morris⁹, Colin L Masters⁷, Jurgen Fripp³, Victor L Villemagne¹⁰; Alzheimer's Disease Neuroimaging Initiative, OASIS3, and the AIBL research group

Affiliations

¹ CSIRO Health and Biosecurity, Brisbane, Australia. Electronic address: Pierrick.bourgeat@csiro.au.
² CSIRO Health and Biosecurity, Brisbane, Australia; Department of Molecular Imaging & Therapy, Austin Health, Melbourne, Australia.
³ CSIRO Health and Biosecurity, Brisbane, Australia.
⁴ Knight Alzheimer Disease Research Center, St. Louis, MO, USA.
⁵ San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA,; Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA.
⁶ Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, USA.
⁷ The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Melbourne, Australia.
⁸ Department of Molecular Imaging & Therapy, Austin Health, Melbourne, Australia; The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Melbourne, Australia.
⁹ Washington University in St. Louis, St. Louis, MO, USA.
¹⁰ Department of Molecular Imaging & Therapy, Austin Health, Melbourne, Australia; Department of Psychiatry, The University of Pittsburgh, Pittsburgh, PA, USA.

Abstract

Introduction: The Centiloid scale was developed to harmonise the quantification of β-amyloid (Aβ) PET images across tracers, scanners, and processing pipelines. However, several groups have reported differences across tracers and scanners even after centiloid conversion. In this study, we aim to evaluate the impact of different pre and post-processing harmonisation steps on the robustness of longitudinal Centiloid data across three large international cohort studies.

Methods: All Aβ PET data in AIBL (N = 3315), ADNI (N = 3442) and OASIS3 (N = 1398) were quantified using the MRI-based Centiloid standard SPM pipeline and the PET-only pipeline CapAIBL. SUVR were converted into Centiloids using each tracer's respective transform. Global Aβ burden from pre-defined target cortical regions in Centiloid units were quantified for both raw PET scans and PET scans smoothed to a uniform 8 mm full width half maximum (FWHM) effective smoothness. For Florbetapir, we assessed the performance of using both the standard Whole Cerebellum (WCb) and a composite white matter (WM)+WCb reference region. Additionally, our recently proposed quantification based on Non-negative Matrix Factorisation (NMF) was applied to all spatially and SUVR normalised images. Correlation with clinical severity measured by the Mini-Mental State Examination (MMSE) and effect size, as well as tracer agreement in ¹¹C-PiB-¹⁸F-Florbetapir pairs and longitudinal consistency were evaluated.

Results: The smoothing to a uniform resolution partially reduced longitudinal variability, but did not improve inter-tracer agreement, effect size or correlation with MMSE. Using a Composite reference region for ¹⁸F-Florbetapir improved inter-tracer agreement, effect size, correlation with MMSE, and longitudinal consistency. The best results were however obtained when using the NMF method which outperformed all other quantification approaches in all metrics used.

Conclusions: FWHM smoothing has limited impact on longitudinal consistency or outliers. A Composite reference region including subcortical WM should be used for computing both cross-sectional and longitudinal Florbetapir Centiloid. NMF improves Centiloid quantification on all metrics examined.

Keywords: Amyloid PET; Centiloid; Harmonisation.

Publication types

Research Support, U.S. Gov't, Non-P.H.S.
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Alzheimer Disease* / diagnostic imaging
Amyloid beta-Peptides*
Aniline Compounds
Cross-Sectional Studies
Humans
Longitudinal Studies
Positron-Emission Tomography / methods

Substances

Amyloid beta-Peptides
Aniline Compounds

Abstract

Publication types

MeSH terms

Substances

Grants and funding