Quantitative Amyloid Imaging in Autosomal Dominant Alzheimer’s Disease: Results from the DIAN Study Group

Abstract

Amyloid imaging plays an important role in the research and diagnosis of dementing disorders. Substantial variation in quantitative methods to measure brain amyloid burden exists in the field. The aim of this work is to investigate the impact of methodological variations to the quantification of amyloid burden using data from the Dominantly Inherited Alzheimer’s Network (DIAN), an autosomal dominant Alzheimer’s disease population. Cross-sectional and longitudinal [11C]-Pittsburgh Compound B (PiB) PET imaging data from the DIAN study were analyzed. Four candidate reference regions were investigated for estimation of brain amyloid burden. A regional spread function based technique was also investigated for the correction of partial volume effects. Cerebellar cortex, brain-stem, and white matter regions all had stable tracer retention during the course of disease. Partial volume correction consistently improves sensitivity to group differences and longitudinal changes over time. White matter referencing improved statistical power in the detecting longitudinal changes in relative tracer retention; however, the reason for this improvement is unclear and requires further investigation. Full dynamic acquisition and kinetic modeling improved statistical power although it may add cost and time. Several technical variations to amyloid burden quantification were examined in this study. Partial volume correction emerged as the strategy that most consistently improved statistical power for the detection of both longitudinal changes and across-group differences. For the autosomal dominant Alzheimer’s disease population with PiB imaging, utilizing brainstem as a reference region with partial volume correction may be optimal for current interventional trials. Further investigation of technical issues in quantitative amyloid imaging in different study populations using different amyloid imaging tracers is warranted.

Metabolic and amyloid PET network reorganization in Alzheimer’s disease: differential patterns and partial volume effects

“…For [ 18 F]AV45-PET data PVEc yielded lower SUVR values for the CN group (−18.4%, p < 0.0001) and higher SUVR values for the AD group (+21.2%, p < 0.0001) with respect to the non-corrected data (Fig. 2, boxplots), which is in line with previous studies (Brendel et al 2015;Gonzalez-Escamilla et al 2017;Su et al 2016;Su et al 2015). The inter-subject variability of the amyloid data in terms of %COV was increased after PVE-correction (CN: from 5.3 to 8.9 %COV; AD: from 14.1 to 17.3 %COV).…”

Longitudinal validity of PET‐based staging of regional amyloid deposition

“…However, the derived cut-offs in the present study are limited to a relatively small sample of young controls with available Florbetapir-PET data (Navitsky et al, 2018a), and larger samples of truly amyloidnegative individuals would be desirable for a more robust estimate of regional amyloid-positivity thresholds. In our regional amyloid staging approach, we apply partial volume effect correction to the Florbetapir-PET images, which has previously been shown to improve amyloid-PET quantification (Brendel et al, 2015;Gonzalez-Escamilla et al, 2017;Rullmann et al, 2016;Su et al, 2016), particularly by accounting for confounding effects of high nonspecific white matter binding (Matsubara et al, 2016). Nonetheless, quantitative amyloid imaging results are also affected by the choice of several other experimental variables, including type of acquisition, preprocessing techniques, and reference tissue selection (Landau et al, 2014;Schmidt et al, 2015).…”

“…Nonetheless, quantitative amyloid imaging results are also affected by the choice of several other experimental variables, including type of acquisition, preprocessing techniques, and reference tissue selection (Landau et al, 2014;Schmidt et al, 2015). Thus, future methodological work should also assess potential effects of other commonly applied reference regions (Landau et al, 2015;Su et al, 2016) or of different approaches to PET signal quantification (e.g., full kinetic modeling instead of the simplified SUVR approach [Ottoy et al, 2017]). Finally, our longitudinal modeling of amyloid progression was limited to two time points with a relatively short interval of 2 years, which previously was associated with less stable amyloid trajectories than models based on three or more time points (Landau et al, 2018).…”

OASIS-3: Longitudinal Neuroimaging, Clinical, and Cognitive Dataset for Normal Aging and Alzheimer Disease

“…Two modeling approaches are implemented in PUP using the cerebellum as a reference region. Binding potential (BPND) is calculated using Logan graphical analysis 42,43,[49][50][51]…”