Measurement of longitudinal β-amyloid change with 18F-florbetapir PET and standardized uptake value ratios

Abstract

The accurate measurement of β-amyloid (Aβ) change using amyloid PET imaging is important for Alzheimer disease research and clinical trials but poses several unique challenges. In particular, reference region measurement instability may lead to spurious changes in cortical regions of interest. To optimize our ability to measure (18)F-florbetapir longitudinal change, we evaluated several candidate regions of interest and their influence on cortical florbetapir change over a 2-y period in participants from the Alzheimer Disease Neuroimaging Initiative (ADNI).

Methods: We examined the agreement in cortical florbetapir change detected using 6 candidate reference regions (cerebellar gray matter, whole cerebellum, brain stem/pons, eroded subcortical white matter [WM], and 2 additional combinations of these regions) in 520 ADNI subjects. We used concurrent cerebrospinal fluid Aβ1-42 measurements to identify subgroups of ADNI subjects expected to remain stable over follow-up (stable Aβ group; n = 14) and subjects expected to increase (increasing Aβ group; n = 91). We then evaluated reference regions according to whether cortical change was minimal in the stable Aβ group and cortical retention increased in the increasing Aβ group.

Results: There was poor agreement across reference regions in the amount of cortical change observed across all 520 ADNI subjects. Within the stable Aβ group, however, cortical florbetapir change was 1%-2% across all reference regions, indicating high consistency. In the increasing Aβ group, cortical increases were significant with all reference regions. Reference regions containing WM (as opposed to cerebellum or pons) enabled detection of cortical change that was more physiologically plausible and more likely to increase over time.

Conclusion: Reference region selection has an important influence on the detection of florbetapir change. Compared with cerebellum or pons alone, reference regions that included subcortical WM resulted in change measurements that are more accurate. In addition, because use of WM-containing reference regions involves dividing out cortical signal contained in the reference region (via partial-volume effects), use of these WM-containing regions may result in more conservative estimates of actual change. Future analyses using different tracers, tracer-kinetic models, pipelines, and comparisons with other biomarkers will further optimize our ability to accurately measure Aβ changes over time.

Keywords: Alzheimer’s disease; PET imaging; amyloid.

FIGURE 1

Candidate reference regions for florbetapir analysis are overlaid an example subject’s MR imaging scan.

FIGURE 2

SUVR change is plotted against baseline SUVR using 6 candidate reference regions for all 520 subjects. Subjects were divided into quartiles based on SUVR change using whole cerebellum reference region (upper left; blue = lowest 25%, purple = highest 25%), and this quartile assignment was used for the other 5 reference regions. Subjects A and B show SUVR pattern that varies across reference regions (see “Results” section).

FIGURE 3

For each quartile defined by SUVR change using whole cerebellum reference (x-axis), percentage of subjects is shown who are assigned to same quartile defined by SUVR change using other reference regions.

FIGURE 4

Sample baseline MR imaging and florbetapir scans at each visit are shown for 2 example subjects (subjects A and B) who had a reference region–dependent pattern of cortical retention (see “Results” section; Figs. 2 and 5). The top row (raw) of PET scans is averaged, coregistered frames that have not been intensity normalized. Subsequent rows show coregistered, averaged frames at uniform voxel size and resolution, normalized using reference region shown. Scale for color display selected for whole cerebellum was arbitrary but consistent across visits. Color scale for other reference regions was selected by transforming upper threshold for whole cerebellum to units consistent with other regions. This was performed using linear regression equations resulting from correlation between whole cerebellum–normalized SUVRs and other reference regions at baseline across all 520 subjects.

FIGURE 5

Relationship is shown between SUVRs at visit 1 vs. visit 2 in Aβ-increasing group calculated using 6 different reference regions. R² values, linear regression equations, and number (and percentage) of decliners are shown for plots, which are arranged in order of increasing WM making up reference region. Example subject (subject B) who showed a discrepant pattern across reference regions is shown (Fig. 4B).