Measurement of neurodegeneration using a multivariate early frame amyloid PET classifier

Abstract

Introduction: Amyloid measurement provides important confirmation of pathology for Alzheimer's disease (AD) clinical trials. However, many amyloid positive (Am+) early-stage subjects do not worsen clinically during a clinical trial, and a neurodegenerative measure predictive of decline could provide critical information. Studies have shown correspondence between perfusion measured by early amyloid frames post-tracer injection and fluorodeoxyglucose (FDG) positron emission tomography (PET), but with limitations in sensitivity. Multivariate machine learning approaches may offer a more sensitive means for detection of disease related changes as we have demonstrated with FDG.

Methods: Using summed dynamic florbetapir image frames acquired during the first 6 minutes post-injection for 107 Alzheimer's Disease Neuroimaging Initiative subjects, we applied optimized machine learning to develop and test image classifiers aimed at measuring AD progression. Early frame amyloid (EFA) classification was compared to that of an independently developed FDG PET AD progression classifier by scoring the FDG scans of the same subjects at the same time point. Score distributions and correlation with clinical endpoints were compared to those obtained from FDG. Region of interest measures were compared between EFA and FDG to further understand discrimination performance.

Results: The EFA classifier produced a primary pattern similar to that of the FDG classifier whose expression correlated highly with the FDG pattern (R-squared 0.71), discriminated cognitively normal (NL) amyloid negative (Am-) subjects from all Am+ groups, and that correlated in Am+ subjects with Mini-Mental State Examination, Clinical Dementia Rating Sum of Boxes, and Alzheimer's Disease Assessment Scale-13-item Cognitive subscale (R = 0.59, 0.63, 0.73) and with subsequent 24-month changes in these measures (R = 0.67, 0.73, 0.50).

Discussion: Our results support the ability to use EFA with a multivariate machine learning-derived classifier to obtain a sensitive measure of AD-related loss in neuronal function that correlates with FDG PET in preclinical and early prodromal stages as well as in late mild cognitive impairment and dementia.

Highlights: The summed initial post-injection minutes of florbetapir positron emission tomography correlate with fluorodeoxyglucose.A machine learning classifier enabled sensitive detection of early prodromal Alzheimer's disease.Early frame amyloid (EFA) classifier scores correlate with subsequent change in Mini-Mental State Examination, Clinical Dementia Rating Sum of Boxes, and Alzheimer's Disease Assessment Scale-13-item Cognitive subscale.EFA classifier effect sizes and clinical prediction outperformed region of interest standardized uptake value ratio.EFA classification may aid in stratifying patients to assess treatment effect.

Keywords: Alzheimer's disease; EFA; amyloid; early frame amyloid; fluorodeoxyglucose; machine learning.

FIGURE 1

Mean CV1 scores (bars = SEM) by training group generated during independent LOO (A) EFA classifier scoring of summed first 6‐minute scans, and (B) FDG classifier scoring of FDG scans for the same subjects and visit where available. Consensus CV1 pattern associated with the primary canonical variates for the (C) EFA and (D) FDG classifiers. Plots showing correlation between (E) EFA and FDG classifier scores, and (F) EFA classifier scores versus scores resulting from evaluation of all FDG scans in the data set (including subjects not used in training or LOO) using the previously developed FDG AD progression classifier. AD, Alzheimer's disease; CV1, first canonical variate; EFA, early frame amyloid; FDG, fluorodeoxyglucose; LOO, leave one out; MCI, mild cognitive impairment; NL, cognitively normal; SEM, standard error of the mean; SMC, subjective memory complaint

FIGURE 2

Mean values (with standard error of the mean bars) for NL/SMC–, NL+, EMCI+, LMCI+, and AD+ subjects for (A) FDG classifier developed using scans from this study; (B) EFA classifier; (C) EFA MetaROIs referenced to whole brain; (D) EFA MetaROIs referenced to cerebellar cortex; (E) EFA MetaROIs referenced to pons; (F) posterior cingulate, angular gyrus, and lateral temporal ROIs referenced to whole brain; and (G) comparison of effect sizes (ES) for FDG classifier developed in this study, FDG AD progression classifier developed using 133 subjects not in this study, EFA classifier LOO results,, , Meta ROI referenced to whole brain, cerebellar cortex, and pons, respectively,, , posterior cingulate, angular gyrus, and lateral temporal ROIs referenced to whole brain. Labels show the ES values for NL+ and AD+ versus NL/SMC–. A complete listing is found in Table S1. AD, Alzheimer's disease; CV1, first canonical variate; EFA, early frame amyloid; EMCI, early mild cognitive impairment; ES, effect size; FDG, fluorodeoxyglucose; LOO, leave one out; LMCI, late mild cognitive impairment; MCI, mild cognitive impairment; NL, cognitively normal; ROI, region of interest; SEM, standard error of the mean; SMC, subjective memory complaint

FIGURE 3

A, Mean fluorodeoxyglucose (FDG) image and (B) mean early frame amyloid (EFA) image representing the average of scans from cognitively normal Am– subjects, intensity normalized to whole brain

FIGURE 4

Correlation between (A) EFA classifier scores and (B) FDG classifier scores with MMSE, CDR‐ sb, and ADAS‐Cog 13 scores at the same visit time point as the scan; (C) EFA classifier scores and (D) FDG classifier scores with the change in MMSE, CDR‐sb, and ADAS‐Cog 13 scores over the 24 months after the scan. The two unfilled circles in each case are the subjects where only 12‐month follow‐up was available and the slope applied to estimate a 24‐month change. E, Comparison of Pearson's R‐values for the FDG classifier, EFA classifier, and EFA MetaROI referenced to whole brain, using only those subjects who had both EFA and FDG PET scans available. Bar labels are R‐values. ADAS‐Cog 13, Alzheimer's Disease Assessment Scale–13‐item Cognitive subscale; CDR‐sb, Clinical Dementia Rating sum of boxes; CV, canonical variate; EFA, early frame amyloid; EMCI, early mild cognitive impairment; ES, effect size; FDG, fluorodeoxyglucose; LOO, leave one out; LMCI, late mild cognitive impairment; MCI, mild cognitive impairment; MMSE, Mini‐Mental State Examination; NL, cognitively normal; PET, positron emission tomography; ROI, region of interest