AV-1451 PET imaging of tau pathology in preclinical Alzheimer disease: Defining a summary measure

Abstract

Utilizing [18F]-AV-1451 tau positron emission tomography (PET) as an Alzheimer disease (AD) biomarker will require identification of brain regions that are most important in detecting elevated tau pathology in preclinical AD. Here, we utilized an unsupervised learning, data-driven approach to identify brain regions whose tau PET is most informative in discriminating low and high levels of [18F]-AV-1451 binding. 84 cognitively normal participants who had undergone AV-1451 PET imaging were used in a sparse k-means clustering with resampling analysis to identify the regions most informative in dividing a cognitively normal population into high tau and low tau groups. The highest-weighted FreeSurfer regions of interest (ROIs) separating these groups were the entorhinal cortex, amygdala, lateral occipital cortex, and inferior temporal cortex, and an average SUVR in these four ROIs was used as a summary metric for AV-1451 uptake. We propose an AV-1451 SUVR cut-off of 1.25 to define high tau as described by imaging. This spatial distribution of tau PET is a more widespread pattern than that predicted by pathological staging schemes. Our data-derived metric was validated first in this cognitively normal cohort by correlating with early measures of cognitive dysfunction, and with disease progression as measured by β-amyloid PET imaging. We additionally validated this summary metric in a cohort of 13 Alzheimer disease patients, and showed that this measure correlates with cognitive dysfunction and β-amyloid PET imaging in a diseased population.

Copyright © 2017 Elsevier Inc. All rights reserved.

Figures

Figure 1

Pictorial representation of FreeSurfer Regions of Interest assigned to Braak stages 1–4, made in consultation with a neuropathologist.

Figure 2

Axial, coronal, and sagittal planes of the mean difference in AV-1451 SUVR between cognitively normal and Alzheimer diseases individuals.

Figure 3

Mean weights for each of the FreeSurfer Regions of Interest from 500 iterations of the sparse k-means algorithm when clustering cognitively normal participants into k=2 groups

Figure 4

Proportion of total iterations that each participant was classified in the high tau cluster, as a function of mean AV-1451 SUVR across top four highest-weighted FreeSurfer regions of interest.

Figure 5

Relationship between mean AV-1451 SUVR across the top four highest-weighted FreeSurfer regions of interest (SKM ROIs) and A) attentional control composite score and B) episodic memory composite score.

Figure 5

Relationship between mean AV-1451 SUVR across the top four highest-weighted FreeSurfer regions of interest (SKM ROIs) and A) attentional control composite score and B) episodic memory composite score.

Figure 6

Relationship between mean AV-1451 SUVR across Braak stages 1–4 FreeSurfer regions of interest (Braak ROIs) and A) attentional control composite score and B) episodic memory composite score.

Figure 6

Relationship between mean AV-1451 SUVR across Braak stages 1–4 FreeSurfer regions of interest (Braak ROIs) and A) attentional control composite score and B) episodic memory composite score.

Figure 7

A) Mean AV-1451 SUVR across the top four highest-weighted FreeSurfer regions of interest in the cognitively normal participants stratified by Aβ positivity and B) Mean AV-45 SUVR in cognitively normal participants, classified as having high tau or low tau based on SUVR cut-off of 1.25.

Figure 7

A) Mean AV-1451 SUVR across the top four highest-weighted FreeSurfer regions of interest in the cognitively normal participants stratified by Aβ positivity and B) Mean AV-45 SUVR in cognitively normal participants, classified as having high tau or low tau based on SUVR cut-off of 1.25.