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.

Tau, amyloid, and cascading network failure across the Alzheimer's disease spectrum

“…Our major findings were: (1) Spatial patterns of tau deposition follow functionally related brain systems, beyond those sampled neuropathologically for Braak staging, in a heterogeneous manner; (2) These atypical, ‘non-Braak-like’ tau-deposition patterns were associated with younger age-of-onset while the typical, ‘Braak-like’ pattern was not related to age-of-onset; (3) Elevated τ-PET signal is detected network-wide rather than focally and sequentially throughout Braak regions as demonstrated by ICA (Figure 2E and Supplementary Figures 2,5, and 14), frequency count (Figure 4 and Supplementary Figure 13), the effect of brain region on the relationship between tau and Aβ (Figure 5), average τ-PET images across IC-5 loading groups (Figure 6), and the presence of network wide patterns in clinically normal subjects (Supplementary Figures 11 and 12 and recently reported in (Mishra et al, 2017)); (4) The typical, ‘Braak-like’ τ-PET deposition pattern is related to large-scale functional network failure, with this relationship being partially mediated by Aβ (Figure 7). These results implicate large-scale brain networks in the pathophysiology of tau and in the pathophysiology that links Aβ to tau as hypothesized by our model outlined in Figure 1.…”

“…Sequential spreading not fully detected by τ-PET until later stages could also produce these observations, but using Braak-staging ROIs, or any other regional approach, would not confer any particular advantage over global measures in such a case (Maass et al, 2017). The lack of utility in using Braak-staging ROIs is also highlighted by the fact that the τ-PET signal in preclinical AD is more widespread than predicted by pathologic staging as was recently shown by others (Mishra et al, 2017) and also observed in this study (see Figure 6 and Supplementary Figures 11–13). Another alternative explanation for the observed widespread signal is that tau-PET is sensitive to abnormalities in vulnerable brain systems even in the absence of NFTs, perhaps manifesting as marginally elevated tau-PET signal.…”

Data-driven approaches for Tau-PET imaging biomarkers in Alzheimer’s disease

“…18 F]AV1451 signal [42][43][44] . As such, even small improvements are important for studies assessing more subtle effects of cortical tau accumulation and studies seeking optimal biomarkers for multimodal classification or disease progression 45 .…”

Automated segmentation of medial temporal lobe subregions on in vivo T1‐weighted MRI in early stages of Alzheimer's disease

“…However, manual segmentation is infeasible for larger data sets like the AD neuroimaging initiative (ADNI), which includes thousands of MRI scans. Among the automated methods available for MTL subregion segmentation on T1w MRI, the specialized modules for MTL provided by FreeSurfer (Fischl, 2012) are of the most widely used in the literature in older populations (Delli Pizzi et al, 2016; Lehmann et al, 2010; Mah, Binns, & Steffens, 2015; Mishra et al, 2017; Pasquini et al, 2016). FreeSurfer includes a module for labeling hippocampal subfields and hippocampal lamina based on an ex vivo atlas (Iglesias et al, 2015).…”