Longitudinal neuroimaging biomarkers differ across Alzheimer's disease phenotypes

Abstract

Alzheimer's disease can present clinically with either the typical amnestic phenotype or with atypical phenotypes, such as logopenic progressive aphasia and posterior cortical atrophy. We have recently described longitudinal patterns of flortaucipir PET uptake and grey matter atrophy in the atypical phenotypes, demonstrating a longitudinal regional disconnect between flortaucipir accumulation and brain atrophy. However, it is unclear how these longitudinal patterns differ from typical Alzheimer's disease, to what degree flortaucipir and atrophy mirror clinical phenotype in Alzheimer's disease, and whether optimal longitudinal neuroimaging biomarkers would also differ across phenotypes. We aimed to address these unknowns using a cohort of 57 participants diagnosed with Alzheimer's disease (18 with typical amnestic Alzheimer's disease, 17 with posterior cortical atrophy and 22 with logopenic progressive aphasia) that had undergone baseline and 1-year follow-up MRI and flortaucipir PET. Typical Alzheimer's disease participants were selected to be over 65 years old at baseline scan, while no age criterion was used for atypical Alzheimer's disease participants. Region and voxel-level rates of tau accumulation and atrophy were assessed relative to 49 cognitively unimpaired individuals and among phenotypes. Principal component analysis was implemented to describe variability in baseline tau uptake and rates of accumulation and baseline grey matter volumes and rates of atrophy across phenotypes. The capability of the principal components to discriminate between phenotypes was assessed with logistic regression. The topography of longitudinal tau accumulation and atrophy differed across phenotypes, with key regions of tau accumulation in the frontal and temporal lobes for all phenotypes and key regions of atrophy in the occipitotemporal regions for posterior cortical atrophy, left temporal lobe for logopenic progressive aphasia and medial and lateral temporal lobe for typical Alzheimer's disease. Principal component analysis identified patterns of variation in baseline and longitudinal measures of tau uptake and volume that were significantly different across phenotypes. Baseline tau uptake mapped better onto clinical phenotype than longitudinal tau and MRI measures. Our study suggests that optimal longitudinal neuroimaging biomarkers for future clinical treatment trials in Alzheimer's disease are different for MRI and tau-PET and may differ across phenotypes, particularly for MRI. Baseline tau tracer retention showed the highest fidelity to clinical phenotype, supporting the important causal role of tau as a driver of clinical dysfunction in Alzheimer's disease.

Keywords: Alzheimer’s disease phenotypes; atrophy; longitudinal tau-PET; neuroimaging biomarkers.

Figure 1

Longitudinal tau-PET uptake and atrophy patterns in Alzheimer’s disease phenotypes. SPM maps of higher annualized rates of tau-PET uptake accumulation and of atrophy (i.e. MRI annualized log Jacobians) for typical Alzheimer’s disease (AD), PCA and LPA participants relative to cognitively unimpaired (CU). Results are shown after FWE correction for multiple comparison (P < 0.05), except for the typical Alzheimer’s disease rates of atrophy map, which is shown uncorrected (P < 0.001).

Figure 2

AUROC estimates of longitudinal regional measures. AUROC estimates for each Alzheimer’s disease (AD) phenotype against cognitively unimpaired individuals. AUROC were calculated using ROI-based rates of tau accumulation (A) and atrophy (B). ROIs are listed according to a decreasing order of their AUROC and only the first 10 regions are reported. The reported P-values were adjusted for multiple comparisons using the Bonferroni method. CI = confidence interval.

Figure 3

Principal component analyses of tau-PET uptake. Variability in baseline tau-PET uptake (A) and annualized rates of tau accumulation (B) as described by the first four principal components. To visualize the variability captured by the principal components, brain maps were reconstructed using the 5th and 95th percentile of each principal component. For example, from A, a participant with a high PC2 score (i.e. closer to the 95th percentile) has high lateral occipital uptake, while a participant with a low PC2 score (i.e. closer to the 5th percentile) has high lateral temporal uptake. The tables below the brain maps show which phenotypes have statistically different principal component scores. For example, from A, PC2 is statistically higher for PCA participants, meaning that PCA participants will resemble more the 95th percentile map of PC2, while LPA and typical Alzheimer’s disease (AD) will resemble more the 5th percentile map of PC2.

Figure 4

Principal component analyses of grey matter volumes. Variability in baseline MRI grey matter volumes (A) and MRI annualized log Jacobians (B) as described by the first four principal components. To visualize the variability captured by the principal components, the Pearson’s R correlation coefficients between each principal component and each regional volume (A) and log Jacobian (B) are reported. For example, from A, a participant with a high PC2 score has high volumes in the medial temporal regions. Only correlations with P < 0.05 are shown. Correlations that survived Bonferroni correction for multiple comparisons at P < 0.05 are marked with an asterisk. The tables on the right show which phenotypes have statistically different principal component scores. For example, from A, PC2 is statistically lower for typical Alzheimer’s disease (AD) participants, meaning that they have lower medial temporal grey matter volumes than PCA and LPA participants.

Figure 5

Diagnostic power of principal components. Diagnostic power of each imaging modality estimated by how many participants were classified into their correct phenotype using penalized multinomial regression models based on increasing numbers of principal components.