Amyloid and tau accumulate across distinct spatial networks and are differentially associated with brain connectivity

Abstract

The abnormal accumulation of amyloid-β and tau targets specific spatial networks in Alzheimer's disease. However, the relationship between these networks across different disease stages and their association with brain connectivity has not been explored. In this study, we applied a joint independent component analysis to ¹⁸F- Flutemetamol (amyloid-β) and ¹⁸F-Flortaucipir (tau) PET images to identify amyloid-β and tau networks across different stages of Alzheimer's disease. We then assessed whether these patterns were associated with resting-state functional networks and white matter tracts. Our analyses revealed nine patterns that were linked across tau and amyloid-β data. The amyloid-β and tau patterns showed a fair to moderate overlap with distinct functional networks but only tau was associated with white matter integrity loss and multiple cognitive functions. These findings show that amyloid-β and tau have different spatial affinities, which can be used to understand how they accumulate in the brain and potentially damage the brain's connections.

Keywords: alzheimer's disease; amyloid; cognition; human; neuroscience; resting-state functional networks; tau; white matter tracts.

Figure 1.. Flow-chart of imaging sequences available for the whole cohort.

All subjects included in the study had a ¹⁸F-Flutemetamol PET, ¹⁸F-Flortaucipir (¹⁸F-AV1451) PET and T1-weighted (T1–w) MRI scans. In addition, a subsample (n = 101) also underwent resting-state functional MRI (rs-fMRI), of which 13 had to be excluded due to errors in spatial normalization or excessive head motion (see Materials and methods) so the final sample with rs-fMRI was 88 subjects. Finally, a subsample of subjects (n = 88) that had ¹⁸F-Flutemetamol PET, ¹⁸F-AV1451 PET and T1-w scans also underwent diffusion tensor imaging (DTI). Four subjects were excluded from the DTI subsample given they were outliers in white matter integrity measures. CN, cognitively normal; MCI, mild cognitive impairment; AD, Alzheimer’s disease; amyloid negative (Aβ-); amyloid positive (Aβ+).

Figure 2.. Networks of amyloid-β and tau accumulation in the whole cohort.

We identified nine joint independent components (ICs) for amyloid-β and tau in the analyses of ¹⁸F-Flutemetamol and ¹⁸F-Flortaucipir PET data in the entire sample of 117 subjects (26 amyloid-β negative cognitively normal subjects, 34 amyloid-β positive cognitively normal subjects, 21 patients with mild cognitive impairment, 36 patients with Alzheimer’s disease). These components were thresholded with a z-score >2.0, which corresponds to a two-tailed significance value of p<0.05. Inclusion and exclusion criteria for the subjects included in this analysis can be found in Materials and methods.

Figure 2—figure supplement 1.. Excluded amyloid-β and tau components.

The components were thresholded with a z-score >2.0.

Figure 2—figure supplement 2.. Amyloid-β networks in different groups.

CN Aβ+, cognitively normal subjects with amyloid pathology (n = 34); MCI Aβ+, mild cognitive impairment patients with amyloid pathology (n = 21); AD Aβ+, patients with Alzheimer’s disease dementia with amyloid pathology (n = 36). The components were thresholded with a z-score >2.0.

Figure 2—figure supplement 3.. Tau networks in different groups.

CN Aβ+, cognitively normal subjects with amyloid pathology (n = 34); MCI Aβ+, mild cognitive impairment patients with amyloid pathology (n = 21); AD Aβ+, patients with Alzheimer’s disease dementia with amyloid pathology (n = 36). The components were thresholded with a z-score >2.0.

Figure 2—figure supplement 4.. Amyloid-β and tau networks in subsample with functional MRI and diffusion tensor imaging data.

These analyses were carried out using the subsample with all imaging modalities (n = 67): 10 amyloid-β negative cognitively normal subjects (CN Aβ-), 24 amyloid-β positive cognitively normal subjects (CN Aβ+), 14 patients with mild cognitive impairment (MCI Aβ+), and 19 patients with Alzheimer’s disease (AD Aβ+). The components were thresholded with a z-score >2.0.

Figure 3.. Spatial overlap between the functional MRI, amyloid-β and tau networks.

Resting-state functional networks that overlapped best with the amyloid-β and tau networks identified in the entire sample of 117 subjects (26 amyloid-β negative cognitively normal subjects, 34 amyloid-β cognitively normal subjects, 21 patients with mild cognitive impairment, 36 patients with Alzheimer’s disease). The Dice coefficients (DC) between these networks indicated poor (<0.2), fair (0.2–0.4) or moderate (0.4–0.6) overlap.

Figure 4.. Association between the tau networks and fractional anisotropy of white matter tracts.

The plots show significant correlations between the SUVRs of different tau networks and fractional anisotropy values of white matter tracts in 66 amyloid-β positive subjects with both imaging modalities, after regressing out the effects of age, gender and cognitive impairment, and adjusting for multiple comparisons using FDR corrections (q < 0.05). Three outliers were excluded: 2 AD patients and 1 MCI patient.

Figure 5.. Association between the tau networks and mean diffusivity of white matter tracts.

The plots show significant correlations between the SUVRs of different tau networks and mean diffusivity values of white matter tracts in 66 amyloid-β positive subjects with both imaging modalities, after regressing out the effects of age, gender and cognitive impairment, and adjusting for multiple comparisons using FDR corrections (q < 0.05). One AD patient with high mean diffusivity values in white matter tracts was excluded from all plots.