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Mapping cerebral blood perfusion and its links to multi-scale brain organization across the human lifespan

Fig 5

Blood perfusion across aging.

(a) Age-effect perfusion map estimated using data from 678 participants in the HCP-Aging dataset (36–100 years). Linear age coefficients () are derived from a linear model (), applied at each vertex/voxel i. Subcortical age-effect values are shown in S11B Fig. Sex-effect and coefficient significance maps are provided in S16 Fig. S17 Fig shows vertex/voxel-wise Spearman correlation maps of blood perfusion and age, stratified by biological sex. See S18 Fig for sex-stratified non-linear modeling of perfusion changes. (b) The age-effect pattern aligns with arterial border-zone cortical regions. The border-zone areas are described based on: (1) Left: First principal component of estimated arterial transit time maps (greater values correspond to regions with later arterial transit time; see S19 Fig for analysis details). In the figure, “ATT” stands for arterial transit time. (2) Right: Intersections of major cerebral arterial territories derived from a stroke-based atlas [142,143]. (c) Correlation between cerebral blood perfusion age-related changes (x-axis) and arterial transit time scores (y-axis) (r = −0.71, ). Red dots represent border-zone regions defined according to (2). (d) Using partial least squares (PLS) analysis, we find a significant latent variable that accounts for 82.0%, and 90.6% of the covariance between cortical blood perfusion and biomarkers (S20 Fig) in male and female participants (in both cases: ). The bar plot visualizes the contribution of individual biomarkers to the first latent variable. The significance of each biomarker’s contribution to the overall pattern is assessed by bootstrap resampling ( bootstraps; see S21 Fig). (e) The brain loadings of the first latent variable are shown for both groups. These maps correlate with the arterial transit time score map shown in (b) (male: r = 0.47, , female: r = 0.52, ). Brain maps in (a), (b) and (e) are shown on the inflated and 2D flat cortical surfaces (fsLR); maps in (b)-right and (e) are parcellated according to the Schaefer-400 functional atlas [126]. Results of PLS analysis after regressing out the linear effect of age and sex from the perfusion maps and the biomarkers are presented in S22 Fig.

doi: https://doi.org/10.1371/journal.pbio.3003277.g005