Cerebrovascular Brain Aging Examined with Arterial Spin Labelling and Applied to Age Prediction
M. Ethan MacDonald1, Nils D. Forkert1, Yuhan Ma2, Rebecca J. Williams1, Alexandru Hanganu1, Hongfu Sun1, Randall Stafford1, Cheryl R. McCreary1, Richard Frayne1, and G. Bruce Pike1

1University of Calgary, Calgary, AB, Canada, 2McGill University, Montreal, QC, Canada


Changes in both cortical thickness and cerebral blood flow are observed with age. In this work, we look at how these parameters are modulated across the lifespan. T1-weighted and arterial spin labelling data from 146 subjects were analyzed, with 68 cortical regions selected in each subject to obtain mean cortical thickness and cerebral blood flow. We calculated rates of change, correlation, and laterality for both parameters. Finally, we explored predictive modeling using cortical thickness, CBF and a model combining the two. Predictive modelling was slightly improved when both measures were included.


Aging is known to have effects on the anatomy and physiology of the brain.[1,2] In fact, age prediction from brain morphology has been investigated by several research groups and has been suggested to be a surrogate measure of brain health.[3-6] However, these studies are usually limited to anatomical T1-weighted MRI data. Chen et al., reported thinning of the cerebral cortex that is associated with cerebral blood flow (CBF) decreases.[7] Thus, we hypothesized that including CBF data for age prediction would improve accuracy. The aim of this study is to assess regional CBF measurements and cortical thickness across the adult lifespan, and incorporate CBF measurements for age prediction to investigate the additional predictive power it can provide to chronological age estimation of healthy subjects.


A total of 146 subjects (58-M, 88-F; 18 to 87 years) were scanned with a 3T MRI (Discovery 750, GE Healthcare). All subjects included in this analysis had a Montreal Cognitive Assessment score >25 (indicating normal cognition). All subjects self-reported normal neurological and vascular medical history. Among others, T1-weighted anatomical and arterial spin labelling (ASL) were acquired and used for this analysis. The T1-weighted MPRAGE scan with an isotropic image resolution of 1 mm was acquired with TI/TR/TE/α of 650/5.84/2.36 ms/8°. The pseudo-continuous ASL was acquired with 5-mm thick slices and a spiral trajectory with an effective in-plane resolution of 2.33 mm, label duration and post label duration were 1.0 s and 2.0 s, respectively.

FreeSurfer was used to calculate cortical thickness measurements from the anatomical scans for a total of 68 volumes-of-interest (VOIs) using the cortical parcellation from the DK atlas.[8] CBF images were registered to the anatomical data and the same VOIs were used to determine the corresponding mean CBF. CBF data were then registered to the ICBM atlas,[9] and group average and aging regression maps were computed.

Linear regression with age was calculated for each region on both the cortical thickness and CBF data. Average laterality index and regional correlation matrices were then calculated for each data type. Multiple linear regression with recursive feature selection using the ReliefF algorithm[10] was used for age prediction, in three input setups: 1) cortical thickness, 2) CBF, and 3) both.


A polar plot is shown in Figure 1 to illustrate the differences in regional regression (R2) with age for the cortical thickness and CBF data. In general, the R2 is lower for the CBF data.

Figures 2 and 3 show the correlation and laterality indices. CBF measurements are more highly correlated across regions than cortical thickness data, while there is more laterality in the CBF data.

Figure 4 shows the CBF group average and regression maps. Generally, the rate of change is higher in gray matter. R2 was most robust near the feeding vessels (MCA, ACA).

Figure 5 shows the outcome of the multiple regression age prediction for the cortical thickness data alone, CBF alone, and for the combination.


While results may vary slightly with different population selection or acquisition parameters, some generalizable observations include: 1) CBF data has lower R2 with age than cortical thickness, 2) there is more laterality in CBF than cortical thickness, 3) cortical thickness is less correlated amongst regions than CBF, 4) group average maps indicate that the reduction of CBF with age is constrained primarily to gray matter with similar rates of change across regions, and 5) age prediction was good for either cortical thickness and CBF alone only, and slightly better with the combination.

Compared to previous similar studies,[11,12] this study adds new information including the laterality analysis. It has been known that cortical thickness changes are typically bilateral, and often only the bilateral average cortical thickness values are reported,[11] while more laterality has been observed in CBF.[12] Structural cortical thickness correlation matrices are often computed,[13] but, to our knowledge, this is the first evaluation of cortical CBF correlation.

Cortical thickness and CBF measurements are not necessarily changing linearly with age, particularly in the younger age ranges. Due to the variability in the data and relatively small sample size, the linear approximation is appropriate. Inclusion of more subjects might allow a more precise piecewise linear regression or non-linear modelling. More advanced techniques, such as support vector machine or random forest modelling, could also improve age prediction based on cortical thickness and CBF.


There is more laterality in CBF than cortical thickness yet high correlation between regional CBF values. While age prediction via cortical thickness or CBF was good, the combination of both provided only a small improvement.


No acknowledgement found.


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Figure 1: Polar plot indicating the R2 regression with age parameter of the cortical thickness and cerebral blood flow in 68 cortex regions. For the vast majority of the regions, the cortical thickness measurements have higher R2 than the cerebral blood flow measurements.

Figure 2: Laterality indices. Hemispheric laterality index for cortical thickness and CBF. Laterality index = ( L – R ) / ( L + R ). Plotted are mean (grey bar) and standard deviation (error bars).

Figure 3: Correlation matrices of cortical thickness (left) and cerebral blood flow (right). The left hemisphere is at the top and right hemisphere is at the bottom. The cerebral blood flow data is more highly correlated than the cortical thickness.

Figure 4: Group analysis of CBF vs. age map. In addition to calculating the average CBF, a regression was performed on each pixel to obtain the slope, R2, and intercept. The gray matter cortical regions have a decrease of 0.4 to 0.6 mL / 100 g / min / year.

Figure 5: Age prediction performance using the cortical thickness, cerebral blood flow, and the combined data. Recursive feature selection using the ReliefF algorithm was used to reduce the number of features, and the mean age difference versus the number of features reduced, and is shown on the left. The minimum mean absolute differences for the cortical thickness, CBF and combined data are 9.22, 10.03, and 8.67, years respectively. The leave-one-out regression analysis is shown on the right.

Proc. Intl. Soc. Mag. Reson. Med. 26 (2018)