Abstract
The objective is to estimate average global and regional percentage brain volume loss per year (BVL/year) of the physiologically ageing brain. Two independent, cross-sectional single scanner cohorts of healthy subjects were included. The first cohort (n = 248) was acquired at the Medical Prevention Center (MPCH) in Hamburg, Germany. The second cohort (n = 316) was taken from the Open Access Series of Imaging Studies (OASIS). Brain parenchyma (BP), grey matter (GM), white matter (WM), corpus callosum (CC), and thalamus volumes were calculated. A non-parametric technique was applied to fit the resulting age–volume data. For each age, the BVL/year was derived from the age–volume curves. The resulting BVL/year curves were compared between the two cohorts. For the MPCH cohort, the BVL/year curve of the BP was an increasing function starting from 0.20% at the age of 35 years increasing to 0.52% at 70 years (corresponding values for GM ranged from 0.32 to 0.55%, WM from 0.02 to 0.47%, CC from 0.07 to 0.48%, and thalamus from 0.25 to 0.54%). Mean absolute difference between BVL/year trajectories across the age range of 35–70 years was 0.02% for BP, 0.04% for GM, 0.04% for WM, 0.11% for CC, and 0.02% for the thalamus. Physiological BVL/year rates were remarkably consistent between the two cohorts and independent from the scanner applied. Average BVL/year was clearly age and compartment dependent. These results need to be taken into account when defining cut-off values for pathological annual brain volume loss in disease models, such as multiple sclerosis.
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Acknowledgements
The OASIS database is made available by the Washington University Alzheimer’s Disease Research Center, Dr. Randy Buckner at the Howard Hughes Medical Institute (HHMI) at Harvard University, the Neuroinformatics Research Group (NRG) at Washington University School of Medicine, and the Biomedical Informatics Research Network (BIRN), and supported by NIH grants P50 AG05681, P01 AG03991, R01 AG021910, P50 MH071616, U24 RR021382, R01 MH56584.
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The study (MPCH cohort) was approved by the Ethics Board of the Ärztekammer, Hamburg, Germany.
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415_2016_8374_MOESM1_ESM.tiff
Supplementary material 1 (TIFF 264 kb) Fig. 3 Two plots show simulated age–volume trajectories. The trajectories are modelled by a set of quadratic functions \({\text{g}}_{\text{i}} \left( {\text{x}} \right) = {\text{a}}_{\text{i}} {\text{x}}^{2} + {\text{b}}_{\text{i}} {\text{x}} + {\text{c}}_{\text{i}}\) with some random coefficients \({\text{a}}_{\text{i}} ,{\text{b}}_{\text{i}} ,{\text{c}}_{\text{i}}\). The range of the coefficients is estimated from our data. The left plot shows 300 random trajectories and the mean trajectory (black curve). In the right plot, one single timepoint on each trajectory was randomly selected (grey dots). Based on these given single timepoints, the true mean trajectory (black curve) was estimated with the non-parametric technique described in the manuscript (dashed line). We can observe that the “true” mean curve is reasonably close to the estimated curve (dashed line)
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Schippling, S., Ostwaldt, AC., Suppa, P. et al. Global and regional annual brain volume loss rates in physiological aging. J Neurol 264, 520–528 (2017). https://doi.org/10.1007/s00415-016-8374-y
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DOI: https://doi.org/10.1007/s00415-016-8374-y