Global and regional annual brain volume loss rates in physiological aging

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.

Changes in the intracranial volume from early adulthood to the sixth decade of life: A longitudinal study

“…ml/year to the ICV at age 20 years that is replaced by a decrease of 1.3 ml/year at age 55. Interestingly, the decade of life where we found the ICV to change direction between growth and decline corresponds well to the decade of life where the amount of white matter in the brain stop growing and starts shrinking or stats accelerated shrinking [37,38,36]. This raises the possibility that these two processes result from a shared genetic cause, which will be interesting hypothesis to check in future works.…”

Longitudinal magnetic resonance imaging in progressive supranuclear palsy: A new combined score for clinical trials

“…The ideal modeling of There is evidence that nonparametric approaches may be advantageous in certain instances. [40][41][42] With only 2 measurements (baseline, follow-up) in the present study, we decided to calculate a simple volume difference for each patient (corresponding to a linear fit) and to determine the mean volume change in the cohort by averaging the patients' results. We deliberately refrained from applying more sophisticated nonlinear models for fitting volume changes because they would only be sufficiently supported by more than 2 measurements in time.…”

Estimates of age-dependent cutoffs for pathological brain volume loss using SIENA/FSL—a longitudinal brain volumetry study in healthy adults

“…Several cross-sectional (Fjell, et al, 2009, Fjell, et al, 2013, Marcus, et al, 2007, Schippling, et al, 2017, Ziegler, et al, 2012, as well as longitudinal studies (Driscoll, et al, 2009, Hedman, et al, 2012, Marcus, et al, 2010, Taki, et al, 2011, found that BVL critically depends on age, in both, MS patients and healthy individuals. A recent study reported age dependent mean BVL per year values in physiological aging (Schippling, et al, 2017). However, the data in that study were extrapolated from cross-sectional brain volumetry data using a non-parametric fitting approach.…”

“…However, the data in that study were extrapolated from cross-sectional brain volumetry data using a non-parametric fitting approach. As discussed before (Schippling, et al, 2017), cross-sectional data allows the determination of mean BVL per year values of a given cohort, but lacks estimates of the biological variability (age-dependent standard deviations) of BVL per year values for a specific age range. To interpret BVL per year in disease models correctly, it is of utmost importance to do so against the background of measurements derived from longitudinal studies in healthy aging populations, to allow a correction for physiological aging.…”

“…To interpret BVL per year in disease models correctly, it is of utmost importance to do so against the background of measurements derived from longitudinal studies in healthy aging populations, to allow a correction for physiological aging. The aim of this study was to validate BVL per year in healthy individuals that some of the authors of this study previously investigated cross-sectionally (Schippling, et al, 2017) using a longitudinal cohort of healthy individuals. We assessed the mean and the variability of the BVL per year for each age range.…”