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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.

Background The aim of this study was to investigate the dynamics of annual whole brain volume loss (BVL/year) and annual thalamic volume loss (ThalaVL/year) in patients with relapsing-remitting multiple sclerosis (PwRRMS) during the course of the disease. Methods A longitudinal database of magnetic resonance imaging (MRI) scans of 195 healthy individuals (age range, 22.8–63.7 years) and longitudinal MRI data of 256 PwRRMS (age range, 20.1–60.8 years) were analyzed and compared. BVL/year and ThalaVL/year were computed for healthy individuals as well as for all patients with MS using a Jacobian integration approach. A linear regression was used to compute the relationship between age and BVL/year and ThalaVL/year for healthy individuals. The linear regression was then used to decompose the BVL/year and ThalaVL/year into a multiple sclerosis (MS)-related and an age-related component for each PwRRMS. PwRRMS were dichotomized into early-phase RRMS (disease duration ≤ 6 years) and later-phase RRMS (disease duration > 6 years), and a t -test was performed to test for differences between these groups. Results The 135 early-phase patients (disease duration, ≤ 6 years) had statistically significantly higher MS-related BVL/year than the later-phase patients ( n  = 121) (− 0.21% vs. − 0.06%, p  = 0.007). For MS-related ThalaVL/year, the difference between the groups was even more pronounced (− 0.39% vs. − 0.00%, p  < 0.0001). Conclusions Our results indicate that in PwRRMS, the MS-related components of BVL/year and ThalaVL/year are accelerated in early phases and slowdown in later phases of the disease. This might explain why early intervention often leads to improved outcomes in patients with MS.