With aging, the brain undergoes several structural changes. These changes reflect the normal aging process and are therefore not necessarily pathologic. In fact, better understanding of these normal changes is an important cornerstone to also disentangle pathologic changes. Several studies have investigated normal brain aging, both cross-sectional and longitudinal, and focused on a broad range of magnetic resonance imaging (MRI) markers. This study aims to comprise the different aspects in brain aging, by performing a comprehensive longitudinal assessment of brain aging, providing trajectories of volumetric (global and lobar; subcortical and cortical), microstructural, and focal (presence of microbleeds, lacunar or cortical infarcts) brain imaging markers in aging and the sequence in which these markers change in aging. Trajectories were calculated on 10,755 MRI scans that were acquired between 2005 and 2016 among 5286 persons aged 45 years and older from the population-based Rotterdam Study. The average number of MRI scans per participant was 2 scans (ranging from 1 to 4 scans), with a mean interval between MRI scans of 3.3 years (ranging from 0.2 to 9.5 years) and an average follow-up time of 5.2 years (ranging from 0.3 to 9.8 years). We found that trajectories of the different volumetric, microstructural, and focal markers show nonlinear curves, with accelerating change with advancing age. We found earlier acceleration of change in global and lobar volumetric and microstructural markers in men compared with women. For subcortical and cortical volumes, results show a mix of more linear and nonlinear trajectories, either increasing, decreasing, or stable over age for the subcortical and cortical volume and thickness. Differences between men and women are visible in several parcellations; however, the direction of these differences is mixed. The presence of focal markers show a nonlinear increase with age, with men having a higher probability for cortical or lacunar infarcts. The data presented in this study provide insight into the normal aging process in the brain, and its variability.
Background Previous studies identifying hearing loss as a promising modifiable risk factor for cognitive decline mostly adjusted for baseline age solely. As such a faster cognitive decline at a higher age, which is expected considering the non-linear relationship between cognition and age, may have been overlooked. Therefore it remains uncertain whether effects of hearing loss on cognitive decline extend beyond age-related declines of cognitive function. Methods 3,590 non-demented participants were eligible for analysis at baseline, and a maximum of 837 participants were eligible for the longitudinal analysis. Hearing loss was defined at baseline. Cognitive function was measured at baseline and at follow-up (4.4 years [SD: 0.2]). Multivariable linear regression analysis was used for the cross-sectional analysis. Linear mixed models were used to assess the longitudinal association between hearing loss and cognitive decline over time while adjusting for confounders and the interaction of age and follow-up time. Results Hearing loss was associated with lower cognitive function at baseline. Moreover, hearing loss was associated with accelerated cognitive decline over time on a memory test. After additionally adjusting for the interaction between age and follow-up time, we found that hearing loss did not accelerate cognitive decline anymore. Conclusions Hearing loss was associated with lower cognitive function at baseline and accelerated cognitive decline on a memory test. The association between hearing loss and accelerated cognitive decline was non-significant after additional adjustment for non-linear age effects. More evidence is needed to ensure the role of hearing loss as a modifiable risk factor for cognitive decline.
Contrast-enhanced ultrasound (CEUS) has been suggested as a new method to measure cerebral perfusion in patients with acute brain injury. In this systematic review, the tolerability, repeatability, reproducibility and accuracy of different CEUS techniques for the quantification of cerebral perfusion were assessed. We selected studies published between January 1994 and March 2017 using CEUS to measure cerebral perfusion. We included 43 studies (bolus kinetics n = 31, refill kinetics n = 6, depletion kinetics n = 6) with a total of 861 patients. Tolerability was reported in 28 studies describing 12 patients with mild and transient side effects. Repeatability was assessed in 3 studies, reproducibility in 2 studies and accuracy in 19 studies. Repeatability was high for experienced sonographers and significantly lower for less experienced sonographers. Reproducibility of CEUS was not clear. The sensitivity and specificity of CEUS for the detection of cerebral ischemia ranged from 75% to 96% and from 60% to 100%. Limited data on repeatability, reproducibility and accuracy may suggest that this technique could be feasible for use in acute brain injury patients.
Background To establish trajectories of cognitive and motor function, and to determine the sequence of change across individual tests in community-dwelling individuals aged 45-90 years. Methods Between 1997-2016, we repeatedly assessed cognitive function with five tests in 9,514 participants aged 45-90 years from the population-based Rotterdam Study. Between 1999-2016, we measured motor function with three tests in 8,297 participants. All participants were free from dementia, stroke, and parkinsonism. We assessed overall and education-specific cognitive and motor trajectories using linear mixed models with age as time scale. Next, we determined the sequence of change across individual tests. Results The number of assessments per participant ranged between 1-6 (mean interval, years[SD]: 5.1[1.4]) for cognitive function, and 1-4 (5.4[1.4]) for motor function. Cognitive and motor trajectories declined linearly between ages 45-65 years, followed by steeper declines after ages 65-70 years. Lower educated participants had lower cognitive function at age 45 years (baseline), and declined faster on most cognitive, but not on motor tests than higher educated participants. Up to a 25-year age difference between the fastest and slowest declining test scores was observed. Conclusions On a population-level, cognitive and motor function decline similarly. Compared to higher educated individuals, lower educated individuals had lower cognitive function at baseline, and a faster rate of decline thereafter. These educational-effects were not seen for motor function. These findings benefit the understanding of the natural course of cognitive and motor function during aging, and highlight the role of education in the preservation of cognitive but not motor function
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