Age-related gray matter volume changes in the brain during non-elderly adulthood

Abstract

Previous magnetic resonance imaging (MRI) studies described consistent age-related gray matter (GM) reductions in the fronto-parietal neocortex, insula and cerebellum in elderly subjects, but not as frequently in limbic/paralimbic structures. However, it is unclear whether such features are already present during earlier stages of adulthood, and if age-related GM changes may follow non-linear patterns at such age range. This voxel-based morphometry study investigated the relationship between GM volumes and age specifically during non-elderly life (18–50 years) in 89 healthy individuals (48 males and 41 females). Voxelwise analyses showed significant (p < 0.05, corrected) negative correlations in the right prefrontal cortex and left cerebellum, and positive correlations (indicating lack of GM loss) in the medial temporal region, cingulate gyrus, insula and temporal neocortex. Analyses using ROI masks showed that age-related dorsolateral prefrontal volume decrements followed non-linear patterns, and were less prominent in females compared to males at this age range. These findings further support for the notion of a heterogeneous and asynchronous pattern of age-related brain morphometric changes, with region-specific non-linear features.

When does brain aging accelerate? Dangers of quadratic fits in cross-sectional studies

“…Also, while the quadratic model indicated non-monotonic trajectories when sampling started at age 50 or earlier in the main sample and at age 40 or earlier in the replication sample, this pattern was not found for the local smoothing spline in either samples. This puts forth the question of whether non-monotonic relationships often reported in aging research (Allen et al, 2005;Jernigan and Gamst, 2005;Kennedy et al, 2008;Terribilli et al, 2009;Walhovd et al, 2005) to some degree are exaggerated by use of quadratic model fits. This question warrants further investigation.…”

“…A quadratic term is added to the list of predictors in a regression analysis, yielding a higher order polynomial function. If the quadratic term is significant, the brain structure in question can be said to have a non-linear age-trajectory (Allen et al, 2005;Good et al, 2001;Jernigan and Gamst, 2005;Kennedy et al, 2008;Lupien et al, 2007;Sowell et al, 2003;Sullivan et al, 1995;Terribilli et al, 2009;Walhovd et al, 2005). In addition, the trajectory of the curve may be used to describe the relationship between age and the brain structure, e.g.…”

Repeated mild traumatic brain injuries is not associated with volumetric differences in former high school football players

“…These analyses showed small-to-medium effect sizes, such that the concussed group was older and reported more concussive-like symptoms compared to the control group. We do not believe that these potential group differences influenced the analysis or interpretation of our data, given that both increased age and concussion-related pathology would likely lead to smaller volumes (Du et al 2006;Ross et al 2014;Terribilli et al 2011). Including age as a covariate did not influence our findings.…”

Comparison of two nonlinear registration techniques to investigate brain atrophy patterns in normal aging

“…Large MRI studies have investigated age-related GM reductions using manual region-based volumetry or VBM [20,[22][23][24][25][26][27][28][29][30]. These studies described a consistent pattern of GM volumetric reductions in the human neocortex that mostly involved the prefrontal region and the parietal-temporal association cortex [28,31,32].…”