Critical ages in the life course of the adult brain: nonlinear subcortical aging

Abstract

Age-related changes in brain structure result from a complex interplay among various neurobiological processes, which may contribute to more complex trajectories than what can be described by simple linear or quadratic models. We used a nonparametric smoothing spline approach to delineate cross-sectionally estimated age trajectories of the volume of 17 neuroanatomic structures in 1100 healthy adults (18-94 years). Accelerated estimated decline in advanced age characterized some structures, for example hippocampus, but was not the norm. For most areas, 1 or 2 critical ages were identified, characterized by changes in the estimated rate of change. One-year follow-up data from 142 healthy older adults (60-91 years) confirmed the existence of estimated change from the cross-sectional analyses for all areas except 1 (caudate). The cross-sectional and the longitudinal analyses agreed well on the rank order of age effects on specific brain structures (Spearman ρ = 0.91). The main conclusions are that most brain structures do not follow a simple path throughout adult life and that accelerated decline in high age is not the norm of healthy brain aging.

Keywords: Aging; Amygdala; Atrophy; Cerebral cortex; Hippocampus; Longitudinal; Magnetic resonance imaging; Thalamus; Trajectory; White matter.

Figure 1. Scatterplots of age-brain structure relationships

The figure shows the individual data points and the cross-sectionally estimated trajectories for the 13 brain structures of interest based on the smoothing spline. Y-axis values represent mean volume across hemispheres, corrected for the influence of sample and intracranial volume (Z-scores). The right bottom figure shows some of the segmented structures of the average brain of Sample 2. The three-dimensional renderings illustrate the average shape, extension and relative position within the brain. The cerebral cortex and underlying white matter are made transparent to allow visualization of the underlying subcortical structures.

Figure 2. Scatterplots of age - ventricular system relationships

The figure shows the individual data points and the cross-sectionally estimated trajectories for the ventricles based on the smoothing spline. Y-axis values represent mean volume across hemispheres, corrected for the influence of sample and intracranial volume (Z-scores).

Figure 3. Estimated age-trajectories and critical ages

The figure shows the estimated age-trajectories from the cross-sectional analyses for the 12 areas that deviated from linearity, based on the smoothing spline. Critical ages, identified by changes in the second derivative, are displayed. Pearson correlations between brain volume and age are shown for each age phase separated by the critical ages. All correlations were significant at p < .05, except for pallidum in middle age (.01), cerebellum WM in young age (−.02) and brain stem in young age (.01). Due to small age-variance, correlations are not presented for age-phases defined by critical ages of 80 years or higher. Critical ages are indicative of phases where estimated changes in brain volumes are in transitions.

Figure 4. A hypothetic model for discontinuous change in rate of atrophy

The figure represents a simplified attempt to visualize how two sets of age-dependent degenerative effects can affect the age-trajectory of a brain volume, and how timing of critical ages reflect the start and endpoint of these effects. The blue line represents the volume of a brain structure through life, e.g. WM volume. In the first part of life, volume increases, caused by the sum of progressive events, e.g. myelination and axonal growth (green line). Before the maturational changes caused by the progressive events have come to an end, degenerative events starts, e.g. selective loss of small-diameter myelinated axons (primary degenerative event) and demyelination of larger connections (secondary degenerative event). The onset of these processes will affect the growth rate of the curve, detected as a change in the second derivative, and this change can be termed early critical age. After this point, the volume increase slowly decelerates. After continuous impact on the volume from these two processes, one of them eventually burns out in higher age while the other continues further. This causes a late critical age, where the volume reductions slowly starts to level off. This is of course a gross simplification of the processes in the brain and the trajectories that may characterize them. The main point is to illustrate that critical ages may be used in the characterisation of estimated age-trajectories of brain volumes, and that they may be related to underlying neurobiological events, both developmental and degenerative.