Brain aging mechanisms with mechanical manifestations

Mech Ageing Dev. 2021 Dec:200:111575. doi: 10.1016/j.mad.2021.111575. Epub 2021 Oct 1.

Abstract

Brain aging is a complex process that affects everything from the subcellular to the organ level, begins early in life, and accelerates with age. Morphologically, brain aging is primarily characterized by brain volume loss, cortical thinning, white matter degradation, loss of gyrification, and ventricular enlargement. Pathophysiologically, brain aging is associated with neuron cell shrinking, dendritic degeneration, demyelination, small vessel disease, metabolic slowing, microglial activation, and the formation of white matter lesions. In recent years, the mechanics community has demonstrated increasing interest in modeling the brain's (bio)mechanical behavior and uses constitutive modeling to predict shape changes of anatomically accurate finite element brain models in health and disease. Here, we pursue two objectives. First, we review existing imaging-based data on white and gray matter atrophy rates and organ-level aging patterns. This data is required to calibrate and validate constitutive brain models. Second, we review the most critical cell- and tissue-level aging mechanisms that drive white and gray matter changes. We focuse on aging mechanisms that ultimately manifest as organ-level shape changes based on the idea that the integration of imaging and mechanical modeling may help identify the tipping point when normal aging ends and pathological neurodegeneration begins.

Keywords: Brain aging mechanisms; Cerebral atrophy; Gray and white matter changes; Morphological changes; Vascular changes.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Review

MeSH terms

  • Aging* / metabolism
  • Aging* / pathology
  • Atrophy
  • Brain* / diagnostic imaging
  • Brain* / metabolism
  • Brain* / pathology
  • Cellular Senescence / physiology*
  • Functional Neuroimaging / methods
  • Functional Neuroimaging / trends
  • Humans
  • Models, Biological