Statistical estimation of physiological brain age as a descriptor of senescence rate during adulthood.

Mapping aging-related brain structure and connectivity changes can be helpful for assessing physiological brain age (PBA), which is distinct from chronological age (CA) because genetic and environmental factors affect individuals differently. This study proposes an approach whereby structural and connectomic information can be combined to estimate PBA as an early biomarker of brain aging. In a cohort of 136 healthy adults, magnetic resonance and diffusion tensor imaging are respectively used to measure cortical thickness over the entire cortical mantle as well as connectivity properties (mean connectivity density and mean fractional anisotropy) for white matter connections. Using multivariate regression, these measurements are then employed to (1) illustrate how CA can be predicated--and thereby also how PBA can be estimated--and to conclude that (2) healthy aging is associated with significant connectome changes during adulthood. Our study illustrates a connectomically-informed statistical approach to PBA estimation, with potential applicability to the clinical identification of patients who exhibit accelerated brain aging, and who are consequently at higher risk for developing mild cognitive impairment or dementia.