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

Abstract

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.

Keywords: Connectome; Cortical thickness; Healthy aging; Mild cognitive impairment; Multivariate regression; Physiological brain age.

Fig. 1

(A) Results of the statistical analysis to determine the extent to which all three feature variables (cortical thickness, CD and mean FA) can predict chronological age. For each cortical location, the F statistic with 3 and 132 d.f. is displayed for the omnibus test of the null hypothesis that none of the three independent variables predicts subject age. Here and throughout, the displayed values of the test statistic are thresholded for significance using FDR < 0.05. (B) Result of testing the null hypothesis that cortical thickness does not contribute to the regression model above and beyond all other predictor variables. The test statistic is Student’s t with 132 d.f. Some areas are colored in red, corresponding to t > 0, whereas others are colored in blue, indicating that t < 0. For areas colored in red, as cortical thickness increases, so does age. For areas colored in blue, as thickness decreases, age increases.

Fig. 2

CA (in years) as a function of cortical thickness (in mm) at 12 locations where thickness contributes to the regression above and beyond all other predictor variables (see text). Each dot represents a subject and ellipses are drawn to indicate the 95% confidence regions for thickness. The linear relationship between CA and cortical thickness within the CA range displayed (18.6 to 61.1 years) is apparent. Outliers (dots located outside the confidence interval) indicate the presence of subjects with atypical cortical thickness values given their CAs. Note the decrease in thickness at all highlighted locations, with the exception of two locations (bottom row, middle) where cortical thickening as a function of age is apparent.

Fig. 3

(A) Results of testing the null hypothesis that neither of the connectomic variables predicts age above and beyond the ability of cortical thickness to do so. As in Figure 1A, the test statistic is the F statistic with 2 and 133 d.f. Color-coded arrows indicate prominent regions where the null hypothesis is rejected, namely the paracentral lobule of the right hemisphere (magenta), the anterior bank of the central sulcus in the left hemisphere (green), the banks of the parieto-occipital sulci (bilaterally, black), and the antero-medial aspects of the superior frontal gyri (bilaterally, cyan). (B) Results of testing the null hypothesis that CD alone cannot predict age above and beyond the ability of cortical thickness to predict it. As in Figure 1B, the test statistic is Student’s t with 133 d.f. (C) As in (B), for mean FA.

Fig. 4

Residual CA (in years) as a function of CD (scaled by 10⁶ for convenience) at 12 locations where CD contributes to the regression above and beyond cortical thickness (see text). Each dot represents a subject; ellipses are drawn to indicate the 95% confidence regions for CD. Note that (1) the residual CA can be either positive or negative, depending on whether thickness alone over- or underestimates CA, and that (2) there are outliers much farther outside the confidence region than in the case of cortical thickness (Figure 2) or mean FA (Figure 5).

Fig. 5

As in Fig. 4, but where residual CA is plotted as a function of mean FA rather than CD. Note that there are both decreases in mean FA as a function of CA (Student’s t < 0, left and right columns), as well as increases (Student’s t > 0, middle, top and bottom).