Biological Brain Age Prediction Using Cortical Thickness Data: A Large Scale Cohort Study

Abstract

Brain age estimation from anatomical features has been attracting more attention in recent years. This interest in brain age estimation is motivated by the importance of biological age prediction in health informatics, with an application to early prediction of neurocognitive disorders. It is well-known that normal brain aging follows a specific pattern, which enables researchers and practitioners to predict the age of a human's brain from its degeneration. In this paper, we model brain age predicted by cortical thickness data gathered from large cohort brain images. We collected 2,911 cognitively normal subjects (age 45-91 years) at a single medical center and acquired their brain magnetic resonance (MR) images. All images were acquired using the same scanner with the same protocol. We propose to first apply Sparse Group Lasso (SGL) for feature selection by utilizing the brain's anatomical grouping. Once the features are selected, a non-parametric non-linear regression using the Gaussian Process Regression (GPR) algorithm is applied to fit the final age prediction model. Experimental results demonstrate that the proposed method achieves the mean absolute error of 4.05 years, which is comparable with or superior to several recent methods. Our method can also be a critical tool for clinicians to differentiate patients with neurodegenerative brain disease by extracting a cortical thinning pattern associated with normal aging.

Keywords: Gaussian process; ROI; Sparse Group Lasso; aging; cortical lobe; cortical thickness; regression analysis.

Figure 1

Overview of the proposed method.

Figure 2

Outlier filtering approach: first, the mean cortical thickness values extracted from brain images are partitioned based on eight age interval groups and LOF algorithm is applied on each age group interval separately to compute outlier scores. The histograms illustrate the distribution of resulting outlier scores. Outliers correspond to the highest values, which are skewed to the right.

Figure 3

Number of samples and identified outliers per age group interval.

Figure 4

Chronological age vs. predicted age using the SGL + GPR model.

Figure 5

Visualization of the features significantly contributing to age estimation: the warm and cold color represent important features in predicting brain age using cortical thinning patterns.