Age-related differences in cerebral blood flow and cortical thickness with an application to age prediction

Abstract

Cerebral cortex thinning and cerebral blood flow (CBF) reduction are typically observed during normal healthy aging. However, imaging-based age prediction models have primarily used morphological features of the brain. Complementary physiological CBF information might result in an improvement in age estimation. In this study, T1-weighted structural magnetic resonance imaging and arterial spin labeling CBF images were acquired in 146 healthy participants across the adult life span. Sixty-eight cerebral cortex regions were segmented, and the cortical thickness and mean CBF were computed for each region. Linear regression with age was computed for each region and data type, and laterality and correlation matrices were computed. Sixteen predictive models were trained with the cortical thickness and CBF data alone as well as a combination of both data types. The age explained more variance in the cortical thickness data (average R² of 0.21) than in the CBF data (average R² of 0.09). All 16 models performed significantly better when combining both measurement types and using feature selection, and thus, we conclude that the inclusion of CBF data marginally improves age estimation.

Keywords: Aging; Cerebral blood flow; Cerebral cortical thickness; Magnetic resonance imaging.