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

2020-11

MacDonald, M Ethan

Williams, Rebecca J

Rajashekar, Deepthi

Stafford, Randall B

Hanganu, Alexandru

Sun, Hongfu

Berman, Avery J L

McCreary, Cheryl R

Frayne, Richard

Forkert, Nils D

Pike, G Bruce

Journal Article

en

Publication

Elsevier Inc

United State of America

10.1016/j.neurobiolaging.2020.06.019

une:1959.11/64386

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 lifespan. 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.

link

Neurobiology of Aging, v.95, p. 131-142

1558-1497

0197-4580

131

142