Research Paper Volume 12, Issue 6 pp 4822—4835
Prediction of cognitive performance in old age from spatial probability maps of white matter lesions
- 1 School of Biomedical Engineering, Capital Medical University, Beijing, China
- 2 Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
received: December 6, 2019 ; accepted: February 5, 2020 ; published: March 19, 2020 ;https://doi.org/10.18632/aging.102901
How to Cite
Copyright © 2020 Zhao et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
The purposes of this study were to explore the association between cognitive performance and white matter lesions (WMLs), and to investigate whether it is possible to predict cognitive impairment using spatial maps of WMLs. These WML maps were produced for 263 elders from the OASIS-3 dataset, and a relevance vector regression (RVR) model was applied to predict neuropsychological performance based on the maps. The association between the spatial distribution of WMLs and cognitive function was examined using diffusion tensor imaging data. WML burden significantly associated with increasing age (r=0.318, p<0.001) and cognitive decline. Eight of 15 neuropsychological measures could be accurately predicted, and the mini-mental state examination (MMSE) test achieved the highest predictive accuracy (CORR=0.28, p<0.003). WMLs located in bilateral tapetum, posterior corona radiata, and thalamic radiation contributed the most prediction power. Diffusion indexes in these regions associated significantly with cognitive performance (axial diffusivity>radial diffusivity>mean diffusivity>fractional anisotropy). These results show that the combination of the extent and location of WMLs exhibit great potential to serve as a generalizable marker of multidomain neurocognitive decline in the aging population. The results may also shed light on the mechanism underlying white matter changes during the progression of cognitive decline and aging.
WMLs: white matter lesions; RVR: relevance vector regression; DTI: diffusion tensor imaging; MMSE: mini-mental state examination; WMH: white matter hyperintensities; FLAIR: fluid-attenuated inverse recovery; MCI: mild cognitive impairment; OASIS-3: Open access series of imaging studies-3; CDR: clinical dementia rating; BIANCA: brain intensity abnormality classification algorithm; CORR: correlation coefficient; norm MSE: normalized mean square error; FA: fractional anisotropy; RD: radial diffusivity; AD: axial diffusivity; MD: mean diffusivity; APOE: apolipoprotein E; PET: positron emission tomography; WAIS: Wechsler adult intelligence scale; BOSTON: Boston naming tests; ER: expected ranking.