title: Estimating the brain pathological age of Alzheimer's disease patients from MR image data based on the separability distance criterion.
abstract: Traditional age estimation methods are based on the same idea that uses the real age as the training label. However, these methods ignore that there is a deviation between the real age and the brain age due to accelerated brain aging. This paper considers this deviation and searches for it by maximizing the separability distance value rather than by minimizing the difference between the estimated brain age and the real age. Firstly, set the search range of the deviation as the deviation candidates according to prior knowledge. Secondly, use the support vector regression (SVR) as the age estimation model to minimize the difference between the estimated age and the real age plus deviation rather than the real age itself. Thirdly, design the fitness function based on the separability distance criterion. Fourthly, conduct age estimation on the validation dataset using the trained age estimation model, put the estimated age into the fitness function, and obtain the fitness value of the deviation candidate. Fifthly, repeat the iteration until all the deviation candidates are involved and get the optimal deviation with maximum fitness values. The real age plus the optimal deviation is taken as the brain pathological age. The experimental results showed that the separability was apparently improved. For normal control-Alzheimer's disease (NC-AD), normal control-mild cognition impairment (NC-MCI), and MCI-AD, the average improvements were 0.178 (35.11%), 0.033 (14.47%), and 0.017 (39.53%), respectively. For NC-MCI-AD, the average improvement was 0.2287 (64.22%). The estimated brain pathological age could be not only more helpful to the classification of AD but also more precisely reflect accelerated brain aging. In conclusion, this paper offers a new method for brain age estimation that can distinguish different states of AD and can better reflect the extent of accelerated aging.
authors.affiliation: College of Communication Engineering, Chongqing University, Chongqing 400044, People's Republic of China. Department of Medical Image, College of Biomedical Engineering, Third Military Medical University, Chongqing 400038, People's Republic of China.
authors.forename: Yongming
authors.initials: Y
authors.lastname: Li
authors.forename: Fan
authors.initials: F
authors.lastname: Li
authors.forename: Pin
authors.initials: P
authors.lastname: Wang
authors.forename: Xueru
authors.initials: X
authors.lastname: Zhu
authors.forename: Shujun
authors.initials: S
authors.lastname: Liu
authors.forename: Mingguo
authors.initials: M
authors.lastname: Qiu
authors.forename: Jingna
authors.initials: J
authors.lastname: Zhang
authors.forename: Xiaoping
authors.initials: X
authors.lastname: Zeng
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000375:Aging / Q000473:pathology*; D000465:Algorithms; D000544:Alzheimer Disease / Q000473:pathology*; D001921:Brain / Q000473:pathology*; D003072:Cognition Disorders; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male