Abstract
ObjectivesTraditional brain age estimation methods are based on the 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 the accelerated brain aging.MethodsThis paper considers this deviation and obtains it by maximizing the correlation between the estimated brain age and the class label 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 the prior knowledge. Secondly, use the support vector regression 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 correlation 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.ResultsThe experimental results showed that the separability of the samples was apparently improved. For normal control- Alzheimer’s disease (NC-AD), normal control- mild cognition impairment (NC-MCI), and mild cognition impairment—Alzheimer’s disease (MCI-AD), the average improvements were 0.164 (31.66%), 0.1284 (34.29%), and 0.0206 (7.1%), respectively. For NC-MCI-AD, the average improvement was 0.2002 (50.39%). The estimated brain pathological age could be not only more helpful for the classification of AD but also more precisely reflect the accelerated brain aging.ConclusionIn conclusion, this paper proposes a new kind of brain age—brain pathological age and offers an estimation method for it that can distinguish different states of AD, thereby better reflecting accelerated brain aging. Besides, the brain pathological age is most helpful for feature reduction, thereby simplifying the relevant classification algorithm.
Highlights
Alzheimer’s disease (AD) is a common neurodegenerative disease
Estimation of brain pathological age Study of kernel function of support vector regression (SVR) For the two classes of samples, the range of age deviation is set according to prior knowledge
Real age has been proven to be related to the classification of AD, but it has poor and unsatisfactory classification capability
Summary
Alzheimer’s disease (AD) is a common neurodegenerative disease. The key for prevention and treatment is early diagnosis [1]. Magnetic resonance imaging (MRI) is a medical imaging technique used in radiology to visualize the anatomy and the physiological processes of the body in both healthy and disease states. It is noninvasive, nonradioactive, and highly cost-effective, and it can reflect changes in anatomical structures and functions in different biological tissue quantitatively, so it has been applied in the early diagnosis of AD with positive results [2, 3]. Research has obtained positive results, the classification accuracy, stability and the number of biomarkers are still not sufficient for clinical applications
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