Dependency criterion based brain pathological age estimation of Alzheimer’s disease patients with MR scans

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.

Retinal Aging in 3× Tg-AD Mice Model of Alzheimer's Disease

“…This appears to contradict what has been observed for AD, where some age-related changes such as, neural tissue thinning, neural activity and functional connectivity impairments were shown to be more pronounced in AD (Fox and Schott, 2004 ; Sperling, 2007 ; Toepper, 2017 ; Chiquita et al, 2019 ). It also appears to contradict findings in Li et al ( 2017 ) and Löwe et al ( 2016 ), where AD patients presented a positive brain-age gap (greater brain aging), and findings in Gaser et al ( 2013 ), where brain-predicted age was a significant predictor of dementia progression within 3 years of baseline MRI scan.…”

CNN-derived brain age gaps of different neurological and cardiovascular diseases in the UK Biobank and identifying affected brain regions

“…Previous research has demonstrated that the brain age gap is not only valuable to obtain a general estimate of brain health in healthy subjects but also to identify subjects at risk for mental or neurological diseases. Specifically, previous research has shown that subjects with schizophrenia, 3 Down syndrome, 4 depression, 3 and Alzheimer's disease 2,[5][6][7] show increased brain age gaps, suggestive of accelerated aging. Furthermore, recent research suggests that cardiovascular risk factors are also associated with elevated positive brain age gaps, 8 reinforcing the concept of a reciprocal heart-brain axis 9 wherein cardiac conditions impact the brain and vice versa.…”

Hierarchical Rough-to-Fine Model for Infant Age Prediction Based on Cortical Features

“…Various age ranges of subjects have been used for studying age prediction. For example, the subjects of 3–20 years of age were chosen for assessment of biological maturity based on age prediction [12]; the study of the advanced BrainAGE in patients with type 2 diabetes mellitus was conducted based on the subjects of 20–86 years of age [13]; the subjects of 21–65 years of age were chosen for studying the brain ageing in schizophrenia [9]; the age range of 65–85 years was used for the discussion of the dependence between brain age and Alzheimer’s disease [14]. Nonetheless, little research has been done on the age prediction of infants before 2 years of age.…”