Dependency criterion based brain pathological age estimation of Alzheimer's disease patients with MR scans.
Objectives: Traditional 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.Methods: This paper considers this deviation and...
| Publicado en: | BioMedical Engineering OnLine Vol. 16; pp. 1 - 21 |
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| Autores principales: | , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
4/24/2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=122739204&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 122739204 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1475925X 1CGX jtl: BioMedical Engineering OnLine issn: 1475925X maglogo: N pubinfo: dt: 4/24/2017 vid: 16 pid: 24147 pub: BioMed Central artinfo: ui: 122739204 122739204 NLM28438167 122739204 10.1186/s12938-017-0342-y NLM28438167 122739204 ppf: 1 ppct: 20 formats: tig: atl: Dependency criterion based brain pathological age estimation of Alzheimer's disease patients with MR scans. aug: au: Yongming Li Yuchuan Liu Pin Wang Jie Wang Sha Xu Mingguo Qiu Li, Yongming Liu, Yuchuan Wang, Pin Wang, Jie Xu, Sha Qiu, Mingguo affil: College of Communication Engineering, Chongqing University, Shapingba District, Chongqing 400044, China sug: subj: Brain Image Interpretation, Computer Assisted Methods Brain Pathology Alzheimer's Disease Aging Alzheimer's Disease Pathology Magnetic Resonance Imaging Methods Algorithms Aged Disease Progression Reproducibility of Results Aged, 80 and Over Severity of Illness Indices Human Male Sensitivity and Specificity Female Validation Studies Comparative Studies Evaluation Research Multicenter Studies Aged: 65+ years Aged, 80 & over Male Female ab: Objectives: Traditional 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.Methods: This 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.Results: The 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.Conclusion: In 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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