Evidence map›Paper›PMID 28438167›Full record

ArticleBiomedical engineering online2017

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

Yongming Li, Yuchuan Liu, Pin Wang, Jie Wang, Sha Xu, Mingguo Qiu, Alzheimer’s Disease Neuroimaging Initiative (ADNI)

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Article in Biomedical engineering online, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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  6. [A review on brain age prediction in brain ageing].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2019
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Yongming LiCollege of Communication Engineering, Chongqing University, Shapingba District, Chongqing, 400044, China. yongmingli@cqu.edu.cn.ORCID http://orcid.org/0000-0002-7542-4356
Yuchuan LiuCollege of Communication Engineering, Chongqing University, Shapingba District, Chongqing, 400044, China.
Pin WangCollege of Communication Engineering, Chongqing University, Shapingba District, Chongqing, 400044, China.
Jie WangCollege of Communication Engineering, Chongqing University, Shapingba District, Chongqing, 400044, China.
Sha XuCollege of Communication Engineering, Chongqing University, Shapingba District, Chongqing, 400044, China.
Mingguo QiuDepartment of Medical Image, College of Biomedical Engineering, Third Military Medical University, Chongqing, 400038, China.
Alzheimer’s Disease Neuroimaging Initiative (ADNI)

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AgedAged, 80 and overAgingAlgorithmsAlzheimer DiseaseBrainDisease ProgressionFemaleHumansImage Interpretation, Computer-AssistedMagnetic Resonance ImagingMaleReproducibility of ResultsSensitivity and SpecificitySeverity of Illness IndexAlzheimer’s diseaseBrain age estimationBrain pathological ageClassificationCorrelation criterionMagnetic resonance imagingSupport vector regression

Identifiers

PMID28438167
PMCPMC5404315

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.