[1]LU Zixiang,TU Liyang,ZU Chen,et al.Prediction of clinical variables in Alzheimer’s disease using brain connective networks[J].CAAI Transactions on Intelligent Systems,2017,12(3):355-361.[doi:10.11992/tis.201607020]
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Prediction of clinical variables in Alzheimer’s disease using brain connective networks
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
12
Number of periods:
2017 3
Page number:
355-361
Column:
学术论文—脑认知基础
Public date:
2017-06-25
- Title:
- Prediction of clinical variables in Alzheimer’s disease using brain connective networks
- Keywords:
- brain function; feature selection; feature extraction; feature fusion; network analysis; regression analysis; Alzheimer’s disease; medical image
- CLC:
- TP181
- DOI:
- 10.11992/tis.201607020
- Abstract:
- Brain functional connectivity networks have been widely used for diagnosing brain diseases. However, a traditional brain network based on classification methods cannot assess the stage or predict the development of the disease. Recent studies show that the values of the clinical variables of brain disease can effectively help doctors evaluate the disease. In this study, a novel brain-connectivity-network-based method was proposed for estimating the values of the clinical variables of Alzheimer’s disease. First, the functional connectivity network was extracted from the brain images. Then, LASSO , which is a regression analysis method, was adopted for feature selection and elimination of redundant features; the clustering coefficients and edge weights of the network were fused as features. Finally, support vector machine regression was used to predict the values of the clinical variables. The proposed method was validated on the ADNI dataset, and the experimental results demonstrate that the proposed method can accurately predict the values of clinical variables and verify the effectiveness of the fusion of multiple features.
- References:
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Last Update:
2017-06-25