Analysis of Alzheimer's Disease Based on the Random Neural Network Cluster in fMRI.
Analysis of Alzheimer's Disease Based on the Random Neural Network Cluster in fMRI.
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DOI：
10.3389/fninf.2018.00060
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发表时间：
2018
影响因子：
3.5
通讯作者：
Liu Y
中科院分区：
文献类型：
--
作者：
Bi XA;Jiang Q;Sun Q;Shu Q;Liu Y
由于阿尔茨海默病（AD）具有退行性和不可逆性，因此AD的早期诊断非常重要。近年来，一些研究人员尝试应用神经网络（NN）根据功能性磁共振成像（fMRI）数据对AD患者与健康对照（HC）进行分类。但大多数研究都集中在单个神经网络上，分类精度不高。因此，本文采用由多个神经网络组成的随机神经网络簇来提高分类性能。 61 名受试者（25 名 AD 和 36 HC）来自阿尔茨海默病神经影像倡议 (ADNI) 数据集。该方法不仅可以用于分类，还可以用于特征选择。首先，根据特征选择的结果，从五种神经网络中选择Elman神经网络作为随机神经网络簇的最优基分类器，随机Elman神经网络簇的准确率最高且稳定，可达92.31%。然后我们使用随机Elman神经网络簇来选择显着特征，这些特征可以用来找出异常区域。最后，我们发现了中央前回、额回、辅助运动区等23个异常区域。这些结果充分表明随机神经网络簇对于AD的诊断是有价值且有意义的。
As Alzheimer’s disease (AD) is featured with degeneration and irreversibility, the diagnosis of AD at early stage is important. In recent years, some researchers have tried to apply neural network (NN) to classify AD patients from healthy controls (HC) based on functional MRI (fMRI) data. But most study focus on a single NN and the classification accuracy was not high. Therefore, this paper used the random neural network cluster which was composed of multiple NNs to improve classification performance. Sixty one subjects (25 AD and 36 HC) were acquired from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. This method not only could be used in the classification, but also could be used for feature selection. Firstly, we chose Elman NN from five types of NNs as the optimal base classifier of random neural network cluster based on the results of feature selection, and the accuracies of the random Elman neural network cluster could reach to 92.31% which was the highest and stable. Then we used the random Elman neural network cluster to select significant features and these features could be used to find out the abnormal regions. Finally, we found out 23 abnormal regions such as the precentral gyrus, the frontal gyrus and supplementary motor area. These results fully show that the random neural network cluster is worthwhile and meaningful for the diagnosis of AD.
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影响因子：
5.7
作者：
Jenkinson, M;Bannister, P;Smith, S
通讯作者：
Smith, S
影响因子：
2.3
作者：
Griffanti, Ludovica;Wilcock, Gordon K.;Zamboni, Giovanna
通讯作者：
Zamboni, Giovanna
影响因子：
2.7
作者：
Hopkins WD;Meguerditchian A;Coulon O;Misiura M;Pope S;Mareno MC;Schapiro SJ
通讯作者：
Schapiro SJ
影响因子：
14.5
作者：
Grossman, M;Koenig, P;Gee, J
通讯作者：
Gee, J
DOI：
10.1073/pnas.0308627101
发表时间：
2004-03-30
影响因子：
11.1
作者：
Greicius, MD;Srivastava, G;Menon, V
通讯作者：
Menon, V