Quantification of Cognitive Function in Alzheimer's Disease Based on Deep Learning.
Quantification of Cognitive Function in Alzheimer's Disease Based on Deep Learning.
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DOI：
10.3389/fnins.2021.651920
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发表时间：
2021
影响因子：
4.3
通讯作者：
Qian H
中科院分区：
文献类型：
--
作者：
He Y;Wu J;Zhou L;Chen Y;Li F;Qian H
阿尔茨海默病（Alzheimer disease， AD）主要表现为起病隐匿、慢性进行性认知能力下降和非认知性神经精神症状，严重影响老年人的生活质量，给社会和家庭造成很大负担。利用图论对构建的脑网络进行分析，提取两种模态脑网络的节点度、节点效率和节点间中心性参数。采用T检验方法分析正常人与AD患者图论参数的差异，选择图论参数差异显著的脑区作为脑网络特征。通过分析常规卷积层和深度可分卷积单元的计算原理，比较了它们的计算复杂度。深度可分卷积单元将传统的卷积过程分解为用于特征提取的空间卷积和用于特征组合的点卷积，大大减少了卷积过程中乘法和加法运算的次数，同时仍然能够获得比较。针对深度可分卷积单元特殊的卷积结构，提出了一种基于卷积结构的信道剪枝方法，并对其剪枝过程进行了说明。多模态神经影像学可以为阿尔茨海默病的量化提供完整的信息。本文提出了一种基于单模态和多模态图像的级联三维神经网络框架，利用MRI和PET图像从正常样本中区分AD和MCI。使用多个三维CNN网络在局部图像块中提取可识别信息。高层次二维CNN网络融合多模态特征，选择判别区域的特征对样本进行定量预测。本文提出的算法可以逐层自动提取和融合多模态、多区域的特征，可视化分析结果表明，受阿尔茨海默病影响的异常变化区域为临床量化提供了重要信息。
Alzheimer disease (AD) is mainly manifested as insidious onset, chronic progressive cognitive decline and non-cognitive neuropsychiatric symptoms, which seriously affects the quality of life of the elderly and causes a very large burden on society and families. This paper uses graph theory to analyze the constructed brain network, and extracts the node degree, node efficiency, and node betweenness centrality parameters of the two modal brain networks. The T test method is used to analyze the difference of graph theory parameters between normal people and AD patients, and brain regions with significant differences in graph theory parameters are selected as brain network features. By analyzing the calculation principles of the conventional convolutional layer and the depth separable convolution unit, the computational complexity of them is compared. The depth separable convolution unit decomposes the traditional convolution process into spatial convolution for feature extraction and point convolution for feature combination, which greatly reduces the number of multiplication and addition operations in the convolution process, while still being able to obtain comparisons. Aiming at the special convolution structure of the depth separable convolution unit, this paper proposes a channel pruning method based on the convolution structure and explains its pruning process. Multimodal neuroimaging can provide complete information for the quantification of Alzheimer’s disease. This paper proposes a cascaded three-dimensional neural network framework based on single-modal and multi-modal images, using MRI and PET images to distinguish AD and MCI from normal samples. Multiple three-dimensional CNN networks are used to extract recognizable information in local image blocks. The high-level two-dimensional CNN network fuses multi-modal features and selects the features of discriminative regions to perform quantitative predictions on samples. The algorithm proposed in this paper can automatically extract and fuse the features of multi-modality and multi-regions layer by layer, and the visual analysis results show that the abnormally changed regions affected by Alzheimer’s disease provide important information for clinical quantification.
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影响因子：
4.6
作者：
Lu D;Popuri K;Ding GW;Balachandar R;Beg MF;Alzheimer’s Disease Neuroimaging Initiative
通讯作者：
Alzheimer’s Disease Neuroimaging Initiative
使用多模态堆叠深度多项式网络进行多模态神经影像特征学习以诊断阿尔茨海默病
DOI：
10.1109/jbhi.2017.2655720
发表时间：
2018-01-01
影响因子：
7.7
作者：
Shi, Jun;Zheng, Xiao;Ying, Shihui
通讯作者：
Ying, Shihui
影响因子：
4.6
作者：
Lee, Garam;Nho, Kwangsik;Fargher, Kristin
通讯作者：
Fargher, Kristin
影响因子：
6.1
作者：
Nguyen Quoc Khanh Le;Tuan-Tu Huynh;Yeh, Hui-Yuan
通讯作者：
Yeh, Hui-Yuan
影响因子：
3.9
作者：
Jain, Rachna;Jain, Nikita;Hemanth, D. Jude
通讯作者：
Hemanth, D. Jude