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.2021 Mar 17:15:651920.
doi: 10.3389/fnins.2021.651920. eCollection 2021.

Quantification of Cognitive Function in Alzheimer's Disease Based on Deep Learning

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Quantification of Cognitive Function in Alzheimer's Disease Based on Deep Learning

Yanxian He et al. Front Neurosci..

Abstract

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.

Keywords: Alzheimer’s disease; channel pruning; convolutional neural network; deep separable convolution; quantification of cognitive function.

Copyright © 2021 He, Wu, Zhou, Chen, Li and Qian.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
The original image preprocessing process of Alzheimer’s disease.
FIGURE 2
FIGURE 2
Schematic diagram of brain function connection network acquisition.
FIGURE 3
FIGURE 3
Convolutional neural network structure.
FIGURE 4
FIGURE 4
Comparison of ordinary convolution, spatial convolution and channel convolution.
FIGURE 5
FIGURE 5
The overall process of channel pruning.
FIGURE 6
FIGURE 6
Age characteristics of Alzheimer’s disease of MRI subjects.
FIGURE 7
FIGURE 7
Age characteristics of Alzheimer’s disease of PET subjects.
FIGURE 8
FIGURE 8
Comparison of parallel 3D-CNNs integration method and other methods.
FIGURE 9
FIGURE 9
Comparison of AD vs. NC accuracy of 3D image blocks at various positions before and after multi-modal fusion.
FIGURE 10
FIGURE 10
Performance comparison of single-mode and multi-mode under three quantitative tasks.
FIGURE 11
FIGURE 11
AD vs. NC fusion quantified ROC curve.
FIGURE 12
FIGURE 12
ROC curve of p MCI vs. NC fusion quantification.
FIGURE 13
FIGURE 13
s MCI vs. NC fusion quantified ROC curve.
FIGURE 14
FIGURE 14
Performance comparison of five multi-modal fusion methods.
FIGURE 15
FIGURE 15
Alzheimer’s disease area that the neural network focuses on.
FIGURE 16
FIGURE 16
Comparison of the quantification accuracy of the multi-modal cascaded 3D-CNNs proposed in this article and other methods.
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References

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