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Review
. 2021 Jul 13:2021:9523039.
doi: 10.1155/2021/9523039. eCollection 2021.

Single and Combined Neuroimaging Techniques for Alzheimer's Disease Detection

Morteza Amini  1 , Mir Mohsen Pedram  2   3 , Alireza Moradi  4   5 , Mahdieh Jamshidi  6 , Mahshad Ouchani  7
Affiliations
Review

Single and Combined Neuroimaging Techniques for Alzheimer's Disease Detection

Morteza Amini et al. Comput Intell Neurosci. .

Abstract

Alzheimer's disease (AD) consists of the gradual process of decreasing volume and quality of neuron connection in the brain, which consists of gradual synaptic integrity and loss of cognitive functions. In recent years, there has been significant attention in AD classification and early detection with machine learning algorithms. There are different neuroimaging techniques for capturing data and using it for the classification task. Input data as images will help machine learning models to detect different biomarkers for AD classification. This marker has a more critical role for AD detection than other diseases because beta-amyloid can extract complex structures with some metal ions. Most researchers have focused on using 3D and 4D convolutional neural networks for AD classification due to reasonable amounts of data. Also, combination neuroimaging techniques like functional magnetic resonance imaging and positron emission tomography for AD detection have recently gathered much attention. However, gathering a combination of data can be expensive, complex, and tedious. For time consumption reasons, most patients prefer to throw one of the neuroimaging techniques. So, in this review article, we have surveyed different research studies with various neuroimaging techniques and ML methods to see the effect of using combined data as input. The result has shown that the use of the combination method would increase the accuracy of AD detection. Also, according to the sensitivity metrics from different machine learning methods, MRI and fMRI showed promising results.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Example of magnetic resonance imaging (MRI).
Figure 2
Figure 2
Example of functional magnetic resonance imaging (fMRI) in different stages. (a) Slice number: 0. (b) Slice number: 10. (c) Slice number: 20. (d) Slice number: 30.
Figure 3
Figure 3
Example of fMRI in different stages.
Figure 4
Figure 4
Example of rs-fMRI in different stages. (a) Slice number: 0. (b) Slice number: 10. (c) Slice number: 20. (d) Slice number: 30.
Figure 5
Figure 5
Example of PET in different stages. (a) Slice number: 0. (b) Slice number: 20. (c) Slice number: 40. (d) Slice number: 60. (e) Slice number: 80. (f) Slice number: 100. (g) Slice number: 120. (h) Slice number: 140. (i) Slice number: 160. (j) Slice number: 180.
Figure 6
Figure 6
Combination of PET with rs-fMRI pictures.
Figure 7
Figure 7
Combination of PET with rs-fMRI pictures in the processing of data preprocessing.
Figure 8
Figure 8
Diagram of feature selection and PCA.
Figure 9
Figure 9
Semisupervised learning method for binary AD classification.
Figure 10
Figure 10
Semisupervised GAN learning method for binary AD classification.
Figure 11
Figure 11
Structure of CNN.
Figure 12
Figure 12
Result of convolutional layer on input image for (a) first convolutional layer, (b) second convolutional layer, and (c) last convolutional layer.
Figure 13
Figure 13
Structure of (a) RNN versus (b) LSTM.
Figure 14
Figure 14
Different accuracy based on different years.
See this image and copyright information in PMC

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