Classification of Alzheimer’s Disease Based on White Matter Connectivity Network
Abstract
:1. Introduction
- (1)
- Calculate the DTI index of the WM connectivity between the brain regions of the whole brain and construct the FA connectivity network as features.
- (2)
- Combine the statistical test and recursive feature elimination (RFE) [31] to screen the combination features with better classification effects. The contribution of features was also calculated using RFE to analyze the pathological mechanisms of AD and MCI.
2. Materials and Methods
2.1. Subjects and Experimental Environment
2.2. System Pipeline
2.3. Image Processing
2.4. White Matter Connectivity Network Construction
2.5. Gaussian Kernel Support Vector Machine
2.6. Statistical Processing
2.7. Evaluation Indicators
3. Results and Discussion
3.1. Analysis of Demographic Information
3.2. Comparison of FA Values
3.3. Feature Extraction
3.4. Results and Discussion
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Characteristics and Ratings | AD | MCI | NC | p-Value |
---|---|---|---|---|
Age | 67.76 ± 5.91 | 67.96 ± 5.27 | 65.64 ± 4.55 | 0.053 |
Gender (M/F) | 22/16 | 23/23 | 23/33 | 0.270 |
Educational attainment | 11.81 ± 3.46 | 12.03 ± 3.76 | 14.05 ± 2.34 | 0.068 |
MMSE | 23.13 ± 2.98 | 28.87 ± 2.10 | 29.48 ± 1.09 | 0.303 |
Mricro Name | MCI | NC | p-Value |
---|---|---|---|
Parietal_Sup_L | 0.19 ± 0.20 | 0.31 ± 0.17 | 9.42 × 10−4 |
Parietal_Sup_R | 0.07 ± 0.18 | 0.24 ± 0.26 | 5.18 × 10−4 |
Frontal_Sup_Orb_L | 0.30 ± 0.23 | 0.41 ± 0.16 | 6.01 × 10−3 |
Frontal_Sup_Orb_R | 0.30 ± 0.21 | 0.40 ± 0.14 | 5.91 × 10−3 |
Cingulum_Post_L | 0.07 ± 0.18 | 0.24 ± 0.26 | 5.18 × 10−4 |
Cuneus_L | 0.19 ± 0.20 | 0.31 ± 0.17 | 9.42 × 10−4 |
Rectus_L | 0.07 ± 0.18 | 0.23 ± 0.23 | 1.59 × 10−3 |
Pallidum_L | 0.12 ± 0.22 | 0.27 ± 0.27 | 3.15 × 10−3 |
Caudate_L | 0.30 ± 0.21 | 0.40 ± 0.14 | 5.91 × 10−3 |
Insula_L | 0.39 ± 0.19 | 0.47 ± 0.10 | 7.33 × 10−3 |
Lingual_L | 0.39 ± 0.19 | 0.47 ± 0.10 | 7.33 × 10−3 |
Cingulum_Mid_L | 0.40 ± 0.17 | 0.46 ± 0.05 | 9.28 × 10−3 |
Postcentral_L | 0.40 ± 0.17 | 0.46 ± 0.05 | 9.28 × 10−3 |
Frontal_Sup_Medial_R | 0.09 ± 0.18 | 0.23 ± 0.23 | 1.59 × 10−3 |
Angular_R | 0.01 ± 0.07 | 0.11 ± 0.22 | 5.49 × 10−3 |
Frontal_Mid_Orb_R | 0.01 ± 0.06 | 0.08 ± 0.17 | 9.65 × 10−3 |
Mricro Name | MCI | AD | p-Value |
---|---|---|---|
Putamen_L | 0.48 ± 0.13 | 0.26 ± 0.25 | 3.31 × 10−6 |
Putamen_R | 0.47 ± 0.15 | 0.25 ± 0.26 | 1.27 × 10−5 |
Thalamus_L | 0.43 ± 0.17 | 0.21 ± 0.24 | 5.55 × 10−6 |
Thalamus_R | 0.41 ± 0.19 | 0.20 ± 0.24 | 3.39 × 10−5 |
Supp_Motor_Area_L | 0.41 ± 0.07 | 0.27 ± 0.19 | 2.49 × 10−5 |
Supp_Motor_Area_R | 0.39 ± 0.15 | 0.19 ± 0.22 | 5.17 × 10−6 |
Caudate_L | 0.31 ± 0.23 | 0.11 ± 0.20 | 7.11 × 10−5 |
Caudate_R | 0.47 ± 0.13 | 0.28 ± 0.26 | 5.10 × 10−5 |
Precuneus_L | 0.36 ± 0.16 | 0.17 ± 0.21 | 1.05 × 10−5 |
Frontal_Sup_Medial_L | 0.41 ± 0.07 | 0.27 ± 0.19 | 2.49 × 10−5 |
Hippocampus_L | 0.47 ± 0.17 | 0.26 ± 0.27 | 2.93 × 10−5 |
Pallidum_L | 0.41 ± 0.22 | 0.19 ± 0.25 | 3.41 × 10−5 |
Cingulum_Post_L | 0.30 ± 0.25 | 0.08 ± 0.18 | 4.02 × 10−5 |
Insula_R | 0.39 ± 0.15 | 0.19 ± 0.22 | 5.17 × 10−6 |
Frontal_Inf_Oper_R | 0.37 ± 0.06 | 0.24 ± 0.19 | 3.33 × 10−5 |
Frontal_Sup_R | 0.37 ± 0.06 | 0.24 ± 0.19 | 3.33 × 10−5 |
Indicators | AD vs. MCI | MCI vs. NC |
---|---|---|
ACC | 89.29% | 91.18% |
SEN | 92.86% | 92.31% |
SPE | 85.71% | 90.48% |
AUC | 0.94 | 0.95 |
Studies | Subjects | Classifier | Feature | Performance | |||
---|---|---|---|---|---|---|---|
AD | MCI | NC | AD/MCI | MCI/NC | |||
Khvostikov et al. [40] | 48 | 108 | 58 | CNN | HIP-related MD | ACC: 80% | ACC: 63% |
Dalboni et al. [9] | 15 | 15 | 15 | Linear SVM | Bilateral parahippocampal gyrus FA | ACC: 90% | ACC: 90% |
Marzban et al. [41] | / | 106 | 185 | CNN | HIP-related and EC-related MD | / | ACC: 71.1% |
SPE: 81.8% | |||||||
AUC: 0.68 | |||||||
Zhou et al. [42] | / | 42 | 54 | SVM-RFE | HIP-related FA | / | ACC: 79.8% |
SEN: 84.1% | |||||||
SPE: 73.8% | |||||||
AUC: 0.901 | |||||||
Bigham et al. [43] | 24 | 24 | 24 | Quadratic SVM | Whole brain FA, MD, RD | ACC: 83.3% | ACC: 83.3% |
SEN: 80.7% | SEN: 94.4% | ||||||
SPE: 86.3% | SPE: 76.6% | ||||||
AUC: 0.93 | AUC: 0.88 | ||||||
Our study | 38 | 46 | 56 | SVM-RFE | Whole brain FA | ACC: 89.29% | ACC: 91.18% |
SEN: 92.86% | SEN: 92.31% | ||||||
SPE: 85.71% | SPE: 90.48% | ||||||
AUC: 0.94 | AUC: 0.95 |
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Yang, X.; Xia, Y.; Li, Z.; Liu, L.; Fan, Z.; Zhou, J. Classification of Alzheimer’s Disease Based on White Matter Connectivity Network. Appl. Sci. 2023, 13, 12030. https://doi.org/10.3390/app132112030
Yang X, Xia Y, Li Z, Liu L, Fan Z, Zhou J. Classification of Alzheimer’s Disease Based on White Matter Connectivity Network. Applied Sciences. 2023; 13(21):12030. https://doi.org/10.3390/app132112030
Chicago/Turabian StyleYang, Xiaoli, Yuxin Xia, Zhenwei Li, Lipei Liu, Zhipeng Fan, and Jiayi Zhou. 2023. "Classification of Alzheimer’s Disease Based on White Matter Connectivity Network" Applied Sciences 13, no. 21: 12030. https://doi.org/10.3390/app132112030
APA StyleYang, X., Xia, Y., Li, Z., Liu, L., Fan, Z., & Zhou, J. (2023). Classification of Alzheimer’s Disease Based on White Matter Connectivity Network. Applied Sciences, 13(21), 12030. https://doi.org/10.3390/app132112030