A Deep Learning Model for Data-Driven Discovery of Functional Connectivity
Abstract
:1. Introduction
2. Materials and Methods
2.1. Materials
2.1.1. Fbirn
2.1.2. Preprocessing
2.2. Method
2.2.1. Cnn Encoder
2.2.2. Self Attention
2.2.3. GNN
2.2.4. Training and Testing
3. Results
3.1. Classification
3.2. Functional Connectivity
3.3. Region Selection
4. Discussion
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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CV Fold | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
AUC | 0.695 | 0.955 | 0.644 | 0.752 | 0.908 | 0.917 | 0.894 | 0.803 | 0.649 | 0.805 | 0.922 | 0.699 | 0.625 | 0.780 | 0.794 | 0.766 | 0.914 | 0.750 | 0.777 |
Test | p Value |
---|---|
Mann-Whitney U Test | 0.0 |
Welch’s t-test | 0.0 |
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Mahmood, U.; Fu, Z.; Calhoun, V.D.; Plis, S. A Deep Learning Model for Data-Driven Discovery of Functional Connectivity. Algorithms 2021, 14, 75. https://doi.org/10.3390/a14030075
Mahmood U, Fu Z, Calhoun VD, Plis S. A Deep Learning Model for Data-Driven Discovery of Functional Connectivity. Algorithms. 2021; 14(3):75. https://doi.org/10.3390/a14030075
Chicago/Turabian StyleMahmood, Usman, Zening Fu, Vince D. Calhoun, and Sergey Plis. 2021. "A Deep Learning Model for Data-Driven Discovery of Functional Connectivity" Algorithms 14, no. 3: 75. https://doi.org/10.3390/a14030075