Understanding the Role of Connectivity Dynamics of Resting-State Functional MRI in the Diagnosis of Autism Spectrum Disorder: A Comprehensive Study
Abstract
:1. Introduction
- The impact of using different atlases, including the automated anatomical labeling (AAL) and Talaraich and Tournoux (TT) atlases.
- The effect of using different preprocessing strategies.
- The effect of using our novel dFC, in comparison to using conventional static FC.
- The dimensionality reduction problem, using two-stage feature selection, with four types of kernels.
- The role of the classification strategy, investigating six different classifiers.
- The ability to highlight the importance of each of the previous choices on the overall performance.
2. Materials and Methods
2.1. Dataset
2.2. Proposed Framework
2.3. Preprocessing
2.4. Feature Representation
2.5. Feature Selection
2.6. Machine Learning
Algorithm1 Step-by-step rs-fMRI diagnosis algorithm |
|
2.7. Performance Metrics
- Specificity:
- Sensitivity (recall):
- Accuracy:
- Balanced accuracy: average of true positive rate (sensitivity) and true negative rate (specificity).
3. Results
3.1. Significance of Data Representation
3.1.1. Preprocessing Pipeline
3.1.2. Atlas Use
3.1.3. Dynamic Connectivity
3.2. Model Results
Identified Brain Areas
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Sample Availability
Abbreviations
ABIDE | autism brain imaging data exchange |
MRI | magnetic resonance imaging |
sMRI | structural MRI |
fMRI | functional MRI |
rs-fMRI | resting-state functional MRI |
ASD | autism spectrum disorder |
CAD | computer-aided diagnosis |
ML | machine learning |
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ASD Group (n = 408) | TD Group (n = 476) | |||
---|---|---|---|---|
M = 358, F = 50 | M = 388, F = 88 | |||
AGE | FIQ | AGE | FIQ | |
count | 408 | 379 | 476 | 442 |
mean | 17.69 | 106.19 | 16.79 | 111.28 |
std | 8.93 | 17.01 | 7.35 | 12.48 |
min | 7 | 41 | 6.47 | 73 |
max | 64 | 148 | 56.2 | 146 |
sum_sq | df | F | PR (>F) | |
---|---|---|---|---|
C(Feat, Sum) | 0.941852 | 1.0 | 163.004715 | |
C(Strat, Sum) | 0.177957 | 3.0 | 10.266230 | |
C(Atls, Sum) | 0.149164 | 1.0 | 25.815521 | |
C(Feat, Sum):C(Strat, Sum) | 0.117433 | 3.0 | 6.774646 | |
C(Feat, Sum):C(Atls, Sum) | 0.013784 | 1.0 | 2.385576 | |
C(Strat, Sum):C(Atls, Sum) | 0.034962 | 3.0 | 2.016924 | |
C(Feat, Sum):C(Strat, Sum):C(Atls, Sum) | 0.084563 | 3.0 | 4.878417 |
sum_sq | df | F | PR (>F) | |
---|---|---|---|---|
C(Feat, Sum) | 0.936316 | 1.0 | 160.682470 | |
C(Filt, Sum) | 0.179172 | 3.0 | 10.249322 | |
C(Atls, Sum) | 0.148105 | 1.0 | 25.416432 | |
C(Feat, Sum):C(Filt, Sum) | 0.117208 | 3.0 | 6.704737 |
Metric | Accuracy | Sensitivity | Specificity | Balanced Accuracy |
---|---|---|---|---|
5-fold value | 0.988 ± 0.004 | 0.987 ± 0.008 | 0.989 ± 0.007 | 0.988 ± 0.004 |
Index | First Selected Features | Top Frequent Region Names | Frequency |
---|---|---|---|
1 | Precentral_L__Rolandic_Oper_R_wk | Temporal_Sup_L | 24 |
2 | Precentral_L__Supp_Motor_Area_L_st | Postcentral_R | 23 |
3 | Precentral_L__Fusiform_L_wk | Frontal_Sup_Medial_R | 22 |
4 | Precentral_L__Parietal_Inf_L_wk | Precuneus_R | 22 |
5 | Precentral_L__SupraMarginal_R_st | SupraMarginal_R | 21 |
6 | Precentral_L__Precuneus_R_wk | Frontal_Sup_R | 21 |
7 | Precentral_L__Temporal_Sup_L_wk | Cingulum_Ant_R | 21 |
8 | Precentral_L__Temporal_Pole_Sup_R_wk | Supp_Motor_Area_L | 21 |
9 | Precentral_L__Cerebelum_Crus2_R_wk | Angular_R | 21 |
10 | Precentral_R__Frontal_Inf_Tri_L_wk | Hippocampus_R | 21 |
Article | Used Classifier | Achieved Accuracy |
---|---|---|
Abraham et al., 2017 [50] | SVM | 67.0% |
Guo et al., 2017 [51] | Deep neural networks with feature selection (DNN-FS) | 86.4% |
Kam et al., 2017 [52] | Discriminative restricted Boltzmann machines (DRBM) | 80.8% |
Sadeghi et al., 2017 [53] | SVM | 92% |
Spera et al., 2019 [54] | SVM | 71.0% |
Tang et al., 2019 [55] | SVM | 62.6% |
Wang et al., 2020 [56] | MLP and a voting strategy | 74.5% |
Rakić et al., 2020 [49] | Ensemble of classifiers | 85.0% |
Subah et al., 2021 [57] | DNN | 87.0% |
Al-Hiyali et al., 2021 [58] | SVM, K-nearest neighbors (KNN) | 85.9% |
Yin et al., 2021 [59] | Autoencoders, CNN, DNN | 79.2% |
Chu et al., 2022 [60] | Multi-scale graph convolutional network (GCN) | 79.5% |
Yang et al., 2022 [61] | LR, SVM, DNN, supervised learning classifier | 69.4% |
Ding et al., 2022 [62] | low-rank domain adaptive method with inter-class difference constraint | 75.5% |
Proposed Work | Best: LSVM | 98.8% |
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ElNakieb, Y.; Ali, M.T.; Elnakib, A.; Shalaby, A.; Mahmoud, A.; Soliman, A.; Barnes, G.N.; El-Baz, A. Understanding the Role of Connectivity Dynamics of Resting-State Functional MRI in the Diagnosis of Autism Spectrum Disorder: A Comprehensive Study. Bioengineering 2023, 10, 56. https://doi.org/10.3390/bioengineering10010056
ElNakieb Y, Ali MT, Elnakib A, Shalaby A, Mahmoud A, Soliman A, Barnes GN, El-Baz A. Understanding the Role of Connectivity Dynamics of Resting-State Functional MRI in the Diagnosis of Autism Spectrum Disorder: A Comprehensive Study. Bioengineering. 2023; 10(1):56. https://doi.org/10.3390/bioengineering10010056
Chicago/Turabian StyleElNakieb, Yaser, Mohamed T. Ali, Ahmed Elnakib, Ahmed Shalaby, Ali Mahmoud, Ahmed Soliman, Gregory Neal Barnes, and Ayman El-Baz. 2023. "Understanding the Role of Connectivity Dynamics of Resting-State Functional MRI in the Diagnosis of Autism Spectrum Disorder: A Comprehensive Study" Bioengineering 10, no. 1: 56. https://doi.org/10.3390/bioengineering10010056
APA StyleElNakieb, Y., Ali, M. T., Elnakib, A., Shalaby, A., Mahmoud, A., Soliman, A., Barnes, G. N., & El-Baz, A. (2023). Understanding the Role of Connectivity Dynamics of Resting-State Functional MRI in the Diagnosis of Autism Spectrum Disorder: A Comprehensive Study. Bioengineering, 10(1), 56. https://doi.org/10.3390/bioengineering10010056