Comparing functional and effective connectivity features in diagnosis of Autism Spectrum Disorder using Stacked Autoencoder by resting-state fMRI data
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Abstract
Abstract
Purpose: The objective of this paper is studying the feasibility of using effective connectivity (Granger causality) obtained from resting-state functional magnetic resonance imaging (rs-fMRI) data and stacked autoencoder for diagnosing autism spectrum disorder (ASD) and comparing the results with those obtained using functional connectivity (Pearson correlation coefficient).
Background: ASD affects the normal development of the brain in the field of social interactions and communication skills. Because diagnosing ASD using behavioral symptoms is a time-consuming subjective process which needs exact collaboration of the ASD subject or his/her relatives, in recent years diagnosing ASD using resting-state functional neuroimaging modalities like rs-fMRI, has been taken into consideration.
Materials and Methods: We used rs-fMRI data and compared the use of functional and effective connectivity features using autoencoder to classify people with ASD from healthy subjects. We used ABIDE dataset and have divided the brain into 100 regions using the Harvard-Oxford Atlas. We calculated the Pearson correlation coefficient in classification using functional connectivity, and we calculated the granger causality in classification using effective connectivity. We used a stacked autoencoder to reduce the dimension of feature-space and a multi-layered perceptron (MLP) neural network as classifier in both classifications. To the best of our knowledge this is the first time that granger causality and stacked autoencoder have been used to diagnose ASD together.
Results: We achieved accuracy of 67.8%, sensitivity of 68.5% and specificity of 66.6% in classification using functional connectivity, and we achieved accuracy of 67.6%, sensitivity of 73.1% and specificity of 60.8% in classification using effective connectivity.
Conclusion: Although the accuracy obtained using the functional and effective connectivity are almost similar, the sensitivity is notably higher using effective connectivity. Since sensitivity is more important than specificity in the medical diagnosis, it seems that using effective connectivity features may outperform the ASD diagnosis in practice.
N. Salari, S. Rasoulpoor, S. Rasoulpoor, S Shohaimi, S. Jafarpour, N. Abdoli, B. Khaledi-Paveh and M. Mohammadi,"The global prevalence of autism spectrum disorder: a comprehensive systematic review and meta-analysis," Italian Journal of Pediatrics, vol. 48, pp. 1-16, 2022.
H. Choi, "Functional connectivity patterns of autism spectrum disorder identified by deep feature learning," arXiv preprint arXiv:1707.07932, 2017.
M. R. Daliri, and M. Behroozi, "Advantages and disadvantages of resting state functional connectivity magnetic resonance imaging for clinical applications," OMICS Journal of Radiology, vol. 3, pp. 1-2, 2013.
A. Khadem, G. A. Hossein-Zadeh, and A. Khorrami, "Long-range reduced predictive information transfers of autistic youths in EEG sensor-space during face processing," Brain topography, vol. 29, pp. 283-295, 2016.
A. A. A. Valliani, D. Ranti, and E. K. Oermann, "Deep learning and neurology: a systematic review," Neurology and therapy, vol. 8, pp. 351-365, 2019.
J. Hu, L. Cao, T. Li, B. Liao, S. Dong, and P. Li, "Interpretable learning approaches in resting-state functional connectivity analysis: the case of autism spectrum disorder," Computational and mathematical methods in medicine, vol. 2020, 2020.
Y. You, H. Liu, S. Zhang, and L. Shao, "Classification of autism based on fMRI data with feature-fused convolutional neural network," in Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health, Springer, 2020, pp. 77-88.
W. Yin, S. Mostafa, and F.X. Wu, "Diagnosis of autism spectrum disorder based on functional brain networks with deep learning," Journal of Computational Biology, vol. 28, pp. 146-165, 2021.
F. Almuqhim, and F. Saeed, "ASD-SAENet: a sparse autoencoder, and deep-neural network model for detecting autism spectrum disorder (ASD) using fMRI data," Frontiers in Computational Neuroscience, vol. 15, p. 654315, 2021.
M. Ingalhalikar, S. Shinde, A. Karmarkar, A. Rajan, D. Rangaprakash, and G. Deshpande, "Functional connectivity-based prediction of Autism on site harmonized ABIDE dataset," IEEE Transactions on Biomedical Engineering, vol. 68, pp. 3628-3637, 2021.
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| Issue | Articles in Press | |
| Section | Original Article(s) | |
| Keywords | ||
| Autism Spectrum Disorder resting-state functional magnetic resonance imaging Functional and Effective Brain Connectivity Pearson Correlation Coefficient Granger Causality Stacked autoencoder | ||
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