Denoising Variational Autoencoder as a Feature Reduction Pipeline for the diagnosis of Autism based on Resting-state fMRI

• URL: http://arxiv.org/abs/2410.00068v1
• Date: Mon, 30 Sep 2024 09:38:47 GMT
• Title: Denoising Variational Autoencoder as a Feature Reduction Pipeline for the diagnosis of Autism based on Resting-state fMRI
• Authors: Xinyuan Zheng, Orren Ravid, Robert A. J. Barry, Yoojean Kim, Qian Wang, Young-geun Kim, Xi Zhu, Xiaofu He,
• Abstract summary: Autism spectrum disorders (ASDs) are developmental conditions characterized by restricted interests and difficulties in communication. We propose an ASD feature reduction pipeline using resting-state fMRI (rs-fMRI) We used Ncuts parcellations and Power atlas to extract functional connectivity data, resulting in over 30 thousand features.
• Score: 11.871709357017416
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autism spectrum disorders (ASDs) are developmental conditions characterized by restricted interests and difficulties in communication. The complexity of ASD has resulted in a deficiency of objective diagnostic biomarkers. Deep learning methods have gained recognition for addressing these challenges in neuroimaging analysis, but finding and interpreting such diagnostic biomarkers are still challenging computationally. We propose an ASD feature reduction pipeline using resting-state fMRI (rs-fMRI). We used Ncuts parcellations and Power atlas to extract functional connectivity data, resulting in over 30 thousand features. Then the pipeline further compresses the connectivities into 5 latent Gaussian distributions, providing is a low-dimensional representation of the data, using a denoising variational autoencoder (DVAE). To test the method, we employed the extracted latent features from the DVAE to classify ASD using traditional classifiers such as support vector machine (SVM) on a large multi-site dataset. The 95% confidence interval for the prediction accuracy of the SVM is [0.63, 0.76] after site harmonization using the extracted latent distributions. Without using DVAE, the prediction accuracy is 0.70, which falls within the interval. This implies that the model successfully encodes the diagnostic information in rs-fMRI data to 5 Gaussian distributions (10 features) without sacrificing prediction performance. The runtime for training the DVAE and obtaining classification results from its extracted latent features (37 minutes) was 7 times shorter compared to training classifiers directly on the raw connectivity matrices (5-6 hours). Our findings also suggest that the Power atlas provides more effective brain connectivity insights for diagnosing ASD than Ncuts parcellations. The encoded features can be used for the help of diagnosis and interpretation of the disease.

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