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Multi-kernel Learning Fusion Algorithm Based on RNN and GRU for ASD Diagnosis and Pathogenic Brain Region Extraction

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Abstract

Autism spectrum disorder (ASD) is a complex, severe disorder related to brain development. It impairs patient language communication and social behaviors. In recent years, ASD researches have focused on a single-modal neuroimaging data, neglecting the complementarity between multi-modal data. This omission may lead to poor classification. Therefore, it is important to study multi-modal data of ASD for revealing its pathogenesis. Furthermore, recurrent neural network (RNN) and gated recurrent unit (GRU) are effective for sequence data processing. In this paper, we introduce a novel framework for a Multi-Kernel Learning Fusion algorithm based on RNN and GRU (MKLF-RAG). The framework utilizes RNN and GRU to provide feature selection for data of different modalities. Then these features are fused by MKLF algorithm to detect the pathological mechanisms of ASD and extract the most relevant the Regions of Interest (ROIs) for the disease. The MKLF-RAG proposed in this paper has been tested in a variety of experiments with the Autism Brain Imaging Data Exchange (ABIDE) database. Experimental findings indicate that our framework notably enhances the classification accuracy for ASD. Compared with other methods, MKLF-RAG demonstrates superior efficacy across multiple evaluation metrics and could provide valuable insights into the early diagnosis of ASD.

Graphical abstract

By fusing functional-structural features, we constructed a disease classification framework MKLF-RAG to diagnose ASD patients and identify pathogenic ROI. The framework consists of three parts. They are construction and selection of functional brain features, construction and selection of structural brain features, and feature fusion and classification

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Code Availability

https://github.com/1998cj/MKLF-RAG.

Data Availability

This study was carried out in accordance with the recommendations of Health Insurance Portability and Accountability Act (HIPAA) guidelines and 1000 Functional Connectomes Project /INDI protocols. All datasets are anonymous, with no protected health information included.

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Acknowledgements

This work was supported by National Key R and D Program of China (No. 2020YFB2104400); National Nature Science Foundation of China (Grant Nos 62362027, 62362028, 62072173, and 62372169); and Natural Science Foundation of Hainan, China (Grant Nos 121RC538 and 120RC588); the Key Open Project of Key Laboratory of Data Science and Intelligence Education (Hainan Normal University), Ministry of Education (No. DSIE202101); Natural Science Foundation of Hunan Province, China (No. DSIE202101); the Innovation and Entrepreneurship Training Program of Hunan Xiangjiang Artificial Intelligence Academy; and the project grant from the College of Mathematics and Statistics, Hainan Normal University (styc202205, styc202206).

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Authors and Affiliations

  1. Key Laboratory of Data Science and Intelligence Education, Ministry of Education, Hainan Normal University, Haikou, 571126, China

    Jie Chen, Huilian Zhang, Bo Liao & Xia-an Bi

  2. Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Quan Zou

  3. College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China

    Xia-an Bi

  4. College of Mathematics and Statistics, Hainan Normal University, Haikou, 571126, China

    Jie Chen, Huilian Zhang, Bo Liao & Xia-an Bi

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  1. Jie Chen
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Correspondence to Xia-an Bi.

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Chen, J., Zhang, H., Zou, Q. et al. Multi-kernel Learning Fusion Algorithm Based on RNN and GRU for ASD Diagnosis and Pathogenic Brain Region Extraction. Interdiscip Sci Comput Life Sci (2024). https://doi.org/10.1007/s12539-024-00629-8

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