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Early diagnosis and clinical score prediction of Parkinson's disease based on longitudinal neuroimaging data

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Abstract

Parkinson's disease (PD) is an irreversible neurodegenerative disease that has serious impacts on patients' lives. To provide timely accurate treatment and delay the deterioration of the disease, the accurate early diagnosis and clinical score prediction of PD are extremely important. Differing from previous studies on PD, we propose a network combining feature selection method with feature learning to obtain the most discriminative feature representation for longitudinal early diagnosis and clinical score prediction. Specifically, we first preprocess the multi-modal neuroimaging data at multiple time points to extract original longitudinal multi-modal features. Then, the feature selection method is performed to preliminary reduce the feature dimensions. Finally, the stacked sparse nonnegative autoencoder (SSNAE) is employed to obtain more discriminative longitudinal multi-modal features to improve the accuracy of early diagnosis and clinical score prediction at multiple time points. To verify our proposed network, diverse and extensive experiments are performed on the Parkinson's Progression Markers Initiative dataset, which aims to identify biological markers of PD risk, onset and progression. The results demonstrate that our proposed method is more efficient and achieves promising performance on both longitudinal early diagnosis and clinical scores prediction compared to state-of-the-art methods.

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Acknowledgements

This work was supported partly by National Natural Science Foundation of China (Nos. 62276172, 61871274, 61801305 and U22A2024), National Natural Science Foundation of Guangdong Province (No. 2019A1515111205 and No. 2020A1515010649), Shenzhen Science and Technology Program (Nos. JCYJ20220818095809021, JCYJ20190808165209410, KCXFZ20201221173213036), (Key) Project of Department of Education of Guangdong Province (No. 2019KZDZX1015), NTUTSZU Joint Research Program (Nos. 2023006 and 2021002). Special Project in Key fields of General Universities of Guangdong Province (No. 2019KZDZX1015). Xiaohua Xiao, Huoyou Hu and Yaohui Huang are the corresponding authors.

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Authors and Affiliations

  1. Key Laboratory of Service Computing and Applications, Guangdong Province Key Laboratory of Popular High Performance Computers, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

    Haijun Lei, Yukang Lei, Zihao Chen, Shiqi Li & Zhongwei Huang

  2. Department of Industrial and Manufacturing, Systems Engineering, The University of Michigan, Dearborn, MI, USA

    Feng Zhou

  3. School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Ee-Leng Tan

  4. First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen University, Shenzhen, China

    Xiaohua Xiao, Yi Lei, Huoyou Hu & Yaohui Huang

  5. Computer Science and Information Engineering, National Taipei University of Technology, Taipei, Taiwan, Republic of China

    Chien-Hung Liu

  6. National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China

    Baiying Lei

  7. Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation, Southern University of Science and Technology, Shenzhen, 518055, China

    Baiying Lei

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  1. Haijun Lei
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Lei, H., Lei, Y., Chen, Z. et al. Early diagnosis and clinical score prediction of Parkinson's disease based on longitudinal neuroimaging data. Neural Comput & Applic 35, 16429–16455 (2023). https://doi.org/10.1007/s00521-023-08508-x

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