Skip to main content
SpringerLink
Log in

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

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Julie L, Patrik B (2002) Pathogenesis of Parkinson’s disease: dopamine, vesicles and α-synuclein. Nat Rev Neurosci 3:932–942

    Google Scholar 

  2. Postuma RB, Daniela B, Matthew S, Werner P, Warren OC, Wolfgang O, José O, Kenneth M, Irene L, Lang AE (2015) MDS clinical diagnostic criteria for Parkinson’s disease. Mov Disord 30:1591–1601

    Google Scholar 

  3. Lang AE, Lozano AM (1998) Parkinson’s disease. Lancet 386:896–912

    Google Scholar 

  4. Dickson DW (2018) Neuropathology of Parkinson disease. Parkinsonism Relat Disord 46:S30–S33

    Google Scholar 

  5. Petra S, Diane R, Edwards MJ, Carmen T, Petra K, Fatima C, Laura SM, Schneider SA, Georg KG, Palomar FJ (2010) Distinguishing SWEDDs patients with asymmetric resting tremor from Parkinson’s disease: a clinical and electrophysiological study. Mov Disord 25:560–569

    Google Scholar 

  6. Nicastro N, Garibotto V, Badoud S, Burkhard PR (2016) Scan without evidence of dopaminergic deficit: a 10-year retrospective study. Parkinsonism Relat Disord 31:53–58

    Google Scholar 

  7. Adeli E, Shi F, An L, Wee CY, Wu G, Wang T, Shen D (2016) Joint feature-sample selection and robust diagnosis of Parkinson’s disease from MRI data. Neuroimage 141:206–219

    Google Scholar 

  8. Lei H, Zhao Y, Wen Y, Lei B (2018) Adaptive sparse learning for neurodegenerative disease classification. In: IEEE international symposium on multimedia, pp 292–295

  9. Péran P, Barbagallo G, Nemmi F, Sierra M, Galitzky M, Traon APL, Payoux P, Meissner WG, Rascol O (2018) MRI supervised and unsupervised classification of Parkinson’s disease and multiple system atrophy. Mov Disord 33:600–608

    Google Scholar 

  10. Zhu X, Suk H, Lee S, Shen D (2016) Subspace regularized sparse multitask learning for multiclass neurodegenerative disease identification. IEEE Trans Biomed Eng 63:607–618

    Google Scholar 

  11. Zhou T, Liu M, Thung KH, Shen D (2019) Latent representation learning for Alzheimer’s disease diagnosis with incomplete multi-modality neuroimaging and genetic data. IEEE Trans Med Imaging 38:2411–2422

    Google Scholar 

  12. Ning Z, Xiao Q, Feng Q, Chen W, Zhang Y (2021) Relation-induced multi-modal shared representation learning for Alzheimer’s disease diagnosis. IEEE Trans Med Imaging 40:1632–1645

    Google Scholar 

  13. Huang Z, Lei H, Chen G, Frangi AF, Xu Y, Elazab A, Qin J, Lei B (2021) Parkinson's disease classification and clinical score regression via united embedding and sparse learning from longitudinal data. IEEE Trans Neural Netw Learn Syst

  14. Suk H-I, Lee S-W, Shen D, Initiative ADN (2014) Subclass-based multi-task learning for Alzheimer’s disease diagnosis. Front Aging Neurosci 6:168

    Google Scholar 

  15. Liu J, Pan Y, Wu F-X, Wang J (2020) Enhancing the feature representation of multi-modal MRI data by combining multi-view information for MCI classification. Neurocomputing 400:322–332

    Google Scholar 

  16. Hao X, Bao Y, Guo Y, Yu M, Zhang D, Risacher SL, Saykin AJ, Yao X, Shen L, A. s. D. N. Initiative, (2020) Multi-modal neuroimaging feature selection with consistent metric constraint for diagnosis of Alzheimer’s disease. Med Image Anal 60:101625

    Google Scholar 

  17. Shao W, Wang T, Sun L, Dong T, Han Z, Huang Z, Zhang J, Zhang D, Huang K (2020) Multi-task multi-modal learning for joint diagnosis and prognosis of human cancers. Med Image Anal 65:101795

    Google Scholar 

  18. Prashanth R, Roy SD, Mandal PK, Ghosh S (2014) Automatic classification and prediction models for early Parkinson’s disease diagnosis from SPECT imaging. Expert Syst Appl 41:3333–3342

    Google Scholar 

  19. Lei H, Huang Z, Zhang J, Yang Z, Tan E-L, Zhou F, Lei B (2017) Joint detection and clinical score prediction in Parkinson’s disease via multi-modal sparse learning. Expert Syst Appl 80:284–296

    Google Scholar 

  20. Lei H, Huang Z, Zhou F, Elazab A, Tan E-L, Li H, Qin J, Lei B (2018) Parkinson’s disease diagnosis via joint learning from multiple modalities and relations. IEEE J Biomed Health Inform 23:1437–1449

    Google Scholar 

  21. Zhang J, Liu M, An L, Gao Y, Shen D (2017) Alzheimer’s disease diagnosis using landmark-based features from longitudinal structural MR images. IEEE J Biomed Health Inform 21:1607–1616

    Google Scholar 

  22. Hong Y, Kim J, Chen G, Lin W, Yap P-T, Shen D (2019) Longitudinal prediction of infant diffusion MRI data via graph convolutional adversarial networks. IEEE Trans Med Imaging 38:2717–2725

    Google Scholar 

  23. Yang P, Zhou F, Ni D, Xu Y, Chen S, Wang T, Lei B (2019) Fused sparse network learning for longitudinal analysis of mild cognitive impairment. IEEE T Cybern 51:233–246

    Google Scholar 

  24. Lei B, Yang M, Yang P, Zhou F, Hou W, Zou W, Li X, Wang T, Xiao X, Wang S (2020) Deep and joint learning of longitudinal data for Alzheimer’s disease prediction. Pattern Recognit 102:107247

    Google Scholar 

  25. Goyal J, Khandnor P, Aseri TC (2021) A hybrid approach for Parkinson’s disease diagnosis with resonance and time-frequency based features from speech signals. Expert Syst Appl 182:115283

    Google Scholar 

  26. Balaji E, Brindha D, Elumalai VK, Vikrama R (2021) Automatic and non-invasive Parkinson’s disease diagnosis and severity rating using LSTM network. Appl Soft Comput 108:107463

    Google Scholar 

  27. A. ul Haq, J. P. Li, B. L. Y. Agbley, C. B. Mawuli, Z. Ali, S. Nazir and S. U. Din, (2022) A survey of deep learning techniques based Parkinson’s disease recognition methods employing clinical data. Expert Syst Appl 208:118045

    Google Scholar 

  28. Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313:504

    MathSciNet  MATH  Google Scholar 

  29. X. Lin, M. Cao, B. Song, J. Zhang and F. E. Alsaadi (2018) Open-Circuit Fault Diagnosis of Power Rectifier using Sparse Autoencoder based Deep Neural Network. Neurocomputing.

  30. Chen Y, Jiao L, Li Y, Zhao J (2017) Multilayer projective dictionary pair learning and sparse autoencoder for PolSAR image classification. IEEE Trans Geosci Remote Sensing 55:6683–6694

    Google Scholar 

  31. Wen L, Gao L, Li X (2017) A new deep transfer learning based on sparse auto-encoder for fault diagnosis. IEEE Trans Syst Man Cybern-Syst 49:136–144

    Google Scholar 

  32. Chen K, Hu J, He J (2016) A framework for automatically extracting overvoltage features based on sparse autoencoder. IEEE Trans Smart Grid 9:594–604

    Google Scholar 

  33. Lv N, Chen C, Qiu T, Sangaiah AK (2018) Deep learning and superpixel feature extraction based on contractive autoencoder for change detection in SAR images. IEEE Trans Ind Inform 14:5530–5538

    Google Scholar 

  34. Suk HI, Wee CY, Lee SW, Shen D (2016) State-space model with deep learning for functional dynamics estimation in resting-state fMRI. Neuroimage 129:292–307

    Google Scholar 

  35. Lei B, Zhao Y, Huang Z, Hao X, Zhou F, Elazab A, Qin J, Lei H (2020) Adaptive sparse learning using multi-template for neurodegenerative disease diagnosis. Med Image Anal 61:101632

    Google Scholar 

  36. Peng S, Ser W, Chen B, Lin Z (2021) Robust semi-supervised nonnegative matrix factorization for image clustering. Pattern Recogn 111:107683

    Google Scholar 

  37. Woo J, Xing F, Prince JL, Stone M, Gomez AD, Reese TG, Wedeen VJ, El Fakhri G (2021) A deep joint sparse non-negative matrix factorization framework for identifying the common and subject-specific functional units of tongue motion during speech. Med Image Anal 72:102131

    Google Scholar 

  38. Won JH, Youn J, Park H (2022) Enhanced neuroimaging genetics using multi-view non-negative matrix factorization with sparsity and prior knowledge. Med Image Anal 77:102378

    Google Scholar 

  39. Gunduz H (2021) An efficient dimensionality reduction method using filter-based feature selection and variational autoencoders on Parkinson’s disease classification. Biomed Signal Process Control 66:102452

    Google Scholar 

  40. Yuan Y, Wan J, Wang Q (2016) Congested scene classification via efficient unsupervised feature learning and density estimation. Pattern Recognit 56:159–169

    Google Scholar 

  41. Ge M-F, Ge Z, Pan H, Liu Y, Xu Y, Liu J (2020) A deep condition feature learning approach for rotating machinery based on MMSDE and optimized SAEs. Meas Sci Technol 32:035101

    Google Scholar 

  42. Xie X, Niu J, Liu X, Chen Z, Tang S, Yu S (2021) A survey on incorporating domain knowledge into deep learning for medical image analysis. Med Image Anal 69:101985

    Google Scholar 

  43. Marek K, Jennings D, Lasch S, Siderowf A, Tanner C, Simuni T, Coffey C, Kieburtz K, Flagg E, Chowdhury S (2011) The Parkinson progression marker initiative (PPMI). Prog Neurobiol 95:629–635

    Google Scholar 

  44. W. D. Penny, K. J. Friston, J. T. Ashburner, S. J. Kiebel and T. E. Nichols (2011) Statistical parametric mapping: the analysis of functional brain images.

  45. Sadananthan SA, Zheng W, Chee MW, Zagorodnov V (2010) Skull stripping using graph cuts. Neuroimage 49:225–239

    Google Scholar 

  46. Tzouriomazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, Mazoyer B, Joliot M (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15:273–289

    Google Scholar 

  47. Beckmann CF, Jenkinson M, Woolrich MW, Behrens TE, Flitney DE, Devlin JT, Smith SM (2006) Applying FSL to the FIAC data: model-based and model-free analysis of voice and sentence repetition priming. Hum Brain Mapp 27:380–391

    Google Scholar 

  48. Shin H-C, Orton MR, Collins DJ, Doran SJ, Leach MO (2012) Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data. IEEE Trans Pattern Anal Mach Intell 35:1930–1943

    Google Scholar 

  49. Chang C-C, Lin C-J (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2:1–27

    Google Scholar 

  50. Lei H, Huang Z, Elazab A, Li H, Lei B (2018) Longitudinal and multi-modal data learning via joint embedding and sparse regression for Parkinson’s disease diagnosis. In: International workshop on machine learning in medical imaging, pp 310–318

  51. Zhang D, Shen D, Initiative ASDN (2012) Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer’s disease. Neuroimage 59:895–907

    Google Scholar 

  52. Xu J, Xiang L, Liu Q, Gilmore H, Wu J, Tang J, Madabhushi A (2014) Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images. In: IEEE international symposium on biomedical imaging, pp 119–130

  53. Bengio Y, Lamblin P, Dan P, Larochelle H (2007) Greedy layer-wise training of deep networks. Adv Neural Inf Process Syst 19:153–160

    Google Scholar 

  54. Xia M, Wang J, He Y (2013) BrainNet viewer: a network visualization tool for human brain connectomics. PLoS ONE 8:e68910

    Google Scholar 

  55. Jolliffe IT, Cadima J (2016) Principal component analysis: a review and recent developments. Philos Trans R Soc A-Math Phys Eng Sci 374:20150202

    MathSciNet  MATH  Google Scholar 

  56. X. He, D. Cai and P. Niyogi (2005) Laplacian score for feature selection. Adv. Neural Inf. Process. Syst. 18:

  57. Liu Y, Ye D, Li W, Wang H, Gao Y (2020) Robust neighborhood embedding for unsupervised feature selection. Knowl Based Syst 193:105462

    Google Scholar 

  58. Roffo G, Melzi S, Castellani U, Vinciarelli A, Cristani M (2020) Infinite feature selection: a graph-based feature filtering approach. IEEE Trans Pattern Anal Mach Intell 43:4396–4410

    Google Scholar 

  59. Huang D, Cai X, Wang C-D (2019) Unsupervised feature selection with multi-subspace randomization and collaboration. Knowl Based Syst 182:104856

    Google Scholar 

  60. Rajammal RR, Mirjalili S, Ekambaram G, Palanisamy N (2022) Binary grey wolf optimizer with mutation and adaptive K-nearest neighbour for feature selection in Parkinson’s disease diagnosis. Knowl-Based Syst 246:108701

    Google Scholar 

  61. Martinez-Eguiluz M, Arbelaitz O, Gurrutxaga I, Muguerza J, Perona I, Murueta-Goyena A, Acera M, Del Pino R, Tijero B, Gomez-Esteban JC (2022) Diagnostic classification of Parkinson’s disease based on non-motor manifestations and machine learning strategies. Neural Comput Appl 35(8):1–15

    Google Scholar 

  62. Sang L, Zhang J, Wang L, Zhang J, Zhang Y, Li P, Wang J, Qiu M (2015) Alteration of brain functional networks in early-stage Parkinson’s disease: a resting-state fMRI study. PLoS ONE 10:e0141815–e0141815

    Google Scholar 

  63. Watanabe H, Senda J, Kato S, Ito M, Atsuta N, Hara K, Tsuboi T, Katsuno M, Nakamura T, Hirayama M, Adachi H, Naganawa S, Sobue G (2013) Cortical and subcortical brain atrophy in Parkinson’s disease with visual hallucination. Mov Disord 28:1732–1736

    Google Scholar 

  64. Lach B, Grimes D, Benoit B, Minkiewicz-Janda A (1992) Caudate nucleus pathology in Parkinson’s disease: ultrastructural and biochemical findings in biopsy material. Acta Neuropathol 83:352–360

    Google Scholar 

Download references

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.

Author information

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

Authors
  1. Haijun Lei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yukang Lei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zihao Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shiqi Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhongwei Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Feng Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ee-Leng Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Xiaohua Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Yi Lei
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Huoyou Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Yaohui Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Chien-Hung Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Baiying Lei
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Baiying Lei.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-023-08508-x

Keywords

Search

Navigation