Abstract
Parkinson’s disease (PD) is a neurodegenerative disorder that affects millions of people worldwide. The diagnosis of PD is based on clinical and neuroimaging data. This work proposes a novel approach that jointly models several Variational Autoencoder (VAE) architectures in order to maximize cross-modality prediction. We hypothesize that 123I-ioflupane SPECT could be related to motor symptomatology and other dopaminergic deficits. We propose a joint modelling of several VAE architectures for maximizing cross-modality prediction of the PD Clinical and Neuroimaging Data. The final model, with 5 common latents and 2 neuroimaging and data specific latents achieve R2 values up to 0.8 for scores related to PD, including well known PD symptomatology scales such as UPDRS (R2 = 0.545), at the same time that provides tools for interpreting the results and the common latent distribution for both clinical data and neuroimaging, paving the way for interpretable machine learning tools in neurodegeneration.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Arco, J.E., Ortiz, A., Castillo-Barnes, D., Górriz, J.M., Ramírez, J.: Ensembling shallow siamese architectures to assess functional asymmetry in Alzheimer’s disease progression. Appl. Soft Comput. 134, 109991 (2023). https://doi.org/10.1016/j.asoc.2023.109991
Bowman, S.R., Vilnis, L., Vinyals, O., Dai, A.M., Jozefowicz, R., Bengio, S.: Generating sentences from a continuous space. In: SIGNLL Conference on Computational Natural Language Learning (CONLL) (2016)
Duong, M.T., Rauschecker, A.M., Mohan, S.: Diverse applications of artificial intelligence in neuroradiology. Neuroimaging Clin. N. Am. 30(4), 505–516 (2020). https://doi.org/10.1016/j.nic.2020.07.003
Górriz, J.M., et al.: Computational approaches to explainable artificial intelligence: advances in theory, applications and trends. Inf. Fus. 100, 101945 (2023)
Górriz, J.M., et al.: Artificial intelligence within the interplay between natural and artificial computation: advances in data science, trends and applications. Neurocomputing 410, 237–270 (2020)
Delgado de las Heras, E., et al.: Revealing patterns of symptomatology in Parkinson’s disease: a latent space analysis with 3D convolutional autoencoders. In: Advances in Signal Processing and Artificial Intelligence: Proceedings of the 5th International Conference on Advances in Signal Processing and Artificial Intelligence, pp. 246–249 (2023)
Kim, M., Kim, J., Lee, S.H., Park, H.: Imaging genetics approach to Parkinson’s disease and its correlation with clinical score. Sci. Rep. 7(1) (2017). https://doi.org/10.1038/srep46700
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: Proceedings of the International Conference on Learning Representations (ICLR) (2014)
Lei, B., et al.: Predicting clinical scores for Alzheimer’s disease based on joint and deep learning. Exp. Syst. Appl. 187, 115966 (2022). https://doi.org/10.1016/j.eswa.2021.115966
Martinez-Murcia, F., Górriz, J., Ramírez, J., Ortiz, A.: Convolutional neural networks for neuroimaging in Parkinson’s disease: is preprocessing needed? Int. J. Neural Syst. 28, 1850035 (2018)
Martinez-Murcia, F.J., Ortiz, A., Gorriz, J.M., Ramirez, J., Castillo-Barnes, D.: Studying the manifold structure of Alzheimer’s disease: a deep learning approach using convolutional autoencoders. IEEE J. Biomed. Health Inf. 24(1), 17–26 (2020). https://doi.org/10.1109/jbhi.2019.2914970
Podell, D., et al.: SDXL: improving latent diffusion models for high-resolution image synthesis (2023)
Ross, B.C.: Mutual Information between discrete and continuous data sets. PLoS ONE 9(2), e87357 (2014). https://doi.org/10.1371/journal.pone.0087357
Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004). https://doi.org/10.1109/tip.2003.819861
Zhao, S., Song, J., Ermon, S.: InfoVAE: information maximizing variational autoencoders. arXiv preprint arXiv:1706.02262 (2017)
Acknowledgements
This research is part of the projects PID2022-137629OA-I00, PID2022-137461NB-C32 and PID2022-137451OB-I00, funded by the MICIU/AEI/10.13039/501100011033 and by “ERDF/EU”, and the C-ING-183-UGR23 project, cofunded by the Consejería de Universidad, Investigación e Innovación and by European Union, funded by Programa FEDER Andalucía 2021–2027. Work by F.J.M.M. is part of the grant RYC2021-030875-I funded by MICIU/AEI/10.13039/501100011033 and by the “European Union NextGenerationEU/PRTR”.
PPMI – a public-private partnership – is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners, including 4D Pharma, Abbvie, AcureX, Allergan, Amathus Therapeutics, Aligning Science Across Parkinson’s, AskBio, Avid Radiopharmaceuticals, BIAL, BioArctic, Biogen, Biohaven, BioLegend, BlueRock Therapeutics, BristolMyers Squibb, Calico Labs, Capsida Biotherapeutics, Celgene, Cerevel Therapeutics, Coave Therapeutics, DaCapo Brainscience, Denali, Edmond J. Safra Foundation, Eli Lilly, Gain Therapeutics, GE HealthCare, Genentech, GSK, Golub Capital, Handl Therapeutics, Insitro, Janssen Neuroscience, Jazz Pharmaceuticals, Lundbeck, Merck, Meso Scale Discovery, Mission Therapeutics, Neurocrine Biosciences, Neuropore, Pfizer, Piramal, Prevail Therapeutics, Roche, Sanofi, Servier, Sun Pharma Advanced Research Company, Takeda, Teva, UCB, Vanqua Bio, Verily, Voyager Therapeutics, the Weston Family Foundation and Yumanity Therapeutics.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Vázquez-García, C. et al. (2024). A Cross-Modality Latent Representation for the Prediction of Clinical Symptomatology in Parkinson’s Disease. In: Ferrández Vicente, J.M., Val Calvo, M., Adeli, H. (eds) Artificial Intelligence for Neuroscience and Emotional Systems. IWINAC 2024. Lecture Notes in Computer Science, vol 14674. Springer, Cham. https://doi.org/10.1007/978-3-031-61140-7_8
Download citation
DOI: https://doi.org/10.1007/978-3-031-61140-7_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-61139-1
Online ISBN: 978-3-031-61140-7
eBook Packages: Computer ScienceComputer Science (R0)