- Cristóbal Vázquez-García27,
- F. J. Martinez-Murcia27,
- Juan E. Arco27,
- Ignacio A. Illán27,
- Carmen Jiménez-Mesa27,
- Javier Ramírez27 &
- …
- Juan M. Górriz27
Part of the book series:Lecture Notes in Computer Science ((LNCS,volume 14674))
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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.
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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.
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Department of Signal Theory, Networking and Communications, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18071, Granada, Spain
Cristóbal Vázquez-García, F. J. Martinez-Murcia, Juan E. Arco, Ignacio A. Illán, Carmen Jiménez-Mesa, Javier Ramírez & Juan M. Górriz
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- F. J. Martinez-Murcia
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- Juan E. Arco
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- Ignacio A. Illán
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Correspondence toF. J. Martinez-Murcia.
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Universidad Politécnica de Cartagena, Cartagena, Spain
José Manuel Ferrández Vicente
Polytechnic University of Valencia, Valencia, Spain
Mikel Val Calvo
Ohio State University, Columbus, OH, USA
Hojjat Adeli
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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
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