Abstract
We extend the sparse, spatially piecewise-contiguous linear classification framework for mesh-based data to ordinal logistic regression. The algorithm is intended for use with subcortical shape and cortical thickness data where progressive clinical staging is available, as is generally the case in neurodegenerative diseases. We apply the tool to Parkinson’s and Alzheimer’s disease staging. The resulting biomarkers predict Hoehn-Yahr and cognitive impairment stages at competitive accuracy; the models remain parsimonious and outperform one-against-all models in terms of the Akaike and Bayesian information criteria.
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
References
Braak, H., Del Tredici, K., Rüb, U., de Vos, R.A.I., Jansen Steur, E.N.H., Braak, E.: Staging of brain pathology related to sporadic parkinson’s disease. Neurobiology of Aging 24(2), 197–211 (2003)
de Pierrefeu, A.: Structured sparse principal components analysis with the tv-elastic net penalty. IEEE Trans. Med. Imaging 37(2), 396–407 (2018)
Dohmatob, E.D., Gramfort,A., Thirion, B., Varoquaux, G.: Benchmarking solvers for tv-l1 least-squares and logistic regression in brain imaging. In: 2014 International Workshop on Pattern Recognition in Neuroimaging, pp. 1–4 (2014)
Doyle, O.M., et al.: Predicting progression of alzheimer’s disease using ordinal regression. PLoS ONE 9(8), e105542 (2014)
Garbarino, S., Lorenzi, M.: Modeling and inference of spatio-temporal protein dynamics across brain networks. In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 57–69. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20351-1_5
Guo, X., Tinaz, S., Dvornek, N.C.: Characterization of early stage parkinson’s disease from resting-state fmri data using a long short-term memory network. Front. Neuroimaging 1 (2022)
Gutman, B.A.: Empowering imaging biomarkers of Alzheimer’s disease. Neurobio. Aging 36, S69–S80 (2014)
Hoehn, M.M., Yahr, M.D., et al.: Parkinsonism: onset, progression, and mortality. Neurology 50(2), 318–318 (1998)
Jack Jr., C.R., et al.: Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol. 9(1), 119–128 (2010)
Jin, D., et al.: Generalizable, reproducible, and neuroscientifically interpretable imaging biomarkers for Alzheimer’s disease. Adv. Sci. (Weinh) 7(14), 2198–3844 (2020)
Kurmukov, A., Zhao, Y., Mussabaeva, A., Gutman, B.: Constraining disease progression models using subject specific connectivity priors. In: Schirmer, M.D., Venkataraman, A., Rekik, I., Kim, M., Chung, A.W. (eds.) CNI 2019. LNCS, vol. 11848, pp. 106–116. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32391-2_11
La Joie, R.: Prospective longitudinal atrophy in alzheimer &x2019;s disease correlates with the intensity and topography of baseline tau-pet. Sci. Trans. Med. 12(524), eaau5732 (2020)
Laansma, M.A., et al.: International multicenter analysis of brain structure across clinical stages of Parkinson’s disease. Mov. Disord. 36(11), 2583–2594 (2021)
Marinescu, R.V., et al.: Dive: a spatiotemporal progression model of brain pathology in neurodegenerative disorders. Neuroimage 192, 166–177 (2019)
McCullagh, P.: Regression models for ordinal data. J. Roy. Stat. Soc.: Ser. B (Methodol.) 42(2), 109–127 (1980)
Nemmi, F., Sabatini, U., Rascol, O., Péran, P.: Parkinson’s disease and local atrophy in subcortical nuclei: insight from shape analysis. Neurobiol. Aging 36(1), 424–433 (2015)
Nir, T.M., et al.: Alzheimer’s disease classification with novel microstructural metrics from diffusion-weighted MRI. In: Fuster, A., Ghosh, A., Kaden, E., Rathi, Y., Reisert, M. (eds.) Computational Diffusion MRI. MV, pp. 41–54. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-28588-7_4
Oxtoby, N.P.: Data-driven sequence of changes to anatomical brain connectivity in sporadic Alzheimer’s disease. Front. Neuro. 8, 580 (2017)
Shangran, Q., et al: Development and validation of an interpretable deep learning framework for alzheimer’s disease classification. Brain 143(6), 1920–1933 (2020)
Rennie, J.D.M., Srebro, N.: Loss functions for preference levels: Regression with discrete ordered labels. In: Proceedings of the IJCAI Multidisciplinary Workshop on Advances in Preference Handling, vol. 1. Citeseer (2005)
Roshchupkin, G.V., Gutman, B.A., et al.: Heritability of the shape of subcortical brain structures in the general population. Nat. Commun. 7, 13738 (2016)
Young, P.N.E., et al.: Imaging biomarkers in neurodegeneration: current and future practices. Alzheimers Res. Ther. 12(1), 49 (2020)
Zhao, Y., Kurmukov, A., Gutman, B.A.: Spatially adaptive morphometric knowledge transfer across neurodegenerative diseases. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 845–849 (2021)
Acknowledgments
Work by BG and YZ was supported by the Alzheimer’s Association grant 2018-AARG-592081, Advanced Disconnectome Markers of Alzheimer’s Disease. ENIGMA-PD (YW, PT, EH, ML) is supported by NINDS award 1RO1NS107513-01A.
Author information
Authors and Affiliations
Consortia
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zhao, Y. et al. (2022). Learning Interpretable Regularized Ordinal Models from 3D Mesh Data for Neurodegenerative Disease Staging. In: Abdulkadir, A., et al. Machine Learning in Clinical Neuroimaging. MLCN 2022. Lecture Notes in Computer Science, vol 13596. Springer, Cham. https://doi.org/10.1007/978-3-031-17899-3_12
Download citation
DOI: https://doi.org/10.1007/978-3-031-17899-3_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-17898-6
Online ISBN: 978-3-031-17899-3
eBook Packages: Computer ScienceComputer Science (R0)
