Learning Interpretable Regularized Ordinal Models from 3D Mesh Data for Neurodegenerative Disease Staging

ENIGMA consortium

doi:10.1007/978-3-031-17899-3_12

Learning Interpretable Regularized Ordinal Models from 3D Mesh Data for Neurodegenerative Disease Staging

ENIGMA consortium

Department of Medical Imaging and Radiological Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

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.

Original language	English
Title of host publication	Machine Learning in Clinical Neuroimaging - 5th International Workshop, MLCN 2022, Held in Conjunction with MICCAI 2022, Proceedings
Editors	Ahmed Abdulkadir, Deepti R. Bathula, Nicha C. Dvornek, Mohamad Habes, Seyed Mostafa Kia, Vinod Kumar, Thomas Wolfers
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	115-124
Number of pages	10
ISBN (Print)	9783031178986
DOIs	https://doi.org/10.1007/978-3-031-17899-3_12
State	Published - 2022
Event	5th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2022, held in Conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022 - Singapore, Singapore Duration: 18 09 2022 → 18 09 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13596 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	5th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2022, held in Conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022
Country/Territory	Singapore
City	Singapore
Period	18/09/22 → 18/09/22

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Imaging biomarker
Neurodegenerative Disease
Ordinal regression

Access to Document

10.1007/978-3-031-17899-3_12

Cite this

ENIGMA consortium (2022). Learning Interpretable Regularized Ordinal Models from 3D Mesh Data for Neurodegenerative Disease Staging. In A. Abdulkadir, D. R. Bathula, N. C. Dvornek, M. Habes, S. M. Kia, V. Kumar, & T. Wolfers (Eds.), Machine Learning in Clinical Neuroimaging - 5th International Workshop, MLCN 2022, Held in Conjunction with MICCAI 2022, Proceedings (pp. 115-124). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13596 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-17899-3_12

ENIGMA consortium. / Learning Interpretable Regularized Ordinal Models from 3D Mesh Data for Neurodegenerative Disease Staging. Machine Learning in Clinical Neuroimaging - 5th International Workshop, MLCN 2022, Held in Conjunction with MICCAI 2022, Proceedings. editor / Ahmed Abdulkadir ; Deepti R. Bathula ; Nicha C. Dvornek ; Mohamad Habes ; Seyed Mostafa Kia ; Vinod Kumar ; Thomas Wolfers. Springer Science and Business Media Deutschland GmbH, 2022. pp. 115-124 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{7391e8ead3d444dc8d2a1bd2efde4b80,

title = "Learning Interpretable Regularized Ordinal Models from 3D Mesh Data for Neurodegenerative Disease Staging",

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{\textquoteright}s and Alzheimer{\textquoteright}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.",

keywords = "Imaging biomarker, Neurodegenerative Disease, Ordinal regression",

author = "{ENIGMA consortium} and Yuji Zhao and Laansma, {Max A.} and {van Heese}, {Eva M.} and Conor Owens-Walton and Parkes, {Laura M.} and Ines Debove and Christian Rummel and Roland Wiest and Fernando Cendes and Rachel Guimaraes and Yasuda, {Clarissa Lin} and Wang, {Jiun Jie} and Anderson, {Tim J.} and Dalrymple-Alford, {John C.} and Melzer, {Tracy R.} and Pitcher, {Toni L.} and Reinhold Schmidt and Petra Schwingenschuh and G{\"a}etan Garraux and Mario Rango and Letizia Squarcina and Sarah Al-Bachari and Emsley, {Hedley C.A.} and Klein, {Johannes C.} and Mackay, {Clare E.} and Dirkx, {Michiel F.} and Rick Helmich and Francesca Assogna and Fabrizio Piras and Bright, {Joanna K.} and Gianfranco Spalletta and Kathleen Poston and Christine Lochner and McMillan, {Corey T.} and Daniel Weintraub and Jason Druzgal and Benjamin Newman and {Van Den Heuvel}, {Odile A.} and Neda Jahanshad and Thompson, {Paul M.} and {van der Werf}, {Ysbrand D.} and Boris Gutman",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 5th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2022, held in Conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022 ; Conference date: 18-09-2022 Through 18-09-2022",

year = "2022",

doi = "10.1007/978-3-031-17899-3_12",

language = "英语",

isbn = "9783031178986",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "115--124",

editor = "Ahmed Abdulkadir and Bathula, {Deepti R.} and Dvornek, {Nicha C.} and Mohamad Habes and Kia, {Seyed Mostafa} and Vinod Kumar and Thomas Wolfers",

booktitle = "Machine Learning in Clinical Neuroimaging - 5th International Workshop, MLCN 2022, Held in Conjunction with MICCAI 2022, Proceedings",

address = "德国",

}

ENIGMA consortium 2022, Learning Interpretable Regularized Ordinal Models from 3D Mesh Data for Neurodegenerative Disease Staging. in A Abdulkadir, DR Bathula, NC Dvornek, M Habes, SM Kia, V Kumar & T Wolfers (eds), Machine Learning in Clinical Neuroimaging - 5th International Workshop, MLCN 2022, Held in Conjunction with MICCAI 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13596 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 115-124, 5th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2022, held in Conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022, Singapore, Singapore, 18/09/22. https://doi.org/10.1007/978-3-031-17899-3_12

Learning Interpretable Regularized Ordinal Models from 3D Mesh Data for Neurodegenerative Disease Staging. / ENIGMA consortium.
Machine Learning in Clinical Neuroimaging - 5th International Workshop, MLCN 2022, Held in Conjunction with MICCAI 2022, Proceedings. ed. / Ahmed Abdulkadir; Deepti R. Bathula; Nicha C. Dvornek; Mohamad Habes; Seyed Mostafa Kia; Vinod Kumar; Thomas Wolfers. Springer Science and Business Media Deutschland GmbH, 2022. p. 115-124 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13596 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Learning Interpretable Regularized Ordinal Models from 3D Mesh Data for Neurodegenerative Disease Staging

AU - ENIGMA consortium

AU - Zhao, Yuji

AU - Laansma, Max A.

AU - van Heese, Eva M.

AU - Owens-Walton, Conor

AU - Parkes, Laura M.

AU - Debove, Ines

AU - Rummel, Christian

AU - Wiest, Roland

AU - Cendes, Fernando

AU - Guimaraes, Rachel

AU - Yasuda, Clarissa Lin

AU - Wang, Jiun Jie

AU - Anderson, Tim J.

AU - Dalrymple-Alford, John C.

AU - Melzer, Tracy R.

AU - Pitcher, Toni L.

AU - Schmidt, Reinhold

AU - Schwingenschuh, Petra

AU - Garraux, Gäetan

AU - Rango, Mario

AU - Squarcina, Letizia

AU - Al-Bachari, Sarah

AU - Emsley, Hedley C.A.

AU - Klein, Johannes C.

AU - Mackay, Clare E.

AU - Dirkx, Michiel F.

AU - Helmich, Rick

AU - Assogna, Francesca

AU - Piras, Fabrizio

AU - Bright, Joanna K.

AU - Spalletta, Gianfranco

AU - Poston, Kathleen

AU - Lochner, Christine

AU - McMillan, Corey T.

AU - Weintraub, Daniel

AU - Druzgal, Jason

AU - Newman, Benjamin

AU - Van Den Heuvel, Odile A.

AU - Jahanshad, Neda

AU - Thompson, Paul M.

AU - van der Werf, Ysbrand D.

AU - Gutman, Boris

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - Imaging biomarker

KW - Neurodegenerative Disease

KW - Ordinal regression

UR - http://www.scopus.com/inward/record.url?scp=85141746165&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-17899-3_12

DO - 10.1007/978-3-031-17899-3_12

M3 - 会议稿件

AN - SCOPUS:85141746165

SN - 9783031178986

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 115

EP - 124

BT - Machine Learning in Clinical Neuroimaging - 5th International Workshop, MLCN 2022, Held in Conjunction with MICCAI 2022, Proceedings

A2 - Abdulkadir, Ahmed

A2 - Bathula, Deepti R.

A2 - Dvornek, Nicha C.

A2 - Habes, Mohamad

A2 - Kia, Seyed Mostafa

A2 - Kumar, Vinod

A2 - Wolfers, Thomas

PB - Springer Science and Business Media Deutschland GmbH

T2 - 5th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2022, held in Conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022

Y2 - 18 September 2022 through 18 September 2022

ER -

ENIGMA consortium. Learning Interpretable Regularized Ordinal Models from 3D Mesh Data for Neurodegenerative Disease Staging. In Abdulkadir A, Bathula DR, Dvornek NC, Habes M, Kia SM, Kumar V, Wolfers T, editors, Machine Learning in Clinical Neuroimaging - 5th International Workshop, MLCN 2022, Held in Conjunction with MICCAI 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 115-124. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-17899-3_12

Learning Interpretable Regularized Ordinal Models from 3D Mesh Data for Neurodegenerative Disease Staging

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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