Prediction of memory impairment with MRI data: A longitudinal study of Alzheimer’s disease

Xiaoqian Wang; Dinggang Shen; Heng Huang

doi:10.1007/978-3-319-46720-7_32

Prediction of memory impairment with MRI data: A longitudinal study of Alzheimer’s disease

Xiaoqian Wang, Dinggang Shen, Heng Huang

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

20 Citations (Scopus)

Abstract

Alzheimer’s Disease (AD),a severe type of neurodegenerative disorder with progressive impairment of learning and memory,has threatened the health of millions of people. How to recognize AD at early stage is crucial. Multiple models have been presented to predict cognitive impairments by means of neuroimaging data. However,traditional models did not employ the valuable longitudinal information along the progression of the disease. In this paper,we proposed a novel longitudinal feature learning model to simultaneously uncover the interrelations among different cognitive measures at different time points and utilize such interrelated structures to enhance the learning of associations between imaging features and prediction tasks. Moreover,we adopted Schatten p-norm to identify the interrelation structures existing in the low-rank subspace. Empirical results on the ADNI cohort demonstrated promising performance of our model.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
Editors	Sebastian Ourselin, Leo Joskowicz, Mert R. Sabuncu, William Wells, Gozde Unal
Publisher	Springer Verlag
Pages	273-281
Number of pages	9
ISBN (Print)	9783319467191
DOIs	https://doi.org/10.1007/978-3-319-46720-7_32
Publication status	Published - 2016
Event	1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 - Athens, Greece Duration: 2016 Oct 21 → 2016 Oct 21

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9900 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016
Country/Territory	Greece
City	Athens
Period	16/10/21 → 16/10/21

Bibliographical note

Funding Information:
X. Wang and H. Huang were supported in part by NSF IIS-1117965, IIS-1302675, IIS-1344152, DBI-1356628, and NIH AG049371. D. Shen was supported in part by NIH AG041721.

Publisher Copyright:
© Springer International Publishing AG 2016.

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-46720-7_32

Cite this

Wang, X., Shen, D., & Huang, H. (2016). Prediction of memory impairment with MRI data: A longitudinal study of Alzheimer’s disease. In S. Ourselin, L. Joskowicz, M. R. Sabuncu, W. Wells, & G. Unal (Eds.), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings (pp. 273-281). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9900 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46720-7_32

Prediction of memory impairment with MRI data: A longitudinal study of Alzheimer’s disease. / Wang, Xiaoqian; Shen, Dinggang; Huang, Heng.
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. ed. / Sebastian Ourselin; Leo Joskowicz; Mert R. Sabuncu; William Wells; Gozde Unal. Springer Verlag, 2016. p. 273-281 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9900 LNCS).

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

Wang, X, Shen, D & Huang, H 2016, Prediction of memory impairment with MRI data: A longitudinal study of Alzheimer’s disease. in S Ourselin, L Joskowicz, MR Sabuncu, W Wells & G Unal (eds), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9900 LNCS, Springer Verlag, pp. 273-281, 1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, Athens, Greece, 16/10/21. https://doi.org/10.1007/978-3-319-46720-7_32

Wang X, Shen D, Huang H. Prediction of memory impairment with MRI data: A longitudinal study of Alzheimer’s disease. In Ourselin S, Joskowicz L, Sabuncu MR, Wells W, Unal G, editors, Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Springer Verlag. 2016. p. 273-281. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-46720-7_32

Wang, Xiaoqian ; Shen, Dinggang ; Huang, Heng. / Prediction of memory impairment with MRI data : A longitudinal study of Alzheimer’s disease. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. editor / Sebastian Ourselin ; Leo Joskowicz ; Mert R. Sabuncu ; William Wells ; Gozde Unal. Springer Verlag, 2016. pp. 273-281 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{7256681ef6d64c31bf6b8cb16007c9a0,

title = "Prediction of memory impairment with MRI data: A longitudinal study of Alzheimer{\textquoteright}s disease",

abstract = "Alzheimer{\textquoteright}s Disease (AD),a severe type of neurodegenerative disorder with progressive impairment of learning and memory,has threatened the health of millions of people. How to recognize AD at early stage is crucial. Multiple models have been presented to predict cognitive impairments by means of neuroimaging data. However,traditional models did not employ the valuable longitudinal information along the progression of the disease. In this paper,we proposed a novel longitudinal feature learning model to simultaneously uncover the interrelations among different cognitive measures at different time points and utilize such interrelated structures to enhance the learning of associations between imaging features and prediction tasks. Moreover,we adopted Schatten p-norm to identify the interrelation structures existing in the low-rank subspace. Empirical results on the ADNI cohort demonstrated promising performance of our model.",

author = "Xiaoqian Wang and Dinggang Shen and Heng Huang",

note = "Funding Information: X. Wang and H. Huang were supported in part by NSF IIS-1117965, IIS-1302675, IIS-1344152, DBI-1356628, and NIH AG049371. D. Shen was supported in part by NIH AG041721. Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 ; Conference date: 21-10-2016 Through 21-10-2016",

year = "2016",

doi = "10.1007/978-3-319-46720-7_32",

language = "English",

isbn = "9783319467191",

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

publisher = "Springer Verlag",

pages = "273--281",

editor = "Sebastian Ourselin and Leo Joskowicz and Sabuncu, {Mert R.} and William Wells and Gozde Unal",

booktitle = "Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings",

}

TY - GEN

T1 - Prediction of memory impairment with MRI data

T2 - 1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016

AU - Wang, Xiaoqian

AU - Shen, Dinggang

AU - Huang, Heng

N1 - Funding Information: X. Wang and H. Huang were supported in part by NSF IIS-1117965, IIS-1302675, IIS-1344152, DBI-1356628, and NIH AG049371. D. Shen was supported in part by NIH AG041721. Publisher Copyright: © Springer International Publishing AG 2016.

PY - 2016

Y1 - 2016

N2 - Alzheimer’s Disease (AD),a severe type of neurodegenerative disorder with progressive impairment of learning and memory,has threatened the health of millions of people. How to recognize AD at early stage is crucial. Multiple models have been presented to predict cognitive impairments by means of neuroimaging data. However,traditional models did not employ the valuable longitudinal information along the progression of the disease. In this paper,we proposed a novel longitudinal feature learning model to simultaneously uncover the interrelations among different cognitive measures at different time points and utilize such interrelated structures to enhance the learning of associations between imaging features and prediction tasks. Moreover,we adopted Schatten p-norm to identify the interrelation structures existing in the low-rank subspace. Empirical results on the ADNI cohort demonstrated promising performance of our model.

AB - Alzheimer’s Disease (AD),a severe type of neurodegenerative disorder with progressive impairment of learning and memory,has threatened the health of millions of people. How to recognize AD at early stage is crucial. Multiple models have been presented to predict cognitive impairments by means of neuroimaging data. However,traditional models did not employ the valuable longitudinal information along the progression of the disease. In this paper,we proposed a novel longitudinal feature learning model to simultaneously uncover the interrelations among different cognitive measures at different time points and utilize such interrelated structures to enhance the learning of associations between imaging features and prediction tasks. Moreover,we adopted Schatten p-norm to identify the interrelation structures existing in the low-rank subspace. Empirical results on the ADNI cohort demonstrated promising performance of our model.

UR - http://www.scopus.com/inward/record.url?scp=84996598828&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-46720-7_32

DO - 10.1007/978-3-319-46720-7_32

M3 - Conference contribution

AN - SCOPUS:84996598828

SN - 9783319467191

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

SP - 273

EP - 281

BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings

A2 - Ourselin, Sebastian

A2 - Joskowicz, Leo

A2 - Sabuncu, Mert R.

A2 - Wells, William

A2 - Unal, Gozde

PB - Springer Verlag

Y2 - 21 October 2016 through 21 October 2016

ER -

Prediction of memory impairment with MRI data: A longitudinal study of Alzheimer’s disease

Abstract

Publication series

Other

Bibliographical note

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this