Network-guided sparse learning for predicting cognitive outcomes from MRI measures

Jingwen Yan, Heng Huang, Shannon L. Risacher, Sungeun Kim, Mark Inlow, Jason H. Moore, Andrew Saykin, Li Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

Alzheimer's disease (AD) is characterized by gradual neurodegeneration and loss of brain function, especially for memory during early stages. Regression analysis has been widely applied to AD research to relate clinical and biomarker data such as predicting cognitive outcomes from MRI measures. In particular, sparse models have been proposed to identify the optimal imaging markers with high prediction power. However, the complex relationship among imaging markers are often overlooked or simplified in the existing methods. To address this issue, we present a new sparse learning method by introducing a novel network term to more flexibly model the relationship among imaging markers. The proposed algorithm is applied to the ADNI study for predicting cognitive outcomes using MRI scans. The effectiveness of our method is demonstrated by its improved prediction performance over several state-of-the-art competing methods and accurate identification of cognition-relevant imaging markers that are biologically meaningful.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages202-210
Number of pages9
Volume8159 LNCS
DOIs
StatePublished - 2013
Event3rd International Workshop on Multimodal Brain Image Analysis, MBIA 2013, Held in Conjunction with the 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 - Nagoya, Japan
Duration: Sep 22 2013Sep 22 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8159 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other3rd International Workshop on Multimodal Brain Image Analysis, MBIA 2013, Held in Conjunction with the 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
CountryJapan
CityNagoya
Period9/22/139/22/13

Fingerprint

Magnetic resonance imaging
Imaging
Imaging techniques
Alzheimer's Disease
Biomarkers
Performance Prediction
Cognition
Regression Analysis
Regression analysis
Brain
Data storage equipment
Learning
Prediction
Term
Model
Relationships

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Yan, J., Huang, H., Risacher, S. L., Kim, S., Inlow, M., Moore, J. H., ... Shen, L. (2013). Network-guided sparse learning for predicting cognitive outcomes from MRI measures. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8159 LNCS, pp. 202-210). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8159 LNCS). https://doi.org/10.1007/978-3-319-02126-3_20

Network-guided sparse learning for predicting cognitive outcomes from MRI measures. / Yan, Jingwen; Huang, Heng; Risacher, Shannon L.; Kim, Sungeun; Inlow, Mark; Moore, Jason H.; Saykin, Andrew; Shen, Li.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8159 LNCS 2013. p. 202-210 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8159 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Yan, J, Huang, H, Risacher, SL, Kim, S, Inlow, M, Moore, JH, Saykin, A & Shen, L 2013, Network-guided sparse learning for predicting cognitive outcomes from MRI measures. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 8159 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8159 LNCS, pp. 202-210, 3rd International Workshop on Multimodal Brain Image Analysis, MBIA 2013, Held in Conjunction with the 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013, Nagoya, Japan, 9/22/13. https://doi.org/10.1007/978-3-319-02126-3_20
Yan J, Huang H, Risacher SL, Kim S, Inlow M, Moore JH et al. Network-guided sparse learning for predicting cognitive outcomes from MRI measures. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8159 LNCS. 2013. p. 202-210. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-02126-3_20
Yan, Jingwen ; Huang, Heng ; Risacher, Shannon L. ; Kim, Sungeun ; Inlow, Mark ; Moore, Jason H. ; Saykin, Andrew ; Shen, Li. / Network-guided sparse learning for predicting cognitive outcomes from MRI measures. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8159 LNCS 2013. pp. 202-210 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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