Identifying AD-Sensitive and Cognition-Relevant Imaging Biomarkers via Joint Classification and Regression
Hua Wang, Feiping Nie, Heng Huang, Shannon L. Risacher, Andrew J. Saykin, Li Shen, ADNI.
MICCAI - 2011
Traditional neuroimaging studies in Alzheimer’s disease (AD) typically employ independent and pairwise analyses between multimodal data, which treat imaging biomarkers, cognitive measures, and disease status as isolated units. To enhance mechanistic understanding of AD, in this paper, we conduct a new study for identifying imaging biomarkers that are associated with both cognitive measures and AD. To achieve this goal, we propose a new sparse joint classification and regression method. The imaging biomarkers identified by our method are AD-sensitive and cognition-relevant and can help reveal complex relationships among brain structure, cognition and disease status. Using the imaging and cognition data from Alzheimer’s Disease Neuroimaging Initiative database, the effectiveness of the proposed method is demonstrated by clearly improved performance on predicting both cognitive scores and disease status.
Links
- View publications from Hua Wang
- View publications presented in MICCAI
- View publications researching Sparsity / Sparse Coding
- View publications applied to Medical Image Computing
Cite this paper
MLA
Wang, Hua, et al. "Identifying AD-sensitive and cognition-relevant imaging biomarkers via joint classification and regression." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Berlin, Heidelberg, 2011.
BibTeX
@inproceedings{wang2011identifying,
title={Identifying AD-sensitive and cognition-relevant imaging biomarkers via joint classification and regression},
author={Wang, Hua and Nie, Feiping and Huang, Heng and Risacher, Shannon and Saykin, Andrew J and Shen, Li and others},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={115--123},
year={2011},
organization={Springer}
}