Predicting Progressions of Cognitive Outcomes via High-Order Multi-Modal Multi-Task Feature Learning
Lyujian Lu, Hua Wang, Xiaohui Yao, Shannon Risacher, Andrew Saykin, Li Shen, for the ADNI
ISBI - 2018
Many existing studies on complex brain disorders, such as Alzheimer’s Disease, usually employed regression analysis to associate the neuroimaging measures to cognitive status. However, whether these measures in multiple modalities have the predictive power to infer the trajectory of cognitive performance over time still remain under-explored. In this paper, we propose a high-order multi-modal multi-mask feature learning model to uncover temporal relationship between the longitudinal neuroimaging measures and progressive cognitive output scores. The regularizations through sparsity-induced norms implemented in the proposed learning model enable the selection of only a small number of imaging features over time and capture modality structures for multi-modal imaging markers. The promising experimental results in extensive empirical studies performed on the ADNI cohort have validated the effectiveness of the proposed method.
Links
- View publications from Lyujian Lu
- View publications from Hua Wang
- View publications presented in ISBI
- View publications in the project, Mining Brain Imaging Genomics Data for Improved Cognitive Health
- View publications in the project, Mining Materials Genome Data for Prediction and Guidance of Nanoparticle Synthesis
- View publications researching Longitudinal / Temporal Learning Models
- View publications researching Multi-Modal/View Data Fusion
- View publications researching Sparsity / Sparse Coding
- View publications applied to Medical Image Computing
Cite this paper
MLA
Lu, Lyujian, et al. "Predicting progressions of cognitive outcomes via high-order multi-modal multi-task feature learning." 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE, 2018.
BibTeX
@inproceedings{lu2018predicting,
title={Predicting progressions of cognitive outcomes via high-order multi-modal multi-task feature learning},
author={Lu, Lyujian and Wang, Hua and Yao, Xiaohui and Risacher, Shannon and Saykin, Andrew and Shen, Li},
booktitle={2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)},
pages={545--548},
year={2018},
organization={IEEE}
}