TY - JOUR
T1 - Sparse Multi-Response Tensor Regression for Alzheimer's Disease Study with Multivariate Clinical Assessments
AU - Li, Zhou
AU - Suk, Heung Il
AU - Shen, Dinggang
AU - Li, Lexin
N1 - Funding Information:
Manuscript received January 25, 2016; revised February 27, 2016; accepted March 01, 2016. Date of current version July 29, 2016. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). This work was supported in part by the National Science Foundation under Grant AG049371, and Grant AG042599. The work of H.-I. Suk was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2015R1C1A1A01052216). The work of D. Shen was supported by the NIH under Grant EB006733, Grant EB008374, Grant EB009634, MH100217, Grant AG041721, Grant AG049371, and Grant AG042599. Asterisk indicates corresponding author.
PY - 2016/8
Y1 - 2016/8
N2 - Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disorder that has recently seen serious increase in the number of affected subjects. In the last decade, neuroimaging has been shown to be a useful tool to understand AD and its prodromal stage, amnestic mild cognitive impairment (MCI). The majority of AD/MCI studies have focused on disease diagnosis, by formulating the problem as classification with a binary outcome of AD/MCI or healthy controls. There have recently emerged studies that associate image scans with continuous clinical scores that are expected to contain richer information than a binary outcome. However, very few studies aim at modeling multiple clinical scores simultaneously, even though it is commonly conceived that multivariate outcomes provide correlated and complementary information about the disease pathology. In this article, we propose a sparse multi-response tensor regression method to model multiple outcomes jointly as well as to model multiple voxels of an image jointly. The proposed method is particularly useful to both infer clinical scores and thus disease diagnosis, and to identify brain subregions that are highly relevant to the disease outcomes. We conducted experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and showed that the proposed method enhances the performance and clearly outperforms the competing solutions.
AB - Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disorder that has recently seen serious increase in the number of affected subjects. In the last decade, neuroimaging has been shown to be a useful tool to understand AD and its prodromal stage, amnestic mild cognitive impairment (MCI). The majority of AD/MCI studies have focused on disease diagnosis, by formulating the problem as classification with a binary outcome of AD/MCI or healthy controls. There have recently emerged studies that associate image scans with continuous clinical scores that are expected to contain richer information than a binary outcome. However, very few studies aim at modeling multiple clinical scores simultaneously, even though it is commonly conceived that multivariate outcomes provide correlated and complementary information about the disease pathology. In this article, we propose a sparse multi-response tensor regression method to model multiple outcomes jointly as well as to model multiple voxels of an image jointly. The proposed method is particularly useful to both infer clinical scores and thus disease diagnosis, and to identify brain subregions that are highly relevant to the disease outcomes. We conducted experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and showed that the proposed method enhances the performance and clearly outperforms the competing solutions.
KW - Alzheimer's Disease
KW - Magnetic Resonance Imaging
KW - Multiple Responses
KW - Region Selection
KW - Tensor Regression
UR - http://www.scopus.com/inward/record.url?scp=84982806170&partnerID=8YFLogxK
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U2 - 10.1109/TMI.2016.2538289
DO - 10.1109/TMI.2016.2538289
M3 - Article
C2 - 26960221
AN - SCOPUS:84982806170
VL - 35
SP - 1927
EP - 1936
JO - IEEE Transactions on Medical Imaging
JF - IEEE Transactions on Medical Imaging
SN - 0278-0062
IS - 8
M1 - 7426368
ER -