干旱区生态环境知识资源中心(Arid): Predicting Brain Amyloid Using Multivariate Morphometry Statistics, Sparse Coding, and Correntropy: Validation in 1,101 Individuals From the ADNI and OASIS Databases

Arid

DOI	10.3389/fnins.2021.669595
	Predicting Brain Amyloid Using Multivariate Morphometry Statistics, Sparse Coding, and Correntropy: Validation in 1,101 Individuals From the ADNI and OASIS Databases
	Wu, Jianfeng; Dong, Qunxi; Gui, Jie; Zhang, Jie; Su, Yi; Chen, Kewei; Thompson, Paul M.; Caselli, Richard J.; Reiman, Eric M.; Ye, Jieping; Wang, Yalin
通讯作者	Wang, YL (corresponding author), Arizona State Univ, Sch Comp Informat & Decis Syst Engn, Tempe, AZ 85281 USA.
来源期刊	FRONTIERS IN NEUROSCIENCE
EISSN	1662-453X
出版年	2021
卷号	15
英文摘要	Biomarker assisted preclinical/early detection and intervention in Alzheimer's disease (AD) may be the key to therapeutic breakthroughs. One of the presymptomatic hallmarks of AD is the accumulation of beta-amyloid (A beta) plaques in the human brain. However, current methods to detect A beta pathology are either invasive (lumbar puncture) or quite costly and not widely available (amyloid PET). Our prior studies show that magnetic resonance imaging (MRI)-based hippocampal multivariate morphometry statistics (MMS) are an effective neurodegenerative biomarker for preclinical AD. Here we attempt to use MRI-MMS to make inferences regarding brain A beta burden at the individual subject level. As MMS data has a larger dimension than the sample size, we propose a sparse coding algorithm, Patch Analysis-based Surface Correntropy-induced Sparse-coding and Max-Pooling (PASCS-MP), to generate a low-dimensional representation of hippocampal morphometry for each individual subject. Then we apply these individual representations and a binary random forest classifier to predict brain A beta positivity for each person. We test our method in two independent cohorts, 841 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and 260 subjects from the Open Access Series of Imaging Studies (OASIS). Experimental results suggest that our proposed PASCS-MP method and MMS can discriminate A beta positivity in people with mild cognitive impairment (MCI) [Accuracy (ACC) = 0.89 (ADNI)] and in cognitively unimpaired (CU) individuals [ACC = 0.79 (ADNI) and ACC = 0.81 (OASIS)]. These results compare favorably relative to measures derived from traditional algorithms, including hippocampal volume and surface area, shape measures based on spherical harmonics (SPHARM) and our prior Patch Analysis-based Surface Sparse-coding and Max-Pooling (PASS-MP) methods.
英文关键词	Alzheimer's disease hippocampal multivariate morphometry statistics Dictionary and Correntropy-induced Sparse Coding beta-amyloid burden ADNI and OASIS database
类型	Article
语种	英语
开放获取类型	Green Submitted, gold, Green Published
收录类别	SCI-E
WOS记录号	WOS:000687469000001
WOS关键词	MILD COGNITIVE IMPAIRMENT ; SURFACE CONFORMAL PARAMETERIZATION ; ALZHEIMERS-DISEASE ; STRUCTURAL DIFFERENCES ; GENETIC INFLUENCE ; HIPPOCAMPAL ; SHAPE ; CLASSIFICATION ; REPRESENTATION ; REGISTRATION
WOS类目	Neurosciences
WOS研究方向	Neurosciences & Neurology
来源机构	Arizona State University
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/363328
作者单位	[Wu, Jianfeng; Dong, Qunxi; Zhang, Jie; Wang, Yalin] Arizona State Univ, Sch Comp Informat & Decis Syst Engn, Tempe, AZ 85281 USA; [Dong, Qunxi] Beijing Inst Technol, Inst Engn Med, Beijing, Peoples R China; [Gui, Jie] Southeast Univ, Sch Cyber Sci & Engn, Nanjing, Peoples R China; [Su, Yi; Chen, Kewei; Reiman, Eric M.] Banner Alzheimers Inst, Phoenix, AZ USA; [Thompson, Paul M.] Univ Southern Calif, Imaging Genet Ctr, Stevens Neuroimaging & Informat Inst, Marina Del Rey, CA USA; [Caselli, Richard J.] Mayo Clin Arizona, Dept Neurol, Scottsdale, AZ USA; [Ye, Jieping] Univ Michigan, Dept Computat Med & Bioinformat, Ann Arbor, MI 48109 USA
推荐引用方式 GB/T 7714	Wu, Jianfeng,Dong, Qunxi,Gui, Jie,et al. Predicting Brain Amyloid Using Multivariate Morphometry Statistics, Sparse Coding, and Correntropy: Validation in 1,101 Individuals From the ADNI and OASIS Databases[J]. Arizona State University,2021,15.
APA	Wu, Jianfeng.,Dong, Qunxi.,Gui, Jie.,Zhang, Jie.,Su, Yi.,...&Wang, Yalin.(2021).Predicting Brain Amyloid Using Multivariate Morphometry Statistics, Sparse Coding, and Correntropy: Validation in 1,101 Individuals From the ADNI and OASIS Databases.FRONTIERS IN NEUROSCIENCE,15.
MLA	Wu, Jianfeng,et al."Predicting Brain Amyloid Using Multivariate Morphometry Statistics, Sparse Coding, and Correntropy: Validation in 1,101 Individuals From the ADNI and OASIS Databases".FRONTIERS IN NEUROSCIENCE 15(2021).