title: Discerning mild cognitive impairment and Alzheimer Disease from normal aging: morphologic characterization based on univariate and multivariate models.
abstract: OBJECTIVE
  Differentiating mild cognitive impairment (MCI) and Alzheimer Disease (AD) from healthy aging remains challenging. This study aimed to explore the cerebral structural alterations of subjects with MCI or AD as compared to healthy elderly based on the individual and collective effects of cerebral morphologic indices using univariate and multivariate analyses.
  
  
  METHODS
  T1-weighted images (T1WIs) were retrieved from Alzheimer Disease Neuroimaging Initiative database for 116 subjects who were categorized into groups of healthy aging, MCI, and AD. Analysis of covariance (ANCOVA) and multivariate analysis of covariance (MANCOVA) were performed to explore the intergroup morphologic alterations indexed by surface area, curvature index, cortical thickness, and subjacent white matter volume with age and sex controlled as covariates, in 34 parcellated gyri regions of interest (ROIs) for both cerebral hemispheres based on the T1WI. Statistical parameters were mapped on the anatomic images to facilitate visual inspection.
  
  
  RESULTS
  Global rather than region-specific structural alterations were revealed in groups of MCI and AD relative to healthy elderly using MANCOVA. ANCOVA revealed that the cortical thickness decreased more prominently in entorhinal, temporal, and cingulate cortices and was positively correlated with patients' cognitive performance in AD group but not in MCI. The temporal lobe features marked atrophy of white matter during the disease dynamics. Significant intercorrelations were observed among the morphologic indices with univariate analysis for given ROIs.
  
  
  CONCLUSIONS
  Significant global structural alterations were identified in MCI and AD based on MANCOVA model with improved sensitivity. The intercorrelation among the morphologic indices may dampen the use of individual morphological parameter in featuring cerebral structural alterations. Decrease in cortical thickness is not reflective of the cognitive performance at the early stage of AD.
authors.affiliation: Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Ave., Shenzhen, Guangdong Province 518055, China.
authors.forename: Weiqi
authors.initials: W
authors.lastname: Liao
authors.affiliation: Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Ave., Shenzhen, Guangdong Province 518055, China.
authors.forename: Xiaojing
authors.initials: X
authors.lastname: Long
authors.affiliation: Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Ave., Shenzhen, Guangdong Province 518055, China.
authors.forename: Chunxiang
authors.initials: C
authors.lastname: Jiang
authors.affiliation: Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Ave., Shenzhen, Guangdong Province 518055, China.
authors.forename: Yanjun
authors.initials: Y
authors.lastname: Diao
authors.affiliation: Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Ave., Shenzhen, Guangdong Province 518055, China.
authors.forename: Xin
authors.initials: X
authors.lastname: Liu
authors.affiliation: Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Ave., Shenzhen, Guangdong Province 518055, China.
authors.forename: Hairong
authors.initials: H
authors.lastname: Zheng
authors.affiliation: Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Ave., Shenzhen, Guangdong Province 518055, China. Electronic address: lj.zhang@siat.ac.cn.
authors.forename: Lijuan
authors.initials: L
authors.lastname: Zhang
mesh_terms: D000368:Aged; D000375:Aging / Q000473:pathology*; D000465:Algorithms; D000544:Alzheimer Disease / Q000150:complications / Q000473:pathology*; D001921:Brain / Q000473:pathology*; D060825:Cognitive Dysfunction / Q000150:complications / Q000473:pathology*; D003198:Computer Simulation; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D015233:Models, Statistical; D015999:Multivariate Analysis; D010363:Pattern Recognition, Automated / Q000379:methods; D012016:Reference Values; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Alzheimer disease; Structural neuroimaging; brain morphology; healthy aging; mild cognitive impairment