Rationale and Objectives
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.
Materials and 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.
As a disease with increasing prevalence, Alzheimer disease (AD) has raised great medical and socioeconomic concerns . The onset of AD is difficult to define. It remains challenging for clinicians to identify the entity at the preclinical stage when patients are asymptomatic or just present with mild cognitive impairment (MCI) . Effective prevention and cure are yet to be developed through intensive research toward the better understanding of the mechanism and the dynamic of the disease. The accumulation of the disease pathology during predementia may result in macroscopic structural alterations in the brain, which allows characterization of the transition events of the disease and differentiating it from healthy aging.
The diagnosis may be reached based on information integrating patient’s overall medical condition, family history, physical and neurologic examinations, neuropsychological tests, and imaging studies . Objective parameters of molecular and structural markers have been investigated as supplementary evidences facilitating diagnosis and characterizing the disease dynamics. Cross-sectional and longitudinal brain research in healthy elders and patients with MCI who eventually developed AD (progressive MCI) has revealed regional structural and functional alterations well associated with cognitive impairment . Region-specific parenchymal tissue loss, amyloid plaque, and neurofibrillary tangle were reported to be associated with the gross architectural destruction identified by multimodality neuroimaging . Magnetic resonance imaging (MRI) with submillimeter resolution and postprocessing techniques of voxel-based morphometry has greatly facilitated the characterization of neuroanatomic features of AD patients relative to the age and gender–matched healthy elders with high reproductivity and accuracy . However, the effectiveness of the region-specific morphologic alterations in identifying the diseased brain based on single measure may be profoundly complicated because of the intercorrelation among the indices , which may lead to discrepancies in discrete studies. In this work, we aimed to investigate the cerebral structural alterations of MCI and AD groups based on cerebral surface area, curvature index, cortical thickness, and subjacent white matter volume collectively by multivariate analysis of covariance (MANCOVA), and reappraise the use of the individual morphologic parameter by analysis of covariance (ANCOVA) in the disease characterization.
Materials and methods
Subjects
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Table 1
Demographic Information of Subject Groups
HC Progressive MCI AD_n_ 44 39 33 Sex (Male/Female) 23/21 27/12 17/16 Age (years) X¯¯¯±S X
¯
±
S 76.3 ± 6.6 75.6 ± 7.3 77.8 ± 7.5 MMSE score X¯¯¯±S X
¯
±
S 29.3 ± 0.8 25.9 ± 2.3 14.9 ± 5.9 CDR scale 0 0–0.5 1–3
AD, Alzheimer disease; HC, healthy elderly control; MCI, mild cognitive impairment.
X¯¯¯±S X
¯
±
S , mean ± standard deviation.
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MR Image Processing
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Table 2
Typical Parameters of the T1-weighted Imaging
Facility TR (milliseconds) TE (milliseconds) TI (milliseconds) FA Weighting GE Medical Systems 8.92 3.9 1000 8° T1 Philips Medical System 8.60 4.0 0 8° T1 Siemens 2400/3000 ∗ 3.5 1000 8° T1
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Statistical Analysis
Univariate analysis
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Multivariate analysis
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Multiple testing corrections
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Results
Univariate Analysis
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Table 3
Number of Regions of Significant Difference with Percentage (%) of Each Morphologic Index Using ANCOVA and Post hoc Analysis
Morphologic Index One-way ANCOVA HC versus MCI ∗ HC versus AD ∗ MCI versus AD ∗ Uncorrected [ n (%)] FDR [ n (%)] Uncorrected [ n (%)] FDR [ n (%)] Uncorrected [ n (%)] FDR [ n (%)] Uncorrected [ n (%)] FDR [ n (%)] Surface area 3 (9) 0 1 (3) 0 1 (3) 0 2 (6) 0 Curvature index 12 (35) 4 (12) 5 (15) 3 (9) 8 (24) 4 (12) 1 (3) 0 Cortical thickness 30 (88) 30 (88) 19 (56) 19 (56) 30 (88) 30 (88) 27 (79) 27 (79) WM Volume 17 (50) 15 (44) 2 (6) 2 (6) 17 (50) 15 (44) 14 (41) 11 (32)
AD, Alzheimer disease; ANCOVA, analysis of covariance; FDR, false discovery rate; HC, healthy elderly control; MCI, mild cognitive impairment; WM, white matter.
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Correlation among the Morphologic Indices and Cognitive Status
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Multivariate Analysis
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Discussion
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Limitations
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Conclusions
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Acknowledgments
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