Structural Lateralization in Brains of Patients with Alzheimer's Disease

Journal of Integration Technology(2013)

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摘要
Hemispheric asymmetry is believed to increase the efficiency of information processing, especially for the multitask performance. Regional variation of cerebral lateralization was observed in healthy elderly as well as in patients with Alzheimer's disease(AD). However, the extent and degree of the alterations in the cerebral lateralization in AD patients remained controversial. In the current study, high-resolution T1-weighted magnetic resonance images(T1WI) and diffusion tensor images(DTI) were retrieved consecutively for a total of 78 AD patients and healthy elderly from the open database of ADNI(http://adni.loni.ucla.edu/). The entire brain of each subject was parcellated into 34 regions of interest(ROIs) for each hemisphere. Five variables including cortical surface area, curvature index, cortical thickness, subjacent white matter volume,and fractional anisotropy(FA) of white matter fiber tracts were calculated for each ROI. Inter-hemisphere difference of the aforementioned parameters was analyzed using univariate and multivariate models for each ROI. The statistics parametric images were mapped on the anatomy template to facilitate visual inspection. Significant morphological variation between hemispheres was identified mainly in the lateral prefrontal cortex and temporal lobe, medial parietal lobe and limbic system which were vulnerable in both healthy aging and AD. Cerebral lateralization reduced significantly in AD patient in comparison with healthy elderly, especially in the entorhinal cortex, the gray matter lateralization and white matter lateralization both vanished. Univariate and multivariate analyses of the alterations in the cerebral lateralization might add extra values of the morphological information in evaluating the disease dynamic of AD and other dementia disorders.
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关键词
morphology,structural MRI,cerebral lateralization,Alzheimer's Disease,fractional anisotropy,diffusion tensor imaging
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