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Deformation-based Statistical Shape Analysis of the Corpus Callosum in Mild Cognitive Impairment and Alzheimer’s Disease

[ Vol. 15 , Issue. 12 ]


Zihan Jiang, Huilin Yang and Xiaoying Tang*   Pages 1151 - 1160 ( 10 )


Objective: In this study, we investigated the influence that the pathology of Alzheimer’s disease (AD) exerts upon the corpus callosum (CC) using a total of 325 mild cognitive impairment (MCI) subjects, 155 AD subjects, and 185 healthy control (HC) subjects.

Method: Regionally-specific morphological CC abnormalities, as induced by AD, were quantified using a large deformation diffeomorphic metric curve mapping based statistical shape analysis pipeline. We also quantified the association between the CC shape phenotype and two cognitive measures; the Mini Mental State Examination (MMSE) and the Alzheimer’s Disease Assessment Scale-Cognitive Behavior Section (ADAS-cog). To identify AD-relevant areas, CC was sub-divided into three subregions; the genu, body, and splenium (gCC, bCC, and sCC).

Results: We observed significant shape compressions in AD relative to that in HC, mainly concentrated on the superior part of CC, across all three sub-regions. The HC-vs-MCI shape abnormalities were also concentrated on the superior part, but mainly occurred on bCC and sCC. The significant MCI-vs-AD shape differences, however, were only detected in part of sCC. In the shape-cognition association, significant negative correlations to ADAS-cog were detected for shape deformations at regions belonging to gCC and sCC and significant positive correlations to MMSE at regions mainly belonging to sCC.

Conclusion: Our results suggest that the callosal shape deformation patterns, especially those of sCC, linked tightly to the cognitive decline in AD, and are potentially a powerful biomarker for monitoring the progression of AD.


Alzheimer`s disease, mild cognitive impairment, corpus callosum, shape analysis, deformation, shape-cognition association.


Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, Guangdong, Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, Guangdong

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