Integrated cortical structural marker for Alzheimer's disease

NEUROBIOLOGY OF AGING

卷:36

Article

Ming, Jing^1,7  Harms, Michael P.²  Morris, John C.^3,4,5  Beg, M. Faisal⁶  Wang, Lei^7,8 

[1] Univ Illinois, Biomed Engn, Chicago, IL USA

[2] Washington Univ, Sch Med, Dept Psychiat, St Louis, MO 63110 USA

[3] Washington Univ, Sch Med, Dept Neurol, St Louis, MO 63110 USA

[4] Washington Univ, Sch Med, Dept Pathol & Immunol, St Louis, MO USA

[5] Washington Univ, Sch Med, Knight Alzheimer Dis Res Ctr, St Louis, MO USA

[6] Simon Fraser Univ, Biomed Engn, Burnaby, BC V5A 1S6, Canada

[7] Northwestern Univ, Feinberg Sch Med, Dept Psychiat & Behav Sci, Chicago, IL 60611 USA

[8] Northwestern Univ, Feinberg Sch Med, Dept Radiol, Chicago, IL 60611 USA

关键词: Neuroimaging;    Cortical thickness;    Cortical geometry;    Convexity;    Metric distortion;    Classification;   

DOI  :  10.1016/j.neurobiolaging.2014.03.042

来源: Elsevier

【 摘 要 】

In this article, we propose an approach to integrate cortical morphology measures for improving the discrimination of individuals with and without very mild Alzheimer's disease (AD). FreeSurfer was applied to scans collected from 83 participants with very mild AD and 124 cognitively normal individuals. We generated cortex thickness, white matter convexity (aka sulcal depth), and white matter surface metric distortion measures on a normalized surface atlas in this first study to integrate high resolution gray matter thickness and white matter surface geometric measures in identifying very mild AD. Principal component analysis was applied to each individual structural measure to generate eigenvectors. Discrimination power based on individual and combined measures are compared, based on stepwise logistic regression and 10-fold cross-validation. Global AD likelihood index and surface-based likelihood maps were also generated. Our results show complementary patterns on the cortical surface between thickness, which reflects gray matter atrophy, convexity, which reflects white matter sulcal depth changes and metric distortion, which reflects white matter surface area changes. The classifier integrating all 3 types of surface measures significantly improved classification performance compared with classification based on single measures. The principal component analysis-based approach provides a framework for achieving high discrimination power by integrating high-dimensional data, and this method could be very powerful in future studies for early diagnosis of diseases that are known to be associated with abnormal gyral and sulcal patterns. (C) 2015 Elsevier Inc. All rights reserved.

【 授权许可】

Free