Cong Shan, Rizkalla Maher, Salama Paul, West John, Risacher Shannon, Saykin Andrew, Shen Li
Dept. of Electrical and Computer Engineering, Purdue University Indianapolis, Indianapolis, IN 46202.
Dept. of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202.
Conf Proc (Midwest Symp Circuits Syst). 2015 Aug;2015. doi: 10.1109/MWSCAS.2015.7282173.
The hippocampus is widely studied with neuroimaging techniques given its importance in learning and memory and its potential as a biomarker for Alzheimer's disease (AD). Its complex folding anatomy often presents analytical challenges. In particular, the critical subfield information is typically not addressed by the existing hippocampal shape studies. To bridge this gap, we present a computational framework for surface-based morphometric analysis of hippocampal subfields. The major strengths of this framework are as follows: (a) it performs detailed hippocampal shape analysis, (b) it embraces, rather than ignores, the important hippocampal subfield information, and (c) it analyzes regular magnetic resonance imaging scans and is applicable to large scale studies. We demonstrate its effectiveness by applying it to the identification of regional hippocampal subfield atrophy patterns associated with mild cognitive impairment and AD.
鉴于海马体在学习和记忆中的重要性及其作为阿尔茨海默病(AD)生物标志物的潜力,人们利用神经成像技术对其进行了广泛研究。其复杂的折叠结构常常带来分析挑战。特别是,现有的海马体形状研究通常未涉及关键的子区域信息。为了弥补这一差距,我们提出了一个用于基于表面的海马体子区域形态计量分析的计算框架。该框架的主要优势如下:(a)它能进行详细的海马体形状分析;(b)它包含而非忽略重要的海马体子区域信息;(c)它能分析常规磁共振成像扫描,适用于大规模研究。我们通过将其应用于识别与轻度认知障碍和AD相关的区域海马体子区域萎缩模式来证明其有效性。