基于多变量形态学特征的健康老年人认知发展预测算法  

Cognitive Development Prediction Algorithm for Healthy Elderly Based on Multi⁃variate Morphological Features

作　　者：张玲玉 王雅琳 赵子阳 黄文静 郑炜豪 姚志军[1] 胡斌[1,6,7,8] ZHANG Lingyu;WANG Yalin;ZHAO Ziyang;HUANG Wenjing;ZHENG Weihao;YAO Zhijun;HU Bin(Gansu Provincial Key Laboratory of Wearable Computing,School of Information Science and Engineering,Lanzhou University,Lanzhou 730000,China;School of Computing,Informatics,and Decision Systems Engineering,Arizona State University,Tempe 85281,USA;Department of Magnetic Resonance,Lanzhou University Second Hospital,Lanzhou 730030,China;Second Clinical School,Lanzhou University,Lanzhou 730030,China;Gansu Province Clinical Research Center for Functional and Molecular Imaging,Lanzhou 730030,China;School of Medical Technology,Beijing Institute of Technology,Beijing 100081,China;Center for Excellence in Brain Science and Intelligence Technology,Shanghai Institutes for Biological Sciences,Chinese Academy of Sciences,Shanghai 200031,China;Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University&Institute of Semiconductors,Chinese Academy of Sciences,Lanzhou 730000,China)

机构地区：[1]兰州大学信息科学与工程学院甘肃省可穿戴装备重点实验室,兰州730000 [2]美国亚利桑那州立大学计算、信息学和决策系统工程学院,坦佩85281 [3]兰州大学第二医院核磁共振科,兰州730030 [4]兰州大学第二临床医学院,兰州730030 [5]甘肃省功能及分子影像临床医学研究中心,兰州730030 [6]北京理工大学医学技术学院,北京100081 [7]中国科学院神经科学研究所脑科学与智能技术卓越创新中心,上海200031 [8]中国科学院半导体研究所-兰州大学认知神经传感技术联合研究中心,兰州730000

出　　处：《数据采集与处理》2023年第4期837-848,共12页Journal of Data Acquisition and Processing

基　　金：国家重点研发计划(2019YFA0706200);国家自然科学基金(U21A20520,62227807,61632014,61627808);科技创新2030重点项目(2021ZD0200701)。

摘　　要：由于体积、表面积等常规形态学指标对于皮层下核团而言过于笼统,因此传统的形态特征获取手段难以检测到其表面形态的细微变化。为解决这一问题,本文提出了一种针对皮层下核团的精细特征提取算法,并将其应用到老年人认知状态预测任务上。通过表面共形参数化、表面共形表示和基于互信息的表面流配准,提取了46名被试双侧海马和杏仁核各15000×2个顶点上的形态学特征;通过斑块选择、稀疏编码与字典学习,和最大池化的降维流程,避免了维度诅咒的同时充分保留了核团的纹理信息;最后,以树为弱学习器,采用GentleBoost算法集成了最终的强分类器做认知预测。结果显示,仅纳入海马和杏仁核两个皮层下结构的新颖特征,即可达到85%的预测准确率,为皮层下结构的精细特征发掘提供了新思路。Because conventional morphological indicators such as volume and surface area are too general for the subcortical nuclei,it is difficult to detect the subtle changes in the surface morphology using traditional morphological feature acquisition methods.To solve this problem,we propose a fine feature extraction algorithm for subcortical nuclei and apply it to the cognitive state prediction task of the elderly.Using surface conformal parameterization,surface conformal representation,and the surface fluid registration based on mutual information,15000×2 morphological features are extracted from both the bilateral hippocampus and amygdala of 46 subjects.Using the dimensionality reduction process,including patch selection,sparse coding and dictionary learning,and max-pooling,we avoid the dimensionality curse while fully preserving the texture information of nuclei.Finally,taking tree as the weak learner,we integrate the final strong classifier using the GentleBoost algorithm for cognitive prediction.The results show that the prediction accuracy of 85%could be achieved only by the novel features of the hippocampus and amygdala,providing a new way perspective for fine feature mining of subcortical structures.

关 键 词：皮层下核团 多变量形态学 精细特征提取 海马体 杏仁核 认知状态预测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]