Finer discrimination of brain activation with local multivariate distance  

作　　者：Zhen Zonglei Tian Jie Zhang Hui 

机构地区：[1]Medical Image Processing Group, Key Laboratory of Complex Systems and Intenigenee Science, Institute of Automation, Chinese Academy of Scienees, Beijing 100080, China

出　　处：《Progress in Natural Science:Materials International》2007年第12期1508-1514,共7页自然科学进展·国际材料（英文版）

基　　金：Supported by Cheung Kong Scholars Program and Innovative Research Teamin University,National Program on Key Basic Research Projects(Grant No .2006CB705700);Joint Research Fundfor Overseas Chinese Young Scholars (Grant No .30528027);National Natural Science Foundationof China (Grant Nos .30600151,30500131 and 60532050);Natural Science Foundation of Beijing (Grant Nos .4051002 and 4071003)

摘　　要：The organization of human brain function is diverse on different spatial scales. Various cognitive states are always represented as distinct activity patterns across the specific brain region on fine scales. Conventional univariate analysis of functional MRI data seeks to determine how a particular cognitive state is encoded in brain activity by analyzing each voxel separately without considering the fine-scale patterns information contained in the local brain regions. In this paper, a local multivariate distance mapping (LMDM) technique is proposed to detect the brain activation and to map the fine-scale brain activity patterns. LMDM directly represents the local brain activity with the patterns across multiple voxels rather than individual voxels, and it employs the multivariate distance between different patterns to discriminate the brain state on fine scales. Experiments with simulated and real fMRI data demonstrate that LMDM technique can dramatically increase the sensitivity of the detection for the fine-scale brain activity patterns which contain the subtle information of the experimental conditions.The organization of human brain function is diverse on different spatial scales. Various cognitive states are always represented as distinct activity patterns across the specific brain region on fine scales. Conventional univariate analysis of functional MRI data seeks to determine how a particular cognitive state is encoded in brain activity by analyzing each voxel separately without considering the fine-scale patterns information contained in the local brain regions. In this paper, a local multivariate distance mapping （LMDM） technique is proposed to detect the brain activation and to map the fine-scale brain activity patterns. LMDM directly represents the local brain activity with the patterns across multiple voxels rather than individual voxels, and it employs the multivariate distance between different patterns to discriminate the brain state on fine scales. Experiments with simulated and real fMRI data demonstrate that LMDM technique can dramatically increase the sensitivity of the detection for the fine-scale brain activity patterns which contain the subtle information of the experimental conditions.

关 键 词：functional magnetic resonance imaging （fMRI） statistical analysis multivariate distance local pattern pattern classification. 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]