Adaptive integration of local region information to detect fine-scale brain activity patterns  

作　　者：ZHEN ZongLei TIAN Jie ZHANG Hui 

机构地区：[1]Medical Image Processing Group,Key Laboratory of Complex Systems and Intelligence Science,Institute of Automation,Chinese Academy of Sciences,Beijing 100080,China [2]Graduate University of the Chinese Academy of Sciences,Beijing 100039,China

出　　处：《Science China(Technological Sciences)》2008年第11期1980-1989,共10页中国科学（技术科学英文版）

基　　金：Chair Professors of Changjiang Scholars Program and CAS Hundred Talents Program;National Program on Key Basic Research Projects (Grant No. 2006CB705700);National High-Tech R&D Program of China (Grant No.2006AA04Z216);National Key Technology R&D Program (Grant No. 2006BAH02A25); Joint Research Fund for Overseas Chinese Young Scholars (Grant No.30528027),;National Natural Science Foundation of China (Grant Nos.30600151, 30500131 and 60532050); Natural Science Foundation of Beijing (Grant Nos. 4051002 and 4071003)

摘　　要：With the rapid development of functional magnetic resonance imaging (fMRI) technology, the spatial resolution of fMRI data is continuously growing. This pro- vides us the possibility to detect the fine-scale patterns of brain activities. The es- tablished univariate and multivariate methods to analyze fMRI data mostly focus on detecting the activation blobs without considering the distributed fine-scale pat- terns within the blobs. To improve the sensitivity of the activation detection, in this paper, multivariate statistical method and univariate statistical method are com- bined to discover the fine-grained activity patterns. For one voxel in the brain, a local homogenous region is constructed. Then, time courses from the local ho- mogenous region are integrated with multivariate statistical method. Univariate statistical method is finally used to construct the interests of statistic for that voxel. The approach has explicitly taken into account the structures of both activity pat- terns and existing noise of local brain regions. Therefore, it could highlight the fine-scale activity patterns of the local regions. Experiments with simulated and real fMRI data demonstrate that the proposed method dramatically increases the sensitivity of detection of fine-scale brain activity patterns which contain the subtle information about experimental conditions.With the rapid development of functional magnetic resonance imaging (fMRI) technology, the spatial resolution of fMRI data is continuously growing. This provides us the possibility to detect the fine-scale patterns of brain activities. The established univariate and multivariate methods to analyze fMRI data mostly focus on detecting the activation blobs without considering the distributed fine-scale patterns within the blobs. To improve the sensitivity of the activation detection, in this paper, multivariate statistical method and univariate statistical method are combined to discover the fine-grained activity patterns. For one voxel in the brain, a local homogenous region is constructed. Then, time courses from the local homogenous region are integrated with multivariate statistical method. Univariate statistical method is finally used to construct the interests of statistic for that voxel. The approach has explicitly taken into account the structures of both activity patterns and existing noise of local brain regions. Therefore, it could highlight the fine-scale activity patterns of the local regions. Experiments with simulated and real fMRI data demonstrate that the proposed method dramatically increases the sensitivity of detection of fine-scale brain activity patterns which contain the subtle information about experimental conditions.

关 键 词：functional magnetic resonance imaging (fMRI) principal component analysis general linear model LOCAL region fine-scale activity PATTERNS 

分 类 号：TB114[理学—概率论与数理统计]