| SPM的数学基础及其在脑功能成像研究中的应用 |
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| 引用本文： | 唐焕文,潘丽丽,唐一源.SPM的数学基础及其在脑功能成像研究中的应用[J].应用基础与工程科学学报,2005,13(3):223-231. |
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| 作者姓名： | 唐焕文 潘丽丽 唐一源 |
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| 作者单位： | 1. 大连理工大学应用数学系,辽宁,大连,116023
2. 大连理工大学神经信息学研究所,辽宁,大连,116023;中国科学院生物物理所脑与认知科学国家重点实验室,北京,100101 |
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| 基金项目： | 科技部国际合作(DF2003)，教育部重大项目(KP0302)，国家自然科学基金(60472017)资助 |
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| 摘 要： | 探讨SPM的数学基础,给出了SPM中利用一般线性模型(General LinearModel,GLM)构建统计参数图的方法;为了适应处理功能磁共振成像(functionalMagnetic Resonance Imaging,fMR I)数据的需要,对一般线性模型做了扩展,推导出扩展的GLM的检验统计量和自由度.最后将扩展的一般线性模型应用于一个具体的fMR I实验,说明了方法的有效性.
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| 关 键 词： | SPM 一般线性模型 回归分析 功能磁共振成像 |
| 文章编号： | 1005-0930（2005）03-0223-09 |
| 收稿时间： | 2004-07-15 |
| 修稿时间： | 2005-06-06 |
The Mathematical Principles of SPM and its Applications to the Functional Neuroimages |
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| TANG Huanwen,PAN Lili,TANG Yiyuan.The Mathematical Principles of SPM and its Applications to the Functional Neuroimages[J].Journal of Basic Science and Engineering,2005,13(3):223-231. |
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| Authors: | TANG Huanwen PAN Lili TANG Yiyuan |
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| Institution: | 1. Department of Applied Mathematics, Dalian University, Dalian 116023, China; 2. Institute of Neuroinformaties, Dalian University of Technology, Dalian 116023, China; 3. State Key Laboratory for Brain and Cogritive Sciences, the Chinese Academy of Sciences, Beijing 100101, China |
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| Abstract: | SPM(Statistical ParametricMapping) method refers to the construction and assessment of spatially extended statistical process used to test hypotheses about specific cerebral area effects.SPM,which is the software for analysis of neruoimage data,is generally used to identify functional specialized brain responses and is the most prevalent approach to characterizing functional anatomy and d isease-related changes.In this essay,the mathematical principles of SPM are d iscussed and the method that constructed statistical parametric maps using general linearmodel is given.To accommodate fMR I(functionalMagnetic Resonance Imaging) data analysis,we extend GLM and induce the test statistics and degree of freedom.At last,the extended GLM is applied to a real fMR I experiment,which shows that the method is effective. |
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| Keywords: | SPM general linear models regression analysis functional magnetic resonance imaging(fMR I) |
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