| Finer discrimination of brain activation with local multivariate distance |
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| 作者姓名： | Zhen Zonglei Tian Jie Zhang Hui |
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| 作者单位： | Medical Image Processing Group，Key Laboratory of Complex Systems and Intelligence Science，Institute of Automation，Chinese Academy of Sciences，Beijing 100080，China |
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| 基金项目： | 国家重点基础研究发展计划(973计划);国家自然科学基金;北京市自然科学基金 |
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| 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.
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Finer discrimination of brain activation with local multivariate distance |
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| Zhen Zonglei,Tian Jie,Zhang Hui.Finer discrimination of brain activation with local multivariate distance[J].Progress in Natural Science,2007,17(12):1508-1514. |
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| Authors: | Zhen Zonglei Tian Jie Zhang Hui |
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| Institution: | Medical Image Processing Group, Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China |
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| Abstract: | 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. |
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| Keywords: | functional magnetic resonance imaging(fMRI) statistical analysis multivariate distance local pattern pattern classification |
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