| 基于时域与空域信息去除fMRI信号生理噪声的无监督方法 |
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| 引用本文： | 吴超,曾卫明,王倪传,陈艳阳.基于时域与空域信息去除fMRI信号生理噪声的无监督方法[J].计算机系统应用,2015,24(11):179-184. |
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| 作者姓名： | 吴超 曾卫明 王倪传 陈艳阳 |
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| 作者单位： | 上海海事大学信息工程学院数字影像与智能计算实验室, 上海 201306,上海海事大学信息工程学院数字影像与智能计算实验室, 上海 201306,上海海事大学信息工程学院数字影像与智能计算实验室, 上海 201306,上海海事大学信息工程学院数字影像与智能计算实验室, 上海 201306 |
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| 摘 要： | fMRI脑功能成像过程中的心跳和呼吸等生理噪声具有较强的自相关结构特性,因而会对后续数据分析造成干扰.结合生理噪声在时间域和空间域的综合特征,通过典型相关分析方法,可稳健地从非神经组织区域的残差数据中识别并去除生理噪声.并且所提方法不需要任何实验先验信息,实现了对fMRI生理噪声的无监督抑制.通过在真实fMRI数据上进行实验,阐明了该方法的有效性及可靠性.
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| 关 键 词： | fMRI 生理噪声 残差 CCA 无监督 |
| 收稿时间： | 2015/3/10 0:00:00 |
| 修稿时间： | 2015/4/15 0:00:00 |
Unsupervised Method to Remove Physiological Noise in fMRI Signals Based on the Temporal and Spatial Information |
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| WU Chao,ZENG Wei-Ming,WANG Ni-Zhuan and CHEN Yan-Yang.Unsupervised Method to Remove Physiological Noise in fMRI Signals Based on the Temporal and Spatial Information[J].Computer Systems& Applications,2015,24(11):179-184. |
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| Authors: | WU Chao ZENG Wei-Ming WANG Ni-Zhuan and CHEN Yan-Yang |
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| Affiliation: | Lab of Digital Image and Intelligent Computation, College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China,Lab of Digital Image and Intelligent Computation, College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China,Lab of Digital Image and Intelligent Computation, College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China and Lab of Digital Image and Intelligent Computation, College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China |
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| Abstract: | The physiological noise such as heart-beating and respiration can cause great interference to the subsequent process of data analysis. Because these noise has considerable autocorrelation characteristics. Combined with the characteristics of physiological noise in time and space, through the method of canonical correlation analysis, the noise components can be steadily identified and removed from the nerve tissues in the residual data after regressing the useful signal components. In this paper, the proposed method does not require any priori information about experiments, realizing the unsupervised reduction of fMRI physiological noise. Through the analysis on the real fMRI data, we illustrate the effectiveness and the reliability of our method. |
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| Keywords: | fMRI physiological noise residual CCA unsupervised |
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