FMRI data analysis by blind source separation algorithms: A comparison study for nongaussian properties

机译：通过盲源分离算法进行FMRI数据分析：非高斯性质的比较研究

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The application of Independent Component Analysis (ICA) to functional magnetic resonance imaging (FMRI) data has proven to be quite fruitful. In this sense, an important problem is different nongaussian properties of FMRI data that should be considered in ICA application. In this paper, we have experimentally compared nongaussian source separation ability of three ICA/BSS approaches for fMRI: Infomax, FastICA and JADE. The comparison study used both simulated fMRI-like data generated using the synthesis model and actual fMRI data performing an audio-visual stimulation task. The results were evaluated by task-related activation maps and associated time-courses. Based on our result, Infomax emerged as a reliable choice for the task followed by JADE. FastICA didn't perform reliably especially for sub-gaussian sources.

机译：事实证明，将独立分量分析（ICA）应用于功能性磁共振成像（FMRI）数据非常有效。从这个意义上说，一个重要的问题是在ICA应用中应考虑的FMRI数据的不同非高斯性质。在本文中，我们通过实验比较了三种MRI的ICA / BSS方法的非高斯源分离能力：Infomax，FastICA和JADE。对比研究使用了使用合成模型生成的模拟fMRI类数据和执行视听刺激任务的实际fMRI数据。通过与任务相关的激活图和相关的时间过程对结果进行了评估。根据我们的结果，Infomax成为JADE紧随其后的任务的可靠选择。 FastICA的性能不可靠，特别是对于次高斯信号源。

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来源
《2010 18th Iranian Conference on Electrical Engineering》|2010年|p.13-17|共5页
会议地点 Isfahan(IR);Isfahan(IR)
作者
Ghasemi Mahdieh; Mahloojifar Ali;
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作者单位

Biomedical Engineering Department, Tarbiat Modares University, Tehran, Iran;

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会议组织
原文格式 PDF
正文语种 eng
中图分类真空电子技术;
关键词
Spatial Independent Component analysis (SICA) algorithms; component; functional Magnetic Resonance Imaging (fMRI); nongaussian distributions;

机译：空间独立分量分析（SICA）算法;分量;功能磁共振成像（fMRI）;非高斯分布;

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专利

1. Performance of blind source separation algorithms for fMRI analysis using a group ICA method [J] . Correa N, Adali T, Calhoun VD Magnetic resonance imaging: An International journal of basic research and clinical applications . 2007,第5期

机译：使用组ICA方法进行fMRI分析的盲源分离算法的性能
2. Blind source separation of multiple signal sources of fMRI data sets using independent component analysis. [J] . Biswal BB, Ulmer JL Journal of computer assisted tomography . 1999,第2期

机译：使用独立的成分分析，对fMRI数据集的多个信号源进行盲源分离。
3. Improved FastICA algorithm in fMRI data analysis using the sparsity property of the sources [J] . Ge Ruiyang, Wang Yubao, Zhang Jipeng, Journal of Neuroscience Methods . 2016,第Null期

机译：利用源稀疏性在fMRI数据分析中改进的FastICA算法
4. FMRI Data Analysis by Blind Source Separation Algorithms: a Comparison Study for Nongaussian Properties [C] . Mahdieh Ghasemi, Ali Mahloojifar Iranian Conference on Electrical Engineering . 2010

机译：盲源分离算法的FMRI数据分析：Nongaussian特性的比较研究
5. Machine learning algorithms for independent vector analysis and blind source separation. [D] . Lee, In Tae. 2009

机译：用于独立矢量分析和盲源分离的机器学习算法。
6. Performance of Blind Source Separation Algorithms for FMRI Analysis using a Group ICA Method [O] . Nicolle Correa, Tülay Adali, Vince D. Calhoun -1

机译：使用组ICA方法进行FMRI分析的盲源分离算法的性能
7. Blind source separation of multiple signal sources of fMRI data sets using independent component analysis [O] . B. B. Biswal, J. L. Ulmer 1999

机译：使用独立分量分析对fmRI数据集的多个信号源进行盲源分离
8. Analysis of fMRI Data by Blind Separation into Independent Spatial Components. [R] . McKeown, M. J., Makeig, S., Brown, G. G., 1997

机译：盲分离成独立空间分量的fmRI数据分析。

FMRI data analysis by blind source separation algorithms: A comparison study for nongaussian properties

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