FMRI signal analysis using empirical mean curve decomposition.
FMRI signal analysis using empirical mean curve decomposition.
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DOI：
10.1109/tbme.2012.2221125
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发表时间：
2013-01
期刊：
影响因子：
--
通讯作者：
Liu T
中科院分区：
文献类型：
--
作者：
Deng F;Zhu D;Lv J;Guo L;Liu T
Functional magnetic resonance imaging (fMRI) time series is non-linear and composed of components at multiple temporal scales, which presents significant challenges to its analysis. In the literature, significant effort has been devoted into model-based fMRI signal analysis, while much less attention has been directed to data-driven fMRI signal analysis. In this paper, we present a novel data-driven multi-scale signal decomposition framework named Empirical Mean Curve Decomposition (EMCD). Targeted on functional brain mapping, the EMCD optimizes mean envelopes from fMRI signals and iteratively extracts coarser-to-finer scale signal components. The EMCD framework was applied to infer meaningful low-frequency information from Blood Oxygenation Level Dependent (BOLD) signals from resting state fMRI, task-based fMRI, and natural stimulus fMRI, and promising results are obtained.
