9 March 2010 The improvement of ICA with projection technique in multitask fMRI data analysis
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Abstract
The existence of the potential non-independency between task-related components in multi-task functional magnetic resonance imaging (fMRI) studies limits the general application of Independent Component Analysis (ICA) method. The ICA with projection (ICAp) method proposed by Long (2009, HBM) demonstrated its capacity to solve the interaction among task-related components of multi-task fMRI data. The basic idea of projection is to remove the influence of the uninteresting tasks through projection in order to extract one interesting task-related component. However, both the stimulus paradigm of each task and the homodynamic response function (HRF) are essential for the projection. Due to the noises in the data and the variability of the HRF across the voxels and subjects, the ideal time course of each task for projection would be deviant from the true value, which might worsen the ICAp results. In order to make the time courses for projection closer to the true value, the iterative ICAp is proposed in this study. The iterative ICAp is based on the assumption that the task-related time courses extracted from the fMRI data by ICAp is more approximate to the true value than the ideal reference function. Simulated experiment proved that both the spatial detection power and the temporal accuracy of time course were increased for each task-related component. Moreover, the results of the real two-task fMRI data were also improved by the iterative ICAp method.
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Rui Li, Rui Li, Kewei Chen, Kewei Chen, Li Yao, Li Yao, Zhiying Long, Zhiying Long, } "The improvement of ICA with projection technique in multitask fMRI data analysis", Proc. SPIE 7626, Medical Imaging 2010: Biomedical Applications in Molecular, Structural, and Functional Imaging, 762606 (9 March 2010); doi: 10.1117/12.843770; https://doi.org/10.1117/12.843770
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