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However, actual fMRI signals are nonlinear and do not automatically meet with the requirement of sICA. To provide a better solution to this problem, we proposed a novel approach termed instantaneous power based fMRI (ip-fMRI) for regularization of fMRI data. Given that the instantaneous power of fMRI signals is a scalar value, it should be a linear mixture that naturally satisfies the \u201clatent variables\u201d model. Based on our simulated data, the curves of accuracy and resulting receiver-operating characteristic curves indicate that the proposed approach is superior to the traditional fMRI in terms of accuracy and specificity by using sICA. Experimental results from human subjects have shown that spatial components of a hand movement task-induced activation reveal a brain network more specific to motor function by ip-fMRI than that by the traditional fMRI. We conclude that ICA decomposition of ip-fMRI may be used to localize energy signal changes in the brain and may have a potential to be applied to detection of brain activity.<\/jats:p>","DOI":"10.1155\/2014\/579652","type":"journal-article","created":{"date-parts":[[2014,3,9]],"date-time":"2014-03-09T05:22:35Z","timestamp":1394342555000},"page":"1-8","source":"Crossref","is-referenced-by-count":1,"title":["Independent Component Analysis of Instantaneous Power-Based fMRI"],"prefix":"10.1155","volume":"2014","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6295-7711","authenticated-orcid":true,"given":"Yuan","family":"Zhong","sequence":"first","affiliation":[{"name":"School of Psychology, Nanjing Normal University, Nanjing 210097, China"},{"name":"Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210002, 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