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Abstract #5339

Prior Knowledge Oriented Independent Component Analysis (pICA) for Component Identification in Functional MRI

Gengyan Zhao1, Vivek Prabhakaran1,2, Elizabeth M. Meyerand1,3, and Rasmus Birn1,4

1Medical Physics, University of Wisconsin - Madison, Madison, WI, United States, 2Radiology, University of Wisconsin - Madison, WI, United States, 3Biomedical Engineering, University of Wisconsin - Madison, WI, United States, 4Psychiatry, University of Wisconsin - Madison, WI, United States

Independent component analysis (ICA), as a data-driven signal decomposition method, has been widely used in fMRI. Sources of the measurement can be separated according to the rule of maximum independency, but it usually cannot naturally generate a source which is highly correlated with the signal we are interested in. To solve this problem, we propose a new method, prior knowledge oriented ICA (pICA), to drive ICA to a set of sources with the SOI among them. Experiments of simulation and fMRI show this new method has higher specificity and accuracy in identifying the SOI and its corresponding spatial map.

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