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1 May 1994 Using principal component analysis to visualize the spatial distribution of functional areas of the brain as studied with MRI during motor and sensory activation
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Abstract
Magnetic resonance imaging (MRI) can be used for functional brain studies. The identification of areas with changed blood oxygenation level dependent (BOLD) signal is usually done by visually inspecting maps of different kinds created through different post-processing procedures of the acquired images. It is desirable that the maps have as good an image quality as possible, and principal component analysis (PCA) can be used for this task. PCA is a data- driven method which does not use information about the timing of the experiment, instead the variance-covariance structure of the image data set is analyzed. PCA results in linear combinations of the analyzed MR images called score images, and the possibility to use score images as functional maps is investigated and compared to another commonly used method.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Finn Pedersen, Ewert W. Bengtsson, Tomas Hindmarsh, Bo Nordell, and Hans Forssberg "Using principal component analysis to visualize the spatial distribution of functional areas of the brain as studied with MRI during motor and sensory activation", Proc. SPIE 2168, Medical Imaging 1994: Physiology and Function from Multidimensional Images, (1 May 1994); https://doi.org/10.1117/12.174399
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