Statistical analysis methods for the fMRI data
Basic and Clinical Neuroscience
67 - 74
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Functional magnetic resonance imaging (fMRI) is a safe and non-invasive way to assess brain functions by using signal changes associated with brain activity. The technique has become a ubiquitous tool in basic, clinical and cognitive neuroscience. This method can measure little metabolism changes that occur in active part of the brain. We process the fMRI data to be able to find the parts of brain that are involve in a mechanism, or to determine the changes that occur in brain activities due to a brain lesion. In this study we will have an overview over the methods that are used for the analysis of fMRI data.
General Linear Model (GLM)
Independent Component Analysis (ICA)
Multi-voxel pattern analysis (mvpa)
Principal Component Analysis (PCA)
data pre processing
event related paradigm
functional magnetic resonance imaging
generalized linear model
independent component analysis
multi voxel pattern analysis
principal component analysis
slice timing correction
Permalink (Please cite this version)http://hdl.handle.net/11693/21683
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