Statistical analysis methods for the fMRI data
Date
2011Source Title
Basic and Clinical Neuroscience
Print ISSN
2008-126X
Volume
2
Issue
4
Pages
67 - 74
Language
English
Type
ArticleItem Usage Stats
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Abstract
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.
Keywords
FmriMachine Learning
MultiVoxel Pattern Analysis (MVPA)
General Linear Model (GLM)
Independent Component Analysis (ICA)
Principal Component Analysis (PCA)