Statistical Analysis Methods for the fMRI Data
Copyright policy )Statistical Analysis Methods for the fMRI Data
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.
- Iran University of Science and Technology Iran (Islamic Republic of)
- Bilkent University Turkey
experimental design, principal component analysis, data analysis, General Linear Model (GLM), Neurosciences. Biological psychiatry. Neuropsychiatry, information processing, MultiVoxel Pattern Analysis (MVPA), Multi-voxel pattern analysis (mvpa), Machine Learning, intensity normalization, mixed paradigm, temporal filtering, blocked paradigm, signal detection, statistical analysis, Fmri, multi voxel pattern analysis, Machine learning, signal processing, Independent ComponentAnalysis (ICA), Principal Component Analysis(PCA)., Principal Component Analysis (PCA), neuroimaging, statistical model, fMRI, article, data pre processing, Independent Component Analysis (ICA), functional magnetic resonance imaging, event related paradigm, 004, univariate analysis, multivariate analysis, Multi-Voxel Pattern Analysis(MVPA), slice timing correction, independent component analysis, generalized linear model, FMRI, brain mapping, connectivity analysis, RC321-571
experimental design, principal component analysis, data analysis, General Linear Model (GLM), Neurosciences. Biological psychiatry. Neuropsychiatry, information processing, MultiVoxel Pattern Analysis (MVPA), Multi-voxel pattern analysis (mvpa), Machine Learning, intensity normalization, mixed paradigm, temporal filtering, blocked paradigm, signal detection, statistical analysis, Fmri, multi voxel pattern analysis, Machine learning, signal processing, Independent ComponentAnalysis (ICA), Principal Component Analysis(PCA)., Principal Component Analysis (PCA), neuroimaging, statistical model, fMRI, article, data pre processing, Independent Component Analysis (ICA), functional magnetic resonance imaging, event related paradigm, 004, univariate analysis, multivariate analysis, Multi-Voxel Pattern Analysis(MVPA), slice timing correction, independent component analysis, generalized linear model, FMRI, brain mapping, connectivity analysis, RC321-571
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