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      • Department of Psychology
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      Statistical analysis methods for the fMRI data

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      Author(s)
      Behroozi, M.
      Daliri, M.R.
      Boyacı, Hüseyin
      Date
      2011
      Source Title
      Basic and Clinical Neuroscience
      Print ISSN
      2008-126X
      Volume
      2
      Issue
      4
      Pages
      67 - 74
      Language
      English
      Type
      Article
      Item 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
      Fmri
      Machine Learning
      MultiVoxel Pattern Analysis (MVPA)
      General Linear Model (GLM)
      Independent Component Analysis (ICA)
      Principal Component Analysis (PCA)
      Permalink
      http://hdl.handle.net/11693/21683
      Collections
      • Aysel Sabuncu Brain Research Center (BAM) 213
      • Department of Psychology 191
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