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Paper Information

Journal:   BASIC AND CLINICAL NEUROSCIENCE   SUMMER 2011 , Volume 2 , Number 4; Page(s) 67 To 74.
 
Paper: 

STATISTICAL ANALYSIS METHODS FOR THE FMRI DATA

 
 
Author(s):  BEHROOZI MEHDI, DALIRI MOHAMMAD REZA*, BOYACI HUSEYIN
 
* BIOMEDICAL ENGINEERING DEPARTMENT, FACULTY OF ELECTRICAL ENGINEERING, IRAN UNIVERSITY OF SCIENCE AND TECHNOLOGY (IUST), NARMAK, 16846-13114 TEHRAN, IRAN
 
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.

 
Keyword(s): FMRI, MACHINE LEARNING, MULTI-VOXEL PATTERN ANALYSIS (MVPA), GENERAL LINEAR MODEL (GLM), INDEPENDENT COMPONENT ANALYSIS (ICA), PRINCIPAL COMPONENT ANALYSIS (PCA)
 
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