Statistical Analysis Methods for the fMRI Data

被引：0

作者：

Behroozi, Mehdi ^{[1
]}

Daliri, Mohammad Reza ^{[1
]}

Boyaci, Huseyin ^{[2
]}

机构：

[1] IUST, Fac Elect Engn, Dept Biomed Engn, Narmak, Tehran 1684613114, Iran

[2] Bilkent Univ, Dept Psychol, TR-06800 Ankara, Turkey

来源：

BASIC AND CLINICAL NEUROSCIENCE | 2011年 / 2卷 / 04期

关键词：

fMRI; Machine Learning; MultiVoxel Pattern Analysis (MVPA); General Linear Model (GLM); Independent Component Analysis (ICA); Principal Component Analysis (PCA);

D O I：

暂无

中图分类号：

Q189 [神经科学];

学科分类号：

071006 ;

摘要：

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.

引用

页码：67 / 74

页数：8

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