著者

川口 淳

出版者

日本計量生物学会

雑誌

計量生物学 (ISSN:09184430)

巻号頁・発行日

vol.33, no.2, pp.145-174, 2013-02-28 (Released:2013-03-07)

参考文献数

116

被引用文献数

2

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Imaging techniques have been used for effectively studying the brain in a non-invasive manner in several fields, for example, psychiatry and psychology. In this review, we focus on two imaging techniques that provide different views of brain structure and function. Structural magnetic resonance imaging (sMRI) provides information about various tissue types in the brain, for example, gray matter, white matter, and cerebrospinal fluid. Functional MRI (fMRI) measures brain activity by detecting changes in cerebral blood flow. These techniques enable high-quality visualization of brain activity or the location of atrophies; moreover, these techniques facilitate the study of disease mechanisms in the healthy brain and might lead to the development of effective therapies or drugs against such diseases. However, raw MRI data must be statistically analyzed to obtain objective answers to clinical questions. Therefore, statistical methods play a very important role in brain research. Here, we briefly review the most commonly used statistical analyses, namely, data pre-processing, general linear model, random field theory, mixed effect model, independent component analysis, network analysis, and discriminant analysis. Further, we provide information about brain imaging data structure and introduce useful software to implement these methods.