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  <title>NITRC News Group Forum: development-of-powermap--a-software-package-for-statistical-power-calculation-in-neuroimaging-studies</title>
  <link>http://stage.nitrcce.org/forum/forum.php?forum_id=3205</link>
  <description>&lt;p class=&quot;abstract&quot;&gt;&lt;div class=&quot;Abstract&quot; lang=&quot;en&quot;&gt;&lt;a name=&quot;Abs1&quot;&gt;&lt;/a&gt;&lt;span class=&quot;AbstractHeading&quot;&gt;Abstract&amp;nbsp;&amp;nbsp;&lt;/span&gt;&lt;div class=&quot;normal&quot;&gt;Although there are a number of statistical software tools for voxel-based massively univariate analysis of neuroimaging data,
 such as fMRI (functional MRI), PET (positron emission tomography), and VBM (voxel-based morphometry), very few software tools
 exist for power and sample size calculation for neuroimaging studies. Unlike typical biomedical studies, outcomes from neuroimaging
 studies are 3D images of correlated voxels, requiring a correction for massive multiple comparisons. Thus, a specialized power
 calculation tool is needed for planning neuroimaging studies. To facilitate this process, we developed a software tool specifically
 designed for neuroimaging data. The software tool, called PowerMap, implements theoretical power calculation algorithms based
 on non-central random field theory. It can also calculate power for statistical analyses with FDR (false discovery rate) corrections.
 This GUI (graphical user interface)-based tool enables neuroimaging researchers without advanced knowledge in imaging statistics
 to calculate power and sample size in the form of 3D images. In this paper, we provide an overview of the statistical framework
 behind the PowerMap tool. Three worked examples are also provided, a regression analysis, an ANOVA (analysis of variance),
 and a two-sample &lt;i&gt;T&lt;/i&gt;-test, in order to demonstrate the study planning process with PowerMap. We envision that PowerMap will be a great aide for
 future neuroimaging research.
 &lt;/div&gt;
 &lt;/div&gt;&lt;/p&gt;&lt;ul&gt;
	&lt;li&gt;&lt;span class=&quot;labelName&quot;&gt;Content Type &lt;/span&gt;&lt;span class=&quot;labelValue&quot;&gt;Journal Article&lt;/span&gt;&lt;/li&gt;&lt;li&gt;Category Original Article&lt;/li&gt;&lt;li&gt;Pages 1-15&lt;/li&gt;&lt;li&gt;DOI 10.1007/s12021-012-9152-3&lt;/li&gt;&lt;li&gt;&lt;span class=&quot;labelName&quot;&gt;Authors&lt;/span&gt;&lt;ul&gt;
		&lt;li&gt;Karen E. Joyce, Department of Biomedical Engineering, Wake Forest School of Medicine, Winston-Salem, NC, USA&lt;/li&gt;&lt;li&gt;Satoru Hayasaka, Department of Biostatistical Sciences, Wake Forest School of Medicine, Medical Center Blvd., Winston-Salem, NC 27157, USA&lt;/li&gt;
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		&lt;li&gt;&lt;span class=&quot;header labelName&quot;&gt;Journal &lt;/span&gt;&lt;span class=&quot;labelValue&quot;&gt;&lt;a href=&quot;http://www.springerlink.com/content/120559/&quot;&gt;Neuroinformatics&lt;/a&gt;&lt;/span&gt;&lt;/li&gt;&lt;li&gt;&lt;span class=&quot;labelName&quot;&gt;Online ISSN &lt;/span&gt;&lt;span class=&quot;labelValue&quot;&gt;1559-0089&lt;/span&gt;&lt;/li&gt;&lt;li&gt;&lt;span class=&quot;labelName&quot;&gt;Print ISSN &lt;/span&gt;&lt;span class=&quot;labelValue&quot;&gt;1539-2791&lt;/span&gt;&lt;/li&gt;
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