An example of a more complicated and realistic fMRI preprocessing pipeline.

<p>Once the code is generated, this can in turn be transformed into a Nipype graph visualisation. Whereas this is usually the end point for a pipeline in Nipype, we here propose to use a visualisation as a starting point of one’s analysis.</p

A screenshot of a Porcupine workflow.

<p>The editor is divided into four panels, each of them targeted at facilitating a more understandable and reproducible analysis. The <i>workflow editor</i> (1) provides a visual overview of one’s analysis. The functions are all listed in the <i>node editor</i> (2), where the parameters for all functions can be orderly stored. This may include links to important parameters that are listed in the <i>parameter editor</i> (3), such that an overview of the main analysis settings can be easily viewed and modified. Readily executable analysis code is generated in the <i>code window</i> (4).</p

mri_f09

All MRI data for each participant separately. (Structural, functional main task, and functional localizer task)

mri_f03

All MRI data for each participant separately. (Structural, functional main task, and functional localizer task)

mri_f02

All MRI data for each participant separately. (Structural, functional main task, and functional localizer task)

mri_f04

All MRI data for each participant separately. (Structural, functional main task, and functional localizer task)

Betweenness centrality hub map.

<p>Average betweenness centrality pial (A) and inflated (B) surface hub map with a mean betweenness centrality of 0.00124±0.00061 (SD). The colour scale for the betweenness centrality values is shown at the right of subfigure (A). See also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0065511#pone.0065511.s001" target="_blank">Table S1</a>.</p

mri_f06

All MRI data for each participant separately. (Structural, functional main task, and functional localizer task)

Node degree hub map.

<p>Average node degree pial (A) and inflated (B) surface hub map with a mean node degree of 102.57±19.78 (SD). The colour scale for the node degree values is shown at the right of subfigure (A). See also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0065511#pone.0065511.s001" target="_blank">Table S1</a>.</p

Gender and hemispheric differences in small world indices.

<p>The differences between left and right hemispheric small-world indices are shown in boxplot (A). Boxplots grouped by gender are: (B) whole brain small world indices, (C) left and right hemispheric small world indices and (D) small world asymmetry indices. See also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0065511#pone.0065511.s003" target="_blank">Tables S3</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0065511#pone.0065511.s004" target="_blank">S4</a>. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0065511#pone-0065511-g005" target="_blank"><u>Figure 5</u></a><u> footnote:</u> *** and * indicate statistical significant differences with p_2-tailed<.001 and with p_2-tailed<.05. The degrees of freedom for the tests are A: df = 124, B-D: df = 61. Each boxplot shows the median (red line), the upper and lower quartile (blue rectangle), the smallest and largest observations (endpoints of the dashed line) and observations which should be considered as outliers (red pluses).</p