ROImap_fs6

<p>A list containing two data frames, 1) listing fsaverage6 vertices and each atlas parcellation number they correspond to, and 2) listing each available atlas and their corresponding labels (1=aparc, 2=Destrieux-148, 3=Glasser-360, 4=Schaefer-100, 5=Schaefer-200, 6=Schaefer-400).</p>

datasets

Provides functions to run statistical analyses on
surface-based neuroimaging data, computing measures including cortical
thickness and surface area of the whole-brain and of the hippocampi.
It can make use of 'FreeSurfer', 'fMRIprep', 'HCP' and 'CAT12' preprocessed
datasets and 'HippUnfold' hippocampal segmentation outputs for a given
sample by restructuring the data values into a single file. The single
file can then be used by the package for analyses independently from
its base dataset and without need for its access.

Charly Billaud

VertexWiseR

Simplified Vertex-Wise Analyses of Whole-Brain and Hippocampal
Surface

Junhong Yu

ROImap_fs6 function


<div class="section" id="roimap-fs-">
<h3><code>ROImap_fs6</code></h3><p></p><p>
</p><p>A list object with two data frame objects: ()</p><dl>
<dt>vertices</dt>
<dd><p>data frame with 81924 rows (vertices), 6 columns (atlases)</p></dd><p></p><p><dt>atlases</dt>
<dd><p>data frame with 400 rows (labels, not all are filled depending on atlas), 6 columns (atlases)</p></dd></p><p>
</p></dl><p></p><p></p></div>


Format

Atlas parcellations of fsaverage6 — ROImap_fs6


<div class='section' id='roimap-fs-'>
<h3><code>ROImap_fs6</code></h3>


<p>A list object with two data frame objects: ()</p><dl>
<dt>vertices</dt>
<dd><p>data frame with 81924 rows (vertices), 6 columns (atlases)</p></dd>

<dt>atlases</dt>
<dd><p>data frame with 400 rows (labels, not all are filled depending on atlas), 6 columns (atlases)</p></dd>


</dl>

</div>


ROImap_fs6: Atlas parcellations of fsaverage6

Description

Usage

Arguments

Format

`ROImap_fs6`