decode_surf_data

Correlates the significant clusters of an earlier vertex-wise analysis with a database of task-based fMRI and voxel-based morphometric statistical maps and associate them with relevant key words. Decoding currently works with surfaces in fsaverage5 space only."

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

decode_surf_data function

<dl><dt>surf_data</dt>
<dd>A numeric vector or object containing the surface data, in fsaverage5 (1 x 20484 vertices) or fsLR32k (1 x 64984 vertices) space. It can only be one row of vertices (not a cohort surface data matrix).</dd>
<dt>contrast</dt>
<dd>A string object indicating whether to decode the positive or negative mask ('positive' or 'negative')</dd>
<dt>VWR_check</dt>
<dd>A boolean object specifying whether to check and validate system requirements. Default is TRUE.</dd></dl>

Arguments

Decode surface data — decode_surf_data

<dl>

<dt>surf_data</dt>
<dd>A numeric vector or object containing the surface data, in fsaverage5 (1 x 20484 vertices) or fsLR32k (1 x 64984 vertices) space. It can only be one row of vertices (not a cohort surface data matrix).</dd>


<dt>contrast</dt>
<dd>A string object indicating whether to decode the positive or negative mask ('positive' or 'negative')</dd>


<dt>VWR_check</dt>
<dd>A boolean object specifying whether to check and validate system requirements. Default is TRUE.</dd>

</dl>

decode_surf_data: Decode surface data

Description

Usage

Value

Arguments

Details

Examples