cgb_smooth

Apply a precomputed CGB graph to volumetric data

A collection of data structures and methods for handling volumetric
brain imaging data, with a focus on functional magnetic resonance imaging (fMRI). Provides efficient
representations for three-dimensional and four-dimensional neuroimaging data through sparse and dense array
implementations, memory-mapped file access for large datasets, and
spatial transformation capabilities. Implements methods for image resampling,
spatial filtering, region of interest analysis, and connected component labeling. General introduction
to fMRI analysis can be found in Poldrack et al. (2024, "Handbook of functional MRI data analysis",
<ISBN:9781108795760>).

Bradley Buchsbaum

neuroim2

Data Structures for Brain Imaging Data

cgb_smooth function

<dl><dt>x</dt>
<dd>A <code>NeuroVec</code> (4D) or <code>NeuroVol</code> (3D).</dd>
<dt>graph</dt>
<dd>Graph list returned by <code>cgb_make_graph</code>.</dd>
<dt>passes</dt>
<dd>Number of smoothing passes (&gt;=1). Each pass multiplies by
<code>W</code>; if <code>lambda &lt; 1</code> a simple diffusion blend
<code>(1 - lambda)I + lambda W</code> is applied per pass.</dd>
<dt>lambda</dt>
<dd>Blend factor in \([0,1]\) controlling diffusion strength.</dd></dl>

Arguments

Apply a precomputed CGB graph to volumetric data — cgb_smooth

<dl>

<dt>x</dt>
<dd>A <code>NeuroVec</code> (4D) or <code>NeuroVol</code> (3D).</dd>


<dt>graph</dt>
<dd>Graph list returned by <code>cgb_make_graph</code>.</dd>


<dt>passes</dt>
<dd>Number of smoothing passes (&gt;=1). Each pass multiplies by
<code>W</code>; if <code>lambda &lt; 1</code> a simple diffusion blend
<code>(1 - lambda)I + lambda W</code> is applied per pass.</dd>


<dt>lambda</dt>
<dd>Blend factor in \([0,1]\) controlling diffusion strength.</dd>

</dl>

cgb_smooth: Apply a precomputed CGB graph to volumetric data

Description

Usage

Value

Arguments