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NIMA stands for Nima's Images from Measure-projection Analysis. Measure Projection Toolbox (MPT) is a published method (Bigdely-Shamlo et al., 2013), and for his wiki page see this link. MPT's nice visualization functions are stripped and re-wrapped into a stand-alone dipole visualizer that does not have dependency on MPT. As a GUI, a menu item appears under STUDY tab. The main function, nimasImagesfromMpA() can be used also as a stand-alone command line function.

What you can do with the optional inputs (12/07/2018 updated)

  • Specifying the colors and Alpha values separately for each blob/voxel clusters.
  • Specifying which MRI image and blob/voxel-cluster projections to show.

P159_separatealpha.png

GUI, Blobs, and Voxels

GUI image can be seen in the screenshot below. This visualization works on 3-D Gaussian-blurred dipole locations, called (probabilistic) dipole density, which requires two parameters to determine the spatial spreading, namely full-width half-maximum (FWHM) in mm and number of sigma to truncate the Gaussian (can be specified as optional input, default 3 sigma; sigma == FWHM/2.355). Also, users are requested to determine the spatial resolution by determining isometric voxel size, which could be from 2 mm to 8 mm by increment of 1 mm. The final visualization can be made either using blob or voxels, and the transparency can be specified as Alpha (0-1 as invisible-solid). As an optional input, the order and the RGB triplets of the color assignment for the selected clusters can be specified as 'blobColor', [1 0 0; 0 1 0; 0 0 1] which will give you R, G, B for the specified three clusters in this order. In the screenshot below, compare scalp topographies, blob images, and voxel images of Cluster 5, 6, 16. My colleague told me the voxel image reminds him of Minecraft.

Nimafigure01.png

Comparison with std_dipoleDensity()

std_dipoleDensity() currently allows up to 5 clusters to be plotted in one head space. Compared with this function, images from NIMA can embody transparency and more fine-tuned spatial resolution.

Dipfitcomparison.png

Voxel Size Comparison for Blob Images

FWHM = 8 mm, number of sigma to truncate Gaussian = 3. From top left to right, Voxel Size = 2 mm, 3 mm, 4 mm. From bottom left to right, 5 mm, 6 mm, and 7 mm.

Blob_from2to7mm.png

Voxel Size Comparison for Voxel Images

FWHM = 8 mm, number of sigma to truncate Gaussian = 3. From top left to right, Voxel Size = 2 mm, 3 mm, 4 mm. From bottom left to right, 5 mm, 6 mm, and 7 mm.

Voxels_from2to7mm.png

FWHM Size Comparison

Number of sigma to truncate Gaussian = 3. From top left to right, FWHM = 8 mm, 12 mm, 16 mm. From bottom left to right, 20 mm, 24 mm, and 28 mm.

Voxels_fwhm8to28mm.png

Alpha Comparison

FWHM = 8 mm, number of sigma to truncate Gaussian = 3. Top row, voxel plot. Bottom row, blob plot. From left to right, Alpha = 0.1, 0.3, 0.5, 0.7, 0.9.

Alphacomparison.png

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