VITLAM - Visualization Toolbox for Latent Variable Modeling of fMRI
The Visualization Toolbox for Latent Variable Modeling of fMRI holds a collection of visualization methods for latent variable analysis in functional magnetic resonance imaging (fMRI). The methods are implemented in Matlab™. All code can be used freely in research and other non-profit applications. If you publish results obtained with this toolbox we kindly ask that our and other relevant sources are properly cited.
This toolbox has been developed at:
The Technical University of Denmark, Department for Applied Mathematics and Computer Science, Section for Cognitive Systems.
The toolbox was developed in connection with the Brain Connectivity project at DTU (https://brainconnectivity.compute.dtu.dk/) .
- Plots 3D view of the brain with the spatial activation of a specified latent component (with intensity and sign).
- Plots three 3D views of the brain (and the associated temporal activation) with the spatial activation of a specified latent component (positive and/or negative).
- Plots 3D view with bloated indicators for spatial active voxels, can compare multiple latent components and assess their overlap.
- Similar to "plotComponent" but with bloated voxels instead.
- Plots the spatial activation of a components, as slices of the brain. It is also possible to show the temporal activation (or its power spectrum) and/or histogram of the component.
- Plots heteroscedastic voxel noise as spatial slices (average and standard deviation across subjects)
- Highly customizable output, see demostration scripts for further detail.
How to call the functions and illustrating the effect of different parameter settings.
For most use cases a link to either this toolbox or the Brain Connectivity project will surfice. The foundations for plotBrain, plotComponents, plotBloatedVoxels and plotBloatedComponents were introduced in . The foundations for plotSpatialSlices is presented in an article currently under review (will be added in the future).
-  Hinrich, J. L., Bardenfleth, S. E., Røge, R. E., Churchill, N. W., Madsen, K. H., & Mørup, M. (2016). Archetypal Analysis for Modeling Multisubject fMRI Data. IEEE Journal of Selected Topics in Signal Processing, 10(7), 1160-1171.