Localized semi-Nonnegative Matrix Factorization (LocaNMF) is a method that efficiently decomposes widefield video data and allows us to directly compare activity across multiple mice by outputting mouse-specific localized functional regions that are significantly more interpretable than more traditional decomposition techniques. Moreover, it provides a natural subspace to directly compare correlation maps and neural dynamics across different behaviors, mice, and experimental conditions, and enables identification of task- and movement-related brain regions.
The NeuroCAAS implementation of LocaNMF works with version 1.1 of LocaNMF. Look for live logging in log.txt as well as standard DATASET_NAME files.
Args:
-Low-rank widefield video: (mat file) The .mat file called 'Vc.mat' should contain a variable 'U' of low-rank spatial components with dimensions [fov_width,fov_height,rank], a variable 'V' of low-rank temporal components with dimensions [rank,timepoints,trials] (note that number of trials can be 1), and a variable 'brainmask' of a mask denoting the brain in the field of view.
-Config: (yaml) a yaml file containing the following parameters:
1) names of the variables in the input data file ('Vc.mat'),
2) maxrank: how many max components per brain region.
3) min_pixels: minimum number of pixels in Allen map for it to be considered a brain region.
4) loc_thresh: Localization threshold, i.e. percentage of area restricted to be inside the 'Allen boundary'.
5) r2_thresh: Fraction of variance in the data to capture with LocaNMF.
6) Path to aligned atlas (mat file): 'atlas.mat' should contain the Allen atlas, registered / aligned to the field of view. Check out https://github.com/ss5513/locaNMF-preprocess to preprocess your data. Example given in template file.
Outputs:
-LocaNMF_Components (folder): The spatial and temporal components in variables 'A' and 'C' respectively, with the corresponding region names for each component in 'areanames'.
-Figures (folder):
CorrelationPlot.png: correlations between different LocaNMF regions;
SummaryComponents.png: A summary of the different spatial and temporal components for every regions.
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