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brainmontageplot
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# brainmontageplot Generate brain ROI figures with multiple surface viewpoints and/or volume slices and combine them together. Can be used from command line. [](https://colab.research.google.com/github/kjamison/brainmontageplot/blob/main/brainmontageplot_example.ipynb) (Note: non-lookup rendering is much faster on local install than colab) Requirements: python3 (<3.11 for now!), nilearn (for main plotting functions), numpy, scipy, nibabel, PIL, pandas. See [requirements.txt](requirements.txt) [atlas_info.json](brainmontage/atlases/atlas_info.json) contains information about the currently supported atlases to map ROIs to surface vertices. * fs86: FreeSurfer Desikan-Killiany 68 cortical gyri + 18 aseg subcortical. * fs86med: Same as fs86, but it projects all of the subcortical structures (except cerebellum) onto the medial wall. See [example](examples/mydata_fs86sub_montage.png) * shen268: 268-region cortical+subcortical atlas from [Shen 2013](https://pubmed.ncbi.nlm.nih.gov/23747961/) * schaefer100(200,300,400,800): 100-400,800 region cortical atlas from [Schaefer 2018](https://pubmed.ncbi.nlm.nih.gov/28981612/). Uses 7Network order. * hcpmmp: 360 region cortical atlas from [Glasser 2016](https://pubmed.ncbi.nlm.nih.gov/27437579/) * coco439: 81 subcortical regions + 360 region cortical atlas from [Glasser 2016](https://pubmed.ncbi.nlm.nih.gov/27437579/) * cifti91k: Renders 32k_fs_LR data (91282 values = 29696 L verts + 29716 R verts + 31870 subcort voxels), e.g., from HCP pipelines * brainmontage also now supports rendering parcellations saved as cifti91k .dlabel.nii or .dscalar.nii. See --subparcellation or creat_montage_figure(...,subparcfile=...) * Can also specify a dscalar.nii or dtseries.nii file as the input data for atlas cifti91k Installation: ``` git clone https://github.com/kjamison/brainmontageplot.git cd brainmontageplot pip install . ``` Usage: ``` brainmontage # Input value options: [--input INPUTFILE] file with value for each ROI. Can be .txt or .mat (or CIFTI for atlas cifti91k) [--inputfield INPUTFIELDNAME] for .mat input with multiple variables, which variable name to use [--inputmapindex INPUTMAXINDEX] 0-based index for which map in a multi-map input file to render (FIRST dimension of input) [--inputvals val1 val2 val3 ...] provide values for each ROI directly from commmand line --views VIEWNAME VIEWNAME ... choose from: dorsal, lateral, medial, ventral, anterior, posterior (or none). default: all --outputimage OUTPUTIMAGE image file to save final montage --surftype SURFTYPE choose from: infl, white, pial, mid, semi (semi-inflated). default: infl --hemis [HEMIS ...] left, right, or both. default: both --colormap CMAPNAME colormap name from matplotlib colormaps (or "lut" for RGB from atlas LUT, or "random") [--cmapfile CMAPFILE] .txt file with R,G,B values on each line for some colormap --clim MIN MAX colormap value range --upscale SCALE higher value to render images at higher resolution (default=1) --backgroundcolor COLORNAME color name for background (default: white) [--backgroundrgb R G B] or specify background as R G B triplet (values 0-1.0) [--noshading] don't apply surface lighting --facemode <[mode],mean,best> face color mode: mode (default, fastest), mean, best (prettier) --bestmodeiters For "best" facemode, how many selection smoothing iterations (default=5) #color bar options --colorbar Add colorbar to output image --colorbarcolor COLORNAME Colorbar tick+label color. Can change to "white" for dark backgrounds --colorbarfontsize FONTSIZE Font size for colorbar label values --colorbarlocation LOCATION right (default), left, top, bottom --colorbartext LABEL Text for colorbar label --colorbartextrotation Flip colorbar label 180deg #optional volume slices --slices <ax,cor,sag> <slice idx> List of slice axes names followed by and indices * Example: axial slices 10,20,30 and sagittal 5,10,15: --slices ax 10 20 30 sag 5 10 15 * order of axes determines order they appear in final montage --axmosaic ROWS COLS How many rows and columns for axial, coronal, or sagittal volume slices? --cormosaic ROWS COLS * default = square. --sagmosaic ROWS COLS * One of ROWS or COLS can be -1 --stackdir <[horizontal],vertical> should slice mosaics be stacked horizontal or vertical (surface views always vertical) --slicebgalpha ALPHAVAL opacity value for brain slice background volume (default=1) --slicezoom ZOOMVAL Zoom (and crop) volume slices (range >=1. default=1) # atlas info option 1: [--atlasname ATLASNAME] atlas name for entry in atlas_info.json (See --atlasname list for available) --subparcellation SUBPARCFILE Sub-parcellation file to use with atlasname. If --atlasname cifti91, this can be any cifti91k .dlabel.nii or .dscalar.nii with 91k (surf+vox) or 59k (cortex only) parcellation indices. # atlas info option 2: [--roilut ROILUTFILE] if not providing atlasname, must provide roilut, lhannot, rhannot files [--lhannot LHANNOTFILE] [--rhannot RHANNOTFILE] [--annotsurfacename ANNOTSURFACE] surface on which annot files are defined (default:fsaverage5) [--lhannotprefix LHANNOTPREFIX] prefix to append to names in lhannot to match ROI LUT (eg: ctx-lh-) [--rhannotprefix RHANNOTPREFIX] same for rhannot (eg: ctx-rh-) [--subcortvolume SUBCORTVOL] NIfTI volume with subcortical ROIs ``` Example command-line usage: Surface views ``` brainmontage --input examples/mydata_fs86.mat --inputfield data --atlasname fs86 \ --colormap magma --clim -1 1 --outputimage mydata_montage.png ``` Example command-line usage: Surface views with column of axial slices ``` brainmontage --input examples/mydata_fs86.mat --inputfield data --atlasname fs86 \ --colormap magma --clim -1 1 --slices ax 23 33 43 53 --axmosaic -1 1 \ --outputimage mydata_montage_withslices.png ``` Example command-line usage: Surface views with LUT ROI colors ``` brainmontage --atlasname fs86med --colormap lut \ --outputimage mydata_fs86med_lut_montage.png ``` Example command-line usage: Surface views with subparc dlabel.nii on cifti91k ``` brainmontage --input examples/mydata_4S1056.mat --inputfield data --atlasname cifti91k --subparc path/to/tpl-fsLR_atlas-4S1056Parcels_den-91k_dseg.dlabel.nii \ --colormap magma --clim -1 1 --slices ax 23 33 43 53 --axmosaic -1 1 \ --outputimage mydata_4S1056_montage_withslices.png ``` Example command-line usage: Surface views and slices for a cifti map: ``` brainmontage --input S1200_7T_Retinotopy181.Fit3_R2_MSMAll.32k_fs_LR.dscalar.nii \ --atlasname cifti91k --colormap turbo --clim 0 50 --slices ax 23 33 43 53 --axmosaic -1 1 \ --outputimage mydata_RetR2_montage_withslices.png ``` Example command-line usage: Surface views and slices for 6th volume of a parcellated timeseries ``` brainmontage --input mydata_fs86_ts.mat --inputfield ts --inputmapindex 5 --atlasname fs86 \ --colormap turbo --clim 0 50 --slices ax 23 33 43 53 --axmosaic -1 1 \ --outputimage mydata_fs86ts_vol5_montage_withslices.png ``` Example python function usage: ```python import numpy as np from brainmontage import create_montage_figure, save_image #note: any ROIs with "nan" will not be displayed roivals=np.arange(86)+1 #example values for each ROI (1-numroi) img=create_montage_figure(roivals,atlasname='fs86', viewnames='all',surftype='infl',clim=[0,86],colormap='magma') save_image(img,'mydata_montage.png') #or you can add outputimagefile='mydata_montage.png' to create_montage_figure() to save directly #create an image with surface views, upscaled by 2, a vertical row of axial slices, # with black background, using smoother "best" face-coloring mode img_with_slices=create_montage_figure(roivals,atlasname='fs86', viewnames='all',surftype='infl',clim=[0,86],colormap='magma', slice_dict={'axial':[23,33,43,53]},mosaic_dict={'axial':[-1,1]}, upscale_factor=2, backgroundcolor="black", face_mode="best", outputimagefile='mydata_montage_withslices.png') #create an image with surface views, upscaled by 2, with a colorbar img_with_slices=create_montage_figure(roivals,atlasname='fs86', viewnames='all',surftype='infl',clim=[0,86],colormap='magma', upscale_factor=2, face_mode="best", add_colorbar=True, colorbar_label='my data', outputimagefile='mydata_montage_withcolorbar.png') #Or an image with axial, coronal, and sagittal slices only, and a light gray background img_slices=create_montage_figure(roivals,atlasname='fs86', viewnames='none',clim=[0,86],colormap='rainbow', slice_dict={'axial':range(0,180,20),'coronal':range(20,200,20),'sagittal':range(10,180,20)}, backgroundcolor="lightgray", outputimagefile='mydata_montage_onlyslices.png') #Or use an existing cifti91k dlabel.nii file and supply parcel values to view roivals=np.arange(1056)+1 #example for 4S1056 subparc img_with_slices=create_montage_figure(roivals,atlasname='cifti91k', subparcfile='path/to/tpl-fsLR_atlas-4S1056Parcels_den-91k_dseg.dlabel.nii', viewnames='all',surftype='infl',clim=[0,1056],colormap='rainbow', slice_dict={'axial':[23,33,43,53]},mosaic_dict={'axial':[-1,1]}, outputimagefile='mydata4S1056_montage_withslices.png') ``` <img src="examples/mydata_montage.png" width=25%> <img src="examples/mydata_montage_whitesurf.png" width=25%> <img src="examples/mydata_montage_withslices.png" width=25%> <img src="examples/mydata_montage_withcolorbar.png" width=25%> <img src="examples/mydata_montage_onlyslices.png" width=25%> <img src="examples/mydata_fs86sub_montage.png" width=25%>
