3-D volume visualization
Contents
- About volume visualisation
- Prepare sample statistical results maps
- The surface() method
- Using the fmridisplay object to create a registry of montages/surfaces
- Using addbrain to load or build surfaces
- Removing blobs and re-adding new colors
- Prepackaged cutaway surfaces
- Creating isosurfaces
- Using isosurface to create a custom surface
- Explore on your own
- Explore More: CANlab Toolboxes
About volume visualisation
Creating 3-D renderings of brain data and results can help localize brain activity and show patterns in a way that allows meaningful comparisons across studies.
The CANlab object-oriented tools have methods built in for rendering volume data on brain surfaces. They generally use Matlab's isosurface and isocaps functions.
Prepare sample statistical results maps
For this walkthrough, we'll use the "Pain, Cognition, Emotion balanced N = 270 dataset" from Kragel et al. 2018, Nature Neuroscience. More information about this dataset and how to download it is in an earlier walkthrough on "loading datasets"
The code below loads it; but you could also use any other brain dataset for this purpose (e.g., the 'emotionreg' dataset in load_image_set.
Our goal is to select subject-level images corresponding to a particular task type and do a t-test on these, and visualize the results in various 3-D ways.
[test_images, names] = load_image_set('kragel18_alldata', 'noverbose'); % This field contains a table object with metadata for each image: metadata = test_images.dat_descrip; metadata(1:5, :) % Show the first 5 rows % Here are the 3 domains: disp(unique(metadata.Domain)) % Find all the images of "Neg_Emotion" and pull those into a separate % fmri_data object: wh = find(strcmp(metadata.Domain, 'Neg_Emotion')); neg_emo = get_wh_image(test_images, wh); % Do a t-test on these images, and store the results in a statistic_image % object called "t". Threshold at q < 0.001 FDR. This is an unusual % threshold, but we have lots of power here, and want to restrict the % number of significant voxels for display purposes below. t = ttest(neg_emo, .001, 'fdr'); % Find the images for other conditions and save them for later: t_emo = t; wh = find(strcmp(metadata.Domain, 'Cog_control')); cog_control = get_wh_image(test_images, wh); t_cog = ttest(cog_control, .001, 'fdr'); wh = find(strcmp(metadata.Domain, 'Pain')); pain = get_wh_image(test_images, wh); t_pain = ttest(pain, .001, 'fdr');
Loading /Users/torwager/Google Drive/CanlabDataRepository/Study_Subject_Level_Maps/2018_Kragel_Generalizability/kragel_2018_nat_neurosci_270_subjects_test_images.mat
ans =
5×4 table
Domain Subdomain imagenames Studynumber
______ _________ ______________ ___________
'Pain' 'Thermal' 'ThermalPain1' 1
'Pain' 'Thermal' 'ThermalPain1' 1
'Pain' 'Thermal' 'ThermalPain1' 1
'Pain' 'Thermal' 'ThermalPain1' 1
'Pain' 'Thermal' 'ThermalPain1' 1
'Cog_control'
'Neg_Emotion'
'Pain'
One-sample t-test
Calculating t-statistics and p-values
Image 1 FDR q < 0.001 threshold is 0.000112
Image 1
91 contig. clusters, sizes 1 to 12490
Positive effect: 20261 voxels, min p-value: 0.00000000
Negative effect: 2960 voxels, min p-value: 0.00000000
One-sample t-test
Calculating t-statistics and p-values
Image 1 FDR q < 0.001 threshold is 0.000000
Image 1
0 contig. clusters, sizes to
Positive effect: 0 voxels, min p-value: 0.00000048
Negative effect: 0 voxels, min p-value: 0.00001979
One-sample t-test
Calculating t-statistics and p-values
Image 1 FDR q < 0.001 threshold is 0.000055
Image 1
100 contig. clusters, sizes 1 to 3574
Positive effect: 6993 voxels, min p-value: 0.00000000
Negative effect: 4406 voxels, min p-value: 0.00000000
The surface() method
Most data objects, including fmri_data, statistic_image, and region objects, have a surface() method. Entering it with no arguments creates a surface or series of surfaces (depending on the object)
The surface() method lets you easily render blobs on a number of pre-set choices for brain surfaces or 3-D cutaway surfaces.
% This generates a series of 6 surfaces of different types, including a % subcortical cutaway surface(t); drawnow, snapnow % This generates a different plot create_figure('lateral surfaces'); surface_handles = surface(t, 'foursurfaces', 'noverbose'); snapnow % You can see more options with: % |help statistic_image.surface| or |help t.surface| % You can also use the |render_on_surface()| to method to change the colormap: render_on_surface(t, surface_handles, 'colormap', 'summer'); snapnow
ans =
Light with properties:
Color: [1 1 1]
Style: 'infinite'
Position: [1.4142 -8.6596e-17 0]
Visible: 'on'
Use GET to show all properties
Anatomical image: Adapted from the 7T high-resolution atlas of Keuken, Forstmann et al.
Keuken et al. (2014). Quantifying inter-individual anatomical variability in the subcortex using 7T structural MRI.
Forstmann, Birte U., Max C. Keuken, Andreas Schafer, Pierre-Louis Bazin, Anneke Alkemade, and Robert Turner. 2014. ?Multi-Modal Ultra-High Resolution Structural 7-Tesla MRI Data Repository.? Scientific Data 1 (December): 140050.
Using the fmridisplay object to create a registry of montages/surfaces
The fmridisplay object is an object class that stores information about a set of display items you have created. It stores information about a series of montages (in the same figure or different figures) and surfaces along with their handles.
You can use this to render activations on a whole set of slices/surfaces at once. You can create your own custom views, or use pre-packaged sets of montages/surfaces created by the function canlab_results_fmridisplay
The code snippet below displays our t-map on a relatively complete series of slices and surfaces, so you can see the "whole picture".
my_display_obj = canlab_results_fmridisplay([], 'montagetype', 'full'); my_display_obj = addblobs(my_display_obj, region(t)); snapnow
Setting up fmridisplay objects
48
Grouping contiguous voxels: 91 regions
sagittal montage: 2340 voxels displayed, 20881 not displayed on these slices
coronal montage: 2237 voxels displayed, 20984 not displayed on these slices
axial montage: 5585 voxels displayed, 17636 not displayed on these slices
axial montage: 5521 voxels displayed, 17700 not displayed on these slices
You can see some prepackaged sets in help canlab_results_fmridisplay These include: 'full' Axial, coronal, and saggital slices, 4 cortical surfaces 'compact' Midline saggital and two rows of axial slices [the default] 'compact2' A single row showing midline saggital and axial slices 'multirow' A series of 'compact2' displays in one figure for comparing different images/maps side by side 'regioncenters' A series of separate axes, each focused on one region
In addition, you can pass in a series of optional keywords that will be passed onto the addblobs object method that controls rendering of the blobs on slices. (They don't all work for surfaces). These control colors, inclusion of an outline around each activation blob/region, and more. See help fmridisplay.addblobs for more information. Here is an example using a few of the options.
my_display_obj = canlab_results_fmridisplay(t, 'compact2', 'color', [1 0 0], 'nooutline', 'trans');
Grouping contiguous voxels: 91 regions Setting up fmridisplay objects axial montage: 5576 voxels displayed, 17645 not displayed on these slices sagittal montage: 442 voxels displayed, 22779 not displayed on these slices
After creating the display, other maps can be added. The addblobs() method requires a region object, so we use region(t) to convert to a region object. This shows us regions activated during negative emotion in red and pain in blue.
my_display_obj = addblobs(my_display_obj, region(t_pain), 'color', [0 0 1], 'nooutline', 'trans'); snapnow
Grouping contiguous voxels: 100 regions axial montage: 2794 voxels displayed, 8605 not displayed on these slices sagittal montage: 485 voxels displayed, 10914 not displayed on these slices
my_display_obj is an fmridisplay-class object. It has its own methods:
methods(my_display_obj) % One particularly useful feature is that you can add or _remove_ activation blobs. % This allows you to create a canonical set of views and then render one % activation map on them, remove the blobs, and render other maps on the % same set of display items. You can add points, spheres (e.g., from % different studies), and more. % % When you operate on an fmridisplay object, pass it back out as an output % argument so that the information you updated will be available. my_display_obj = removeblobs(my_display_obj); snapnow
Methods for class fmridisplay: activate_figures legend title_montage addblobs montage transparency_change addpoints removeblobs zoom_in_on_regions addthreshblobs removepoints fmridisplay surface
Using addbrain to load or build surfaces
You can also build any surface object you want and render colors onto it. This is a very versatile method.
Matlab creates handles to surfaces (and other graphics objects). You can use the handles to manipulate the objects in a many ways. They have, for example, color, line, face color, edge color, and many more properties. If h is a figure handle, get(h) shows a list of its properties. set(h, 'MyPropertyName', myvalue) sets the property 'MyPropertyName' to myvalue.
Here, we'll use addbrain to build up a set of surfaces with handles, and then render colored blobs on them using surface(). Instead of the object method, you can also use its underlying function: cluster_surf
Try help addbrain for a list of surfaces, and help cluster_surf for other color/display/scaling options.
% Let's build a surface by starting with a group of thalamic nuclei, and % adding the parabrachial complex and the red nucleus. % Then, we'll render our t-statistics in colors onto those surfaces. create_figure('cutaways'); axis off surface_handles = addbrain('thalamus_group'); surface_handles = [surface_handles addbrain('pbn')]; surface_handles = [surface_handles addbrain('rn')]; surface_handles = [surface_handles addbrain('pag')]; drawnow, snapnow
ans =
Light with properties:
Color: [1 1 1]
Style: 'infinite'
Position: [0.7873 0.9382 0.7071]
Visible: 'on'
Use GET to show all properties
Now render the statistic image stored in t onto those surfaces. All non-activated areas turn gray.
render_on_surface(t, surface_handles, 'colormap', 'summer'); set(surface_handles, 'FaceAlpha', .85); set(surface_handles(5:6), 'FaceAlpha', .15); % Make brainstem and thalamus shell more transparent drawnow, snapnow % Emphasize positive values, in a hot colormap render_on_surface(t, surface_handles, 'clim', [0.01 3]); drawnow, snapnow % Show positive and negative values, in a hot colormap render_on_surface(t, surface_handles, 'clim', [-3 3]); drawnow, snapnow
Now render the statistic image stored in t onto those surfaces. All non-activated areas turn gray.
t.surface('surface_handles', surface_handles, 'noverbose'); drawnow, snapnow
Transparent surfaces are a little hard to see. We can use Matlab's powerful handle graphics system to inspect and change all kinds of properties. See the Matlab documentation for more details. Let's just make the surfaces solid:
set(surface_handles, 'FaceAlpha', 1);
drawnow, snapnow
These surfaces can be rotated too (or zoom in/out, pan, set lighting, etc.) Let's shift the angle.
view(222, 15); % rotate camzoom(0.8); % zoom out % A helpful CANlab function to re-set the lighting after rotating is: lightRestoreSingle; drawnow, snapnow
addbrain also has keywords for composites of multiple surfaces
create_figure('cutaways'); axis off surface_handles = addbrain('limbic'); t.surface('surface_handles', surface_handles, 'noverbose');
ans =
Light with properties:
Color: [1 1 1]
Style: 'infinite'
Position: [1.4142 -8.6596e-17 0]
Visible: 'on'
Use GET to show all properties
Removing blobs and re-adding new colors
There is a special command to re-set the surface colors to gray. Then we can add a t-test for different contrasts without re-drawing surfaces.
surface_handles = addbrain('eraseblobs',surface_handles); title('Cognitive Control'); t = ttest(cog_control, .001, 'unc'); t.surface('surface_handles', surface_handles, 'noverbose'); drawnow, snapnow surface_handles = addbrain('eraseblobs',surface_handles); title('Pain'); t = ttest(pain, .001, 'unc'); t.surface('surface_handles', surface_handles, 'noverbose'); drawnow, snapnow surface_handles = addbrain('eraseblobs',surface_handles); title('Negative Emotion'); t = ttest(neg_emo, .001, 'unc'); t.surface('surface_handles', surface_handles, 'noverbose'); drawnow, snapnow
One-sample t-test Calculating t-statistics and p-values Image 1 63 contig. clusters, sizes 1 to 3466 Positive effect: 9740 voxels, min p-value: 0.00000048 Negative effect: 12440 voxels, min p-value: 0.00001979
One-sample t-test Calculating t-statistics and p-values Image 1 163 contig. clusters, sizes 1 to 4705 Positive effect: 11213 voxels, min p-value: 0.00000000 Negative effect: 13204 voxels, min p-value: 0.00000000
One-sample t-test Calculating t-statistics and p-values Image 1 97 contig. clusters, sizes 1 to 15628 Positive effect: 30689 voxels, min p-value: 0.00000000 Negative effect: 6850 voxels, min p-value: 0.00000000
Prepackaged cutaway surfaces
Addbrain has many 3-D surfaces, including cutaways that show various 3-D sections. The code below visualizes the various options for cutaways.
You can also pass any of these keywords into surface() to generate the surface and render colored blobs. Let's render these with and without an unthresholded pain map
t = ttest(pain, 1, 'unc'); t_thr = threshold(t, .05, 'fdr'); keywords = {'left_cutaway' 'right_cutaway' 'left_insula_slab' 'right_insula_slab' 'accumbens_slab' 'coronal_slabs' 'coronal_slabs_4' 'coronal_slabs_5'}; for i = 1:length(keywords) create_figure('cutaways'); axis off surface_handles = surface(t_thr, keywords{i}); % This essentially runs the code below: % surface_handles = addbrain(keywords{i}, 'noverbose'); % render_on_surface(t, surface_handles, 'clim', [-7 7]); % Alternative: This command creates the same surfaces: % surface_handles = canlab_canonical_brain_surface_cutaways(keywords{i}); % render_on_surface(t, surface_handles, 'clim', [-7 7]); drawnow, snapnow end
One-sample t-test
Calculating t-statistics and p-values
Image 1
36 contig. clusters, sizes 1 to 206720
Positive effect: 77667 voxels, min p-value: 0.00000000
Negative effect: 129110 voxels, min p-value: 0.00000000
Image 1 FDR q < 0.050 threshold is 0.012018
Image 1
245 contig. clusters, sizes 1 to 26536
Positive effect: 17917 voxels, min p-value: 0.00000000
Negative effect: 31782 voxels, min p-value: 0.00000000
ans =
Light with properties:
Color: [1 1 1]
Style: 'infinite'
Position: [1.4142 -8.6596e-17 0]
Visible: 'on'
Use GET to show all properties
Anatomical image: Adapted from the 7T high-resolution atlas of Keuken, Forstmann et al.
Keuken et al. (2014). Quantifying inter-individual anatomical variability in the subcortex using 7T structural MRI.
Forstmann, Birte U., Max C. Keuken, Andreas Schafer, Pierre-Louis Bazin, Anneke Alkemade, and Robert Turner. 2014. ?Multi-Modal Ultra-High Resolution Structural 7-Tesla MRI Data Repository.? Scientific Data 1 (December): 140050.
ans =
Light with properties:
Color: [1 1 1]
Style: 'infinite'
Position: [1.4142 -8.6596e-17 0]
Visible: 'on'
Use GET to show all properties
Anatomical image: Adapted from the 7T high-resolution atlas of Keuken, Forstmann et al.
Keuken et al. (2014). Quantifying inter-individual anatomical variability in the subcortex using 7T structural MRI.
Forstmann, Birte U., Max C. Keuken, Andreas Schafer, Pierre-Louis Bazin, Anneke Alkemade, and Robert Turner. 2014. ?Multi-Modal Ultra-High Resolution Structural 7-Tesla MRI Data Repository.? Scientific Data 1 (December): 140050.
Anatomical image: Adapted from the 7T high-resolution atlas of Keuken, Forstmann et al. Keuken et al. (2014). Quantifying inter-individual anatomical variability in the subcortex using 7T structural MRI. Forstmann, Birte U., Max C. Keuken, Andreas Schafer, Pierre-Louis Bazin, Anneke Alkemade, and Robert Turner. 2014. ?Multi-Modal Ultra-High Resolution Structural 7-Tesla MRI Data Repository.? Scientific Data 1 (December): 140050.
Anatomical image: Adapted from the 7T high-resolution atlas of Keuken, Forstmann et al. Keuken et al. (2014). Quantifying inter-individual anatomical variability in the subcortex using 7T structural MRI. Forstmann, Birte U., Max C. Keuken, Andreas Schafer, Pierre-Louis Bazin, Anneke Alkemade, and Robert Turner. 2014. ?Multi-Modal Ultra-High Resolution Structural 7-Tesla MRI Data Repository.? Scientific Data 1 (December): 140050.
Anatomical image: Adapted from the 7T high-resolution atlas of Keuken, Forstmann et al. Keuken et al. (2014). Quantifying inter-individual anatomical variability in the subcortex using 7T structural MRI. Forstmann, Birte U., Max C. Keuken, Andreas Schafer, Pierre-Louis Bazin, Anneke Alkemade, and Robert Turner. 2014. ?Multi-Modal Ultra-High Resolution Structural 7-Tesla MRI Data Repository.? Scientific Data 1 (December): 140050.
Anatomical image: Adapted from the 7T high-resolution atlas of Keuken, Forstmann et al. Keuken et al. (2014). Quantifying inter-individual anatomical variability in the subcortex using 7T structural MRI. Forstmann, Birte U., Max C. Keuken, Andreas Schafer, Pierre-Louis Bazin, Anneke Alkemade, and Robert Turner. 2014. ?Multi-Modal Ultra-High Resolution Structural 7-Tesla MRI Data Repository.? Scientific Data 1 (December): 140050.
Anatomical image: Adapted from the 7T high-resolution atlas of Keuken, Forstmann et al. Keuken et al. (2014). Quantifying inter-individual anatomical variability in the subcortex using 7T structural MRI. Forstmann, Birte U., Max C. Keuken, Andreas Schafer, Pierre-Louis Bazin, Anneke Alkemade, and Robert Turner. 2014. ?Multi-Modal Ultra-High Resolution Structural 7-Tesla MRI Data Repository.? Scientific Data 1 (December): 140050.
Anatomical image: Adapted from the 7T high-resolution atlas of Keuken, Forstmann et al. Keuken et al. (2014). Quantifying inter-individual anatomical variability in the subcortex using 7T structural MRI. Forstmann, Birte U., Max C. Keuken, Andreas Schafer, Pierre-Louis Bazin, Anneke Alkemade, and Robert Turner. 2014. ?Multi-Modal Ultra-High Resolution Structural 7-Tesla MRI Data Repository.? Scientific Data 1 (December): 140050.
Plot thresholded and unthresholded maps side by side
create_figure('cutaways',1, 2); axis off surface_handles = surface(t, 'right_cutaway'); title('Pain, Unthresholded') subplot(1, 2, 2); surface_handles = surface(t_thr, 'right_cutaway'); title('Pain, thresholded') drawnow, snapnow
ans =
Light with properties:
Color: [1 1 1]
Style: 'infinite'
Position: [1.4142 -8.6596e-17 0]
Visible: 'on'
Use GET to show all properties
Anatomical image: Adapted from the 7T high-resolution atlas of Keuken, Forstmann et al.
Keuken et al. (2014). Quantifying inter-individual anatomical variability in the subcortex using 7T structural MRI.
Forstmann, Birte U., Max C. Keuken, Andreas Schafer, Pierre-Louis Bazin, Anneke Alkemade, and Robert Turner. 2014. ?Multi-Modal Ultra-High Resolution Structural 7-Tesla MRI Data Repository.? Scientific Data 1 (December): 140050.
ans =
Light with properties:
Color: [1 1 1]
Style: 'infinite'
Position: [1.4142 -8.6596e-17 0]
Visible: 'on'
Use GET to show all properties
Anatomical image: Adapted from the 7T high-resolution atlas of Keuken, Forstmann et al.
Keuken et al. (2014). Quantifying inter-individual anatomical variability in the subcortex using 7T structural MRI.
Forstmann, Birte U., Max C. Keuken, Andreas Schafer, Pierre-Louis Bazin, Anneke Alkemade, and Robert Turner. 2014. ?Multi-Modal Ultra-High Resolution Structural 7-Tesla MRI Data Repository.? Scientific Data 1 (December): 140050.
Controlling options in render_on_surface()
When the t map has positive and negative values, render_on_surface creates a special bicolor split colormap that has warm colors for positive vals, and cool colors for negative vals. You can set the color limits (here, in t-values)
create_figure('cutaways'); axis off surface_handles = surface(t_thr, 'coronal_slabs'); render_on_surface(t_thr, surface_handles, 'clim', [-4 4]); drawnow, snapnow % You can set the colormap to any Matlab colormap: render_on_surface(t_thr, surface_handles, 'colormap', 'summer'); drawnow, snapnow % If your image is positive-valued only (or negative-valued only), a % bicolor split map will not be created: t = threshold(t_thr, [2 Inf], 'raw-between'); render_on_surface(t, surface_handles, 'colormap', 'winter', 'clim', [2 6]); drawnow, snapnow
Anatomical image: Adapted from the 7T high-resolution atlas of Keuken, Forstmann et al. Keuken et al. (2014). Quantifying inter-individual anatomical variability in the subcortex using 7T structural MRI. Forstmann, Birte U., Max C. Keuken, Andreas Schafer, Pierre-Louis Bazin, Anneke Alkemade, and Robert Turner. 2014. ?Multi-Modal Ultra-High Resolution Structural 7-Tesla MRI Data Repository.? Scientific Data 1 (December): 140050.
Keeping vals between 2.000 and Inf: 25038 voxels in .sig
Creating isosurfaces
The fmri_data.isosurface() method allows you to create and save isosurfaces for any image, parcel, or blob.
You can also save the meshgrid output and surface structure that lets you render this surface easily in the future.
Here's a simple example visualizing all of the parcels in an atlas as 3-D blobs:
t = ttest(pain, .01, 'unc'); atlas_obj = load_atlas('thalamus'); create_figure('isosurface'); surface_handles = isosurface(atlas_obj); % Now we'll set some lighting and figure properties axis image vis3d off material dull view(210, 20); lightRestoreSingle drawnow, snapnow % You can render blobs on these surfaces too. render_on_surface(t, surface_handles, 'colormap', 'hot'); drawnow, snapnow
One-sample t-test
Calculating t-statistics and p-values
Image 1
244 contig. clusters, sizes 1 to 24024
Positive effect: 17224 voxels, min p-value: 0.00000000
Negative effect: 29956 voxels, min p-value: 0.00000000
Loading atlas: /Users/torwager/Documents/GitHub/Neuroimaging_Pattern_Masks/Atlases_and_parcellations/2018_Wager_combined_atlas/Thalamus_combined_atlas_object.mat
ans =
Light with properties:
Color: [1 1 1]
Style: 'infinite'
Position: [-0.6645 1.1509 0.4837]
Visible: 'on'
Use GET to show all properties
Let's load another atlas, and pull out all the "default mode" regions We'll use isosurface to visualize these
atlas_obj = load_atlas('canlab2018_2mm'); atlas_obj = select_atlas_subset(atlas_obj, {'Def'}, 'labels_2'); create_figure('isosurface'); surface_handles = isosurface(atlas_obj); % let's add a cortical surface for context % We'll make the back surface (right) opaque, and the front (left) transparent han = addbrain('hires right'); set(han, 'FaceAlpha', 1); han2 = addbrain('hires left'); set(han2, 'FaceAlpha', 0.1); axis image vis3d off material dull view(-88, 31); lightRestoreSingle drawnow, snapnow
Loading atlas: CANlab_combined_atlas_object_2018_2mm.mat
ans =
Light with properties:
Color: [1 1 1]
Style: 'infinite'
Position: [-1.2115 -0.0423 0.7284]
Visible: 'on'
Use GET to show all properties
Using isosurface to create a custom surface
You can build a cutaway surface or a series of them with the isosurface method, from any suitable image. The image used here is a mean anatomical image that renders nicely. As always, you can use Matlab's rendering to change the look of the surfaces
anat = fmri_data(which('keuken_2014_enhanced_for_underlay.img'), 'noverbose'); create_figure('cutaways'); axis off surface_handles = isosurface(anat, 'thresh', 140, 'nosmooth', 'xlim', [-Inf 0], 'YLim', [-30 Inf], 'ZLim', [-Inf 40]); render_on_surface(t, surface_handles, 'colormap', 'hot'); view(-127, 33); set(surface_handles, 'FaceAlpha', .85); snapnow
Explore on your own
1. Try to create your own custom brain surface to visualize one of the 3 statistic image maps we've been working on. Can you make a plot that really shows the important results in a clear way?
2. Try exploring movie_tools to create a movie where you rotate, zoom, and change the transparency of your surfaces in some way. You can write movie files to disk for use in presentations, too.
3. Try rendering another atlas, or subset of an atlas, with isosurface() Maybe you can pull out and render all the "ventral attention" network regions.
Explore More: CANlab Toolboxes
Tutorials, overview, and help: https://canlab.github.io
Toolboxes and image repositories on github: https://github.com/canlab
| CANlab Core Tools | https://github.com/canlab/CanlabCore | CANlab Neuroimaging_Pattern_Masks repository | https://github.com/canlab/Neuroimaging_Pattern_Masks | CANlab_help_examples | https://github.com/canlab/CANlab_help_examples | M3 Multilevel mediation toolbox | https://github.com/canlab/MediationToolbox | M3 CANlab robust regression toolbox | https://github.com/canlab/RobustToolbox | M3 MKDA coordinate-based meta-analysis toolbox | https://github.com/canlab/Canlab_MKDA_MetaAnalysis |
Here are some other useful CANlab-associated resources:
| Paradigms_Public - CANlab experimental paradigms | https://github.com/canlab/Paradigms_Public | FMRI_simulations - brain movies, effect size/power | https://github.com/canlab/FMRI_simulations | CANlab_data_public - Published datasets | https://github.com/canlab/CANlab_data_public | M3 Neurosynth: Tal Yarkoni | https://github.com/neurosynth/neurosynth | M3 DCC - Martin Lindquist's dynamic correlation tbx | https://github.com/canlab/Lindquist_Dynamic_Correlation | M3 CanlabScripts - in-lab Matlab/python/bash | https://github.com/canlab/CanlabScripts |
Object-oriented, interactive approach The core basis for interacting with CANlab tools is through object-oriented framework. A simple set of neuroimaging data-specific objects (or classes) allows you to perform interactive analysis using simple commands (called methods) that operate on objects.
Map of core object classes:
