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codespell: fix typos surfaced on master; whitelist hask (#84)
The codespell gate (merged via #53) was red on master because newer code (merged via #53/#70/#79) contained typos the original sweep never saw. Fix real comment typos: exlude->exclude, meaninful->meaningful (canlab_pattern_similarity.m); INTIALIZE->INITIALIZE (hansen_neurotransmitter_maps.m); and saggital/immmediately/iamges/waht/ sigificant/Treshold in the vendored walkthrough snapshot scripts (Unit_tests/walkthroughs/private/canlab_help_*.m). All are in comments. Whitelist 'hask' -- an intentional "has key" anonymous-function helper in canlab_colormap.m and @fmridisplay/render_layer_surfaces.m. codespell now exits clean. Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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‎.codespellrc‎

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@@ -26,4 +26,6 @@ ignore-regex = https?://\S+|^% data: \{"dataType":"image".*
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# atleast, whch, whn, includ -- variable names (renaming would break code; "atleast" -> "at least" would be a syntax error)
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# efficency -- legacy function file name OptimizeDesign11/core_functions/efficency.m (sibling efficiency.m is the newer version; both retained)
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# ony, nam, tha, thre, firt, jus -- ambiguous variable/string fragments in context
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ignore-words-list = ons,ans,fpr,ttests,myu,whos,ser,htmp,fwe,dout,ttest,indx,alph,te,ist,selt,multy,shft,delt,dum,datin,fixin,runn,nneeded,figurestyle,cxan,losin,commun,pres,ues,assignin,evalin,inout,ment,sepulcre,shepard,shure,claus,lew,nd,ore,fo,atmost,sessons,lightening,prctiles,atleast,whch,whn,includ,efficency,ony,nam,tha,thre,firt,jus
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# hask -- local "has key" anonymous-function helper (`hask = @(k) any(strcmp(args,k))`)
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# in canlab_colormap.m and @fmridisplay/render_layer_surfaces.m
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ignore-words-list = ons,ans,fpr,ttests,myu,whos,ser,htmp,fwe,dout,ttest,indx,alph,te,ist,selt,multy,shft,delt,dum,datin,fixin,runn,nneeded,figurestyle,cxan,losin,commun,pres,ues,assignin,evalin,inout,ment,sepulcre,shepard,shure,claus,lew,nd,ore,fo,atmost,sessons,lightening,prctiles,atleast,whch,whn,includ,efficency,ony,nam,tha,thre,firt,jus,hask

‎CanlabCore/@image_vector/hansen_neurotransmitter_maps.m‎

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end
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% -------------------------------------------------------------------------
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% INTIALIZE OUTPUT
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% INITIALIZE OUTPUT
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% -------------------------------------------------------------------------
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stats = struct();
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[hh, hhfill] = deal(' ');

‎CanlabCore/Statistics_tools/canlab_pattern_similarity.m‎

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% By default, data images with values of zero or NaN are considered out-of-mask, as they
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% are not valid values. That is, 0 is typically treated as a missing data value in images
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% This can be changed by setting ['treat_zero_as_data', true].
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% This function does not exlude 0 voxels from the pattern_mask input, where it is assumed
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% that 0 is a meaninful value (for a neuromarker or binary mask).
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% This function does not exclude 0 voxels from the pattern_mask input, where it is assumed
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% that 0 is a meaningful value (for a neuromarker or binary mask).
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% Thus, this function treats values of 0 in DATA images, not pattern masks,
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% as missing values, and excludes these voxels from both image and
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% mask when calculating similarity.

‎CanlabCore/Unit_tests/walkthroughs/private/.!15888!canlab_help_2c_loading_datasets.m‎

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%% Datasets used in CANlab tutorials
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%
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% Note: this report was generated from |canlab_help_2c_loading_datasets.m|
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% in the repository <https://github.com/canlab/CANlab_help_examples>
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%
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%% The Neuroimaging_pattern_masks Github repository and website
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%
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% For these walkthroughs, we'll load several image datasets.
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% Some are stored in Github, if the files are small enough.
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% The Wager 2008 emotion regulation dataset, for example, is
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% in the CANlab Core repository. Others are on figshare or Neurovault.
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%
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% Many tutorials apply pre-trained patterns and masks.
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% These are stored in this Github repository:
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%
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% <https://github.com/canlab/Neuroimaging_Pattern_Masks>
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%
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% In addition, you can explore these and find more information here:
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% <https://sites.google.com/dartmouth.edu/canlab-brainpatterns/home>
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%
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% To run this tutorial and many others in this series, you'll need to
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% download the Neuroimaging_Pattern_Masks Github repository and add it to
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% your Matlab path with subfolders.
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% The Github site has three main types of datasets, shown here:
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%
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% <<Neuroimaging_pattern_masks_site.png>>
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%
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%% Registries for easy loading
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% There are several functions that contain sets of images that you can load
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% by name. For example:
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%
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% |load_image_set()| includes a registry of datasets that you can access by
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% name. Try |help load_image|set| for a list of images you can load by
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% keyword.
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%
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% |load_atlas()| also has a named set of atlases/brain parcellations you can load.
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%
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% |canlab_load_ROI| has a registry of many named regions derived from previous
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% studies. This is particularly useful for loading a subcortical ROI and
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% visualizing it or applying it to data.
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%% Emotion regulation dataset
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% "Wager_et_al_2008_Neuron_EmotionReg"
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% The dataset is a series of contrast images from N = 30 participants.
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% Each image is a contrast image for [reappraise neg vs. look neg]
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%
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% These data were published in:
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% Wager, T. D., Davidson, M. L., Hughes, B. L., Lindquist, M. A.,
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% Ochsner, K. N.. (2008). Prefrontal-subcortical pathways mediating
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% successful emotion regulation. Neuron, 59, 1037-50.
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%
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% To load, try the following:
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[data_obj, subject_names, image_names] = load_image_set('emotionreg', 'noverbose');
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montage(mean(data_obj), 'trans', 'compact2');
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drawnow, snapnow
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% data_obj is an fmri_data object containing all 30 images.
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% subject_names is a list of short names for each image.
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% image_names is a list of the full image names with their path.
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%% Buckner lab resting-state connectivity maps
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%
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% A popular series of masks is a set of "networks" developed from resting-state fMRI
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% connectivity in 1,000 people. For our purposes, the "network" maps
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% consist of a set of voxels that load most highly on each of 7 ICA
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% components from the 1,000 Functional Connectomes project.
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% These were published by Randy Buckner's lab in 2011 in three papers:
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% Choi et al., Buckner et al., and Yeo et al. We'll use the cortical
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% networks from Yeo et al. here.
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%
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[bucknermaps, mapnames] = load_image_set('bucknerlab', 'noverbose'); % loads 7 masks from Yeo et al.
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disp(char(mapnames))
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% Create a montage plot
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o2 = montage(get_wh_image(bucknermaps, 1), 'trans', 'compact2', 'color', rand(1, 3));
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for i = 2:7, o2 = addblobs(o2, region(get_wh_image(bucknermaps, i)), 'trans', 'color', rand(1, 3)); end
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drawnow, snapnow
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%% Pain, Cognition, Emotion balanced N = 270 dataset
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%
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% This dataset is an fmri_data object class object created using CANlab tools (canlab.github.io).
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% It contains 270 single-participant fMRI contrast maps across 18 studies (with 15 participants per study).
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% Studies are grouped into three domains: Pain, Cognitive Control, and Negative Emotion, with 9 studies each.
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% Each domain includes 3 subdomains, with 3 studies each.
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%
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% The dataset is from Kragel et al. 2018, Nature Neuroscience.
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% It is too large for Github, and it's stored on Neurovault.org
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% as collection #504. You could get it using CANlab tools like this:
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%
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% |[files_on_disk, url_on_neurovault, mycollection, myimages] = retrieve_neurovault_collection(504);|
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%
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% It is also available on Figshare, with DOI 10.6084/m9.figshare.24033402
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% https://figshare.com/ndownloader/files/42143352
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%
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% If you download from Neurovault, you'd
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% have to add metadata for the study category labels to use it.
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% Therefore, we suggest you use the CANLab load_image_set() function, as below.
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% It saves a file on your hard drive the first time you run it:
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%
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% |kragel_2018_nat_neurosci_270_subjects_test_images.mat|
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%
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% This file also has metadata that is not necessarily on Neurovault.
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% If you save this file somewhere on your Matlab path, you'll be able to
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% reload and reuse the dataset easily.
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[test_images, names] = load_image_set('kragel18_alldata', 'noverbose');
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% This field contains a table object with metadata for each image:
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metadata = test_images.metadata_table;
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disp('Metadata available in test_images.metadata_table:')
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metadata(1:5, :)
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% Make a plot of the spatial correlation of the average image for each study
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imgs = cellstr(test_images.image_names);
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m = mean(test_images);
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for i = 1:length(names)
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% Create a mean image for each study and store in "m" object.
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wh = metadata.Studynumber == i;
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studymean = mean(get_wh_image(test_images, find(wh)));
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m.dat(:, i) = studymean.dat;
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end
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disp('Map of spatial correlations across the mean images for each study');
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plot_correlation_matrix(m.dat, 'names', names, 'partitions', [ones(1, 6) 2*ones(1, 6) 3*ones(1, 6)], 'partitionlabels', {'Pain', 'Cognition', 'Emotion'});
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drawnow, snapnow
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%% Pain across six intensity levels per person (BMRK3)
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%
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% The dataset contains data from 33 participants, with brain responses to six levels
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% of heat (non-painful and painful). Each image is the average over several
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% (4-8) trials of heat delivered at a single stimulus intensity, ranging
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% from 44.3 - 49.3 degrees C in one-degree increments. Each image is also
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% paired with an average reported pain value for that set of trials, rated
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% immediately after heat experience.
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%
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% This dataset is interesting for mixed-effects and predictive analyses, as
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% it has both within-person and between-person sources of variance.
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%
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% Aspects of this data appear in these papers:
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% Wager, T.D., Atlas, L.T., Lindquist, M.A., Roy, M., Choong-Wan, W., Kross, E. (2013).
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% An fMRI-Based Neurologic Signature of Physical Pain. The New England Journal of Medicine. 368:1388-1397
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% (Study 2)
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%
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% Woo, C. -W., Roy, M., Buhle, J. T. & Wager, T. D. (2015). Distinct brain systems
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% mediate the effects of nociceptive input and self-regulation on pain. PLOS Biology. 13(1):
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% e1002036. doi:10.1371/journal.pbio.1002036
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%

‎CanlabCore/Unit_tests/walkthroughs/private/.!15889!canlab_help_3b_atlases_and_ROI_analysis.m‎

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%% Using the atlas object for Region of Interest (ROI) analyses
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%% About the atlas object
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% An atlas-class object is a specialized subclass of fmri_data that stores
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% information about a series of parcels, or pre-defined regions, and in
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% some cases the probalistic maps that underlie the final parcellation.
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%
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% Some common uses of atlas objects include:
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% * Labeling regions by best-matching atlas regions, as in region.table()
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% * Analysis within specific ROIs, or on ROI averages
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% * Defining regions for connectivity and graph theoretic analyses
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%
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% A list of pre-defined atlases is contained in the function |load_atlas|.
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% The default atlas for some CANlab functions is the "CANlab combined 2018"
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% This was created by Tor Wager from other published atlases. It includes:
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%
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% * Glasser 2016 Nature 180-region cortical parcellation (in MNI space, not indivdidualized)
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% * Pauli 2016 PNAS basal ganglia subregions
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% * Amygdala/hippocampal and basal forebrain regions from SPM Anatomy Toolbox
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% * Thalamus regions from the Morel thalamus atlas
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% * Subthalamus/Basal forebrain regions from Pauli "reinforcement learning" HCP atlas
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% * Diedrichsen cerebellar atlas regions
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% * Multiple named brainstem nuclei localized based on individual papers
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% * Additional Shen atlas parcels to cover areas (esp. brainstem) not otherwise named
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%
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% References for the corresponding papers are stored in the atlas object, and
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% printed in tables generated with the region.table() method.
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%
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% There are many methods for atlas, including all the fmri_data methods.
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% You can see those with |methods(atlas)|. For example:
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%
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% Using the |select_atlas_subset()| method:
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% You can select any subset of atlas regions by name or number, and return
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% them in a new atlas object. You can also 'flatten' regions, combining
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% them into a single new region.
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%
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% % Using the |extract_data()| method:
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% You can extract average data from every image in an atlas, for a series of target images.
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%
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% We will explore these here.
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%% load an atlas
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% 'atlas' objects are a class of objects specially designed for brain
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% atlases. Here is more information on this class (also try |doc atlas|)
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help atlas
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% The function load_atlas in the CANlab toolbox loads a number of named
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% atlases included with the toolbox. Here is a list of named atlases:
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help load_atlas
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% Now load the "CANlab combined 2018" atlas:
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atlas_obj = load_atlas('canlab2018_2mm');
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%% visualize the atlas regions
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orthviews(atlas_obj);
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o2 = montage(atlas_obj);
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drawnow, snapnow
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%% Selecting regions of interest
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% There are several ways of selecting regions of interest. You can do it:
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% * By name or part of name (in the .labels, .labels_2, or other fields)
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% * By number
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% * Graphically, or by entering a coordinate and a search radius
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%%
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% *Select regions graphically or by coordinate*
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% First bring up the orthviews display:
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orthviews(atlas_obj);
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% Now click on a coordinate in the center of vmPFC
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% We'll use the SPM code below to do this here:
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spm_orthviews('Reposition', [0 38 -11])
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% Running this will create a subset atlas containing only regions with centers
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% within 20 mm of that coordinate.
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[obj_within_20mm,index_vals] = select_regions_near_crosshairs(atlas_obj, 'thresh', 20);
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orthviews(obj_within_20mm);
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drawnow, snapnow
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% To reproduce this later in a script, you can enter the coordinates as
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% input instead:
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[obj_within_20mm,index_vals] = select_regions_near_crosshairs(atlas_obj, 'coords', [0 38 -11], 'thresh', 20);
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% This function returns index values, so you can use these directly to
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% specify the regions in select_atlas_subset as well.
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find(index_vals)
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test_obj = select_atlas_subset(atlas_obj, find(index_vals));
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% % This should yield the same map of vmPFC regions: orthviews(test_obj)
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%%
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% *Select regions by name*
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% Let's select all the regions in the thalamus. All regions are labeled in
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% the atlas object, so we can select them by name.
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% Select all regions with "Thal" in the label:
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thal = select_atlas_subset(atlas_obj, {'Thal'})
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% Print the labels:
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thal.labels
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% Select a few thalamus/epithalamus regions of interest:
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thal = select_atlas_subset(atlas_obj, {'Thal_Intra', 'Thal_VL', 'Thal_MD', 'Thal_LGN', 'Thal_MGN', 'Thal_Hb'});
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thal.labels
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% Select all the regions with "Thal" in the label, and collapse them into a single region:
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whole_thal = select_atlas_subset(atlas_obj, {'Thal'}, 'flatten');
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%% Extract and analyze data from atlas regions
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%
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%%
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% First, we'll load a dataset to extract data from for an ROI analysis
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% The dataset contains data from 33 participants, with brain responses to six levels
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% of heat (non-painful and painful).
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%
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% Aspects of this data appear in these papers:
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% Wager, T.D., Atlas, L.T., Lindquist, M.A., Roy, M., Choong-Wan, W., Kross, E. (2013).
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% An fMRI-Based Neurologic Signature of Physical Pain. The New England Journal of Medicine. 368:1388-1397
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% (Study 2)
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%
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% Woo, C. -W., Roy, M., Buhle, J. T. & Wager, T. D. (2015). Distinct brain systems
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% mediate the effects of nociceptive input and self-regulation on pain. PLOS Biology. 13(1):
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% e1002036. doi:10.1371/journal.pbio.1002036
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%

‎CanlabCore/Unit_tests/walkthroughs/private/canlab_help_2b_basic_image_visualization.m‎

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% slices, so as to show the "whole picture" and not omit any results.
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%
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% You can customize this a lot, as it uses the fmridisplay() object class,
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% which allows you to add custom montages (in axial, saggital, and coronal
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% which allows you to add custom montages (in axial, sagittal, and coronal
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% orientations, add blobs of various types, and remove them and re-plot.
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% See |help fmridisplay| and |help fmridisplay.addblobs| for more details.
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%

‎CanlabCore/Unit_tests/walkthroughs/private/canlab_help_2c_loading_datasets.m‎

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% (4-8) trials of heat delivered at a single stimulus intensity, ranging
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% from 44.3 - 49.3 degrees C in one-degree increments. Each image is also
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% paired with an average reported pain value for that set of trials, rated
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% immmediately after heat experience.
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% immediately after heat experience.
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%
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% This dataset is interesting for mixed-effects and predictive analyses, as
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% it has both within-person and between-person sources of variance.

‎CanlabCore/Unit_tests/walkthroughs/private/canlab_help_3b_atlases_and_ROI_analysis.m‎

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%
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% With the pain dataset, for a real analysis we might worry that multiple
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% images come from the same subject. We can't do stats across all the
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% iamges without accounting for the correlations among images belonging to
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% the same subject. But first, let's plot the averages and see waht we get:
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% images without accounting for the correlations among images belonging to
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% the same subject. But first, let's plot the averages and see what we get:
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% Concatenate the region averages into a matrix:
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roi_averages = cat(2, r.dat);
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% NAc = select_atlas_subset(atlas_obj, {'V_Str'});
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%
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% 2. try the t-test on 48 degree only images above. What do you see? Which
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% regions show sigificant activation?
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% regions show significant activation?
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%
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% 3. Try to identify which prefrontal regions show the greatest emotion
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% regulation effect, and which show the greatest response during pain.

‎CanlabCore/Unit_tests/walkthroughs/private/canlab_help_4b_3D_visualization.m‎

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%%
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% You can see some prepackaged sets in |help canlab_results_fmridisplay|
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% These include:
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% 'full' Axial, coronal, and saggital slices, 4 cortical surfaces
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% 'compact' Midline saggital and two rows of axial slices [the default]
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% 'compact2' A single row showing midline saggital and axial slices
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% 'full' Axial, coronal, and sagittal slices, 4 cortical surfaces
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% 'compact' Midline sagittal and two rows of axial slices [the default]
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% 'compact2' A single row showing midline sagittal and axial slices
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% 'multirow' A series of 'compact2' displays in one figure for comparing different images/maps side by side
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% 'regioncenters' A series of separate axes, each focused on one region
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%

‎CanlabCore/Unit_tests/walkthroughs/private/canlab_help_5_regression_walkthrough.m‎

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% Do a standard OLS regression and view the results:
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out = regress(image_obj_untouched, 'nodisplay');
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% Treshold at q < .05 FDR and display
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% Threshold at q < .05 FDR and display
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t_untouched = threshold(out.t, .05, 'fdr');
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o2 = montage(t_untouched);

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