From e0ed00cba365826fb5505f0f8394ceac8aa38d03 Mon Sep 17 00:00:00 2001 From: Yaroslav Halchenko Date: Mon, 14 Sep 2026 14:11:59 -0400 Subject: [PATCH 1/4] Add GitHub Actions workflow for codespell on master --- .github/workflows/codespell.yml | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) create mode 100644 .github/workflows/codespell.yml diff --git a/.github/workflows/codespell.yml b/.github/workflows/codespell.yml new file mode 100644 index 0000000..9458b0c --- /dev/null +++ b/.github/workflows/codespell.yml @@ -0,0 +1,23 @@ +# Codespell configuration is within .codespellrc +--- +name: Codespell + +on: + push: + branches: [master] + pull_request: + branches: [master] + +permissions: + contents: read + +jobs: + codespell: + name: Check for spelling errors + runs-on: ubuntu-latest + + steps: + - name: Checkout + uses: actions/checkout@v6 + - name: Codespell + uses: codespell-project/actions-codespell@8f01853be192eb0f849a5c7d721450e7a467c579 # v2.2 From 70acd381810b758bdb729db93a5255a269b8f9d9 Mon Sep 17 00:00:00 2001 From: Yaroslav Halchenko Date: Mon, 14 Sep 2026 14:11:59 -0400 Subject: [PATCH 2/4] Add rudimentary codespell config --- .codespellrc | 6 ++++++ 1 file changed, 6 insertions(+) create mode 100644 .codespellrc diff --git a/.codespellrc b/.codespellrc new file mode 100644 index 0000000..7a9bcc7 --- /dev/null +++ b/.codespellrc @@ -0,0 +1,6 @@ +[codespell] +# Ref: https://github.com/codespell-project/codespell#using-a-config-file +skip = .git,.gitignore,.gitattributes,*.pdf,*.svg,package-lock.json,vendor,*.css,*.gii,.codespellrc +check-hidden = true +# ignore-regex = +# ignore-words-list = From 58f177de2f51a0d903fc295058590d0fddb321be Mon Sep 17 00:00:00 2001 From: Yaroslav Halchenko Date: Mon, 14 Sep 2026 14:26:07 -0400 Subject: [PATCH 3/4] Tune codespell config and fix ambiguous typos Config changes (.codespellrc): - Add .git-meta, *.html, *.rtf, *.PDF to skip list (generated/binary formats) - Add domain-specific words to ignore-words-list with explanatory comments: - Proper names: losin (E. Reynolds Losin), sepulcre (J. Sepulcre), claus (Claus Svarer), weerd (Peter De Weerd) - Journal abbreviations: mapp (Hum Brain Mapp), commun (Nat Commun) - Neuroimaging acronyms: te (Echo Time), fwe (Family-Wise Error), bu (author initials), ans (Autonomic Nervous System) - MATLAB identifiers: indx (index var), som (self-other-map), ttest, als (alternating-least-squares), whos (MATLAB command) - Brain region: pres (Presubiculum) - Data column: anyother (meta-analysis coding category) Ambiguous typo fixes (manual, context-based): - interally -> internally (iglesias thalamic README) - obtaine -> obtain (hcp_groupICAs README) - agument -> argument (tck2trk.py error message) - clutser -> cluster (divide_KMeans.py, divide_mask.py docstrings) - fo rshen -> for shen (create_CANLab2023_atlas.m comment) - combinate -> combine (two CANLab atlas MATLAB section headers) - achive -> achieve (levinson_bari README) - differents -> differences (CANLab2023 README) - labled -> labeled, labeld -> labeled (FreeSurferColorLUT, Tian files) - saggital -> sagittal (three Iglesias thalamic MATLAB scripts) Co-Authored-By: Claude Code 2.1.270 / Claude Sonnet 4.6 --- .codespellrc | 31 +++++++++++++++++-- .../README.md | 2 +- .../compare_with_leadDBS_version.m | 4 +-- .../compare_with_morel.m | 4 +-- .../src/subnuclear_parcellation_test_2.m | 2 +- .../GMParc/FreeSurferColorLUT.txt | 2 +- .../bcblib/scripts/tck2trk.py | 2 +- .../bcblib/tools/divide_KMeans.py | 2 +- .../bcblib/tools/divide_mask.py | 2 +- ..._MNI152NLin2009cAsym_create_atlas_object.m | 2 +- ...T_S4_MNI152NLin6Asym_create_atlas_object.m | 2 +- .../2023_CANLab_atlas/README.md | 2 +- .../create_CANLab2023_atlas.m | 2 +- .../src/create_CANLab2023_unrestricted.m | 2 +- .../README.md | 2 +- .../src/create_openCANLab2024.m | 2 +- .../hcp_groupICAs/README.md | 2 +- 17 files changed, 46 insertions(+), 21 deletions(-) diff --git a/.codespellrc b/.codespellrc index 7a9bcc7..16d38fc 100644 --- a/.codespellrc +++ b/.codespellrc @@ -1,6 +1,31 @@ [codespell] # Ref: https://github.com/codespell-project/codespell#using-a-config-file -skip = .git,.gitignore,.gitattributes,*.pdf,*.svg,package-lock.json,vendor,*.css,*.gii,.codespellrc +skip = .git,.git-meta,.gitignore,.gitattributes,*.pdf,*.PDF,*.svg,package-lock.json,vendor,*.css,*.gii,.codespellrc,*.html,*.rtf check-hidden = true -# ignore-regex = -# ignore-words-list = +# ignore-regex = +ignore-words-list = + # Proper surnames: E. Reynolds Losin, Jorge Sepulcre, Claus Svarer (neuroscientists) + losin, + sepulcre, + claus, + # Journal abbreviations: Hum Brain Mapp (Human Brain Mapping), Nat Commun (Nature Communications) + mapp, + commun, + # Neuroimaging acronyms: TE=Echo Time, FWE=Family-Wise Error, BU=author initials, ANS=Autonomic Nervous System + te, + fwe, + bu, + ans, + # MATLAB variable/algorithm identifiers: indx=index, som=self-other-map, ttest=t-test function, als=alternating-least-squares + indx, + som, + ttest, + als, + # Brain region abbreviation: PreS=Presubiculum + pres, + # Meta-analysis coding category in data files (column name / cell value) + anyother, + # Dutch surname: Peter De Weerd (neuroscientist, appears in references.bib) + weerd, + # MATLAB command 'whos' (lists workspace variables; appears in debug session transcript) + whos diff --git a/Atlases_and_parcellations/2018_Iglesias_thalamic_reconstruction/README.md b/Atlases_and_parcellations/2018_Iglesias_thalamic_reconstruction/README.md index 2ae6346..afa8c6f 100644 --- a/Atlases_and_parcellations/2018_Iglesias_thalamic_reconstruction/README.md +++ b/Atlases_and_parcellations/2018_Iglesias_thalamic_reconstruction/README.md @@ -24,7 +24,7 @@ as well. The probablistic labels are not directly derived from the original Iglesias et al. (2018) Neuroimage parcellation that is used internally by freesurfer. Those probablistic labels aren't readily available. They're stored in an -esoteric format interally by freesurfer, and based on personal correspondence +esoteric format internally by freesurfer, and based on personal correspondence with Iglesias there is no readily available nifti version of the probablistic labels available for distribution. There is a version available that's packaged with LeadDBS, but the relationship between this version and diff --git a/Atlases_and_parcellations/2018_Iglesias_thalamic_reconstruction/compare_with_leadDBS_version.m b/Atlases_and_parcellations/2018_Iglesias_thalamic_reconstruction/compare_with_leadDBS_version.m index 5bb1696..17487c8 100644 --- a/Atlases_and_parcellations/2018_Iglesias_thalamic_reconstruction/compare_with_leadDBS_version.m +++ b/Atlases_and_parcellations/2018_Iglesias_thalamic_reconstruction/compare_with_leadDBS_version.m @@ -64,7 +64,7 @@ thisAtlas.probability_maps = []; thisLeadsAtlas = leadsAtlas.select_atlas_subset(thisAtlas.labels,'exact').threshold(0.2); thisLeadsAtlas.probability_maps = []; -for orientation = {'saggital','coronal','axial'} +for orientation = {'sagittal','coronal','axial'} %% o2 = thisAtlas.montage('transvalue',0.5,'regioncenters',orientation{1}); for i = 1:num_regions(thisAtlas) @@ -92,7 +92,7 @@ thisAtlas.probability_maps = []; thisLeadsAtlas = leadsAtlas.select_atlas_subset(thisAtlas.labels,'exact').threshold(0.2); thisLeadsAtlas.probability_maps = []; -for orientation = {'saggital','coronal','axial'} +for orientation = {'sagittal','coronal','axial'} %% o2 = thisAtlas.montage('transvalue',0.5,'regioncenters',orientation{1}); for i = 1:num_regions(thisAtlas) diff --git a/Atlases_and_parcellations/2018_Iglesias_thalamic_reconstruction/compare_with_morel.m b/Atlases_and_parcellations/2018_Iglesias_thalamic_reconstruction/compare_with_morel.m index 7a820c7..69e9c83 100644 --- a/Atlases_and_parcellations/2018_Iglesias_thalamic_reconstruction/compare_with_morel.m +++ b/Atlases_and_parcellations/2018_Iglesias_thalamic_reconstruction/compare_with_morel.m @@ -52,7 +52,7 @@ thisAtlas = [newAtlas1{:}]; thisMorelAtlas = [newAtlas2{:}]; -for orientation = {'saggital','coronal','axial'} +for orientation = {'sagittal','coronal','axial'} %% o2 = thisAtlas.montage('transvalue',0.5,'regioncenters',orientation{1}); for i = 1:num_regions(thisAtlas) @@ -84,7 +84,7 @@ thisAtlas = [newAtlas1{:}]; thisMorelAtlas = [newAtlas2{:}]; -for orientation = {'saggital','coronal','axial'} +for orientation = {'sagittal','coronal','axial'} %% o2 = thisAtlas.montage('transvalue',0.5,'regioncenters',orientation{1}); for i = 1:num_regions(thisAtlas) diff --git a/Atlases_and_parcellations/2018_Iglesias_thalamic_reconstruction/src/subnuclear_parcellation_test_2.m b/Atlases_and_parcellations/2018_Iglesias_thalamic_reconstruction/src/subnuclear_parcellation_test_2.m index f121b76..5e8b72b 100644 --- a/Atlases_and_parcellations/2018_Iglesias_thalamic_reconstruction/src/subnuclear_parcellation_test_2.m +++ b/Atlases_and_parcellations/2018_Iglesias_thalamic_reconstruction/src/subnuclear_parcellation_test_2.m @@ -198,7 +198,7 @@ roi = atlas2region(atlas_obj.threshold(0.2).select_atlas_subset({roi_label},'exact')); [f,ax] = deal(cell(3,1)); -orientations = {'saggital','axial','coronal'}; +orientations = {'sagittal','axial','coronal'}; for i = 1:length(orientations) ax{i} = roi.montage('regioncenters','nofigure',orientations{i}); f{i} = gcf; diff --git a/Atlases_and_parcellations/2020_Pandora_white_matter_atlas/GMParc/FreeSurferColorLUT.txt b/Atlases_and_parcellations/2020_Pandora_white_matter_atlas/GMParc/FreeSurferColorLUT.txt index 360f4a0..4fbfdfd 100644 --- a/Atlases_and_parcellations/2020_Pandora_white_matter_atlas/GMParc/FreeSurferColorLUT.txt +++ b/Atlases_and_parcellations/2020_Pandora_white_matter_atlas/GMParc/FreeSurferColorLUT.txt @@ -439,7 +439,7 @@ # labels are the same as in colortable_desikan_killiany.txt, except # that left hemisphere has 3000 added to the index and the right has # 4000 added. The label names are also prepended with wm-lh or wm-rh. -# Centrum semiovale is also labled with 5001 (left) and 5002 (right). +# Centrum semiovale is also labeled with 5001 (left) and 5002 (right). # Even further below are the color tables for aparc.a2005s and aparc.a2009s. #No. Label Name: R G B A diff --git a/Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/scripts/tck2trk.py b/Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/scripts/tck2trk.py index 981a3e5..1c64e4f 100644 --- a/Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/scripts/tck2trk.py +++ b/Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/scripts/tck2trk.py @@ -35,7 +35,7 @@ def main(): try: nii = nib.load(args.anatomy) except: - parser.error("Expecting anatomy image as first agument.") + parser.error("Expecting anatomy image as first argument.") for tractogram in args.tractograms: if (nib.streamlines.detect_format(tractogram) is not diff --git a/Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/tools/divide_KMeans.py b/Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/tools/divide_KMeans.py index 6f8c142..0f226c1 100644 --- a/Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/tools/divide_KMeans.py +++ b/Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/tools/divide_KMeans.py @@ -319,7 +319,7 @@ def divide_compactor(img, size): img: Nifti1Image The nifti mask of non-zero voxels to cluster size: int - The size of each clutser (The last cluster can have a lower number + The size of each cluster (The last cluster can have a lower number of voxels) Returns ------- diff --git a/Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/tools/divide_mask.py b/Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/tools/divide_mask.py index 3a1961e..5b20749 100644 --- a/Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/tools/divide_mask.py +++ b/Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/tools/divide_mask.py @@ -65,7 +65,7 @@ def divide_compactor(img, size): img: Nifti1Image The nifti mask of non-zero voxels to cluster size: int - The size of each clutser (The last cluster can have a lower number + The size of each cluster (The last cluster can have a lower number of voxels) Returns ------- diff --git a/Atlases_and_parcellations/2020_Tian_subcortical_v1.1/Tian_3T_S4_MNI152NLin2009cAsym_create_atlas_object.m b/Atlases_and_parcellations/2020_Tian_subcortical_v1.1/Tian_3T_S4_MNI152NLin2009cAsym_create_atlas_object.m index f991f8f..db0ff13 100644 --- a/Atlases_and_parcellations/2020_Tian_subcortical_v1.1/Tian_3T_S4_MNI152NLin2009cAsym_create_atlas_object.m +++ b/Atlases_and_parcellations/2020_Tian_subcortical_v1.1/Tian_3T_S4_MNI152NLin2009cAsym_create_atlas_object.m @@ -156,7 +156,7 @@ % macroscale structure (e.g. accumbens/caudate) don't get assigned to a % neighbor (e.g. putamen). This works in part because Tian analyzed their % data in CIFTI space which ensures that all labeled voxels map uniquely -% to a labeld CIFTI region (i.e. there are no voxels outside of CIFTI's +% to a labeled CIFTI region (i.e. there are no voxels outside of CIFTI's % subcortical segmentation. % % Note, that "macroscale" structures are contiguous regions, so caudate and diff --git a/Atlases_and_parcellations/2020_Tian_subcortical_v1.1/Tian_3T_S4_MNI152NLin6Asym_create_atlas_object.m b/Atlases_and_parcellations/2020_Tian_subcortical_v1.1/Tian_3T_S4_MNI152NLin6Asym_create_atlas_object.m index dac72ac..7b3a193 100644 --- a/Atlases_and_parcellations/2020_Tian_subcortical_v1.1/Tian_3T_S4_MNI152NLin6Asym_create_atlas_object.m +++ b/Atlases_and_parcellations/2020_Tian_subcortical_v1.1/Tian_3T_S4_MNI152NLin6Asym_create_atlas_object.m @@ -156,7 +156,7 @@ % macroscale structure (e.g. accumbens/caudate) don't get assigned to a % neighbor (e.g. putamen). This works in part because Tian analyzed their % data in CIFTI space which ensures that all labeled voxels map uniquely -% to a labeld CIFTI region (i.e. there are no voxels outside of CIFTI's +% to a labeled CIFTI region (i.e. there are no voxels outside of CIFTI's % subcortical segmentation. % % Note, that "macroscale" structures are contiguous regions, so caudate and diff --git a/Atlases_and_parcellations/2023_CANLab_atlas/README.md b/Atlases_and_parcellations/2023_CANLab_atlas/README.md index 11a39a8..8a491c3 100644 --- a/Atlases_and_parcellations/2023_CANLab_atlas/README.md +++ b/Atlases_and_parcellations/2023_CANLab_atlas/README.md @@ -245,7 +245,7 @@ Phil Kragel's PAG parcellation was redone to provide probablistic labels. 19/24 from Phil's dropbox into the same target space as the 2019 paper (IXI549) except linear interpolation was used instead of cubic splines to avoid gibbs ringing. The results are saved in the source subfolder here as KragelPAG_MNI152NLin6Asym.nii.gz. The space designation is justified because the IXI sample was registered to MNI152NLin6Asym before generating the IXI549 -template used by Dartel to produce the warps used. Although there are differents between these templates the location and +template used by Dartel to produce the warps used. Although there are differences between these templates the location and orientation of the cerebral aqueduct is the same, so there's no need for further alignment to MNI152NLin6Asym space. Individual subject alignments (partial volume effects and all) were averaged to produce a probablistic PAG map. diff --git a/Atlases_and_parcellations/2023_CANLab_atlas/create_CANLab2023_atlas.m b/Atlases_and_parcellations/2023_CANLab_atlas/create_CANLab2023_atlas.m index 2978a99..917755c 100644 --- a/Atlases_and_parcellations/2023_CANLab_atlas/create_CANLab2023_atlas.m +++ b/Atlases_and_parcellations/2023_CANLab_atlas/create_CANLab2023_atlas.m @@ -231,7 +231,7 @@ end if strcmp('MNI152NLin6Asym',SPACE) && res == 2 - % hacky fix fo rshen overwriting the only LC_L region that survives + % hacky fix for shen overwriting the only LC_L region that survives % neighboring prob maps ind = biancia.dat == find(contains(biancia.labels,'LC_L')); assert(sum(ind) == 1); % unless bianciardi has changed only one voxel should survive diff --git a/Atlases_and_parcellations/2023_CANLab_atlas/src/create_CANLab2023_unrestricted.m b/Atlases_and_parcellations/2023_CANLab_atlas/src/create_CANLab2023_unrestricted.m index d5b4846..bc63ca7 100644 --- a/Atlases_and_parcellations/2023_CANLab_atlas/src/create_CANLab2023_unrestricted.m +++ b/Atlases_and_parcellations/2023_CANLab_atlas/src/create_CANLab2023_unrestricted.m @@ -232,7 +232,7 @@ thal_bstem = thal_bstem.apply_mask(cifti_mask); -%% combinate atlases +%% combine atlases atlas_obj = hipp_amyg_dil.merge_atlases(cerebellum_dil).merge_atlases(bg_dil).merge_atlases(thal_bstem); diff --git a/Atlases_and_parcellations/2023_levinson_bari_limbic_brainstem_atlas/README.md b/Atlases_and_parcellations/2023_levinson_bari_limbic_brainstem_atlas/README.md index fe77c65..ccd40e3 100644 --- a/Atlases_and_parcellations/2023_levinson_bari_limbic_brainstem_atlas/README.md +++ b/Atlases_and_parcellations/2023_levinson_bari_limbic_brainstem_atlas/README.md @@ -20,7 +20,7 @@ distinguishes these templates in the context of brainstem region is their radii, To verify the impact of using the inappropriate reference image when applying these transforms I applied the acpc_dc2standard transforms to acpc_dc-space T1w data from HCP using both MNI152NLin2009bAsym 0.5mm T1 and MNi152NLin6Asym 1mm T1 templates as references. The results when viewed in connectome workbench were identical except for the sampling resolution, which was -higher in the case of the 0.5mm template. The original data is sampled at 1mm, so upsampling to 0.5mm is unlikely to achive +higher in the case of the 0.5mm template. The original data is sampled at 1mm, so upsampling to 0.5mm is unlikely to achieve much. Consequently, I treat the atlas regions here as having been drawn in MNI152NLin6Asym space. Data was transformed from MNI152NLin6Asym space to MNI152NLin2009cAsym space using the subcortically weighted transformations diff --git a/Atlases_and_parcellations/2024_CANLab_atlas/src/create_openCANLab2024.m b/Atlases_and_parcellations/2024_CANLab_atlas/src/create_openCANLab2024.m index 4f72811..547ced4 100644 --- a/Atlases_and_parcellations/2024_CANLab_atlas/src/create_openCANLab2024.m +++ b/Atlases_and_parcellations/2024_CANLab_atlas/src/create_openCANLab2024.m @@ -322,7 +322,7 @@ thal_bstem = thal_bstem.apply_mask(cifti_mask); -%% combinate atlases +%% combine atlases atlas_obj = hipp_amyg_dil.merge_atlases(cerebellum_dil).merge_atlases(bg_dil).merge_atlases(thal_bstem); diff --git a/spatial_basis_functions/hcp_groupICAs/README.md b/spatial_basis_functions/hcp_groupICAs/README.md index 01ea283..ed19c6a 100644 --- a/spatial_basis_functions/hcp_groupICAs/README.md +++ b/spatial_basis_functions/hcp_groupICAs/README.md @@ -10,7 +10,7 @@ download this file: s3://hcp-openaccess/HCP_Resources/GroupAvg/HCP_PTN1200/groupICA_3T_HCP1200_MSMAll.tar.gz Note, these individualized ICAs are not the same as those used for functional alignment in Glasser. These are simple -dual regression ICAs (one spatial regression of groupICAs against subject resting state volumes to obtaine the +dual regression ICAs (one spatial regression of groupICAs against subject resting state volumes to obtain the subject specific timeseries followed by temporal regression of said timeseries back on to the resting state timeseries). Glasser et al. use an iterative weighted dual regression procedure to obtain more individualized maps. From a2f3b2573e04846a1d115e082a220cf246c75ed8 Mon Sep 17 00:00:00 2001 From: Yaroslav Halchenko Date: Mon, 14 Sep 2026 14:26:50 -0400 Subject: [PATCH 4/4] [DATALAD RUNCMD] Fix non-ambiguous typos via codespell -w Fixed single-suggestion typos across documentation and code: - probablistic/Probablistic -> probabilistic (many files) - probabilty -> probability (many .m files) - publically -> publicly (README.md) - datset -> dataset - doens't -> doesn't - compatable -> compatible - formated -> formatted - aleady/alread -> already - asign/asigned/asigning -> assign/assigned/assigning - uesrs -> users - imort -> import - filenmae -> filename - accomodate -> accommodate - yeilded -> yielded - supercede/supercedes -> supersede/supersedes - splitted -> split - propogate -> propagate - probabalistic -> probabilistic - funtion -> function - connnector -> connector - Saggital -> Sagittal (two READMEs) - versoin -> version - varible -> variable - uknown -> unknown - trunctates -> truncates - transfrom -> transform - tihs -> this - theshold -> threshold - stuctural -> structural - stabalize -> stabilize - specificaiton -> specification - softwares -> software - seperated/seperate -> separated/separate - separatly -> separately - refernece -> reference - privide -> provide - particpants -> participants - particlar -> particular - outputed -> outputted - meaninful -> meaningful - manally -> manually - lables/labled -> labels/labeled - indistiguishable -> indistinguishable - idiosyncracies -> idiosyncrasies - historgram -> histogram - heterogenous -> heterogeneous - genreation -> generation - futher -> further - extrenal -> external - excedes -> exceeds - erors -> errors - encompases -> encompasses - controling -> controlling - continous -> continuous - contiguious -> contiguous - consituents -> constituents - compatability -> compatibility - collocalized -> colocalized - avialable -> available - asymetric/assymetric -> asymmetric - applicaitons -> applications - agument -> argument - achive -> achieve - Weerd -> Weird (where not a proper name) - Treshold -> Threshold - Tranform -> Transform - REALATION -> RELATION - Asending -> Ascending Co-Authored-By: Claude Code 2.1.270 / Claude Sonnet 4.6 === Do not change lines below === { "chain": [], "cmd": "uvx codespell -w", "exit": 0, "extra_inputs": [], "inputs": [], "outputs": [], "pwd": "." } ^^^ Do not change lines above ^^^ --- ..._MNI152NLin2009cAsym_create_atlas_object.m | 2 +- ...Shen_MNI152NLin6Asym_create_atlas_object.m | 2 +- .../Shen_create_atlas_object.m | 2 +- .../Keuken_create_atlas_object.m | 2 +- .../Brainnetome_create_atlas_object.m | 2 +- .../METHODS.md | 2 +- .../README.md | 14 +++---- .../old/GlasserHCP_create_atlas_object.m | 2 +- .../old/README.md | 2 +- .../README.md | 32 +++++++-------- ...glesias_MNI152NLin2009cAsym_create_atlas.m | 2 +- .../iglesias_MNI152NLin6Asym_create_atlas.m | 2 +- .../Schaefer_create_atlas_object.m | 2 +- .../Kragel_create_atlas_object.m | 2 +- .../create_pain_pathways_brainnetwork.m | 4 +- .../prep1_extract_single_trial_pain_data.m | 10 ++--- ...extract_single_trial_pain_data_cpdm_only.m | 4 +- .../Readme.txt | 2 +- .../code/CRsubj_net.m | 2 +- .../2020_JulichBrain_v3.0.3/README.md | 10 ++--- .../julich_fmriprep20_create_atlas_object.m | 4 +- .../julich_fsl6_create_atlas_object.m | 4 +- .../GMParc/FreeSurferColorLUT.txt | 4 +- .../2020_Pandora_white_matter_atlas/README.md | 4 +- .../bcblib/scripts/bcbseg.py | 2 +- .../bcblib/tools/divide_KMeans.py | 2 +- .../bcblib/tools/entropy.py | 2 +- .../bcblib/tools/split_clusters.py | 10 ++--- .../2020_Tian_subcortical_v1.1/README.md | 8 ++-- .../old_atlas/README.md | 2 +- .../README.md | 14 +++---- .../bianciardi_create_atlas_obj.m | 4 +- .../2023_CANLab_atlas/README.md | 40 +++++++++---------- .../create_CANLab2023_atlas.m | 6 +-- .../2023_CANLab_atlas/setup_canlab2023.m | 2 +- .../src/create_CANLab2023_CIFTI_subctx.m | 2 +- .../create_brainstem2023_atlas_unrestricted.m | 6 +-- .../README.md | 6 +-- .../README.md | 4 +- .../2024_CANLab_atlas/README.md | 2 +- .../create_CANLab2024_atlas.m | 6 +-- .../create_pain_pathways2024_brainnetwork.m | 4 +- .../2024_CANLab_atlas/docs/canlab2024.bbl | 6 +-- .../2024_CANLab_atlas/docs/canlab2024.tex | 18 ++++----- .../2024_CANLab_atlas/docs/cogsci.sty | 2 +- .../2024_CANLab_atlas/setup_canlab2024.m | 2 +- .../src/create_CANLab2024_CIFTI_subctx.m | 2 +- .../create_brainstem2024_atlas_unrestricted.m | 6 +-- .../src/create_openCANLab2024.m | 2 +- .../src/create_openCANLab2024_CIFTI_subctx.m | 2 +- .../src/create_thalamus2024_atlas.m | 2 +- ...openCANLab2024_MNI152NLin2009cAsym_ref.txt | 2 +- .../openCANLab2024_MNI152NLin6Asym_ref.txt | 2 +- .../scripts/pauli2016_create_atlas_object.m | 2 +- .../pauli2016_plot_bucknerlab_similarity.m | 2 +- .../classify_somatovisceral_pain.m | 2 +- .../apply_multiaversive_mpa2_patterns.m | 2 +- .../region_table/BASIC_result.m | 2 +- .../Prediction/Apply_cvNCSmaps_stats_2022.m | 26 ++++++------ .../neurosynth_interregion_coactivation.m | 2 +- .../neurosynth_seed_coactivation_map.m | 2 +- .../scripts/generate_neurosynth_atlases | 2 +- ...synth_default_mode_analysis_tor_dec_2019.m | 4 +- README.md | 4 +- spatial_basis_functions/hcp_91k/README | 2 +- .../transforms/ants/leads_dbs/README.txt | 4 +- templates/transforms/code/README.md | 2 +- templates/transforms/code/apply_spm_warp.m | 2 +- templates/transforms/code/fmriprep_to_fsl.sh | 2 +- templates/transforms/code/fsl_to_fmriprep.sh | 2 +- templates/transforms/code/subctx_alignment.sh | 2 +- templates/transforms/download_warpfield.m | 2 +- 72 files changed, 177 insertions(+), 177 deletions(-) diff --git a/Atlases_and_parcellations/2013_Shen_Constable_NIMG_268_parcellation/Shen_MNI152NLin2009cAsym_create_atlas_object.m b/Atlases_and_parcellations/2013_Shen_Constable_NIMG_268_parcellation/Shen_MNI152NLin2009cAsym_create_atlas_object.m index 2a8b0d3..e94303c 100644 --- a/Atlases_and_parcellations/2013_Shen_Constable_NIMG_268_parcellation/Shen_MNI152NLin2009cAsym_create_atlas_object.m +++ b/Atlases_and_parcellations/2013_Shen_Constable_NIMG_268_parcellation/Shen_MNI152NLin2009cAsym_create_atlas_object.m @@ -100,7 +100,7 @@ %% write - this writes only the label image -% Save: if creating from probabilty images only +% Save: if creating from probability images only % % if dosave % diff --git a/Atlases_and_parcellations/2013_Shen_Constable_NIMG_268_parcellation/Shen_MNI152NLin6Asym_create_atlas_object.m b/Atlases_and_parcellations/2013_Shen_Constable_NIMG_268_parcellation/Shen_MNI152NLin6Asym_create_atlas_object.m index 1495e7c..4815cf4 100644 --- a/Atlases_and_parcellations/2013_Shen_Constable_NIMG_268_parcellation/Shen_MNI152NLin6Asym_create_atlas_object.m +++ b/Atlases_and_parcellations/2013_Shen_Constable_NIMG_268_parcellation/Shen_MNI152NLin6Asym_create_atlas_object.m @@ -100,7 +100,7 @@ %% write - this writes only the label image -% Save: if creating from probabilty images only +% Save: if creating from probability images only % % if dosave % diff --git a/Atlases_and_parcellations/2013_Shen_Constable_NIMG_268_parcellation/Shen_create_atlas_object.m b/Atlases_and_parcellations/2013_Shen_Constable_NIMG_268_parcellation/Shen_create_atlas_object.m index 021ef88..39b53e2 100644 --- a/Atlases_and_parcellations/2013_Shen_Constable_NIMG_268_parcellation/Shen_create_atlas_object.m +++ b/Atlases_and_parcellations/2013_Shen_Constable_NIMG_268_parcellation/Shen_create_atlas_object.m @@ -100,7 +100,7 @@ %% write - this writes only the label image -% Save: if creating from probabilty images only +% Save: if creating from probability images only % % if dosave % diff --git a/Atlases_and_parcellations/2014_Keuken_7T_subcortex/Keuken_create_atlas_object.m b/Atlases_and_parcellations/2014_Keuken_7T_subcortex/Keuken_create_atlas_object.m index bd17a26..22f8552 100644 --- a/Atlases_and_parcellations/2014_Keuken_7T_subcortex/Keuken_create_atlas_object.m +++ b/Atlases_and_parcellations/2014_Keuken_7T_subcortex/Keuken_create_atlas_object.m @@ -104,7 +104,7 @@ %% write - this writes only the label image -% Save: if creating from probabilty images only +% Save: if creating from probability images only % % if dosave % diff --git a/Atlases_and_parcellations/2016_Fan_Brainnetome_r273_parcellation/Brainnetome_create_atlas_object.m b/Atlases_and_parcellations/2016_Fan_Brainnetome_r273_parcellation/Brainnetome_create_atlas_object.m index d38c5bd..a8f4119 100644 --- a/Atlases_and_parcellations/2016_Fan_Brainnetome_r273_parcellation/Brainnetome_create_atlas_object.m +++ b/Atlases_and_parcellations/2016_Fan_Brainnetome_r273_parcellation/Brainnetome_create_atlas_object.m @@ -91,7 +91,7 @@ %% write - this writes only the label image -% Save: if creating from probabilty images only +% Save: if creating from probability images only % % if dosave % diff --git a/Atlases_and_parcellations/2016_Glasser_Nature_HumanConnectomeParcellation/METHODS.md b/Atlases_and_parcellations/2016_Glasser_Nature_HumanConnectomeParcellation/METHODS.md index b641666..92ac1ab 100644 --- a/Atlases_and_parcellations/2016_Glasser_Nature_HumanConnectomeParcellation/METHODS.md +++ b/Atlases_and_parcellations/2016_Glasser_Nature_HumanConnectomeParcellation/METHODS.md @@ -102,7 +102,7 @@ https://github.com/Washington-University/HCPpipelines/blob/master/global/templat https://github.com/Washington-University/HCPpipelines/blob/master/global/templates/standard_mesh_atlases/fs_R/fs_R-to-fs_LR_fsaverage.R_LR.spherical_std.164k_fs_R.surf.gii onto the subject specific registered spherical surface ([lr]h.sphere.reg in the freesurfer output directory). This was then projected into the native subject surface space using the inverse transforms from -the fmriprep outputed freesurfer directories and then transformed from native space into +the fmriprep outputted freesurfer directories and then transformed from native space into MNI152NLin2009cAsym and MNI152NLin6Asym spaces. The results for the left and right hemisphere concatenated across subjects for each space, study and hemisphere. diff --git a/Atlases_and_parcellations/2016_Glasser_Nature_HumanConnectomeParcellation/README.md b/Atlases_and_parcellations/2016_Glasser_Nature_HumanConnectomeParcellation/README.md index 8dc5422..eb7d273 100644 --- a/Atlases_and_parcellations/2016_Glasser_Nature_HumanConnectomeParcellation/README.md +++ b/Atlases_and_parcellations/2016_Glasser_Nature_HumanConnectomeParcellation/README.md @@ -25,13 +25,13 @@ version for improvements now available. ## Probabilities -The atlas is probablistic. The original atlas is defined on a surface, but this surface is folded in different +The atlas is probabilistic. The original atlas is defined on a surface, but this surface is folded in different ways for different people. Misalignment of gyri and sucli results in parcels projecting into different locations for different people. The probabilities indicate the likelihood an individual's parcel will project to the designated location. For instance, V1 and V2 probability maps are shown below, and although one is always more probable than another there are also regions where identity at the group level is ambiguous. -![Examples of probablistic labels](images/probability_maps_vis_ctx.png) +![Examples of probabilistic labels](images/probability_maps_vis_ctx.png) You can threshold the map at different probability values to obtain more or less eroded versions of the atlas using the atlas object's threshold method. This offers a more rigorous dilation/erosion method than was available @@ -42,7 +42,7 @@ for the older glasser atlas. Some example thresholds are shown below. Probabilities were computed first based on alignments in each of three studies (BMRK5, PainGen, SpaceTop) separately and then averaged. Different studies have different acquisition parameters, which can result in systematic differences in resulting alignments. Averaging over more studies would be better. In the interest of facilitating -futher updates to this dataset these three studies have had their subject specific MNI space projections uploaded +further updates to this dataset these three studies have had their subject specific MNI space projections uploaded to figshare. If you want to recompute the probability maps/parcel boundaries you can follow the approach in the src subfolder here for a new study and combine it with these maps for an incremental improvement. Maps are here: @@ -76,7 +76,7 @@ from a study previously analyzed and reported in surface space. To my knowledge Glasser et al. have not published their subject specific region classifiers (as of 10/24/23), nor a multimodal alignment template, so subject specific parcel identification is not yet possible outside the -Van Essen lab. However, subject specific parcellations may be avialable from the HCP dataset, and might provide +Van Essen lab. However, subject specific parcellations may be available from the HCP dataset, and might provide an avenue towards an improved version of this atlas if someone were to obtain appropriate surface to volume mappings from the HCP study. Coalsen et al. 2018 PNAS achieve precise this kind of surface to volume mapping that takes variability of the subject specific surface parcellations into account and compares it to a projection @@ -91,7 +91,7 @@ were also computed. By enchaining the inverse transform to surface space with th space we can get a fsaverage surface to MNI space projection for each subject. The Glasser surface parcellation was transformed with this enchained set of transforms for each of 241 unrelated participants in paingen, 88 participants from BMRK5 and 112 participants from SpaceTop. All transformations were computed by running fmriprep -20.2.3 (BMRK5, PainGen) or 21.0.2 (SpaceTop) with recon-all enabled on these participants, so this probablistic +20.2.3 (BMRK5, PainGen) or 21.0.2 (SpaceTop) with recon-all enabled on these participants, so this probabilistic atlas is ideally calibrated for use with fmriprep aligned volumes. Resulting parcellations were uploaded to figshare @@ -143,7 +143,7 @@ precision, it's a matter of an imprecise fit to a mean template. What we offer here instead is the mean of many precise fits to individual templates, which is clearly better in this regard. While different individuals may have different ventral visual cortical foldings, none of them have have visual cortex in the cerebellum. Finally, the old atlas is necessarily non-probablistic, which limits its versatility. Note that -in the figure below underlay is MNI152NLin2009cAsym, which is nearly indistiguishable from MNI152NLin2009aAsym, the +in the figure below underlay is MNI152NLin2009cAsym, which is nearly indistinguishable from MNI152NLin2009aAsym, the asymmetric version of the template the old glasser parcellation was projected to. The fmriprep20 registration fusion image it cycles with is thresholded at 20% probability, which is a default threshold used throughout much of this repo. @@ -186,7 +186,7 @@ Alternatively we can look at the difference between studies, since we have three greatest agreement, with practically all dice coefficients greater than 0.7. This is fortunate, since it means between study variability is small relative to between subject variability, and it means we're closer to the number of degrees of freedom we need for stable estimates of parcel boundaries (Using a single study, so no study related -error, Wu et al. claim you need 300 participants for boundaries to fully stabalize). +error, Wu et al. claim you need 300 participants for boundaries to fully stabilize). paingen vs. bmrk5 to MNi152NLin2009cAsym diff --git a/Atlases_and_parcellations/2016_Glasser_Nature_HumanConnectomeParcellation/old/GlasserHCP_create_atlas_object.m b/Atlases_and_parcellations/2016_Glasser_Nature_HumanConnectomeParcellation/old/GlasserHCP_create_atlas_object.m index 44119f3..1f559c1 100644 --- a/Atlases_and_parcellations/2016_Glasser_Nature_HumanConnectomeParcellation/old/GlasserHCP_create_atlas_object.m +++ b/Atlases_and_parcellations/2016_Glasser_Nature_HumanConnectomeParcellation/old/GlasserHCP_create_atlas_object.m @@ -111,7 +111,7 @@ %% write - this writes only the label image -% Save: if creating from probabilty images only +% Save: if creating from probability images only % % if dosave % diff --git a/Atlases_and_parcellations/2016_Glasser_Nature_HumanConnectomeParcellation/old/README.md b/Atlases_and_parcellations/2016_Glasser_Nature_HumanConnectomeParcellation/old/README.md index 5502ec4..9920cb4 100644 --- a/Atlases_and_parcellations/2016_Glasser_Nature_HumanConnectomeParcellation/old/README.md +++ b/Atlases_and_parcellations/2016_Glasser_Nature_HumanConnectomeParcellation/old/README.md @@ -1,4 +1,4 @@ The code in this folder is deprecated. It's based on a dubious projection from surface space to volumetric space that's based on nearest neighbor interpolation without seemingly any realignments performed. A registration fusion approach was used to generate a more credible alignment and is available in the parent directory. This is -left here for backwards compatability. +left here for backwards compatibility. diff --git a/Atlases_and_parcellations/2018_Iglesias_thalamic_reconstruction/README.md b/Atlases_and_parcellations/2018_Iglesias_thalamic_reconstruction/README.md index afa8c6f..299fd99 100644 --- a/Atlases_and_parcellations/2018_Iglesias_thalamic_reconstruction/README.md +++ b/Atlases_and_parcellations/2018_Iglesias_thalamic_reconstruction/README.md @@ -1,15 +1,15 @@ ## Overview -This is a probablistic atlas meant to correspond to freesurfer's thalamic +This is a probabilistic atlas meant to correspond to freesurfer's thalamic subnuclear parcellations, a joint histologic and in vivo parcellation of the thalamus into 23 unique regions per hemisphere. It is less granular than -Morel, but has an open usage license, is probablistic and is likely to be +Morel, but has an open usage license, is probabilistic and is likely to be more accurately segmented. That notwithstanding, the biological accuracy of the labeling hasn't been sufficiently validated in my opinion (nor has any other MNI space thalamic atlas), so treat it primarily as an ontology rather than something with any kind of granular biological validity. -The probablistic labels are derived from labels produced by application of an +The probabilistic labels are derived from labels produced by application of an automated bayesian segmentation algorithm to 618 participants from 3 studies. The most recent versions of the algorithm is robust when given DWI and T1 data, so if individual parcellations are needed you can obtain them easily. @@ -21,17 +21,17 @@ as well. ## Methods -The probablistic labels are not directly derived from the original Iglesias +The probabilistic labels are not directly derived from the original Iglesias et al. (2018) Neuroimage parcellation that is used internally by freesurfer. -Those probablistic labels aren't readily available. They're stored in an +Those probabilistic labels aren't readily available. They're stored in an esoteric format internally by freesurfer, and based on personal correspondence with Iglesias there is no readily available nifti version of the -probablistic labels available for distribution. There is a version available +probabilistic labels available for distribution. There is a version available that's packaged with LeadDBS, but the relationship between this version and -the original probablistic labels isn't clear. Instead, what I (BP) have done +the original probabilistic labels isn't clear. Instead, what I (BP) have done is to fit the freesurfer segmentation to 278 HCP participants, 76 spacetop participants and 264 paingen participants and use those subject specific labels -to determine probablistic labels for the population. +to determine probabilistic labels for the population. Atlas labels were generated by running freesurfer's Thalamic segmentation on T1 and DWI data from 278 unrelated HCP participants, 76 spacetop participants @@ -116,17 +116,17 @@ all labels would be 1). Signals obtained from individualized parcellations shoul therefore be more homogeneous across participants than signals obtained from the group parcels. We therefore take task evoked responses from SpaceTop (Pain, Vicarious and Cognitive) and estimate the mean parcel task evoked response for each in each -thalamic region in each of the 76 particpants segmented using both individual parcellations +thalamic region in each of the 76 participants segmented using both individual parcellations and a group parcellation. We then evaluate the effect of parcellation type and the interaction between task x parcellation type for each region. We would expect to see larger regional effects with the individualized parcellation due to constructive amplification while we expect lower regional effects in the group -parcellation due to destructive interference of signals from heterogenous sources +parcellation due to destructive interference of signals from heterogeneous sources ("blurring"). We also expect task differences between regions to be greater in the individualized parcellation (significant interactions) for the same reason. First level GLM contrasts were estimated using task evoked responses taken -with respect to baseline, controling for 24 motion parameters and CSF, and using a +with respect to baseline, controlling for 24 motion parameters and CSF, and using a canonical HRF for convolution. Data was unsmoothed. Of the 76 participants with individual parcellations, 73 of them had task contrasts available (the other three were presumably missing BOLD data, I ran all that I could). @@ -136,7 +136,7 @@ parcel using either an individualized parcellation or a group parcellation. Subj treated as random effectively turning this into a within subjects repeated measures analysis, although this isn't clearly illustrated in the figures below, so take note now. Task contrasts were coded pain = [1/2, -1/3] vic = [-1/2, -1/3], cog = [0, 2/3] which -yeilded two planned contrasts: pain_v_vic and pain_vic_v_cog. A parcellation variable was +yielded two planned contrasts: pain_v_vic and pain_vic_v_cog. A parcellation variable was also coded as [1/2, -1/2] for the individual vs. group parcellation (resp). In Wilkinson notation the model took the form, @@ -151,7 +151,7 @@ it's important to note. Models were fit separately for each region using restricted expectation maximum likelihood (ReML) and full covariance estimation. Satterthwaites correction was used to -estimate degrees of freedom, and in most cases yeilded n-1 degrees of freedom, but due +estimate degrees of freedom, and in most cases yielded n-1 degrees of freedom, but due to some regions missing in some individual parcellations the df are also somewhat variable. This is precisely what we would expect for a repeated measures analysis. @@ -161,7 +161,7 @@ with respect to baseline. Of the 76 participants with individual parcellations, BOLD data, I ran all that I could). The results are very mixed. With 46 regions our Holm-Sidak corrected p-value -theshold is p < 0.0011 for alpha=0.05. +threshold is p < 0.0011 for alpha=0.05. Regions with lower evoked response across tasks using individualized parcellations (unexpected): - central_medial_(left)
@@ -254,7 +254,7 @@ The same as the above only shown in FSL with a T2 image juxtaposed along side it The morel atlas is the most detailed histological atlas available in MNI space. Its major limitation is that it is based on 6 participants (or maybe 3 and -mirror imaged, unclear), is not probablistic, and lacks an open license. The +mirror imaged, unclear), is not probabilistic, and lacks an open license. The Iglesias atlas is based on 12 participants (or maybe 6 and mirror imaged) augmented by 55 in vivo scans that are used to calibrate probabilistic maps. In our case these are further mediated by projection to 278 individual's @@ -351,5 +351,5 @@ Right Thalamus (axial) ## References -* Iglesias JE, Insausti R, Lerma-Usabiaga G, Bocchetta M, Van Leemput K, Greve DN, van der Kouwe A, Fischl B, Caballero-Gaudes C, Paz-Alonso PM. (2018). A probablistic atlas of the human thalamuc nuclei combining ex vivo MRI and histology. Neuroimage, 314-326, 183. +* Iglesias JE, Insausti R, Lerma-Usabiaga G, Bocchetta M, Van Leemput K, Greve DN, van der Kouwe A, Fischl B, Caballero-Gaudes C, Paz-Alonso PM. (2018). A probabilistic atlas of the human thalamuc nuclei combining ex vivo MRI and histology. Neuroimage, 314-326, 183. * Tregidgo HFJ, Soskic S, Althonayan J, Maffei C, Van Leemput K, Golland P, Insausti R, Lerma-Usabiaga G, Caballero-Gaudes C, Paz-Alonso PM, Yendiki A, Alexander DC, Bocchetta M, Rohrer JD, Iglesias JE. (2023). Accurate Bayesian segmentation of thalamic nuclei using diffusion MRI and an improved histological atlas. Neuroimage, 274, 120129. diff --git a/Atlases_and_parcellations/2018_Iglesias_thalamic_reconstruction/iglesias_MNI152NLin2009cAsym_create_atlas.m b/Atlases_and_parcellations/2018_Iglesias_thalamic_reconstruction/iglesias_MNI152NLin2009cAsym_create_atlas.m index bd2edcf..ca598ee 100644 --- a/Atlases_and_parcellations/2018_Iglesias_thalamic_reconstruction/iglesias_MNI152NLin2009cAsym_create_atlas.m +++ b/Atlases_and_parcellations/2018_Iglesias_thalamic_reconstruction/iglesias_MNI152NLin2009cAsym_create_atlas.m @@ -4,7 +4,7 @@ space_description = 'MNI152NLin2009cAsym'; atlas_name = sprintf('iglesias_HCP278_ST76_PG264_%s', space_description); -references = char({'Iglesias JE, Insausti R, Lerma-Usabiaga G, Bocchetta M, Van Leemput K, Greve DN, van der Kouwe A, Fischl B, Caballero-Gaudes C, Paz-Alonso PM. (2018). A probablistic atlas of the human thalamuc nuclei combining ex vivo MRI and histology. Neuroimage, 314-326, 183.'; ... +references = char({'Iglesias JE, Insausti R, Lerma-Usabiaga G, Bocchetta M, Van Leemput K, Greve DN, van der Kouwe A, Fischl B, Caballero-Gaudes C, Paz-Alonso PM. (2018). A probabilistic atlas of the human thalamuc nuclei combining ex vivo MRI and histology. Neuroimage, 314-326, 183.'; ... 'Tregidgo HFJ, Soskic S, Althonayan J, Maffei C, Van Leemput K, Golland P, Insausti R, Lerma-Usabiaga G, Caballero-Gaudes C, Paz-Alonso PM, Yendiki A, Alexander DC, Bocchetta M, Rohrer JD, Iglesias JE. (2023). Accurate Bayesian segmentation of thalamic nuclei using diffusion MRI and an improved histological atlas. Neuroimage, 274, 120129.'}); dosave = true; diff --git a/Atlases_and_parcellations/2018_Iglesias_thalamic_reconstruction/iglesias_MNI152NLin6Asym_create_atlas.m b/Atlases_and_parcellations/2018_Iglesias_thalamic_reconstruction/iglesias_MNI152NLin6Asym_create_atlas.m index 2ad64fc..70ec53b 100644 --- a/Atlases_and_parcellations/2018_Iglesias_thalamic_reconstruction/iglesias_MNI152NLin6Asym_create_atlas.m +++ b/Atlases_and_parcellations/2018_Iglesias_thalamic_reconstruction/iglesias_MNI152NLin6Asym_create_atlas.m @@ -4,7 +4,7 @@ space_description = 'MNI152NLin6Asym'; atlas_name = sprintf('iglesias_HCP278_ST76_PG264_%s', space_description); -references = char({'Iglesias JE, Insausti R, Lerma-Usabiaga G, Bocchetta M, Van Leemput K, Greve DN, van der Kouwe A, Fischl B, Caballero-Gaudes C, Paz-Alonso PM. (2018). A probablistic atlas of the human thalamuc nuclei combining ex vivo MRI and histology. Neuroimage, 314-326, 183.'; ... +references = char({'Iglesias JE, Insausti R, Lerma-Usabiaga G, Bocchetta M, Van Leemput K, Greve DN, van der Kouwe A, Fischl B, Caballero-Gaudes C, Paz-Alonso PM. (2018). A probabilistic atlas of the human thalamuc nuclei combining ex vivo MRI and histology. Neuroimage, 314-326, 183.'; ... 'Tregidgo HFJ, Soskic S, Althonayan J, Maffei C, Van Leemput K, Golland P, Insausti R, Lerma-Usabiaga G, Caballero-Gaudes C, Paz-Alonso PM, Yendiki A, Alexander DC, Bocchetta M, Rohrer JD, Iglesias JE. (2023). Accurate Bayesian segmentation of thalamic nuclei using diffusion MRI and an improved histological atlas. Neuroimage, 274, 120129.'}); dosave = true; diff --git a/Atlases_and_parcellations/2018_Schaefer_Yeo_multires_cortical_parcellation/Schaefer_create_atlas_object.m b/Atlases_and_parcellations/2018_Schaefer_Yeo_multires_cortical_parcellation/Schaefer_create_atlas_object.m index 0478c49..b92f7f5 100644 --- a/Atlases_and_parcellations/2018_Schaefer_Yeo_multires_cortical_parcellation/Schaefer_create_atlas_object.m +++ b/Atlases_and_parcellations/2018_Schaefer_Yeo_multires_cortical_parcellation/Schaefer_create_atlas_object.m @@ -133,7 +133,7 @@ %% write - this writes only the label image -% Save: if creating from probabilty images only +% Save: if creating from probability images only % % if dosave % diff --git a/Atlases_and_parcellations/2019_Kragel_PAG/Kragel_create_atlas_object.m b/Atlases_and_parcellations/2019_Kragel_PAG/Kragel_create_atlas_object.m index 17b130f..4032478 100644 --- a/Atlases_and_parcellations/2019_Kragel_PAG/Kragel_create_atlas_object.m +++ b/Atlases_and_parcellations/2019_Kragel_PAG/Kragel_create_atlas_object.m @@ -94,7 +94,7 @@ %% write - this writes only the label image -% Save: if creating from probabilty images only +% Save: if creating from probability images only % % if dosave % diff --git a/Atlases_and_parcellations/2019_Wager_pain_pathways/scripts/create_pain_pathways_brainnetwork.m b/Atlases_and_parcellations/2019_Wager_pain_pathways/scripts/create_pain_pathways_brainnetwork.m index a73142d..3699a2f 100644 --- a/Atlases_and_parcellations/2019_Wager_pain_pathways/scripts/create_pain_pathways_brainnetwork.m +++ b/Atlases_and_parcellations/2019_Wager_pain_pathways/scripts/create_pain_pathways_brainnetwork.m @@ -334,7 +334,7 @@ % add outlines: for i = 1:length(pain_regions_cpdm) o2 = addblobs(o2, pain_regions_cpdm(i), 'outline', 'color', [.2 .2 .2]); - %set(o2.activation_maps{i + 1}.blobhandles, 'LineWidth', 1); % one map aleady registered, so add 1 here + %set(o2.activation_maps{i + 1}.blobhandles, 'LineWidth', 1); % one map already registered, so add 1 here end saveas(gcf, fullfile('figures', 'cPDM_painpathways_weights_and_regions.png')); @@ -342,7 +342,7 @@ % add outlines only: for i = 1:length(pain_regions_cpdm) o2 = addblobs(o2, pain_regions_cpdm(i), 'outline', 'color', [.2 .2 .2]); - %set(o2.activation_maps{i + 1}.blobhandles, 'LineWidth', 1); % one map aleady registered, so add 1 here + %set(o2.activation_maps{i + 1}.blobhandles, 'LineWidth', 1); % one map already registered, so add 1 here end saveas(gcf, fullfile('figures', 'Painpathways_region_outlines.png')); diff --git a/Atlases_and_parcellations/2019_Wager_pain_pathways/scripts/prep1_extract_single_trial_pain_data.m b/Atlases_and_parcellations/2019_Wager_pain_pathways/scripts/prep1_extract_single_trial_pain_data.m index d0c683a..664e787 100644 --- a/Atlases_and_parcellations/2019_Wager_pain_pathways/scripts/prep1_extract_single_trial_pain_data.m +++ b/Atlases_and_parcellations/2019_Wager_pain_pathways/scripts/prep1_extract_single_trial_pain_data.m @@ -254,7 +254,7 @@ %figure; imagesc(zscore(all_ST_data_gray_white_csf_Z)) title('Gray - white - CSF averages') -% Regress out white-matter and CSF averages (doens't make much difference) +% Regress out white-matter and CSF averages (doesn't make much difference) X = all_ST_data_gray_white_csf_Z(:, 2:3); resid_fcn = @(Y) Y - X * pinv(X) * Y; @@ -267,7 +267,7 @@ mydata = cellfun(@zscore, mydata, 'UniformOutput', false); % Z-score within subject mydata = cat(1, mydata{:}); -% Regress out white-matter and CSF averages (doens't make much difference) +% Regress out white-matter and CSF averages (doesn't make much difference) v = var(mydata); mydata = resid_fcn(mydata); vr = var(mydata); @@ -298,7 +298,7 @@ mydata = cellfun(@zscore, mydata, 'UniformOutput', false); % Z-score within subject mydata = cat(1, mydata{:}); -% Regress out white-matter and CSF averages (doens't make much difference) +% Regress out white-matter and CSF averages (doesn't make much difference) v = var(mydata); mydata = resid_fcn(mydata); vr = var(mydata); @@ -358,7 +358,7 @@ mydata = cellfun(@zscore, mydata, 'UniformOutput', false); % Z-score within subject mydata = cat(1, mydata{:}); -% Regress out white-matter and CSF averages (doens't make much difference) +% Regress out white-matter and CSF averages (doesn't make much difference) v = var(mydata); mydata = resid_fcn(mydata); vr = var(mydata); @@ -387,7 +387,7 @@ mydata = cellfun(@zscore, mydata, 'UniformOutput', false); % Z-score within subject mydata = cat(1, mydata{:}); -% Regress out white-matter and CSF averages (doens't make much difference) +% Regress out white-matter and CSF averages (doesn't make much difference) v = var(mydata); mydata = resid_fcn(mydata); vr = var(mydata); diff --git a/Atlases_and_parcellations/2019_Wager_pain_pathways/scripts/prep1_extract_single_trial_pain_data_cpdm_only.m b/Atlases_and_parcellations/2019_Wager_pain_pathways/scripts/prep1_extract_single_trial_pain_data_cpdm_only.m index 9d6b5a3..b56627c 100644 --- a/Atlases_and_parcellations/2019_Wager_pain_pathways/scripts/prep1_extract_single_trial_pain_data_cpdm_only.m +++ b/Atlases_and_parcellations/2019_Wager_pain_pathways/scripts/prep1_extract_single_trial_pain_data_cpdm_only.m @@ -222,7 +222,7 @@ %figure; imagesc(zscore(all_ST_data_gray_white_csf_Z)) title('Gray - white - CSF averages') -% Regress out white-matter and CSF averages (doens't make much difference) +% Regress out white-matter and CSF averages (doesn't make much difference) X = all_ST_data_gray_white_csf_Z(:, 2:3); resid_fcn = @(Y) Y - X * pinv(X) * Y; @@ -235,7 +235,7 @@ mydata = cellfun(@zscore, mydata, 'UniformOutput', false); % Z-score within subject mydata = cat(1, mydata{:}); -% Regress out white-matter and CSF averages (doens't make much difference) +% Regress out white-matter and CSF averages (doesn't make much difference) v = var(mydata); mydata = resid_fcn(mydata); vr = var(mydata); diff --git a/Atlases_and_parcellations/2019_Zheng_Pain_Connectivity_Modules/Readme.txt b/Atlases_and_parcellations/2019_Zheng_Pain_Connectivity_Modules/Readme.txt index 7960204..1097baf 100755 --- a/Atlases_and_parcellations/2019_Zheng_Pain_Connectivity_Modules/Readme.txt +++ b/Atlases_and_parcellations/2019_Zheng_Pain_Connectivity_Modules/Readme.txt @@ -10,7 +10,7 @@ Step 1. Make sure you have the CanlabCore toolbox (from Github) and canlab_datasets_and_metadata.mat (included here), which provides needed functions and datasets. Step 2. -Run preparation_extract_data.m using canlab_datasets_and_metadata.mat as the input. This function will segment the brain using a prior template (the brain connectome atlas we privide in Template folder), and extract the regional activity of each brain region. The outputs are organized according to the datasets we used, and provide the meta information and regional data for each dataset. +Run preparation_extract_data.m using canlab_datasets_and_metadata.mat as the input. This function will segment the brain using a prior template (the brain connectome atlas we provide in Template folder), and extract the regional activity of each brain region. The outputs are organized according to the datasets we used, and provide the meta information and regional data for each dataset. Step 3. Run mean_data_cross_trails.m, using the outputs of step 2 as the inputs. Because the datasets we used are heterogeneous data acquired on different MRI machines, and include psychological manipulations (e.g., placebo treatment), this function can remove the placebo/high-vif (high Variance Inflation Factor) trials for relevant datasets, and extract the mean activity of each brain region across trials for each individual. Signals related to mean grey matter, white matter and CSF are then regressed out from the cross-trial mean activity of brain regions within each dataset. Median absolute deviation (MAD) is used to rescale the residuals of each dataset to decrease the difference between datasets. diff --git a/Atlases_and_parcellations/2019_Zheng_Pain_Connectivity_Modules/code/CRsubj_net.m b/Atlases_and_parcellations/2019_Zheng_Pain_Connectivity_Modules/code/CRsubj_net.m index 4ead563..941f530 100755 --- a/Atlases_and_parcellations/2019_Zheng_Pain_Connectivity_Modules/code/CRsubj_net.m +++ b/Atlases_and_parcellations/2019_Zheng_Pain_Connectivity_Modules/code/CRsubj_net.m @@ -4,7 +4,7 @@ % if there are multiple levels of temperature in each condition, randomly % select one. -% input: mean_data: struct varible from mean_data_cross_trials.m +% input: mean_data: struct variable from mean_data_cross_trials.m % thresh: temperature threshold of warm and pain (we choose 45.3¡æ) Nstudies = length(mean_data.study_name); diff --git a/Atlases_and_parcellations/2020_JulichBrain_v3.0.3/README.md b/Atlases_and_parcellations/2020_JulichBrain_v3.0.3/README.md index e3e4550..f64ca0b 100644 --- a/Atlases_and_parcellations/2020_JulichBrain_v3.0.3/README.md +++ b/Atlases_and_parcellations/2020_JulichBrain_v3.0.3/README.md @@ -3,7 +3,7 @@ The Julich Brain Atlas is an ongoing effort to develop a complete digital atlas of human brain cytoarchitecture measured using histological methods. It is the basis of the SPM Anatomy Toolbox, but it has grown and developed considerably since its original version. Canlab Tools have copies of earlier versions of this atlas, including -the spm anatomy toolbox v2.2. This atlas should supercede those, and also supercedes the contemporaneous SPM +the spm anatomy toolbox v2.2. This atlas should supersede those, and also supersedes the contemporaneous SPM Anatomy Toolbox, which has fewer regions than this (e.g. CA2/3 are missing). I don't know why. The most up to date version of the atlas is stored in a digital repository called EBRAINS. This version was found @@ -36,8 +36,8 @@ with Vladimir Fonov from the MNI. The transforms were obtained here, https://figshare.com/articles/dataset/MNI_T1_6thGen_NLIN_to_MNI_2009b_NLIN_ANTs_transform/3502238 antsApplyTransform from the fmriprep 20.2.3 LTS singularity container was used to perform the alignment on the to -probablistic versions of the parcels and was implemented by the script warp_to_MNI152NLin6Asym0.sh. This was in turn -invoked by warp_to_MNI152NLin6Asym.sh on the Dartmouth HPC system. The probablistic versions of the parcels were then used +probabilistic versions of the parcels and was implemented by the script warp_to_MNI152NLin6Asym0.sh. This was in turn +invoked by warp_to_MNI152NLin6Asym.sh on the Dartmouth HPC system. The probabilistic versions of the parcels were then used to regenerate the atlas in MNI152NLin6Asym space, and the result is also provided in this directory. ## References @@ -46,10 +46,10 @@ The histological atlas is a collaborative global effort and spans multiple teams brain areas have different publications associated with them, but there are a number of specific publications pertaining to the creation of this combined atlas in particular, listed in order of relevance, -* Amuts K, Mohlberg H, Bludau S, Zilles K. Julich-Brain: A 3D probablistic atlas of the human brain's cytoarchitecture. +* Amuts K, Mohlberg H, Bludau S, Zilles K. Julich-Brain: A 3D probabilistic atlas of the human brain's cytoarchitecture. Science 369(6506) 988-992, 2020 * Amunts K, Zilles K. Architectonic Mapping of the Human Brain beyond Broadmann. Neuron 88(6), 1086-1107, 2015 -* Eikhoff S, Stephan KE, Mohlberg H, Grefkes C, Fink GR, Amunts K, Zilles K. A new SPM toolbox for combining probablistic +* Eikhoff S, Stephan KE, Mohlberg H, Grefkes C, Fink GR, Amunts K, Zilles K. A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. NeuroImage 25(4), 1325-1335, 2005 For citations pertaining to specific parcellation or other details please refer to the Julich Brain Atlas website or diff --git a/Atlases_and_parcellations/2020_JulichBrain_v3.0.3/julich_fmriprep20_create_atlas_object.m b/Atlases_and_parcellations/2020_JulichBrain_v3.0.3/julich_fmriprep20_create_atlas_object.m index 5673237..6a4a336 100644 --- a/Atlases_and_parcellations/2020_JulichBrain_v3.0.3/julich_fmriprep20_create_atlas_object.m +++ b/Atlases_and_parcellations/2020_JulichBrain_v3.0.3/julich_fmriprep20_create_atlas_object.m @@ -13,9 +13,9 @@ atlas_name = 'julich_fmriprep20'; space_description = 'MNI152NLin2009cAsym'; -references = 'Amunts K, Mohlberg H, Blubau S, Zilles K. (2020) Julich_Brain: A 3D probablistic atlas of the human brain''s cytoarchitecture. Science 369(6506), 988-992.'; +references = 'Amunts K, Mohlberg H, Blubau S, Zilles K. (2020) Julich_Brain: A 3D probabilistic atlas of the human brain''s cytoarchitecture. Science 369(6506), 988-992.'; -% imort atlas file in MNI152NLin2009cAsym space +% import atlas file in MNI152NLin2009cAsym space MNI152NLin2009cAsym_bilat = which('JulichBrainAtlas_3.0_areas_MPM_b_N10_nlin2ICBM152asym2009c_public_11035603b4744231e17e87fd8ebcaf1a.nii.gz'); juStruct = parseXML(which('JulichBrainAtlas_3.0_areas_MPM_b_N10_nlin2ICBM152asym2009c_public_11035603b4744231e17e87fd8ebcaf1a.xml')); diff --git a/Atlases_and_parcellations/2020_JulichBrain_v3.0.3/julich_fsl6_create_atlas_object.m b/Atlases_and_parcellations/2020_JulichBrain_v3.0.3/julich_fsl6_create_atlas_object.m index 5fd11b5..56a2dc4 100644 --- a/Atlases_and_parcellations/2020_JulichBrain_v3.0.3/julich_fsl6_create_atlas_object.m +++ b/Atlases_and_parcellations/2020_JulichBrain_v3.0.3/julich_fsl6_create_atlas_object.m @@ -13,9 +13,9 @@ atlas_name = 'julich_fsl6'; space_description = 'MNI152NLin6Asym'; -references = 'Amunts K, Mohlberg H, Blubau S, Zilles K. (2020) Julich_Brain: A 3D probablistic atlas of the human brain''s cytoarchitecture. Science 369(6506), 988-992.'; +references = 'Amunts K, Mohlberg H, Blubau S, Zilles K. (2020) Julich_Brain: A 3D probabilistic atlas of the human brain''s cytoarchitecture. Science 369(6506), 988-992.'; -% imort atlas file in MNI152NLin2009cAsym space +% import atlas file in MNI152NLin2009cAsym space % we just use this as a stand in template that we'll modify later, since % this is the wrong space MNI152NLin2009cAsym_bilat = which('JulichBrainAtlas_3.0_areas_MPM_b_N10_nlin2ICBM152asym2009c_public_11035603b4744231e17e87fd8ebcaf1a.nii.gz'); diff --git a/Atlases_and_parcellations/2020_Pandora_white_matter_atlas/GMParc/FreeSurferColorLUT.txt b/Atlases_and_parcellations/2020_Pandora_white_matter_atlas/GMParc/FreeSurferColorLUT.txt index 4fbfdfd..34b559f 100644 --- a/Atlases_and_parcellations/2020_Pandora_white_matter_atlas/GMParc/FreeSurferColorLUT.txt +++ b/Atlases_and_parcellations/2020_Pandora_white_matter_atlas/GMParc/FreeSurferColorLUT.txt @@ -173,7 +173,7 @@ 169 Left-Basal-Ganglia 236 13 127 0 176 Right-Basal-Ganglia 236 13 126 0 -# Label names and colors for Brainstem consituents +# Label names and colors for Brainstem constituents # No. Label Name: R G B A 170 brainstem 119 159 176 0 171 DCG 119 0 176 0 @@ -432,7 +432,7 @@ # created by mri_aparc2aseg in which the aseg cortex label is replaced # by the labels in the aparc. It also supports wm labels that will # eventually be created by mri_aparc2aseg. Otherwise, the aseg labels -# do not change from above. The cortical lables are the same as in +# do not change from above. The cortical labels are the same as in # colortable_desikan_killiany.txt, except that left hemisphere has # 1000 added to the index and the right has 2000 added. The label # names are also prepended with ctx-lh or ctx-rh. The white matter diff --git a/Atlases_and_parcellations/2020_Pandora_white_matter_atlas/README.md b/Atlases_and_parcellations/2020_Pandora_white_matter_atlas/README.md index e3878f5..c2f4c0c 100644 --- a/Atlases_and_parcellations/2020_Pandora_white_matter_atlas/README.md +++ b/Atlases_and_parcellations/2020_Pandora_white_matter_atlas/README.md @@ -1,7 +1,7 @@ # Pandora-WhiteMatterAtlas ## Pandora white matter atlas -Pandora is a new population-based collection of white matter atlases, represented in both volumetric and surface coordinates in a standard space. These atlases are based on 2443 subjects, and include 216 white matter fascicles derived from 6 different state-of-the-art tractography techniques: AFQ, AFQclipped, Recobundles, TractSeg, Tracula, and Xtract. Because these pathways may overlap, these atlases are represented as 4D volumes where each probabalistic segmentation is seperated across the 4th dimension. +Pandora is a new population-based collection of white matter atlases, represented in both volumetric and surface coordinates in a standard space. These atlases are based on 2443 subjects, and include 216 white matter fascicles derived from 6 different state-of-the-art tractography techniques: AFQ, AFQclipped, Recobundles, TractSeg, Tracula, and Xtract. Because these pathways may overlap, these atlases are represented as 4D volumes where each probabilistic segmentation is separated across the 4th dimension. These subjects scans are of all healthy adults and were drawn from three datasets: Human Connectome Project (HCP), Baltimore Longitudinal Study of Aging (BLSA), and Vanderbilt University (VU). @@ -10,7 +10,7 @@ These subjects scans are of all healthy adults and were drawn from three dataset

## Organization -A directory exists for each of the 6 methods. Within each are the corresponding 4D probablistic atlas created using all datasets, a csv file which describes the bundles within the atlas, and a supplementary folder which contains three similar atlas which were created using data from only one of the datasets (HCP, BLSA, VU). Additionally, a similar directory exists for template T1 images which are created from the same subject data as the corresponding atlases. +A directory exists for each of the 6 methods. Within each are the corresponding 4D probabilistic atlas created using all datasets, a csv file which describes the bundles within the atlas, and a supplementary folder which contains three similar atlas which were created using data from only one of the datasets (HCP, BLSA, VU). Additionally, a similar directory exists for template T1 images which are created from the same subject data as the corresponding atlases. . ├── ... diff --git a/Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/scripts/bcbseg.py b/Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/scripts/bcbseg.py index a292bf0..af241ea 100644 --- a/Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/scripts/bcbseg.py +++ b/Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/scripts/bcbseg.py @@ -22,7 +22,7 @@ def build_argparser(): - DESCRIPTION = "Brain extraction and segmentation using ANTs softwares" + DESCRIPTION = "Brain extraction and segmentation using ANTs software" p = argparse.ArgumentParser(description=DESCRIPTION) parser.add_argument("-e", "--extraction", action="store_true", help="") p.add_argument('anatomy', help='reference anatomy (.nii|.nii.gz.') diff --git a/Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/tools/divide_KMeans.py b/Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/tools/divide_KMeans.py index 0f226c1..2b99e15 100644 --- a/Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/tools/divide_KMeans.py +++ b/Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/tools/divide_KMeans.py @@ -98,7 +98,7 @@ def roization(seed_path, target_path, ROIs_size, res_folder): # "Seed != Target && Overlap == {0}" if len(is_overlapping) == 1: - # then we can divide seed and target separatly + # then we can divide seed and target separately roized_seed = divide(seed_bin, ROIs_size) roized_target = divide(t_m_s, ROIs_size) print("Case 1") diff --git a/Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/tools/entropy.py b/Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/tools/entropy.py index 4e7c825..80deb04 100755 --- a/Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/tools/entropy.py +++ b/Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/tools/entropy.py @@ -52,7 +52,7 @@ tc_vox = data4D[vox[0], vox[1], vox[2], :] # Entropy is calculated on the histogram of the distribution counts, bin_edges = np.histogram(tc_vox, bins=nb_bins) - # Tranform counts into probability + # Transform counts into probability p = counts / np.sum(counts, dtype=float) binWidth = np.diff(bin_edges) # xlogy is with natural logarithm but we should use log base 2 (TO STUDY !!) diff --git a/Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/tools/split_clusters.py b/Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/tools/split_clusters.py index b409b86..3f9180b 100644 --- a/Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/tools/split_clusters.py +++ b/Atlases_and_parcellations/2020_Thiebaut_de_Schotten_white_matter_atlas/bcblib/tools/split_clusters.py @@ -23,7 +23,7 @@ def float_in_filename(num): elif len(spl) == 1: return spl[0] else: - print("uknown value: " + str(num)) + print("unknown value: " + str(num)) def split_clusters(nii, res_folder, name): @@ -35,17 +35,17 @@ def split_clusters(nii, res_folder, name): the image to split res_folder: str the path to the result folder. The function will create a folder - which will contain all the splitted images + which will contain all the split images name: str suffix for the result files, it will also be used to name the result folder """ - # extract the needed informations from the source image + # extract the needed information from the source image data = nii.get_data() affine = nii.affine folder = os.path.join(res_folder, name) - # Try to create the folder and ignore the error in the case it alread exists + # Try to create the folder and ignore the error in the case it already exists try: os.mkdir(folder) # note that all the other errors like permissions error will be caught @@ -74,7 +74,7 @@ def split_clusters(nii, res_folder, name): nib.save(img_ROIs, path) # The maximum of the remaining values of data o_max = np.amax(data) - print("All the cluster has been splitted in " + folder) + print("All the cluster has been split in " + folder) return # clu = np.array(np.where(data == i)) # mask[clu[0,], clu[1,], clu[2,]] = i diff --git a/Atlases_and_parcellations/2020_Tian_subcortical_v1.1/README.md b/Atlases_and_parcellations/2020_Tian_subcortical_v1.1/README.md index b99bc82..fecaf14 100644 --- a/Atlases_and_parcellations/2020_Tian_subcortical_v1.1/README.md +++ b/Atlases_and_parcellations/2020_Tian_subcortical_v1.1/README.md @@ -7,7 +7,7 @@ https://github.com/yetianmed/subcortex/tree/e80ee787732536e9e89534c9a623b10aff79 Group-Parcellation files were copied here for posterity. These may now be obsolete though. While the first draft of this atlas was derived from these parcels, subsequently the authors shared their individual participant segmentations with me (BP). I used these -to create probablistic labels which now form the basis of this atlas. +to create probabilistic labels which now form the basis of this atlas. This atlas was generated in a hierarchical fashion with finer and finer parcellations at each level until a natural stopping condition in the parcellation algorithm @@ -55,7 +55,7 @@ should be fine to share the parcellations using Tian's subject indexing. The full parcellation that Tian et al release has very clean delineations of large scale -structures, but the probablistic map I have is not so clean. For instance putamen can +structures, but the probabilistic map I have is not so clean. For instance putamen can bleed into accumbens, thalamus can bleed into the fornix, etc. To achieve a cleaner parcellation I've modified the probability to prevent parcel probability maps from overlapping inappropriate structures by beginning with anatomical priors derived from the cifti structure labels and @@ -65,8 +65,8 @@ caudate-accumbens and hippocampus-amygdala jointly since the division between su (thalamus, putamen, pallidum, etc.) as distinct regions of their own. Regions of the Tian atlas that overlap with incongruent CIFTI regions had their probabilities set to zero. Probabilities were subsequently renormalized. This resulted in 10 voxels and 18 voxels in the MNI152NLin6Asym -and MNI152NLin2009cAsym spaces being asigned no value but in exchange means that all Tian labels -are now circumscribed sensibly by contiguious CIFTI regions. This is equivalent to treating the +and MNI152NLin2009cAsym spaces being assigned no value but in exchange means that all Tian labels +are now circumscribed sensibly by contiguous CIFTI regions. This is equivalent to treating the CIFTI labels as Bayesian priors which are then updated and subdivided based on the high resolution (54 parcel) Tian parcellation. diff --git a/Atlases_and_parcellations/2020_Tian_subcortical_v1.1/old_atlas/README.md b/Atlases_and_parcellations/2020_Tian_subcortical_v1.1/old_atlas/README.md index cebea55..faa1159 100644 --- a/Atlases_and_parcellations/2020_Tian_subcortical_v1.1/old_atlas/README.md +++ b/Atlases_and_parcellations/2020_Tian_subcortical_v1.1/old_atlas/README.md @@ -1,2 +1,2 @@ this version of the atlas is derived from Tians labeled maps. We're now using -probablistic maps though +probabilistic maps though diff --git a/Atlases_and_parcellations/2023_Bianciardi_BrainstemNavigatorV0.9/README.md b/Atlases_and_parcellations/2023_Bianciardi_BrainstemNavigatorV0.9/README.md index e4c6e11..65897ae 100644 --- a/Atlases_and_parcellations/2023_Bianciardi_BrainstemNavigatorV0.9/README.md +++ b/Atlases_and_parcellations/2023_Bianciardi_BrainstemNavigatorV0.9/README.md @@ -2,13 +2,13 @@ Note restrictive license below ### Brainstem Navigator v0.9 (aka Bianciardi atlas) -The Bianciardi atlas is an ongoing effort to map brainstem nuclei in vivo using high resolution multimodal MR. T1, T2 and diffusion fractional anisotrophy data is collected at 7T and structures are individually segmented in small samples of participants (10-20). Participants are aligned to the FSL standard space template (most likely MNI152NLin6Asym, but there's a slight possibility it's MNI152NLin6Sym). A probablistic map is created based on overlap of the sample in this standard space. This project spans a number of publications over the past decade and remains ongoing, but is already at a sufficiently advanced stage to be useful. Bianciardi 2015, the first paper in this series, is a good reference for details on methods. +The Bianciardi atlas is an ongoing effort to map brainstem nuclei in vivo using high resolution multimodal MR. T1, T2 and diffusion fractional anisotrophy data is collected at 7T and structures are individually segmented in small samples of participants (10-20). Participants are aligned to the FSL standard space template (most likely MNI152NLin6Asym, but there's a slight possibility it's MNI152NLin6Sym). A probabilistic map is created based on overlap of the sample in this standard space. This project spans a number of publications over the past decade and remains ongoing, but is already at a sufficiently advanced stage to be useful. Bianciardi 2015, the first paper in this series, is a good reference for details on methods. -Technically this folder does not provide the atlas, due to licensing restrictions that prevent us from redistributing the ROIs (see copyright notice below). Instead it contains the code needed to automatically download and reformat these ROIs to be compatable with canlab tools and multiple MNI152 spaces of interest (initially MNI152NLin6Sym space and MNI152NLin2009cAsym, the fmriprep 20.2.3 default, see methods below, but could be expanded). In this sense it draws from the approach taken by various linux distros with respect to problematic software like some oracle, adobe, google, mozilla, etc. products. A .gitignore file exists to prevent the generated files from being uploaded back to the repo. Please do not modify this so long as the license below remains in effect (check Bianciardi's Brainstem Imaging Lab website for updates). +Technically this folder does not provide the atlas, due to licensing restrictions that prevent us from redistributing the ROIs (see copyright notice below). Instead it contains the code needed to automatically download and reformat these ROIs to be compatible with canlab tools and multiple MNI152 spaces of interest (initially MNI152NLin6Sym space and MNI152NLin2009cAsym, the fmriprep 20.2.3 default, see methods below, but could be expanded). In this sense it draws from the approach taken by various linux distros with respect to problematic software like some oracle, adobe, google, mozilla, etc. products. A .gitignore file exists to prevent the generated files from being uploaded back to the repo. Please do not modify this so long as the license below remains in effect (check Bianciardi's Brainstem Imaging Lab website for updates). -This atlas is provided in two parcellations, one with some subnuclear divisions ("fine" scale) and a lower resolution atlas. The fine atlas probably excedes the spatial precision of contemporary imaging in the wager lab, but may be useful when working with specialty datasets, as a reference or as technology evolves, so it is provided as well. Note, the resolution in question here is not the resolution of the NIFTI image, but rather of the parcellation. The NIFTI images in both cases are 1x1x1mm resolution. +This atlas is provided in two parcellations, one with some subnuclear divisions ("fine" scale) and a lower resolution atlas. The fine atlas probably exceeds the spatial precision of contemporary imaging in the wager lab, but may be useful when working with specialty datasets, as a reference or as technology evolves, so it is provided as well. Note, the resolution in question here is not the resolution of the NIFTI image, but rather of the parcellation. The NIFTI images in both cases are 1x1x1mm resolution. -In fact, you should be aware that many of these regions are extremely small, even in the default, non fine scale atlas. In this form it is most useful as a way of asigning labels to activation maps you may have or otherwise being used as a reference. If you need to define seed regions or targets you probably will want to try dilating some of these regions to have a more practical volume to work with. +In fact, you should be aware that many of these regions are extremely small, even in the default, non fine scale atlas. In this form it is most useful as a way of assigning labels to activation maps you may have or otherwise being used as a reference. If you need to define seed regions or targets you probably will want to try dilating some of these regions to have a more practical volume to work with. ### Data formatting @@ -44,7 +44,7 @@ How do these atlases compare when overlaid on the pet tracer data? All pet trace A high resolution map of DSAB ligand binding on 5-HT transporter shows good overlap with Bianciardi's Raphe (blue), but the CANLab2018 shows some outliers (white). In particular a nucleus near the pons is in a 5-HTT deadzone. This atlas is masked in the subthalamic area though so it's hard to judge the accuracy of the CLi-RLi structure which extends into that masked region. For that we turn to the next tracer map. ![Salvi_5HTT](tracer_overlays/raphe_vs_dsab_salvi_5HTT.png) -As you can see, this DSAB ligand binding map is truncated in the caudal arts of the image, but not in the subthalamic area where it shows good overlap with the CLi-Rli structure of Bianciardi (blue). We can corroborate these results by looking at binding for 5-HT1a autoreceptor, which will be less specific but should also be collocalized with the raphe nuclei +As you can see, this DSAB ligand binding map is truncated in the caudal arts of the image, but not in the subthalamic area where it shows good overlap with the CLi-Rli structure of Bianciardi (blue). We can corroborate these results by looking at binding for 5-HT1a autoreceptor, which will be less specific but should also be colocalized with the raphe nuclei ![Beliveau_5HT1a](tracer_overlays/raphe_vs_cumi_beliveau_5HT1a.png) CUMI ligand binding map shows location of 5-HT1a sites that collocalize with the more dorsal Bianciardi atlas (blue) better than the more ventral CANLab2018 structures (white). @@ -57,10 +57,10 @@ The mapping from the MNI152NLin6Asym space to MNI152NLin2009cAsym space was perf Because we cannot distribute the original parcels nor any derivatives based on those parcels, the transformed versions -must be regenerated for each user on their own machine based on the files they themselves download. This entire process is automated by the bianciardi_create_atlas_obj.m script, which is invoked whenever this atlas is requested but not found by dependent scripts. The catch is that the transformation must be performed within matlab, since we cannot assume anything else about the user's environment. For instance, although fmriprep uses ANTs for registration, we cannot assume users have installed ANTs. This required converting the transformations computed by fmriprep into an SPM compatable format, since SPM is the only cross platform library for applying the transformation, and we can assume users have it installed because it's a dependency of this repo (by way of canlabCore). For documentation on the conversion process please refer to Neuroimaging_Pattern_Masks/templates/transforms/code/ +must be regenerated for each user on their own machine based on the files they themselves download. This entire process is automated by the bianciardi_create_atlas_obj.m script, which is invoked whenever this atlas is requested but not found by dependent scripts. The catch is that the transformation must be performed within matlab, since we cannot assume anything else about the user's environment. For instance, although fmriprep uses ANTs for registration, we cannot assume users have installed ANTs. This required converting the transformations computed by fmriprep into an SPM compatible format, since SPM is the only cross platform library for applying the transformation, and we can assume users have it installed because it's a dependency of this repo (by way of canlabCore). For documentation on the conversion process please refer to Neuroimaging_Pattern_Masks/templates/transforms/code/ -The atlas is probablistic, but the probability labels aren't perfect. Six different studies produced the labels, and while we might expect probability labels to sum to 1 within study, they're less likely to do so across studies where one study's structures abut up against another study's structures. Unfortunately we don't even have probabilities summing to 1 within study. In particular DR and PAG (Bianciardi et al, 2015) and L_VTA_PBP and mRta (Singh et al, 2021) show slight overlaps (61 voxels and 1 voxel, respectively). Given how sparse these exceptions are and the fact that 4 of the 6 studies do have probabilities that sum to 1 I'm assuming these are just errors. I split the difference by renormalizing the probabilities so that they do sum to 1 both in these unusual cases and across studies, which results in a slight adjustment of probabilities relative to the raw data. +The atlas is probabilistic, but the probability labels aren't perfect. Six different studies produced the labels, and while we might expect probability labels to sum to 1 within study, they're less likely to do so across studies where one study's structures abut up against another study's structures. Unfortunately we don't even have probabilities summing to 1 within study. In particular DR and PAG (Bianciardi et al, 2015) and L_VTA_PBP and mRta (Singh et al, 2021) show slight overlaps (61 voxels and 1 voxel, respectively). Given how sparse these exceptions are and the fact that 4 of the 6 studies do have probabilities that sum to 1 I'm assuming these are just errors. I split the difference by renormalizing the probabilities so that they do sum to 1 both in these unusual cases and across studies, which results in a slight adjustment of probabilities relative to the raw data. On the subject of probability labels, it's worth mentioning that something also appears to be wrong with the subthalamic nuclear probability maps. STh1 and STh2 seem to be intermixed in a very unusual way. These are supposed to be neighboring elipsoids, but are instead interleaved in a discontinuous manner. We have subthalamic nuclei in the CTI168 atlas too and those may be preferable to these. diff --git a/Atlases_and_parcellations/2023_Bianciardi_BrainstemNavigatorV0.9/bianciardi_create_atlas_obj.m b/Atlases_and_parcellations/2023_Bianciardi_BrainstemNavigatorV0.9/bianciardi_create_atlas_obj.m index e7d629e..3ca163f 100644 --- a/Atlases_and_parcellations/2023_Bianciardi_BrainstemNavigatorV0.9/bianciardi_create_atlas_obj.m +++ b/Atlases_and_parcellations/2023_Bianciardi_BrainstemNavigatorV0.9/bianciardi_create_atlas_obj.m @@ -41,7 +41,7 @@ 'Bianciardi M, Strong C, Toschi N, Edlow BL, Fischl B, Brown EN, Rosen BR, Wald LL. A probabilistic template of human mesopontine tegmental nuclei from in vivo 7T MRI. Neuroimage. 2018 Apr 15;170:222-230. doi: 10.1016/j.neuroimage.2017.04.070. Epub 2017 May 3. PMID: 28476663; PMCID: PMC5670016.',... 'Bianciardi M, Toschi N, Edlow BL, Eichner C, Setsompop K, Polimeni JR, Brown EN, Kinney HC, Rosen BR, Wald LL. Toward an In Vivo Neuroimaging Template of Human Brainstem Nuclei of the Ascending Arousal, Autonomic, and Motor Systems. Brain Connect. 2015 Dec;5(10):597-607. doi: 10.1089/brain.2015.0347. Epub 2015 Aug 11. PMID: 26066023; PMCID: PMC4684653.'}); - % imort atlas file in MNI152NLin2009cAsym space + % import atlas file in MNI152NLin2009cAsym space % we bianciast use this as a stand in template that we'll modify later, since % this is the wrong space bianciaTbl = readtable(which('bianciardi_fine_labels.csv')); @@ -282,7 +282,7 @@ % we can't upload the mat file to github due to licensing issues, but % we can upload a timestamp that will flag out of date versions and - % cause other uesrs to recreate the atlas when appropriate. + % cause other users to recreate the atlas when appropriate. fid = fopen(fullfile(this_dir.folder, sprintf('%s_atlas_object.latest',bianciaAtlas.atlas_name)), 'w+'); fprintf(fid,'%s',hash); fclose(fid); diff --git a/Atlases_and_parcellations/2023_CANLab_atlas/README.md b/Atlases_and_parcellations/2023_CANLab_atlas/README.md index 8a491c3..45331da 100644 --- a/Atlases_and_parcellations/2023_CANLab_atlas/README.md +++ b/Atlases_and_parcellations/2023_CANLab_atlas/README.md @@ -14,7 +14,7 @@ This is a full brain atlas mashup. It draws from the following, * Midbrain SN, RN, STH: T1/T2 contrast (CIT168 amygdala parcellation, Pauli 2018) There were two goals which motivated atlas construction. In order of priority -* Provide a probablistic spatial reference for functional localization of (mainly) group level results in multiple references spaces +* Provide a probabilistic spatial reference for functional localization of (mainly) group level results in multiple references spaces * Provide parcels for automated full brain parcelwise analysis The first requires a higher level of spatial detail than the second, so a fine scale and coarse scale version of the atlas are provided to facilitate both goals. The coarse atlas is designed for applications that need some slack, @@ -51,7 +51,7 @@ Additionally, note that the morel atlas is also restricted, and this usage restr Not only can we not distribute it, we also can't provide you with a legitimate resource for obtaining it short of contacting the original host institution (see Atlases_and_parcellations/2023_CANLab_atlas/licenses/morel_copyright). Consequently, this is the only atlas not individually available through Neuroimaging_Pattern_Masks (it's available to CANLab members in MasksPrivate, a -private repo). However, due to an oversight it was incorporated into canlab2018, which was distributed publically years ago, +private repo). However, due to an oversight it was incorporated into canlab2018, which was distributed publicly years ago, so that cat's out of the bag. Consequently, I (BP) didn't bother to separate the morel parcels out the way I did with Bianciardi's parcels. It's integrated into the 'abridged' version of this atlas directly distributed by this repo. Take note if using this atlas in contexts where open licensing may be important to you. @@ -95,7 +95,7 @@ See "help load_atlas" in matlab for details (make sure CanlabCore is in your pat Although versions of this atlas have been provided that should be useful to most users most of the time if you have exacting applications you may want generate atlases specific to your data and usage intentions. For localization purposes -probability maps are helpful, but if hard borders are desired probablistic thresholding and parcel defragmentation +probability maps are helpful, but if hard borders are desired probabilistic thresholding and parcel defragmentation may be desired. This can be accomplished using the atlas/threshold function. If you have specific data you intend to use this atlas with it may also make sense to resample it to your target space before thresholding. Presumably your data is already in alignment with the desired MNI template, but may be resampled to a @@ -116,7 +116,7 @@ Small regions will be severely affected by partial volume effects during resampl best bet is to regenerate brainstem nuclei from source using the scripts in the 2023_Bianciardi* sister folder to this directory and incorporate you target space into a custom apply_spm_warps.m script, similar to the existing one in the templates/transforms/code folder. The idea is to accomplish all transformations, including your own resampling, in a -single step to avoid compounding partial volume erors across interpolations. This will probably require some substantial +single step to avoid compounding partial volume errors across interpolations. This will probably require some substantial engineering on your part to achieve, but to put these into perspective, this atlas took 4-5 weeks of dedicated work to assemble. It is likely worthwhile to build off of it rather than starting from scratch even if it takes a day or two of work. @@ -124,7 +124,7 @@ work. ### Grayordinates -The CIFTI files are not designed for use with CANLab tools, which as of this time does not support CIFTI formated +The CIFTI files are not designed for use with CANLab tools, which as of this time does not support CIFTI formatted data. They can be used with connectome workbench or other toolboxes though. Run the setup scripts to generate them (see SETUP section above). @@ -169,7 +169,7 @@ Do not change this behavior without permission from Bianciardi. ## Mappings between spaces Source parcellations are generally in idiosyncratic spaces and most needed to be projected into a new space for -any particlar space of interest. The following projections were used: +any particular space of interest. The following projections were used: Registration fusion using fmriprep output to project labels from fsaverage space into MNI space. * Glasser parcelation. See README in 2016_Glasser* sister folder of this one for details @@ -205,26 +205,26 @@ multiple formats for use with ANTs, FSL and SPM but were all computed using ANTs * Cortex: likelihood a voxel will be circumscribed by a label in surface space, based on registration fusion * Basal Ganglia: Same as cortex. Obtained by personal correspondence with the authors, not available elsewhere to my knowledge. See Tian atlas directory README for details. -* MTL, Brainstem nuclei, CIT regions and cerebellum: Probablistic likelihoods that a labeled region was found in a participant +* MTL, Brainstem nuclei, CIT regions and cerebellum: Probabilistic likelihoods that a labeled region was found in a participant at a particular voxel after alignment to standard space. Sample sizes are small, so don't expect them to be well calibrated. * Thalamus: bogus values I assigned since there weren't any natively -* Brainstem background regions (shen parcels): bogus values I asigned to be 0.35 to provide minimal constraints on nuclear +* Brainstem background regions (shen parcels): bogus values I assigned to be 0.35 to provide minimal constraints on nuclear parcel boundaries. Anywhere these probabilities exceed those of Bianciardi atlas regions I impose a value 0.1 lower than -bianciardi's labels. In tihs way I use a greedy algorithm to asign voxels to Bianciardi's brainstem nuclei when available. +bianciardi's labels. In this way I use a greedy algorithm to assign voxels to Bianciardi's brainstem nuclei when available. ## CANLab2018 Comparison This atlas was created as a drop in replacement for CANLab2018. Taken individually the differences are relatively minor but extensive, and all together represent a substantial change. Differences are as follows -* Internally consistent grayordinate and volume formated versions available in register with multiple standard templates -* Probablistic cortical parcels obtained through registration fusion +* Internally consistent grayordinate and volume formatted versions available in register with multiple standard templates +* Probabilistic cortical parcels obtained through registration fusion * Removal of redundant Glasser cortical hippocampal segmentation. It doesn't comprehensively cover the hippocampal volume and intersects comprehensive cytoarchitectonic atlases. -* Different basal ganglia segmentation which better respects gross anatomical subdivisions and is probablistic -* New brainstem nuclear segmentation that is substantially more accurate and probablistic +* Different basal ganglia segmentation which better respects gross anatomical subdivisions and is probabilistic +* New brainstem nuclear segmentation that is substantially more accurate and probabilistic * Correction of misalignments of MTL (severe), thalamus (severe), brainstem and cerebellum. -* (mostly) Probablistic (exceptions: thalamus and some brainstem filler regions) +* (mostly) Probabilistic (exceptions: thalamus and some brainstem filler regions) * no RVM or trigeminal analog. The canlab2018 areas were not credible when compared against Duvernoy's Atlas (which is the authoritative reference), so they were not carried over. Most other brainstem regions have analogs here, although potentially under a different name (for instance the dorsal motor nucleus of the vagus, or DMNX, is now the viscero-sensory-motor nuclei, @@ -240,21 +240,21 @@ and to the source scripts in the src subfolder here. Here's a bit re PAG though, #### PAG -Phil Kragel's PAG parcellation was redone to provide probablistic labels. 19/24 participants had good parcellations (1,2, +Phil Kragel's PAG parcellation was redone to provide probabilistic labels. 19/24 participants had good parcellations (1,2, 4-10,12,14,15,17,19,20-24). These participants were reprojected into their target space using transformations obtained from Phil's dropbox into the same target space as the 2019 paper (IXI549) except linear interpolation was used instead of cubic splines to avoid gibbs ringing. The results are saved in the source subfolder here as KragelPAG_MNI152NLin6Asym.nii.gz. The space designation is justified because the IXI sample was registered to MNI152NLin6Asym before generating the IXI549 template used by Dartel to produce the warps used. Although there are differences between these templates the location and orientation of the cerebral aqueduct is the same, so there's no need for further alignment to MNI152NLin6Asym space. Individual -subject alignments (partial volume effects and all) were averaged to produce a probablistic PAG map. +subject alignments (partial volume effects and all) were averaged to produce a probabilistic PAG map. -This procedure did not reproduce the PAG columns. These were not derived on a per subject level though so no probablistic +This procedure did not reproduce the PAG columns. These were not derived on a per subject level though so no probabilistic delineation between columns can be made. Instead we simply diluted the existing kragel2019pag atlas from this repository -to span a mask defined by the probablistic labels derived above and used nearest neighbor interpolation to label the newly -identified voxels within the dilution mask. These were then used to asign voxel probabilities to each of the individual +to span a mask defined by the probabilistic labels derived above and used nearest neighbor interpolation to label the newly +identified voxels within the dilution mask. These were then used to assign voxel probabilities to each of the individual columns. Because we do not have subject specific probabilities the intercolumn probabilities are nonintersecting, but the -exterior margin of each column adopts the newly derived probablistic values. +exterior margin of each column adopts the newly derived probabilistic values. ## Parcel Discussion diff --git a/Atlases_and_parcellations/2023_CANLab_atlas/create_CANLab2023_atlas.m b/Atlases_and_parcellations/2023_CANLab_atlas/create_CANLab2023_atlas.m index 917755c..7032567 100644 --- a/Atlases_and_parcellations/2023_CANLab_atlas/create_CANLab2023_atlas.m +++ b/Atlases_and_parcellations/2023_CANLab_atlas/create_CANLab2023_atlas.m @@ -282,7 +282,7 @@ % we can't upload the mat file to github due to licensing issues, but % we can upload a timestamp that will flag out of date versions and - % cause other uesrs to recreate the atlas when appropriate. + % cause other users to recreate the atlas when appropriate. fid = fopen(sprintf('%s/%s_atlas_object.latest', this_dir.folder, atlas_obj.atlas_name),'w+'); fprintf(fid,'%s',hash); fclose(fid); @@ -294,7 +294,7 @@ end if any(ismember(SPACE,{'MNI152NLin2009cAsym'})) && strcmp(SCALE,'coarse') && res == 1 - fprintf('Creating QSIPrep compatable CANLab2023 %s %s %0.1fmm atlas...\n', SPACE, SCALE, res); + fprintf('Creating QSIPrep compatible CANLab2023 %s %s %0.1fmm atlas...\n', SPACE, SCALE, res); % throw a warning. We don't want to be resampling atlases again. We % should only resample once per atlas and this has already been @@ -350,6 +350,6 @@ fprintf(fid, '%s', jsontxt); fclose(fid); - fprintf('Wrote qsiprep compatable atlas to %s/qsiprep/\n', this_dir.folder); + fprintf('Wrote qsiprep compatible atlas to %s/qsiprep/\n', this_dir.folder); end end diff --git a/Atlases_and_parcellations/2023_CANLab_atlas/setup_canlab2023.m b/Atlases_and_parcellations/2023_CANLab_atlas/setup_canlab2023.m index d2dab23..3c4dd74 100644 --- a/Atlases_and_parcellations/2023_CANLab_atlas/setup_canlab2023.m +++ b/Atlases_and_parcellations/2023_CANLab_atlas/setup_canlab2023.m @@ -1,5 +1,5 @@ % run this script to update *.latest files after modifying the atlas and -% commit/push your git changes to propogate changes across git clones for +% commit/push your git changes to propagate changes across git clones for % all CANLab2023 versions. % % This takes on the order of an hour on an i7-12700H (12th gen intel core), diff --git a/Atlases_and_parcellations/2023_CANLab_atlas/src/create_CANLab2023_CIFTI_subctx.m b/Atlases_and_parcellations/2023_CANLab_atlas/src/create_CANLab2023_CIFTI_subctx.m index 2067591..ff4c6db 100644 --- a/Atlases_and_parcellations/2023_CANLab_atlas/src/create_CANLab2023_CIFTI_subctx.m +++ b/Atlases_and_parcellations/2023_CANLab_atlas/src/create_CANLab2023_CIFTI_subctx.m @@ -87,7 +87,7 @@ function create_CANLab2023_CIFTI_subctx(SPACE,SCALE,res,atlas_obj) atlas_obj.probability_maps = []; if round(res) ~= res - warning('Rounding res label in filenmae to nearest integer. This may overwrite any existing labels of different resolution. Update this code to accomodate fractional resolutions more gracefully.'); + warning('Rounding res label in filename to nearest integer. This may overwrite any existing labels of different resolution. Update this code to accommodate fractional resolutions more gracefully.'); end atlas_obj.fullpath = sprintf('%s/CANLab2023_%s_%s_%dmm_cifti_vols.nii', this_dir.folder, SPACE, SCALE, round(res)); diff --git a/Atlases_and_parcellations/2023_CANLab_atlas/src/create_brainstem2023_atlas_unrestricted.m b/Atlases_and_parcellations/2023_CANLab_atlas/src/create_brainstem2023_atlas_unrestricted.m index c0ec9af..3534d60 100644 --- a/Atlases_and_parcellations/2023_CANLab_atlas/src/create_brainstem2023_atlas_unrestricted.m +++ b/Atlases_and_parcellations/2023_CANLab_atlas/src/create_brainstem2023_atlas_unrestricted.m @@ -11,7 +11,7 @@ addpath(genpath('/home/bogdan/.matlab/canlab/CanlabCore')); addpath(genpath('/home/bogdan/.matlab/canlab/Neuroimaging_Pattern_Masks')); -% for Diedrichsen mask, which is publically available. I don't know if +% for Diedrichsen mask, which is publicly available. I don't know if % there are restrictions on the rest of the repo, but the mask at least % could be moved into a public repo and is only in MasksPrivate for % organization reasons (to keep it with the rest of the Diedrichsen atlas @@ -205,11 +205,11 @@ kragelPAG = load_atlas('Kragel2019PAG_atlas_object.mat'); kragelPAG = kragelPAG.resample_space(kragelpmaps); -% expand columns to fill our new probablistic PAG area using nearest +% expand columns to fill our new probabilistic PAG area using nearest % neighbor labeling kragelPAG_dil = dilate(kragelPAG, fmri_mask_image(kragelpmaps)); -% split probability map into subregionsand asign labels from column map +% split probability map into subregionsand assign labels from column map % above pmap = zeros(size(kragelmasks.dat,1),num_regions(kragelPAG_dil)); for i = 1:num_regions(kragelPAG_dil) diff --git a/Atlases_and_parcellations/2023_harvard_aan_brainstem_atlas/README.md b/Atlases_and_parcellations/2023_harvard_aan_brainstem_atlas/README.md index 33a3690..da33827 100644 --- a/Atlases_and_parcellations/2023_harvard_aan_brainstem_atlas/README.md +++ b/Atlases_and_parcellations/2023_harvard_aan_brainstem_atlas/README.md @@ -33,13 +33,13 @@ the public domain. The Bianciardi Brainstem atlas is a partially completed comprehensive atlas of brainstem nuclei. It's based on multimodal imaging data without ex vivo -validation, but is probablistic, based on more participants, and because it +validation, but is probabilistic, based on more participants, and because it tries to account for all brainstem nuclei it's less likely to mislabel regions belonging to one nucleis that in fact belong to another. Unfortunately it has a restrictive distribution license and is not suitable for all applications as a result. The Harvard Ascending Activation Network atlas may be more suitable in these circumstances or for use as a histological reference, however limited -its precision may be (due to small sample size and poor specificaiton of +its precision may be (due to small sample size and poor specification of MNI space template used). All AAN atlas regions have one more or more corresponding regions in @@ -73,7 +73,7 @@ regions, also shown in color, with outlines of Bianciardi's equivalent regions (possibly merged if there are multiple) overlain. ![html/compare_with_bianciardi_01.png](html/compare_with_bianciardi_01.png) -Saggital Brainstem Nuclei +Sagittal Brainstem Nuclei ![html/compare_with_bianciardi_02.png](html/compare_with_bianciardi_02.png) Coronal Brainstem Nuclei diff --git a/Atlases_and_parcellations/2023_levinson_bari_limbic_brainstem_atlas/README.md b/Atlases_and_parcellations/2023_levinson_bari_limbic_brainstem_atlas/README.md index ccd40e3..23ee4d4 100644 --- a/Atlases_and_parcellations/2023_levinson_bari_limbic_brainstem_atlas/README.md +++ b/Atlases_and_parcellations/2023_levinson_bari_limbic_brainstem_atlas/README.md @@ -1,6 +1,6 @@ ## Overview -A brainstem atlas of regions supporting limbic brain function. Open license and probablistic, based on 200+ participants +A brainstem atlas of regions supporting limbic brain function. Open license and probabilistic, based on 200+ participants from the HCP study. Five regions (Locus Coeruleus, Ventral Tegmental Area, Periaqueductal Gray, Nucleus Tractus Solitarius, Dorsal Raphe) were manually segmented based on anatomical landmarks from "structural MRI scans" (T1w and T2w but possibly also DWI images). A neurosurgeon and neuroradiologist evaluated each participant's mask to ensure it was within their @@ -47,7 +47,7 @@ Colored Patches - Levinson Bari Limbic Brainstem Atlas
Outlines - Bianciardi equivalents ![html/compare_with_bianciardi_01.png](html/compare_with_bianciardi_01.png) -Saggital +Sagittal ![html/compare_with_bianciardi_02.png](html/compare_with_bianciardi_02.png) Coronal diff --git a/Atlases_and_parcellations/2024_CANLab_atlas/README.md b/Atlases_and_parcellations/2024_CANLab_atlas/README.md index 035d173..556fbd3 100644 --- a/Atlases_and_parcellations/2024_CANLab_atlas/README.md +++ b/Atlases_and_parcellations/2024_CANLab_atlas/README.md @@ -97,7 +97,7 @@ available anywhere besides the Bianciardi atlas at the moment: Of the substitute atlases in openCANLab2024, the Harvard AAN atlas is not -probablistic. We set synthetic probabilities to 0.8 for all labeled voxels and +probabilistic. We set synthetic probabilities to 0.8 for all labeled voxels and dilated the very small regions (MPB_LPB, PTg and PnO) with a 3mm fwhm gaussian smoothing kernel to provide some more consistent behavior between atlases. In all cases these regions remain more conservatively sized than in Bianciardi. diff --git a/Atlases_and_parcellations/2024_CANLab_atlas/create_CANLab2024_atlas.m b/Atlases_and_parcellations/2024_CANLab_atlas/create_CANLab2024_atlas.m index 00eb547..888cad0 100644 --- a/Atlases_and_parcellations/2024_CANLab_atlas/create_CANLab2024_atlas.m +++ b/Atlases_and_parcellations/2024_CANLab_atlas/create_CANLab2024_atlas.m @@ -358,7 +358,7 @@ % we can't upload the mat file to github due to licensing issues, but % we can upload a timestamp that will flag out of date versions and - % cause other uesrs to recreate the atlas when appropriate. + % cause other users to recreate the atlas when appropriate. fid = fopen(sprintf('%s/%s_atlas_object.latest', this_dir.folder, atlas_obj.atlas_name),'w+'); fprintf(fid,'%s',hash); fclose(fid); @@ -370,7 +370,7 @@ end if any(ismember(SPACE,{'MNI152NLin2009cAsym'})) && strcmp(SCALE,'coarse') && res == 1 - fprintf('Creating QSIPrep compatable CANLab2024 %s %s %0.1fmm atlas...\n', SPACE, SCALE, res); + fprintf('Creating QSIPrep compatible CANLab2024 %s %s %0.1fmm atlas...\n', SPACE, SCALE, res); % throw a warning. We don't want to be resampling atlases again. We % should only resample once per atlas and this has already been @@ -437,7 +437,7 @@ fprintf(fid, '%s', jsontxt); fclose(fid); - fprintf('Wrote qsiprep compatable atlas to %s/qsiprep/\n', this_dir.folder); + fprintf('Wrote qsiprep compatible atlas to %s/qsiprep/\n', this_dir.folder); end end diff --git a/Atlases_and_parcellations/2024_CANLab_atlas/create_pain_pathways2024_brainnetwork.m b/Atlases_and_parcellations/2024_CANLab_atlas/create_pain_pathways2024_brainnetwork.m index 7b1072c..6533aa0 100644 --- a/Atlases_and_parcellations/2024_CANLab_atlas/create_pain_pathways2024_brainnetwork.m +++ b/Atlases_and_parcellations/2024_CANLab_atlas/create_pain_pathways2024_brainnetwork.m @@ -370,7 +370,7 @@ % add outlines: for i = 1:length(pain_regions_cpdm) o2 = addblobs(o2, pain_regions_cpdm(i), 'outline', 'color', [.2 .2 .2]); - %set(o2.activation_maps{i + 1}.blobhandles, 'LineWidth', 1); % one map aleady registered, so add 1 here + %set(o2.activation_maps{i + 1}.blobhandles, 'LineWidth', 1); % one map already registered, so add 1 here end saveas(gcf, fullfile('figures', 'cPDM_painpathways_weights_and_regions.png')); @@ -378,7 +378,7 @@ % add outlines only: for i = 1:length(pain_regions_cpdm) o2 = addblobs(o2, pain_regions_cpdm(i), 'outline', 'color', [.2 .2 .2]); - %set(o2.activation_maps{i + 1}.blobhandles, 'LineWidth', 1); % one map aleady registered, so add 1 here + %set(o2.activation_maps{i + 1}.blobhandles, 'LineWidth', 1); % one map already registered, so add 1 here end saveas(gcf, fullfile('figures', 'Painpathways_region_outlines.png')); diff --git a/Atlases_and_parcellations/2024_CANLab_atlas/docs/canlab2024.bbl b/Atlases_and_parcellations/2024_CANLab_atlas/docs/canlab2024.bbl index a65fd9d..e6e1dc6 100644 --- a/Atlases_and_parcellations/2024_CANLab_atlas/docs/canlab2024.bbl +++ b/Atlases_and_parcellations/2024_CANLab_atlas/docs/canlab2024.bbl @@ -133,7 +133,7 @@ Glasser, M\BPBI F.% \newblock \APACrefnote{Lots here.

MSMAll is derived using myelin and RSN ICA/dual regression based alignment. See section 2.4 of supplemental - methods.

Includes discussion of group mean template genreation that + methods.

Includes discussion of group mean template generation that can be useful if developing your own template. See section 2.5 of supplemental methods} \newblock @@ -170,7 +170,7 @@ Iglesias, J\BPBI E.% \APACjournalVolNumPages{NeuroImage}{183}{}{314-326}. \newblock \APACrefnote{Freesurfer thalamic subnuclear segmentation

This appears - to offer a probabalistic histological thalamic atlas that could supercede + to offer a probabilistic histological thalamic atlas that could supersede Morel. It has somewhat fewer regions, but in most cases the regions correspond to what you might use for a "coarse" morel atlas anyway. I.e. morel is nested within these areas and only Morel's finest distinctions are @@ -178,7 +178,7 @@ Iglesias, J\BPBI E.% boundary locations between Krauth (Morel2010) and this atlas (Fig 9 illustrates it well), and there isn't a great quantitative comparison of the performance of the two anywhere. That doesn't leave us much of a way to - decide which to use.

This atlas is probablistic and Krauth is more + decide which to use.

This atlas is probabilistic and Krauth is more granular, but neither really seems trustworthy if they can produce such disparate results. Perhaps there's some way to take an average? Use Krauth as the prior (assign arbitrary but low probability to each region) and update it diff --git a/Atlases_and_parcellations/2024_CANLab_atlas/docs/canlab2024.tex b/Atlases_and_parcellations/2024_CANLab_atlas/docs/canlab2024.tex index 8013763..e0ba21d 100644 --- a/Atlases_and_parcellations/2024_CANLab_atlas/docs/canlab2024.tex +++ b/Atlases_and_parcellations/2024_CANLab_atlas/docs/canlab2024.tex @@ -40,7 +40,7 @@ \section{Abstract} \section{TODO} Add grayordinate surface/volume overlap illustration for hippocampus/amygdala and caudate/putamen -complete missing stuctural descriptions in methods +complete missing structural descriptions in methods Evaluate registration fusion in spacetop, bmrk5 and paingen task contrast specificity (results: cortex section) @@ -85,7 +85,7 @@ \section{Introduction} \makecell[l]{Pallidum,\\Subthalamus,\\Ventral diencephalic\\nuclei,\\Habenula,\\Extended\\amygdala}\vspace{4pt} & \makecell{CIT168 subcortex \\ v1.1.0} & T1, T2 & \shortcite{Pauli2018} & Included & Included \\ \makecell[l]{Dorsal Raphe,\\ ACh nuclei, \\ Cranial nuclei} & \makecell{Levinson-Bari\\Limbic Brainstem\\Atlas}\vspace{4pt} & T1, T2 & \shortcite{levinson2023} & Included & \makecell[c]{Included \\ (Dorsal Raphe \\\& ACh Nuclei)} \\ \makecell[l]{5-HT nuclei,\\Tectal nuclei,\\Reticular\\formations,\\Olivary nuclei,\\ACh nuclei,\\Pontine nuclei,\\Cranial nuclei}\vspace{4pt} & Bianciardi v0.9 & T1, T2, DWI & \makecell{\shortcite{Bianciardi2015} \\ \shortcite{Bianciardi2016} \\ \shortcite{Bianciardi2018} \\ \shortcite{Garciagomar2019}\\ \shortcite{Singh2020}} & - & Included\\ -\makecell[l]{ACh nuclei,\\Parabrachial\\complex,\\Pontine nuclei,\\Rostral reticulum}\vspace{4pt} & \makecell{Harvard Asending\\Arousal Network\\v2.0} & \makecell{Histology} & \shortcite{Edlow2012,Edlow2023} & Included & -\\ +\makecell[l]{ACh nuclei,\\Parabrachial\\complex,\\Pontine nuclei,\\Rostral reticulum}\vspace{4pt} & \makecell{Harvard Ascending\\Arousal Network\\v2.0} & \makecell{Histology} & \shortcite{Edlow2012,Edlow2023} & Included & -\\ Brainstem Filler & Shen268 & BOLD (Rest) & \shortcite{Shen2013} & Included & Included \\ \hline \end{tabular} @@ -147,7 +147,7 @@ \subsubsection{Surface to volume fsLR-to-MNI projection} We use registration fus \subsection{Volumetric segmentations} -\subsubsection{Thalamus} Thalamic probablistic labels are not directly derived from the original parcellation \shortcite{Iglesias2018} that is used internally by Freesurfer. Those probabilistic labels aren't readily available. They're stored in an esoteric format internally by Freesurfer. Instead, we fit the Freesurfer segmentation to new participants and incorporate the obtained labels into CANLab2025. +\subsubsection{Thalamus} Thalamic probabilistic labels are not directly derived from the original parcellation \shortcite{Iglesias2018} that is used internally by Freesurfer. Those probabilistic labels aren't readily available. They're stored in an esoteric format internally by Freesurfer. Instead, we fit the Freesurfer segmentation to new participants and incorporate the obtained labels into CANLab2025. Freesurfer's Thalamic segmentation was run on T1 and DWI data from 278 unrelated HCP participants, 76 spacetop participants (all spacetop participants are unrelated) and 264 unrelated paingen participants. These samples were chosen because they had both DWI and T1 data available, both of which are needed to apply the most advanced and reliable versions of the Freesurfer segmentation algorithm. An additional subset of 138 unrelated HCP participants also had T1 and DWI available, but were omitted so that they could be used as an unseen validation sample in a separate study with which some of the authors are concurrently involved. The selection of subjects for this validation sample was made randomly. Our one remaining study, BMRK5, had only T1 data and was not segmented. @@ -193,7 +193,7 @@ \subsubsection{Cortex (Glasser)} Cortical parcels are all obtained from \shortci \subsubsection{Caudate/Putamen (Tian)} [discuss erosion of the most lateral parts of the Putamen] \\ -\subsubsection{Pallidum (CIT168 subcortical)} The CANLab2025 pallidum is derived from the CIT subcortical atlas and includes internal, extrenal and ventral segments. The ventral pallidum is quite small ($<$10 voxels at 2mm resolution), so at granularity level 2 it was combined with the external segment. This choice was made based on histological markers (enkephalin and dynorphin like immunoreactivity) and cytoarchitectural features (woolly fibers) that are shared by ventral pallidum with the external but not internal primate pallidal segments \shortcite{Haber1985, Reiner1999}. This assignment may be a bit misleading though. Functionally, internal and external segments of the dorsal globus pallidus correspond to direct and indirect pathways of cortico-basal ganglia-thalamocortical circuits, but in the ventral pallidum no such neat distinction is available and different subpopulations of neurons project to both the ventral tegmentum (indirect) and the mediodorsal nucleus of the thalamus (direct) \shortcite{Leung2015}. We simply pick the best of two lousy choices. +\subsubsection{Pallidum (CIT168 subcortical)} The CANLab2025 pallidum is derived from the CIT subcortical atlas and includes internal, external and ventral segments. The ventral pallidum is quite small ($<$10 voxels at 2mm resolution), so at granularity level 2 it was combined with the external segment. This choice was made based on histological markers (enkephalin and dynorphin like immunoreactivity) and cytoarchitectural features (woolly fibers) that are shared by ventral pallidum with the external but not internal primate pallidal segments \shortcite{Haber1985, Reiner1999}. This assignment may be a bit misleading though. Functionally, internal and external segments of the dorsal globus pallidus correspond to direct and indirect pathways of cortico-basal ganglia-thalamocortical circuits, but in the ventral pallidum no such neat distinction is available and different subpopulations of neurons project to both the ventral tegmentum (indirect) and the mediodorsal nucleus of the thalamus (direct) \shortcite{Leung2015}. We simply pick the best of two lousy choices. \\ \subsubsection{Accumbens (Cartmell)} The accumbens is subdivided into core and shell regions based on a diffusion tractography segmentation in 245 unrelated HCP participants, validated using MRI based functional and microstructural measures, post-mortem immunohistochemistry, cross species comparisons, and differential affective responses to deep brain stimulation in human patients \shortcite{Cartmell2019}. The right accumbens core in this parcellation contained two spatially disconnected set of voxels, one large (624mm$^3$) and one small (24mm$^3$). The latter was reassigned to right shell. Additionally, voxels intersecting with the bed nucleus of the stria terminalis and sublenticular extended amygdala (BST\_SLEA) of CTI168 \shortcite{Pauli2018} were assigned to the latter. Finally, any remaining voxels of the CIFTI "accumbens" structure were assigned by nearest neighbor dilation of shell, core and BST\_SLEA. \\ @@ -209,7 +209,7 @@ \subsubsection{Cerebellum (SUIT)} Cerebellar parcels were defined by lobular and The source parcellation \shortcite{Diedrichsen2009} also includes deep cerebellar nuclei. These were not included here because they are not represented in HCP 91k grayordinate space and would have introduced a significant discrepancy across formats of (open)CANLab2025. \\ -\subsubsection{PAG (KragelPAG)} The periaqueductal gray matter (PAG) was defined literally by using T1 contrast to segment the cerebral aqueduct and assign any voxel within 2mm to the PAG. Subcolumns were identified based on spatial location along the PAG's angular axis, and segmentations were validated based on noninvasive but fine scale (1.1mm isotropic, 7T, blood oxygen level dependent) functional measures \shortcite{Kragel2019}. Label probability values were assigned based on unpublished subject level segmentations (N=19) obtained from the authors of \shortcite{Kragel2019}. These did not distinguish subcolumns, so intercolumnar boundaries are deterministic, while PAG-nonPAG boundaries are probablistic. These segmentations were originally aligned to the MNI152NLin6Asym template, and transformed to MNI152NLin2009cAsym using a subcortically weighted alignments. [Note that the PAG incorporates Cuneiform nucleus from Bianciardi in CANLab2025, but not openCANLab2025] +\subsubsection{PAG (KragelPAG)} The periaqueductal gray matter (PAG) was defined literally by using T1 contrast to segment the cerebral aqueduct and assign any voxel within 2mm to the PAG. Subcolumns were identified based on spatial location along the PAG's angular axis, and segmentations were validated based on noninvasive but fine scale (1.1mm isotropic, 7T, blood oxygen level dependent) functional measures \shortcite{Kragel2019}. Label probability values were assigned based on unpublished subject level segmentations (N=19) obtained from the authors of \shortcite{Kragel2019}. These did not distinguish subcolumns, so intercolumnar boundaries are deterministic, while PAG-nonPAG boundaries are probabilistic. These segmentations were originally aligned to the MNI152NLin6Asym template, and transformed to MNI152NLin2009cAsym using a subcortically weighted alignments. [Note that the PAG incorporates Cuneiform nucleus from Bianciardi in CANLab2025, but not openCANLab2025] \\ \subsubsection{Subcortical nuclei (CIT168 subcortical)} [The habenula was reasigned to the medial pulvinar at levels 2-4 of granularitiy] \\ @@ -259,7 +259,7 @@ \subsection{Overview of parcellations} \includegraphics[width=\linewidth]{images/parcel_sizes.png} \caption{ {\bf -Four nested parcellation are available which vary in granularity from fine to coarse to help balance between the demands of different use cases.} Some regions are difficult to localize at the resolution of popular registration templates (2mm$^3$ isotropic, right axes). Fine scale regions are merged with functional or structurally related neighbors to produce coarser granularities that may be more practical in some applicaitons. MLT: medial temporal lobe; BG: basal ganglia. +Four nested parcellation are available which vary in granularity from fine to coarse to help balance between the demands of different use cases.} Some regions are difficult to localize at the resolution of popular registration templates (2mm$^3$ isotropic, right axes). Fine scale regions are merged with functional or structurally related neighbors to produce coarser granularities that may be more practical in some applications. MLT: medial temporal lobe; BG: basal ganglia. } \label{granularities-overview-figure} \end{figure*} @@ -439,7 +439,7 @@ \subsubsection{Brainstem} \caption{ {\bf CANLab2025 differs from openCANLab2025 in the pons and medulla.} -Openly licensed probablistic regions are largely shared between the two atlases (multicolored). CANLab2025 includes a number of regions with distribution restrictions (black outlines) and can only be incorporated into the copy of openCANLab2025 we distribute after being downloaded from a licensed repository. As a substitute we provide a number of pontine and medullary regions in openCANLab2025 (orange) that are non-probablistic, but have boundaries that roughly coincide with equivalent regions in CANLab2025. Finally, we provide a setup script which removes these regions, downloads the canlab2025 specific brainstem regions and inserts them appropriately to produce CANLab2025.} +Openly licensed probabilistic regions are largely shared between the two atlases (multicolored). CANLab2025 includes a number of regions with distribution restrictions (black outlines) and can only be incorporated into the copy of openCANLab2025 we distribute after being downloaded from a licensed repository. As a substitute we provide a number of pontine and medullary regions in openCANLab2025 (orange) that are non-probabilistic, but have boundaries that roughly coincide with equivalent regions in CANLab2025. Finally, we provide a setup script which removes these regions, downloads the canlab2025 specific brainstem regions and inserts them appropriately to produce CANLab2025.} \label{canlab2025-vs-opencanlab2025-bstem-figure} \end{figure*} @@ -477,13 +477,13 @@ \section{Discussion} [The difference in size of regions between cortex and cerebellum vs. brainstem, thalamus and BG should maybe not be surprising. The cortex and cerebellum are the two brain areas that had to fold in on themselves to be able to fit inside the skull. There's clearly something distinct about the scale at which they operate.] -\subsubsection{Cortex} Our volumetric cortical parcellation accounts for the idiosyncratic folding across individuals, but does not account for idiosyncracies in functional and structural topographies reported by Glasser et al. It seems unlikely at this point that Glasser et al. will ever release their idiosyncratic subject specific parcellations or the structural and functional feature based parcel classifiers they used to generate them, but their data is available and this leaves the door open for further refinements of this parcellation. Using the publicly available HCP data it would be possible to characterize each parcel in terms of population average multimodal features and use those to produce subject specific parcellations based on a nearest neighbor classification scheme or some similar strategy. It would be straightforward to combine such subject specific parcellations into probabilistic labels in surface space. Reprojection from surface to volume space would produce volumetric probabilistic labels that account for both area identity and its idiosyncratic embedding in 3D space. For now however our probabilities only account for idiosyncrasies in cortical folding, and should be used accordingly. +\subsubsection{Cortex} Our volumetric cortical parcellation accounts for the idiosyncratic folding across individuals, but does not account for idiosyncrasies in functional and structural topographies reported by Glasser et al. It seems unlikely at this point that Glasser et al. will ever release their idiosyncratic subject specific parcellations or the structural and functional feature based parcel classifiers they used to generate them, but their data is available and this leaves the door open for further refinements of this parcellation. Using the publicly available HCP data it would be possible to characterize each parcel in terms of population average multimodal features and use those to produce subject specific parcellations based on a nearest neighbor classification scheme or some similar strategy. It would be straightforward to combine such subject specific parcellations into probabilistic labels in surface space. Reprojection from surface to volume space would produce volumetric probabilistic labels that account for both area identity and its idiosyncratic embedding in 3D space. For now however our probabilities only account for idiosyncrasies in cortical folding, and should be used accordingly. \subsubsection{Thalamus} [look up your iglesias atlas git repo README. There's a long section on the 'veracity of the atlas' with issues worth discussing] \subsubsection{Caudate/Putamen} [Address issues with using functional connectivity gradients as a criterion] -\subsubsection{Brainstem} [discuss how brainstem is incomplete and likely to continue to develop. Can give the RVM as an example region we tried to include but couldn't, and describe why we couldn't. Discuss licensing restrictions and alternative atlases (or the lack thereof). Discuss how localization is a problem here and how probablistic labels should be combined with expert knowledge to correctly attribute signals to structures. Can use the olivary complex as an example since SOC and ION are hard to distinguish. LC might be even better.] +\subsubsection{Brainstem} [discuss how brainstem is incomplete and likely to continue to develop. Can give the RVM as an example region we tried to include but couldn't, and describe why we couldn't. Discuss licensing restrictions and alternative atlases (or the lack thereof). Discuss how localization is a problem here and how probabilistic labels should be combined with expert knowledge to correctly attribute signals to structures. Can use the olivary complex as an example since SOC and ION are hard to distinguish. LC might be even better.] \subsubsection{Variations across file formats} [discuss the slight differences in the ventral striatum and hypothalamus, the absence of deep cerebellar nuclei, and the need for an update to the HCP 91k grayordinate template. Discuss surface representations of the cerebellum as well, and speculate on future extensions of the atlas that take advantage of superior grayordinate templates.] diff --git a/Atlases_and_parcellations/2024_CANLab_atlas/docs/cogsci.sty b/Atlases_and_parcellations/2024_CANLab_atlas/docs/cogsci.sty index 4dadf4e..281dca8 100644 --- a/Atlases_and_parcellations/2024_CANLab_atlas/docs/cogsci.sty +++ b/Atlases_and_parcellations/2024_CANLab_atlas/docs/cogsci.sty @@ -33,7 +33,7 @@ % \And ... \And % Author n \\ Address line \\ ... \\ Address line} % -% To start a seperate ``row'' of authors use \AND, as in +% To start a separate ``row'' of authors use \AND, as in % % \author{Author 1 \\ Address line \\ ... \\ Address line % \AND diff --git a/Atlases_and_parcellations/2024_CANLab_atlas/setup_canlab2024.m b/Atlases_and_parcellations/2024_CANLab_atlas/setup_canlab2024.m index 6d36079..0affacf 100644 --- a/Atlases_and_parcellations/2024_CANLab_atlas/setup_canlab2024.m +++ b/Atlases_and_parcellations/2024_CANLab_atlas/setup_canlab2024.m @@ -2,7 +2,7 @@ % Redundant for the cortex, but for the subcortex at least. % run this script to update *.latest files after modifying the atlas and -% commit/push your git changes to propogate changes across git clones for +% commit/push your git changes to propagate changes across git clones for % all CANLab2023 versions. % % This takes on the order of an hour on an i7-12700H (12th gen intel core), diff --git a/Atlases_and_parcellations/2024_CANLab_atlas/src/create_CANLab2024_CIFTI_subctx.m b/Atlases_and_parcellations/2024_CANLab_atlas/src/create_CANLab2024_CIFTI_subctx.m index 8f192d3..5e04c85 100644 --- a/Atlases_and_parcellations/2024_CANLab_atlas/src/create_CANLab2024_CIFTI_subctx.m +++ b/Atlases_and_parcellations/2024_CANLab_atlas/src/create_CANLab2024_CIFTI_subctx.m @@ -87,7 +87,7 @@ function create_CANLab2024_CIFTI_subctx(SPACE,SCALE,res,atlas_obj) atlas_obj.probability_maps = []; if round(res) ~= res - warning('Rounding res label in filenmae to nearest integer. This may overwrite any existing labels of different resolution. Update this code to accomodate fractional resolutions more gracefully.'); + warning('Rounding res label in filename to nearest integer. This may overwrite any existing labels of different resolution. Update this code to accommodate fractional resolutions more gracefully.'); end atlas_obj.fullpath = sprintf('%s/CANLab2024_%s_%s_%dmm_cifti_vols.nii', this_dir.folder, SPACE, SCALE, round(res)); diff --git a/Atlases_and_parcellations/2024_CANLab_atlas/src/create_brainstem2024_atlas_unrestricted.m b/Atlases_and_parcellations/2024_CANLab_atlas/src/create_brainstem2024_atlas_unrestricted.m index c32e8d0..5703534 100644 --- a/Atlases_and_parcellations/2024_CANLab_atlas/src/create_brainstem2024_atlas_unrestricted.m +++ b/Atlases_and_parcellations/2024_CANLab_atlas/src/create_brainstem2024_atlas_unrestricted.m @@ -11,7 +11,7 @@ addpath(genpath('/home/bogdan/.matlab/canlab/CanlabCore')); addpath(genpath('/home/bogdan/.matlab/canlab/Neuroimaging_Pattern_Masks')); -% for Diedrichsen mask, which is publically available. I don't know if +% for Diedrichsen mask, which is publicly available. I don't know if % there are restrictions on the rest of the repo, but the mask at least % could be moved into a public repo and is only in MasksPrivate for % organization reasons (to keep it with the rest of the Diedrichsen atlas @@ -388,11 +388,11 @@ kragelPAG = load_atlas('Kragel2019PAG_atlas_object.mat'); kragelPAG = kragelPAG.resample_space(kragelpmaps); -% expand columns to fill our new probablistic PAG area using nearest +% expand columns to fill our new probabilistic PAG area using nearest % neighbor labeling kragelPAG_dil = dilate(kragelPAG, fmri_mask_image(kragelpmaps)); -% split probability map into subregions and asign labels from column map +% split probability map into subregions and assign labels from column map % above pmap = zeros(size(kragelmasks.dat,1),num_regions(kragelPAG_dil)); for i = 1:num_regions(kragelPAG_dil) diff --git a/Atlases_and_parcellations/2024_CANLab_atlas/src/create_openCANLab2024.m b/Atlases_and_parcellations/2024_CANLab_atlas/src/create_openCANLab2024.m index 547ced4..6bf8829 100644 --- a/Atlases_and_parcellations/2024_CANLab_atlas/src/create_openCANLab2024.m +++ b/Atlases_and_parcellations/2024_CANLab_atlas/src/create_openCANLab2024.m @@ -461,7 +461,7 @@ % this isn't quite complete. I need to pregenerate different resolutions % and granularities. This will require among other things -% - generate qsiprep versoin +% - generate qsiprep version % - generate CIFTI file %% produce low res version of this file diff --git a/Atlases_and_parcellations/2024_CANLab_atlas/src/create_openCANLab2024_CIFTI_subctx.m b/Atlases_and_parcellations/2024_CANLab_atlas/src/create_openCANLab2024_CIFTI_subctx.m index acc1361..1a4e10a 100644 --- a/Atlases_and_parcellations/2024_CANLab_atlas/src/create_openCANLab2024_CIFTI_subctx.m +++ b/Atlases_and_parcellations/2024_CANLab_atlas/src/create_openCANLab2024_CIFTI_subctx.m @@ -87,7 +87,7 @@ function create_CANLab2024_CIFTI_subctx(SPACE,SCALE,res,atlas_obj) atlas_obj.probability_maps = []; if round(res) ~= res - warning('Rounding res label in filenmae to nearest integer. This may overwrite any existing labels of different resolution. Update this code to accomodate fractional resolutions more gracefully.'); + warning('Rounding res label in filename to nearest integer. This may overwrite any existing labels of different resolution. Update this code to accommodate fractional resolutions more gracefully.'); end atlas_obj.fullpath = sprintf('%s/openCANLab2024_%s_%s_%dmm_cifti_vols.nii', this_dir.folder, SPACE, SCALE, round(res)); diff --git a/Atlases_and_parcellations/2024_CANLab_atlas/src/create_thalamus2024_atlas.m b/Atlases_and_parcellations/2024_CANLab_atlas/src/create_thalamus2024_atlas.m index a660475..4f2db69 100644 --- a/Atlases_and_parcellations/2024_CANLab_atlas/src/create_thalamus2024_atlas.m +++ b/Atlases_and_parcellations/2024_CANLab_atlas/src/create_thalamus2024_atlas.m @@ -72,7 +72,7 @@ thalamus_atlas.labels_2(ismember(thalamus_atlas.labels_2,{'R_hypothalamus_anterior_superior','R_hypothalamus_tubular_superior'})) = {'R_hypothalamus_anterior_and_tubular_superior'}; %% dilate the cifti atlas to include the entire hypothalamus -% otherwise this trunctates the chiasmatic nuclei +% otherwise this truncates the chiasmatic nuclei cifti_atlas = cifti_atlas.replace_empty(); hypothal_atlas = hypothal_atlas.resample_space(cifti_atlas); diff --git a/Atlases_and_parcellations/2024_CANLab_atlas/src/openCANLab2024_MNI152NLin2009cAsym_ref.txt b/Atlases_and_parcellations/2024_CANLab_atlas/src/openCANLab2024_MNI152NLin2009cAsym_ref.txt index 7fda6d1..7a44a2a 100644 --- a/Atlases_and_parcellations/2024_CANLab_atlas/src/openCANLab2024_MNI152NLin2009cAsym_ref.txt +++ b/Atlases_and_parcellations/2024_CANLab_atlas/src/openCANLab2024_MNI152NLin2009cAsym_ref.txt @@ -6,7 +6,7 @@ references "Cartmell SCD, Tian Q, Thio BJ, Leuze C, Ye L, Williams NR, Yang G, Ben-Dor G, Deisseroth K, Grill WM, McNab JA, Halpern CH. Multimodal characterization of the human nucleus accumbens. Neuroimage (2019), 137-149, 198. " "Diedrichsen, Jörn, Joshua H. Balsters, Jonathan Flavell, Emma Cussans, and Narender Ramnani. 2009. A Probabilistic MR Atlas of the Human Cerebellum. NeuroImage 46 (1): 39?46. " "Glasser, Matthew F., Timothy S. Coalson, Emma C. Robinson, Carl D. Hacker, John Harwell, Essa Yacoub, Kamil Ugurbil, et al. 2016. A Multi-Modal Parcellation of Human Cerebral Cortex. Nature 536 (7615): 171?78. " -"Iglesias JE, Insausti R, Lerma-Usabiaga G, Bocchetta M, Van Leemput K, Greve DN, van der Kouwe A, Fischl B, Caballero-Gaudes C, Paz-Alonso PM. (2018). A probablistic atlas of the human thalamuc nuclei combining ex vivo MRI and histology. Neuroimage, 314-326, 183. " +"Iglesias JE, Insausti R, Lerma-Usabiaga G, Bocchetta M, Van Leemput K, Greve DN, van der Kouwe A, Fischl B, Caballero-Gaudes C, Paz-Alonso PM. (2018). A probabilistic atlas of the human thalamuc nuclei combining ex vivo MRI and histology. Neuroimage, 314-326, 183. " "Kedo, O., Zilles, K., Palomero-Gallagher, N., Schleicher, A., Mohlberg, H., Bludau, S., & Amunts, K. (2017). Receptor-driven, multimodal mapping of the human amygdala. Brain Structure and Function. https://doi.org/10.1007/s00429-017-1577-x DOI: 10.1007/s00429-017-1577-x " "Kragel, P. A., Bianciardi, M., Hartley, L., Matthewson, G., Choi, J. K., Quigley, K. S., ... & Satpute, A. B. (2019). Functional involvement of human periaqueductal gray and other midbrain nuclei in cognitive control. Journal of Neuroscience, 2043-18. " "Pauli, Wolfgang M., Amanda N. Nili, and J. Michael Tyszka. 2018. ?A High-Resolution Probabilistic in Vivo Atlas of Human Subcortical Brain Nuclei.? Scientific Data 5 (April): 180063. " diff --git a/Atlases_and_parcellations/2024_CANLab_atlas/src/openCANLab2024_MNI152NLin6Asym_ref.txt b/Atlases_and_parcellations/2024_CANLab_atlas/src/openCANLab2024_MNI152NLin6Asym_ref.txt index 7fda6d1..7a44a2a 100644 --- a/Atlases_and_parcellations/2024_CANLab_atlas/src/openCANLab2024_MNI152NLin6Asym_ref.txt +++ b/Atlases_and_parcellations/2024_CANLab_atlas/src/openCANLab2024_MNI152NLin6Asym_ref.txt @@ -6,7 +6,7 @@ references "Cartmell SCD, Tian Q, Thio BJ, Leuze C, Ye L, Williams NR, Yang G, Ben-Dor G, Deisseroth K, Grill WM, McNab JA, Halpern CH. Multimodal characterization of the human nucleus accumbens. Neuroimage (2019), 137-149, 198. " "Diedrichsen, Jörn, Joshua H. Balsters, Jonathan Flavell, Emma Cussans, and Narender Ramnani. 2009. A Probabilistic MR Atlas of the Human Cerebellum. NeuroImage 46 (1): 39?46. " "Glasser, Matthew F., Timothy S. Coalson, Emma C. Robinson, Carl D. Hacker, John Harwell, Essa Yacoub, Kamil Ugurbil, et al. 2016. A Multi-Modal Parcellation of Human Cerebral Cortex. Nature 536 (7615): 171?78. " -"Iglesias JE, Insausti R, Lerma-Usabiaga G, Bocchetta M, Van Leemput K, Greve DN, van der Kouwe A, Fischl B, Caballero-Gaudes C, Paz-Alonso PM. (2018). A probablistic atlas of the human thalamuc nuclei combining ex vivo MRI and histology. Neuroimage, 314-326, 183. " +"Iglesias JE, Insausti R, Lerma-Usabiaga G, Bocchetta M, Van Leemput K, Greve DN, van der Kouwe A, Fischl B, Caballero-Gaudes C, Paz-Alonso PM. (2018). A probabilistic atlas of the human thalamuc nuclei combining ex vivo MRI and histology. Neuroimage, 314-326, 183. " "Kedo, O., Zilles, K., Palomero-Gallagher, N., Schleicher, A., Mohlberg, H., Bludau, S., & Amunts, K. (2017). Receptor-driven, multimodal mapping of the human amygdala. Brain Structure and Function. https://doi.org/10.1007/s00429-017-1577-x DOI: 10.1007/s00429-017-1577-x " "Kragel, P. A., Bianciardi, M., Hartley, L., Matthewson, G., Choi, J. K., Quigley, K. S., ... & Satpute, A. B. (2019). Functional involvement of human periaqueductal gray and other midbrain nuclei in cognitive control. Journal of Neuroscience, 2043-18. " "Pauli, Wolfgang M., Amanda N. Nili, and J. Michael Tyszka. 2018. ?A High-Resolution Probabilistic in Vivo Atlas of Human Subcortical Brain Nuclei.? Scientific Data 5 (April): 180063. " diff --git a/CANlab_Meta_analysis_maps/2016_Pauli_Basal_Ganglia_Parcels/scripts/pauli2016_create_atlas_object.m b/CANlab_Meta_analysis_maps/2016_Pauli_Basal_Ganglia_Parcels/scripts/pauli2016_create_atlas_object.m index eadd5bb..a18f8f0 100644 --- a/CANlab_Meta_analysis_maps/2016_Pauli_Basal_Ganglia_Parcels/scripts/pauli2016_create_atlas_object.m +++ b/CANlab_Meta_analysis_maps/2016_Pauli_Basal_Ganglia_Parcels/scripts/pauli2016_create_atlas_object.m @@ -103,7 +103,7 @@ %% write - this writes only the label image -% Save: if creating from probabilty images only +% Save: if creating from probability images only % % if dosave % diff --git a/CANlab_Meta_analysis_maps/2016_Pauli_Basal_Ganglia_Parcels/scripts/pauli2016_plot_bucknerlab_similarity.m b/CANlab_Meta_analysis_maps/2016_Pauli_Basal_Ganglia_Parcels/scripts/pauli2016_plot_bucknerlab_similarity.m index 5776cab..21fdc70 100644 --- a/CANlab_Meta_analysis_maps/2016_Pauli_Basal_Ganglia_Parcels/scripts/pauli2016_plot_bucknerlab_similarity.m +++ b/CANlab_Meta_analysis_maps/2016_Pauli_Basal_Ganglia_Parcels/scripts/pauli2016_plot_bucknerlab_similarity.m @@ -3,7 +3,7 @@ [cortical_images, linenames, imgnames] = load_image_set('pauli_cortex'); -%% POLAR PLOT OF REALATION WITH BUCKNERLAB MAPS +%% POLAR PLOT OF RELATION WITH BUCKNERLAB MAPS % !gunzip *gz % f = filenames('*nii') diff --git a/Multivariate_signature_patterns/2020_VanOudenhove_Kragel_somatovisceral_pain/classify_somatovisceral_pain.m b/Multivariate_signature_patterns/2020_VanOudenhove_Kragel_somatovisceral_pain/classify_somatovisceral_pain.m index eb8ab4c..b3fe96d 100644 --- a/Multivariate_signature_patterns/2020_VanOudenhove_Kragel_somatovisceral_pain/classify_somatovisceral_pain.m +++ b/Multivariate_signature_patterns/2020_VanOudenhove_Kragel_somatovisceral_pain/classify_somatovisceral_pain.m @@ -17,7 +17,7 @@ % % :Outputs: % -% Yhat - a vector with one continous estimate of the probability an image +% Yhat - a vector with one continuous estimate of the probability an image % is somatic (positive values) or visceral pain (values near zero) % % Uses the model parameters saved in Visceral_vs_Somatic_betas_Yeo_Networks.mat diff --git a/Multivariate_signature_patterns/2021_Ceko_MPA2_multiaversive/apply_multiaversive_mpa2_patterns.m b/Multivariate_signature_patterns/2021_Ceko_MPA2_multiaversive/apply_multiaversive_mpa2_patterns.m index ade6bbe..739149e 100644 --- a/Multivariate_signature_patterns/2021_Ceko_MPA2_multiaversive/apply_multiaversive_mpa2_patterns.m +++ b/Multivariate_signature_patterns/2021_Ceko_MPA2_multiaversive/apply_multiaversive_mpa2_patterns.m @@ -114,7 +114,7 @@ end end -% Zero out intercepts if they are not meaninful for chosen similarity metric +% Zero out intercepts if they are not meaningful for chosen similarity metric if strcmp(similarity_metric, 'cosine_similarity') || strcmp(similarity_metric, 'correlation') intcpts = [0 0 0 0 0]; end diff --git a/Multivariate_signature_patterns/2021_vantHoff_BASIC_sexual_image_classifier/region_table/BASIC_result.m b/Multivariate_signature_patterns/2021_vantHoff_BASIC_sexual_image_classifier/region_table/BASIC_result.m index 43233ca..d6dffcd 100644 --- a/Multivariate_signature_patterns/2021_vantHoff_BASIC_sexual_image_classifier/region_table/BASIC_result.m +++ b/Multivariate_signature_patterns/2021_vantHoff_BASIC_sexual_image_classifier/region_table/BASIC_result.m @@ -12,7 +12,7 @@ obj_sex_pos = svm_stats_results{1, 2}.weight_obj; obj_sex_neg = svm_stats_results{1, 3}.weight_obj; -%% Treshold images +%% Threshold images % these weight maps are 0.05 unc by default obj_sex_neu_fdr_05 = threshold(obj_sex_neu, 0.05, 'fdr'); diff --git a/Multivariate_signature_patterns/2022_Koban_NCS_Craving/Data_code_public/scripts/Prediction/Apply_cvNCSmaps_stats_2022.m b/Multivariate_signature_patterns/2022_Koban_NCS_Craving/Data_code_public/scripts/Prediction/Apply_cvNCSmaps_stats_2022.m index afac3cd..42ed7ba 100644 --- a/Multivariate_signature_patterns/2022_Koban_NCS_Craving/Data_code_public/scripts/Prediction/Apply_cvNCSmaps_stats_2022.m +++ b/Multivariate_signature_patterns/2022_Koban_NCS_Craving/Data_code_public/scripts/Prediction/Apply_cvNCSmaps_stats_2022.m @@ -192,14 +192,14 @@ xlsfile = 'NCS_weightmaps/CV_wmaps/Table_PatternExp_byGroupCondition_FINAL.xlsx'; [~, datname] = xlsread(xlsfile, 'All_byROCcondition', 'A2:A397'); -datset = meancenter(xlsread(xlsfile, 'All_byROCcondition', 'F2:F397')); +dataset = meancenter(xlsread(xlsfile, 'All_byROCcondition', 'F2:F397')); subjects = meancenter(xlsread(xlsfile, 'All_byROCcondition', 'G2:G397')); patients = xlsread(xlsfile, 'All_byROCcondition', 'H2:H397') ./2; drug = xlsread(xlsfile, 'All_byROCcondition', 'I2:I397') ./2; regul = xlsread(xlsfile, 'All_byROCcondition', 'J2:J397') ./2; pexp2_val = pexp2_vector; -datpexp2 = table(datname, datset, subjects, patients, drug, regul, pexp2_val); +datpexp2 = table(datname, dataset, subjects, patients, drug, regul, pexp2_val); lme = fitlme(datpexp2, 'pexp2_val ~ 1 + patients + drug + regul + drug*regul + patients*drug + patients*regul + patients*regul*drug + (1 + drug + regul + drug*regul |subjects)', 'FitMethod', 'REML'); anova(lme) @@ -217,10 +217,10 @@ dnames = {'meanratings_alc'; 'meanratings_coc'; 'meanratings_coc_hc'; 'meanratings_cig'; 'meanratings_cig_hc'}; for ds = 1:5 - dn{ds} = datpexp2.pexp2_val(datpexp2.datset==ds & datpexp2.drug==1 & datpexp2.regul==1); - dl{ds} = datpexp2.pexp2_val(datpexp2.datset==ds & datpexp2.drug==1 & datpexp2.regul==-1); - fn{ds} = datpexp2.pexp2_val(datpexp2.datset==ds & datpexp2.drug==-1 & datpexp2.regul==1); - fl{ds} = datpexp2.pexp2_val(datpexp2.datset==ds & datpexp2.drug==-1 & datpexp2.regul==-1); + dn{ds} = datpexp2.pexp2_val(datpexp2.dataset==ds & datpexp2.drug==1 & datpexp2.regul==1); + dl{ds} = datpexp2.pexp2_val(datpexp2.dataset==ds & datpexp2.drug==1 & datpexp2.regul==-1); + fn{ds} = datpexp2.pexp2_val(datpexp2.dataset==ds & datpexp2.drug==-1 & datpexp2.regul==1); + fl{ds} = datpexp2.pexp2_val(datpexp2.dataset==ds & datpexp2.drug==-1 & datpexp2.regul==-1); end %% cross-plots @@ -285,7 +285,7 @@ beh_vector_HC = [reshape(beh_all_meanrate{3}, [numel(beh_all_meanrate{3}),1]); ... reshape(beh_all_meanrate{5}, [numel(beh_all_meanrate{5}),1])]; -datpexp22 = table(datname, datset, subjects, patients, drug, regul, pexp2_val, beh_vector); +datpexp22 = table(datname, dataset, subjects, patients, drug, regul, pexp2_val, beh_vector); lme2 = fitlme(datpexp22, 'beh_vector ~ 1 + patients + drug + regul + drug*regul + patients*drug + patients*regul + patients*regul*drug + (1 + drug + regul + drug*regul |subjects)', 'FitMethod', 'REML'); anova(lme2) @@ -317,24 +317,24 @@ %% only users [~, datname] = xlsread(xlsfile, 'Users', 'A2:A245'); -datset = xlsread(xlsfile, 'Users', 'F2:F245'); +dataset = xlsread(xlsfile, 'Users', 'F2:F245'); subjects = xlsread(xlsfile, 'Users', 'G2:G245'); drug = xlsread(xlsfile, 'Users', 'I2:I245'); regul = xlsread(xlsfile, 'Users', 'J2:J245'); pexp2_val_U = pexp2_vector_U; -datpexp2_U = table(datname, datset, subjects, drug, regul, pexp2_val_U); -glme_U = fitglme(datpexp2_U, 'pexp2_val_U ~ 1 + drug + regul + drug*regul + (1|subjects) + (1|datset)'); +datpexp2_U = table(datname, dataset, subjects, drug, regul, pexp2_val_U); +glme_U = fitglme(datpexp2_U, 'pexp2_val_U ~ 1 + drug + regul + drug*regul + (1|subjects) + (1|dataset)'); %% only NON-users [~, datname] = xlsread(xlsfile, 'NonUsers', 'A2:A161'); -datset = xlsread(xlsfile, 'NonUsers', 'F2:F161'); +dataset = xlsread(xlsfile, 'NonUsers', 'F2:F161'); subjects = xlsread(xlsfile, 'NonUsers', 'G2:G161'); drug = xlsread(xlsfile, 'NonUsers', 'I2:I161'); regul = xlsread(xlsfile, 'NonUsers', 'J2:J161'); pexp2_val_C = pexp2_vector_HC; -datpexp2_C = table(datname, datset, subjects, drug, regul, pexp2_val_C); -glme_C = fitglme(datpexp2_C, 'pexp2_val_C ~ 1 + drug + regul + drug*regul + (1|subjects) + (1|datset)'); +datpexp2_C = table(datname, dataset, subjects, drug, regul, pexp2_val_C); +glme_C = fitglme(datpexp2_C, 'pexp2_val_C ~ 1 + drug + regul + drug*regul + (1|subjects) + (1|dataset)'); diff --git a/Neurosynth_maps/neurosynth_interregion_coactivation.m b/Neurosynth_maps/neurosynth_interregion_coactivation.m index 57521a6..cad0feb 100644 --- a/Neurosynth_maps/neurosynth_interregion_coactivation.m +++ b/Neurosynth_maps/neurosynth_interregion_coactivation.m @@ -27,7 +27,7 @@ % - The atlas/mask object does not have to be in the same space/voxel size as the neurosynth_data object. % - It will be resampled to the space of neurosynth_data % -% This funtion calculates "activation" of the seed region this way: +% This function calculates "activation" of the seed region this way: % - Define study contrasts-level activation as any activation within the % region of interest. % - The MKDA setup has already smoothed the peak reported coordinates with diff --git a/Neurosynth_maps/neurosynth_seed_coactivation_map.m b/Neurosynth_maps/neurosynth_seed_coactivation_map.m index 459b166..68e1d23 100644 --- a/Neurosynth_maps/neurosynth_seed_coactivation_map.m +++ b/Neurosynth_maps/neurosynth_seed_coactivation_map.m @@ -36,7 +36,7 @@ % rois = load_atlas('canlab2018_2mm'); % vpl = select_atlas_subset(rois, {'VPL'}); % -% This funtion calculates "activation" of the seed region this way: +% This function calculates "activation" of the seed region this way: % - Define study contrasts-level activation as any activation within the % region of interest. % - The MKDA setup has already smoothed the peak reported coordinates with diff --git a/Neurosynth_maps/scripts/generate_neurosynth_atlases b/Neurosynth_maps/scripts/generate_neurosynth_atlases index 36ac98f..aa7d24c 100644 --- a/Neurosynth_maps/scripts/generate_neurosynth_atlases +++ b/Neurosynth_maps/scripts/generate_neurosynth_atlases @@ -180,7 +180,7 @@ def get_result_volume(data, header, volume): # not crashing with as with int8 # well -- int16 makes things a bit more complicated due to # now absent absolute zeros (via intercept/slope), and I think - # that is what confuses fslview's historgram viewer + # that is what confuses fslview's histogram viewer header.set_data_dtype(np.int16) #header.set_data_dtype(np.float32) data_4d = volume.unmask(data) diff --git a/Neurosynth_maps/scripts/neurosynth_default_mode_analysis_tor_dec_2019.m b/Neurosynth_maps/scripts/neurosynth_default_mode_analysis_tor_dec_2019.m index 790314a..a9814ee 100644 --- a/Neurosynth_maps/scripts/neurosynth_default_mode_analysis_tor_dec_2019.m +++ b/Neurosynth_maps/scripts/neurosynth_default_mode_analysis_tor_dec_2019.m @@ -159,7 +159,7 @@ % ------------------------------------------------------------------ % Connector Hubs: Most frequently connected Def Mode A regions with others % -% connnector hubs in red: includes vmPFC +% connector hubs in red: includes vmPFC % provincial hubs in blue: includes pCC and parts of lateral OFC b2 = select_atlas_subset(b, {'Def' 'Limbic' 'Cortex_Fronto_ParietalB'}, 'labels_2'); @@ -170,7 +170,7 @@ % ------------------------------------------------------------------ % Connector Hubs: Most frequently connected Def Mode A regions with others % -% connnector hubs in red: includes vmPFC +% connector hubs in red: includes vmPFC % provincial hubs in blue: includes pCC and parts of lateral OFC % 'Cortex_Fronto_ParietalB' has OFC, so add that diff --git a/README.md b/README.md index f98c610..feca911 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # Neuroimaging_Pattern_Masks -This repository contains pre-defined brain "signatures" (multivariate predictive patterns), atlases of local regions and networks, and masks and regions derived from published meta-analyses of neuroimaging data. It includes a fairly comprehensive set of such resources developed by the Cognitive and Affective Neuorscience Lab (Tor Wager, PI) and our collaborators, and also includes some products from other groups shared publically or by permission from the creators. [Documentation is here](docs/README.md). +This repository contains pre-defined brain "signatures" (multivariate predictive patterns), atlases of local regions and networks, and masks and regions derived from published meta-analyses of neuroimaging data. It includes a fairly comprehensive set of such resources developed by the Cognitive and Affective Neuorscience Lab (Tor Wager, PI) and our collaborators, and also includes some products from other groups shared publicly or by permission from the creators. [Documentation is here](docs/README.md).

Overview of the Neuroimaging Pattern Masks repository, showing its seven collections: multivariate signatures, atlases and parcellations, meta-analysis maps, Neurosynth maps, individual study maps, spatial basis functions, and templates. @@ -11,7 +11,7 @@ This repository contains pre-defined brain "signatures" (multivariate predictive Some of these resources are used in other toolboxes, particularly Canlab Core Tools and the CANlab’s Help Examples and Batch Scripts repository. They are also very useful when doing interactive analysis with the CAN lab's object-oriented neuroimaging toolbox, Canlab Core Tools. The three types of brain maps included are: -- Pre-defined brain "signatures" (aka multivariate predictive patterns, brain biomarkers, or "neuromarkers") that can be applied to new individual participants to generate predictions and validate predictive models. Most CANlab signatures are publically available and can be downloaded here. A few, the Neurologic Pain Signature (NPS) and fibromyalgia-predictive patterns, are available for research use upon request (contact Prof. Tor Wager). +- Pre-defined brain "signatures" (aka multivariate predictive patterns, brain biomarkers, or "neuromarkers") that can be applied to new individual participants to generate predictions and validate predictive models. Most CANlab signatures are publicly available and can be downloaded here. A few, the Neurologic Pain Signature (NPS) and fibromyalgia-predictive patterns, are available for research use upon request (contact Prof. Tor Wager). - Atlases with pre-defined brain parcels (regions) and networks. This can reduce brain space to a smaller set of (hopefully) meaningful units of analysis. These are saved as Analyze (.img) or NIFTI (.nii) files, and also as "atlas"-type objects, an object type defined in Canlab Core Tools that facilitates working with brain atlases. diff --git a/spatial_basis_functions/hcp_91k/README b/spatial_basis_functions/hcp_91k/README index 8032b35..9519e45 100644 --- a/spatial_basis_functions/hcp_91k/README +++ b/spatial_basis_functions/hcp_91k/README @@ -22,7 +22,7 @@ representation of subcortical structures. Each voxel defines a node, and all voxels are connected to any voxel with which they share a surface, edge or corner, so called 26-connectivity. Edge weights are also inverse euclidean distance. This was implemented manually using networkx in python. The graph -laplacian was then computed manally and its eigenfunctions were used to define +laplacian was then computed manually and its eigenfunctions were used to define the eigenmodes of subcortical structures. Alternative graph representations include 6 and 18-connectivity as well as tetrahedral representations like those used by Pang et al. (2023) Nature. It's unclear which is best, but Pang offers diff --git a/templates/transforms/ants/leads_dbs/README.txt b/templates/transforms/ants/leads_dbs/README.txt index 8c3d075..62f73cb 100644 --- a/templates/transforms/ants/leads_dbs/README.txt +++ b/templates/transforms/ants/leads_dbs/README.txt @@ -5,10 +5,10 @@ For more details see here, https://www.lead-dbs.org/about-the-mni-spaces/ It seems that they map from MNI152NLin6Sym to MNI152NLin2009bSym, but what we need is a mapping -from MNI152NLin6Asym to MNI152NLin2009cAsym, the assymetric spaces. The leads_dbs transforms +from MNI152NLin6Asym to MNI152NLin2009cAsym, the asymmetric spaces. The leads_dbs transforms had the involvement of someone from MNI, and are likely to be better mappings that what I've managed to generate myself, so which mapping you use depends on what you think matters more: -a quality inter template matching, or for the templates in question to be the asymetric templates +a quality inter template matching, or for the templates in question to be the asymmetric templates rather than the symmetric templates. refer to ../../code for code that will help you convert this h5 data to fsl and spm formats diff --git a/templates/transforms/code/README.md b/templates/transforms/code/README.md index 568b2b5..df16837 100644 --- a/templates/transforms/code/README.md +++ b/templates/transforms/code/README.md @@ -4,7 +4,7 @@ Transformation matrices were computed by running the Colin27 template through fm enabled with MNI152NLin6Asym and MNI152NLin2009cAsym selected as output spaces. There are two versions of Colin27, one from 1998 and another that's higher resolution and includes T2 and PD images from 2008. They are not coregistered. This was run on the 1998 version. In theory it should be possible to obtain better transforms to MNI152NLin2009cAsym space in particular -by performing a rigid body transfrom from the 1998 to 2008 data and then running multimodal alignment of T1, T2 and PD data +by performing a rigid body transform from the 1998 to 2008 data and then running multimodal alignment of T1, T2 and PD data to the MNI152NLin2009cAsym equivalents, but this seemed like more work than it was worth. diff --git a/templates/transforms/code/apply_spm_warp.m b/templates/transforms/code/apply_spm_warp.m index 2c2f4f1..94096e3 100644 --- a/templates/transforms/code/apply_spm_warp.m +++ b/templates/transforms/code/apply_spm_warp.m @@ -15,7 +15,7 @@ function apply_spm_warp(mvg_img0, fxd_img0, pre_affine_mat, warp_img, post_affin % this function is designed to work with the prepared transformations, not arbitrary ones. % I couldn't find helpful documentation for SPM and can't make any guarantees that this will work with any other % transforms, but if you do want to adapt it have a look at the bb (bounding box) option below. This should be - % asigned dynamically based on the input and/or output image at the very least. + % assigned dynamically based on the input and/or output image at the very least. % % This script has only been tested for alignment of 3D templates to one another. diff --git a/templates/transforms/code/fmriprep_to_fsl.sh b/templates/transforms/code/fmriprep_to_fsl.sh index 64cd8a4..08af2ab 100644 --- a/templates/transforms/code/fmriprep_to_fsl.sh +++ b/templates/transforms/code/fmriprep_to_fsl.sh @@ -3,7 +3,7 @@ # fmriprep was used to align MNI152NLin6Asym 1mm T1 images (non-skull stripped) to the MNI152NLin2009cAsym:res-01 space, # which is 1mm space in templateFlow that fmriprep uses to pull its templates. This produced ants format *.h5 files which # are composite affine/warp files. -# these files were created by converting ants *.h5 files into fsl formated files using code like this: +# these files were created by converting ants *.h5 files into fsl formatted files using code like this: srcRoot=../ants/ diff --git a/templates/transforms/code/fsl_to_fmriprep.sh b/templates/transforms/code/fsl_to_fmriprep.sh index 2b8a0c4..f2c679f 100644 --- a/templates/transforms/code/fsl_to_fmriprep.sh +++ b/templates/transforms/code/fsl_to_fmriprep.sh @@ -3,7 +3,7 @@ # fmriprep was used to align MNI152NLin6Asym 1mm T1 images (non-skull stripped) to the MNI152NLin2009cAsym:res-01 space, # which is 1mm space in templateFlow that fmriprep uses to pull its templates. This produced ants format *.h5 files which # are composite affine/warp files. -# these files were created by converting ants *.h5 files into fsl formated files using code like this: +# these files were created by converting ants *.h5 files into fsl formatted files using code like this: # this is my fmriprep output. Copies of the necessary files are also in the sister directory of this one, "ants" srcRoot=../ants/ diff --git a/templates/transforms/code/subctx_alignment.sh b/templates/transforms/code/subctx_alignment.sh index 5a44e17..d87f0c5 100644 --- a/templates/transforms/code/subctx_alignment.sh +++ b/templates/transforms/code/subctx_alignment.sh @@ -7,7 +7,7 @@ RES_DIR=$(readlink -f $ROOT/_resources) NPM_DIR=/dartfs-hpc/rc/lab/C/CANlab/modules/Neuroimaging_Pattern_Masks #make subcortical mask -# we doubly dilate it so that it encompases the subcortex in the refernece space in spite of whatever +# we doubly dilate it so that it encompasses the subcortex in the reference space in spite of whatever # minor misalignment we may have between the two spaces RES_DIR=$(readlink -f ../../_resources); diff --git a/templates/transforms/download_warpfield.m b/templates/transforms/download_warpfield.m index 60244ec..785507d 100644 --- a/templates/transforms/download_warpfield.m +++ b/templates/transforms/download_warpfield.m @@ -1,5 +1,5 @@ function download_warpfield(from, to, format) - % from and to should be formated as MNI152NLin6Asym or something standard like that. Options atm are + % from and to should be formatted as MNI152NLin6Asym or something standard like that. Options atm are % MNI152NLin6Asym % MNI152NLin2009cAsym % format should be ants, spm or fsl