Toolbox contents¶
Datasets¶
| Data | Overview | Available templates |
|---|---|---|
| profiles | cell-staining intensities sampled at each vertex and across 50 equivolumetric surfaces. This is stored as a single vector to reduce the size. Reshape to 50 rows for use. | bigbrain, fsaverage, fs_LR (164k and 32k) |
| white | grey/white matter boundary | bigbrain (histological and sym), fsaverage, fs_LR (164k and 32k) |
| sphere | spherical representation of surface mesh | bigbrain, fsaverage, fs_LR (164k and 32k) |
| confluence | continuous surface that includes isocortex and allocortex (hippocampus) from Paquola et al., 2020. Only available for the right hemisphere. | bigbrain |
| Hist-G* | first two eigenvectors of cytoarchitectural differentiation derived from BigBrain, accounting for approximately 41.9 and 29.5% of variance, respectively. | bigbrain, fsaverage, fs_LR (164k and 32k), icbm |
| Micro-G* | first two eigenvector of microstructural differentiation derived from quantitative in-vivo T1 imaging, accounting for approximately 59.0 and 10.5% of variance, respectively. | bigbrain, fsaverage |
| Func-G* | first three eigenvectors of functional differentiation derived from rs-fMRI, accounting for approximately 12.9, 6.5 and 5.3% of variance, respectively. | bigbrain, fsaverage |
| Yeo2011_7Networks_N1000 | 7 functional clusters from Yeo & Krienen et al., 2011 | bigbrain |
| Yeo2011_17Networks_N1000 | 17 functional clusters from Yeo & Krienen et al., 2011 | bigbrain |
| layer*_thickness | Approximate layer thicknesses estimated from Wagstyl et al., 2020 | bigbrain, fsaverage, fs_LR |
Naming conventions¶
In an effort to standardise and simplify the naming of files across BigBrainWarp, we have adopted the BIDS-like structure of TemplateFlow https://www.templateflow.org/. This means that files likely have a different name than their source. The output of BigBrainWarp is automatically named according to this convention.
| Indicator | Description |
|---|---|
| tpl | Template of output data. Determined based on the required argument “out_space”. |
| hemi | L for left, R for right. Only included in surface-based transformations where data is separated by hemisphere. |
| den | Density of output data on mesh. Currently only specified for fs_LR, where 164k and 32k options are possible. Can be indicated using the out_den parameter |
| desc | General description of the data. Determined based on the required argument “desc”. In the “Datasets” table above, the desc is given in the first column. |
Data origins¶
BigBrainWarp depends upon collation of data from various sources. We’ll reference these sources throughout the documentation. Here is an overview of the data that was directly used in BigBrainWarp. In other words, this list encompasses data that was not generated specifically for BigBrainWarp.
| Source | Data | Hyperlink | Reference |
|---|---|---|---|
| BigBrain FTP 3D_Surfaces | BigBrain and BigBrainSym meshes | https://bigbrain-ftp.loris.ca/bigbrain-ftp/BigBrainRelease.2015/3D_Surfaces | Amunts et al., 2013 |
| BigBrain FTP BigBrainWarp_Support | Rotated meshes and MSM registration surfaces for BigBrain, fsaverage and fs_LR (32k) | https://bigbrain-ftp.loris.ca/bigbrain-ftp/BigBrainRelease.2015/BigBrainWarp_Support | Lewis et al., 2020 |
| “Accurate registration of the BigBrain dataset with the MNI PD25 and ICBM152 atlases” OSF | Volume-based transformation matrices and segmentation labels for BigBrain and ICBM. (Not stored in BigBrainWarp repository; automatically pulled with initialisation). | https://osf.io/xkqb3/ | Xiao et al., 2019 |
| BIC packages | Volume-based transformation matrices for BigBrainSym. (Not stored in BigBrainWarp repository; automatically pulled with initialisation). | https://packages.bic.mni.mcgill.ca/mni-models/PD25/ | Xiao et al., 2019 |
| Diedrichsen Lab Github | Inflated, sphere and reference sulcus surface maps for fs_LR 32k | https://github.com/DiedrichsenLab/fs_LR_32 | Van Essen et al., 2012 |
| WashU HCP pipelines Github | Reference sulcus map for fs_LR (164k) | https://github.com/Washington-University/HCPpipelines/tree/master/global/templates/standard_mesh_atlases | Van Essen et al., 2012 |
Scripts¶
The bigbrainwarp function calls a range of scripts that may also be helpful for independent use:
- af_dist.py: calculates distance between transformed and set anatomical fiducials
- bigbrain_to_fsaverage.sh: called by bigbrainwarp
- bigbrain_to_icbm.sh: called by bigbrainwarp
- bigbrainsurf_to_icbm.sh: called by bigbrainwarp
- compile_profiles.py: collates and saves out intensities into profiles
- demo_dockerbased.sh: key examples of transformations using the docker installation
- demo_gitbased.sh: walkthrough of the toolbox utilities using the github installation
- evaluate_warp.sh: estimates accuracy of warp based on anatomical fiducials and region overlaps
- fsaverage_to_bigbrain.sh: called by bigbrainwarp
- icbm_to_bigbrain.sh: called by bigbrainwarp
- icbm_to_bigbrainsurf.sh: called by bigbrainwarp
- init.sh: initialises the environment
- io_mesh.py: scripts from Surface Tools that help with loading .obj files
- nn_surface_indexing.mat: contains mesh decimation output
- obj2fs.sh: wrapper script to convert .obj surface meshes to a freesurfer style mesh (.pial), which can be loaded into Freeview for visualisation
- sample_intensity_profiles.sh: wrapper script for generating staining intensity profiles
- txt2curv.sh: wrapper script to convert .txt files to.curv, helpful for visualisation with Freesurfer