The BigBrainWarp toolbox for integration of BigBrain 3D histology with multimodal neuroimaging
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Canada
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, Germany
- Department of Computer Science and Software Engineering, Concordia University, Canada
- Wellcome Trust Centre for Neuroimaging, University College London, United Kingdom
- Brain and Mind Institute, University of Western Ontario, Canada
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Germany
- Institute of Neuroscience and Medicine (INM-7), Forschungszentrum Jülich, Germany
- Department of Medical Biophysics, Schulich School of Medicine & Dentistry, University of Western Ontario, Canada
Figures
Figure 1
Magnification of cytoarchitecture using BigBrain, from (A) whole brain 3D reconstruction (taken on https://atlases.ebrains.eu/viewer) to (B) a histological section at 20 µm resolution (available from bigbrainproject.org) to (C) an intracortical staining profile.
The profile represents variations in cellular density and size across cortical depths. Distinctive features of laminar architecture are often observable i.e., a layer IV peak. Note, the presented …
Figure 2
Evaluating BigBrain–MRI transformations.
(A) Volume-based transformations. (i) Jacobian determinant of deformation field shown with a sagittal slice and stratified by lobe. Subcortical+ includes the shape priors (as described in Materials …
Figure 3
Overview of spaces and transformations included within BigBrainWarp.
(A) The flow chart illustrates the extant transformation procedures that are wrapped in by the bigbrainwarp function. (B) Arrows indicate the transformations possible using the bigbrainwarp …
Figure 4
Intrinsic functional connectivity of the iso-to-allocortical axis of the mesiotemporal lobe.
(A) i. BigBrain surface models of the isocortex and hippocampal subfields are projected on a 40 µm resolution coronal slice of BigBrain. (ii–iii) The continuous surface model bridges the inner …
Figure 5
Concordance of imaging-derived effects with histological gradients.
(A) Four stages of histological gradient construction. (i) Vertex-wise staining intensity profiles (dotted lines) are averaged within parcels (solid lines). Colours represent different parcels. (ii) …
Figure 6
Prediction of functional network by cytoarchitecture.
(A) Surface-based transformation of 17-network functional atlas to the BigBrain surface, operationalised with BigBrainWarp, allows staining intensity profiles to be stratified by functional network. …
Appendix 1—figure 1
Influence of sampling parameters on staining intensity profiles.
(A) Line plots show how the shape of an exemplar profile is changed by various sampling parameters. Far left is the raw profile constructed with 50 surfaces. Centre left are raw profiles constructed …
Tables
Table 1
Surface constructions for BigBrain.
| Surfaces | Utility | Reference |
|---|---|---|
| Grey and white | Initialisation and visualisation | Lewis et al., 2014 |
| Layer 1/2 and layer 4 | Boundary conditions | Wagstyl et al., 2018a |
| Equivolumetric | Staining intensity profiles | Waehnert et al., 2014 |
| Deep learning laminar | Laminar thickness | Wagstyl et al., 2020 |
| Hippocampal | Initialisation and visualisation | DeKraker et al., 2019 |
| Mesiotemporal confluence | Initialisation and visualisation | Paquola et al., 2020a |
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Note: Initialisation broadly refers to an input for feature generation, for example creation of staining intensity profiles or surface transformations.
Table 2
Input parameters for the bigbrainwarp function.
| Parameter | Description | Conditions | Options |
|---|---|---|---|
| in_space | Space of input data | Required | bigbrain, bigbrainsym, icbm, fsaverage, fs_LR |
| out_space | Space of output data | Required | bigbrain, bigbrainsym, icbm, fsaverage, fs_LR |
| wd | Path to working directory | Required | |
| desc | Prefix for output files | Required | |
| in_vol | Full path to input data, whole brain volume. | Requires either in_vol, or in_lh and in_rh | Permitted formats: mnc, nii or nii.gz |
| ih_lh | Full path to input data, left hemisphere surface | Permitted formats: label.gii, annot, shape.gii, curv or txt | |
| ih_rh | Full path to input data, right hemisphere surface | ||
| interp | Interpolation method | Required for in_vol. Optional for txt input. Not permitted for other surface inputs. | For in_vol, can be trilinear (default), tricubic, nearest or sinc.For txt, can be linear or nearest |
| out_type | Specifies whether output in surface or volume space | Optional function for bigbrain, bigbrainsym and icbm output. Defaults to the same type as the input. | surface, volume |
| out_res | Resolution of output volume | Optional where out_type is volume. Default is 1 | Value provided in mm |
| out_den | Density of output mesh | Optional where out_type is surface. Default is 164 | For fs_LR out_space, 164 or 32 |
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Note: the options are subject to change as the toolbox is expanded. Updates will be posted on https://bigbrainwarp.readthedocs.io/en/latest/pages/updates.html.
Table 3
BigBrainWarp contents.
| Data | Definition | Original space | Transformed spaces |
|---|---|---|---|
| Profiles | Staining intensity profiles, sampled at each vertex and across 50 equivolumetric surfaces | BigBrain | fsaverage, fs_LR (164 k and 32 k) |
| White | Grey/white matter boundary | BigBrain, fsaverage, fs_LR | |
| Sphere | Spherical representation of surface mesh | BigBrain, fsaverage, fs_LR | |
| Confluence | Continuous surface that includes isocortex and allocortex (hippocampus) from Paquola et al., 2020a | BigBrain | |
| Histological gradients | First two eigenvectors of cytoarchitectural differentiation derived from BigBrain | BigBrain | fsaverage, fs_LR (164 k and 32 k), icbm |
| Microstructural gradients | First two eigenvector of microstructural differentiation derived from quantitative in-vivo T1 imaging | fsaverage | BigBrain, |
| Functional gradients | First three eigenvectors of functional differentiation derived from rs-fMRI | fsaverage | BigBrain |
| Seven functional networks | Seven functional networks from Yeo et al., 2011 | fsaverage | BigBrain |
| 17 Functional networks | 17 Functional networks from Yeo et al., 2011 | fsaverage | BigBrain, icbm |
| Layer thickness | Layer thicknesses estimated from Wagstyl et al., 2020 | BigBrain | fsaverage, fs_LR (164 k and 32 k) |
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Note: Datasets Are Named According to BIDS and Align with Recommendations From TemplateFlow (Ciric et al., 2021).
