Database of marks of the sulci on the endocasts and brains obtained on the same sample of 75 volunteers

To achieve this, the automatic identification functionality of brain sulci within the software BrainVISA (Anatomist) was used as a reference. In Blender, each tube corresponding to a specific type of sulcus was selected, separated and isolated from the others so a color code could be applied to it. The endocast of each volunteer was reconstructed using the image obtained with an ultrashort time-to-echo (UTE) MRI sequence following our previously published experience (Labra et al., 2024).

The segmentation was made automatically using the Morphologist toolbox from BrainVISA (Cointepas et al., 2001; Rivière et al., 2009; Fischer et al., 2012). Nevertheless, the obtained models for 26 of the 75 specimens were not satisfactory because of the lack of contrast between the soft tissues and the bone and the difficulty encountered by the automatic process to reconstruct the precise interface. In those cases, we performed manual segmentation using avizo software.

This consisted of selecting an intermediate threshold value between the values observed for the bone and those of the tissues in contact inside the cranial vault. This setting had to be modified according to the variations observed for these different tissues in order to obtain as much as possible the precise characterization of the interface. Slightly less detail is visible on the surface of manually obtained endocasts compared to those obtained automatically.

Nevertheless, this allowed us to reconstruct all endocasts for a broad comparative analysis. In the same imaging session at the Brain Institute, a T1-MPRAGE image was acquired. It was used to segment the brain, to generate the pial surface mesh and to extract and automatically label the sulci (Labra et al., 2024).

Those procedures were also conducted using the Morphologist toolbox from BrainVISA (Cointepas et al., 2001; Fischer et al., 2012; Labra et al., 2024). In order to facilitate our determination, we used the automatically assigned sulcal labels as used by Morphologist (Perrot et al., 2011). This nomenclature was simplified to facilitate the work of description of those traits on endocasts where the diversity of the sulci does not appear in its completeness.

This allowed us to focus on the main imprints sought on endocasts, i.e. those which separate the different lobes, which border important functional areas or which have been regularly found to be visible on endocasts. The final step of the protocol was the identification and labelling of the sulci observed on the endocasts. To achieve this, the automatic identification functionality of brain sulci within the software BrainVISA (Anatomist) was used as a reference.

In Blender, each tube corresponding to a specific type of sulcus was selected, separated and isolated from the others so a color code could be applied to it. It corresponds to 75 zbrush files including marks on the endocast related to brain sulci, MNAS (for endocranial Marks Not Associated with Sulci) and sulci on the brain of the same sample of volunteers. The corresponding 3D data of the endocasts and brains are available in another online database (doi.org/10.48579/PRO/KZMMLM)