HAL Id: hal-02791117
https://hal.inrae.fr/hal-02791117
Submitted on 5 Jun 2020
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Surface-based cortical parcellation and atlas creation of the sheep brain
Scott Love, Marine Siwiaszczyk, Marie Auge, Christophe Destrieux, Frédéric Andersson, Elodie Chaillou
To cite this version:
Scott Love, Marine Siwiaszczyk, Marie Auge, Christophe Destrieux, Frédéric Andersson, et al..
Surface-based cortical parcellation and atlas creation of the sheep brain. Workshop on Animal Popu- lation Imaging, Jun 2018, Paris, France. 1p., 2018. �hal-02791117�
1. Dale, AM., et al (1999). Cortical surface-based analysis. I. Segmentation and surface reconstruction. NeuroImage 9(2):179-94.
2. Fischl, B., et al (1999). Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system NeuroImage 9(2):195-207.
3. Destrieux, C., et al (2010). Automatic parcellation cortical gyri and sulci using standard anatomical nomenclature. NeuroImage 53: 1-15.
4. Landacre, F.L., 1930. The Major and Minor Sulci of the Brain of the Sheep. The Ohio Journal of Science 30, 36–51.
Surface-based cortical parcellation of the sheep brain
Scott Love 1,2 , Marine Siwiaszczyk 1,2 , Marie Auge 2 , Christophe Destrieux 2 , Frédéric Andersson 2 , Elodie Chaillou 1
1
PRC, INRA, CNRS, IFCE, Université de Tours, Nouzilly, France
2
UMR 1253, iBrain, Université de Tours, Inserm, Tours, France
Introduction
● The sheep model is a compelling yet underused model for understanding structure and function of the cortex.
● Brain atlases have largely focused on subcortical regions in sheep, very little information is available about the cortical parcellation.
● Neuroanatomical labeling facilitates the generalizability and comparability of experimental results across studies and laboratories.
● The current work aimed to produce a surface-based labeling of the entire sheep cortex.
Atlas of the Sheep Neocortex
Methods
● High-resolution (0.6 mm
3) T1-weighted MR images were collected from 20, two-year old Ile de France ewes using a 3T Siemens Magnetom Verio.
● Standard processing streams of the Freesurfer software suite (http://freesurfer.net, Dale 1999; Fischl 1999) that produce automatically labeled cortical surfaces of the human cortex (Destrieux 2010) were modified to enable semi-automatic labelling of the sheep cortex.
● Anatomical nomenclature is based on previous literature (e.g., Landacre 1930) and the Nomina Anatomica Veterinaria (5
thEdition).
Right
Left
Right
Left Right
Left 1) Gyrus cinguli
2) Gyrus compositus 3) Gyrus cruciatus
4) Gyrus ectomarginalis caudalis 5) Gyrus ectomarginalis medius 6) Gyrus ectomarginalis rostralis 7) Gyrus ectosylvius
8) Insula
9) Gyrus marginalis 10) Gyrus proreus
11) Gyrus sylvius caudalis 12) Gyrus sylvius rostralis 13) Sulcus ansatus
14) Sulcus coronalis
15) Sulcus cruciatus 16) Sulcus diagonalis
17) Sulcus ectomarginalis 18) Sulcus endomarginalis 19) Sulcus genualis
20) Sulcus marginalis
21) Sulcus occipitotemporalis
22) Sulcus olfactorius 23) Sulcus posticus 24) Sulcus presylvius
25) Sulcus rostralis internus 26) Sulcus splenialis
27) Sulcus suprasylvius 28) Sulcus sylvius
Right
Left
Right
Left Right
Left
Superior Lateral Medial
14 13
15
16
17
19 20
23
24
26 27
28
7
25 15
27
5 4 3 9
2 6
7 10
8 2
12 6 11
4 5 9
1 3
4 5
12 11
18
5) Bias correction 1
6) Normalize white-matter 7) Extract the cortex
8) Bias correction 2
9) Segment white-matter 10) Separate hemispheres 1) Image denoising
2) Talairach-like orientation 3) Neocortex mask
4) Conform the image
● 1mm
3voxel size
● 256
3matrix size
11) Surface reconstruction:
● White-matter surface
● Inflated white-matter
● Pial surface
● Local mean curvature 12) Manual parcellation
Conclusions
● A computerized labeling of the entire sheep neocortex containing 28 regions (12 gyri, 16 sulci) per hemisphere is provided.
● We also aim to produce a probabilistic parcellation that will optimize automatic labelling of the entire sheep brain.
● As well as providing an atlas of the sheep cortex it is hoped that the atlas, along with the pipelines used to create it, will also facilitate neuroimaging research of this valuable large animal model.
Project NeuroGeo (00091714)
Funded by Region Centre Val de Loire
