LP13: Activity in the brain network for dynamic manipulation of unstable objects is robust to acute tactile deafferentation: an fMRI study

Abstract

malized CT images were segmented to identify cranial bones using simple thresholding and morphological operations. A signed distance function was first generated for each CT dataset based on its segmented cranial bones. A shape model for cranial bones was then reconstructed by applying the principal component analysis (PCA) on the signed distance functions. The fontanels were identified as gaps between the cranial bones in the shape model. Using a probabilistic atlas, the intracranial tissues (brain and CSF) were segmented from the normalized and averaged MR images. The scalp was then obtained by removing the segmented tissues from the averaged MR images. Finally, the segmented tissues were surface rendered and used to generate a realistic head model. Results: Figure 1 shows the different compartments of the realistic neonatal head model including brain, CSF, scalp, cranial bones and fontanels (Fig. 1a) as well as their 3D reconstruction (Fig. 1b). Conclusion: In this paper we developed a method to create a realistic neonatal head model based on the co-registration of CT and MR images from neonates. The model included the geometry of brain, CSF, scalp, cranial bones and fontanels. With a specific conductivity value assigned to each compartment, the model can be used for EEG/MEG source localization in neonates.