3D reconstruction of ultra-high resolution neurotransmitter receptor atlases in human and non-human primate brains
Funck, T.; Wagstyl, K.; Lepage, C.; Omidyeganeh, M.; Toussaint, P. J.; Amunts, K.; Thiel, A.; Palomero-Gallagher, N.; Evans, A. C.
Show abstract
Quantitative maps of neurotransmitter receptor densities are important tools for characterising the molecular organisation of the brain and key for understanding normal and pathologic brain function and behaviour. We describe a novel method for reconstructing 3-dimensional cortical maps for data sets consisting of multiple different types of 2-dimensional post-mortem histological sections, including autoradiographs acquired with different ligands, cell body and myelin stained sections, and which can be applied to data originating from different species. The accuracy of the reconstruction was quantified by calculating the Dice score between the reconstructed volumes versus their reference anatomic volume. The average Dice score was 0.91. We were therefore able to create atlases with multiple accurately reconstructed receptor maps for human and macaque brains as a proof-of-principle. Future application of our pipeline will allow for the creation of the first ever set of ultra-high resolution 3D atlases composed of 20 different maps of neurotransmitter binding sites in 3 complete human brains and in 4 hemispheres of 3 different macaque brains.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Tensor Image Registration Library: Automated Deformable Registration of Stand-Alone Histology Images to Whole-Brain Post-Mortem MRI Data 96%
- Automated joint skull-stripping and segmentation with Multi-Task U-Net in large mouse brain MRI databases 96%
- Fusion of quantitative susceptibility maps and T1-weighted images improve braintissue contrast in primates 95%
Similar papers in this journal
Similar papers in this journal
- Lesion aware automated processing pipeline for multimodal neuroimaging stroke data and The Virtual Brain (TVB) 95%
- OpenMAP-T1: A Rapid Deep Learning Approach to Parcellate 280 Anatomical Regions to Cover the Whole Brain 95%
- A framework for evaluating correspondence between brain images using anatomical fiducials 95%
Similar papers in this journal
- Longitudinal deformation based morphometry pipeline to study neuroanatomical differences in structural MRI based on SyN unbiased templates 96%
- QRATER: a collaborative and centralized imaging quality control web-based application. 95%
- Visual QC Protocol for FreeSurfer Cortical Parcellations from Anatomical MRI 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.