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Modelling Cortical Laminar Connectivity in the Macaque Brain
- Source :
- Neuroinformatics. 20:559-573
- Publication Year :
- 2021
- Publisher :
- Springer Science and Business Media LLC, 2021.
-
Abstract
- In 1991, Felleman and Van Essen published their seminal study regarding hierarchical processing in the primate cerebral cortex. Their work encompassed a widescale analysis of connections reported through tracing between 35 regions in the macaque visual cortex, extending from cortical regions to the laminar level. In this work, we revisit laminar-level connectivity in the macaque brain using a whole-brain MRI-based approach. We use multi-modal ex-vivo MRI imaging of the macaque brain in both white and grey matter, which are then integrated via a simple model of laminar connectivity. This model uses a granularity-based approach to define a set of rules that expands cortical connections to the laminar level. Different fiber tracking routines are then examined in order to explore the ability of our model to infer laminar connectivity. The network of macaque cortical laminar connectivity resulting from the chosen routine is then validated in the visual cortex by comparison to findings from Felleman and Van Essen with an 83% accuracy level. By using a more comprehensive definition of the cortex that addresses its heterogenous laminar composition, we can explore a new avenue of structural connectivity on the laminar level.
- Subjects :
- Connectomics
genetic structures
Mri imaging
Computer science
Grey matter
Macaque
White matter
Neuroimaging
biology.animal
Cortex (anatomy)
medicine
Animals
Visual Cortex
Cerebral Cortex
Brain Mapping
biology
General Neuroscience
Primate cerebral cortex
Brain
Laminar flow
Magnetic Resonance Imaging
eye diseases
Cortex (botany)
medicine.anatomical_structure
Visual cortex
Macaca
Neuroscience
Software
Information Systems
Subjects
Details
- ISSN :
- 15590089 and 15392791
- Volume :
- 20
- Database :
- OpenAIRE
- Journal :
- Neuroinformatics
- Accession number :
- edsair.doi.dedup.....1d8c949d5d71a4e9d56db27d1e3e85af