A central feature of theories of spatial navigation involves the representation of spatial relationships between objects in complex environments. The parietal cortex has long been linked to the processing of spatial visual information and recent evidence from single unit recording in rodents suggests a role for this region in encoding egocentric and world-centered frames. The rat parietal cortex can be subdivided into four distinct rostral-caudal and medial-lateral regions, which includes a zone previously characterized as secondary visual cortex. At present, very little is known regarding the relative connectivity of these parietal subdivisions. Thus, we set out to map the connectivity of the entire anterior-posterior and medial-lateral span of this region. To do this we used anterograde and retrograde tracers in conjunction with open source neuronal segmentation and tracer detection tools to generate whole brain connectivity maps of parietal inputs and outputs. Our present results show that inputs to the parietal cortex varied significantly along the medial-lateral, but not the rostral-caudal axis. Specifically, retrosplenial connectivity is greater medially, but connectivity with visual cortex, though generally sparse, is more significant laterally. Finally, based on connection density, the connectivity between parietal cortex and hippocampus is indirect and likely achieved largely via dysgranular retrosplenial cortex. Thus, similar to primates, the parietal cortex of rats exhibits a difference in connectivity along the medial-lateral axis, which may represent functionally distinct areas.
Background Understanding the neurobiological basis of cognition and behavior, and disruptions to these processes following injury and disease, requires a large-scale assessment of neural populations, and knowledge of their patterns of connectivity. New Method We present an analysis platform for large-scale investigation of functional and neuroanatomical connectivity in the rodents. Retrograde tracers were injected and in a subset of animals behavioral tests to drive immediate-early gene expression were administered. This approach allows users to perform whole-brain assessment of function and connection in a semi-automated quantitative manner. Brains were cut in the coronal plane, and an image of the block face was acquired. Wide-field fluorescent scans of whole sections were acquired and analyzed using Matlab software. Results The toolkit utilized open-source and custom platforms to accommodate a largely automated analysis pipeline in which neuronal boundaries are automatically segmented, the position of segmented neurons are co-registered with a corresponding image acquired during vibratome sectioning, and a 3-D representation of neural tracer (and other products) throughout the entire brain is generated. Comparison with Existing Methods Current whole brain connectivity measures primarily target mice and use anterograde tracers. Our focus on segmented units of interest (e.g., NeuN labeled neurons) and restricting measures to these units produces a flexible platform for a variety of whole brain analyses (measuring activation, connectivity, markers of disease, etc.). Conclusions This open-source toolkit allows an investigator to visualize and quantify whole brain data in 3-D, and additionally provides a framework that can be rapidly integrated with user-specific analyses and methodologies.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.