Chemoarchitectural studies of the rat hypothalamus and zona incerta.Chemopleth 1.0, a downloadable interactive Brain Maps spatial database of five co-visualizable neurochemical systems, with novel feature- and grid-based mapping tools
Navarro, V. I.; Arnal, A.; Peru, E.; Balivada, S.; Toccoli, A. R.; Sotelo, D.; Fuentes, O.; Khan, A. M.
Show abstract
The hypothalamus and zona incerta of the brown rat (Rattus norvegicus), a model organism important for translational neuroscience research, contain diverse neuronal populations essential for survival, but how these populations are structurally organized as systems remains elusive. With the advent of novel gene-editing technologies and artificial intelligence, there is an apparent research need for high-spatial-resolution maps of rat hypothalamic neurochemical cell types to aid in their gene-directed targeting, to validate their expression in transgenic lines, or to supply precious ground-truth training data for machine learning algorithms. Here, we present Chemopleth 1.0 [available at: https://doi.org/10.5281/zenodo.15788189], a chemoarchitecture database for the rat hypothalamus (HY) and zona incerta (ZI), which features downloadable interactive maps featuring the census distributions of five immunoreactive neurochemical systems: (1) vasopressin (as detected from its gene co-product, copeptin); (2) neuronal nitric oxide synthase (EC 1.14.13.39); (3) hypocretin 1/orexin A; (4) melanin-concentrating hormone; and (5) alpha-melanocyte-stimulating hormone. These maps are formatted for the widely used Brain Maps 4.0 (BM4.0) open-access rat brain atlas (RRID:SCR_017314). Importantly, this dataset retains atlas stereotaxic coordinates that facilitate the precise targeting of the cell bodies and/or axonal fibers of these neurochemical systems, thereby potentially serving to streamline delivery of viral vectors for gene-directed manipulations. The maps are presented together with novel open-access tools to visualize the data, including a new workflow to quantify cell positions and fiber densities for BM4.0. The workflow produces "heat maps" of neurochemical distributions from multiple subjects: 1) isopleth maps that represent consensus distributions independent of underlying atlas boundary conditions, and 2) choropleth maps that provide distribution differences based on cytoarchitectonic boundaries. The database files, generated using the Adobe(R) Illustrator(R) vector graphics environment, can also be opened using the free vector graphics editor, Inkscape. We also introduce a refined grid-based coordinate system for this dataset, register it with previously published spatial data for the HY and ZI, and introduce novel grid-based annotation of experimental observations. This database provides critical spatial targeting information for these neurochemical systems unavailable from mRNA-based maps and allows readers to place their own datasets in register with them. It also provides a space for the continued buildout of a community-driven atlas-based spatial model of rat hypothalamic chemoarchitecture, allowing experimental observations from multiple laboratories to be registered to a common spatial framework.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- SMART: An open source extension of WholeBrain for iDISCO+ LSFM intact mouse brain registration and segmentation 94%
- AxoDen: An Algorithm for the Automated Quantification of Axonal Density in defined Brain Regions 93%
- The Complex Hodological Architecture Of The Macaque Dorsal Intraparietal Areas As Emerging From Neural Tracers And Dw-MRI Tractography 92%
Similar papers in this journal
- Software and pipelines for registration and analyses of rodent brain image data in reference atlas space 94%
- A Smart Region-Growing algorithm for single-neuron segmentation from confocal and 2-photon datasets 92%
- Developing a Multiscale Neural Connectivity Knowledgebase of the Autonomic Nervous System 92%
"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.