Neuromaps-mouse: integrating multiscale, multimodal annotations across the mouse brain
Poster No:
1843
Submission Type:
Abstract Submission
Authors:
Zhen-Qi Liu1, Vincent Bazinet1, Justine Hansen1, Silvia Gini2, Jean-Charles Mariani2, Alessandro Gozzi2, Bratislav Misic1
Institutions:
1Montreal Neurological Institute, Montreal, Canada, 2Istituto Italiano di Tecnologia, Rovereto, Italy
First Author:
Co-Author(s):
Introduction:
Lab mouse (Mus musculus), the most commonly used mammalian model organism, resembles close homology to human in many aspects, and allows understanding the brain organization at a much smaller and feasible scale. The mouse brain offers indispensable opportunities for developing hypotheses and testing findings, from microscopic cellular composition to macroscopic neural networks. However, datasets describing the mouse brain are intrinsically multiscale and multimodal, scattered across several scientific subfields (imaging, genetics, cellular), forming high barriers preventing practical application. Here we present neuromaps-mouse, a new addition to the neuromaps ecosystem (Markello & Hansen et al., 2022). Neuromaps-mouse features (1) a hand-curated collection of mouse brain annotations, (2) flexible functions for working with atlas ontology, and (3) convenient visualization utilities.
Methods:
In neuromaps-mouse, we curated a large collection of reference annotations of the mouse brain in the form of regional maps, tabular dataframes, and connectivity matrices from a range of modalities covering anatomical, microstructural, functional, and molecular aspects. We implemented functions for standardizing & harmonizing different sets of regions. Finally, we provide tools for statistical inference and visualization. The package will be openly available at https://github.com/netneurolab/neuromaps-mouse. The workflow and technical details are described below.
Results:
The template and atlas conventions in neuromaps-mouse are mainly based on the Allen Mouse Brain Common Coordinate Framework (CCFv3; Wang & Ding & Li et al., 2020), but can be easily extended and customized to new atlases. We first curate datasets by aggregating them from databases, supplementary materials, and by directly getting in touch with the investigators. Then we semi-manually map the original region labels to the standard CCFv3 regions, correcting duplicates, ontology updates, and errors. To facilitate comparisons between annotations in different region sets, we implement a function to match the sets of regions by tracing the regions in the hierarchical ontology tree.
For neuromaps-mouse, we no longer limit datasets into scalar-valued brain maps as in neuromaps due to the variety of datasets available for the mouse brain. We conceptualized specialized categories, including scalar (for regional maps), tabular (for a panel of scalar maps, such as regional gene expressions), and matrix (for inter-regional connectivity data). Tabular-formatted datasets are accompanied by a metadata table for columnar variables. To efficiently visualize the annotations, we implemented a set of matplotlib-based plotting functions from the ground up, allowing streamlined production of publication-ready figures. The functions support rendering brain sections colored by regional values in both orthographic and lightbox views.They also support flexible custom options, allow asymmetric values across hemispheres, and greatly reduce the common dependency issues. To complete the workflow of annotation comparison, we implemented a spatial autocorrelation-preserving null model based on volumetric Moran's spectral randomization. We are also working with collaborators on the possibility of extending surface-based "spin tests" to the mouse brain. Finally, to help with ingressing raw imaging files, we also include a module for registration and parcellation from volumetric data.
For neuromaps-mouse, we no longer limit datasets into scalar-valued brain maps as in neuromaps due to the variety of datasets available for the mouse brain. We conceptualized specialized categories, including scalar (for regional maps), tabular (for a panel of scalar maps, such as regional gene expressions), and matrix (for inter-regional connectivity data). Tabular-formatted datasets are accompanied by a metadata table for columnar variables. To efficiently visualize the annotations, we implemented a set of matplotlib-based plotting functions from the ground up, allowing streamlined production of publication-ready figures. The functions support rendering brain sections colored by regional values in both orthographic and lightbox views.They also support flexible custom options, allow asymmetric values across hemispheres, and greatly reduce the common dependency issues. To complete the workflow of annotation comparison, we implemented a spatial autocorrelation-preserving null model based on volumetric Moran's spectral randomization. We are also working with collaborators on the possibility of extending surface-based "spin tests" to the mouse brain. Finally, to help with ingressing raw imaging files, we also include a module for registration and parcellation from volumetric data.
Conclusions:
The neuromaps-mouse toolbox offers unified access to a wide range of multiscale, multimodal mouse brain datasets from the published literature. It also allows transparent mapping between regions, rigorous statistical comparison, and flexible visualization.
We hope neuromaps-mouse will grow into a standardized workflow for aggregating and contextualizing result for the mouse brain, as well as enable effectively comparative and translational studies of brain organization.
We hope neuromaps-mouse will grow into a standardized workflow for aggregating and contextualizing result for the mouse brain, as well as enable effectively comparative and translational studies of brain organization.
Modeling and Analysis Methods:
Methods Development 2
Neuroinformatics and Data Sharing:
Workflows 1
Informatics Other
Novel Imaging Acquisition Methods:
Imaging Methods Other
Keywords:
Other - brain networks; cross-species; open-source development
1|2Indicates the priority used for review
·Figure 1
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
Provide references using APA citation style.
Wang, Q., Ding, S. L., Li, Y., Royall, J., Feng, D., Lesnar, P., ... & Ng, L. (2020). The Allen mouse brain common coordinate framework: a 3D reference atlas. Cell, 181(4), 936-953.