Whole-brain mapping in adult zebrafish and identification of the functional brain network underlying the novel tank test
Data files
Feb 08, 2025 version files 851.96 KB
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15min_correlation_matrix.csv
250.43 KB
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Bench_protocol_for_whole_brain_activity_mapping.pdf
251.80 KB
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Brain_region_names_and_ontological_levels.csv
21.69 KB
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cfos_density_by_region_and_timepoint.csv
170.64 KB
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Extended_table_5-2_15min_vs_HT_cell_counts.csv
15.32 KB
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Graph_15min.cys
72.58 KB
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Graph_15min.graphml
65.19 KB
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README.md
4.30 KB
Abstract
Zebrafish have gained prominence as a model organism in neuroscience over the past several decades, generating key insight into the development and functioning of the vertebrate brain. However, techniques for whole brain mapping in adult stage zebrafish are lacking. Here, we describe a pipeline built using open-source tools for whole-brain activity mapping in adult zebrafish. Our pipeline combines advances in histology, microscopy, and machine learning to capture cfos activity across the entirety of the brain. Following tissue clearing, whole brain images are captured using light-sheet microscopy and registered to the recently created adult zebrafish brain atlas (AZBA) for automated segmentation. By way of example, we used our pipeline to measure brain activity after zebrafish were subject to the novel tank test, one of the most widely used behaviors in adult zebrafish. Cfos levels peaked 15 minutes following behavior and several regions, including those containing serotoninergic and dopaminergic neurons, were active during exploration. Finally, we generated a novel tank test functional brain network. This revealed that several regions of the subpallium form a cohesive sub-network during exploration. Functional interconnections between the subpallium and other regions appear to be mediated primarily by ventral nucleus of the ventral telencephalon (Vv), the olfactory bulb, and the anterior part of the parvocellular preoptic nucleus (PPa). Taken together, our pipeline enables whole-brain activity mapping in adult zebrafish while providing insight into neural basis for the novel tank test.
https://doi.org/10.5061/dryad.k3j9kd5js
Description of the data and file structure
See methods of associated paper for details on the creation of the dataset
Files include:
*cfos_density_byregionand_timepoint.csv
Associated with figure 4 of the paper. This is the cfos cell density for individual fish for each brain region at each time point.
Data fields
- fish.id: internal reference for the individual fish
- Sex: Sex of the animals (male or female)
- Time point: Which time point the animals were euthanized, either off the housing rack (rack), after remaining in their hometank (Home tank) or time after exposure to the novel tank test (5, 15, 30, 60, or 120 minutes)
- Brain regions: Abbreviations for brain regions; full region names are in Extended table 5-1 of the published manuscript and taken from the adult zebrafish brain atlas (azba.wayne.edu; Kenney et al, 2021) and are available in this repository in "Brain_region_names_and_ontological_levels.csv"
Extended_table_5-2_15min_vs_HT_cell_counts.csv
Associated with figure 5 of the paper.
This is the average counts per brain region for the home tank and fifteen minute groups, associated p-values from t-tests for each brain region, the effect size (Cohen's D) and the fdr corrected p-value.
Data fields
IDX: Region index number (from azba.wayne.edu; Kenney et al, 2021), can also be found in "Brain_region_names_and_ontological_levels.csv"
Abbreviation: Abbreviation for brain region
Name: Full name of brain region
Home.tank.mean: Average cfos density for each brain region from the hometank fish
fifteen.minute.mean: Average cfos density for each brain region from the group of fish euthanized fifteen minutes after the novel tank test
fold.change: The fold increase in cfos density in the 15 minute group versus the hometank group
p.value: The p-value from the independent samples t-test comparing the hometank to the 15 minute group
cohens.D: The effect size (Cohen's D) for the comparison between the hometank and the 15 minute group
fdr: False discovery rate (FDR) corrected p-values
15min_correlation_matrix.csv
Associated with figure 6 of the paper.
Each row and column represent a brain region (abbreviated). Each entry is the correlated cfos activity between those brain regions across animals for the group of fish euthanized 15 minutes after the novel tank test.
Graph_15min.graphml and Graph_15min.cys
Associated with figure 7 of the paper. The 15 minute correlation with the top 2.5% of correlations to generate a network. In these network files, the nodes represent brain regions and the edges between nodes represent the presence of above threshold correlations. The .cys file is a Cytoscape session file that can be used to look at the network using Cytoscape (https://cytoscape.org/).
Brain_region_names_and_ontological_levels.csv
List of brain regions, abbreviations, index numbers, and ontological levels. These correspond to the adult zebrafish brain atlas (AZBA) nomenclautre (azba.wayne.edu; Kenney et al , 2021).
Data fields
IDX: Region index number
Abbreviation: The abbreviation for the brain region
Name: The full name of the brain region
Ont.1-6: The ontological levels for each region from most coarse/highest (1) to finest (2)
Display: The ontological level used for display in figure 6 of the associated paper.
Bench_protocol_for_whole_brain_activity_mapping.docx
Detailed bench protocol for performing whole-brain activity mapping in adult zebrafish using iDISCO, CellFinder, and ANTs (advanced normalization tools) for registration to the adult zebrafish brain atlas.
References
Kenney, J.W., Steadman, P.E., Young, O., Shi, M.T., Polanco, M., Dubaishi, S., Covert, K., Mueller, T. and Frankland, P.W., 2021. A 3D adult zebrafish brain atlas (AZBA) for the digital age. Elife, 10, p.e69988.
Code/software
remove_overlaps.py
Python code for removing overlaps after CellFinder:
Kenney_Data_setup_cellfinder_registration.R
R code for combining the output of segmentation from ANTS and cell counts from CellFinder:
Please see methods in the associated paper
