Blue Brain Atlas Pipeline

Table of Contents

• Introduction
• Installation
  • Git repository
  • Singularity image on BB5
  • Docker image
• Run the pipeline
  • Running the reference Atlas pipeline
  • Customize a pipeline rule
  • Useful Snakemake options
• Blue Brain Atlas Pipeline
  • Rules and modules
  • Configuration
  • Additional information
• Appendix
  • Placement hints data catalog json format
• Acknowledgment

Introduction

The Blue Brain Atlas Pipeline (BBAP) is a set of processing modules that generate new data such as:

• Annotation volume, brain region hierarchy, direction vectors, orientations and placement hints for selected brain regions,
• Cell density volumes for several cell types,
• CellComposition summary of the brain regions.

To view the command for creating the Atlas as it is pushed to Nexus and consumed by OBP (the "reference" Atlas), see the below section Running the Reference Atlas Pipeline.

Installation

The Blue Brain Atlas Pipeline (BBAP) can be installed in three different ways:

• via this Git repository,
• via a Singularity image (recommended),
• via a Docker image.

For computation time reason and ease of installation, it is recommended to run the pipeline on the BB5 cluster via the Singularity image described hereafter. You can log in to the cluster with
ssh -l <your-Gaspar-username> bbpv1.epfl.ch
and your Gaspar password, or via the OpenOnDemand service.

Once the installation step is completed, go to Run the pipeline for the instructions to run the pipeline.

Git repository

The BBAP can be installed directly from the setup.py file available in this repository:

1. git clone https://github.com/BlueBrain/bbp-atlas-pipeline.git
2. pip install blue_brain_atlas_pipeline/
3. cd blue_brain_atlas_pipeline

Dependencies

Each package run as part of the pipeline is considered a pipeline dependency:

• token-fetch
• nexusforge
• bba-datafetch
• atlas-direction-vectors
• atlas-splitter
• atlas-placement-hints
• atlas-densities
• parcellationexport
• bba-data-integrity-check
• bba-data-push

On BB5, most packages are available also as modules:

module load unstable \
snakemake \
py-token-fetch \
py-nexusforge \
py-bba-datafetch \
py-atlas-building-tools \
py-bba-webexporter \
py-data-integrity-check \
py-bba-data-push

Or they can be installed following the ‘Installation’ section in their Confluence documentation page.

Now you can go to Run the pipeline for the instructions to run the pipeline.

Singularity image on BB5

A Singularity image (created from the Docker image) is available on BB5 in:
/gpfs/bbp.cscs.ch/data/project/proj84/atlas_singularity_images/

The folder contains

• blue_brain_atlas_pipeline_dev.sif: development image regularly updated,
• blue_brain_atlas_pipeline_<tag>.sif: production image corresponding to a repository tag (such as v0.5.2).

One can spawn the corresponding container with (example with dev)

1. module load unstable singularityce
2. singularity shell /gpfs/bbp.cscs.ch/data/project/proj84/atlas_singularity_images/blue_brain_atlas_pipeline_dev.sif
   and run the following commands to copy the pipeline files in a path (e.g $HOME) where snakemake can write:
3. cp -r /pipeline/blue_brain_atlas_pipeline $HOME
4. cd blue_brain_atlas_pipeline

Now you can go to Run the pipeline for the instructions to run the pipeline.

Docker image

A Docker image containing all the pipeline dependencies is available in the Git registry:
bbpgitlab.epfl.ch:5050/dke/apps/blue_brain_atlas_pipeline:<tag>
where <tag> = dev or a repository tag.

It can be pulled and run with

1. docker login bbpgitlab.epfl.ch:5050 -u <your-Gaspar-username> -p <your-Gaspar-password>
2. docker pull bbpgitlab.epfl.ch:5050/dke/apps/blue_brain_atlas_pipeline:<tag>
3. docker run -it bbpgitlab.epfl.ch:5050/dke/apps/blue_brain_atlas_pipeline:<tag> bash
4. cd blue_brain_atlas_pipeline

or converted into an Apptainer image with
apptainer pull --docker-login docker://bbpgitlab.epfl.ch:5050/dke/apps/blue_brain_atlas_pipeline:<tag>

A benchmark of the resources to provision as required by the different pipeline steps is available here.

Now you can go to Run the pipeline for the instructions to run the pipeline.

Run the pipeline

Once the pipeline environment is installed, from the root directory execute
export PYTHONPATH=.:$PYTHONPATH
and the general command to run the pipeline is available:

bbp-atlas  --target-rule <target_rule>  --snakemake-options '<options>'

where

• <target_rule> represents the target action to execute.
• <options> represents the snakemake options.
  A set of most common options is available here. The option --cores <number_of_cores> is mandatory unless the --dryrun option is used, and must be provided as last option.

Note: If running multicore on a BB5 node, the step transplant_mtypes_densities_from_probability_map may exceed the available memory and cause a node failure. Therefore, it is recommended to use a maximum of 70 cores.

A benchmark of the resources required by the different pipeline steps is available here.

Running the reference Atlas pipeline

The command that is used to run the version of the Blue Brain Atlas Pipeline that is pushed to Nexus and then used for OBP is as follows:

bbp-atlas  --target-rule push_atlas_datasets  --user-config-path customize_pipeline/user_config.json  --snakemake-options '--config NEXUS_REGISTRATION=True  --cores all'

Note that unless you have special permissions, the push_... rules are expected to fail because only some users have write access to Nexus.

The main entities generated by the pipeline are stored under the paths and names defined in the config file located at $HOME/blue_brain_atlas_pipeline/rules_config_dir_templates/push_dataset_config_template.yaml.

AtlasRelease

The following command:

  bbp-atlas  --target-rule push_atlas_release  --snakemake-options '--config NEXUS_REGISTRATION=False  --cores 1'

will generate (locally, without registering in Nexus) the following AtlasRelease (see the prod AtlasRelease entity) datasets:

• parcellationVolume: annotation volume nrrd file generated at the location defined in the config under GeneratedDatasetPath.VolumetricFile.annotation_ccfv3_l23split_barrelsplit
• parcellationOntology: brain region hierarchy generated at the location defined in the config under HierarchyJson.hierarchy_ccfv3_l23split_barrelsplit
• directionVector: direction vector volume generated at the location defined in the config under GeneratedDatasetPath.VolumetricFile.direction_vectors_ccfv3
• cellOrientationField: orientation field volume generated at the location defined in the config under GeneratedDatasetPath.VolumetricFile.cell_orientations
• hemisphereVolume: orientation field volume generated at the location defined in the config under GeneratedDatasetPath.VolumetricFile.hemispheres
• placementHintsDataCatalog: json catalog of placement hints volumes generated at the location WORKING_DIR/ph_catalog_distribution.json This catalog has the format described in the Appendix and groups the placement hints by regions and layers. The set of actual placement hints nrrd files are generated at the location defined in the config under GeneratedDatasetPath.VolumetricFile.placement_hints

CellComposition

The following command:

  bbp-atlas  --target-rule push_cellComposition  --snakemake-options '--config NEXUS_REGISTRATION=False  --cores 1'

will generate (locally, without registering in Nexus) the following CellComposition (see the prod CellComposition entity) datasets:

• cellCompositionVolume: json file generated at the location WORKING_DIR/cellCompositionVolume_payload.json, containing the ids of selected ME-type density nrrd files registered in Nexus, grouped by M-type and E-type.
  The whole set of ME-type densities is generated at the location defined in the config under GeneratedDatasetPath.VolumetricFile.mtypes_densities_probability_map_transplant
• cellCompositionSummary: json file generated at the location WORKING_DIR/cellCompositionSummary_payload.json, containing the values of the ME-type densities in the cellCompositionVolume, grouped by regions

---

NOTE for versions < v1.0.0
The selected ME-type densities that enter the CellCompositionVolume are those having a layer in their Nexus Resource property brainLocation, plus the two
Generic{Inhibitory,Excitatory}NeuronMType-Generic{Inhibitory,Excitatory}NeuronEType.

For an ME-type density Resource to be registered by the pipeline with such a layer property, the Nexus Ontology Class of its M-type must have the hasLayerLocationPhenotype attribute:

res = forge.resolve("<M-type label>", scope="ontology", target="CellType", strategy="EXACT_MATCH")
res_layer = res.hasLayerLocationPhenotype

---

Miscellanea

The rules in the previous commands trigger many intermediate dependent rules as described here.

The pipeline consumes a configuration file described here, by default named config.yaml and located in the directory from which the pipeline is run.
A specific config file can be provided via the --configfile option:

bbp-atlas  --target-rule <target_rule>  --snakemake-options '--configfile <config_file_path>'

NOTE
To run the pipeline skipping the generation of datasets already available (in case a previous run failed at an intermediate step for instance), the option --rerun-trigger mtime can be used as in the following command:

bbp-atlas  --target-rule <target_rule>  --snakemake-options '--rerun-trigger mtime  --cores 1'

Such an option allows to skip the execution of the pipeline steps whose output files exist and have a modification time (mtime) more recent than any of their input files.

---

Customize a pipeline rule

It is possible to customize a pipeline rule that generates a (set of) volumetric file (.nrrd) in order to change the values of a specific region of the volume (and leave the rest of the volume unchanged). The customization happens via the configuration file customize_pipeline/user_config.json with the following structure:

• rule: name of the rule to customize from the default pipeline;
• brainRegion: ID of the brain region to customize;
• CLI:
  • command: CLI to execute in order to produce the volumetric file with the desired values for the brain region of interest;
  • args: CLI arguments that can reference variables between curly brackets (see below);
• output_dir: path of the folder where the volumetric file(s) is generated by the CLI;
• container: URL of the Docker image to use in order to spawn a container where the CLI will be executed. This parameter is optional: if not provided, the CLI will be executed in the same environment of the default pipeline (in such a case, the user must ensure that the provided CLI is defined therein).

Note: the Snakemake option --use-singularity must be provided for the configuration parameter container to be considered.

The CLI args can reference one or more variables which points to files generated by pipeline rules executed before the rule to customize. The list of variables is available in customize_pipeline/available_vars.yaml.

Filename convention

The user must ensure that the files generated by the provided CLI have the same names as the files generated by the rule to customize.
For example, the rule direction_vectors_placeholder_ccfv3 in the sample configuration generates one output file direction_vectors_ccfv3.nrrd.
The placement_hints rule generates seven volumetric files: [PH]y.nrrd and [PH]layer_n.nrrd where n = 1, ..., 6. The mapping between each nrrd file and the layer it refers to for each region is available in this dictionary, which the user needs to extend with the "region acronym": {"layer ID", "layer label"} of its customized region. A layer is considered associated to a region if the corresponding layer ID appears in the regions-to-layers mapping for that region or for at least one of that region's offspring.

Customized pipeline

Once the configuration file is ready, the customized pipeline can be run with the following command:

bbp-atlas  --target-rule <target_rule>  --user-config-path customize_pipeline/user_config.json  --snakemake-options '<options>'

When a rule is customized as described above, the pipeline will run

1. the default rule to generate the default output file(s),
2. the CLI provided in the configuration file to produce the corresponding region-specific output file(s),
3. a merge step to override the specific region in the default file(s) (step 1) with the values of that region from the region-specific file(s) (step 2).

Integration

In case a user wants to request the integration of the customized version of a dataset:

1. Open a Merge Request (MR) in this repository including the updated user_config.json and any additional input metadata required.
2. The MR is then reviewed and, if approved, a new Atlas pipeline dev image is produced accordingly.
3. The new pipeline is run and the new datasets are registered in Nexus staging for wider tests.
4. When the new version of the datasets is validated, a new tag of the Atlas pipeline is cut and the corresponding image is used to register the datasets in Nexus prod.

Metadata

Some pipeline steps require metadata as input, which are fetched from Nexus.
Currently, the files available in the metadata directory are automatically synchronized with their Nexus versions.
If you want to update/add one metadata file, make sure to update/add also the corresponding documentation file in the metadata/docs directory, keeping the current naming convention (probability_map_*{.csv,.txt}).

Direction-vectors and placement-hints

If you want to add creation of your region's direction-vectors, placement-hints, or other NRRD files that need to be merged with files containing data from other regions, you should add them using the instructions in Customize a pipeline rule into customize_pipeline/user_config.json instead of the snakefile directly.

Useful Snakemake options

Snakemake being a command-line tool, it comes with a multitude of optional arguments to execute, debug, and visualize workflows. Here is a selection of the most used:

• --cores <number_of_cores>, -c <number_of_cores> → Specify the number of cores snakemake can use.
• --dry-run, -n → Perform a dry run (execute nothing but print the list of rules that would be executed).
• --rerun-trigger mtime → Use only the modification time (mtime) of the existing output files to determine which rules to execute.
• --forcerun <some_rule> → Force a given rule to be re-executed (overwrite the output if it already exists).
• --list, -l → Print a list of all the available rules from the snakefile.

Every Snakemake command line argument is listed and described in the Snakemake official documentation page.

Blue Brain Atlas Pipeline

Its workflow consists of the following steps:

1. Fetch the required datasets from Nexus. These input data consist of the original AIBS ccfv3 brain parcellation, the AIBS Mouse CCF Atlas regions hierarchy file and a series of Nissl and ISH volumes as described in the documentation page Allen Mouse CCF Compatible Data.
2. The fetched datasets are then fed to the Snakemake rules, and under the hood consumed by atlas modules to generate products.
3. Each product can (optionally) be pushed into Nexus with a set of metadata automatically filled up and be visualised in the Blue Brain Atlas.

This workflow is illustrated on the following diagram containing the directed acyclic graph (DAG) of the Snakemake rules of the BBAP:

A more detailed DAG listing the input and output files for each step is available here.

Rules and modules

In this document, a “module” is a CLI encapsulated inside one of the components of the pipeline. Such component is called a “rule”. This terminology comes from SnakeMake, where a “rule” can leverage one or more modules and where a module can be used by one of more rules, usually using a different set of arguments.
You can find more information on rules in the SnakeMake documentation.

To only visualize the command that a given rule will execute without running it, one can use the --dryrun option as in the following command:

  bbp-atlas  --target-rule <target_rule>  --snakemake-options '--dryrun'

The documentation of each command is available in the corresponding pipeline dependency.

Profiling

A detailed profiling of the most resource-intensive rules (sorted by execution order) is available in the following table, corresponding to a single core of an Intel Xeon Gold 6140 CPU (BB5 node).
Some rules can exploit multiple cores, in which case a second entry for such rules appears in the table along with the number of cores ("--cores n") used for the profiling.

The total multicore wall clock time required by the two final rules push_atlas_release (which depends on direction vectors, orientation field, placement hints) and push_cellComposition (which depends on all the density generation rules) is respectively 1 h (with an RSS peak of 10 GB) and 4 h (with an RSS peak of 8 GB).

Rule name	wall clock time [s]	wall clock time [h:m:s]	max RSS [MB]	max VMS [MB]	max USS [MB]	max PSS [MB]	I/O in [B]	I/O out [B]	average CPU load [%]	CPU time [s]
direction_vectors_default_ccfv3	352.1527	0:05:52	3345.09	4503.41	3309.88	3321.13	0.07	0.00	98.53	347.43
direction_vectors_isocortex_ccfv3	376.2279	0:06:16	5438.86	6049.21	5401.71	5412.96	0.00	0.00	92.23	347.29
orientation_field	248.3647	0:04:08	8423.66	9010.55	8388.29	8399.54	0.00	0.00	91.73	228.17
split_isocortex_layer_23_ccfv3	147.5079	0:02:27	1945.17	2977.68	1866.67	1877.88	0.83	0.00	92.70	137.05
create_leaves_only_hierarchy_annotation_ccfv3	46.3272	0:00:46	5897.06	6542.66	5818.63	5830.39	0.04	0.00	36.30	17.06
split_barrel_ccfv3_l23split	141.6368	0:02:21	715.49	2420.59	679.82	691.35	0.06	0.00	96.49	137.23
validate_annotation_v3	4.8913	0:00:04	908.17	1749.66	865.54	876.86	0.03	0.00	71.09	3.81
placement_hints	924.7421	0:15:24	6600.64	7225.64	6524.41	6537.06	7.41	0.00	99.36	919.29
create_hemispheres_ccfv3	6.3128	0:00:06	547.75	1233.48	514.01	524.86	0.00	0.00	68.63	4.67
export_brain_region	27482.2978	7:38:02	2649.79	3214.40	3497.71	3509.16	0.40	0.00	99.68	27395.00
export_brain_region (--cores 70)	1162.2325	0:19:22	143397.54	193202.21	115383.76	115749.06	0.04	0.00	4307.04	50067.62
combine_v2_annotations	17.5364	0:00:17	1030.33	1581.99	1002.29	1015.24	1.09	0.00	85.00	15.44
direction_vectors_isocortex_ccfv2	299.9846	0:04:59	5483.91	6050.02	5454.11	5467.01	0.04	0.00	95.15	285.99
split_isocortex_layer_23_ccfv2	153.1193	0:02:33	1999.78	2979.68	1937.86	1950.75	0.00	0.00	88.49	135.96
create_leaves_only_hierarchy_annotation_ccfv2	31.2190	0:00:31	6027.33	6836.57	6276.27	6289.61	0.04	0.00	51.64	16.60
split_barrel_ccfv2_l23split	112.8031	0:01:52	737.26	2421.75	706.36	719.25	0.06	0.00	93.59	106.04
validate_annotation_v2	2.3923	0:00:02	1116.59	1736.83	1076.46	1089.53	0.03	0.00	66.48	2.12
cell_density_correctednissl	55.1323	0:00:55	2867.18	3418.92	2839.32	2852.28	0.00	0.00	82.24	45.88
validate_cell_density	4.8925	0:00:04	1252.33	2355.65	1767.52	1780.59	0.00	0.00	76.66	4.27
combine_markers	555.3407	0:09:15	5147.12	5697.71	5119.06	5132.01	0.00	0.00	94.58	525.61
glia_cell_densities_correctednissl	221.8078	0:03:41	7373.72	8061.20	7298.36	7311.31	0.00	0.00	87.87	195.19
validate_neuron_glia_cell_densities	17.4026	0:00:17	3845.63	4698.86	4109.74	4122.81	0.00	0.00	89.61	16.00
average_densities_correctednissl	2886.3829	0:48:06	3889.09	5342.21	3841.21	3854.18	0.00	0.00	99.22	2864.22
fit_average_densities_correctednissl	2185.0088	0:36:25	5605.01	7106.13	5410.92	5423.89	0.00	0.00	99.52	2174.67
inhibitory_neuron_densities_linprog_correctednissl	2859.1158	0:47:39	4799.59	18207.76	4771.89	4784.86	0.00	0.00	99.16	2834.94
compute_lamp5_density	53.7274	0:00:53	3652.39	4176.77	4964.55	4977.10	0.00	0.00	83.84	45.36
create_mtypes_densities_from_probability_map	31310.7357	8:41:50	31178.52	32289.28	31150.59	31163.56	0.00	0.00	99.69	31209.90
create_mtypes_densities_from_probability_map (--cores 70)	6276.3961	1:44:36	394077.36	2285360.80	63690.27	82778.46	1.04	0.00	5992.08	376431.41
excitatory_split	222.6994	0:03:42	3437.98	3989.99	3410.12	3423.07	0.00	0.00	87.40	195.18
create_cellCompositionVolume_payload	414.9835	0:06:54	0	0	0	0	0.00	0.00	0.25	0
create_cellCompositionSummary_payload	1205.1099	0:20:05	2414.04	5000.99	2230.68	2308.75	3.73	0.00	84.15	1030.68
create_cellCompositionSummary_payload (--cores 70)	206.0507	0:03:26	21817.64	67112.53	12911.70	13023.87	0.00	0.00	635.94	1314.64

Fetch rules

The rules starting with "fetch_" are used to download a given file from Nexus.
The IDs of the corresponding Nexus Resource (containing a description of the file to fetch) are listed in the nexus_ids.json (the explicit link between a fetch rule and the corresponding Resource ID lays in the nexus_id parameter of the rule definition in the snakefile).
Note: the rule "fetch_genes_correctednissl" is not linked to a specific Resource, it's used just to trigger the execution of a set of single "fetch_gene_" rules needed by the "fit-average-densities" step.

In order to run the pipeline with a different version of a fetched file, one can just execute the corresponding fetch rule and subsequently replace the downloaded file with the desired version, by keeping the same name of the originally fetched file.
The --rerun-trigger mtime option may be useful here.

Configuration

The configuration of the pipeline is provided in the config.yaml file. The most important variables that a user can customize are:

• WORKING_DIR: the output directory of the pipeline files,
• NEXUS_IDS_FILE: the json file containing the Ids of the Nexus Resources to fetch,
• FORGE_CONFIG: the configuration file (yaml) to instantiate nexus-forge,
• NEW_ATLAS: boolean flag to trigger the creation of a brand-new atlas release,
• RESOLUTION: resolution (in μm) of the input volumetric files to be consumed by the pipeline (default to 25),
• NEXUS_REGISTRATION: boolean flag to trigger data registration in Nexus
• RESOURCE_TAG: string to use as tag of the data registered in Nexus
• IS_PROD_ENV: boolean flag to indicate whether the target Nexus environment is production or not (staging),
• NEXUS_DESTINATION_ORG/NEXUS_DESTINATION_PROJ: Nexus organization/project where register the pipeline products,
• DISPLAY_HELP: boolean flag to display every rule of the snakefile with its descriptions.

It is possible to override the config variables at runtime using the snakemake argument --config:
--config <VAR_NAME>=<VALUE>

Additional information

The release notes are available here.

More information about The Blue Brain Atlas Pipeline (BBAP) are available in its confluence documentation.
This space contains several documentation pages describing:
The Allen Mouse CCF Compatible Data : https://bbpteam.epfl.ch/project/spaces/display/BBKG/Allen+Mouse+CCF+Compatible+Data
The Atlas Modules : https://bbpteam.epfl.ch/project/spaces/display/BBKG/Atlas+Modules

Appendix

Brain region layers

Some brain areas have a subdivision in layers.
The mapping adopted in the BBP between a brain region and the layers it belongs to is provided in this dictionary, where the keys are brain region IDs and the layers are identified with Uberon classes.
One layer - "Neocortex layer 6a" - is not present in the Uberon ontology and is defined as follows:

<https://bbp.epfl.ch/ontologies/core/bmo/neocortex_layer_6a> rdf:type owl:Class ;
    rdfs:subClassOf <http://purl.obolibrary.org/obo/UBERON_0002301> ;
    rdfs:label "L6a"^^xsd:string ;
    <http://www.w3.org/2004/02/skos/core#definition> "Neocortex layer 6a."^^xsd:string ;
    <http://www.w3.org/2004/02/skos/core#altLabel> "layer 6a"^^xsd:string ;
    <http://www.w3.org/2004/02/skos/core#altLabel> "neocortex layer 6a"^^xsd:string  ;
    <http://www.w3.org/2004/02/skos/core#prefLabel> "L6a"^^xsd:string ;
    <http://www.w3.org/2004/02/skos/core#notation> "L6a"^^xsd:string .

Placement hints data catalog json format

{
  "placementHints": [
    {
      "@id": "https://bbp.epfl.ch/data/bbp/atlas/f1049c1b-f1af-4d33-acd9-099f05c56bbf",
      "_rev": 13,
      "distribution": {
        "atLocation": {
          "location": "file:///gpfs/bbp.cscs.ch/data/project/proj39/nexus/bbp/atlas/9/b/1/3/3/7/7/9/%5BPH%5Dlayer_1.nrrd"
        },
        "name": "[PH]layer_1.nrrd"
      },
      "regions": {
        "Isocortex": {
          "@id": "http://api.brain-map.org/api/v2/data/Structure/315",
          "hasLeafRegionPart": [
            "PL1",
            "..."
          ],
          "layer": {
            "@id": "http://purl.obolibrary.org/obo/UBERON_0005390",
            "label": "L1"
          }
        },
        "Hippocampal formation": {
          "@id": "http://api.brain-map.org/api/v2/data/Structure/1089",
          "hasLeafRegionPart": [
            "CA1sp",
            "..."
          ],
          "layer": {
            "@id": "http://purl.obolibrary.org/obo/UBERON_0002313",
            "label": "SP"
          }
        },
        "...": {}
      }
    },
    {
      "@id": "https://bbp.epfl.ch/data/bbp/atlas/74ba22b1-39ee-486d-ab3c-cb960d006a5d",
      "_rev": 13,
      "distribution": {
        "atLocation": {
          "location": "file:///gpfs/bbp.cscs.ch/data/project/proj39/nexus/bbp/atlas/a/9/3/0/d/e/a/8/%5BPH%5Dlayer_2.nrrd"
        },
        "name": "[PH]layer_2.nrrd"
      },
      "regions": {
        "Isocortex": {
          "@id": "http://api.brain-map.org/api/v2/data/Structure/315",
          "hasLeafRegionPart": [
            "AUDp2",
            "..."
          ],
          "layer": {
            "@id": "http://purl.obolibrary.org/obo/UBERON_0005391",
            "label": "L2"
          }
        },
        "Hippocampal formation": {
          "@id": "http://api.brain-map.org/api/v2/data/Structure/1089",
          "hasLeafRegionPart": [
            "CA1so",
            "..."
          ],
          "layer": {
            "@id": "http://purl.obolibrary.org/obo/UBERON_0005371",
            "label": "SO"
          }
        },
        "...": {}
      }
    },
   "..."
  ],
  "voxelDistanceToRegionBottom": {
    "@id": "https://bbp.epfl.ch/data/bbp/atlas/59a2bca3-d8b6-43b1-870e-a0c19a020175",
    "_rev": 13,
    "distribution": {
      "atLocation": {
        "location": "file:///gpfs/bbp.cscs.ch/data/project/proj39/nexus/bbp/atlas/3/9/e/b/6/d/8/b/%5BPH%5Dy.nrrd"
      },
      "name": "[PH]y.nrrd"
    }
  }
}

Funding & Acknowledgment

The development of this software was supported by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL), from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology.

Copyright © 2020-2024 Blue Brain Project/EPFL