BlueRecording

BlueRecording is used to produce an input file (also refered to as an electrodes file or a weights file) for the calculation of extracellular signals in neurodamus.

This branch provides code that produces an electrodes file compatible with the SONATA format. For scripts to produce an electrode file compatible with the old BlueConfig format, see the non-sonata branch of this repo.

User instructions

System requirements

Our documentation and examples assume that you are running BlueRecording on a Linux system with slurm and the spack package manager. BlueRecording has not been tested on any other system.

Dependencies

Bluerecording requires mpi4py, h5py, and hdf5 built with MPI support. These should be installed with spack, as per the instructions in the following section. Bluerecording also depends on several other python packages, which are automatically installed with setuptools when Bluerecording is installed.

Running neural simulations to generate inputs to BlueRecording (and to calculate extracellular signals using weights files produced by BlueRecording) requires neurodamus, the BBP Simulation Control application for Neuron, neurodamus-models, which contains mod files for neural mechanisms. These should be installed in a separate spack environment

Installation

BlueRecording

We recommend using a combimation of a spack environment and a virtulenvironment to install BlueRecording and its dependencies

First create a spack environment, which is used to satisfy the h5py+mpi and mpi4py dependencies

spack env create bluerecording-dev
spack env activate -p bluerecording-dev
spack install --add py-h5py+mpi 
spack install --add py-mpi4py

Then install bluerecording in a virtualenv:

git clone https://github.com/BlueBrain/BlueRecording.git
cd BlueRecording
python -m venv bluerecording-dev
source bluerecording-dev/bin/activate
pip install -e .

Neurodamus

We recommend installing Neurodamus and the Neurodamus-Models mechanisms in a separate spack environment.

First, run

git clone https://github.com/BlueBrain/spack.git
. spack/share/spack/setup-env.sh
cd spack
git checkout tharayil/neurodamus-updates
spack env create neurodamus
spack env activate -p neurodamus
spack install --add neurodamus-models@develop+coreneuron

Then, create modules for neurodamus and its dependencies. To do so, make sure that py-neurodamus,neurodamus-models,and neuron are included in your ~/.spack/modules.yaml file. An example is provided here. Then, run

spack module tcl refresh
module use $SPACK_INSTALL_PREFIX/modules/linux-rhel7-skylake

The second of the two lines above must be run every time you begin a new terminal session.

Neurodamus-models expects that you have modules available on your system for python/3.11.6, intel-oneapi-mkl/2023.2.0, and hpe-mpi/2.27.p1.hmpt. The launch scripts provided in the examples folder assume that these modules are in an archive called unstable

Testing

Please note that some of the unit tests rely on the following configuration files tests/data/simulation_config.json, tests/data/configuration/circuit_config.json, examples/compare-to-reference-solutions/data/simulation/simulation_config.json, and examples/compare-to-reference-solutions/data/simulation/configuration/circuit_config.json. These configuration files contain relative paths to files, which must be changed to absolute paths before running BlueRecording. You must also download the folder atlas.zip from our Zenodo repository (10.5281/zenodo.10927050) and unzip it into the folder examples/data/atlas.

After following the instructions above, run pytest tests

Steps to produce electrode files

1. Produce a compartment report from a target including the cells that will be used in the extracellular recording. Complete documentation for this calculation can be found here.

2. Create a csv file containing information about the electrodes. Each row of the file contains information about one electrode contact. The format of the csv file is defined as follows:

   • The header is name,x,y,z,layer,region,type
   • The first column is the name of the electrode contact. It is either a string or an integer
   • The second through fourth columns are the x, y, and z coordinates of the contact in Cartesian space. They are floats.
   • The fifth column is the cortical layer in which the electrode is located. It is a string in the format LN, where N is an integer.
     • If the electrode is outside of the brain, the value in the column is the strign Outside
     • If the electrode is in a region without laminar oraginzation, the value in the column is the string NA
   • The sixth column is the brain region in which the electrode is located. It is a string.
     • If the electrode is outside the brain, the value in the column is the strong Outside
   • The seventh column is the calculation method used to determine the compartment weights. Supported values are PointSource, LineSource, Reciprocity, and DipoleReciprocity. The PointSource and LineSource methods assume that the neurons are in an infinite homogeneous medium. They should be used only for recordings made inside the brain tissue. If they are used, the tissue conductivity should be provided in step 6. Reciprocity and DipoleReciprocity assign the compartment weights based on a lead-field calculated in step 3. These should be used for EEG or ECoG recordings. In general, we recommend using the Reciprocity method.

   The folder examples/makeCsvFiles contains an example python script that will generate a csv file for a Neuropixels probe.

3. If the Reciprocity or DipoleReciprocity methods are used, you must calculate a lead-field. The lead field is the potential field (for the reciprocity method) or the E-field (for the dipole reciprocity method) produced in the neural tissue by running a current of 1 nA between the recording electrode and the reference electrode. BlueRecording assumes that this field is calculated using the Sim4Lfie finite element solver and exported as an h5 file. Other calculation methods are possible, asusming the field is exported in the same format.

4. Run the function bluerecording.getPositions.getPositions(path_to_simconfig, neurons_per_file, files_per_folder, path_to_positions_folder,replace_axons=True). This loads the compartment report produced in step 1, and will create folders containing pickle files listing the (x,y,z) position of each segment in each cell in the target. The argument neurons_per_file refers to the number of neurons whose positions are stored in each pickle file, and files_per_folder refers to the number of such files in each folder (which should be adjusted based on your filesystem)

5. Run the function bluerecording.writeH5_prelim.initializeH5File(path_to_simconfig,outputfile,electrode_csv). This loads the compartment report produced in step 1 and the csv file produced in step 2, and will create the electrodes file, with the name outputfile, populating all coefficients with 1s.

6. Run the function bluerecording.writeH5.writeH5File(path_to_simconfig,path_to_segment_position_folder,outputfile,neurons_per_file,files_per_folder,sigma=0.277,path_to_fields=None). This loads the position files created in step 4 and the electrode file created in step 4, populates the electrode file with the correct coefficients. Here sigma is the conductivity of the tissue, and must be provided if one of the analytic methods is used. path_to_fields is the path to the finite element output calculated in step 3, which must be provided if the reciprocity-based methods are used. This two-step procedure is used because the calculation of the LFP coefficients for large neural populatons is not feasible without parallelization, but MPI cannot be used when H5 files are created, since parallel writing of variable length strings is not supported.

Running an extracellular recording simulation

Once the electrode file has been generated, it can be used in a Neurodamus simulation that includes extracellular recording. Instructions for this step are found here

Examples

See here

Contribution Guidelines

Here

Citation

If you use this software, we kindly ask you to cite the following publication: Tharayil et al. BlueRecording: A Pipeline for efficient calculation of extracellular recordings in large-scale neural circuit models. bioRxiv, (2024)

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 (c) 2023 Blue Brain Project/EPFL