Data pipeline for Guo, Inagaki et al., 2017 from Svoboda Lab.
Data consists of both extracellular and whole-cell recording at the ALM and thalamus regions. The recordings were performed on mice performing tactile discrimination task with and without unilateral photoinhibition.
This study revealed the thalamic neurons exhibited selective persistent delay activity that predicted movement direction, which is similar to the anterior lateral motor cortex (ALM). This data pipeline contains the ephys and behavior data, and their corresponding analysis that replicate the figures in the paper.
This project presents a DataJoint pipeline design for the data accompanying the paper
Zengcai V. Guo, Hidehiko K. Inagaki, Kayvon Daie, Shaul Druckmann, Charles R. Gerfen & Karel Svoboda. "Maintenance of persistent activity in a frontal thalamocortical loop" (2017) Nature
https://dx.doi.org/10.1038/nature22324
The data: https://dx.doi.org/10.6080/K03F4MH (Not available)
This repository contains the Python 3.7 code of the DataJoint data pipeline design for this dataset, as well as scripts for data ingestions and visualization
This repository contains the Python 3.7 code to convert the DataJoint pipeline into NWB 2.0 format (See https://neurodatawithoutborders.github.io/) Each NWB file represents one recording session. The conversion script can be found here
Data queries and usages are demonstrated in this Jupyter Notebook, where several figures from the paper are reproduced.
After cloning this repository, download the original data. Once downloaded, you should find 2 folders containing
intracellular and extracellular data, whole_cell_nwb2.0
and extracellular
respectively.
dj_local_conf.json
is a configuration file for DataJoint, which minimally specifies the
database connection information, as well as several other optional configurations.
Create a new dj_local_conf.json
at the root of your project directory (where you have this repository cloned),
with the following format:
{
"database.host": "database_hostname",
"database.user": "your_username_here",
"database.password": "your_password_here",
"database.port": 3306,
"database.reconnect": true,
"loglevel": "INFO",
"safemode": true,
"custom": {
"database.prefix": "gi2017_",
"extracellular_directory": "/path_to_downloaded_data/extracellular/datafiles",
"intracellular_directory": "/path_to_downloaded_data/whole_cell_nwb2.0"
}
}
Note: make sure to provide the correct database hostname, username and password. Then specify the path to the downloaded data directories for intracellular and extracellular data.
On a new terminal, navigate to the root of your project directory, then execute the following commands:
python scripts/ingest_nwb_extracellular.py
python scripts/ingest_nwb_wholecell.py
python scripts/populate.py
You now have a functional pipeline up and running, with data fully ingested. You can explore the data, starting with the provided demo notebook.
From your project root, launch jupyter notebook:
jupyter notebook
Data from this DataJoint pipeline can be exported in NWB 2.0 format using this datajoint_to_nwb.py script.
To perform this export for all ingested data, specify the export location (e.g. ./data/exported_nwb2.0
), execute this command from the project root:
python scripts/datajoint_to_nwb.py ./data/exported_nwb2.0