Economo et al., 2018 - A DataJoint example

This notebook presents data and results associated with the following papers:

Michael N. Economo, Sarada Viswanathan, Bosiljka Tasic, Erhan Bas, Johan Winnubst, Vilas Menon, Lucas T. Graybuck, Thuc Nghi Nguyen, Kimberly A. Smith, Zizhen Yao, Lihua Wang, Charles R. Gerfen, Jayaram Chandrashekar, Hongkui Zeng, Loren L. Looger & Karel Svoboda. "Distinct descending motor cortex pathways and their roles in movement" (2018) Nature (https://doi.org/10.1038/s41586-018-0642-9)

The study identified two types of pyramidal track (PT) neuron in the mouse motor cortex, referred to as PT_upper and PT_lower. Further investigation using extracellular recordings in the anterior lateral motor cortex (ALM) during a delay-response task, the study revealed specialized roles for the two neuron types: i) PT_upper neurons are preferentially involed in motor planning, ii) PT_lower neurons are more involved in movement execution.

A DataJoint data pipeline has been constructed for this study, with the presented data ingested into this pipeline. This notebook demonstrates the queries, processing, and reproduction of several figures from the paper. From the pipeline, export capability to NWB 2.0 format is also available.

About the data

The dataset comprises of extracellular recordings and spike sorted results of the mouse's ALM during a delay-response task. The behavior data includes detailed description of the trial structure (e.g. trial timing, trial instruction, trial response, etc.) and the timing of the lick events (e.g. lick left, lick right onset).

Original data is publically available at: doi: 10.25378/janelia.7007846

The data in original MATLAB format (.mat) have been ingested into a DataJoint data pipeline presented below.

Data are also exported into NWB 2.0 format. See NWB export code here

Design DataJoint data pipeline

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.

Conversion to NWB 2.0

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

Demonstration of the data pipeline

Data queries and usages are demonstrated in this Jupyter Notebook, where several figures from the paper are reproduced.

Instruction to execute this pipeline

Download original data

After cloning this repository, download the original data. Once downloaded, you should find a folder named 7007846 containing multiple subfolders. In which, the electrophysiology data is at .../7007846/Ephys/Code/ProcessedData

Setup "dj_local_conf.json"

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": "economo2018_",
       "data_directory": ".../7007846/Ephys/Code/ProcessedData"
   }
}

Note: make sure to provide the correct database hostname, username and password. Then specify the path to the downloaded data directories (fill in the ... portion).

Ingest data into the pipeline

On a new terminal, navigate to the root of your project directory, then execute the following command:

python scripts/ingestion.py

Mission accomplished!

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

Export to NWB 2.0

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