Overview¶

The following notebook demonstrates how to load neural data for all imaging planes in one 2-photon imaging session into a single 'tidy' dataframe, make simple event-triggered plots, and do some basic analysis using scikit-learn.

This is designed to demonstrate a simple method for interacting with the visual behavior data. Many aspects of the dataset are not explored here.

Set up environment and import packages¶

We have built a package called mindscope_utilities which contains some useful convenience functions. The allenSDK is a dependency of this package and will be automatically installed when you install mindscope_utilities per the instrutions below.

We will first install mindscope_utilities into our colab environment by running the commands below. When this cell is complete, click on the RESTART RUNTIME button that appears at the end of the output. Note that running this cell will produce a long list of outputs and some error messages. Clicking RESTART RUNTIME at the end will resolve these issues.

You can minimize the cell after you are done to hide the output.

Next we will import packages we need later in the notebook¶

Load the session and experiment summary tables¶

The AllenSDK provides functionality for downloading tables that describe all sessions and experiments (individual imaging planes) in the Visual Behavior 2P dataset. We first download the data cache:

• Ophys_session_table contains metadata describing imaging sessions. If more than one plane was imaged during a session, one ophys session id will be associated multiple ophys experiment ids. Each ophys session id will also have a unique behavior session id.
• Behavior_session_table contains metadata describing behavioral sessions, which may or may not be during imaging. Behavior session ids that do not have ophys session ids were training sessions.
• Ophys_experiment_table contains metadata describing imaging experiments (aka imaging planes). When mesoscope is used, one ophys session may contain up to 8 unique experiments (two visual areas by four imaging depths). Some imaging planes may not be released due to quality control issues, thus each ophys session id is associated with anywhere from one to eight unique experiment ids. Ophys experiment ids are unique and do not repeat across sessions. To find the same imaging plane that was matched across multiple sessions, use the ophys_container_id column that can be found in both ophys_session_table and ophys_experiment_table.

Then we can access the session and experiment tables directly.

Note that a 'session' is a single behavioral session. Sessions that are performed on the mesoscope will have multiple (up to 8) 'experiments' associated with them, where an experiment is a distinct imaging plane.

We can then view the contents of the session table. Note that this contains a lot of useful metadata about each session. One of the columns, ophys_experiment_id provides a list of the experiments (aka imaging planes) that are associated with each session.

The experiment table has one row per experiment. Note that the ophys_session_id column links each experiment to its associated session in the session_table.

Load one example session¶

We are going to select one session from this table, session 854060305. This is a session with Sst-IRES-Cre mouse, which expressed GCaMP6f in Sst+ inhibitory interneurons. There were 6 simultaneously acquired imaging planes for this session. We can view metadata for this session as follows:

Download all associated experiments¶

Each session consists of one or more 'experiments', in which each experiment is a single imaging plane

Each mesoscope session has up to 8 experiments associated with the session. We will load all sessions into a dictionary with the experiment IDs as the keys

The first time that this cell is run, the associated NWB files will be downloaded to your local data_storage_directory. Subsequent runs of this cell will be faster since the data will already be cached locally.

View the max projection and one cell ROI for one of the experiments¶

We can view the cell_specimen_table for one experiment, which contains information about each identified cell in that experiment

We can then visualize the max projection and one of the identified ROIs

Load neural data into memory¶

The cell below will load the neural data into memory in the pandas 'tidy' format by iterating over each of the 6 experiments and using some helpful tools from the visual_behavior_ophys module of the mindscope_utilities package that was imported above as ophys.

It will also include a subset of metadata from ophys_experiment_table to facilitate splitting by depth, structure (aka cortical area), cre line (aka cell class), etc.

Note that 'tidy' data means that each row represents only one observation. Observations are stacked vertically. Thus, the timestamps colums will repeat for every cell in the dataset.

We can then look at some attributes of the neural_data dataframe we have created.

It is ~2.5 million rows long:

It is so long because has one row for each timestamp for each cell.

Below are the first 5 entries. Again, note that the tidy format means that each row has only one observation, which represents a single GCaMP6 fluorescnce value for a single neuron.

• The cell_roi_id column contains unique roi ids for all cells in a given experiment, which do not repeat across ophys sessions.
• The cell_specimen_id column contains unique ids for cells that were matched across ophys sessions. Thus, a cell that was imaged in more than one session has multiple roi ids but one cell specimen id.

Examine Cell IDs¶

We can get the unique Cell IDs in our dataset as follows:

If we wanted to get the timeseries for one cell, we could query the neural_data dataframe. For example, to get the full timeseries for the cell with cell_specimen_id = 1086557208:

Each cell has three types of traces:

• dff column is the Calcium fluorescence signal, normalized to background fluorescence.
• events column is deconvolved events from dff trace, which approximates neural firing rate and removes the slow decay of the Calcium signal (for more details, you can read EVENT DETECTION section in Visual Behavior whitepaper).
• filtered_events column is events smoothed with a half-gaussian kernel.

We can then plot DeltaF/F for this cell for the full experiment as follows:

Load stimulus data into memory¶

The stimulus table is shared across all experiments (imaging planes) in a session. We can therefore use the stimulus table for just one experiment.

We are going to drop the image_set column because it is not informative for our purposes. We can then view the first 10 rows of the stimulus table.

View the stimulus_templates attribute¶

Note that the unwarped column contains the image before the application of a spherical warp. All of the pixels labeled 'NaN' will be off-screen (not visible to the mouse) after the warp is applied.

All experiments in a given session will share the same stimulus_templates

View the unwarped images¶