Ronny_Bergmann_AirtrackMotorPlanning_RawData

Raw data for: Bergmann, Sehara et al., 2022

The raw-data repository for the project "AirtrackMotorPlanning" (examines whether whiskers and pupil positions predict the animal's motor plans)

The dataset stored here is part of the root data repository.

Note that this dataset contains the un-aligned, unprocessed data. If you intend to look at the 'final' dataset (processed and aligned), refer to the formatted dataset.

Contents

• File organization
• Acquisition
• Behavioral state annotation

File organization

File names

The files are organized as: <ANIMAL>/<SESSION>/<DOMAIN>/<ANIMAL>_<SESSION>_<DOMAIN>_run<TRIAL>, where:

• ANIMAL representing the ID of the animal (in the pattern MLAxxxxxx).
• SESSION being the name of the session (in the pattern sessionYYYY-MM-DD-001).
• DOMAIN representing the domain of recording (see below).
• TRIAL being the trial index during the session.

Domain names

Domain name	Description
top	AVI files taken at 200 Hz using ZR View (a custom video acquisition software) under the near-UV lighting, trial-based. See acquisition for more details.
states	CSV files containing manual annotation of behavioral states based on the top-view videos, trial-based. See behavioral state annotation for more details.
left-eye	AVI files of left eye videos taken at 100 Hz using PylonPD (a custom video acquisition software) under the IR-lighting, trial-based.
right-eye	AVI files of right eye videos taken at 100 Hz using PylonPD (a custom video acquisition software) under the IR-lighting, trial-based.

Acquisition

Below is the schematics of acquisition. The "Start/Stop" signal from the Arduino Mega controls the timings of the beginning and the end of acquisition for each domain.

Behavioral state annotation

Names of states

Basically the idea is to take notes of the period of frames when the subject does a particular action:

1. Standing still
   • at the end of a corridor
     • before starting another trial (AtEnd)
     • waiting for the lick port to come (Expect)
   • at the junction between a corridor and the central arena (AtCenter)
   • in the midway in a corridor (AtMidpoint)
2. Moving backward in a corridor (Backward)
3. Moving forward in a corridor (Forward)
4. Turning in the central arena
   • in the left (Left)
   • in the right (Right)
5. Licking from the lick port (Lick)

Criteria of transition

Transitions from Backward to Left, or vice versa, would be one of the the most difficult decisions. Sina used the level of the subject's eyes as the reference point, and considered "in the lane (i.e. Backward or Forward) if their eyes are still in the corridor.

Another difficulty is determining on when the animal starts to move. You will have to decide case-by-case in accordance with e.g. consistency of motion and positions.

Format

You can keep it as a CSV file, consisting of entries like below:

Trial	FromFrame	ToFrame	State	notes
1	1	4036	AtEnd
1	4037	4385	Backward
1	4386	4473	Right	(ambiguous transition)
1	4480	4702	Forward
...	...	...	...	...

1. You can enter multiple trials in one file, if you want. It is better to have a column holding the trial numbers, in any case.
2. Frames start from one. This is in accordance with how ZR View works.
3. Frames must contain the start (inclusive) and the stop (inclusive) indexes. This is because it is sometimes hard to determine the transitions, and you may want to skip some ambiguous frames for annotation.

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Copyright (c) 2022, Ronny Bergmann, Keisuke Sehara, Sina E. Dominiak, Julien Colomb, Jens Kremkow, Matthew E. Larkum, Robert N. S. Sachdev, CC-BY 4.0