Lever Reaching Task Analysis

File hierarchy

The root directory to your project should have this hierarchy:

root
|-- Laser_ChR2                          <-- optogenetic manipulation with CHR2
|       |-- Animal_0                    <-- data for animal with ChR2 expression
|       |       |-- StimType0_intensity0_mW_protocol0_Nf_NumberOfFrames0        <-- folders separate protocols e.g. 'Beta_15_mW_all-RT_Nf_500'
|       |       |       |-- Right/Left  <-- specifying the side that was filmed
|       |       |       |       |-- DLC <-- contains body part trackings of all sessions derived from DLC
|       |       |       |       |       |-- session_0_DLC.xlsx
|       |       |       |       |       .
|       |       |       |       |       .
|       |       |       |       `-- Laser <-- contains laser detection files derived from LED_deetection.py
|       |       |       |               |-- session_0_laser.xlsx
|       |       |       |               |-- session_1_laser.xlsx
|       |       |       |               .
|       |       |       |               .
|       |       |       |
|       |       |       `config_protocol0.ymal <-- config file for this specific protocol and animal
|       |       |
|       |       |-- StimType1_intensity1_mW_protocol1_Nf_NumberOfFrames1
|       |       |
|       |       .
|       |       .
|       .       |
|       .       `-- StimType1_intensity1_mW_protocol1_Nf_NumberOfFrames1
|       |
|       `-- Animal_n            <-- same for all other animals
|
|-- Laser_ArchT
|
|-- 6OHDA
|       |-- Animal_0
|       .
|       .
|       |
|       `-- Animal_m
|
|
`-- Subplots

Experiment

There are different experiment protocols for laser stimulation:

  • all-RT : where laser stimulation starts when auditory cue is ended and lasts until paw leaves the pad.

  • all-MT : where laser stimulation starts when paw leaves the pad until lever is pressed (successful trials) or untill the end of trial (failed trials)

  • partial-MT : where laser stimulation starts when paw leaves the pad and continues for a certain time period smaller than the whole movement time (usually 250 ms, see example bellow)

../../_images/Lever_reaching_position.png ../../_images/Lever_reaching_velocity.png

Config file

When calling the function set_conf_and_df a template of the config file is generated and the corressponding information about the protocol are added to it using the folder name. You have to make sure before any attempt to integrate all sessions, all the protocols do have their corresponding config.yaml file. In case you wanted to modify any information about the config file, open, edit, save and make sure you import the cfg file manually again.

Measures

Traveled distance :

The whole distance travelled by the paw during MT, starting movement until lever press for successful trials and until the end of trial for failures

../../_images/Tortuosity.png
Tursoisity:

The ratio of the travelled distance to the direct line connecting the position of the paw on the pad to the lever.

MT:

Movement time

RT:

Reaction time

Maximum velocity:

The maximum velocity reached within one trial

Misdetection Correction

../../_images/mis_tracking_lever_reaching.png

Even with an optimized trained network it so happens that there are mistakes in the trackings of body parts. Since the movement of the hand is rather fast, even with a recording of 250 fps, the time resolution is not high enough to be able to detect where DeepLabCut is making a mistake. However, each detection derived from the DeepLabCut is reported together with a likelihood value. Here we get use of this value for detecting these misdetections by setting a p_cutoff.

Here are the steps taken:

@startuml if (<size:16>**Less than "n_good_points_ratio" likelihood>p_cutoff detections?**</size>) then (yes) :Dicard trial; stop else (no) if (Check for likelihood>p_cutoff detection above \n the pad within a certain x vicinity of it) then (Found) :Put equidistant points for frames starting \n from the pad to the point found above; else (Not found) :Discard trial; stop endif :Detect single frame misdetections \n among >p_cutoff detections; :Correct by the average position of a \n number of detections before and after; if (<size:16>** Check for multiple consecutive high likelihood misdetections?**</size>) then (Found) :Replace the points with a set of equidistant points between \n the start and end of misdetected points; else (Not found) endif :Replace likelihood<p_cutoff detections with sets of \n equidistant points between the likelihood>p_cutoff points; stop @enduml

Metadata

The data derived from DeepLabCut and LED detections is then structured into a class object after misdetection handling for easier access. All the trials with the same protocol from different animals is integrated into one class. The class hold information about the following parameters for all trials (and in some cases detailed down to frames of each trial):

path : full path to the session which contains the trial

rat_no : rat number the trial belongs to

pad_left_x : far left x end of the pad in the session in cm

pad_right_x : far right x end of the pad in the session in cm

pad_y : y position of the pad in cm

lever_x : x position of the lever for each trial

lever_y : y position of the lever for each trial

folder : the folder to this protocol

n_pad_miss_detection : number of pad LED misbehavior in all sessions

fp_trial : frame per second of video recording

tortuosity : tortuosity of the trial

distance : the distance travelled by the paw

epochs_x : x position of one body land mark for the whole trial (frames dedicated to timebins before leaving the pad and after reaching the lever are set to -1)

epochs_y : y position of one body land mark for the whole trial (frames dedicated to timebins before leaving the pad and after reaching the lever are set to -1)

likelihood : likelihood of each DLC detection

velocity_r : absolute velocity of the paw during the whole trial (this value for frames dedicated to timebins before leaving the pad and after reaching the lever are set to velocity_mask)

steps : the absolute displacement within each frame

pad_off_t : times when paw left the pad relative to start of trial (-1 if didn’t) reported in frame numbers

got_reward_t : times of lever press in each trial (-1 for failed) reported in frame numbers

pad_miss_detection : set True for mis detected pad trials

starts : start of each trial in the session timeline reported in frame numbers

ends : stop of each trial in the session timeline reported in frame numbers

MT : The whole movement time reported in frame numbers

failure : True for trials where either paw didn’t leave the pad or didn’t press the lever

to_keep : set True for trials that are cleared without any problems

n_trials : number of trials in sessions

n_failed : number of failed trials in sessions

n_succeeded : number of successful trials in all sessions

n_laser_trials : number of trials containing laser stimulation

laser_trial : set True for trials that contain laser stimulation

laser_ind: stores the indices for laser-on time points

laser_duration : laser duration reported in number of frames

laser_start : time of the laser onset reported in number of frames

laser_pulse : laser pulse type (e.g. Square, Beta etc.)

laser_intensity : intensity of the laser stimulation in mW

laser_protocol : laser stimulation protocol (e.g. all-RT, all-MT etc.)

you can save the data containing the session in form of .pickle or a .csv file.

Jupyter Notebook and Data Analysis

You will have the chance to look at a variety of measures for the sessions:

  • Visualize the traces of the paw and the absolute velocity in time with highlighting the laser-on time points as shown above for:

    • one trial

    • trials in one session

    • all trials of one animal

    • all trials of same protocol in multiple animals

  • Compare Successful and failed trial traces.

  • Compare trials containing laser with trials without laser stimulation

  • Compare statistical values of parameters bellow for laser vs. non laser sessions:

    • RT

    • MT

    • Travelled distance

    • Maximum Velocity

    • Tortuosity

  • Look at mean trajectory of trials showing variances in x bins.