BRAIN CoGS mini VR rigs

# Pupillometry Pipeline Guide

  • This documentation will guide the researcher through the process of register, monitor & review pupillometry data.

# Instructions to Set up Pupillometry Pipeline on a Rig Machine

  1. Ask your lab manager for helo to set up a camera in the rig.
  2. If needed, add these parameters in RigParameters.m
%% Pupilometry video parameters
video_parent_path               =   'E:/VideoData'
video_ext                       =   '.mj2'
video_acquisition_rate          =   30
video_record                    =   true
video_gain                      =   8
preview                         =   true
  1. In Tranining GUI subject task selection menu, under PupillometryVideo select 2. In RigParameters:
Training GUI for pupillometry
  1. In Experiment file add the following lines in initializationcodeFun function (just after vr = initializeGradedExperiment(vr); line):
% Start video acquisition
if ~isempty(vr.trainee.pupillometryVideo) && vr.trainee.pupillometryVideo ~= 1
    vr = startVideoAcquisition(vr);
end
  1. In Experiment file add the following lines in runtimeCodeFun function (just after catch err displayException(err); line):
% Stop video acquisition
if ~isempty(vr.trainee.pupillometryVideo) && vr.trainee.pupillometryVideo ~= 1
        vr = stopVideoAcquisition(vr);
end
  1. In Experiment file add the following lines in terminationCodeFun function (the very first line):
% Stop video acquisition
if ~isempty(vr.trainee.pupillometryVideo) && vr.trainee.pupillometryVideo ~= 1
        vr = stopVideoAcquisition(vr);
end

# Pupillometry backup videos task schedule

  1. On Windows type "Task Scheduler"
  2. Open Task Scheduler "App"
  3. On right hand side menu, click on "Create Task" Action
Task scheduler menu
  1. Name new task as video_backup
  2. Add a trigger to run task daily at 11:30 pm
Task scheduler Trigger Tab
  1. Add an action: add this line to the Program/script edit: C:\Experiments\U19-pipeline-matlab\scripts\cmd_copy_video_files
  2. Hit OK button

# Pupillometry DB and data organization

  • Raw data (video files) are located in: \\cup.pni.princeton. edu\braininit\Data\Raw\video_pupillometry\(userid)\(subjectfullname)\(session_date)_g(session_number)\(video_file)

  • Processed data (h5 files) are located in: \\cup.pni.princeton. edu\braininit\Data\Processed\video_pupillometry\(userid)\(subjectfullname)\(session_date)_g(session_number)\(h5_file)

  • Video models are located in: \\cup.pni.princeton. edu\braininit\Data\Raw\video_models\(video_model_directory)

  • Check DB Organization (pupillometry) for a description for each pupillometry table.

Pupillometry pipeline DB schema

# Steps executed in Pupillometry pipeline

  1. When pupillometry_video -> 2. In RigParameters is selected in training GUI a new session_video record is registered in the DB while ingesting Session. (Code here)

  2. The cmd_copy_video_files task scheduler takes session_video records and store new videos in this location: \\cup.pni.princeton. edu\braininit\Data\Raw\video_pupillometry\(userid)\(subjectfullname)\(session_date)_g(session_number)\(video_file). Script run by pupillometry backup videos task schedule

  3. During night cronjob: if video is found on cup location:

  • Populate pupillometry_session table
  • Execute sync behavior code for video. Code here.
  • Register default model to process video in pupillometry_session_model and pupillometry_session_model_data.
  1. Queue/Checks job in spockvm2

  • Similar to ephys/imaging automation pipeline, pupillometry pipeline has a main (more simple) class to process all pupillometry jobs called PupillometryProcessingHandler. This class is located here .

  • Execute pupillometry_queue_jobs script. This scripts calls PupillometryProcessingHandler.check_pupillometry_sessions_queue function. Queues a processing job to all sessions where a model was registered.

  • Execute pupillometry_check_jobs script. This scripts calls PupillometryProcessingHandler.check_processed_pupillometry_sessions function. If processing job is finished get final result (pupil_diameter) and stores it in pupillometry_session_model_data table.

# Monitor pupillometry processing sessions

  • A few MATLAB functions were built to check how pupillometry jobs are doing:

  • Get processed data from a session:

key = struct('subject_fullname', 'efonseca_ef317_act116', 'session_date', '2024-02-21')
pupillometry_data = fetch(pupillometry.PupillometrySessionModelData * pupillometry.PupillometrySyncBehavior & key, '*')
pupillometry_data = 

  struct with fields:

           subject_fullname: 'efonseca_ef317_act116'
               session_date: '2024-02-21'
             session_number: 0
                   model_id: 2
        pupillometry_job_id: 2984
             pupil_diameter: [108177×1 double]
    sync_video_frame_matrix: [108177×4 single]
       sync_behavior_matrix: [150227×5 single]

pupil_diameter: Pupil diameter for each video frame.
sync_video_frame_matrix: Time & Block/Trial/Iteration for each video frame.
sync_behavior_matrix: Time & video frame for each Block/Trial/Iteration.

  • Restart processing for failed processing Sometimes processing fails for external factors (processing system was down, model was not properly selected, etc). As a first attempt to solve the issue a restart processing function was created:
psmd = pupillometry.PupillometrySessionModelData()
key = struct('subject_fullname', 'efonseca_ef317_act116', 'session_date', '2024-02-21')
psmd.restart_pupillometry_failed_job(key)

If processing fails again, contact Software Developer and check #automation_pipeline_errors slack channel for more information about the error.

  • Check status for all sessions:
psmd = pupillometry.PupillometrySessionModelData()
all_sessions_table = psmd.check_status_pupillometry_jobs()
  • Check status for a specific session(s):
psmd = pupillometry.PupillometrySessionModelData()
key = struct('subject_fullname', 'efonseca_ef317_act116', 'session_date', '2024-02-21')
session_status = psmd.check_status_pupillometry_jobs(key)
  • Get all succesfully processed sessions:
psmd = pupillometry.PupillometrySessionModelData()
pupillometry_finished_sessions = psmd.get_finished_jobs_pupillometry()

# Add a new video model to the pipeline

  1. Check Deeplabcut documentation for step by step guide.

  2. Ask for tips and tricks to our BRAINCoGS personnel that have created video models in the past:

  • Joshua Julian (jjulian@princeton.edu)
  • Juan Lopez (juanlopez@princeton.edu)

  1. After video model has been created, copy the main directory of the model to this location: \\cup.pni.princeton. edu\braininit\Data\Raw\video_models\(video_model_directory)

  2. Add model to the DB (u19_pupillometry.PupillometryModels table):

  • MATLAB code:
new_model_key = struct()
new_model_key.model_description = 'New model to insert'
new_model_key.model_path = 'video_models/new_model_directory_name'
insert(pupillometry.PupillometryModels,new_model_key)
  • Always add video_models/ to the model_path, before model_directory_name.
  1. Get model_id of model you want to use for your sessions:
>> pupillometry.PupillometryModels

ans = 
Object pupillometry.PupillometryModels
 :: Table to store reference for each model ::
    MODEL_ID      model_description                        model_path                  
    ________    _____________________    ______________________________________________

       1        {'Pupillometry_2022'}    {'video_models/Pupillometry2-Ryan-2022-04-07'}
       2        {'Pupillometry_2023'}    {'video_models/twolickspouts-esme-2023-06-22'}

fetch(pupillometry.PupillometryModels,'*')

Pupillometry model_id selection
  1. Insert into u19_pupillometry.PupillometrySessionModel sessions to be processed with the new model:
key = struct('subject_fullname', 'efonseca_ef317_act116', 'session_date', '2024-02-21')
key.model_id = 2
insert(pupillometry.PupillometrySessionModel, key, 'IGNORE');
  1. Sessions will be processed with new model overnight.

# Note: All pupillometry sessions are processed with a default model (model_id = 2, Pupillometry_2023).

# In the future we plan a way to select model for subjects and or rigs instead of being processed with default model.

# Review processed data

← Subtask pipeline →