Ephys/Imaging Automation Pipeline (Developer Guide)

The main goals of the Ephys/Imaging Automation Pipeline in BRAINCoGS are:

• Automate spike sorting and imaging segmentation for all recordings
• Centralize and standardize paths for recording data storage
• Unify and register ephys/imaging processing
• Store processed data in the BRAINCoGS database (DJ)

To accomplish this, we developed three tools:

• Ephys/Imaging Automation GUI (RecordingProcessJobGUI)
• Recording Workflow management (Automatic_job directory in U19-pipeline_python )
• Collab repositories to handle Ephys/Imaging Processing (BrainCogsEphysSorters and BrainCogsImagingSegmentation )

Ephys/Imaging Automation GUI

This mini guide for the Automation GUI shows the relationship between the GUI and the database: which tables supply values and which records the GUI writes.

Create Executable and install Ephys/Imaging Automation GUI

• Follow these steps to create the executable:

1. Connect to the 185a Recording machine.
2. Open the C:\Experiments\RecordingProcessJobGUI directory.
3. Open the Recording_Automation_GUI.prj file.
4. Click the Package button.
5. When packaging is done, all files will be available in \cup.pni.princeton.edu\braininit\Shared\AutomationGUI_Installation

• To install the GUI:

1. Connect to the machine where the GUI will be installed (182-Imaging-Rig1 and 165A-OneboxRecording at the moment).
2. If installing for the first time:
   • Copy the AutomationGUI_update directory from braininit\Shared\AutomationGUI_Installation onto the desktop.
   • Execute firstTimeAutomationGUI.BAT from the AutomationGUI_update directory on the desktop.
3. Execute update_AutomationGUI.BAT from the AutomationGUI_update directory on the desktop.
4. Follow the instructions to install the GUI (create a shortcut on the desktop).
5. Open the Recording Automation GUI.

Automation GUI main screen

Ephys Preprocessing (precluster) parameters organization

Main tables

• u19_pipeline_ephys_element.#pre_cluster_method List of methods (or algorithms) supported for ephys preprocessing.
• u19_pipeline_ephys_element.pre_cluster_param_set Specific set of parameters (mainly a dictionary) for a given preprocessing method. Multiple sets of parameters can be stored for the same method.
• u19_pipeline_ephys_element.pre_cluster_param_steps (Ephys) Reference to a set of steps to perform in ephys preprocessing.
• u19_pipeline_ephys_element.pre_cluster_param_steps__step These records indicate which sets of parameters for given preprocessing methods will be executed (and in which order).

• Depicted in the above image:

1. Suppose precluster_param_steps_name = new_preprocessing_steps_1 (precluster_param_steps_id = 10) is selected for preprocessing.
2. According to pre_cluster_param_steps__step:
   • paramset_idx = 9 will be executed 1st
   • paramset_idx = 2 2nd
   • paramset_idx = 3 3rd.
3. Checking pre_cluster_param_set for paramset_idx = 9, 2, 3, we conclude that preprocessing will comprise:
   • Tprime (Tprime ParamSet 1)
   • Catgt (Catgt ParamSet for Towers Task)
   • PreClustMethod1 (PreClusterMethod1 Paramset Mika)

Ephys Processing (cluster) parameters organization

• This structure is simpler than preprocessing (there are no multiple steps involved): two tables organize the ephys processing parameters.

Main tables

• u19_pipeline_ephys_element.#clustering_method List of methods (or algorithms) supported for ephys processing.

• u19_pipeline_ephys_element.#clustering_param_set Specific set of parameters (mainly a dictionary) for a given processing method. Multiple sets of parameters can be stored for the same method.

• Each recording (or, to be precise, each recording process) can be processed with a different set of parameters. Default parameters are used for the majority of recordings in BRAINCoGS.

Default parameters for preprocessing and processing

• As seen in the Automation GUI main screen, u19_recording.#modality stores default parameters for each modality.
• As a developer, manually update the default parameters for all modalities when the project requires it.

• The u19_recording.#modality table stores a reference to the default parameters most commonly used for processing ephys and imaging.
• Main table to store preprocessing parameters:
  1. u19_pipeline_ephys_element.pre_cluster_param_steps: (Ephys) Reference to a set of steps to perform in ephys preprocessing.
  2. Imaging, u19_pipeline_imaging_element.pre_process_param_steps: (Imaging) Reference to a set of steps to perform in imaging preprocessing (no imaging preprocessing for any user at the moment).
• Main table to store processing parameters:
  1. Ephys, u19_pipeline_ephys_element.#clustering_param_set: (Ephys) Reference to a set of parameters for the chosen sorting algorithm.
  2. Imaging, u19_pipeline_imaging_element.#processing_param_set: (Imaging) Reference to a set of parameters for the chosen segmentation algorithm.

Imaging equivalence parameter tables:

• Everything described for the ephys preprocessing and processing tables applies to the imaging counterparts.

Tables written when recording is registered:

• When a new recording is created, three tables are written:

  1. u19_recording.recording: Main table for recordings. The recording_id created here identifies the recording throughout the entire process.
  2. u19_recording.recording__behavior_session: Reference to which behavior session this recording corresponds to.
  3. u19_recording.default_params: Set of parameters chosen for this recording.

• If there is no behavior session attached to the recording:

• u19_recording.recording__recording_session: The subject and datetime of the recording are stored as a reference in this table.

u19_recording.default_params design:

• default_params works as a "guide" to know which parameters were chosen for the recording.

• Explanation of all fields of this table:

  1. recording_id Reference to which recording the parameters are being selected for.
  2. fragment_number Reference to which "fragment" (or job) the parameters apply to. (See the next section to learn how recordings are split into fragments.)
  3. default_same_preparams_all If default_same_preparams_all = 1 (the default case), the same preprocessing parameters will be applied to all fragments of the recording.
  4. preprocess_param_steps_id Preprocessing parameter id chosen for this recording-fragment. Taken from u19_recording.#modality by default.
  5. default_same_params_all If default_same_params_all = 1 (the default case), the same processing parameters will be applied to all fragments of the recording.
  6. paramset_idx Processing parameter id chosen for this recording-fragment. Taken from u19_recording.#modality by default.

• In the default case (Automation GUI main screen), default_same_preparams_all = 1 and default_same_params_all = 1, so default parameters will be applied to all fragments of the recording.

Workflow management description

The workflow management code creates and coordinates a set of tasks for all recordings registered with the GUI to ensure they are fully processed.

Shell code executed as a cronjob for workflow management: (call_cronjob_automatic_job.sh )

Workflow management is composed mainly of two classes that handle recordings and recording_processes (recording_processes, or jobs, are how recordings are composed):

• Ephys recordings are composed of one or many independent probe electrophysiology recordings. Each probe recording corresponds to a job in the workflow management.
• Calcium imaging recordings are composed of one or many independent field-of-view image stacks. Each field-of-view image stack corresponds to a job in the workflow management.

The class that manages workflow at the recording level is (RecordingHandler)

Main functions and variables in recording workflow manager

• recording_status_dict in (Params Config file): This dictionary defines status definitions and the corresponding functions to execute.
• pipeline_handler_main in (RecordingHandler): Main function in the recording workflow.
  1. Executes the corresponding functions based on status.
  2. Runs every 30 minutes to check for new recordings to handle.
  3. Sends notifications for processed and failed functions.
  • exception_handler in (RecordingHandler): Python decorator for error handling.
• modality_preingestion in (RecordingHandler): Main ingestion function from the recording to the recording_process tables. There are subcalls depending on the modality of the recording (ephys or imaging).

Imaging preingestion main steps:

• imaging_preingestion in (RecordingHandler): Ingestion to the recording_process table for an imaging recording. Gets all FOVs (TIFF stacks) for the recording and assigns a new job to each one, with the corresponding parameters fetched from the selection made in the Automation GUI. Make function in AcquiredTiff in (AcquiredTiff make function ): Population calls to:

1. u19_imaging_pipeline.AcquiredTiff: Each recording is divided into Tiff Splits (e.g. Mesoscope recordings contain multiple tiff stacks that are processed independently).
2. u19_imaging_pipeline.SyncImagingBehavior: Finds the correspondence between the virtual reality frame in the behavior experiment and the calcium imaging frame in the recording. (Code here). Since most users use MATLAB to read sync data, this table is populated in the general populate tables cronjob script. (populate tables script description).

Ephys preingestion main steps:

• electrophysiology_preingestion in (RecordingHandler): Ingestion to the recording_process table for an ephys recording. Gets all probes for the recording and assigns a new job to each one, with the corresponding parameters fetched from the selection made in the Automation GUI.

1. Ingest the ephys_pipeline.EphysPipelineSession table.
2. Ingest the ephys_element.ProbeInsertion table.
3. Ingest the ephys_element.EphysRecording table.
4. Ingest the ephys_pipeline.BehaviorSync table: Find the corresponding iteration in the ephys recording with the frame from the Virmen behavior task (Code here ) ( and here ).
5. For each probe (insertion_number) in the EphysSession, insert a Processing (job) in u19_recording_process.Processing.

Main functions and variables in recording_process workflow manager

• recording_process_status_dict in (Params Config file): This dictionary defines status definitions and the corresponding functions to execute.
• pipeline_handler_main in (RecProcessHandler): Main function in the recording process workflow.

1. Executes the corresponding functions based on status.
2. Runs every 30 minutes to check for new recordings to handle.
3. Sends notifications for processed and failed functions.

• transfer check/review in (transfer_check/review): Executes and monitors the globus transfer from PNI to Princeton University clusters. (Deprecated)
• slurm_job_queue/check in (slurm_job_functions): Generates the slurm file and queues the job on the cluster that will process the recording process. Monitors the job to check whether it has finished.
• populate_element in (slurm_job_queue): After processing jobs, populates the imaging or ephys element tables downstream from the results file.

Collab repositories to handle Ephys/Imaging Processing

BrainCogsEphysSorters

• BrainCogsEphysSorters is the electrophysiology processing pipeline used by BrainCOGS to preprocess, sort, and post-process Neuropixels recordings. This repository works with parameters defined in previous steps of the Automation Pipeline.

• Location: Current location of the repository: /mnt/cup/braininit/Shared/repos/AutomaticPipelineProcessing/electrophysiology_processing/BrainCogsEphysSorters

• System: The repository is installed on g-bcogs-u19proc2.pni.princeton.edu and is run through the slurm job scheduler.

• Logs locations:

  • ErrorLogs: /mnt/cup/braininit/Shared/repos/AutomaticPipelineProcessing/u19_pipeline/automatic_job/ErrorLog
  • OutputLogs: /mnt/cup/braininit/Shared/repos/AutomaticPipelineProcessing/u19_pipeline/automatic_job/OutputLog

• The repository acts as a unified orchestration layer around multiple electrophysiology tools:

  • CatGT (preprocessing)
  • Kilosort 2
  • Kilosort 3
  • Kilosort 4
  • IBL Atlas post-processing pipeline

High-Level Workflow

Raw Neuropixels Recording │ ▼ Preprocessing (CatGT) │ ▼ Spike Sorting (Kilosort2/3/4) │ ▼ Partial Cleanup │ ▼ IBL Atlas Conversion │ ▼ Processed Output

Main Components

1. main_script

• Coordinates the entire pipeline.
• File: main_script.py, the main entry point of the repository.

# Get recording process and data directories
recording_process_id = os.environ['recording_process_id']
raw_data_directory = os.environ['raw_data_directory']
processed_data_directory = os.environ['processed_data_directory']

# Get absolute paths to raw and processed
raw_data_directory = pathlib.Path(config.root_raw_data_dir,raw_data_directory)
processed_data_directory = pathlib.Path(config.root_processed_data_dir,processed_data_directory)

# Execute selected preprocessing steps
new_raw_data_directory = pw.preprocess_main(recording_process_id, raw_data_directory, processed_data_directory)

# Execute selected sorter
sorter_processed_directory = sw.sorter_main(recording_process_id, new_raw_data_directory, processed_data_directory)

# Post process data
pw.post_process_partial_results(recording_process_id, raw_data_directory, processed_data_directory)
ppw.post_process_main(raw_data_directory, processed_data_directory, sorter_processed_directory)

2. Preprocessing Layer

• Checks which preprocessing steps to perform based on the preprocessing param file, then executes them (for now, only CatGT is implemented as a preprocessing stage).
• File: u19_sorting/preprocess_wrappers.py
• Output result Location: braininit/Data/Processed/electrophysiology/(user)/(subject)/(session_date)_g(session#)/(g#_spikeglx_dir)/(imec#spikeglx_dir)/job_id(jobid)/catGT_output

def preprocess_main(recording_process_id, raw_data_directory, processed_data_directory):

    preprocess_parameters = json.load(preprocess_param_file)

    for this_preparam in preprocess_parameters:
        if config.preproc_tools['catgt'] in this_preparam:
            catgt_output_dir = pathlib.Path(processed_data_directory, config.preproc_tools['catgt']+"_output")
            new_raw_data_directory = cat_gt.run_cat_gt(new_raw_data_directory, catgt_output_dir, this_preparam[config.preproc_tools['catgt']])

3. Sorting Layer

• Executes the selected spike sorting algorithm.
• File: u19_sorting/sorter_wrappers.py
• Output result Location: braininit/Data/Processed/electrophysiology/(user)/(subject)/(session_date)_g(session#)/(g#_spikeglx_dir)/(imec#spikeglx_dir)/job_id(jobid)/(sorter)_output

 sorter = config.sorters_names[process_parameters['clustering_method']]

sorter_processed_directory = pathlib.Path(processed_directory, process_parameters['clustering_method']+'_output')

  if sorter == config.sorters_names['kilosort2']:
      Kilosort2.run_Kilosort2(raw_directory, sorter_processed_directory, process_parameters_filename, chanmap_filename)
  elif sorter == config.sorters_names['kilosort3']:
      Kilosort3.run_Kilosort3(raw_directory, sorter_processed_directory, process_parameters_filename, chanmap_filename)
  elif sorter == config.sorters_names['kilosort4']:
      print('running Kilosort 4 here xxxxxxxx')
      Kilosort4.run_Kilosort4(raw_directory, sorter_processed_directory, process_parameters_filename, chanmap_filename)

4. Post-Processing

• Convert sorter outputs into the formats required by downstream analysis pipelines.
• File: u19_sorting/postprocess_wrappers.py
• Output result Location: braininit/Data/Processed/electrophysiology/(user)/(subject)/(session_date)_g(session#)/(g#_spikeglx_dir)/(imec#spikeglx_dir)/job_id(jobid)/ibl_data

  # For the moment we just call ibl_data transformation to run atlas
  ibl_atlas_post_processing.run_ibl_atlas_post_processing(raw_data_directory, processed_data_directory, sorter_processed_directory)

Design Patterns

• The repository isolates third-party tools behind wrappers:

  • CatGT
  • Kilosort2
  • Kilosort3
  • Kilosort4

• This makes it easier to:

  • Replace sorters
  • Add new preprocessing tools
  • Keep a common interface
  • Configuration-Driven Execution

• Behavior is controlled entirely by JSON files (created by the Automation Pipeline):

  • preprocess_paramset_(id).json
  • process_paramset_(id).json