Healthy Volunteer Protocol Upload Process

Code

Requirements and Installation

 pyctf
 General Utilities to interface with CTF data using python.  Also provides bids processing utilities.
 https://megcore.nih.gov/index.php/Pyctf  -  A more recent update will be coming available soon

 hv_proc
 Python scripts to extract and mark HV specific stimuli and validate trigger/response timing and data QA.
 **Open Access in development

 NIH MEG Bids processing
 Routines to convert the CTF MEG data into BIDs format using mne_bids and bids_validator
 https://github.com/nih-fmrif/meg_bids/blob/master/1_mne_bids_extractor.ipynb

 mne_bids
 https://mne.tools/mne-bids/stable/index.html
 pip install -U mne
 pip install -U mne-bids

 Afni
 Required for extracting HPI coil locations.
 https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/background_install/install_instructs/index.html

Bids Validator

 https://github.com/bids-standard/bids-validator

BIDS format / OpenNeuro

All data is be converted to BIDS format and uploaded to OpenNeuro as an open access dataset. Data triggers are cleaned using several routines listed below. These have been used to realign stimulus triggers to optical onset of the projector. Datasets that have logfiles have been merged with the trigger data to label triggers and responses.

Processing on Biowulf

 To process the scripts on biowulf:
 pyctf & hv_proc must be in your conda path  (if necessary add filepaths to a .pth file in the conda site-packages folder)
 module load ctf
 module load afni

Processing Steps

 Acquire data
 Copy raw data to Biowulf: $MEG_folder/NIMH_HV/MEG
 Copy data to bids_staging: $MEG_folder/NIMH_HV/bids_staging

 sinteractive --mem=8G --cpus-per-task=4   #Start small sized server
 conda activate hv_proc 
 module load afni
 module load ctf  #Required for addmarks

 process_hv_data -subject_folder $Subject_Folder   #Loops over all task processing steps and creates QA documents

 Remove history files and replace paths/dates/names in text files

Processing steps in process_hv_data.py

 Loop over the following scripts:
 CONVERT MRI FIDUCIALS TO TAGs >> get code name
 pyctf.bids.extract_tags $subjid_anat+orig.BRIK  > tagfile

 Process Tasks and assert ouputs match expected:
 airpuff_processing.py
 oddball_processing.py
 hariri_processing.py
 sternberg_processing.py
 gonogo_processing.py

 Calculate the noise level
 Scrub path info and history text files from the datasets to remove date and identifiers

General Task Analysis Processing

Invert Trigger

During some tasks the trigger randomly inverts. For triggers a histogram is created with the top two bins representing the ON and OFF state of the stimuli. The expectation is that the OFF state should represent more time during the task than the ON state, if not the trigger is inverted prior to analysis.

Threshold Detection

Amplitude detection based on normalizing analog signal between 0 and 1 and derivative threshold. Temporary filters may be applied to the data during this operation.

Merge Logfile

For psychoPy tasks (Sternberg, Hariri, oddball), the text logfile is imported. The first few timepoints representing instruction screens are removed from the file and the rest are added to the dataframe.

Coding Analog stimuli with Parallel port Values

Analog triggers are generally converted to single states (ON/OFF), the parallel port can be used to code the output values

Reset Timing to Optical Trigger

Due to projector frame buffering there can be timing jitter in the presentation of visual stimuli to the subject. The stimuli computer sends a parallel port trigger to the acquisition computer and the stimuli to the projector. The visual tasks have been designed to activate the upper right corner with a specific bit value The projector - Propix (model ????) has an output BNC signal that triggers when the

Response Coding

The correct response is calculated....

Task Specific Processing

Oddball Task

Description: 3 sound stimuli presented to the participant. The participant attends to the "standard" tone stimuli (210 epochs) and is required to respond to the "target" tone stimuli (45 epochs) that are intermixed with the standard tone. Additionally a broadband noise stimuli is presented as a "distractor" stimuli (45 epochs).

 UADC003 - Left Ear auditory stimuli
 UADC004 - Right Ear auditory stimuli
 UADC005 - Participant response
 UPPT001 - Stimuli Coding (standard:1, target:2, distractor:3)

Hariri Hammer

Emotional processing task contrasting happy and sad faces. Shapes are used as neutral baselines. An initial "top stim" will be shown followed by a fixation crosshair. The subject is supposed to respond during the "choice stim" by pressing the left or right response button that corresponds to the face matching the "top stim" presentation.

 UADC006 - Left response
 UADC007 - Right response
 UADC016 - Projector onset
 UPPT001 - Parallel port stimuli

Temporal Coding (UPPT001)

 Top Stim
 Choice Stim
 Response Value

Stimulus Trigger codes (UPPT001)

 diamond,0x1
 moon,0x2
 oval,0x3
 plus,0x4
 rectangle,0x5
 trapezoid,0x6
 triangle,0x7
 hapmale,0xB
 hapfem,0xC
 sadmale,0x15
 sadfem,0x16

Processing Scripts:

 Files have been compiled into a folder hv_proc
 This contains script interfaces into the megblocks package

Data Quality Assurance and PreUpload

 Verify that all tasks have the appropriate number of triggers
 hv_proc/utilities/marker_quality_assurance.py -data_folder $DATA