PyPEER Integration#

PEER is a previously developed support vector regression-based method for retrospectively estimating eye gaze from the fMRI signal in the eye’s orbit. PyPEER is the Python package created by Jake Son [https://github.com/ChildMindInstitute/PyPEER] which implements the PEER method in Python.

C-PAC can prepare your pipeline results directly for Predictive Eye Estimation, given the appropriate input files. The PyPEER package comes pre-installed in the C-PAC Docker and Singularity containers, so no setup is necessary.

Configuring CPAC to Run PyPEER#

../_images/pypeer_gui.png

  1. PEER Eye Scan Names: Scan/series names of the PEER calibration scans to be used for training the model.

  2. PEER Data Scan Names: Scan/series names of the naturalistic viewing data to be used for estimation.

  3. Eye Mask Path: Path of the file of a template-space eye mask. The default selection is bundled with the C-PAC container from the FSL installation.

  4. PyPEER Stimulus File Path: This is a file describing the stimulus locations from the calibration sequence.

  5. Perform Global Signal Regression: PyPEER employs a minimal preprocessing strategy. If global signal regression is desired, this can be selected here. C-PAC will not employ its standard nuisance regression selections on scans selected for PEER calibration and estimation.

  6. Perform Motion Scrubbing: PyPEER employs a minimal preprocessing strategy. If motion scrubbing is desired, this can be selected here. C-PAC will not employ its standard nuisance regression selections on scans selected for PEER calibration and estimation.

  7. Motion Scrubbing Threshold: The threshold for motion (in mm) to use for motion scrubbing, if it is selected. PyPEER employs the Power (2012) measurement for framewise displacement.

Configuration Without the GUI#

The following nested key/value pairs will be set to these defaults if not defined in your pipeline configuration YAML.

PyPEER:

  # Training of eye-estimation models. Commonly used for movies data/naturalistic viewing.
  run: Off

  # PEER scan names to use for training
  # Example: ['peer_run-1', 'peer_run-2']
  eye_scan_names: []

  # Naturalistic viewing data scan names to use for eye estimation
  # Example: ['movieDM']
  data_scan_names: []

  # Template-space eye mask
  eye_mask_path: $FSLDIR/data/standard/MNI152_T1_${func_resolution}_eye_mask.nii.gz

  # PyPEER Stimulus File Path
  # This is a file describing the stimulus locations from the calibration sequence.
  stimulus_path: None

  minimal_nuisance_correction:

    # PyPEER Minimal nuisance regression
    # Note: PyPEER employs minimal preprocessing - these choices do not reflect what runs in the main pipeline.
    #       PyPEER uses non-nuisance-regressed data from the main pipeline.

    # Global signal regression (PyPEER only)
    peer_gsr: True

    # Motion scrubbing (PyPEER only)
    peer_scrub: False

    # Motion scrubbing threshold (PyPEER only)
    scrub_thresh: 0.2

References#

Jake Son, Lei Ai, Ryan Lim, Ting Xu, Stanley Colcombe, Alexandre Rosa Franco, Jessica Cloud, Stephen LaConte, Jonathan Lisinski, Arno Klein, R. Cameron Craddock, Michael Milham. Evaluating fMRI-Based Estimation of Eye Gaze during Naturalistic Viewing. https://doi.org/10.1101/347765

Fox, M.D., Zhang, D., Snyder, A.Z., Raichle, M.E., 2009. The global signal and observed anticorrelated resting state brain networks. J Neurophysiol 101, 3270-3283.

Power, J.D., Barnes, K.A., Snyder, A.Z., Schlaggar, B.L., Petersen, S.E., 2011. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59, 2142-2154.

Power, J.D., Barnes, K.A., Snyder, A.Z., Schlaggar, B.L., Petersen, S.E., 2012. Steps toward optimizing motion artifact removal in functional connectivity MRI; a reply to Carp. Neuroimage.