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pBFSLab / DeepPrep

DeepPrep: An accelerated, scalable, and robust pipeline for neuroimaging preprocessing empowered by deep learning
https://deepprep.readthedocs.io
Apache License 2.0
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Question about the confounds `confounds_series.txt` file #111

Closed julfou81 closed 2 months ago

julfou81 commented 3 months ago

Hi,

Thank you for providing this very nice tool! I use DeepPrep on a HPC with singularity and GPU and so far, so good! I have a question about the column labels in the file desc-confounds_timeseries.txt : What do they mean? Are compX the values of aCompCor components? If yes, where are they calculated? WM, CSF? Combined mask of WM and CSF?

Frame dL dP dS pitch yaw roll dL_d dP_d dS_d pitch_d yaw_d roll_d WB WB_d vent vent_d wm wm_d comp1 ...

Thank you!

Ireneyou33 commented 3 months ago

Hi,

The explanations are provided below, hope you find this helpful :)

  1. Frame: the current frame of BOLD.

  2. The first 6 parameters are the original head motion parameters (6HMP). dL: translational displacement in the left direction (X-axis) dP: translational displacement in the posterior direction (Y-axis) dS: translational displacement in the superior direction (Z-axis) pitch: rotation around the pitch-axis (right/left) yaw: rotation around the yaw-axis (anterior/posterior) roll: rotation around the roll-axis (inferior/superior)

  3. The following 6 parameters (dL_d, dP_d, dS_d, pitch_d, yaw_d, roll_d) are the temporal derivatives of each of the original 6HMP, resulting in 12 head motion parameters (12HMP) in total.

  4. The WB, vent, and wm represent the within brainmask signal, ventrical signal, and white matter signal, while the WB_d, vent_d, and wm_d are their corresponding temporal derivatives.

  5. The last 10 parameters (comp1-comp10) correspond to the first 10 PCA components of the signal outside the brainmask.