Denoising strategies to consider

[tsalo]

1. Multi-echo ICA
   • See tedana.readthedocs.io.
   • Separation of BOLD from non-BOLD noise (e.g., motion).
   • Use tedana.
2. Phase regression
   • Improved spatial localization by suppressing macrovascular signal.
   • Use phaseprep.
3. Physiological trace regression
   • Presumably separation of neural BOLD signal and non-neural BOLD-based noise, but limited to (1) variations in heart rate and respiratory volumes and (2) carbon dioxide.
   • Use phys2denoise.
4. Dynamic global signal regression
   • See Tong, Hocke, and Frederick (2019).
   • Separation of neural BOLD signal and non-neural BOLD-based noise.
   • Likely sources of this non-neural BOLD-based noise include:
     • Variations in heart rate and respiratory volumes
     • Carbon dioxide
     • Vasomotion from oscillations in the vascular tone
     • Gastric oscillations
   • Use rapidtide.
5. Marchenko-Pastur PCA dimensionality reduction
   • See Adhikari et al. (2019).
   • Removal of thermal noise(?)
   • Per anecdotal evidence, dwidenoise can improve ME-ICA, including more subcortical regions in components with high-resolution fMRI data.
   • Also, the ENIGMA consortium uses it for fMRI data.
   • Use dwidenoise function in mrtrix3.
6. Filtering of motion parameters before regression or censoring
   • See Gratton et al. (2020).
   • Respiration-induced factitious head motion via B0 perturbations along the phase-encoding direction.

[tsalo]

Regarding phase regression, per a call on NORDIC this week: Luca compared phase regression to NORDIC in one of his papers. He says it didn't perform well, and it probably doesn't work well with low-resolution data. Tim Laumann says it sounds similar in purpose to the good-voxels procedure. Essa says the resolution's still an issue, but looking across echoes might be interesting.