Nice publication from Jonathan Power. I like the look of these plots (which I've been seeing in talks at Wash U for a few years now).

# Prospective motion correction of fMRI using optical tracking→

/The PMC system did not introduce extraneous artifacts for the no motion conditions and improved the time series temporal signal-to-noise by 30% to 40% for all combinations of low/high resolution and slow/fast head movement relative to the standard acquisition with no prospective correction. The numbers of activated voxels (p < 0.001, uncorrected) in both task-based experiments were comparable for the no motion cases and increased by 78% and 330%, respectively, for PMC on versus PMC off in the slow motion cases.

# Resting state nuisance regressors remove variance with network structure→

/Really interesting work from Bright and Murphy. The "highlights":

- Data variance removed by nuisance regressors contains network structure.
- Simulated regressors unrelated to noise also extract data with network structure.
- Random sampling of original data (as few as 10% of volumes) reveals robust networks.
- After optimal number, motion regressors remove similar variance as simulated ones.
- Excessive nuisance regressors extract random signal variance with network structure.

# ICA methods for motion correction in resting state fMRI→

/From the abstract:

Results demonstrated that ICA-AROMA, spike regression, scrubbing, and ICA-FIX similarly minimized the impact of motion on functional connectivity metrics. However, both ICA-AROMA and ICA-FIX resulted in significantly improved resting-state network reproducibility and decreased loss in tDoF compared to spike regression and scrubbing. In comparison to ICA-FIX, ICA-AROMA yielded improved preservation of signal of interest across all datasets.