Alternative ICA based denoise (sequential)
What is the best way to denoise the data after ICA?
Regression based:
Aggressive approach: nuisance regression using only the rejected components.
Non aggressive (partial regression) approach: all the components are considered, but only the rejected components are regressed out of the data.
Orthogonalised approach: the rejected components are orthogonalised with respect to the other components.
4D-based approach (similar to M/EEG): reconstruct volumes on noise, then subtract it from the original data.
\( Y = \) \(A\) \(+\) \(R\) \(+ n \)
Multivariate:
Alternative ICA based denoise (sequential)
Effect of denoising approach is significant for slope (F(5,354)=177.6, p<0.001) and intercept (F(5,354)=225.7, p<0.001) of the linear regression model
DVARS vs FD
Group level, DVARS vs FD
Slope
Intercept
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Alternative ICA based denoise (sequential)
Average % BOLD and DVARS across all trials
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Aggressive denoise removes signal of interest
OC and E-02 denoise affects the signal of interest more than ICA denoise
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Alternative ICA based denoise (sequential)
Lag reliability
CVR reliability
Notes on Denoising
By Stefano Moia
