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help > ICA: Parameter Choice and 2nd Level Effects ?
Sep 18, 2016 06:09 PM | Shady El Damaty - Georgetown University
ICA: Parameter Choice and 2nd Level Effects ?
Dear fellow CONN-artists,
I've recently begun diving in ICA based methods for extracting functional networks in a modestly large data set (110 subjects, 3 time points). One major question when running ICA is the choice of parameters for data reduction and # of components to be estimated.
The Calhoun paper presents an AIC and MDL approach for performing these estimates. Is this algorithm implemented in CONN? If not, how would one go about extracting the parameters to compute the AIC and MDL? The major parameters of interest would be the log of the maximum likelihood estimate of the model parameters as estimated from the fMRI data, ML (the number of time points following individual subject data reduction) and the number of sources N.
Also, is there an easy way to reconstruct individual subject ICA maps? How would one go about reconstructing individual subject time courses for time course ICA decomposition?
Additionally, how should one pick a Z value threshold? Calhoun suggests calculating the mean and variance of each component across the subjects and then perform random effects inference for a one-sample t-test. Using a standard 0.001 threshold often results in over-saturated maps for some components, whereas other components might tolerate only low thresholds. How does one go about picking a consistent threshold across components?
And lastly, what exactly happens when you contrast an ICA component at the group level? I've performed a contrast between two subject groups (drug users vs non users, [1 -1]) with a single ICA component selected. However the difference between those two subject groups is a cluster that falls outside of the spatial extent of that ICA component (after thresholding). Shouldn't the difference between these two subjects be constrained to only the spatial extent of the chosen ICA component?
I've recently begun diving in ICA based methods for extracting functional networks in a modestly large data set (110 subjects, 3 time points). One major question when running ICA is the choice of parameters for data reduction and # of components to be estimated.
The Calhoun paper presents an AIC and MDL approach for performing these estimates. Is this algorithm implemented in CONN? If not, how would one go about extracting the parameters to compute the AIC and MDL? The major parameters of interest would be the log of the maximum likelihood estimate of the model parameters as estimated from the fMRI data, ML (the number of time points following individual subject data reduction) and the number of sources N.
Also, is there an easy way to reconstruct individual subject ICA maps? How would one go about reconstructing individual subject time courses for time course ICA decomposition?
Additionally, how should one pick a Z value threshold? Calhoun suggests calculating the mean and variance of each component across the subjects and then perform random effects inference for a one-sample t-test. Using a standard 0.001 threshold often results in over-saturated maps for some components, whereas other components might tolerate only low thresholds. How does one go about picking a consistent threshold across components?
And lastly, what exactly happens when you contrast an ICA component at the group level? I've performed a contrast between two subject groups (drug users vs non users, [1 -1]) with a single ICA component selected. However the difference between those two subject groups is a cluster that falls outside of the spatial extent of that ICA component (after thresholding). Shouldn't the difference between these two subjects be constrained to only the spatial extent of the chosen ICA component?
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Title | Author | Date |
---|---|---|
Shady El Damaty | Sep 18, 2016 | |
Alfonso Nieto-Castanon | Sep 29, 2016 | |
David Pagliaccio | Jan 10, 2018 | |
Julia Binnewies | Oct 12, 2016 | |
Julia Binnewies | Oct 18, 2016 | |
Shady El Damaty | Oct 18, 2016 | |
Jeff Browndyke | Oct 18, 2016 | |
Shady El Damaty | Oct 18, 2016 | |
Shady El Damaty | Oct 12, 2016 | |
Alfonso Nieto-Castanon | Oct 14, 2016 | |
Shady El Damaty | Oct 16, 2016 | |
Alfonso Nieto-Castanon | Oct 17, 2016 | |
Shady El Damaty | Oct 17, 2016 | |
Shady El Damaty | Oct 21, 2016 | |
Alfonso Nieto-Castanon | Oct 25, 2016 | |
Jeff Browndyke | Oct 19, 2016 | |
Alfonso Nieto-Castanon | Oct 20, 2016 | |
Jeff Browndyke | Oct 20, 2016 | |
Shady El Damaty | Sep 28, 2016 | |