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Correction Motion Artifacts - Day4 of 2020 course

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David Boas
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Lisa raised several general questions about correcting MA as discussed in Day 4 of the fnirs course 2020.

  1. Is it inappropriate or problematic to run multiple types (i.e., 2+) of motion correction on a single run/subject? I assume too many corrections will result in lost brain activation signal, but sometimes it seems a single correction is not reducing enough MA.
  2. Is it okay to run different types of motion correction across runs/subjects? (Subject A gets better HRF with tPCA, but Subject B with Spline, for example). Is it okay to do different MA corrections prior to running the group averages or should the previous steps be universal across files (runs or subjects)?
  3. How is the above (#2) accomplished? By selecting the run/subject in ‘Current Processing Element’, editing the parameters on the processing stream, then running at that level? Does ‘apply to all’ need to be unchecked? After individually processing the separate files, will running at the Group level with ‘apply to all’ checked apply the most recent run processing stream to all the subjects and thereby negate any individualization that was done?
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David Boas
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Re question 1:
We strongly encourage you to use only one motion correction method when analyzing your data. You can try different methods, one at a time, to visualize the impact it has on your data. But at the end, choose only one to use.

Re question 2:
You really should use the same processing stream and function parameters for all subjects. Tailoring your processing stream functions and parameters to get the "optimal" result for each run means that you are introducing your own subjective bias into the analysis to get the result you want.

Re question 3:
This is why we have "apply all" checked, to make sure you use the same processing parameters for all subjects. Of course, you can uncheck this and then "optimize" parameters for each subject. But then you are at risk for producing the results you want to see. This can easily bias the results of your study to achieve your expected result.

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Lisa Bunker
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@dboas Thank you!

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jessie leuk
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@dboas Hi, you mentioned that it is best to have the same processing stream and parameters for all subjects. For my case, I used the same processing stream for all but the parameters (i.e. motion correction by channel threshold, I changed them according to visual inspection as some subjects have higher motion artifacts, thus I need to input smaller AMPthreshold for some subjects to pick those spikes up). Is this common among fNIRS analysis with Homer?

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David Boas
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@jessieleuk I hear many people say they have to do this. I don't fully understand why we can't use the same amplitude threshold for all subjects. I know some people use a threshold on the standard deviation as they find that this may be more consistent across subjects. It is a good topic that deserves further investigation. It should be possible to have an algorithm with set parameters that work across all subjects. The literature is growing rapidly and I am not up on all the literature any more. Maybe this has recently been addressed.

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Edgar Guevara
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(@codina)
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Hello,

I have a question regarding

hmrR_StimRejection

Should this function be placed before the M.A. correction algorithms (tPCA, spline, wavelet) (panel A in the attached file)

or after? (panel B)

Thank you

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David Boas
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I would recommend that you place it after motion correction. You do motion correction so that you can preserve trials that would otherwise be rejected because of motion artifacts.

 

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