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help > RE: Extremely high values of contrast estimates in SPM after using CONN preprocessed and denoised data
Mar 7, 2019 05:03 PM | avantika mathur - UNL
RE: Extremely high values of contrast estimates in SPM after using CONN preprocessed and denoised data
Hi Alfonso,
The Global Normalization is already set to "None" in spm first level.
Attached is the image of the same.
Do you mean something else is supposed to be changed?
Avantika
Originally posted by Alfonso Nieto-Castanon:
The Global Normalization is already set to "None" in spm first level.
Attached is the image of the same.
Do you mean something else is supposed to be changed?
Avantika
Originally posted by Alfonso Nieto-Castanon:
Hi
Avantika,
I would suggest trying to set the "grand mean scaling" option in SPM first-level estimation off, since that looks like a possible culprit for this behavior (after band-pass filtering, the mean functional data is zero at every voxel, so global signal scaling -and similarly any other default mechanisms that rely on the average BOLD signal containing anatomical information/features- are likely to fail in rather unexpected ways). Let me know if that works
Best
Alfonso
Originally posted by avantika mathur:
I would suggest trying to set the "grand mean scaling" option in SPM first-level estimation off, since that looks like a possible culprit for this behavior (after band-pass filtering, the mean functional data is zero at every voxel, so global signal scaling -and similarly any other default mechanisms that rely on the average BOLD signal containing anatomical information/features- are likely to fail in rather unexpected ways). Let me know if that works
Best
Alfonso
Originally posted by avantika mathur:
Hi Conn users,
After following the following posts,
https://www.nitrc.org/forum/message.php?...
I used the alternative method to import conn preprocessed data in SPM which is the following :
Entering the preprocessed/denoised timeseries into SPM to perform the first-level analyses.
The data I am analyzing is children data thus, ART was used at liberal threshold in preprocessing [Global signal z value threshold 10, subject motion 5 mm]. I did not have the "effect of Condition X" entered as confounding effects during Denoising.
I used the file generated after conn preprocessing and denoising...the niftiDATA_Subject001_Condition000 and further defined first-level design matrices within SPM, specified masking threshold to -Inf in first level analysis [https://www.nitrc.org/forum/message.php?msg_id=14852].
After doing first level analysis and group level analysis [10 subjects], I get weird beta estimate values - which are extremely high . Attached are the bar plots for the same [1st bar-chart - single subject, 2nd bar chart - group of 10 subjects]. Beta values should not be this high.
Can someone direct me where I am going wrong?
Avantika
After following the following posts,
https://www.nitrc.org/forum/message.php?...
I used the alternative method to import conn preprocessed data in SPM which is the following :
Entering the preprocessed/denoised timeseries into SPM to perform the first-level analyses.
The data I am analyzing is children data thus, ART was used at liberal threshold in preprocessing [Global signal z value threshold 10, subject motion 5 mm]. I did not have the "effect of Condition X" entered as confounding effects during Denoising.
I used the file generated after conn preprocessing and denoising...the niftiDATA_Subject001_Condition000 and further defined first-level design matrices within SPM, specified masking threshold to -Inf in first level analysis [https://www.nitrc.org/forum/message.php?msg_id=14852].
After doing first level analysis and group level analysis [10 subjects], I get weird beta estimate values - which are extremely high . Attached are the bar plots for the same [1st bar-chart - single subject, 2nd bar chart - group of 10 subjects]. Beta values should not be this high.
Can someone direct me where I am going wrong?
Avantika
Threaded View
| Title | Author | Date |
|---|---|---|
| avantika mathur | Mar 2, 2019 | |
| Alfonso Nieto-Castanon | Mar 5, 2019 | |
| avantika mathur | Mar 7, 2019 | |
| Sneha Sheth | Sep 27, 2021 | |