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help > RE: Cerebellum ROI analysis in CONN
Nov 23, 2021 03:11 AM | beckerestes
RE: Cerebellum ROI analysis in CONN
Originally posted by Alfonso Nieto-Castanon:
Hi
Alfonso,
I have one last question about this as I seem to
still be stuck. I need my T1 image, functional data and ROI's to
all be in the same space (that is, for the purposes of my analyses,
subject-space). I preprocessed a version of my functional data
outside of conn and imported that and labeled as secondary dataset
independent of primary. I coregistered my subjects T1 image to
their preprocessed (in native space, without normalisation or
smoothing) functional data, then coregistered the participant ROI
seeds which are derived from their T1 image, to the functional
data. All looks good.
However when i preprocess my primary dataset
using the default pipeline in CONN (so that my SBC maps can be
compared across subjects), it obviously normalizes the T1 image
into MNI space. So when i do my signal extraction for my seeds and
I specify that signal extraction will be from the secondary
dataset, the T1 image will no longer be in subject space. Is
there a way to specify more than one 'version' of your subject's T1
image? Or is there another obvious way to do this that I have
missed?
Thank you,
Bec
Hi Bec,
Yes, I am suggesting the latter, that you extract ROI-level BOLD time series from the ROIs in native space (the space where your ROIs are defined) and extract voxel-level BOLD timeseries from the data in MNI-space (so that SBC maps can be meaningfully compared across subjects). In CONN, voxel-level BOLD timeseries are always extracted from the 'primary dataset', so in your case you would want the 'primary dataset' to have your functional data in MNI-space, while ROI-level BOLD timeseries can be extracted from any arbitrary dataset (either primary or secondary, you can specify which), so in your case you would want at least one 'secondary dataset' to have your functional data in subject-space (and select that data when defining your dentate and cerebellum ROIs).
Hope this helps clarify
Alfonso
Originally posted by beckerestes:
Yes, I am suggesting the latter, that you extract ROI-level BOLD time series from the ROIs in native space (the space where your ROIs are defined) and extract voxel-level BOLD timeseries from the data in MNI-space (so that SBC maps can be meaningfully compared across subjects). In CONN, voxel-level BOLD timeseries are always extracted from the 'primary dataset', so in your case you would want the 'primary dataset' to have your functional data in MNI-space, while ROI-level BOLD timeseries can be extracted from any arbitrary dataset (either primary or secondary, you can specify which), so in your case you would want at least one 'secondary dataset' to have your functional data in subject-space (and select that data when defining your dentate and cerebellum ROIs).
Hope this helps clarify
Alfonso
Originally posted by beckerestes:
Thanks, Alfonso. Just so I'm clear then, what
you're saying is that using this approach you have recommended is
normalising the raw data as opposed to normalising the beta maps
(SBC maps), because the beta maps derived from the connectivity
analysis will be in native space?
So, then the workflow looks like this:
1.Define ROIs in subject space
2. Preprocess data outside on CONN in native space, and dont smooth. IMPORT into CONN and label as secondary dataset.
3. Import data from step 2 above and apply additional normalisation and smoothing into MNI space (call this primary dataset)
4. In ROIs set up tab, select extract average BOLD signal from secondary dataset (subject-space data).
My point of confusion then, is if i set up 1st level analysis and do a seed-to-voxel with native-space ROIs as my seed and secondary dataset (native space) as the voxel, then the resulting SBC maps are still in native space and can't be normalised. So what is the purpose of creating another version of the data here that is normalised into MNI space? Or are you suggesting the better (recommended) approach is to do the 1st level analysis using the extracted time series from the ROIs in native space and the 'voxel' is the rest of the brain in 'MNI' space?
Thank you again,
Bec
So, then the workflow looks like this:
1.Define ROIs in subject space
2. Preprocess data outside on CONN in native space, and dont smooth. IMPORT into CONN and label as secondary dataset.
3. Import data from step 2 above and apply additional normalisation and smoothing into MNI space (call this primary dataset)
4. In ROIs set up tab, select extract average BOLD signal from secondary dataset (subject-space data).
My point of confusion then, is if i set up 1st level analysis and do a seed-to-voxel with native-space ROIs as my seed and secondary dataset (native space) as the voxel, then the resulting SBC maps are still in native space and can't be normalised. So what is the purpose of creating another version of the data here that is normalised into MNI space? Or are you suggesting the better (recommended) approach is to do the 1st level analysis using the extracted time series from the ROIs in native space and the 'voxel' is the rest of the brain in 'MNI' space?
Thank you again,
Bec
Threaded View
| Title | Author | Date |
|---|---|---|
| beckerestes | Sep 28, 2021 | |
| Alfonso Nieto-Castanon | Sep 28, 2021 | |
| beckerestes | Oct 12, 2021 | |
| Alfonso Nieto-Castanon | Oct 12, 2021 | |
| beckerestes | Oct 13, 2021 | |
| Alfonso Nieto-Castanon | Oct 13, 2021 | |
| ela | Feb 19, 2022 | |
| beckerestes | Nov 23, 2021 | |
| beckerestes | Sep 29, 2021 | |