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help > RE: voxel-to-voxel level connectivity matrix
Jul 29, 2014 08:07 AM | Alfonso Nieto-Castanon - Boston University
RE: voxel-to-voxel level connectivity matrix
Since other people had requested something along these lines as
well, I am attaching a few such ROI parcellations in a format that
can be entered directly into CONN as standard ROI files (I will add
these files to conn/utils/otherrois/ in the next release of the
toolbox as well).
Each of the P#.img files there represents a simple MNI-space parcellation of all gray matter voxels into cube ROIs, each file using different cube lengths (and resulting in different total number of ROIs):
P20.img: 304 ROIs; each ROI is a 20mm cube
P18.img: 406 ROIs; each ROI is a 18mm cube
P16.img: 576 ROIs; each ROI is a 16mm cube
P14.img: 830 ROIs; each ROI is a 14mm cube
P12.img: 1292 ROIs; each ROI is a 12mm cube
P10.img: 2186 ROIs; each ROI is a 10mm cube
You may use any of these to compute low-resolution voxel-to-voxel matrices using the standard ROI-to-ROI processing pipeline in CONN.
Hope this helps
Alfonso
Originally posted by Alfonso Nieto-Castanon:
Each of the P#.img files there represents a simple MNI-space parcellation of all gray matter voxels into cube ROIs, each file using different cube lengths (and resulting in different total number of ROIs):
P20.img: 304 ROIs; each ROI is a 20mm cube
P18.img: 406 ROIs; each ROI is a 18mm cube
P16.img: 576 ROIs; each ROI is a 16mm cube
P14.img: 830 ROIs; each ROI is a 14mm cube
P12.img: 1292 ROIs; each ROI is a 12mm cube
P10.img: 2186 ROIs; each ROI is a 10mm cube
You may use any of these to compute low-resolution voxel-to-voxel matrices using the standard ROI-to-ROI processing pipeline in CONN.
Hope this helps
Alfonso
Originally posted by Alfonso Nieto-Castanon:
Dear
Yifei,
The typical way this is done is by using a considerably lower-resolution resampling of the functional data to bring the number of "voxels" to something more manageable (e.g. closer to ~1000 or below). In CONN the simplest way to do this is to create a ROI parcellation with your desired "low-resolution" parcels (e.g. 10mm cubes) and then perform standard ROI-to-ROI analyses (which will create, and allow you to analyze, your desired voxel-to-voxel connectivity matrices).
Hope this helps and let me know if you would like me to further clarify any of this
Best
Alfonso
Originally posted by Yifei Zhang:
The typical way this is done is by using a considerably lower-resolution resampling of the functional data to bring the number of "voxels" to something more manageable (e.g. closer to ~1000 or below). In CONN the simplest way to do this is to create a ROI parcellation with your desired "low-resolution" parcels (e.g. 10mm cubes) and then perform standard ROI-to-ROI analyses (which will create, and allow you to analyze, your desired voxel-to-voxel connectivity matrices).
Hope this helps and let me know if you would like me to further clarify any of this
Best
Alfonso
Originally posted by Yifei Zhang:
Dear all,
I am wondering how to get the voxel-to-voxel level connectivity matrix. According to the previous discussion, it needs a lot of calculation for the voxel-to-voxel level matrix and conn will not save it during the calculation, but there are several articles using this method. Can anyone tell me how or where I can find a tool to realise this analysis?
Any help would be appreciated!
Best regards,
Yifei
I am wondering how to get the voxel-to-voxel level connectivity matrix. According to the previous discussion, it needs a lot of calculation for the voxel-to-voxel level matrix and conn will not save it during the calculation, but there are several articles using this method. Can anyone tell me how or where I can find a tool to realise this analysis?
Any help would be appreciated!
Best regards,
Yifei
Threaded View
| Title | Author | Date |
|---|---|---|
| Yifei Zhang | Jul 24, 2014 | |
| Alfonso Nieto-Castanon | Jul 29, 2014 | |
| Alfonso Nieto-Castanon | Jul 29, 2014 | |
| Pravesh Parekh | Feb 29, 2016 | |
| Alfonso Nieto-Castanon | Mar 14, 2016 | |
| Pravesh Parekh | Mar 14, 2016 | |
| Yifei Zhang | Jul 29, 2014 | |