Hello AFNI expert,
I would like to compare the ability of different atlases to extract a homogeneous BOLD signal in each ROI.
I created different atlases based on ICA. I built one atlas per component number (for ex: from 5 to 30 components so I have 25 atlases).
I was thinking of using 3dReHo with -in_rois one each atlas and calculating the curve of an increase of ReHo per atlas.
Since I increase the number of regions and so the number of subdivisions in each atlas I may expect a relatively linear increase of the mean ReHo for each atlas?
The idea is to identify one or two atlases that display a significantly high ReHo for their component number.
Does this make sense? Does this will really identify an atlas that could be “ideal” for a correlation matrix analysis?
If not, is their another way to do that?
I hope that I am not saying non-sense.
thank you for help!
Clément
Hi, Clément-
That sounds a reasonable thing to do: ReHo should provide a measure of homogeneity of time series across ROIs.
Re. “Since I increase the number of regions and so the number of subdivisions in each atlas I may expect a relatively linear increase of the mean ReHo for each atlas?”
Well, I’m not sure about that. I don’t really know what to expect, to be honest. I don’t know what to expect as ROIs get larger. I guess you will just have to calculate and find out.
In my view, how you equate ROIs across the different ICA splits seems harder-- inherently, at each level you will have a different number of ROIs, so how will you track the splits? (And indeed, people always talk about ICA being “data driven” and better than seedbased correlation for providing non seed-location-driven maps, but the number of ICs selected heavily affects the output maps and divisions of networks…)
Re. “Does this will really identify an atlas that could be ‘ideal’ for a correlation matrix analysis?”
Well, I don’t know about being able to define and ideal correlation matrix-- my guess is that smaller ROIs have a better chance of being homogeneous: consider, an ROI of one voxel has a pretty homogeneous time series! So, there are probably other considerations than just homogeneity that one might want to account for. However, as a general way to proceed, looking at homogeneity seems useful; how that is balanced with size of ROIs, for example, is a separate (and harder?) question.
NB: based on this recent thread:
https://afni.nimh.nih.gov/afni/community/board/read.php?1,163366,163366#msg-163366
… I have updated the output format of the ROI-based ReHo calcs to a (hopefully) nicer format. So, you might want to update your AFNI version before embarking on these calcs:
@update.afni.binaries -d
–pt
Hi, Pt-
First, thank you for your answer,
I have done the analysis and:
Re. “Since I increase the number of regions and so the number of subdivisions in each atlas I may expect a relatively linear increase of the mean ReHo for each atlas?”*
Re. “Does this will really identify an atlas that could be ‘ideal’ for a correlation matrix analysis?”
–Yes, the increase is really linear and it actually doesn’t allow the identification of an atlas with interesting homogenous properties.
In my view, how you equate ROIs across the different ICA splits seems harder-- inherently, at each level you will have a different number of ROIs, so how will you track the splits?
In the first place, I didn’t track the split but average the ReHo score of all the regions of a given atlas.
I was thinking of creating a script that allows the tacking of the split, based on the overlap score of a given component of an ICAn and an ICAn+1. However, it is very time consuming and since the first step is not very interesting I will stop here.
Thank you again!
Clément
Hi, Clément–
OK, thanks for the feedback.
(And I agree, tracking the splits would be tough, carrying a bit of arbitrariness, too. 3dMatch might be able to help with this, using successive layers as “refsets”, but anyways, such a project would be pretty time consuming.)
–pt