NITRC Functional Connectivity Community Forum: open-discussion

Functional connectivity on (FDG)PET data

Enrique Franky — Wed, 23 Oct 2024 16:58:33 GMT

Hi all,
I am a researcher exploring the possibility of applying functional connectivity analysis to some FDG PET data. This data is static (or sum over the 4D dynamic image), one volume per subject, and each subject has a 30-month follow-up session. Despite the various tools designed for connectivity analysis (Conn, network-based statistics, Graphvar, Nilearn, etc.), all are built to aim for fMRI/rsfMRI studies. The problem when trying to adapt these toolboxes to my PET data is that they require analysis of the time-series signal (BOLD, in the case of fMRI/rsfMRI).
The Conn toolbox has a step to denoise the images according to covariables generated for each study from quality assessments of the 4D alignment/motion, HRF model (I think?), and PCA 5 components of the WM and CSF. GraphVar expects to work on time signals from a .mat file. I haven’t explored the in-depth network-based statistic (NBS) toolbox or Nilearn yet, but they are also explicitly built for analyzing fMRI.
So my doubts are the following:
<ul>
<li>If I expect to find a condition-specific network for both of the sessions acquired independently and jointed, should I first consider the subsets of my subjects related to that condition on each sessions as a time series and create the first level analysis, considering a subject in this case as the sample that represents a condition, to form the network that represents the condition and then apply a second-level analysis for a cross-sectional study?, and when consider the both sessions jointed (longitudinal analysis) should I form the time-series signal of FDG as the two scans from both sessions for each subject and generate the network on a individual level for then apply the second analysis to see the differences on both conditions?</li>
</ul>
I plan my methodology this way because we are interested on both cross-sectional study (on both sessions) and a longitudinal study.
<ul>
<li>Also, Is it fine to try to run this analysis on these fMRI-oriented toolboxes? or do these toolboxes have a more specific model design that won’t let me analyze the PET images without modelling them as fMRI? Should I start from scratch and design all the pipeline without a framework?.</li>
</ul>
Thank you, any help is welcome!!,

please help : aboout using GraphVar_2.03

Yongqin Xiong — Tue, 26 May 2020 6:28:02 GMT

Hi

After setting path, I try to run the GraphVar. But here is what I got

[color=#ff0000]>> GraphVar[/color]
[color=#ff0000]Error using cd[/color]
[color=#ff0000]Cannot CD to /Users/JK/Desktop/GraphVar_2.02b/workspaces/SampleWorkspace (Name is nonexistent or not a[/color]
[color=#ff0000]directory).[/color]

[color=#ff0000]Error in GraphVar_OpeningFcn (line 100)[/color]
[color=#ff0000]cd(workspacePath);[/color]
[color=#ff0000]Error in gui_mainfcn (line 220)[/color]
[color=#ff0000]feval(gui_State.gui_OpeningFcn, gui_hFigure, [], guidata(gui_hFigure), varargin{:});[/color]
[color=#ff0000]Error in GraphVar (line 40)[/color]
[color=#ff0000]gui_mainfcn(gui_State, varargin{:});[/color]

[color=#000000]This is the first time I use GraphVar, please help me with the above problem.[/color]

[color=#000000]Thank you very much,[/color]

[color=#000000]best regards.[/color]

[color=#000000]Yongqin[/color]

RE: Simplest Tool?

Alfonso Nieto-Castanon — Thu, 03 Sep 2009 22:15:11 GMT

I have just added the latest version of Sue's functional connectivity toolbox to the NITRC site: http://www.nitrc.org/projects/conn . In addition to extracting the ROI timecourses and estimating ROI-to-ROI correlation measures, it performs seed-to-voxel analyses (maps of functional connectivity values between an ROI and every voxel), includes a pre-processing step that removes confounds (regressing-out motion parameters and noise signals obtained from white matter and CSF areas), and performs a number of second-level random effect analyses. Hope you find it useful

RE: Simplest Tool?

Shelli Kesler — Wed, 26 Aug 2009 20:51:47 GMT

I like the connectivity toolbox by Sue Whitfield-Gabrielli: http://web.mit.edu/swg/software.htm. It does preprocessing and seed based analyses.

RE: Simplest Tool?

David Kennedy — Thu, 22 May 2008 21:28:31 GMT

Excellent suggestions. In the spirit of the NITRC projects, let's annotate how to get at these various tools you mention:

PLSGUI - hmmm, not in NITRC! It should be! But no worries, it's at http://www.rotman-baycrest.on.ca/index.php?section=84

BrainVoyager - also not in NITRC, darn. I keep asking them, but to no avail as yet. If anyone else can try to suggest their adding a project, feel free. Still no worries, it's at http://www.brainvoyager.com/

SPM - ah yes, here we go: http://www.nitrc.org/projects/spm/

MarsBar - alas, not at NITRC, but I've pinged them on occasion, so if anyone has any influence there, feel free to exert it. https://sourceforge.net/projects/marsbar/ Since it's SourceForge, it'll be easy to include in NITRC!

AFNI - yeah! http://www.nitrc.org/projects/afni/

RE: Simplest Tool?

Jordan Poppenk — Thu, 22 May 2008 20:49:09 GMT

PLSGUI has a nice built-in intensity extraction feature for voxels / regions and so does BrainVoyager, but I think SPM requires the MarsBar add-on and requires some fiddling. I'm not sure about AFNI. I think these tools would be about equally difficult to use as the bulk of the preparation time will be in programming your design matrix. That said, if you go with PLSGUI, you have the advantage of being able to set up exploratory connectivity analyses later without much extra effort (e.g., task analysis, seed analysis).

RE: Simplest Tool?

David Kennedy — Thu, 22 May 2008 20:19:14 GMT

Yes, operationally that is simple, but which 'tool' makes this easiest for me to do, by the time I create the ROIs, extract the signal, and perform the correlation? FSL, SPM, BrainVoyager, AFNI, other???

RE: Simplest Tool?

Jordan Poppenk — Thu, 22 May 2008 15:46:06 GMT

Wouldn't the simplest approach would be to extract mean intensity data for the two regions or peaks and compute the correlation?

Simplest Tool?

David Kennedy — Wed, 21 May 2008 23:35:55 GMT

If I have a simple fMRI motor (hand squeezing) task, what is the simplest tool to use to make functional connectivity estimates between, for example, the primary motor and primary somatosensory activation foci?

Welcome to Open-Discussion

Christian Haselgrove — Thu, 20 Mar 2008 19:14:26 GMT

Welcome to Open-Discussion