Closed smeisler closed 2 years ago
Hi Steven,
I'm glad you are interested in the Functionnectome.
First, just so that we are on the same page: The Functionnectome takes 4D brain images as input (usually fMRI, but could also be brain images with another metric measured) and project the grey matter metric (usually BOLD signal for fMRI) onto the white matter. The input and the priors must be in the same space, with the same voxel size (which is MNI space with 2mm isotropic voxels with our priors).
As for your questions:
I hope I could clarify some of the questions you had. I'll keep this issue open as long as there are some points that you need answers to, so don't hesitate to ask.
Cheers, Victor
Thanks for the response!
0) This would be for denoising the 4d time series (so the regression would be against the 4th / time dimension). In the paper you say you regress out motion realignment parameter and wm/csf signal, so I was curious if this was specifically chosen or if one could play around with other denoising configurations.
1) Understood. The DWI would have been used to generate priors (related to point 3).
2) Got it!
3) Yes that definitely poses some interesting conceptual challenges, but I think it's a worthwhile endeavor given inter-individual variability in white matter pathways.
You're welcome!
I hope you got all the answers you needed, but of course, I remain available if you need more precisions or have other questions.
Cheers
Hi Steven,
I just realised that I haven't updated the manual, but with the newest version of the Functionnectome(version >= 1.1.0), you have to be careful with the priors you enter: the new algo uses probability maps from the WM voxels, instead of the probability maps from the GM voxels (in a sense, instead of "projecting" the GM signal onto the WM, the algo "grabs" the GM signal into the WM, it's a shift in perspective but doesn't change anything in practice). In fact, for now, it uses both GM and WM voxels maps.
In our priors, we already had both GM and WM voxels probability maps, so I didn't have to change anything, but if you are making custom priors, you have to generate probability maps of voxels in the WM in order for the algo to work... I don't know if you are still working on it or if you had troubles with it, but don't hesitate to come back to me if there is any problem.
Best wishes, Victor
Sorry to reopen an old thread, but I was curious while reading your manuscript why you decided to only apply extra fMRI preprocessing steps to the resting state fMRI. More specifically:
Additionally, the resting-state acquisitions were further preprocessed with despiking, detrending of motion and CSF, white matter and grey-matter signal, temporal filtering (0.01–0.1 Hz), and spatial smoothing (5 mm FWHM).
Also I am a bit confused as to whether images should be spatially smoothed. The above suggests yes, but then later in the manuscript:
Specifically, no spatial smoothing is required for the functionnectomes 4D volumes. Usual smoothing aims at improving the signal/noise ratio (SNR) using a weighted average of the local signal, assuming that neighbouring voxels share some signal of interest. The functionnectome method combines the signal from distant yet structurally linked voxels, which has an analogous effect of improving the SNR, but is guided by actual brain circuits.
Thanks again, Steven
Hey, no problem. As I am not a specialist of task activation fMRI (I'm more of a resting-state guy), I decided to stick with the preprocessing used for such data in HCP papers (see https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4011498/ "fMRI Data Processing"). As I understand it, FSL's FILM used for the analysis applies whitening and temporal filtering of the time-series, which complete the preprocessing. On the other hand, the additional steps we used for the resting-state data are just the usual preprocessing steps used for this kind of data, needed to properly extract RSNs.
Concerning the smoothing, note that we did not use the resting-state fMRI with the Functionnectome, we only applied it to the task fMRI. In that case, we used smoothing when computing the traditional task activation maps, and not when feeding the data to the Functionnectome (which would act like a spatial filter itself). Now, should you smooth your data before using the Functionnectome? Well, I think that depends on the data. if it's noisy or you are not sure about your grey matter mask, maybe you should yes, to ensure that the relevant signal is at least partially at the right place. In the article, we prefered to skip the smoothing as we thought it was not necessary (with the filtering effect of the Functionnectome), and because it would likely make the functionnectome activation maps "fatter", less specific.
I hope that answered your question :) If not, don't hesitate to come back to me, I'll do my best to clarify.
Cheers, Victor
That makes perfect sense, I understand now the traditional smoothing was for the analogous task activation maps as a reference method. I also really like the notion you describe of smoothing based on white matter connectivity; reminds me of the kind of white-matter local neighborhood smoothing used in fixel-based analyses.
Hello,
Really interested in this bimodal tool! But I have some questions about the inputs. I would like to use data that have already been minimally processed by fMRIPrep (fmri), QSIPrep (dwi), and QSIRecon (tractography), which should be comparable to HCP pipelines.
0) Should one feel open to explore with fMRI regressors? For example, would it be alright to add in the squared and derivative expansions of motion realignment parameters? 1) Do the fMRI and DWI have to be in the same space? The QSIPrep has been rotated for ACPC alignment, so although it is still native space it is not aligned to the functional scan. 2) Is the isotropic 2mm resolution required? And does that only apply for the functional image (not the dwi)? 3) Instead of using the HCP "pretrained" anatomical priors, is it possible / well-advised to use subject-specific priors derived from probabilistic tractography, such that each subject has a unique probability map?
Thanks, Steven