Hi Lingqiang, I did not have a strong reason not to do the low pass filtering. I think I tried it once and it did not seem to make much difference, so I did not implement it in FSFAST. The differences may be due to filtering, but there are a lot of other places where things can be different too. Can you turn off the filtering in spm? doug

konglq@nmr.mgh.harvard.edu wrote:

Thanks Doug. The labels work perfectly now.

I also have a methodological question about the fsfast connectivity analysis: functional connectivity analyses in the literature (Biswal, etc) only looks at low-frequency fluctuations from BOLD signals. After preprocessing and regressing out nuisance regressors, the BOLD signal is low-pass filtered (usually 0.01-0.1Hz) before the activity within the seeds are averaged and calculating the whole brain correlation map.

I couldn't find anything like that within the fsfast connectivity processing streamline. Is that something that can be turned on and off ? If so, is there a reason to do it or not do it?

PS. We have ran the same data in the same subject with same seeds using fsfast connectivity streamline and the SPM functional connectivity toolbox (Gabrieli lab). The results are somewhat similar but there are also marked differences. I wonder if the low-pass step has anything to do with that.

Thank you.

Lingqiang

...

Hi Lingqiang, convert your .label file to a volume using mri_label2vol for each subject, something like

cd $SUBJECTS_DIR/$subject/mri mri_label2vol --label lh.yourlabel.label --temp orig.mgz --regheader orig.mgz --subject $subject --hemi lh --proj frac 0 1 .1 --o yourlabel.mgz

When you run fcseed-config specify -segmentation yourlabel.mgz -segid 1

doug

konglq@nmr.mgh.harvard.edu wrote:

...

Hi Freesurfers,

We are trying to do a resting-state functional connectivity analysis using freesurfer. Instead of using the aparc+aseg segmentations as seeds, we would like to use a subject-specific surface labels that we have defined apriori.

In the fcseed-config step, we then don't have a "segid" as in the anatomical seeds case. What should we specify as the -segid and -seg (segmentation)?

We also looked into the ROI-based options using "-roi" but this takes seeds from output of "funcroi-config" and requires that we specify a contrast and an analysis. How should we just specify a surface label without generating a new analysis and contrast?

Any help or suggestions are appreciated.

Lingqiang _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer

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