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Thank you so much Martin, Matthew and Bruce for all the detailed information.
Just to answer your question regarding MRI data: T1-weighted data were collected using a 3.0 Tesla Siemens Tim Trio scanner (Siemens, Erlangen, Germany). T1-weighted magnetization-prepared rapid gradient-echo (MPRAGE) images were collected over 176 sagittal slices (TR/TE/flip angle = 2.1 s/2.25 ms/12°, 256 × 256 matrix) with voxel size = 1 × 1 × 1 mm.
Also, I did not perform any gradient unwarping manually, so I am not sure if this is done automatically.
So as far as I understand from the discussion, to reply reviewer's question, can I handle that something like this:
*"Geometric distortion of T1-weighted data was not manually corrected for our current morphometric analysis. This is because T1-weighted data for this study were collected using a 3.0 Tesla Siemens Tim Trio scanner. Gradient nonlinearity correction in most of the scanners, including Siemens, has a relatively small/no effect on morphometric measures such thickness, area and volume, because morphometric measures are spatially smooth and they warp both the white and pial surfaces similarly. Therefore, the distance between these surface doesn't change much. However, such effects can be very prominent in the periphery of the FOV. For instance, B0 effects can be large and localized to regions near air/tissue interfaces (mostly inferior frontal and inferior temporal). Since our results showed the association between morphometric measures of MPFC and behavior (XYZ), therefore, our findings are not due to geometric distortion."*
I am not expert in this field, so I would really appreciate any further help.
Thanks.
On Tue, May 14, 2019 at 7:13 AM Bruce Fischl fischl@nmr.mgh.harvard.edu wrote:
the gradient nonlinearities have almost no effect on thickness. Since they are spatially smooth they warp both the white and pial surfaces similarly, so the distance between them doesn't change much.
B0 effects can be large locally depending on your field strength and bandwidth. These are pretty localized to regions near air/tissue interfaces (mostly inferior frontal and inferior temporal). If those are not the regiosn you are finding thickness effects, then I think you are safe concluding it is not geometric distortion. In any case unless the condition you are looking at modifies the air/tissue interface it's hard to see why it would create an effect (although it can substantially distort thickness values for low-ish bandwidth sequences).
cheers Bruce
On Tue, 14 May 2019, Martin Reuter wrote:
Hi Martin,
probably the reviewer does not mean anything related to FS. Rather, there is distortion during image acquisition (e.g. gradient non- linearities). The reviewer probably wants to know if gradient unwarparing was done prior to running FS. That is why Matthew asked what MRI data was collected exactly (sequence, scanner etc).
Best, Martin
On Mon, 2019-05-13 at 21:06 -0700, Martin Juneja wrote:
External Email - Use CautionThank you Matthew for your reply.
I just ran recon-all to get cortical thickness, area and volume values for specific ROIs using the steps here: https://surfer.nmr.mgh.harvard.edu/fswiki/VolumeRoiCorticalThickness and correlated the morphometric values with behavioral data. I am not sure how can I address reviewer's comment.
Any help would be really appreciated.
On Mon, May 13, 2019 at 5:41 PM Glasser, Matthew glasserm@wustl.edu wrote:
External Email - Use CautionThere are different kinds of geometric distortion that affect your data to differing extents. What kind of MRI data was in your study?
Matt.
From: freesurfer-bounces@nmr.mgh.harvard.edu on behalf of Martin Juneja mj70481@gmail.com Reply-To: Freesurfer support list freesurfer@nmr.mgh.harvard.edu Date: Monday, May 13, 2019 at 6:53 PM To: Freesurfer support list freesurfer@nmr.mgh.harvard.edu Subject: [Freesurfer] Geometric distortion
External Email - Use CautionHi experts,
I have following question from one of the reviewers regarding recon-all pipeline: "Was geometric distortion corrected? If not, this should be discussed or mentioned in the study limitation." I was wondering if the reviewer is referring to following step in the recon-all pipeline. Could you please help me in addressing this concern by the reviewer?
Thanks. MJ Cortical Parcellation (-<no>cortparc, -<no>cortparc2) Assigns a neuroanatomical label to each location on the cortical surface. Incorporates both geometric information derived from the cortical model (sulcus and curvature), and neuroanatomical convention. Calls mris_ca_label. -cortparc creates label/?h.aparc.annot, and -cortparc2 creates /label/?h.aparc.a2005s.annot.
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