Dear Tim and Matt, thank you very much for your detailed and insightful answers. I learned a lot and agree to nearly everything you said. Especially, I totally agree that we should not use T1w -> T1w Template nonlinear volumetric warps + smoothing nowadays anymore. Regarding the sulcus-on-gyrus mismatches for techniques such as DARTEL/Shoot/ANTs that Matt pointed out, I wonder to what extent the inclusion of FA and e.g. rs-fMRI eigenvector centrality maps in multispectral warps could minimize such mismatches. I agree that it would be nontrivial to add a real connectome (i.e. edges) to the volumetric deformation problem. And again, I lack empirical data to be able to say how much impact either method would really have on results – a fair comparison study would be great and important to the field in my view. On the other hand, I agree that MSM is an awesome technique and why should we not just use it since it’s available.
Best, Andy Am 11.08.2016 um 17:49 schrieb Timothy Coalson <tsc...@mst.edu<mailto:tsc...@mst.edu>>: Sorry, I was not precise enough in my language - my earlier comments should be considered in the context of cortex only (the MMP v1.0 is cortex-only). The "volume-based group-average methods" methods I meant to refer to are when people analyze the whole brain, including cortical data, by doing some T1w MNI registration, and then smooth the volumes (to partially make up for deficiencies in the cortical registration), and then average all signal across subjects (including cortical), still in the volume. This has a whole host of problems, but some people persist in doing things this way. Other replies inline. Tim On Thu, Aug 11, 2016 at 10:03 AM, Horn,Andreas (BIDMC - Neurology) <aho...@bidmc.harvard.edu<mailto:aho...@bidmc.harvard.edu>> wrote: Hi Tim, Yes, of course it’s compared to an average surface – but isn’t that the final goal of brain mapping to somehow define regions within a well-defined space? I would say it is more specific than that - the ideal goal is to define, for each subject, the locations in each atlas region. In the case of the HCP MMP v1.0, it only defines cortical regions, in terms of areal features (for instance, functional connectivity). A group-average cortical surface has very little of the folding (geometric definition) you will find in any contributing subject, which makes it a notably inferior method for transferring cortical data to or from a nonlinear-registered volume template (we aren't enthusiastic about the MMP cortical data being used as a volume file, but there are better ways to get there than group average surfaces). I totally agree with you and Matt that there are a lot of advantages of surface based processing, especially when we are predominantly interested in only the cortex. However, I find it a bit too dogmatic to say that something is only feasible using surface-based analyses and that surface-based approaches are the (only) thing the field should be doing. Our method of generating the HCP MMP v1.0 relied heavily on the MSMAll surface registration. It was a very critical step to being able to do what we did, and I don't know of volume registrations that achieve comparable areal feature alignment in cortex (and most of the ones that I know people use only register anatomy, and don't try to use things like functional connectivity). Perhaps Matt or David have done a more thorough survey of volume methods for registering areal features in cortex. Some things are definitely easier using surfaces (since we can reduce the 3D problem to a 2D problem by projecting the surface to a sphere). Also, it’s much easier to inflate resolution since the data points are drastically reduced. However, my feeling is that volume based approaches have also improved a lot over the last years with multispectral diffeomorphic processes that are often segmentation based, i.e. reduce the warping techniques between single subject’s cortices and an average mean to a more or less 2-D problem as well. In the end, any warp is a set of coordinates projected to another set of coordinates, no matter if doing this on a surface or a volume, right? The warp is just sometimes more constrained on a surface. Surface registration also doesn't require changing the anatomical shape of the cortex in order to enable cross-subject comparisons or group averaging. This makes it easier to regularize the registration in a way that is not penalized for an unusual folding pattern. As before, Matt or David may be in a better position to comment regarding the current state of the field in volume registration of cortical areal features. I’d be very interested in good comparison studies that show superior results using the most advanced surface-based techniques in comparison to most-advanced volume-based techniques (like e.g. multispectral ANTs SyN deformations using the OASIS templates or MNI 2009b NLIN series or similar). This would be an interesting comparison to do (and perhaps include a more "traditional" volume method as well to put any performance difference in perspective). I’d still guess that the surface based approaches would be superior on the cortex but I wonder how much impact it would really have. Really curious about how you did this in the upcoming Nature Neuroscience article and to which volumetric analyses you compared your results. As I said, my comments were intended in the context of cortex, that is, where the HCP MMP v1.0 is defined. It is likely that for a future version including subcortical components, we would use volume registration and voxel representation for those, as they are not as challenging for volume registration (don't have cortical folding variability). Personally, I am interested in deep brain stimulation and small subcortical structures like the subthalamic nucleus. This structure is not visible on T1 (but T2) and is not represented on tissue probability maps at all (there is an enhanced TPM including it by Bogdan Draganski available as a side-note). In my view, the surface-based world it not at all ready to deal with such structures (correct me if I’m wrong). As I understand it, most of these structures do not have a sheetlike nature the way cortex does, and thus we probably would not advocate using surfaces to represent them. When assessing connectivity from these structures to the rest of the brain, it makes it a lot easier to stick to the volume-based approach (and not do volume-to-surface projections at all). Moreover, we are often merely interested in connectivity to „motor“, „sensory“, „limbic“ and „associative“ regions. It could be seen as methodological over engineering to implement volume-to-surface based methods for such trivial parcellations. So this could maybe illustrate an example where it is – at least in my view – still totally fine to use multispectral volumetric deformations for connectometric analyses. I was not intending to say that subcortical structures should be analyzed on a surface. Then, the volumetric version I put up on figshare is really for comparative reasons with atlases that used different techniques and are available in MNI space. Using group average cortical surfaces (which lack significant folding definition) to generate it means it won't align well with any subject's cortex post-registration (because it doesn't align well with the template's cortex features), which is what I was trying to say (with the comment about viewing overlaid on the template slices). Thus, this representation of it will fall short of other volume atlases in terms of cortical overlap with the volume template. I guess this is something many people are interested in. For instance, the histological atlas by the Jülich group exhibits anatomical detail and has been used by the field by coregistering nonlinear warps to it for decades – totally accepting the fact that histology was originally based on different brains than used to construct the MNI templates. In my view, we’d do nothing else with your averaged anatomy atlas if we would compare our results to your map. Per my other comments, this comparison will be compromised when using any group-average cortical surface to translate any cortical data between surface and volume (either direction). We should be aware of likely mismatches in classifications in the same way as we have always been using e.g. the SPM anatomy toolbox or comparisons to the Harvard Oxford atlas. Still, such a comparison could be helpful (in my view). I hope we may agree on a few points I raised. Of course, if the volumetric MMP version bothers you, I’ll gladly put it offline again. People obviously want a volume representation of it, despite the caveats of it being hard to faithfully represent as a volume, and I don't know whether we have reached a decision on whether there is a method of generating it that we think is a reasonable approximation. I'll defer to Matt or David on the question of whether it bothers us. Best, Andreas -- Andreas Horn, MD Laboratory for Brain Network Imaging and Modulation Berenson-Allen Center for Noninvasive Brain Stimulation Department for Neurology, Beth Israel Deaconess Center Harvard Medical School 330 Brooklin Avenue, Kirstein Building KS 158 02215 Boston t: +1 6174077649<tel:%2B1%206174077649> w: http://www.brainnetworkstim.com<https://urldefense.proofpoint.com/v2/url?u=http-3A__www.brainnetworkstim.com&d=CwMFaQ&c=WknmpdNpvrlj2B5K1aWVqL1SOiF30547pqSuOmtwXTQ&r=BTWOP47EynCdRIxIQsxjKLHVp-ucrpC9iMHv26t1NFM&m=gxnHAaHBn2ma4u-R-Z4YZFC3vjjntoi7ICOm3GGoWN8&s=VUZJHmOG8XwpATQMWNnsUgM8RGIDMbdBlrbWWMsbGgY&e=> Am 08.08.2016 um 18:18 schrieb Timothy Coalson <tsc...@mst.edu<mailto:tsc...@mst.edu>>: Thanks for putting a note on that page about how we don't recommend volume-based group-average methods. It should be noted that the similarity between the two representations in that figure is due to the use of a group average surface for display, so that the surface representation shown is also lacking in folding definition. Display of the volume data as a slice overlaid on a T1 volume would show this lack of folding more clearly. However, it looks like the coloring scheme has been changed. Do the left and right labels still have different values in your version? Tim On Mon, Aug 8, 2016 at 1:38 PM, Andreas Horn <andy_h...@icloud.com<mailto:andy_h...@icloud.com>> wrote: Hi Andrew, I made a projection here: https://figshare.com/articles/HCP-MMP1_0_projected_on_MNI2009a_GM_volumetric_in_NIfTI_format/3501911<https://urldefense.proofpoint.com/v2/url?u=https-3A__figshare.com_articles_HCP-2DMMP1-5F0-5Fprojected-5Fon-5FMNI2009a-5FGM-5Fvolumetric-5Fin-5FNIfTI-5Fformat_3501911&d=CwMFaQ&c=WknmpdNpvrlj2B5K1aWVqL1SOiF30547pqSuOmtwXTQ&r=BTWOP47EynCdRIxIQsxjKLHVp-ucrpC9iMHv26t1NFM&m=H5xXuSmR3u1QpacRdYn9m0eZGn-PQpp4kLn56KftEvo&s=Lr8aIWvpWl0qlTOgPrMh6zgmoTWlS1uVtjLW48nAtNA&e=> Best, Andy Am 08.08.2016 um 14:34 schrieb David Van Essen <vanes...@wustl.edu<mailto:vanes...@wustl.edu>>: Hi Andrew, 1) As noted in a previous thread, -cifti-separate should solve this problem. On Jul 20, 2016, at 7:37 PM, Chris Gorgolewski <krzysztof.gorgolew...@gmail.com<mailto:krzysztof.gorgolew...@gmail.com>> wrote: Awesome - this did the trick. Thanks! On Wed, Jul 20, 2016 at 5:03 PM, Timothy Coalson <tsc...@mst.edu<mailto:tsc...@mst.edu>> wrote: Use -cifti-separate with the -label repeatable option to make the left and right cortex gifti label files. Tim 2) As noted in other recent hap-users threads, mapping the HPC_MMP1.0 surface parcellation via a group average midthickness to a group-average volume pays a steep price in the fidelity of spatial relationships, particularly in regions of high individual variability in folding patterns. We have a paper in press (Nature Neuroscience, appearing Aug 28) that discusses this and related issues and suggests alternative analysis strategies for more faithfully preserving spatial fidelity. David On Aug 8, 2016, at 9:14 AM, Reid, A.T. (Andrew) <a.r...@psych.ru.nl<mailto:a.r...@psych.ru.nl>> wrote: Hi all, For comparison purposes, we want to project the excellent new surface parcellation to a NIFTI volume. We tried to do this in two steps using wb_command: 1. Convert CIFTI to GIFTI: wb_command -cifti-convert -to-gifti-ext Q1-Q6_RelatedParcellation210.L.CorticalAreas_dil_Colors.32k_fs_LR.dlabel.nii glasser_labels.gii 2. Project labels to volume (using the nearest-vertex option): wb_command -label-to-volume-mapping glasser_labels.gii Q1-Q6_RelatedParcellation210.L.midthickness_MSMAll_2_d41_WRN_DeDrift.32k_fs_LR.surf.gii MACM_F1_RostroMiddle_red.nii EssaiMap.nii -nearest-vertex 3 Unfortunately, this gives an error: ERROR: input surface and label file have different number of vertices Most likely because the labels are for both hemispheres, and the surface is only for the left hemisphere. Not sure where to go from here. Is there a command to combine surfaces, or conversely to split the labels? Is there a combined surface file somewhere available? Thanks, Andrew _______________________________ Andrew Reid Postdoctoral Fellow Department of Cognitive Artificial Intelligence Donders Institute for Brain, Cognition and Behaviour Radboud University Nijmegen Web: http://andrew.modelgui.org/<https://urldefense.proofpoint.com/v2/url?u=http-3A__andrew.modelgui.org_&d=CwMFAg&c=WknmpdNpvrlj2B5K1aWVqL1SOiF30547pqSuOmtwXTQ&r=BTWOP47EynCdRIxIQsxjKLHVp-ucrpC9iMHv26t1NFM&m=y4DiXP3EHKT-ppnuw6sH4JENi0SMn5XcrCnpGj4jVJw&s=xPD2VFCvGeS108si8baJMaVxSi1T7aOzOX8Cd0KJL5s&e=> Tel: +31 (0)24 36 55931 _______________________________________________ HCP-Users mailing list HCP-Users@humanconnectome.org<mailto:HCP-Users@humanconnectome.org> http://lists.humanconnectome.org/mailman/listinfo/hcp-users<https://urldefense.proofpoint.com/v2/url?u=http-3A__lists.humanconnectome.org_mailman_listinfo_hcp-2Dusers&d=CwMFaQ&c=WknmpdNpvrlj2B5K1aWVqL1SOiF30547pqSuOmtwXTQ&r=BTWOP47EynCdRIxIQsxjKLHVp-ucrpC9iMHv26t1NFM&m=H5xXuSmR3u1QpacRdYn9m0eZGn-PQpp4kLn56KftEvo&s=nXC9IaR3o5CplPnCZdR3k-G-LbrNdeWS67y__YZY5og&e=> _______________________________________________ HCP-Users mailing list HCP-Users@humanconnectome.org<mailto:HCP-Users@humanconnectome.org> http://lists.humanconnectome.org/mailman/listinfo/hcp-users<https://urldefense.proofpoint.com/v2/url?u=http-3A__lists.humanconnectome.org_mailman_listinfo_hcp-2Dusers&d=CwMFaQ&c=WknmpdNpvrlj2B5K1aWVqL1SOiF30547pqSuOmtwXTQ&r=BTWOP47EynCdRIxIQsxjKLHVp-ucrpC9iMHv26t1NFM&m=H5xXuSmR3u1QpacRdYn9m0eZGn-PQpp4kLn56KftEvo&s=nXC9IaR3o5CplPnCZdR3k-G-LbrNdeWS67y__YZY5og&e=> _______________________________________________ HCP-Users mailing list HCP-Users@humanconnectome.org<mailto:HCP-Users@humanconnectome.org> http://lists.humanconnectome.org/mailman/listinfo/hcp-users<https://urldefense.proofpoint.com/v2/url?u=http-3A__lists.humanconnectome.org_mailman_listinfo_hcp-2Dusers&d=CwMFaQ&c=WknmpdNpvrlj2B5K1aWVqL1SOiF30547pqSuOmtwXTQ&r=BTWOP47EynCdRIxIQsxjKLHVp-ucrpC9iMHv26t1NFM&m=H5xXuSmR3u1QpacRdYn9m0eZGn-PQpp4kLn56KftEvo&s=nXC9IaR3o5CplPnCZdR3k-G-LbrNdeWS67y__YZY5og&e=> _______________________________________________ HCP-Users mailing list HCP-Users@humanconnectome.org<mailto:HCP-Users@humanconnectome.org> http://lists.humanconnectome.org/mailman/listinfo/hcp-users<https://urldefense.proofpoint.com/v2/url?u=http-3A__lists.humanconnectome.org_mailman_listinfo_hcp-2Dusers&d=CwMFaQ&c=WknmpdNpvrlj2B5K1aWVqL1SOiF30547pqSuOmtwXTQ&r=BTWOP47EynCdRIxIQsxjKLHVp-ucrpC9iMHv26t1NFM&m=gxnHAaHBn2ma4u-R-Z4YZFC3vjjntoi7ICOm3GGoWN8&s=wTR7xZ2UMZqFeyrRXQ7gK0A-J9qnPiIvf5cewlJw0GQ&e=> ________________________________ This message is intended for the use of the person(s) to whom it may be addressed. 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