Thanks for the references - I'll have a look! On Fri, Aug 12, 2016 at 8:17 PM, Glasser, Matthew <[email protected]> wrote:
> I’ve seen this study come up several times an there are a few things to > consider about it: > > 1. There is a significant literature that has shown that surface-based > alignment is better than volume-based methods (e.g. Fischl et al 1999, > Anticevic et al 2008, Fischl et al 2008, Van Essen et al 2012, Frost et al > 2012, Tuchola et al 2012, Smith et al 2013). But the Klein et al 2010 > study is the only one that I am aware of that has come to a different > conclusion. Thus, it is worth considering why this study may have come to > a discordant conclusion and whether it really is “the best paper” on this > topic. > 2. One important difference between the literature which has shown > converging evidence of the superiority of surface-based alignment to volume > and the Klein et al 2010 study is that these studies based on their > findings on measures tied closely to cortical areas or the areas themselves > (such architecture, function, connectivity, or topography). On the other > hand, the Klein et al 2010 study’s findings are based on manual defined > gyral and sulcal labels that will frequently have little to do with the > areal organization of the cerebral cortex. In fact, we have shown that as > one aligns cortical folds more tightly, functional alignment may actually > decrease some (Robinson et al 2014). > 3. When evaluating the quality of a registration, there are two > important considerations: 1) Accuracy of alignment and 2) Distortion > induced by the alignment. The best approach will maximize accuracy of > alignment while minimizing the distortion induced by the alignment (keeping > it within neurobiologically reasonable limits). Neither the Klein et al > 2010 nor the volume only paper that preceded it in 2009 considered > distortion in their ranking of algorithms. Thus, the best performing > algorithms in these studies may well simply be the ones with the most > distortion. As I mentioned above, however fitting cortical folds very > tightly (leading to higher distortion) doesn’t improve functional alignment > (and indeed we found that we could achieve much better multi-modal areal > feature alignment than folding-based approaches with less distortion than a > standard FreeSurfer folding-based registration). > 4. Given the discordance between folding and areas, I don’t know that > a paper that focuses on aligning folding-based labels really relates to the > question of aligning cortical areas to an areal parcellation, regardless of > the above issues. > > The whole point of cortical registration is to align cortical areas across > subjects (and ideally the topographic organization within these areas) as > well as is feasible. Doing so makes group average results much more > interpretable, both visually and in terms of statistical sensitivity. If > folks want to compare volume-averaged data with the multi-modal > parcellation, I’d rather the burden of inaccuracy be bourn by the > volume-averaged data than making the parcellation less accurate to enable > such comparisons. The recommended way to compare data to this parcellation > is to align across subjects on the surface, ideally driving the alignment > based on areal features (e.g. architecture, connectivity, and topography > like in MSMAll) instead of cortical folds. This will allow the most > definitive comparisons. > > Peace, > > Matt. > > From: Chris Gorgolewski <[email protected]> > Date: Friday, August 12, 2016 at 1:31 PM > To: Matt Glasser <[email protected]> > Cc: "Horn,Andreas (BIDMC - Neurology)" <[email protected]>, > Timothy Coalson <[email protected]>, "Reid, A.T. (Andrew)" < > [email protected]>, "[email protected]" < > [email protected]> > > Subject: Re: [HCP-Users] Surface parcellation to volume > > Is there any comprehensive quantitative comparison of volume and surface > based (potentially multimodal) registration methods? The best paper I know > of (Klein 2010 - http://www.sciencedirect.com/science/article/pii/ > S105381191000114X) recommended using custom study templates over external > templates (like fsaverage or MNI152). The authors could not, however, > recommend volume over surface (or other way around) due to resampling > errors. Here's the relevant paragraph: > > "The resampling tests demonstrate that, for almost every region, the > resampling error is too great to distinguish between the performance of > top-ranking volume and surface registration methods (SyN, FreeSurfer, and > Spherical Demons, all using customized optimal average templates). Based on > these results, it may not be possible to directly compare evaluations of > these surface and volume registration methods using the present resampling > methods, when considering the full surface or full volume or the full > extent of their label boundaries." > > I was wondering if there is some other literature I'm missing that > overcomes the aforementioned resampling problems and provides a > quantitative comparison between the two registration approaches. > > Best, > Chris > > On Fri, Aug 12, 2016 at 10:36 AM, Glasser, Matthew <[email protected]> > wrote: > >> Use of diffusion fiber orientation information might indeed improve >> volume-based alignment of the white matter and is worth pursuing. >> >> I don’t think using areal features in the volume will address the core >> limitation of volume-based cortical areal registration. This will not >> change the fundamental issue of incompatibilities in folding patterns >> across subjects creating topological matching issues. This sort of thing >> occurs in 2D on the surface as well, though much less frequently, where the >> spatial relationships between an area and its neighbors are so different >> that one would have to tear the surface to align the areas. When this >> happens something like the cortical areal classifier is needed to achieve >> correspondence across subjects. For the same reason that topological >> incompatibilities are not fixable in 2D on the surface, the more frequent >> ones that occur in 3D in the volume will also not be fixable. >> >> The overall point is that when we compare across subjects, we need to be >> sure that we are comparing like with like. If we are not doing that we >> aren’t making a valid comparison. >> >> Peace, >> >> Matt. >> >> From: "Horn,Andreas (BIDMC - Neurology)" <[email protected]> >> Date: Friday, August 12, 2016 at 8:49 AM >> To: Timothy Coalson <[email protected]> >> Cc: "Reid, A.T. (Andrew)" <[email protected]>, " >> [email protected]" <[email protected]>, Matt >> Glasser <[email protected]> >> Subject: Re: [HCP-Users] Surface parcellation to volume >> >> 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 <[email protected]>: >> >> 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) < >> [email protected]> 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 >>> 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 <[email protected]>: >>> >>> 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 <[email protected]> >>> wrote: >>> >>>> Hi Andrew, >>>> >>>> I made a projection here: >>>> https://figshare.com/articles/HCP-MMP1_0_projected_on_MNI200 >>>> 9a_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 <[email protected]>: >>>> >>>> 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 < >>>> [email protected]> wrote: >>>> Awesome - this did the trick. Thanks! >>>> On Wed, Jul 20, 2016 at 5:03 PM, Timothy Coalson <[email protected]> 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) <[email protected]> >>>> 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 >>>> [email protected] >>>> 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 >>>> [email protected] >>>> 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 >>>> [email protected] >>>> 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 >>> [email protected] >>> 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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