On Tue, Aug 16, 2016 at 8:11 AM, Reid, A.T. (Andrew) <[email protected]> wrote:
> Hi all, > > In response to Tim’s comments, particularly: > > “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).” > > Agreed, but in our specific case, we have a pre-defined set of volumetric > ROIs in standard space. These have been produced by meta-analysis and > therefore there are no individual subjects to reference. We want to be able > to compare these ROIs to those produced by the MMP method. I am not sure of > what better ways there are to perform such a comparison than by using the > population average surface that has been provided, and projecting this into > standard volumetric space. One alternative might be to project from surface > to volume in individual subjects, and then warp these individual volumes to > the template image. > Yes, this is one way to go about it. We already provide surfaces that have been fnirt nonlinear registered to the MNINonLinear template, in the MNINonLinear folder of each subject. Using these per-subject surfaces to map the group parcellation (or even the individual parcellations) would give a result that better aligns with the volume template, but has other major caveats. > Done cleverly, this could also yield voxel-wise probabilities about the > assignment of particular ROIs (a la the Juelich probabilistic atlas); but > in the end you are still faced with the necessity of averaging across > subjects. > Those probabilities will be quite revealing of what is lost in the translation of cortex to the standard volume space: you can expect that practically all of the smaller cortical areas will have much lower peak probabilities than on the surface, and may in effect "vanish" by no longer having any location where they are the most probable area. If you examined the probabilities at any frontal MNI coordinate, you might get 24% chance of this area, 18% chance of that area, 14% chance of this other area, and so on. This reflects several things: the fine detail in our parcellation (getting overlap of smaller areas is harder in general), the disconnect between cortical folding patterns and areal boundaries (because the MNINonLinear alignment is based on T1, not on any fMRI or myelin contrasts), and anywhere that volume registration failed to even overlap cortex with cortex (folding variability and regularization constraints). This should illustrate why we aren't enthusiastic about projecting the HCP MMP v1.0 into the volume, as we aren't sure that it is feasible to make a volume form of the cortical parcellation that is particularly useful with popular volume methods. When we do group analysis, we compare cortex across subjects on the surface, which neatly sidesteps the problems of 3D registration and coordinates (using a 2D registration instead). This is what we advocate for cortex in general, and for using the MMP v1.0 in particular. But yes, as long as you are focused on a *volume* form of the MMP v1.0, you will encounter problems of this nature. > Loss of precision is an intrinsic property of any population template. > To some extent, yes. You may be surprised at the sharpness and consistency of group-average MSMAll-registered myelin or task maps, though, see here: https://balsa.wustl.edu/WDpX > Whether the averaging is performed on surfaces or on volumes, individual > gyral patterns will always be compromised. I do not see how this limitation > can be avoided in our case — but am of course quite open to suggestions. > MSMAll and the MMP v1.0 don't focus on gyral patterns, as they don't correspond all that well to areal boundaries. As we weren't planning to make a volume version of the cortical MMP v1.0, this didn't bother us. > > Apart from that I am completely sold on this approach and believe it > should become the standard for cortical analysis in the future. > Good to hear! > > Cheers, > 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/ > Tel: +31 (0)24 36 55931 > > > > > > On Aug 16, 2016, at 12:33 AM, [email protected] wrote: > > Send HCP-Users mailing list submissions to > [email protected] > > To subscribe or unsubscribe via the World Wide Web, visit > http://lists.humanconnectome.org/mailman/listinfo/hcp-users > or, via email, send a message with subject or body 'help' to > [email protected] > > You can reach the person managing the list at > [email protected] > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of HCP-Users digest..." > > > Today's Topics: > > 1. Re: Surface parcellation to volume (Arno Klein) > 2. Re: Surface parcellation to volume (Arno Klein) > 3. Re: Surface parcellation to volume (Matthew Brett) > 4. Re: Surface parcellation to volume (Timothy Coalson) > 5. Re: Changing grayordinate space (Raquel Viejo Sobera) > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Mon, 15 Aug 2016 20:59:24 +0000 > From: Arno Klein <[email protected]> > Subject: Re: [HCP-Users] Surface parcellation to volume > To: [email protected] > Message-ID: > <CAPEs=C8dH32aPdYigp8FxsZ=0-v6K=wjafstntgxdf5yiji...@mail.gmail.com> > Content-Type: text/plain; charset="utf-8" > > Dear Matthew and others, > > Thank you for the interesting discussion. I was not on the HCP mailing > list*, but Thomas Yeo was kind enough to suggest that I respond to the two > primary points you made about the 2009/2010 registration evaluation > studies: > > 1. "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). > > The 2009/2010 studies were interested in evaluating anatomical registration > methods, and as such, used anatomical labels as gold standard data. We know > little about how anatomical boundaries correspond to > function/connectivity/receptor maps, and even less about how anatomical > correspondence across brains relates to the correspondence of > function/connectivity/receptors across brains. It seems to me that if one > wishes to compare different brains, it would be best to do so within a > given modality, then try to reconcile the intermodal mappings if desired. > > 2. "Neither the Klein et al 2010 nor the volume only paper that preceded it > in 2009 considered distortion in their ranking of algorithms." > > This is true. I raise a number of caveats in the 2009 Discussion's Caveats > section, and the one most relevant portion is: > > "The evaluation measures and analysis methods used in this paper are > predicated on the assumption that, at the macroscopic scale of topographic > anatomical regions, there are correspondences across a majority of brains > that can effectively guide registrations. It is very important to stress > that we cannot make inferences about the accuracy of registrations within > these macroscopic regions. Therefore our overlap evaluation measures not > only ignore misregistration within a labeled region but are insensitive to > folding in the deformations, which would impact studies such as > deformation-based morphometry. More generally, our evaluation measures rely > on information which is not directly included in the images, which is good > for evaluating the registrations, but they do not inform us about the > intrinsic properties of the spatial transformations. Example measures of > the intrinsic properties of spatial transformations include inverse > consistency error, transitivity error, and ?mean harmonic energy? (where > the Jacobian determinant of the transformation is averaged over the > volume)." > > Cheers, > @rno > > *It's a shame people don't use publicly accessible forums for scientific > debate. Isn't that what Neurostars.org and other stack overflow forks are > for? > > On Sat, Aug 13, 2016 at 11:17 AM, Glasser, Matthew <[email protected]> > wrote: > > I?ve seen this study come up several times and there are a few things to > consider about it: > > 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. 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). 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). 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 > > <SNIP> _______________________________________________ HCP-Users mailing list [email protected] http://lists.humanconnectome.org/mailman/listinfo/hcp-users
