Right, I wasn't very precise in my wording. I was thinking of the "tractography distance bias" as the amount of the bias that is above and beyond the real biological distance relationship.
Tim On Fri, Oct 6, 2017 at 4:36 PM, Glasser, Matthew <glass...@wustl.edu> wrote: > It is worth noting that there IS a biological distance bias in connections > that has been found with invasive tracers, though the mechanism by which > this occurs in in tractography is different from the biological mechanism > as Tim says. There’s more discussion of this in the paper I referenced. > > Peace, > > Matt. > > From: Timothy Coalson <tsc...@mst.edu> > Date: Friday, October 6, 2017 at 5:30 PM > To: "Gopalakrishnan, Karthik" <gkart...@gatech.edu> > Cc: Matt Glasser <glass...@wustl.edu>, "hcp-users@humanconnectome.org" < > hcp-users@humanconnectome.org> > > Subject: Re: [HCP-Users] Distance between surface ROIs in MMP > > Tractography's distance bias is in its reported strengths. The distances > reported by tractography should not have a significant bias in the same way > - while it takes longer paths less often, it doesn't often take paths that > are even more windy and longer than the real path, and it generally can't > take a shorter path, right? > > Since these paths are through the white matter, and generally follow real > fiber directions, they are more plausible than any other available method > of computing connection distances between areas (3D distance is wrong > because connections don't go through CSF, geodesic distance is wrong > because long-distance connections aren't transmitted through gray matter > the whole way). Moreover, the tractography-reported distances should have > a much better relationship to the tractography strength biases. > > To put it another way, the distance bias of tractography is not a > *biological* effect, it is an effect of the *method* of tractography. In > particular, the longer a probabilistic streamline gets, the wider the area > that it could have hit gets, but much of this area gets intercepted by > pieces of cortex before the streamline gets as long as it "should" be - > therefore this spreading effect causes long streamlines to be rarer than > they should be, by virtue of the streamline length itself (not as a > function of the biological tract length). > > Tim > > > On Fri, Oct 6, 2017 at 4:15 PM, Gopalakrishnan, Karthik < > gkart...@gatech.edu> wrote: > >> Hi Matt/Tim, >> >> My goal is to improve network inference from tractography data by better >> accounting for the distance bias in tractography, so I want to use some >> proxy for actual connection distance between ROI pairs. Using tractography >> itself to account for its own bias against long-distance connections >> doesn’t make sense to me. >> >> Do you have any suggestions on how I could best compute this proxy? >> >> Regards, >> Karthik >> >> On Oct 5, 2017, at 8:50 AM, Glasser, Matthew <glass...@wustl.edu> wrote: >> >> Indeed I think we would need to know what you needed the distance for to >> know how best to compute it. For things like MR artifacts, a 3D distance >> might be most appropriate. For something like smoothing, a geodesic >> distance would be appropriate. For something neurobiological, the >> tractography distance might be most appropriate. >> >> Peace, >> >> Matt. >> >> From: <hcp-users-boun...@humanconnectome.org> on behalf of Timothy >> Coalson <tsc...@mst.edu> >> Date: Tuesday, October 3, 2017 at 6:30 PM >> To: "Gopalakrishnan, Karthik" <gkart...@gatech.edu> >> Cc: "hcp-users@humanconnectome.org" <hcp-users@humanconnectome.org> >> Subject: Re: [HCP-Users] Distance between surface ROIs in MMP >> >> Since ROIs are not points, distance between them becomes a trickier >> question. Since areas are connected through white matter rather than gray >> matter, that also implies that the easy ways to calculate distance may not >> be all that biologically relevant. This would point to using tractography >> to find distances. So, I don't think there is an easy answer, sorry. >> >> If you want to compute distance along the gray matter anyway, a >> possibility is to find the center of gravity of each ROI, translate them >> back to surface vertices (the centers will not actually be on the surface >> anymore, so you may want to double check them), and then find geodesic >> distances between those points (you can use -surface-geodesic-distance, >> running it once per area - you can then get the values from the other >> vertices near the centers to build the all-to-all matrix a row at a time). >> Note, however, that this will not give you a distance to areas in the other >> hemisphere. >> >> Tim >> >> >> On Tue, Oct 3, 2017 at 5:06 PM, Gopalakrishnan, Karthik < >> gkart...@gatech.edu> wrote: >> >>> Hi, >>> >>> I’m working with the Glasser multi-modal parcellation and I’d like to >>> know if there is some prevalent notion of distance between any two surface >>> ROIs in the parcellation? If there is, could you please tell me how I could >>> obtain it or point me to a source? >>> >>> Thanks a lot! >>> >>> Regards, >>> Karthik >>> >>> _______________________________________________ >>> HCP-Users mailing list >>> HCP-Users@humanconnectome.org >>> http://lists.humanconnectome.org/mailman/listinfo/hcp-users >>> >> >> _______________________________________________ >> HCP-Users mailing list >> HCP-Users@humanconnectome.org >> http://lists.humanconnectome.org/mailman/listinfo/hcp-users >> >> >> > _______________________________________________ HCP-Users mailing list HCP-Users@humanconnectome.org http://lists.humanconnectome.org/mailman/listinfo/hcp-users
