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<mailto:tsc...@mst.edu>> Date: Friday, October 6, 2017 at 5:30 PM To: "Gopalakrishnan, Karthik" <gkart...@gatech.edu<mailto:gkart...@gatech.edu>> Cc: Matt Glasser <glass...@wustl.edu<mailto:glass...@wustl.edu>>, "hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>" <hcp-users@humanconnectome.org<mailto: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<mailto: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<mailto: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<mailto:hcp-users-boun...@humanconnectome.org>> on behalf of Timothy Coalson <tsc...@mst.edu<mailto:tsc...@mst.edu>> Date: Tuesday, October 3, 2017 at 6:30 PM To: "Gopalakrishnan, Karthik" <gkart...@gatech.edu<mailto:gkart...@gatech.edu>> Cc: "hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>" <hcp-users@humanconnectome.org<mailto: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<mailto: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<mailto:HCP-Users@humanconnectome.org> http://lists.humanconnectome.org/mailman/listinfo/hcp-users _______________________________________________ HCP-Users mailing list HCP-Users@humanconnectome.org<mailto: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
