<div dir="ltr">Hi Timo (and all),<div><br></div><div>So there's two separate 'corrections' for streamline count applied in Patric Hagmann's 2008 paper:</div><div><ol><li>Weighting according to the inverse of the streamline length, in order to 'compensate' for the increased WM seeding volume of longer pathways.</li><li>Weighting according to the inverse of the surface areas of the nodes of interest, to 'compensate' for the fact that a larger node is likely to have a larger number of streamlines terminating within it than a smaller one.</li></ol><div>In the case of 1., you're absolutely right: as soon as you use interface-based seeding and/or SIFT, the mechanism leading to the bias that the heuristic is trying to correct for is no longer present, so that correction is not 'not needed' so much as erroneous. Note however that this is the 'invlength' metric in the tck2connectome command.</div></div><div><br></div><div>2. is the 'invnodevolume' metric. It calculates the volume of each parcellated node simply by counting voxels and dividing by the voxel volume, with the underlying assumption that the grey matter - white matter interface surface area of a node is directly proportional to this volume (I believe the same assumption was made in Hagmann's manuscript). The contribution of each streamline to the connectome is then modulated by the reciprocal of the mean of the two node volumes.</div><div>Personally, I would expect that a physically larger parcel of grey matter <i>should</i> have a greater number of axons entering/exiting it, and a larger number of streamlines is therefore reflective of this, with or without SIFT. (In fact, there's evidence to suggest that the physical size of cortical regions is largely driven by the density of white matter connections necessary for their function). By applying this heuristic, the values in your connectome matrix become more akin to 'the proportion of all connections to/from node A that connect to node B', rather than an absolute connection count between A and B (not a precise analogy, but close enough). So it really depends on what you're trying to achieve with a connectome analysis, and will affect the interpretation of any network metrics you derive.</div><div><br></div><div>Bear in mind that these heuristics were derived to try to compensate for biases in streamline counts relative to biological fibre counts, but they do so in a relatively naive way, i.e. there is no feedback regarding whether or not these biases have actually been corrected. In the case of 1., we can prove that the reconstruction errors are <i>not</i> adequately corrected. SIFT directly corrects the mismatch between streamline count and underlying fibre density, without making any assumptions about how the mismatch arises mechanistically. And of course: if the local streamlines densities are proportional to the underlying fibre densities, and the streamline trajectories are correct, then the streamline connection counts should be proportional to the underlying fibre connection counts <font size="1">(insert many asterisks here)</font>; so the streamline count is the most likely connectivity metric of interest.</div><div><br></div><div>Happy ... connectomics-ing? :-P</div><div>Rob</div></div><div class="gmail_extra"><br clear="all"><div><div class="gmail_signature"><div dir="ltr"><br>--<br><br><span style="color:rgb(255,102,0)"><b>Robert Smith, Ph.D</b><br>Research Officer, Imaging Division</span><br><br>The Florey Institute of Neuroscience and Mental Health<br>Melbourne Brain Centre - Austin Campus<br>245 Burgundy Street<br>Heidelberg Vic 3084<br>Ph: +61 3 9035 7128<br>Fax: +61 3 9035 7301<br><a href="http://www.florey.edu.au/" target="_blank">www.florey.edu.au</a><br></div></div></div>
<br><div class="gmail_quote">On Tue, Nov 18, 2014 at 10:28 PM, Roine Timo <span dir="ltr"><<a href="mailto:timo.roine@uantwerpen.be" target="_blank">timo.roine@uantwerpen.be</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">Hello everyone,<br>
<br>
Another question regarding tck2connectome:<br>
How is the invnodevolume metric calculated and could it be used to calculate the density-weighted connectivity?<br>
<br>
In Hagmann et al. 2008 this kind of weighting is calculated with respect to average cortical surface area of the end point nodes (number of connections per unit surface). When using WM-GM interface seeding and/or SIFT, I believe the correction term 1/l(f) (in Hagmann et al., 2008) for the bias in tractography towards longer fibers is not needed anymore.<br>
<br>
Thanks in advance!<br>
<br>
Cheers,<br>
Timo<br>
<br>
PhD candidate,<br>
iMinds-Vision Lab, University of Antwerp, Belgium<br>
<a href="mailto:timo.roine@uantwerpen.be">timo.roine@uantwerpen.be</a><br>
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