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Hi

What bedpostX effectively estimates is the peaks of the fODF directly. I.e. instead of calculating an fODF and then look for local maxima, it estimates these maxima as the model parameters. BedpostX does Bayesian inference and therefore estimates the uncertainty of these model parameters given the data as well. These uncertainties around the peaks of the fODF are what probtrackx is using.

I am not sure why you want to reduce the uncertainty in tracking. Your result is a spatial distribution and you can then control in a more principled manner which part of this distribution you want to consider. If you want to do deterministic tracking, you can do it in probtrackx, even though in a suboptimal way. See an older  relevant thread:

https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=ind1304&L=FSL&P=R141736&1=FSL&9=A&I=-3&J=on&d=No+Match%3BMatch%3BMatches&z=4<https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=ind1304&L=FSL&P=R141736&1=FSL&9=A&I=-3&J=on&d=No+Match;Match;Matches&z=4>

Cheers
Stam






On 17 Aug 2017, at 07:28, JunHyuk Woo <[log in to unmask]<mailto:[log in to unmask]>> wrote:

To Stam,

Thank you for your kind answer.

I didn't know that fodfs in bedpostx are constructed by delta functions.

Then how can Probtrackx become probabilistic tracking?

Actually, what I want to do is to lower the "temperature" while doing ProbtrackX and do the fiber tracking less "probabilistic".

Thank you!



Sincerely,
JunHyuk Woo