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Thanks a lot. Another question, if the fsl versions are same, the data should be same? I means that I want do the bedpostx at MacOS, can I used the resulted data in Microsoft OS? From: Matt Glasser Sent: Friday, April 08, 2011 10:52 PM To: [log in to unmask] Subject: Re: [FSL] About the Advanced Option of BedPostX? If you have a lot of directions (>60) and/or a high bvalue (>1000), you can try to reconstruct 3 fibers with your dataset. The higher your bvalue and the more directions you have, the more successful you will be. Peace, Matt. -------------------------------------------------------------------------------- From: FSL - FMRIB's Software Library [mailto:[log in to unmask]] On Behalf Of Yibao Wang Sent: Friday, April 08, 2011 7:26 PM To: [log in to unmask] Subject: [FSL] About the Advanced Option of BedPostX? FSL User: First Question: when the quality of diffusion data in high, we could increase the Fibers value?? Second: I have no idea for the other two parameters? could someone explain it briefly? Advanced Options You may change some options before running bedpostx, depending on the questions you want to ask or the quality of your diffusion data. The default values of these parameters are the ones used in the corresponding paper (Behrens et al, NeuroImage 2007, 34:144-55). a.. Fibres: Number of fibres modelled per voxel. b.. Weight: Multiplicative factor for the prior on the additional modelled fibres. A smaller factor means more weighting for the additional fibres. c.. Burnin: Number of iterations before starting the sampling. These might be increased if the data are noisy, and the MCMC needs more iterations to converge. -- ++++++++++++++++++++++++++++++ Yibao Wang M.D, Ph.D Department of Neurosurgery The Affiliated Hospital of China Medical University 155 Nanjingbei Street ,Heping District, Shenyang ,P.R.China 110001 Tell: 086-024-83283302(Dept.); 086-024-25115597(Home) Cell: 13940352894 ++++++++++++++++++++++++++++++ 王义宝 教授、主任医师、博士生导师 沈阳，中国医科大学附属第一医院神经外科，110001 博客网址：http://blog.sina.com.cn/neurosurgeonwang