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Please remove me from the listserve. Thank you. Sláinte, -m Marc D. Yelle Dept. Neurobiology and Anatomy Wake Forest School of Medicine Winston-Salem, NC 27157 [log in to unmask] -----Original Message----- From: FSL - FMRIB's Software Library on behalf of FSL automatic digest system Sent: Sat 11-Aug-07 19:00 To: [log in to unmask] Subject: FSL Digest - 10 Aug 2007 to 11 Aug 2007 (#2007-217) There are 3 messages totalling 260 lines in this issue. Topics of the day: 1. mcflirt for concatenated runs (2) 2. post-stats on a group of .feat directories ---------------------------------------------------------------------- Date: Sat, 11 Aug 2007 12:55:16 +0100 From: Guido Biele <[log in to unmask]> Subject: Re: mcflirt for concatenated runs Dear Steve, dear Stephane,=20 thanks for your responses! I am aware that concatenating is generally discouraged. However, I fear t= hat my data is more messy than my first email suggested: We were running an (event related) experiment where the conditions of interest are determine= d by participants decisions. As a result, some types of decisions (conditions) were made infrequently = and are in addition scattered accross runs; lets say 0,4,10, and 11 events in= runs 1,2,3, and 4, repectively. Would you say that, for this kind of data, we get more reliable parameter= estimetes by running a multi level analysis for each individual, compared= to concatenating runs? best - guido On Fri, 10 Aug 2007 17:26:31 +0100, Steve Smith <[log in to unmask]> wr= ote: >That's correct - we strongly discourage temporal concatenation of >datasets across sessions for FEAT analysis. >Cheers. > > >On 10 Aug 2007, at 17:14, Stephane Jacobs wrote: > >> Hi Guido, >> >> If the only reason why you want to concatenate your runs is because >> you >> want to contrast conditions belonging to separate runs, I *think* (and= >> please anybody correct me if I'm wrong!) that you don't need to do >> that. >> Instead, you could just model each run individually at the 1st level, >> and then contrast conditions as you wish at the 2nd level (for each >> subject) using cope images from the 1st level .feat directory as >> inputs. >> After that, you can average across subjects at the 3rd level. >> >> Hope this helps, >> >> Stephane >> >> Guido Biele wrote: >>> Hi, >>> >>> I am comparing two motion correction methods for concatenated >>> functional data (something I have >>> to do, because conditions to be contrasted are not always in the >>> same run). >>> >>> The methods are: >>> a) simply concatenating the functional data anf then using mcflirt. >>> b) first using mcflirt for seperate runs, then using mclirt to >>> register each run`s example_func to the >>> global example_func, and finally concatenating the two MAT_... >>> files to register each volume on the >>> global example_func. >>> >>> I have two questions in this context. >>> Does the second, rather complicated method make any sense to you, >>> or should the results be the >>> same as for the first, simpler method? >>> Can one combine the transformation paramteres (from the xz.par >>> files ) by simply adding them? >>> >>> Cheers, guido >>> >>> >>> > > >------------------------------------------------------------------------= >--- >Stephen M. Smith, Professor of Biomedical Engineering >Associate Director, Oxford University FMRIB Centre > >FMRIB, JR Hospital, Headington, Oxford OX3 9DU, UK >+44 (0) 1865 222726 (fax 222717) >[log in to unmask] http://www.fmrib.ox.ac.uk/~steve >------------------------------------------------------------------------= >--- >=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D ------------------------------ Date: Sat, 11 Aug 2007 17:19:37 +0100 From: Steve Smith <[log in to unmask]> Subject: Re: mcflirt for concatenated runs Hi, This should be ok - you can run a separate second-level analysis for each subject to combine across sessions; it's probably best to choose the fixed-effects modelling option for this. See the FEAT manual for more examples of higher-level FEAT models. Cheers, Steve. On 11 Aug 2007, at 12:55, Guido Biele wrote: > Dear Steve, dear Stephane, > > thanks for your responses! > > I am aware that concatenating is generally discouraged. However, I > fear that > my data is more messy than my first email suggested: We were > running an > (event related) experiment where the conditions of interest are > determined > by participants decisions. > As a result, some types of decisions (conditions) were made > infrequently and > are in addition scattered accross runs; lets say 0,4,10, and 11 > events in > runs 1,2,3, and 4, repectively. > > Would you say that, for this kind of data, we get more reliable > parameter > estimetes by running a multi level analysis for each individual, > compared to > concatenating runs? > > best - guido > > > On Fri, 10 Aug 2007 17:26:31 +0100, Steve Smith > <[log in to unmask]> wrote: > >> That's correct - we strongly discourage temporal concatenation of >> datasets across sessions for FEAT analysis. >> Cheers. >> >> >> On 10 Aug 2007, at 17:14, Stephane Jacobs wrote: >> >>> Hi Guido, >>> >>> If the only reason why you want to concatenate your runs is because >>> you >>> want to contrast conditions belonging to separate runs, I *think* >>> (and >>> please anybody correct me if I'm wrong!) that you don't need to do >>> that. >>> Instead, you could just model each run individually at the 1st >>> level, >>> and then contrast conditions as you wish at the 2nd level (for each >>> subject) using cope images from the 1st level .feat directory as >>> inputs. >>> After that, you can average across subjects at the 3rd level. >>> >>> Hope this helps, >>> >>> Stephane >>> >>> Guido Biele wrote: >>>> Hi, >>>> >>>> I am comparing two motion correction methods for concatenated >>>> functional data (something I have >>>> to do, because conditions to be contrasted are not always in the >>>> same run). >>>> >>>> The methods are: >>>> a) simply concatenating the functional data anf then using mcflirt. >>>> b) first using mcflirt for seperate runs, then using mclirt to >>>> register each run`s example_func to the >>>> global example_func, and finally concatenating the two MAT_... >>>> files to register each volume on the >>>> global example_func. >>>> >>>> I have two questions in this context. >>>> Does the second, rather complicated method make any sense to you, >>>> or should the results be the >>>> same as for the first, simpler method? >>>> Can one combine the transformation paramteres (from the xz.par >>>> files ) by simply adding them? >>>> >>>> Cheers, guido >>>> >>>> >>>> >> >> >> --------------------------------------------------------------------- >> --- >> --- >> Stephen M. Smith, Professor of Biomedical Engineering >> Associate Director, Oxford University FMRIB Centre >> >> FMRIB, JR Hospital, Headington, Oxford OX3 9DU, UK >> +44 (0) 1865 222726 (fax 222717) >> [log in to unmask] http://www.fmrib.ox.ac.uk/~steve >> --------------------------------------------------------------------- >> --- >> --- >> ===================================================================== >> ==== ------------------------------------------------------------------------ --- Stephen M. Smith, Professor of Biomedical Engineering Associate Director, Oxford University FMRIB Centre FMRIB, JR Hospital, Headington, Oxford OX3 9DU, UK +44 (0) 1865 222726 (fax 222717) [log in to unmask] http://www.fmrib.ox.ac.uk/~steve ------------------------------------------------------------------------ --- ------------------------------ Date: Sat, 11 Aug 2007 21:04:05 +0100 From: Jorge Mallen <[log in to unmask]> Subject: post-stats on a group of .feat directories Hello, Is it OK to run post-stats on a group of .feat directories at once, even = though each directory had a=20 different EV's? In my case, the EV's differed in the mean lag of the gamm= a function that was=20 convolved with the square wave. Thanks! ------------------------------ End of FSL Digest - 10 Aug 2007 to 11 Aug 2007 (#2007-217) **********************************************************