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)
**********************************************************
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