Hi,
Thanks very much for your swift reply! I've had a go at inputting feat
directories of individual subjects where both event types have been
included as separate EVs but can't see how the event types are
distinguished between when you put them into higher level analysis. Is
there a particular way of setting up the EVs at the higher level so that
the event types can be differentiated?
Also, do the post-stats thresholds set at the individual subject level
affect stats done at higher levels?
Many thanks,
Carolyn
-----Original Message-----
From: FSL - FMRIB's Software Library [mailto:[log in to unmask]] On
Behalf Of Christian Beckmann
Sent: 20 July 2007 16:33
To: [log in to unmask]
Subject: Re: [FSL] event-related fMRI analysis
Hi
> In my study there were 16 subjects who were scanned on 2 separate
> days. On day 1 they performed a cognitive task at baseline and again
> following the administration of a placebo or drug, and on day 2 this
> was repeated, such that each subject performed the task 4 times: at
> baseline ('BP') then placebo ('P') and at baseline ('BD') then drug
> ('D'). The task consisted of 2 event types A and B, and reaction times
> (RTs) were obtained for each event.
>
> Firstly, I would like to use RT as a covariate. My questions are the
> following:
> 1. Can I combine the 2 different event types with their corresponding
> RTs in the same analysis or do they have to be analysed separately?
> i.e. will
> I need to analyse event type A for each subject and feed these feat
> directories into higher level analysis and repeat this for event type
> B?
>
No, if your experiment involved two different event types you should
model both of these in a single lower-level design. Each event type will
become a separate EV. RT information will also be a separate EV.
> 2. Will I need to analyse the sessions (BP, P, BD and D) separately or
> can each individual and each session be put into the same higher level
> analysis with RT as a covariate?
I guess the simplest would be to model this as a triple t-test, see
http://www.fmrib.ox.ac.uk/fsl/feat5/index.html
At an intermediate level you'd then use a fixed-effects model to
combine the two BP sessions.
> 3. I'm unclear as to what people mean by demeaning covariates, please
> can you explain?!
You remove the mean value from the time series of RTs - in the GLM as
applied to fMRI you use covariates to 'explain' intensity changes in the
data.The mean image intensity is of no interest (at the first
level) and therefore is removed, i.e. the data is mean zero.
Therefore, all the covariates should be zero mean, too.
> 4. I'm also not sure whether it would be appropriate to tick the
> orthogonalise boxes?
>
I suggest you do not orthogonalise the RT regressor wrt any of the other
ones - if you do and your EVs are positively correlated with RTs you end
up boosting the EVs for A and/or B because by orthogonalising RTs any
amount of variance which could be explained either by A/B or RT ends up
being attributed to A/B only.
> I'd also like to look at the mean group BOLD responses in the 4
> different conditions, and compare between them (e.g. D versus BD, D
> versus P).
> 1. Again, can I combine the 2 different event types in the same
> analysis or do they have to be analysed separately?
see above
> 2. Which is the best approach to do this, to put all data (each
> subject and each session) into a one factor 4 level repeated measures
> ANOVA?
>
I suggest triple t-test, , just specify the appropriate contrasts to
compare e.g. D to P hope this helps Christian
> A couple of other things, do the post-stats set at the individual
> subject level affect stats done at higher levels? Also, I'm confused
> about what the difference is between inputting feat directories or
> cope images into higher level analysis.
>
> Many thanks!
> Carolyn
____
Christian F. Beckmann
University Research Lecturer
Oxford University Centre for Functional MRI of the Brain (FMRIB) John
Radcliffe Hospital, Headington, Oxford OX3 9DU, UK.
[log in to unmask] http://www.fmrib.ox.ac.uk/~beckmann
tel: +44 1865 222551 fax: +44 1865 222717
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