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
|