Laura,
I agree with Helmut, in that deactivations are not really magical.
Increases and decreases are all part of normal brain function. Here is
an example of where a deactivation is simply a relative change in
activity. Consider a visual processing task and the subject shifts her
attention from color features to motion features. When attending to the
color features the activity in color processing regions will be
increased. However, when attention is shifted to the motion features the
activity level will decrease in the color processing areas.
There is also the idea of a "default mode" of brain activity and
deactivations in that network is proposed to be due to modulating
internal processing during task performance. Such deactivations are
compared to a resting baseline rather than to active conditions.
Although the default mode refers to a specific network, other brain
areas can be deactivated during task performance. You should look at the
work by Shmuel (Neuron 2002) where it was demonstrated that stimulation
of different parts of the visual field reduced the BOLD signal in other
portions of visual cortex.
------------
Paul J. Laurienti, MD, PhD
Department of Radiology
Wake Forest University School of Medicine
Medical Center Blvd
Winston-Salem, NC 27157
336-716-3261
[log in to unmask]
www.fmri.wfubmc.edu/laurienti.htm
-----Original Message-----
From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]]
On Behalf Of Laura Mancini
Sent: Friday, July 01, 2005 5:34 AM
To: [log in to unmask]
Subject: Re: [SPM] deactivation
Hi Helmut,
Sorry I haven't been too clear. My mistake.
The contrast for when I use the hrf+time derivatives has got, in fact,
more
columns:
To see only the effect of task A for example (which is repeated twice,
as
also tasks B and C) I used the contrast:
[0 0 0 0 1 -1 1 -1 0 0 0 0]
With "B C A A C B" I meant the order in which I acquired the fMRIs and
therefore for consistency I put the data from those sections in the same
order in the large final density matrix.
I meant to say that I have got a large density matrix which includes 6
sessions, and therefore 6 small matrices. In the case of only hrf every
small matrix has got just one column or regressor (apart from the
constant
always put at the end by SPM).
In the case of hfr+time derivative the small matrix has got in fact two
columns.
I am not sure what you mean when you ask if I included the dispersion, I
guess I didn't otherwise I would have known ... At least I hope.
I must say that the change of perspective you mention is quite
interesting.
But it means I haven't got a clue of how brain really works.
Does it really happen sometimes that it is more active at the
"off-times"
instead of at the "on-times", or is it just an indication that something
has
gone wrong in the subtraction between data on the on and off condition?
Laura
-----Original Message-----
From: Helmut Laufs [mailto:[log in to unmask]]
Sent: 01 July 2005 08:28
To: Mancini,Laura
Cc: SPM (Statistical Parametric Mapping)
Subject: Re: deactivation
Hi Laura,
could you please clarify what your regressors in your model are (you say
you
have 3 conditions and model either hrf or hrf+ time derivatives [incl.
dispersion?], but it appears you always have 6 colums in your design). I
cannot work it out, even should you have put one colum per block (B C A
A C
B), which gives six colums, including the derivatives should give you
more...
In principle: onbe [/another] way to look at 'deactivations' is this:
change
your perspective and see it as: the set of brain areas that
'deactivates'
during your respective condition is in fact more active at the
"off-times"
of your condition.
Maybe this is a start?
Helmut
----- Original Message -----
From: "Laura Mancini" <[log in to unmask]>
Sent: Thursday, June 30, 2005 6:52 PM
Subject: deactivation
> Dear SPMers,
>
> I wonder if you can help soving a puzzle.
>
> I have 6 fmri acquisition, two for conditions A versus rest, two for B
> versus rest, two for C versus rest. The order of the acquisitions is:
> B C A A C B A = only hand movement
> B = only speech production
> C = hand mov + speech prod
> I did only t-tests, not F-tests.
> If as basis set for the SPM analysis I consider only the hrf I have
the
> following.
> When I consider the contrast [0 0 1 1 0 0] I see a huge activation in
the
> motor cortex, while with [0 0 -1 -1 0 0] there is little or no
> (de)activation
> When I consider [1 0 0 0 0 1] or [0 1 0 0 1 0], I see little
activation in
> some parts of the brain, while with [-1 0 0 0 0 -1] or [0 -1 0 0 -1 0]
I
> see
> large (de)activation almost everywhere in the brain.
>
> If as basis set for the SPM analysis I consider only the hrf+time
> derivatives I have the following. When I consider the contrast [0 0 1
> 1 0 0] I see much less activation in the
> motor cortex, while with [0 0 -1 -1 0 0] there is maybe more
> (de)activation
> When I consider [1 0 0 0 0 1] or [0 1 0 0 1 0], I see much more
activation
> in some parts of the brain, and with [-1 0 0 0 0 -1] or [0 -1 0 0 -1
0] I
> see less (de)activation.
>
> I am quite puzzled. Has anybody find something similar?
> Is it more likely that there was a problem during the acquisition or
> the analysis of the data, or that it is a real behaviour of the brain?
>
> Many thanks,
> Laura
>
>
> **********************************************************************
> This email is confidential and intended solely for the person or
> entity to
> whom it is addressed. If this email was not intended for you please
> notify the UCLH Mail Administrator at [log in to unmask]
> This footnote confirms that the email and attachments contained no
viruses
> when they left UCLH.
>
**********************************************************************
This email is confidential and intended solely for the person or entity
to whom it is addressed. If this email was not intended for you please
notify the UCLH Mail Administrator at [log in to unmask]
This footnote confirms that the email and attachments contained no
viruses when they left UCLH.
|