Dear Sun,

On Thu, Oct 28, 2010 at 3:32 PM, Sun Delin <[log in to unmask]> wrote:
> Dear Vladimir,
>
>    Thank you so much for the detailed reply. Could I conclude your replies as follows?
> 1. Try to do correction for multiple comparisons to avoid false positive.
> 2. If there is no hypothesis IN ADVANCE, SPM is better than SPSS because the former can provide a significant map with both temporal and spatial information.
> 3. Use small time window of interest to do analysis.

This is all correct.


> 4. Cluster-level inference is welcome, so large extent threshold is good.
>

You don't need to put any extent threshold to do cluster-level
inference. What you should do is present the results uncorrected, lets
say at 0.05. Then press 'whole brain' to get the stats table and look
under where it says 'cluster-level'. You will see a column with title
'p FWE-corr' (third column from the left of the table). This is the
column you should look at and if there is something below p = 0.05
there you can report it saying that it was significant FWE-corrected
at the cluster level. You can use higher extent threshold if you get
many small clusters that you want to get rid of.

>    However, I would still like to ask more clearly
> 1. If there is no significance left (I am often unlucky to meet such results) after correction for multiple comparisons (FWE or FDR), could I use uncorrected p value (p < 0.05) with large extent threshold such as k > 400? Because it seems impossible that more than 400 adjacent voxels are all false positive. If you are the reviewer, could you accept that result?

No. You can't do it like that because although it is improbable you
can't put a number on how improbable it is. What you should do is look
in the stats table as I explained above.

> 2. You said that it is "absolutely statistically invalid thing to do is to find an uncorrected effect in SPM and then go and
> test the same channel and time window in SPSS." However, I found that if the uncorrected effect (e.g. p < 0.05 uncorrected, k > 400) appeared at some sites in SPM, SPSS analysis involving the same channel and time window would show a more significant result. Because most ERP researchers now accept the results by SPSS, is it a way to use SPM as a guide to show the possible significant ROI (temporally and spatially) and use SPSS to get the statistical significance?

No that's exactly the thing that is wrong. You can only use SPSS if
you have an a-priori hypothesis. As I explained you will get more
significant results in SPSS than in SPM because SPSS assumes
(incorrectly in your case) that you are only doing a single point test
and it doesn't know about all the other points you tried to test in
SPM whereas SPM does know about them and corrects for this.

> 3. If the small time window of interest is more sensitive, could I use several consecutive small time window (e.g. 50 ms) of interest to analysis long component such as LPC (I know some researchers use consecutive time window to analysis LPC component by SPSS) or as an exploring tool to investigate the possible significant result on dataset without hypothesis IN ADVANCE?

If the windows are consecutive (i.e. there are no gaps between them)
then you should just take one long window. If there are gaps you can
use a mask image that will mask those gaps out and SPM will
automatically account for the multiple windows.

> 4. Because of the head shape and some other reasons, the 2D projection map of each individual' sensors on scalp is some different from the standard template provided by SPM. Is it correct to put each subjects' images based on their own 2D sensors' map into the GLM model for specification, or use images based on standard 2D sensors' map instead? I have tested both ways and found that the previous method may lead to some stripe like significance at the border of mask. I do no know why.

Both ways are possible. You can either mask out the borders if you
know there is a problem there or use standard locations for all
subjects.

Best,

Vladimir


>
>    Sorry for asking some weak questions, however, I really like the EEG/MEG module of SPM8.
>
> Bests,
> Sun Delin
>
>