[Eeglablist] comparing normal and clinical populations

[Jose Rebola]

Hi and thank you for the responses!

The problem in getting a value of "effect" per subject is that i don't have
two conditions per subject, I have only one...
Should i compare it against baseline?
Is it the same procedure?

Jose

On Tue, Dec 6, 2011 at 10:18 AM, Guillaume Rousselet <
Guillaume.Rousselet at psy.gla.ac.uk> wrote:

> Hey Jose,
>
> following Arnaud's comment, I would strongly encourage you to perform
> analyses in each subject and report a confidence interval around the
> difference for each subject. You could then report how many subjects show
> an effect in each group. See similar strategy here:
>
> http://www.frontiersin.org/perception_science/10.3389/fpsyg.2011.00137/full
>
> If you want to make inferences at the group level, other strategies would
> involve mixed effect models (R might be better suited than Matlab for that)
> or using a strategy in which you build confidence intervals using data from
> the normal group, and then classify subjects from the patient group as
> being above, below, or inside the control confidence interval - a strategy
> that Cyril Pernet used to investigate dyslexia:
>
> http://www.biomedcentral.com/1471-2202/10/67/abstract
>
> This strategy could be extended to multivariate classifiers if you wanted
> to throw other ERP markers in the mix.
>
> Finally, you should have a look at Elizabeth Milne paper on autism for
> ideas of P1 analyses:
>
> http://www.frontiersin.org/perception_science/10.3389/fpsyg.2011.00051/full
>
> Best wishes,
>
> Guillaume
>
>
>
>
>
>
> ************************************************************************************
> Guillaume A. Rousselet, Ph.D., senior lecturer & deputy post-graduate
> convenor
>
> Centre for Cognitive Neuroimaging (CCNi)
> Institute of Neuroscience and Psychology
> College of Medical, Veterinary and Life Sciences
> University of Glasgow
> 58 Hillhead Street
> G12 8QB
>
> http://www.psy.gla.ac.uk/staff/index.php?id=GAR01
>
> Email: Guillaume.Rousselet at glasgow.ac.uk
> Fax. +44 (0)141 330 4606
> Tel. +44 (0)141 330 6652
> Cell +44 (0)791 779 7833
>
> The University of Glasgow, charity number SC004401
>
> ************************************************************************************
>
> On 6 Dec 2011, at 03:27, Arnaud Delorme wrote:
>
> > Dear Jose,
> >
> > using 1000 trials, you may perform inferential testing on your subject's
> trial population. For instance, you H0 null hypothesis may be "There is no
> difference between the trial activity between condition A and condition B
> in subject X". If you reject the null hypothesis, then you may conclude
> that there is a significant difference between A and B for subject X. In
> other words, you make an inference about the trial population of subject X.
> If you were to record additional trials of conditions A and B in subject X,
> you should be able to reproduce your significant effect (subject to the
> p-value uncertainty obviously).
> >
> > Now, if you have several subjects, you may perform inferential testing
> on the subjects' population (i.e. the general population assuming your
> subject sample is appropriate). Your H0 null hypothesis may be "There is no
> difference between the trial activity between condition A and condition B
> in the general population". If you record additional subjects (not
> necessarily the same ones), you should be able to reproduce your effect.
> >
> > To combine the two (subjects and trials) means that you need to change
> the null hypothesis. Now, your H0 null hypothesis may be "There is no
> difference between the trial activity between condition A and condition B
> in subject X, Y, and Z". Your are making an inference about the trial
> population of subject X, Y, and Z. If you reject this null hypothesis, you
> should be able to record more data for subject X, Y, and Z and reproduce
> the effect.
> >
> > The last approach is valid (and possible in EEGLAB) but less interesting
> than the one using one value per subject - because you usually want to make
> inference about the general population, not about a few subjects.
> >
> > This is my take on it but I am not a statistician, so there might be
> different approaches/interpretations.
> > Best,
> >
> > Arno
> >
> > On Dec 4, 2011, at 11:02 AM, Jose Rebola wrote:
> >
> >> Hi
> >>
> >> I am running a study to investigate differences between  williams
> syndrome patients and controls in a visual task. I have eight subjects of
> each population.
> >>
> >> How do I compare the amplitude of the P100 between the populations?
> >>
> >> Should i include only one value (the peak around 100ms on each of the
> subject's average) per subject ?
> >> It seems to me that if i do this i will only have one value per subject
> and i am "throwing away" the 100 trials per condition that i have
> >>
> >> Isn't there a way that i can compare between populations while
> retaining within-subject variability?
> >> Otherwise, how does it compensate to perform 1000 trials instead of 10?
> >>
> >> I only know how to do two-level analysis when all subjects perform two
> conditions for example, thus getting one value of "effect" per subject and
> moving to the next level...
> >>
> >> Isn't there any paralle of this to two populations?
> >>
> >> Hope somebody can help me,
> >>
> >> José Rebola
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