Saaussan Madi wrote:
> Foucher Jack wrote:
>
> Dear Jack ;
>
> I was replying to the mail by John ollinger.
>
> Sorry for using the term dc and Ac. I should have used (1/f low frequency envelope)
> and (periodic noise). It just makes visualizing the spectral density of the noise
> easier.
>
> Drexel university is in philadelphia, pennsylvania, USA.
>
> > Dear Saaussan,
> >
> > Just a few comments (again authoritative control will be appreciated) :
> >
> > Here is my understanding to noise in fmri experiments. Please correct me if
> > i'm
> > wrong.
> >
> > If you take an roi (could be one voxel) under any condition and plot the
> > density
> > power spectrum using the autocorrelation function, you will get the "1/f"
> > graph
> > and possibly some "squiggles".
> > The "1/f" graph is the so called "DC" component of the spectrum while the
> > "squiggles" are the "ac" components.
> >
> > I am not kind on auto-correllogram, and I don't know what "squiggles" are
> > (may I guess that they represent some small signs of periodicity as small
> > waves on each side of the auto-correlogram - thanks to correct me). However
> > I wouldn't qualify 1/f noise as DC since the DC reefers to the 0 frequency
> > (== decay from zero).
> > 1/f noise do have a clear higher power spectra in low frequencies with a
> > reduction of it proportional to 1/f. Most of the higher part of the spectra
> > is flat == white noise (although I remember a abstract mentioning some
> > higher frequency => this could depend of your TR).
> > I think that you may found a better explanation by reading this chapter
> > (part 3)
> > http://www.fil.ion.bpmf.ac.uk/spm/course/notes97/Ch9.pdf
> >
> > A better view for the "ac" components may be obtained by removing the "DC"
> > component, which hides lower "ac" freqeuncy "squiggles". [Simply subtract
> > the
> > mean of spectral density]
> >
> > In our lab, we reduce the noise by applying a bandpass filter (hamming for
> > example), with the cuttoff frequencies based on the experimental design.
> > Example1: For a block design 30secON/30secOFF, we use (4*30sec, 30sec) as
> > cuttof
> > frequencies.
> > Example 2: For an experimental design which has 30secON/30secOFF &
> > 15secON/15secOFF, we use (4*30sec,15sec) cutoff frequencies.
> >
> > I would appreciate another input on that point, but it seems to me that 15
> > and 30 sec high pass filtering is much too large. Sure that if you filter
> > at quite the paradigm frequency you will get something. But how do you
> > build your statistic after that tremendous loss of independence ? Not
> > taking this lost of degree of freedom into account may give you a too
> > permissive test.
> > Another point is that you will loose some interesting temporal evolution of
> > the signal.
> >
> > Our statistics improves (for our data where physiologic noise density is
> > comparable to our signal) by applying a bandpass filter compared to
> > modeling the
> > the "i/f" noise. However, we didn't test for zero mean, and guassian
> > distribution
> > of the noise after applying the bandpass filter.
> >
> > This is very close to what is doing SPM => it removes low frequency noise
> > with appropriate covariates, and high frequencies by convolving the signal
> > with an 'hemodynamic response' like function (something also important for
> > statistical inference since it serves as an evaluation of loss of
> > independence between each observation).
> >
> > -s madi
> > Drexel university
> >
> > This is very much apart, but I have looked in all my atlas, and haven't
> > found Drexel, could you provide me some help ?
> >
> > I hope that I haven't missed your question and that it could be of some
> > help
> > Best regards
> >
> > Jack
> >
> > ________________________________________________________________
> > | Jack Foucher Universite Louis Pasteur |
> > | Institut de Physique Biologique UPRES-A 7004 du CNRS |
> > | 4 rue Kirschleger Tel: 33 (0)3 88 77 89 90 |
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