Dear Peter,
Bayesian inference has some advantages over the frequentist approach
(also termed classical for some reason, but classical statistics is even
older than frequentist statistics... that's another topic):
* you don't have to correct for multiple comparisons, although some
might disagree (see Woolrich 2012, Neuroimage)
* your answer will be a probability that the effect is there given the
data, as opposed to rejecting the null hypothesis given a small enough
p-value
* with frequentist inference having a lot of data or sensitivity can
declare an activation for every voxel in the brain, i.e. it will reject
the null hypothesis for for every voxel in the brain
* in SPM for first level Bayesian inference a spatial regularization
prior is used on the unsmoothed data, meaning that the smoothing is
automatic -- a much better approach than smoothing with a fixed width
kernel over the whole brain
* you can compare non-nested models, something you can't do with
frequentist inference (Rosa, 2010 Neuroimage; Harrison, 2011; Frontiers
in Human Neuroscience)
If you look at the examples in the SPM manual will get a bit of an idea
of the steps you need to take in Bayesian inference in SPM. However it
doesn't tell you much about 2nd level inference. Regardless, I would
suggest you go through those examples step by step. What you will find,
and what I found was that Bayesian inference finds expected activations
where the frequentist approach doesn't -- so you could say it is more
specific, although that's not really a right term to use in Bayesian
statistics.
The biggest drawback is the long computation time. Just to give you an
idea, on an 8 core intel i5 with 8 parallelized processes it took around
26 hours for 51 subjects with one session each for the first level
Bayesian estimation. Mind you I also asked for the log model evidence.
To get an idea of Bayesian inference I would first suggest you look at
the videos on the SPM website, specifically:
http://www.fil.ion.ucl.ac.uk/spm/course/video/#MEEG_Bayes
http://www.fil.ion.ucl.ac.uk/spm/course/video/#Bayes
I would watch both!
Concerning reading material you can take a look at the SPM book chapters
22,23 maybe 24 and maybe 25. The latter two are maths heavy. You can
find some of the PDFs on the SPM website
http://www.fil.ion.ucl.ac.uk/spm/doc/books/hbf2/
If you need a general overview of Bayesian statistics then check out
Kruschke's Doing Bayesian Data Analysis or his video on it:
http://www.indiana.edu/~video/stream/launchflash.html?format=mp4&folder=ssrc&filename=2012-10-05_wim_kruschke_bayesian.mp4
And if you really want to get into Bayesian statistics then check out
David Draper's video lectures and exercises:
http://users.soe.ucsc.edu/~draper/eBay-Google-2013.html
And last but not least, the scholaropedia entry for Bayesian statistics
is nice and short:
http://www.scholarpedia.org/article/Bayesian_statistics
I hope this helps!
Have fun with Bayes!
Glad
On 29.05.2014 01:02, SPM automatic digest system wrote:
> Date: Tue, 27 May 2014 23:34:09 +0000
> From: Peter Goodin <[log in to unmask]>
> Subject: Second level bayesian modelling - Activation threshold = 0?
>
> Hi SPM list,
>
> I'm doing a second level analysis on my fmri data and have been reading about the Bayesian method, which for my interests has been suggested to be robust to the effects of outliers (is this true)?
>
> Having a read through the mailing list I see that the initial PPM threshold doesn't have to be used but I was wondering if it's acceptable to have y = 0?
>
> Can anyone recommend some further reading on the pros and cons of low threshold PPMs?
>
> Thanks,
>
> Peter
--
Glad MIHAI, M.Sc. Biomedical Physics
Functional Imaging | University Clinic Greifswald
Walther-Rathenau-Straße 46 | 17475 Greifswald | Germany
Tel: +49 3834 86 69 44 | Fax: +49 3834 86 68 98
www.baltic-imaging-center.de
|