Dear Georgos, Francesco, and all,
thanks for taking the time to respond, and for your clear replies. Problem (1) is solved, however I am still puzzled by (2). (1) I was estimating my noise variance by importing the data blindly into SPM. There was a scaling or unit transform being applied to the data which I had not accounted for. Thanks for clearing this up. (2) The aspect of the source variance profile which is confusing is not purely a function of depth bias: there is a bright plane which is unusually strong over a wide range of depths. Firstly, a clarification: We frequently work with pseudo-z stats for source power (see Vrba and Robinson 2001, link below). This normalises source power estimates by the power of the projected sensor noise. Computing source power with normalised weight vectors is equivalent to performing this normalisation post-hoc. I've slightly altered my scripts to clarify this, if it helps. Secondly, thanks for the scripts you sent over. They conveyed your points clearly. When we apply corrections for depth bias, we tend to exclusively use the 2-norm; although I understand that changing the norm will change the sensitivity to the depth bias, as your 3rd script illustrated nicely. However, I don't think that tailoring the normalisation for depth bias resolves the issue. The unusual source profile we observe is present in both the source variance computed using normalised lead fields, and in the pseudo z-stats, and remains visible under a range of choices of norm for the lead fields (e.g. (lf(:).^2)^0.5, (lf(:).^2)^1). Indeed, the bright plane/stripe is clearly not solely a function of depth. It's perhaps easier to see this effect at a higher resolution: try running the script on a 4mm grid, under a range of depth normalisations. Thanks for your help, Giles Vrba and Robinson 2001 http://www.sciencedirect.com/science/article/pii/S1046202301912381 On 16 February 2015 at 13:33, Georgios Michalareas < [email protected]> wrote: > Dear Giles, > > I ve looked a bit into your questions and your code. I have used the same > data file you used. I am sending you 3 matlab scripts , more or less based > on your code, in which I ve put some analysis which I hope can help with > your questions. I have put most of my comments and suggestions as Comments > in the M-files, preceded by my name i.e. %GIORGOS: . Please go through > them(they are not very long and I tried to keep them a bit tidy ) and let > me know if you have any questions. > > 1. The variance of the empty room noise scans and the resting state scan , > at the sensor level, are very comparable. > See "code4Giles1.m" > In your code you mention as "source variance" to the diagonal of the > covariance matrix in source space. The value order and range of this > parameter largely depends on the Spatial Filter used to project the sensor > covariance matrix. In your code for example you normalized the Beamformer > Spatial Filters but their norm and then projected the data. If you dont do > this normalisation the diagonal of the covariance matrix takes a completely > different range of values. And if you normalise the Leadfield by each norm > , as is frequently done in beamformer solutions, (rather than the Spatial > Filters after they have been computed) then the range of values is alos > different. And in this case the exponent of the normalizing norm , also > affects the range of values. > See "code4Giles2.m" > > 3. If no leadfield normalization is performed the beamformers have an > inherent bias towards the center. This is because in order to produce the > given sensor measurements from a dipole in the center of the brain , one > needs much more power than from a dipole on the surface closer to the > sensors. The higher the regularisation (as expressed by the exponent of > the normalising norm) the more the bias shifts from the center towards the > surface. When the exponent of the normalizing norm is 0.5 (or the square > root of the sum of squares), the bias is neither in the center nor on the > surface but spread in-between. If you would like your solution to be more > biased towards the surface then an exponent of 1 is more appropriate. > Please see "code4Giles3.m" > > > Please have a look at the files and let me know for any questions or > comments you have. > Best > Giorgos > > > P.S. > ------------------- > Better to use the latest Fieldtrip version > > > > > On 12/02/2015 12:48, Giles Colclough wrote: > > Hi, > > I have two queries I'm looking for help with. > > > 1. Why is there a scaling difference between the magnitude of the data > recorded in the noise scans, and the output of the rmegpreproc pipeline? > > I find about 30 orders of magnitude difference between the variance of > the empty room scan and the variance of the data outputted by rmegpreproc. > > Have the data been uniformly scaled? If so, by how much? This would be > useful information, for example, when source localising using the empty > room data as an estimate of the noise. > > > 2. When source localizing with a beamformer, I find an unusual variance > profile in the resting state data. > > Normally when we look at resting data, the source variance is > concentrated in a 'halo' around the outside of the cortex. In the HCP data, > this halo is present, but there is also a bright stripe bisecting the brain > around the central sulcus. We find this suprising, but have been unable to > determine what's causing it. > > The effect is very repeatable between participants. > > I attach a screenshot of the effect, and two scripts to replicate the > analysis. > > Any help or insights would be greatly appreciated. > > > > > Many thanks, > Giles > > > _______________________________________________ > HCP-Users mailing list > [email protected] > http://lists.humanconnectome.org/mailman/listinfo/hcp-users > > > > > ------------------------------ > <http://www.avast.com/> > > This email is free from viruses and malware because avast! Antivirus > <http://www.avast.com/> protection is active. > > _______________________________________________ HCP-Users mailing list [email protected] http://lists.humanconnectome.org/mailman/listinfo/hcp-users
hcp_weird_source_variance2.m
Description: application/vnd.wolfram.mathematica.package
