[FieldTrip] fieldtrip Digest, Vol 65, Issue 14
Maris Skujevskis
icelandhouse at gmail.com
Mon Apr 18 10:45:33 CEST 2016
Dear Fieldtrip users,
When constructing an EEG forward model, the function ft_prepare_sourcemodel
has an option cfg.inwardshift.
When plotting the grid of the source model over the brain volume, I get the
following differences between a negative and a zero inwardshift (skull and
scalp layers not shown):
https://drive.google.com/file/d/0B9QSyDy3RhMIUkdxaHBPV2FtODg/view?usp=sharing
>From this I conclude that a negative inward shift means that grid points
that in reality are outside the brain get treated as inside the brain in
the source model.
Based on this observation, I have two questions:
a) why could a shift be necessary? -- naively, it doesn't make sense that a
grid point needs to be labeled as being in the brain when it actually is
outside.
b) how can a shift be justified? -- it seems that the outer grid points
that get labeled as 'inside' due to the shift would have incorrect
conductivity values when the leadfield matrix is calculated. This would be
the case because in the volume conduction model the coordinates of those
points correspond to the skull and not to the brain tissue.
Any explanations/corrections of the above outline of what cfg.inwardshift
does will be highly appreciated,
Maris.
On Sat, Apr 16, 2016 at 12:00 PM, <fieldtrip-request at science.ru.nl> wrote:
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> Today's Topics:
>
> 1. Re: TFR mask stats question; Baselined vs unbaselined
> statistics question (Max Cantor)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Fri, 15 Apr 2016 13:48:38 -0600
> From: Max Cantor <Max.Cantor at Colorado.EDU>
> To: FieldTrip discussion list <fieldtrip at science.ru.nl>
> Subject: Re: [FieldTrip] TFR mask stats question; Baselined vs
> unbaselined statistics question
> Message-ID:
> <
> CAOv1Hy1VumqCGBuzCpTOo7KPLDfTPXMGMoWVrhzbO2+cvqjRdQ at mail.gmail.com>
> Content-Type: text/plain; charset="utf-8"
>
> As a followup, I'd like to note that there was a mistake in the multiplot
> code in my prior post (I had in that code 1,3,5 instead of 1,2,3 as my
> indices for the stat cell array), and the comment for zlim was not relevant
> (that was for when I was plotting powspctrm, since the data were
> freqbaselined).
>
> Since the time I posted this, I've tried a few more things that have been
> somewhat successful. I have set frequency to [1 30], because gamma seemed
> to be very noisy and the data were bandpass filtered at [0.1 50]hz in the
> first place so I figured there would be no meaningful effect as high as
> 50hz. I have also played with the latency, testing a priori assumptions of
> the time window, as well as a general [0 1.8] window for TFR (and [0 1]
> window for ERP).
>
> Finally, I have tried the analyses using the nonparametric permutation
> test, but without the cluster correction (and setting cfg.alpha to 0.05 as
> opposed to 0.025, for the cluster statistics). When I do just the
> permutation test, I get the effects that I was anticipating in both ERP and
> TFR. It's only when I add in the cluster correction that I get no
> significant effects.
>
> Does anyone have an idea as to why this might be happening?
>
> Best,
>
> Max
>
> On Fri, Apr 8, 2016 at 3:02 PM, Max Cantor <Max.Cantor at colorado.edu>
> wrote:
>
> > Hi fieldtrippers,
> >
> > I have two unrelated stats questions, but I figured I'd post them
> together:
> >
> > 1. I've run within subjects permutation statistics testing the difference
> > between an anomaly and control condition for three different language
> > conditions; preposition, morphosyntax, and semantics. The code is nearly
> > identical for both my ERPs and TFRs, except that when I plot my ERPs with
> > cfg.maskparameter = 'mask', cfg.parameter = 'stat', and cfg.maskalpha =
> > 0.025, it seems to multiplot just fine, whereas when I try to do the same
> > for the TFRs, my multiplotted plots are blank, but when I comment out the
> > mask, the stat plots are fine. I'll attach the code, but is there
> something
> > I'm doing obviously wrong? Also, am I correct that my maskalpha should
> > correspond to my alpha, and not my cluster alpha?
> >
> > This is the TFR code, but the ERP code is the exact same except for with
> > timelocked data as the input and ft_timelockstatistics as the function.
> >
> > cfg = [];
> > cfg.method = 'montecarlo';
> > cfg.statistic = 'depsamplesT';
> > cfg.correctm = 'cluster';
> > cfg.clusteralpha = 0.05;
> > cfg.clusterstatistic = 'maxsum';
> > cfg.minnbchan = 2;
> > cfg.neighbours = neighbours;
> > cfg.tail = 0;
> > cfg.clustertail = 0;
> > cfg.alpha = 0.025;
> > cfg.numrandomization = 1000;
> >
> > nsubj = length(tot_tfr2_bl{1});
> > design = zeros(2,2*nsubj);
> > for i = 1:nsubj
> > design(1,i) = i;
> > end
> > for i = 1:nsubj
> > design(1,nsubj+i) = i;
> > end
> > design(2,1:nsubj) = 1;
> > design(2,nsubj+1:2*nsubj) = 2;
> >
> > cfg.design = design;
> > cfg.uvar = 1;
> > cfg.ivar = 2;
> >
> > % Anomaly vs Control
> > stat_tfr{1} = ft_freqstatistics(cfg, tot_tfr2_bl{1}{:},
> > tot_tfr2_bl{2}{:}); % Preposition
> > stat_tfr{2} = ft_freqstatistics(cfg, tot_tfr2_bl{3}{:},
> > tot_tfr2_bl{4}{:}); % Semantics
> > stat_tfr{3} = ft_freqstatistics(cfg, tot_tfr2_bl{5}{:},
> > tot_tfr2_bl{6}{:}); % Morphosyntax
> >
> > This is the plotting code. Again, it is virtually the same for the ERPs
> > except the non-stats configurations would be what makes sense for ERPs.
> >
> > % Anomaly vs Control TFR stats multiplot
> >
> > cfg = [];
> > cfg.xlim = [-0.2 1];
> > cfg.zlim = [-3 3]; % proportion from baseline
> > cfg.layout = [max_dir '/quickcap64.mat'];
> > cfg.maskparameter = 'mask';
> > cfg.parameter = 'stat';
> > cfg.maskalpha = 0.025;
> >
> > ft_multiplotTFR(cfg, stat_tfr{1}); title('Preposition');
> > ft_multiplotTFR(cfg, stat_tfr{3}); title('Semantic');
> > ft_multiplotTFR(cfg, stat_tfr{5}); title('Morphosyntactic');
> >
> >
> > 2. The second question is about analyzing baselined vs unbaselined data.
> > I've seen in the tutorials that there is a somewhat different method for
> > analyzing trial vs baseline period. However, what I've done instead (for
> > both ERP and TFR), is use the same within-subjects statistics as with
> > anomaly vs control, except with the inputs being baselined vs unbaselined
> > ERPs or TFRs, e.g. Baselined Preposition Anomaly vs Unbaselined
> Preposition
> > Anomaly, etc. I understand that the proposed method and my method are not
> > testing the same thing exactly, but is this a reasonable way of testing
> the
> > effect of the baselining anyway?
> >
> > Thanks,
> >
> > Max
> >
> > --
> > Max Cantor
> > Graduate Student
> > Cognitive Neuroscience of Language Lab
> > University of Colorado Boulder
> >
>
>
>
> --
> Max Cantor
> Graduate Student
> Cognitive Neuroscience of Language Lab
> University of Colorado Boulder
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