[FieldTrip] Fwd: head motion regression with ft_regressconfound on a continuous MEG data
McGowin, Inna
mcgoiv0 at wfu.edu
Mon Dec 9 19:04:20 CET 2013
Thanks you
Your code works fine. Here is the Matlab output for this code:
*the input is timelock data with 2 channels and 5 timebinsremoving confound
1 2 3 keeping confound normalizing the confounds, except the constant
estimating the regression weights and removing the confounds updating
descriptives the input is timelock data with 2 channels and 5
timebinsWarning: the data does not contain a trial definition > In
utilities\private\warning_once at 158 In utilities\private\fixsampleinfo
at 66 In ft_datatype_raw at 154 In ft_checkdata at 298 In
ft_timelockanalysis at 105 In ft_regressconfound at 313Warning:
reconstructing sampleinfo by assuming that the trials are consecutive
segments of a continuousrecording > In utilities\private\warning_once at
158 In utilities\private\fixsampleinfo at 79 In ft_datatype_raw at 154
In ft_checkdata at 298 In ft_timelockanalysis at 105 In
ft_regressconfound at 313averaging trialsaveraging trial 20 of 20the call
to "ft_timelockanalysis" took 1 secondsthe call to "ft_regressconfound"
took 4 seconds*
The data I am trying to correct for the motion is real. It was collected
with a current phantom generating the signal by a dipole. The data is
continuous but can be broken into trials of 1 sec (the dipole generates 7
Hz sinusoidal signal continuously). The phantom was mechanically moved
during the scan for a precisely known amount (e.g. 1 cm down). The data
was recorded by CTF 275-sensor MEG unit with the continuous head coils
localization. I was able to apply the SSS motion correction algorithm
successfully to this data set. If there is a way I could share this data
set.
Thanks,
Inna
Inna McGowin
On Mon, Dec 9, 2013 at 9:48 AM, Arjen Stolk <a.stolk8 at gmail.com> wrote:
> Hi Inna,
>
> Testing ft_regressconfound with some fake data, everything seems to be
> fine here:
>
> timelock = [];
> timelock.label = {'1' '2'};
> timelock.time = 1:5;
> timelock.dimord = 'rpt_chan_time';
> timelock.trial = randn(20,2,5);
> timelock.avg = randn(2,5);
>
> cfg = [];
> cfg.confound = randn(20,3);
> cfg.reject = [1:3];
> timelock_out = ft_regressconfound(cfg, timelock);
>
> Does this example code work for you? I could not detect any mistake in
> your lines of code. What does your 'timelock' structure look like? Are
> there any real values in the data? (don't think this is causing it, but
> please update to newest Ft version just to be sure)
>
> Yours,
> Arjen
>
>
> 2013/12/2 McGowin, Inna <mcgoiv0 at wfu.edu>
>
> Thank you Arjen,
>> Actually, I am testing the FieldTrip motion regression on MEG data
>> specifically collected with a current phantom and known motion. I can treat
>> the data as ERP data and average over 1 sec trials if needed.
>> Following your advise on reduction of the regressors number (on 120
>> trials of 1 sec long) I was able to avoid the "out of memory" issue. Though
>> I run into the different set of problems now that I need help to understand:
>> ______________________________________________
>> ??? Error using ==> mxSerialize
>> Error during serialization of (null)
>>
>> Error in ==> ft_postamble_provenance at 91
>> cfg.callinfo.outputhash{iargout} = CalcMD5(mxSerialize(tmparg));
>>
>> Error in ==> ft_postamble at 55
>> evalin('caller', ['ft_postamble_' cmd]);
>>
>> Error in ==> ft_timelockanalysis at 370
>> ft_postamble provenance timelock
>>
>> Error in ==> ft_regressconfound at 313
>> dataout = ft_timelockanalysis(tempcfg, dataout);% reaveraging
>> _________________________________________________________
>>
>> Here is the code I run to regress the head motion:
>> %%
>> addpath C:\Userdata\MATLAB\FieldTrip\fieldtrip-20131023
>> ft_defaults
>> cfg.dataset = 'InnaTest_Current-Phantom_20131004_09.ds';
>> cfg.trialdef.triallength=1;
>> cfg.trialdef.ntrials=120;
>> cfg.continuous = 'yes';
>> cfg = ft_definetrial(cfg);
>> cfg.channel = {'MEG'};
>> cfg.demean = 'yes';
>> data = ft_preprocessing(cfg);
>>
>> %%
>> cfg = [];
>> cfg.keeptrials = 'yes';
>> timelock = ft_timelockanalysis(cfg, data);
>>
>> %%
>> cfg = [];
>> cfg.dataset = 'InnaTest_Current-Phantom_20131004_09.ds';
>> cfg.trialdef.triallength=1;
>> cfg.trialdef.ntrials=120;
>> cfg.continuous = 'yes';
>> cfg = ft_definetrial(cfg);
>>
>> %%
>> cfg.channel = {'HLC0011','HLC0012','HLC0013', ...
>> 'HLC0021','HLC0022','HLC0023', ...
>> 'HLC0031','HLC0032','HLC0033'};
>> headpos = ft_preprocessing(cfg);
>>
>> ntrials = length(headpos.sampleinfo)
>> for t = 1:ntrials
>> coil1(:,t) = [mean(headpos.trial{1,t}(1,:));
>> mean(headpos.trial{1,t}(2,:)); mean(headpos.trial{1,t}(3,:))];
>> coil2(:,t) = [mean(headpos.trial{1,t}(4,:));
>> mean(headpos.trial{1,t}(5,:)); mean(headpos.trial{1,t}(6,:))];
>> coil3(:,t) = [mean(headpos.trial{1,t}(7,:));
>> mean(headpos.trial{1,t}(8,:)); mean(headpos.trial{1,t}(9,:))];
>> end
>>
>> cc = circumcenter(coil1, coil2, coil3);
>> cc_dem = [cc - repmat(mean(cc,2),1,size(cc,2))]';
>>
>> %%
>> confound = [cc_dem ...
>> ones(size(cc_dem,1),1)];
>>
>> %%
>> cfg = [];
>> cfg.confound = confound;
>> cfg.reject = [1:6];
>> regr = ft_regressconfound(cfg, timelock);
>> %%
>>
>> _________________________________________________________________
>> Sorry for the lengthy letter and thanks for the help!
>>
>> Inna
>>
>>
>>
>>
>>
>> On Thu, Nov 28, 2013 at 12:23 PM, Stolk, A. (Arjen) <
>> a.stolk at fcdonders.ru.nl> wrote:
>>
>>> Hi Inna,
>>>
>>> Are you using the same design matrix as on the wiki page? That one
>>> covers the primary head translations and rotations, but also their
>>> derivatives. You could try using the primary measures only (3
>>> translastions, and 3 rotations of the circumcenter, totaling 6 + 1 constant
>>> regressors). It is also mentionworthy that there should be a balance
>>> between the number of regressors and the number of observations
>>> (i.e. trials). I believe on the respective wiki page on head movement
>>> regression it states that the ratio is recommended to be not larger
>>> than 1:10 (regressors:trials), accompanied with a reference. Accordingly,
>>> I'd suggest to use those 7 regressors, and your data (120s resting state)
>>> split in segments of 1 or 2 seconds, totaling 60 or 120 trials. These
>>> specifications might hopefully also allow working around your memory issue.
>>>
>>> Second, it may be worth mentioning what you would like to do with the
>>> data post-movement-regression. Namely, the movement compensation will
>>> remove contributions from head movement to the signal. But this requires
>>> that signal to behave consistently over trials, which does not seem the
>>> case with the signal in your resting study? That is, with an ERP study, the
>>> signal fluctuations appear consistent over trials, with amplitudes being
>>> modulated by head movement (i.e. sensor-source distance, see our neuroimage
>>> paper - 2013) in a predictable fashion. So if you're planning on doing
>>> frequency analysis, or maybe connectivity analysis, after head movement
>>> compensation, I'd recommend reversing that order. The rule of thumb is to
>>> use ft_regressconfound just prior to ft_xxxstatistics, removing otherwise
>>> unexplained variance (over trials) due to head movement, benefitting your
>>> statistical assessments.
>>>
>>> Hope these suggestions may help you with further analyses. I'm
>>> not familiar with anyone implementing this method to resting state
>>> analyses, but maybe someone else has some first-hand experience here.
>>> Furthermore, too late for your current dataset, but for a next one I'd
>>> recommend using the online head position monitoring/respositioning tool
>>> that we have developed for CTF systems (a neuromag version is approaching
>>> the final stage). This tool is routinely used in our lab to monitor
>>> and reduce head movment throughout recording (after which
>>> ft_regressconfound is being used to deal with the trial-by-trial crumbles).
>>>
>>> Yours,
>>> Arjen
>>>
>>> _______________________________________________
>>> fieldtrip mailing list
>>> fieldtrip at donders.ru.nl
>>> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>>>
>>
>>
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>
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