[FieldTrip] calculating behavioural-power correlation

Hwee Ling Lee hweeling.lee at gmail.com
Wed Feb 18 08:58:51 CET 2015

```Dear Frederic,

>From my limited understanding, the way you specify your design matrix seems
correct to me. I did the same thing as well, however, I was not interested
in the correlation along the time dimension, and I averaged some
frequencies to examine my behavioural-power change correlation with
specific frequency bands (e.g. 2 - 4 Hz for Delta band, 4 - 8 Hz for Theta
band, etc).

I think the main reason for the long computation time of your design matrix
is because the statistics is calculating the correlation for every
frequency and every time point and every channel. You should probably ask
yourself if the behavioural is going to change along the time dimension. If
not, then probably averaging across time might be a good idea, or pick a
time period that you hypothesized to be most sensitive to your behavioural
measure. Also, I would suggest to look into some specify frequency bands
based on your hypothesis, and averaged across a specified frequency band

I hope this helps.

Cheers,
Hweeling

On 17 February 2015 at 18:03, Frédéric Roux <f.roux at bcbl.eu> wrote:

> Dear Arjen, dear Hweeling,
>
> I would be interested in trying this method as well.
>
> May I ask you how to specify the design matrix?
>
> For instance if I want to measure the correlation between a TFR-matrix
> and some behavioral measure (Y) across participants would something like
> this make sense:
>
> AVG =
>      powspctrm:[4-D double]
>      label:{186x1 cell}
>      freq:[1x49 double]
>      time:[1x121 double]
>      dimord: 'subj_chan_freq_time'
>      cfg:[1x1 stuct]
>
>
> dum = AVG;
> dum.powspctrm = repmat(Y,[1 size(AVG.powspctrm,2) size(AVG.powspctrm,3)
> size(AVG.powspctrm,4)]);
> %here Y is a vector with the behavioral measure
>
> cfg = [];
> cfg.method = 'montecarlo';
> cfg.parameter = 'powspctrm';
> cfg.statistic = 'ft_statfun_correlationT';
> etc
>
> cfg.design = [];
> cfg.design(1,:) = [ones(1,lenght(Y)) 2*ones(1,length(Y))];
> cfg.design(2,:) = [1:length(Y) 1:length(Y)];
>
> freq_stat = ft_freqstatistica(cfg,AVG,dum);
>
>
> This, however, results in extremely long computing times, which makes me
> doubt that this is the correct way.
>
> Best,
>
> Fred
>
> Frédéric Roux
>
> ----- Original Message -----
> From: "Hwee Ling Lee" <hweeling.lee at gmail.com>
> To: "arjen stolk" <arjen.stolk at donders.ru.nl>
> Cc: "FieldTrip discussion list" <fieldtrip at science.ru.nl>
> Sent: Tuesday, February 17, 2015 5:39:29 PM
> Subject: Re: [FieldTrip] calculating behavioural-power correlation
>
>
>
> Thanks! One last question, just to be sure, what is the reference for this
> correlation method? I tried to search for your publications but not sure
> which one to cite.
>
>
> Cheers,
> Hweeling
>
>
> On 17 February 2015 at 16:44, arjen stolk < a.stolk8 at gmail.com > wrote:
>
>
>
>
>
> Yes it does. ;)
> Arjen
>
>
> -------- Oorspronkelijk bericht --------
> Van: Hwee Ling Lee < hweeling.lee at gmail.com >
> Datum:
> Aan: "Stolk, A. (Arjen)" < a.stolk at donders.ru.nl >
> Cc: FieldTrip discussion list < fieldtrip at science.ru.nl >
> Onderwerp: Re: [FieldTrip] calculating behavioural-power correlation
>
>
>
> Dear Arjen,
>
>
> Thanks! It works well now.
>
>
> I plotted the results using ft_clusterplot, and it only shows the
> significant clusters that show significant correlation of power and
> behavioural measure, right? Or is there a better way I can display the
> results?
>
>
> Thanks again.
>
>
> Cheers,
> Hweeling
>
>
>
>
>
>
> On 17 February 2015 at 11:45, Stolk, A. (Arjen) < a.stolk at donders.ru.nl >
> wrote:
>
>
>
>
> Hey Hweeling,
>
> "Just to ensure that I get this right, I should create a variable for the
> behavioural measure such that the variable contains a powspctrm field with
> the behavioural information for every frequency?"
> > indeed
>
> "What I'm confused is that in the walkthrough website, under the
> subsection on correlation, it is suggested to create the cfg.design with
> the behavioural measure that one wants to correlate. So is this information
> in the walkthrough website incorrect?"
> > the walkthough may refer to a GLM-based statistical implementation, for
> which the FT implementation differs from the correlationT statfun. Namely,
> the former uses the behavioral measure as a regressor in a data model
> whereas the latter uses the behavioral measure as a datapoint series for
> correlation with another datapoint series (and then converts to a T value).
> The correlationT statfun is relatively 'new', hence not yet addressed in
> the walkthrough.
>
> Yours,
> arjen
>
>
>
>
> From: fieldtrip-bounces at science.ru.nl [ fieldtrip-bounces at science.ru.nl ]
> on behalf of Hwee Ling Lee [ hweeling.lee at gmail.com ]
> Sent: Tuesday, February 17, 2015 11:34 AM
> To: FieldTrip discussion list
>
>
> Subject: Re: [FieldTrip] calculating behavioural-power correlation
>
>
>
>
>
>
> Dear Arjen,
>
>
> Thanks for the prompt reply again!
>
>
> Just to ensure that I get this right, I should create a variable for the
> behavioural measure such that the variable contains a powspctrm field with
> the behavioural information for every frequency?
>
>
> What I'm confused is that in the walkthrough website, under the subsection
> on correlation, it is suggested to create the cfg.design with the
> behavioural measure that one wants to correlate. So is this information in
> the walkthrough website incorrect?
>
>
> Cheers,
> Hweeling
>
>
>
>
> On 17 February 2015 at 11:23, Stolk, A. (Arjen) < a.stolk at donders.ru.nl >
> wrote:
>
>
>
>
> Hey Hweeling,
>
> It seems you're only inserting one input variable into the statistics
> function, i.e. " [c200_delta_stat] = ft_freqstatistics(cfg,
> sub_LF_c200{:});"
>
> Could you try something along this line: ft_freqstatistics(cfg, freq1,
> freq2)
>
> where freq1 is the original freq data, and freq2 is a copy of freq but
> with the relevant values (say, in powspctrm) replaced with behavior values
> (ensure this behavior matrix is matched in terms of size and dimensions to
> the original freq values).
>
> Hope this helps,
> Arjen
>
>
>
> --
> Donders Institute for Brain, Cognition and Behaviour
> Centre for Cognitive Neuroimaging
>
> Email: a.stolk at donders.ru.nl
> Phone: +31 (0)243 68294
> Web: www.arjenstolk.nl
>
>
> From: Hwee Ling Lee [ hweeling.lee at gmail.com ]
> Sent: Tuesday, February 17, 2015 10:33 AM
> To: Stolk, A. (Arjen)
> Cc: FieldTrip discussion list
> Subject: Re: [FieldTrip] calculating behavioural-power correlation
>
>
>
>
>
>
> Dear Arjen,
>
>
> Thanks for the prompt reply. I keep getting an error message when I set up
> my correlation cluster statistics, and I'm not sure what I could have done
> wrong. Here's my script:
>
>
>
> cfg = [];
> cfg.layout = 'EEG1010.lay';
> cfg.neighbours = neighbours;
> cfg.channel = 'all';
> cfg.latency = 'all';
> cfg.avgovertime = 'no';
> cfg.avgoverchan = 'no';
> cfg.avgoverfreq = 'yes';
> cfg.parameter = 'powspctrm';
> cfg.method = 'montecarlo';
> cfg.statistic = 'ft_statfun_correlationT';
> cfg.correctm = 'cluster';
> cfg.clusteralpha = 0.05;
> cfg.clusterstatistics = 'maxsum';
> cfg.minnbchan = 2;
> cfg.tail = 0;
> cfg.clustertail = 0;
> cfg.alpha = 0.025;
> cfg.numrandomization = 1000;
> cfg.ivar = 1;
> cfg.uvar = 1;
>
>
> % design matrices
> clear design;
> % change in MMSE score relative to baseline
> design(1,:) = [0.095238095 -0. 045454545 - 0.533333333 0.238095238
> -0.157894737 0.117647059];
> design(2,:) = 1:6;
> cfg.design = design;
>
>
> % for delta band
> cfg.frequency = [2 4];
> [c200_delta_stat] = ft_freqstatistics(cfg, sub_LF_c200{:});
> [c210_delta_stat] = ft_freqstatistics(cfg, sub_HF_c210{:});
>
>
> Here's the output from Matlab:
>
>
>
> computing statistic over the frequency range [2.000 4.000]
> the call to "ft_appendfreq" took 0 seconds
> the call to "ft_selectdata" took 0 seconds
> using "ft_statistics_montecarlo" for the statistical testing
> using "ft_statfun_correlationT" for the single-sample statistics
> constructing randomized design
> total number of measurements = 6
> total number of variables = 2
> number of independent variables = 1
> number of unit variables = 1
> number of within-cell variables = 0
> number of control variables = 0
> using a permutation resampling approach
> repeated measurement in variable 1 over 6 levels
> number of repeated measurements in each level is 1 1 1 1 1 1
> computing a parametric threshold for clustering
> Error using ft_statfun_correlationT (line 90)
> Invalid specification of the design array.
> Error using ft_statistics_montecarlo (line 254)
> could not determine the parametric critical value
> for clustering
>
>
> Error in ft_freqstatistics (line 319)
> [stat, cfg] = statmethod(cfg, dat, cfg.design);
>
> Would you please tell what I have done wrong in this case?
>
>
> Thanks!
>
>
> Cheers,
> Hweeling
>
>
>
>
> On 17 February 2015 at 10:18, Stolk, A. (Arjen) < a.stolk at donders.ru.nl >
> wrote:
>
>
>
>
> Hi Hweeling,
>
> Have a look at the help of ft_statfun_correlationT, which might be the
> function you're looking for. This function calculates correlations between
> two variables (e.g. subjects' behaviors and brain activities) and converts
> the resulting correlation coefficients to t-statistics.
>
> Best,
> arjen
>
>
>
>
> --
> Donders Institute for Brain, Cognition and Behaviour
> Centre for Cognitive Neuroimaging
>
> Email: a.stolk at donders.ru.nl
> Phone: +31 (0)243 68294
> Web: www.arjenstolk.nl
>
>
> From: fieldtrip-bounces at science.ru.nl [ fieldtrip-bounces at science.ru.nl ]
> on behalf of Hwee Ling Lee [ hweeling.lee at gmail.com ]
> Sent: Tuesday, February 17, 2015 10:06 AM
> To: FieldTrip discussion list
> Subject: [FieldTrip] calculating behavioural-power correlation
>
>
>
>
>
>
>
>
> Dear all,
>
>
> I read on the "walkthrough" that it is possible to calculate
> behavioural-power correlation across subjects. However, I was not sure what
> type of descriptive statistics (i.e. cfg.statistics) I should use when
> performing correlation cluster statistics.
>
>
> Would someone please enlighten me which type of statistics I should input
> for cfg.statistics?
>
>
> Thanks!
>
>
> Best regards,
> Hweeling
>
>
>
>
>
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>
>
>
> --
>
>
> =================================================
> Dr. rer. nat. Lee, Hwee Ling
> Postdoc
> German Center for Neurodegenerative Diseases (DZNE) Bonn
>
> Email 1: hwee-ling.lee<at> dzne.de
> Email 2: hweeling.lee<at> gmail.com
>
>
>
> Ernst-Robert-Curtius Strasse 12, 53117, Bonn, Germany
> =================================================
>
>
>
>
> --
>
>
> =================================================
> Dr. rer. nat. Lee, Hwee Ling
> Postdoc
> German Center for Neurodegenerative Diseases (DZNE) Bonn
>
> Email 1: hwee-ling.lee<at> dzne.de
> Email 2: hweeling.lee<at> gmail.com
>
>
>
> Ernst-Robert-Curtius Strasse 12, 53117, Bonn, Germany
> =================================================
>
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--
=================================================
Dr. rer. nat. Lee, Hwee Ling
Postdoc
German Center for Neurodegenerative Diseases (DZNE) Bonn

Email 1: hwee-ling.lee<at>dzne.de
Email 2: hweeling.lee<at>gmail.com