Hi Katie, this is a problem in version 4 in which the scaling is a
little weird. It has no effect on the statistics (ie, p-values), but
when you extract the percent signal change, they appear to be very low.
This has been fixed in 5.X. For 4.X, you will need to compute a scaling
factor. This can be done in matlab with the following commands:
cd session/bold/analysis
x = load('X');
tirf = x.flac0.ev(3).tirf;
Xirf = x.flac0.ev(3).Xirf;
scalefOld = sum(Xirf .* repmat(tirf,[1 size(Xirf,2)]));
TR = x.flac0.TR;
dt = tirf(2)-tirf(1);
scalefNew = sum(Xirf)*TR/dt;
You would then multiply your % signal change that you get from the ROI
analysis by scalefNew.
doug
Katie Bettencourt wrote:
> Hi all,
>
> I'm doing ROI analysis on functional data with an anatomical label,
> and while the activity looks strong and robust in tksurfer, the %
> signal changes I am getting are very low (and much lower than we've
> previously found), and I'm not sure what's going on.
>
> I'm running Freesurfer 4.5 on red hat linux.
>
> The design is a blocked design with 4 non-null conditions, baseline is
> fixation. Attached is a picture of the activity I get for 2 subjects
> for on of the conditions vs fixation (baseline) at a p<0.05 threshold
> with the ROI outlined in black as well as the output from
> roisummary-sess for these two subjects in that roi. I calculate %
> signal change based of the output from roisummary-sess, dividing each
> condition by the baseline in that file and then multiplying by 100. %
> signal changes are listed below, along with all commands run.
>
> % signal change:
> 110222SKJ_connected 110222SP_connected
> 0.000988998467052 0.000533852720367
> 0.00105349836708 0.000556814127695
> 0.000408499366826 0.000327200054419
> 0.000440749316839 0.000436266739225
>
> commands:
> mkanalysis-sess -analysis connect -TR 2 -paradigm connect.dat
> -designtype blocked -funcstem fmc -motioncor -runlistfile
> connect_runs.txt -inorm -tpexclude tpexclude.dat -nconditions 4
> -timewindow 20 -gammafit 2.25 1.25 -noautostimdur
>
> mkcontrast-sess -analysis connect -contrast connected_vs_unconnected
> -a 1 -c 2
> mkcontrast-sess -analysis connect -contrast connected_vs_noise -a 1 -c 3
> mkcontrast-sess -analysis connect -contrast connected_vs_phase -a 1 -c 4
> mkcontrast-sess -analysis connect -contrast connected_vs_fix -a 1 -c 0
> mkcontrast-sess -analysis connect -contrast unconnected_vs_noise -a 2 -c 3
> mkcontrast-sess -analysis connect -contrast unconnected_vs_phase -a 2 -c 4
> mkcontrast-sess -analysis connect -contrast unconnected_vs_fix -a 2 -c 0
> mkcontrast-sess -analysis connect -contrast noise_vs_phase -a 3 -c 4
> mkcontrast-sess -analysis connect -contrast noise_vs_fix -a 3 -c 0
> mkcontrast-sess -analysis connect -contrast shape_vs_noise -a 1 -a 2 -c 3
> mkcontrast-sess -analysis connect -contrast shape_vs_phase -a 2 -a 1 -c 4
> mkcontrast-sess -analysis connect -contrast shape_vs_noisephase -a 2
> -a 1 -c 3 -c 4
> mkcontrast-sess -analysis connect -contrast act_vs_fix -a 2 -a 1 -a 3
> -a 4 -c 0
>
> selxavg3-sess -sf connect-sess -df connect.dir -analysis connect
>
> #run ROIs
> setenv anal connect
> foreach roi ( infIPS supIPS loc )
> foreach h (lh rh)
> func2roi-sess -roidef ${roi}-${anal}-${h} -analysis $anal -anatlabel
> ips_loc/${roi}-${h} -sf connect-sess -df connect.dir
> roisummary-sess -sumfile ips-rois/${anal}_${roi}-${h}.dat -roidef
> ${roi}-${anal}-${h} -analysis ${anal} -sf connect-sess -df connect.dir
> python roi_reader.py -r ${roi} -a ${anal} -z ${h} -f ips-rois -c 4
> end
> end
>
> Thanks,
>
> Katie
>
>
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--
Douglas N. Greve, Ph.D.
MGH-NMR Center
gr...@nmr.mgh.harvard.edu
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Fax: 617-726-7422
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