Deconvolution Analysis of FMRI Time Series Data - Waisman ...

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model parameters, and P columns, each column corresponding to one of the P parametersin the full model.See the Examples in Section 1.4.4 for illustrations of the use of this command.-glt label k glabelThe optional -glt label command is used to specify the string glabel for labeling theoutput corresponding to the kth general linear test.-iresp k iprexThe optional -iresp command instructs program 3dDeconvolve to save the estimated impulseresponse function corresponding to the kth input stimulus. That is, each impulseresponse function for each voxel is saved as a time series of length p +1, and stored intoan AFNI 3d+time dataset. The output dataset has prex lename iprex.If -iresp is used in conjunction with the -input1D command, then the single estimatedimpulse response function time series will be written to le iprex.1D. Note: This willautomatically overwrite a pre-existing iprex.1D le.-tshiftUse cubic spline interpolation to time shift the estimated impulse response function, inorder to correct for dierences in slice acquisition times. (See Section 1.2.14 for furtherdiscussion.) Note that this option eects only the 3d+time output dataset generated bythe -iresp option.Note: It is very important that the slice acquisition time osets, which are stored inthe .HEAD les, be correct. The -tshift option uses these values to determine how muchto shift the impulse response functions for each slice. The time osets are listed under thelabel TAXIS OFFSETS in the header les, and should be inspected by the user.-sresp k sprexThe optional -sresp command instructs program 3dDeconvolve to save the array ofstandarddeviations for the impulse response function corresponding to the kth input stimulus.That is, the standard deviations for the time points of the impulse response function aremade into a time series of length p +1,and stored into an AFNI 3d+time dataset. Theoutput dataset has prex lename sprex.If -sresp is used in conjunction with the -input1D command, then the single arrayofstandarddeviations will be written to le sprex.1D. Note: This will automatically overwritea pre-existing sprex.1D le.-tts fprexThe optional -tts command instructs program 3dDeconvolve to save the full model tto the input time series data for each voxel. The tted time series are stored into an AFNI3d+time dataset. The output dataset has prex lename fprex.If -tts is used in conjunction with the -input1D command, then the single tted timeseries will be written to le fprex.1D. Note: This will automatically overwrite a preexistingfprex.1D le.24
-errts eprexThe optional -errts command instructs program 3dDeconvolve to save the residual errors(i.e., error = data - full model t) into an AFNI 3d+time dataset. The output dataset hasprex lename eprex.If -errts is used in conjunction with the -input1D command, then the single residualerror time series will be written to le eprex.1D. Note: This will automatically overwritea pre-existing eprex.1D le.-bucket bprexThe -bucket command is used to create a single AFNI \bucket" type dataset havingmultiple sub-bricks. The output is written to the le with the user specied prex lenamebprex. Each of the individual sub-bricks can then be accessed for display within programafni. The purpose of this command is to simplify le management, since most of the outputresults for a particular problem can now becontained within a single AFNI bucket dataset.See Examples 1.4.2.5, 1.4.3.6, 1.4.4.6, and 1.4.6.5 for illustrations of the format of the bucketdataset.The following commands control the contents of the output bucket dataset:-fout Flag to output the partial- and full model F-statistics-rout Flag to output the partial- and full model R 2-tout Flag to output the t-statistic for each regression parameter-vout Flag to output the sample variance (MSE) map-nocout Flag to suppress output of the regression coecients(and associated statistics)-full rst Flag to specify that the full model statistics willappear rst in the bucket dataset output1.4 Examples1.4.1 Evaluation of the Experimental DesignIn the following Examples, we consider evaluation of the experimental design prior tocollecting data. This is done by using the -nodata option in place of the -input command.Example 1.4.1.1 Evaluation of a Block DesignSuppose that we wish to evaluate a \block" type design, with a repeating stimuluspattern of 4 \O" TR, followed by 4\On"TR,as indicated below.f(t) = f 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 01 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 10 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 gSave the above f(t), one row at a time, as a single column of 0's and 1's into leBlock.1D. Suppose that we wish to estimate the system IRF for 0-4 TR's. Consider thefollowing script:25
Page 1: Deconvolution Analysis of FMRI Time
Page 7 and 8: Since the system is linear, the ope
Page 9: the above equation can be written:Z
Page 13 and 14: where s(h k ) is the square root of
Page 15 and 16: Here, SSE(F )isthe error sum of squ
Page 17 and 18: concatenated runs, this is not the
Page 19: This equation relates the output y
Page 22 and 23: value is pnum = 1 (corresponding to
Page 26 and 27: Program 3dDeconvolve Command Line f
Page 28 and 29: f(t) = f 0 0 1 1 1 0 1 1 0 0 1 0 1
Page 30 and 31: If we model the measured response b
Page 32 and 33: Note that, since there is only one
Page 34 and 35: Full Model:MSE = 0.9618R^2 = 0.9835
Page 36 and 37: that are not of inherent interest,
Page 40 and 41: Now, model the measured data as a c
Page 42 and 43: -stim label 1 Random -stim label 2
Page 44 and 45: Note: This example is for illustrat
Page 46 and 47: The output is the same as for Examp
Page 48 and 49: After program 3dDeconvolve has nish
Page 52 and 53: Program:3dDeconvolveAuthor:B. Dougl
Page 54 and 55: cat y.1D y.1D > ycat.1DBe sure to r
Page 56 and 57: Recall that in Example 1.4.4.6, we
Page 58 and 59: and the noisy measurement data:wp(t
Page 60 and 61: The model that we will use for the
Page 62 and 63: Program 3dConvolve Command Line for
Page 64 and 65: 2 Program plug deconvolve2.1 Purpos
Page 66 and 67: voxels indicated by the crosshairs,
Page 68 and 69: Deconvolution Plugin Screen Output
Page 70 and 71: The following Random Permutation an
Page 72 and 73: Shuffled array:0 5 6 1 3 5 0 2 1 1
Page 74 and 75:
Program RSFgen Command Line for Exa
Page 76 and 77:
Example 3.3.3.1 Initial Random Bloc
Page 78 and 79:
Compare the output for this example
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Markov chain time series:0 0 1 5 0
Page 82 and 83:
(X'X) inverse matrix::065 ;:000 ;:0
Page 84 and 85:
Program 3dDeconvolve Screen Output
Page 86 and 87:
then the standard deviation for the
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4 Program 3dConvolve4.1 PurposeAs t
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-input1DThe -input1D command specie
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-stim minlag k m-stim maxlag k n-st
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parameters b 0 and b 1 , and impuls
Page 96 and 97:
Program 3dConvolve Command Line for
Page 98 and 99:
Example 4.4.6 Single Stimulus No No
Page 100:
5 References[1 ]F.Stremler, Fourier
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