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

(Can you prove this claim?)The command -tts myFit causes the tted time series to be written to le myFit.1D.You can plot the input time series and the tted time series in separate graph windowswith the following commands (suppressing the rst 6 points):1dplot -ignore 6 029 029 008.1D1dplot -ignore 6 myFit.1DHowever, if you wish to plot the tted time series on top of the FMRI time series data,the following commands can be used to combine the two time series into a single le (e.g.,Overlay.1D) for plotting:Script for Plotting Fitted Data on top of Actual Data1dtranspose 029 029 008.1D 029 029 008.1Dt1dtranspose myFit.1D myFit.1Dtcat 029 029 008.1Dt myFit.1Dt > Overlay.1Dtrm -f Overlay.1D1dtranspose Overlay.1Dt Overlay.1Drm -f 029 029 008.1Dtrm -f myFit.1Dtrm -f Overlay.1Dt1dplot -ignore 6 -one Overlay.1DYou should see the tted time series (in red) plotted on top of the original time series (inblack). This result is similar to that which you will obtain in Example 2.3.1 (q.v.) usingthe Deconvolution plugin.Example 1.4.3.2 Multiple Linear Regression using Estimated Motion ParametersIn this Example, we repeat the above analysis, but will include in the full model theestimated motion parameters. The reason for doing so is to account for variation in themeasured response due to subject motion. This should help both to reduce false positives(by subtracting out the eect of stimulus correlated motion) and to increase true positives(by reducing the measurement noise estimate).The rst step is to estimate the subject motion. This can be done using program3dvolreg, e.g.:3dvolreg -base 34 -1Dfile v2.motion.1D v2:time+origThis will write to le v2.motion.1D the estimated motion parameters: roll, pitch, yaw,dS. dL, and dP, as 6 columns of numbers. Now, these separate columns can be entered asindividual stimulus functions, as indicated below:37