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

One advantage of this approach is that the measure of the magnitude of each dierencehas been reduced to a single sub-brick, either A-B LC[0], or A-C LC[0]. This simpliessubsequent statistical analysis.1.4.5 Multiple Linear Regression Approach to ANOVAProgram 3dDeconvolve always includes an intercept (b0) term. This does not preclude usinga cell means model, if the time series data, unlike data for a \pure" ANOVA experiment,contains data points where no combination of factor levels (i.e., treatments) applies, sincethis allows an independent estimate of the baseline. However, even for experiments whereit is not possible to estimate the baseline, an ANOVA type analysis is still posssible, as isillustrated below.Example 1.4.5.1 Cell means ANOVA modelAs an illustration of using 3dDeconvolve to solve a pure ANOVA type problem, we willconsider the \Castle Bakery" example from Ref.[2]. In that example, there are 2 factors:Factor A, at3levels and Factor B, at 2 levels. Letting 1 if Factor A is at level iandFactor B is at level jA i B j =0 otherwisethen the cell means model would be given by:Y = aA 1 B 1 + bA 1 B 2 + cA 2 B 1 + dA 2 B 2 + eA 3 B 1 + fA 3 B 2The data for this experiment are presented in the following table:Y A 1 B 1 A 1 B 2 A 2 B 1 A 2 B 2 A 3 B 1 A 3 B 247 1 0 0 0 0 043 1 0 0 0 0 046 0 1 0 0 0 040 0 1 0 0 0 062 0 0 1 0 0 068 0 0 1 0 0 067 0 0 0 1 0 071 0 0 0 1 0 041 0 0 0 0 1 039 0 0 0 0 1 042 0 0 0 0 0 146 0 0 0 0 0 1The data from the above table, without the column headings, should be entered into leCastle.data.1D. Note that the rst column contains the measured data, and the following6 columns contain the indicator variables A 1 B 1 ,...,A 3 B 2 .One would like toenter the indicator variables as stim functions, as in the above equation.Try to do this, by executing the following script:49