Please see inline responses below.On Sun, Mar 1, 2015 at 12:56 PM, Lisa McDermott <[log in to unmask]> wrote:Hello all,I feel I am close to understanding the definition of contrasts, but I am still confused about some issues. I was wondering if I could ask your guidance for defining contrasts in my fMRI experiment. Here are the onsets and durations of my experiment:Onset DurationFixation 0 8Recrangle 8 20Fearful_f 28 20Circle 48 20Neutral_f 68 20Triangle 88 20Fearful_m 108 20Circle 128 20Neutral_m 148 20Rectangle 168 20Neutral_f 188 20Triangle 208 20Fearful_m 228 20Rectangle 248 20Neutral_m 268 20Triangle 288 20Fearful_f 308 20Circle 328 20Fixation 348 2I've color coded as well as I can. In the 1st level analysis, I modeled the following (in order):Shapes (black, not bolded): abbreviated S from now onFearfulFaces (color coded red): abbreviated FF from now onNeutralFaces (color coded purple): abbreviated NF from now onAnd I am calling the set of all faces (both fearful and neutral) as FI am not concerned about the differences between males and females right now.However, I am having a lot of confusion with the contrasts. I want to do the following contrasts:FF vs. SFF vs. NFNF vs. SF vs. SIn the contrast manager, I defined the contrasts as follows:>> These all look correct.I am not sure as to the blood flow implications of what I am measuring when I am defining the above contrasts. I would appreciate it if someone could explain the blood flow implications for each contrast to me.>> They mean that one condition has a larger BOLD response than the other condition, or less of a deactivation. Blood flow is only one component of the BOLD response, so I would not intermix the two phrases.
In particular, I am very confused about the 4th contrast: Faces vs. ShapesI am told that the sum of the terms in the contrasts should be zero (I was previously using [-1 1 1] for the Faces vs. Shapes contrast before I was corrected by my colleague). Why is that?If one were to use [-1 1 1], this means that the sum of the BOLD response for FF and NF is greater than that for shapes. Hypothetically, if shapes and faces both had a BOLD response of 2, then you would incorrectly conclude that faces had a larger BOLD response than shapes because the contrast*b would be positive: -1*2+1*2+2*2=2. With the new contrast [-2 1 1], you would correctly get 0 as all three stimuli had the same response. Note, the correct choice really depends on the question; although in this case, I'm not sure what hypothesis would lead to using the initial contrast.
I would suggest that instead of defining the contrasts, you should first define the null hypothesis. In the above case:Ho: shapes=facesthen make that equal to 0Ho: shapes-faces=0 or faces-shapes=0now since faces is actually 2 stimuli, you need to replace faces with what the actual value. Faces is the average of NF and FF.Ho: (NF+FF)/2-shapes=0Now, get the coefficients for each term in the model. Any term not in the null hypothesis has a weight of 0.NF --> 1/2FF --> 1/2S --> -1So the contrast would be [-1 1/2 1/2]. This will give yo the same t-statistic as [-2 1 1], but the contrast value will be half of [-2 1 1]. The contrast value will match the individual conditions better with [-1 1/2 1/2].
When there is a simple activation paradigm, say finger tapping vs. rest, I know the contrast is just [1, 0], which doesn't sum to zero. So why does the 4th contrast have to sum to zero? What is the criteria for the sum of the digits in the contrast vector summing to zero?It all depends on the null hypothesis, which should be set to 0 before making the contrast. Null hypothesis that are already equal at the beginning of the process to 0 won't sum to 0. All other should sum to 0.Hope this helps.
Thank you.- Lisa