 |
| Mine's bigger |
I recently received an email with some relatively basic questions about FMRI, and both the question and answers might apply to some of you. Admittedly, I am not 100% sure about the weighting of the regressors for the ANOVA, but I think it's pretty close. Whatever, man; I'm union.
Some of the words have been changed to protect the identity of the author.
Dear Andrew,
Thanks for your message. My background is in medicine and I am trying to do fmri research!
I will be grateful for your help:
1.
How do you interpret the results of the higher and first level fsl
analysis - I am used to p values and Confidence intervals - are fMRI
results read in a similar way?
2. Importantly- I have a series of subjects and we are interested
to look at effect of [a manipulation] on their response to [cat images] over
one year, we have four time points one before the operation and three
after. These time points are roughly 4 months apart.
Our Idea was to see how the response to [cat images] changes over
time- with each subject serving as their own control- How do I analyse
that? We have some missing time points as well- subjects did not come
for all the time points!
Regards,
Simon Legree
Hi Simon,
Congratulations on your foray into FMRI research; I wish you the best of luck, and I hope you find it enjoyable and rewarding!
In response to your questions:
1. FMRI results also use p-values and confidence intervals, but these
are calculated at every single voxel in the brain. For example, if you
are looking at the average BOLD response to [cat images] at each voxel, a
parameter will be estimated at that voxel, along with a particular
p-value and confidence interval. What you'll notice in the FSL GUI is a
cluster thresholding which will only display a specified number of
spatially contiguous voxels all passing the same p-threshold.
One crucial difference between first and higher-level analyses
in FSL (and any FMRI analysis, really) is the degrees of freedom. At
the first-level, the degrees of freedom is specified as the number of
time points minus the number of regressors; at the second-level (or
higher level) the degrees of freedom is specified as the number of time
points that went into that higher-level analysis - which is usually the
number of subjects included in the analysis. Unless you are doing a case
study, you usually will not be dealing with the degrees of freedom at
the individual level. (However, see documentation on mixed-effect
analyses like AFNI's 3dMEMA, which will take individual variance and
degrees of freedom into account.)
2. For an analysis with each patient serving as their own
control, you would probably want to do a paired t-test or
repeated-measures ANOVA for each subject. For the paired t-test, you
would need to weight each cluster of regressors so that they sum to +1
and -1, respectively; in your case, +1*Before, -0.33*After1,
-0.33*After2, -0.33*After3. However, if you hypothesize that there is a
linear response over time, you might want to do an ANOVA and weight the
timepoints linearly; e.g., for a decreasing response over time,
+0.66*Before, +0.33*After1, -0.33*After2, -0.66*After3. There are a
number of different ways you could do this. As for the subjects with
missing time points, you would need to take that into account when
weighting your regressors; I also recommend doing a sanity check by
doing the analysis both with the timepoint-less subjects and with them.
If there is a huge discrepancy between the two analyses, it might
suggest that there is something else correlated with missing time
points.
Hope this helps!
-Andy
