Difference between revisions of "Tech Reports"

Revision as of 09:55, 30 March 2009

Timecourse methods

Sometime around June 2007 the lab switched from using raw averages to compute timecourses to using the "-iresp" flag in afni's 3dDeconvolve command to create timecouses. To insure that the methods are compatible we directly compared the output from each method on a set of 53 healthy subjects who's age ranged form 20-80 on the MID task. (If you're looking for information on how to use iresp to make timecourses look here in the lab manual under "How to run IRESP").

Iresp method

The best information about computing timecourses in general and how the iresp method works can be found on the gablab site at MIT: http://mindhive.mit.edu/node/86

Here is a little info on the method taken from that site:

"The [Finite Impulse Response] FIR model is a modification of the standard GLM which is designed precisely to deconvolve different conditions' peristimulus timecourses from each other. The main modification from the standard GLM is that instead of having one column for each effect, you have as many columns as you want timepoints in your peristimulus timecourse. If you want a 30-second timecourse and have a 3-second TR, you'd have 10 columns for each condition. Instead of having a single model of activity over time in one column, such as a boxcar convolved with a canonical HRF, or a canonical HRF by itself, each column represents one timepoint in the peristimulus timecourse. So the first column for each condition codes for the onset of each trial; it has a single 1 at each TR that condition has a trial onset, and zeros elsewhere. The second column for each condition codes for the onset + 1 point for each trial; it has a single 1 at each TR that's right after a trial onset, and zeros elsewhere. The third column codes in the same way for the onset + 2 timepoint for each trial; it has a single 1 at each TR that's two after a trial onset, and zeros elsewhere. Each column is filled out appropriately in the same fashion.

With this very wide design matrix, one then runs a standard GLM in the multiple regression style. Given enough timepoints and a properly randomized design, the design matrix then assigns beta weights to each column in the standard way - but these beta weights each represent activity at a certain temporal point following a trial onset. So for each condition, the first column tells you the effect size at the onset of a trial, the second column tells you the effect size one TR after the onset, the third columns tells you the effect size two TRs after the onset, and so on. This clearly translates directly into a peristimulus timecourse - simply plot each column's beta weight against time for a given condition, and voila! A nice-looking timecourse."

Raw Average Method

The Raw average method of building a timecouse is simply to dump out the raw data from the complete experiment (after preprocessing and converting to % signal change) and take an average over all of a particular trial type. For example if you want to look at $5.00 gain trials and the subject had 15 of these trials over the experiment, you would pull out each of these 15 trials from the beginning TR to the end TR (or past the end) for each trial and average them together at each TR.

While the iresp method can easily incorporate motion correction in the model, the raw averaging method does not necessarily include motion correction.

Comparison

Over all it looks like the two methods produce nearly identical results even when the number of subjects is low (which is good news for our data consistency!). However, there may be slight differences on an individual subject level. For example in the gain $0 trials for subject #2.

Physiological Corrections

<img src=All_NaccGain.jpg>