Thanks for the response. I was having a difficult time conceptualizing 
what exactly they represent in this design. From Eric Zarahn's email, 
3/23/2009:
https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=ind0903&L=SPM&D=0&O=D&T=0&P=153402

The ith parameter in a general linear model is a partial slope that  
represents the expected change in the dependent variable given a unit  
change in the ith independent variable, all other independent  
variables being held constant.

Since all timepoints are modeled, there is never a time where there is a 
unit change in any one variable,  holding the others constant. So, I 
couldn't figure out what the real-world, non-abstract meaning of 
beta(MT) would mean.
Does this also preclude getting beta values from an ROI and comparing 
between the graphs (e.g. creating a bar graph with error bars)?

I'm considering combining the rest conditions into one, and leaving them 
unspecified (thus giving me 3 columns in X: metronome tapping, self 
tapping, session effect). Is this a better solution?

Thanks,
Chris

MCLAREN, Donald wrote:
> If every timepoint has an event that is modeled, then its really hard 
> to interpret individual betas because you don't have a clearly defined 
> reference point. If you were to model only 1 rest condition, then you 
> could intepret the betas because they would be in reference to the 
> unmodelled condition.
>
> Best Regards, Donald McLaren
> =================
> D.G. McLaren
> University of Wisconsin - Madison
> Neuroscience Training Program
> Office: (608) 520-0586
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> On Fri, Oct 9, 2009 at 10:40 AM, Chris Watson 
> <[log in to unmask] 
> <mailto:[log in to unmask]>> wrote:
>
>     Hello,
>     I have a block design consisting of tapping to a metronome,
>     self-paced tapping, and 2 different rest conditions. I have 2
>     groups, and was interested in checking for differences between the
>     groups of just one particular condition, e.g. if MT is metronome
>     tapping, I'm interested in Group1(MT) > Group2(MT). The reason I'm
>     interested, is that I found that the reason that Group1(MT>rest) >
>     Group2(MT>rest) shows a lot of significant voxels is that the
>     beta(MT) values are smaller than beta(rest) values for Group2.
>     Is it valid to do a 2nd-level analysis, 2-sample t-test, using
>     just the beta images? (I didn't create a contrast for just the MT
>     condition at the 1st-level, because I have 2 types of rest
>     conditions modeled, so every timepoint is modeled in X). I've
>     already done it, but I want to be sure that it is a reliable measure.
>
>     I can give more information if necessary.
>     Thanks,
>     Chris Watson
>
>