Hi Eric and Dan,

 

Thanks, it seems that this question would be difficult to answer if there is no baseline. 

 

If there was a baseline in between two condtions one task and the other control, what would be the contrast that you would use to answer the original question.  Can this be done with just one time analysis with an appropriate contrast or would you have to mask with other contrasts?

 

Amit



	-----Original Message----- 

	From: Eric Zarahn [mailto:[log in to unmask]] 

	Sent: Sun 5/29/2005 6:48 PM 

	To: Daniel H. Mathalon 

	Cc: Anand, Amit; [log in to unmask] 

	Subject: Re: [SPM] Inference fo contrast

	

	

	Yes to your statement if there are null events; the null events would be a third condition. Whether the third condition is implicitly or explicitly modeled would affect the form of the contrasts.

	 

	No to your statement if there are not null events (or some other condition), in which case there is no baseline (implicit or otherwise) with which to compare. If there are only two total conditions (that's total, including both implicitly and explicitly modeled conditions) in the experiment, A and B (i.e., the rank of the design matrix is 2 == there are only 2 estimable parameters in the model), then a contrast of the type you suggest will give one of two unusable answers depending on how the design is parameterized: 

	 

	1) If the 2 conditions are explicitly modeled and there is in addition an overall intercept term (which I believe is the default in SPM) such that the design matrix is not of full column rank, then the contrasts [x 0 0] and [0 x 0], where x is any scalar, are each inestimable (or so unstable that for pratical purposes they are inestimable). 

	 

	2) If the 2 conditions are explicitly modeled and there is no additional overall intercept term (which might be irrelevant for SPM, but nevertheless) then what you'd get from [x 0] or [0 x] would be dominated by non-physiological T2* signal, and both would be quite positive, always.

	 

	One needs to be careful from a modeling perspective when discussing "baselines", "rest condition", "null events", and implicit/explicit modeling of them. In your question you said



		Assuming that there are some time periods that are not modelled (i.e., null events), or even if there aren't, 



	That last bit is not inconsequential. There is a qualitative difference vis a vis modeling between there being a baseline either implicit or explicit (which you can call a null event) or not. Assuming there is an overall intercept term (and I believe there always is one used in all SPM programs to date), what this affects is whether the design matrix is of full column rank (corresponding to my (2) above) or not (corresponding to my (1)). 

	 

	And finally, the null event does not need to be a "rest" condition per se. It needs to be whatever you want it to be to make your statements about the signal in each of the remaining conditions meaningful scientifically.

	 

	Eric

	 



		 

		 

		 

		couldn't amit do a one sample t-test on the beta images for each condition separately, testing whether beta values are significantly different from zero?  By looking at t contrasts of 1, then -1, wouldn't he be able to assess whether beta values at a particular voxel location are significantly positive or significantly negative relative to implicit baseline?

		

		

		

		

		

		

		

		

		

		

		

		

		

		



			Hi Amit,



			 



			 



			----- Original Message -----



			From: "Amit" <[log in to unmask] <mailto:[log in to unmask]> >



			To: <[log in to unmask] <mailto:[log in to unmask]> >



			Sent: Sunday, May 29, 2005 4:57 PM



			Subject: [SPM] Inference fo contrast



			

			



			> Dear SPMers,

			>

			> The experimental contrasts (a difference between two conditions A and B)

			> is interpreted in usual SPM analysis as indicating that condition A shows

			> a larger response than condition B. However, difference measures,

			> as conceptualized in SPM could have three potential ways in which

			> conditions may differ: positive activity in A may be greater than positive

			> activity in B, positive activity in A may be greater than negative activity

			> in B, and finally, negative activity in B may be greater than negative

			> activity in A. All lead to a positive difference between conditions.

			



			 



			Yes, absolutely. But implicit in your explanations is a third conditiion C to which you are comparing A and B.



			 



			 



			 



			 



			>

			> Is there a way of teasing out these three differences?



			 



			If you have a meaningful C in your experimental design, then you can compute 2 new contrasts to get your answers:



			 



			(1) A minus C



			 



			and



			 



			(2) B minus C.



			 



			 



			

			>

			> Also, how does one get a contrast for areas which are getting inhibited by

			> a particular task - does one just reverse the contrast for the active

			> versus control condition in a block-design experiment or is there a more

			> sophisticated way for doing this?



			 



			The use of the word "inhibited" has connotations that I am not sure you intended to convey. But if you simply meant "how do see where the signal in B is greater than that in A?", then yes just compute the contrast B minus A.



			 



			 



			Eric