So I created a weighted regression analysis to look at the effect of memory load in a particular brain region. Basically, I weighted the paradigms by a behavioral measure that reflected the number of items actually remembered (as set size was increased).  As far as Doug told me there are basically 2 ways to weight your paradigm files.<div>




<br></div><div>Version 1:</div><div>Have 2 conditions, baseline (condition 0) and all the set sizes (condition 1).  Condition 1 would then be weighted by the behavioral measure.</div><div><br></div><div>Version 2:</div><div>




Have 3 conditions, baseline (condition 0), and then I represented <span style="border-collapse:collapse;font-family:arial, sans-serif;font-size:14px">each presentation as two different conditions, one with a weight that is always 1 (condition 1), the other weighted according</span> to the behavioral measure (condition 2).</div>



<div><br></div><div><br></div><div>The difference, as far as I understand it, in version 1, it is assumed that the response amplitude is ) when the weight is 0 (ie. that when you are attending to 0 items, brain activity = 0).  Whereas, version 2, tests the slope of the HRF amplitude vs weight without the assumption above.</div>

<div><br></div><div>However, I&#39;m a bit confused as to the results I got.  When I looked at the data from both versions, version 1 provided a much higher amount of activation and more areas activated than version 2.  However, I believe version 2 better fits with the multiple regression analysis that is done in Brain Voyager. </div>

<div><br></div><div>Can anyone give me a better explanation of what the difference between these analysis models is?</div><div><br></div><div>Katie</div>
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