Nadine Gaab wrote:
> Hello list!
>
> I am trying to convolve four sets of images with a "hand made" hemodynamic
> response function (covariate A-C) using a simple regression analysis
>
> I tried two approaches and got two different results and now I hope that
> someone can explain the differences between the approaches to me
>
> 1) fMRI models, no conditions, one regressor (the "hand made" hr
> function), applied high pass filter and furthermore I chose finite impulse
> function but thought that will not influence the design matrix since I
> only have a regressor and 0 conditions (*stupid me?*)
>
> I used four sets
>
> 1a) 160 images covariate A
>
> 1b) 516 images covariateB
>
> 1c) 66 images covariate C
>
> 1d) 66 images taken from 1b and covariate C
> I got nice activations that fit my hypothesis in 1a-1d
>
>
> Then I used the what I thought more straight forward way
>
> 2)basic models/simple regression
>
> I used the four sets
>
> 2a) 160 images covariate A
>
> 2b) 516 images covariateB
>
> 2c) 66 images covariate C
>
> 2d) 66 images taken from 1b and covariate C
>
> I got similar activations in 2a-2c in comparison to 1a-1c but the t-values
> for conditions 2a and 2b are much higher (ROI analysis).
>
> I get much lower t-values for 2c than 1c...(again ROI) could this be a
> result of the number of images? Or what?
>
But the number of images is the same for approach 1 as for
approach 2. Am I understanding this
correctly ?
Best,
Will.
--
William D. Penny
Wellcome Department of Imaging Neuroscience
University College London
12 Queen Square
London WC1N 3BG
Tel: 020 7833 7475
FAX: 020 7813 1420
Email: [log in to unmask]
URL: http://www.fil.ion.ucl.ac.uk/~wpenny/
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