In SPM, there is also a high-pass filter that is applied to the data.
If you look in spm_spm:
W will be an identity matrix if you turn off AR(1)
xX.K is the high-pass filter.
xX.xKXs = spm_sp('Set',spm_filter(xX.K,W*xX.X)); % KWX
xX.xKXs.X = full(xX.xKXs.X);
xX.pKX = spm_sp('x-',xX.xKXs);
KWY = spm_filter(xX.K,W*Y);
beta = xX.pKX*KWY;
Best Regards, Donald McLaren
D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General Hospital and
Harvard Medical School
Office: (773) 406-2464
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On Sun, Jul 10, 2011 at 2:17 PM, Hongjian He <[log in to unmask]> wrote:
> Dear all,
> Does anyone have any idea about the beta calculation in SPM? I found
> it different with what I got from matlab regression.
> I run SPM with a simple first level model with the design matrix
> including two task condition, a linear trend term and a dc-term. No
> global normalization and AR(1) are specified. After the estimation, I
> can find the beta value for a example voxel, such as 0.5288.
> I then take the time series of that voxel (Y), and do the regression
> in matlab. The design matrix (or regressors X) has been set to be
> exactly the same with SPM.xX.X. To find the beta value, I did the
> calculation as (X' * X) \ X' * Y. However, the value I got is
> -4.9671. I also considered the percentage unit, and did the
> normalization with a factor of 100/dc-term. The result of that is
> Could anyone help me to understand the procedure I missed to find the
> same regression value for the two cases?
> Thanks in advance!
> Hongjian He
> Zhejiang University, Hangzhou, China.
> Email: hehongj(AT)gmail.com