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There is no p-value map. Rather, you create contrasts (after you estimate the model) that create either: (1) spmT_ and con_ images or (2) spmF_ and ess_ images. The con_ images provide you with a measure of the magnitude of the contrast. The spmT_ and spmF_ images provide you with either the T-statsistics and F-stastistics at each voxel. From those values, you can determine the p-value. The ess_ is the extra sums of squares associated with the numerator for the F-test. For a general introduction, I'd read up on statistics and the general linear model. All of the classical analyses in SPM use the general linear model. 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 ===================== This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email. On Fri, May 13, 2011 at 8:28 AM, 王媛 <[log in to unmask]> wrote: > Hello, > > Just a simple question as the subject. > The second level analysis produces a lot of files, I wonder which one > contains or is the p-value map. > RPV? mask? ResMS? > And what exactly does the p-value map show? Is it simply a mask which sets > '1s' for the pixels larger than the value,'0s' for others? > I am totally a freshman for the analysis procedure, maybe you can explain > in a easier way:) > Thank you so much. > > Best Regards, > Yuan >