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In doing PPI analysis, the AR(1) term is recomputed for each model (e.g. each seed region). Since the goal of AR(1) is to remove the autocorrelation from the data and its computed at the first level, does it also need to be computed for each PPI model? Since the temporal autocorrelation in the data is the same, I was thinking that one could use the AR(1) parameters/estimates from the first-level model instead of re-estimating the AR(1) term. Does anyone have any thoughts on using the AR(1) term from the first-level versus re-estimating it for each and every seed region? The estimates seem to change slightly between the first level and each ppi seed region. 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 Website: http://www.martinos.org/~mclaren 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.