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JW
March 31, 2021 10:46AM
Dear AFNI experts,

I am interested in looking at the resting-state component of the time series during task-based fMRI data. So for task-based fMRI, we are trying to find what parts of the time-series match the task design, and we are ignoring all of the residual error which in this case actually is the resting-state component of the time series. I understand how can we sort out task-related activation using proc.py's regress function, but I was wondering if it is possible to investigate the residual error (non task-related time-series) portion of the time series using AFNI. If it is possible, could you please let me know how can we do that?

Best,
JW
Subject Author Posted

quantifying resting state component of task-based time series

JW March 31, 2021 10:46AM

Re: quantifying resting state component of task-based time series

JW April 13, 2021 11:49AM

Re: quantifying resting state component of task-based time series

ptaylor April 13, 2021 04:46PM

Re: quantifying resting state component of task-based time series

JW April 15, 2021 09:52PM

Re: quantifying resting state component of task-based time series

ptaylor April 16, 2021 07:16AM

Re: quantifying resting state component of task-based time series

JW April 16, 2021 05:20PM

Re: quantifying resting state component of task-based time series

ptaylor April 16, 2021 05:27PM



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