The problem of constrained modeling of parametric modulation across events, allowing for flexible relationship between signal and parameter.
svn co http://imaging.mrc-cbu.cam.ac.uk/svn/parameters/devel parameters
Then edit some file.
svn commit -m 'A comment'
cd parameters; svn update
to get the latest repository data into your working copy in the parameters subdirectory.
Work so far
The event-related study of Brett et al:
Parametric modulation approach described in:
- Estimating effects on effects using the Varying Coefficient Models by Ferath Kherif, Emmanuel A Stamatakis, Cristina Ramponi,Matthew Brett, Ian Nimmo-Smith, and Lorraine K Tyler
Described in Brett et al abstract and poster above.
Visual checkerboard events at random intervals. Sessions for which events are rapid (mean ISI 1 second) to slow (mean ISI 10 seconds). Evidence that interval between current and last event influences height and possibly shape of response. We wanted to be able to fit a curve relating the height of the event to the parametric modulator of time since last event.
We used region of interest time courses from the visual cortex to stabilize the signal.
Code so far
Implemented in matlab, by Ferath and Ian. No modeling of
Ian has written a technical note in latex:
and the current pdf version is at:
Approach to problem
- Proper modeling of autocorrelation - maybe using R routines
- Modeling on ROI time-courses
- Model individual events, and constrain to have smooth relationship to paramter
Reimplement model for ROI time course in NIPY - done - see http://imaging.mrc-cbu.cam.ac.uk/svn/parameters/devel/scripts/
- Address autocorrelation - partly done - can now run AR models with scipy,models code
- Implement parametric modulation within scipy.models
- Find suitable interface for expressing constrained modulation for models
See WorkingOutNipy for musings on the code structure of NIPY
Analyses in R via rpy
See SCodebits for the SPlus code that we used in 2003. Will try to get these running in R and use the rpy R-to-python interface to compare these analyses with the current NIPY approach.