142013-03-08 10:28:25localhostconverted to 1.6 markup132008-06-03 17:12:04devel03.mrc-cbu.cam.ac.uk122007-07-30 17:26:21IanNimmoSmith112007-07-25 18:24:01devel03.mrc-cbu.cam.ac.uk102007-07-25 14:30:46devel03.mrc-cbu.cam.ac.uk92007-07-25 14:30:22devel03.mrc-cbu.cam.ac.uk82007-07-24 16:23:50devel06.mrc-cbu.cam.ac.uk72007-07-10 12:50:55devel06.mrc-cbu.cam.ac.uk62007-07-10 11:21:39devel06.mrc-cbu.cam.ac.uk52007-07-10 11:17:21devel06.mrc-cbu.cam.ac.uk42007-07-10 11:08:42devel06.mrc-cbu.cam.ac.uk32007-07-10 10:45:17devel06.mrc-cbu.cam.ac.uk22007-07-10 10:37:45devel06.mrc-cbu.cam.ac.uk12007-07-10 10:34:57devel06.mrc-cbu.cam.ac.uk

The problem of constrained modeling of parametric modulation across events, allowing for flexible relationship between signal and parameter.

At: For example: svn co http://imaging.mrc-cbu.cam.ac.uk/svn/parameters/devel parameters Then edit some file. Then: svn commit -m 'A comment' And: cd parameters; svn update to get the latest repository data into your working copy in the parameters subdirectory.

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.

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:

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 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

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. IanNimmoSmith, MatthewBrett