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Subject:

Announcing release of genetic algorithm for experimental design

From:

Tor Dessart Wager <[log in to unmask]>

Reply-To:

Tor Dessart Wager <[log in to unmask]>

Date:

Mon, 9 Sep 2002 12:41:53 -0400

Content-Type:

TEXT/PLAIN

Parts/Attachments:

Parts/Attachments

TEXT/PLAIN (63 lines)

Dear SPM list,

We'd like to announce the release of a new toolbox for experimental design
in fMRI.  The beta version of the toolbox is a set of Matlab routines that
implement a genetic algorithm for experimental design.  The algorithm can
be used to optimize:

a)      The efficiency of estimating one or more contrasts

b)      The efficiency of estimating hemodynamic response functions (HRF)
        for different trial types

c)      Counterbalancing the frequency with which each trial type follows
        each other one, n steps back in time (useful for, e.g., selective
        averaging analysis)
d)      Linear combinations of these specified by the user

The algorithm works by optimizing the order of presentation of different
types of events, including rest intervals.  The calculation of efficiency
can take into account nonlinearity in BOLD response (with a very crude
model), high- and low-pass filtering, inclusion of rest or probe intervals
periodically throughout the run, and scanner noise autocorrelation.  In
addition, there is some provision for analysis of events whose identity
depends on the nature of previous events and mixed block/event-related
designs.

Also included is a copy of a recent draft of the paper describing the
genetic algorithm, which is in press at Neuroimage.

We have seen substantial improvement in design efficiency using the
toolbox (approx. 6 x for contrast estimation, 1.5 x for HRF estimation,
and (3 x / 1.25 x for simultaneous optimization of both).

The functions and documentation can be downloaded from:

http://www.lsa.umich.edu/psych/research&labs/jjonides/download.html

although they may become available soon from the spm toolboxes website.
Some experience with Matlab may be very helpful in using the functions.
To install after downloading, you will need to unzip the gz (unix/linux)
or zip (windows) file and put the directories it contains in your Matlab
path.  To get started, see the documentation file
Genetic_Algorithm_readme.rtf or .doc.

Thank you,

Tor Wager
Tom Nichols


_____________________________
Tor Wager
Department of Psychology
University of Michigan
Cognition and Perception Area
525 East University
Ann Arbor, MI 48109-1109

Office: 734-936-1295
Home:   734-995-8975
Email:  [log in to unmask]
_____________________________

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