JSM 2005 Online Program

Abstract #304490

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	481
Type:	Contributed
Date/Time:	Thursday, August 11, 2005 : 8:30 AM to 10:20 AM
Sponsor:	Biometrics Section
Abstract - #304490
Title:	A Maximum Likelihood Approach to Assessing Neuron-muscle Relationship
Author(s):	Vera Bulaevskaya*+ and Robert E. Kass
Companies:	Carnegie Mellon University and Carnegie Mellon University
Address:	Department of Statistics, Pittsburgh, PA, 15213, United States
Keywords:	neuroscience ; mixture models ; maximum likelihood ; neuron-muscle association
Abstract:	The standard method neuroscientists use to assess the relationship between the activity of a cortical neuron and that of an associated muscle group is called spike-triggered averaging (STA). Statistically, the STA is a measure of the association between a point process and a continuous-time stochastic process. The problem is complicated by the fact that, due to various factors, even in the presence of an association between a neuron and a muscle group, a neuron spike leads to an impact in the muscle's electrical activity only a small fraction of the time. The STA method does not take this into account, as it simply averages electromyography (EMG) signals obtained from the muscle of interest at time points equal to the neuron's spike times plus the signal travel time. We propose a mixture model for muscle response to neuron activity and use maximum likelihood estimation to evaluate the strength of association between the neuron and the muscle group of interest.

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