[Abstract Title]. - Society for Neuroscience

(MEP). Three active contraction positions- standing resisted hip extension, single leg bridging
(SLB), and double leg bridging were tested for consistency in eliciting contraction of GM at 20%
of maximum voluntary contraction. Five healthy individuals participated in the study. A double
cone coil was used to identify the „hotspot‟ for GM in motor cortex, then a single stimulus
intensity was selected for each subject that yielded a MEP of at least 50 κV (40-60% maximum
stimulator output (MSO)) in the first test position. The same MSO was used in each test
condition, in which 6-10 single TMS pulses were delivered every 5-10 seconds. The dependent
measures were frequency count of MEPs per condition and MEP amplitude. Results: SLB
yielded the most consistent and largest MEP. Conclusions: Future studies using SLB to assess
CE of GM may reveal central mechanisms associated with training-induced improvements in
postural control.
Disclosures: E.A. Pitsch, None; A. Wu, None; C. Powers, None; B. Fisher, None.
Poster
275. Posture: Kinematics and Muscle Activity
Time: Sunday, November 16, 2008, 1:00 pm - 5:00 pm
Program#/Poster#: 275.8/JJ23
Topic: D.16.d. Kinematics and Muscle Activity
Support: 1R01AR053608-01A2
T32HD07418-12
Title: Evolution of lower limb muscle design for standing posture
Authors: *A. M. JARC, M. C. TRESCH;
Northwestern Univ., Chicago, IL
Abstract: The majority of studies that examine the control of movement focus on how the
central nervous system (CNS) forms simplifying strategies to confront the complexities
associated with movement. These studies treat the musculoskeletal system as simply a plant to be
controlled. However, several researchers have taken a slightly different point of view, suggesting
that the musculoskeletal system is part of the overall controller and performs „computations‟ in
the periphery. In this research, we leverage this perspective to computationally evolve designs of
the musculoskeletal system to better understand the role of the CNS in coordinating complex
behaviors. We believe that one can learn aspects of how the CNS controls movement by
thoroughly examining the musculoskeletal system and how task performance fluctuates with
changes in its design. We developed a computational framework to optimally design the muscles