Loss of balance leading to falls is a primary cause of injury and accidental death in older adults. This proposal articulates an innovative and quantitative framework for both investigating and understanding temporal and spatial features of muscle activation patterns for human balance control. The objective of the proposal is to develop and validate a quantitative model to predict spatiotemporal muscle activation patterns during postural control. Using engineering tools, we will integrate novel experimental methods and computer simulations to understand how feedback control of posture is executed by the nervous system. Our framework will demonstrate that complex, high-dimensional muscle coordination patterns for postural control can be explained by just a few parameters related to task-level variables. Advancing our ability to quantify and predict muscle coordination patterns is critical to achieving our long-term goal of using paired experimental measures and engineering models to predict the functional consequences of neuromotor impairments and interventional therapies.
In Specific Aim 1, we will identify muscle synergies and task variables governing spatial organization of muscle activity during multidirectional perturbations to standing posture. Our approach is to extract muscle synergies from experimental data using optimization and correlate the activation of each muscle synergy to the production of a task-related variable. We will use a musculoskeletal model of the leg to determine the structure of synergies required to produce the muscle activation patterns and task-variables measured experimentally.
In Specific Aim 2, we will identify the task- level feedback loops governing temporal organization of muscle activity during sagittal perturbations to standing posture. Our approach is to characterize the feedback relationships between center of mass motion and temporal muscle activation patterns experimentally, and then use a simple inverted pendulum model to test the feasibility of the feedback loops in generating appropriate temporal muscle activation patterns and center of mass kinematics. Our models of postural control will allow us to predict motor dysfunction resulting from changes in motor patterns. Our results will therefore allow us to develop quantitative diagnostic tools for balance and movement disorders and facilitate the design of effective interventional therapies, neural prostheses, and neural repair strategies for motor rehabilitation.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project (R01)
Project #
5R01NS053822-04
Application #
7778280
Study Section
Motor Function, Speech and Rehabilitation Study Section (MFSR)
Program Officer
Chen, Daofen
Project Start
2007-03-05
Project End
2012-02-29
Budget Start
2010-03-01
Budget End
2011-02-28
Support Year
4
Fiscal Year
2010
Total Cost
$279,874
Indirect Cost
Name
Emory University
Department
Biomedical Engineering
Type
Schools of Medicine
DUNS #
066469933
City
Atlanta
State
GA
Country
United States
Zip Code
30322
Welch, Torrence D J; Ting, Lena H (2014) Mechanisms of motor adaptation in reactive balance control. PLoS One 9:e96440
Joseph Jilk, D; Safavynia, Seyed A; Ting, Lena H (2014) Contribution of vision to postural behaviors during continuous support-surface translations. Exp Brain Res 232:169-80
Bolger, Darren; Ting, Lena H; Sawers, Andrew (2014) Individuals with transtibial limb loss use interlimb force asymmetries to maintain multi-directional reactive balance control. Clin Biomech (Bristol, Avon) 29:1039-47
Safavynia, Seyed A; Ting, Lena H (2013) Long-latency muscle activity reflects continuous, delayed sensorimotor feedback of task-level and not joint-level error. J Neurophysiol 110:1278-90
Bingham, Jeffrey T; Ting, Lena H (2013) Stability radius as a method for comparing the dynamics of neuromechanical systems. IEEE Trans Neural Syst Rehabil Eng 21:840-8
McKay, J Lucas; Welch, Torrence D J; Vidakovic, Brani et al. (2013) Statistically significant contrasts between EMG waveforms revealed using wavelet-based functional ANOVA. J Neurophysiol 109:591-602
Safavynia, Seyed A; Ting, Lena H (2013) Sensorimotor feedback based on task-relevant error robustly predicts temporal recruitment and multidirectional tuning of muscle synergies. J Neurophysiol 109:31-45
Ting, Lena H; Chvatal, Stacie A; Safavynia, Seyed A et al. (2012) Review and perspective: neuromechanical considerations for predicting muscle activation patterns for movement. Int J Numer Method Biomed Eng 28:1003-14
Chvatal, Stacie A; Ting, Lena H (2012) Voluntary and reactive recruitment of locomotor muscle synergies during perturbed walking. J Neurosci 32:12237-50
Bingham, Jeffrey T; Choi, Julia T; Ting, Lena H (2011) Stability in a frontal plane model of balance requires coupled changes to postural configuration and neural feedback control. J Neurophysiol 106:437-48

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