- Human Performance - Exercise Physiology
- Department of Neuroscience
- Rockefeller Neuroscience Institute (SOM)
- BS, Kharkiv National University, 1997
- PhD, University of Alberta, 2004
- Yakovenko, S., Sobinov, A., Gritsenko, V. (2018) Analytical CPG model driven by limb velocity input generates accurate temporal locomotor dynamics. PeerJ. 6:e5849. PMID: 30425886
- Sobinov, A., Yakovenko, S., Gritsenko, V., Hardesty, R., Boots, M. (2018) Approximation of complex musculoskeletal dynamics. U.S. Patent Application Serial No. 62/559,711 Filing Date September 18, 2017. (Full patent pending, submitted 2018)
- Popov, A., Olesh, E. V., Yakovenko, S., and Gritsenko, V. (2018) A novel method of identifying motor primitives using wavelet decomposition. IEEE Xplore: 2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN). DOI: 10.1109/BSN.2018.8329674.
- Sobinov A, Yakovenko S (2017) Model of a bilateral Brown-type central pattern generator for symmetric and asymmetric locomotion. J Neurophysiol: jn.00443.2017, PMID: 29187551, DOI: 10.1152/jn.00443.2017
- Yakovenko, S., Boots, M. (2017) Self-paced treadmill algorithm using ground signal to predict subject’s velocity. U.S. Patent Application Serial No. 62/512,432 Filing Date May 30, 2017.
- Tuntevski K, Ellison R, Yakovenko S. Asymmetric walkway: A novel behavioral assay for studying asymmetric locomotion. J Vis Exp (2016) (107):e52921. PMCID: PMC4780838.
- Yakovenko, S., Boots, M., Tuntevski, K., Ellison, R. (2016) Walkway device for quantitative analysis of gait and its modification in rodents. U.S. Patent Application Serial No. 15/259,216 Filing Date September 8, 2016. (Full patent granted, 2018)
- Gritsenko, V., Hardesty, R. L., Boots, M. T., & Yakovenko, S. (2016). Biomechanical constraints underlying motor primitives derived from the musculoskeletal anatomy of the human arm. PloS One, 11(10), e0164050.
- Yakovenko S, Drew T. Similar motor cortical control mechanisms for precise limb control during reaching and locomotion. J Neurosci (2015) 35(43): 14476-90. PMCID: PMC4623226.
- Popov, A. and Yakovenko, S. (2015) Muscle synergy decomposition analysis using wavelet detection in human locomotor activity. IEEE Xplore, Proceedings of the IEEE. Signal Processing Symposium (SPSympo), 1-5. DOI: 10.1109/SPS.2015.7168260
- Olesh EV, Yakovenko S, Gritsenko V. Automated assessment of upper extremity movement impairment due to stroke. PLoS One (2014) 9(8): e104487. PMCID: PMC4123984.
- Yakovenko S (2011) A hierarchical perspective on rhythm generation for locomotor control. Prog. Brain Res. 188: 151-166.
- Yakovenko S, Krouchev N and Drew T. (2011) Sequential activation of motor cortical neurons contributes to intralimb coordination during reaching in the cat by modulating muscle synergies. J. Neurophysiol. 105(1): 388-409.
- Yakovenko S (2011) Does the nervous system need a Dz driver? In L. Giuliani, ed.Wissenschaftskolleg zu Berlin / Institute for Advanced Study: Jahrbuch 2009-2010 Berlin, p. 241–254.
- Yakovenko S and Drew T (2009) A motor cortical contribution to the anticipatory postural adjustments that precede reaching in the cat. J. Neurophysiol. 102: 853-874.
- Gritsenko V, Yakovenko S and Kalaska JF (2009) Integration of predictive feedforward and sensory feedback signals for online control of visually-guided movement. J. Neurophysiol. 102: 914-930.
- Drew T, Andujar JE, Lajoie K, and Yakovenko S (2008) Cortical mechanisms involved in visuomotor coordination during precision walking. Brain Res. Rev. 57(1): 199-211.
- Yakovenko S, Kowalczewski J, and Prochazka A (2007) Intraspinal stimulation caudal to spinal cord transections in rats. Testing the propriospinal hypothesis. J. Neurophysiol. 97: 2570-2574.
- Prochazka A, and Yakovenko S (2007) Predictive and reactive tuning of the locomotor CPG. Integr. Comp. Biol. 47: 474-481.
- Prochazka A, and Yakovenko S. The neuromechanical tuning hypothesis (2007). Prog. Brain Res. 165: 255-265.
About Sergiy Yakovenko
The focus of our interdisciplinary research is to study the coordinated action of neural, muscular, and skeletal systems controlling goal-directed and stereotypic movements.
The neuro-musculo-skeletal (NMS) system evolved to control mechanical actions, e.g. walking, running, balancing and reaching. Since these and other common behaviors are produced by the integrated action of multiple components of the NMS system, investigating the details of this emergent organization of movement control is a major challenge. Our laboratory addresses this challenge with an interdisciplinary approach that combines multi-electrode recording and stimulation techniques with advanced computational tools in the field of neuromechanics and innovative methodology.
Besides the general question of understanding how the brain works we are working on the following specific topics:
-- What is the pattern of interactions between multiple neuromechanical mechanisms involved in the control of movement?
-- What are the evolutionary constraints of neuromechanical organization?
-- What mechanisms are responsible for the way we acquire new motor skills?
-- How can we use the hierarchy and redundancy of control pathways to overcome movement deficits due to stroke and spinal cord injury?
Neural Engineering Laboratory (PI: Yakovenko) web: https://sites.google.com/site/neuralengineeringlab/
I have recently established my laboratory engaged in independent neurophysiological research in motor cortex and spinal cord using new types of electrodes placed in neural structures and using new computational methods for the analysis of large neural and behavioral datasets. My team has multidisciplinary expertise from chronic long-term recording capabilities in animals with brain trauma to computational analytical skills to describe the organization principles of movement control. With our current methods we can observe diverse neural processes over large cortical areas in the nervous system, and the analysis of this activity requires the development of parallel theoretical framework to overcome the limitations of standard reductionist methods. My strategy for the ‘reverse engineering’ of neural controller is to integrate the simplest computational models to aid in the interpretation of experimental data collected to test these same models and allow us to see the ‘big picture’. I am convinced that this framework will yield the highest degree of insight into the complex interactions within the neuro-musculo-skeletal system.
My current research direction is focused on the principles of interactions between the mechanisms of neuromechanical hierarchy both the context of stroke and spinal cord injury using animal models and in the context of improving control of advanced arm prosthesis for human amputees. One of the challenges for the current brain-machine interface is the lack of functional understanding of how neural processes interact within and across the different levels of neuraxis. Specifically, we have limited understanding of how cortical synergies or motor primitives are controlled to produce coupled sequential activation observed in reaching movements and locomotion. Lissencephalic (smooth) rat cortex is the perfect target for the microelectrode arrays with recording-stimulation capabilities to address this question. Building on my experience in recording and stimulation of cat motor cortex and brainstem structures I have collected preliminary data in rats using floating microelectrode arrays to demonstrate the feasibility of the methods. We have developed a new type of walkway specifically designed to create a dextrous locomotor task that requires cortical contribution in rodents. In addition, we are developing neuromechanical models for data processing that will guide our analysis.
My research experience and expertise in conducting multidisciplinary studies are advantageous prerequisites to the success of proposed experimental and theoretical studies and the development of innovative technologies for rehabilitation. Results of these studies may lead to the development of novel therapies using closed-loop stimulation systems to quantify and to restore impaired motor functions.
BAA-14-30 HAPTIX, Yakovenko (Site PI) 02/09/15 to 01/31/19
DARPA (Director: Gaunt, Co-Director Boninger)
Spinal root sensory feedback for intramuscular myoelectric prostheses. Biomimetic prosthesis control.
The goal of this grant is to develop reliable peripheral interfaces and advanced robotic devices for transradial amputee veterans.
P20 GM109098, Yakovenko (Project PI, Director Simpkins) 09/01/14 to 07/31/17
Corticospinal control of sensorimotor synergies in health or disease. West Virginia Stroke CoBRE
The goal of this grant is to study the reorganization of corticospinal mechanisms of gross and precise movement control in rodents before and after stroke.
Completed Research Support
IDeA CTR U54GM104942 Yakovenko (PI) 09/01/14 to 05/31/17
Quantitative measurement of spinal and cortical impairments and interventions to restore neuromuscular gait control
The major goal of this grant is to document reorganization of the CNS after partial spinal cord injury.
Pilot Grant, WVU Gritsenko (PI) 7/1/2016 – 1/31/2017
Biomimetic integrated system for targeted neuroplasticity and neuroprosthesis.
The project’s goal is to develop a flexible electrode grid and algorithms for manipulation of neural signals.
Pilot Grant, WV Clinical and Translational Science Institute Yakovenko (PI) 11/01/14 - 12/31/16
Quantitative measurement of spinal and cortical impairments and interventions to restore neuromuscular gait control.
The goal of this proposal is to understand interactions among cortical and spinal hierarchical levels for the control of locomotion in rats and humans with spinal cord injury.
Byars-Tarnay Endowment Yakovenko (PI) 7/1/2015 – 6/30/2016
Development of a subject-specific motor recruitment model that utilizes motor unit anisotropy and precise muscle architecture for the next-generation biomimetic prosthetics.
The project’s goal is to develop methodology for measuring individual anatomic parameters from structural MRI and DTI for subject-specific musculoskeletal models.
DARPA BAA11-08 Weber (PI) 10/01/11-09/01/13
Reliable Spinal Nerve Interfaces. My contribution is to the TA3 section on Reliable Decoding Algorithms and Neuromuscular Models.