Sergiy Yakovenko, PhD

SY laboratory combines expertise in neurophysiology and computational neuroscience to address basic and applied questions in system motor control.

Professor

Organization:

West Virginia University School of Medicine

Department:

Human Performance - Exercise Physiology

Classification:

Faculty

Associate Professor

Organization:

West Virginia University School of Medicine

Department:

Department of Neuroscience

Classification:

Faculty

Associate Professor

Organization:

West Virginia University School of Medicine

Department:

Rockefeller Neuroscience Institute (SOM)

Classification:

Faculty

• BS, Kharkiv National University, 1997
• PhD, University of Alberta, 2004

Recent Publications in Refereed Journals

1. Bahdasariants S, Barela AMF, Gritsenko V, Bacca O, Barela JA, Yakovenko S. Does joint impedance improve dynamic leg simulations with explicit and implicit solvers? PLoS One. 2023 Jul 3;18(7):e0282130. PMID: 37399198
2. Lis DJ, Carey HD, Yakovenko S, Allen JL. Young adults perceive small disturbances to their walking balance even when distracted. Gait Posture. 2022 Jan;91:198-204. PMID: 34740056
3. Smirnov Y, Smirnov D, Popov A, Yakovenko S. Solving musculoskeletal biomechanics with machine learning. PeerJ Comput Sci. 2021 Aug 26;7:e663. PMID: 34541309
4. Bacca O, Celestino ML, Barela JA, Yakovenko S, Silva de Lima AJ, Barela AMF. Compensatory strategies due to knee flexion constraint during gait of non-disabled adults. J Mot Behav. 2021 Aug 17;1-10. PMID: 34402739
5. Sobinov A, Boots MT, Gritsenko V, Fisher LE, Gaunt RA, Yakovenko S. Approximating complex musculoskeletal biomechanics using multidimensional autogenerating polynomials. PLoS Comput Biol. 2020 Dec 16;16(12):e1008350. PMID: 33326417.
6. Hardesty RL, Boots MT, Yakovenko S, Gritsenko V. Computational evidence for nonlinear feedforward modulation of fusimotor drive to antagonistic co-contracting muscles. Sci Rep. 2021 Jun 30;10(1):10625. PMID: 32606297
7. Yakovenko S, Sobinov A, Gritsenko V. Analytical CPG model driven by limb velocity input generates accurate temporal locomotor dynamics. PeerJ. 2018 Oct 8;6:e5849. PMID: 30425886
8. Sobinov A, Yakovenko S. Model of a bilateral Brown-type central pattern generator for symmetric and asymmetric locomotion. J Neurophysiol. 2018 Mar 1;119(3):1071-1083. PMID: 29187551
9. Gritsenko V, Hardesty RL, Boots MT, Yakovenko S. Biomechanical constraints underlying motor primitives derived from the musculoskeletal anatomy of the human arm. PLoS One. 2016 Oct 13;11(10):e0164050. PMID: 27736890. [Correction issued 2018 Sep 7, PMID 30192901]
10. Tuntevski K, Ellison R, Yakovenko S. Assymetric walkway: A novel behavioral assay for studying asymmetric locomotion. J Vis Exp. 2016 Jan 15;(107):e52921. PMID: 26863182

Recent Published Conference Proceedings

1. Gritsenko V, Moon T, Boone BA, Yakovenko S. Quantifying performance in robotic surgery training using muscle-based activity metrics. 2021 IEEE 11th International Conference on System Engineering and Technology (ICSET). Virtual Conference, Malaysia. 2021 Nov.
2. Hanna K, Yakovenko S. The same muscle synergies are used to control symmetric and asymmetric locomotion. 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER). Virtual Conference. 2021 May.
3. Yough MG, Hardesty RL, Yakovenko S, Gritsenko V. A segmented forearm model of hand pronation-supination approximates joint moments for real time applications. 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER). Virtual Conference. 2021 May.
4. Popov A, Olesh EV, Yakovenko S, Gritsenko V. A novel method of identifying motor primitives using wavelet decomposition. 2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN). Las Vegas, NV. 2018 March.
5. Hardesty RL, Boots MT, Yakovenko S, Gritsenko V. The non-linear relationship between sensory and motor primitives during reaching movements. School and Symposium on Advanced Neurorehabilitation. Baiona (Spain). 2016 Jun.

​Recent Patents

1. Yakovenko S, Boots M. Systems and methods for controlling a self-paced treadmill using predicted subject velocity. US Patent No. 10751561.
2. Sobinov A, Yakovenko S, Gritsenko V, Hardesty R, Boots M. Systems and methods for approximating musculoskeletal dynamics. US Patent Pending.
3. Yakovenko S, Boots M, Ellison R, Tuntevski K. Walkway device and method for quantitative analysis of gait and its modification in rodents. US Patent No. 10058077.

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/

Exercise Physiology

I have 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.

This video shows an example project from the Neural Engineering Lab. Here, we manufactured a prosthetic and improved it to study challenges in design and implementation.  

This video shows equipment used to study human locomotor control.

Current Grants and Contracts

PI unless otherwise stated

• DOD CDMRP W81XWH-21-1-0138, Restoring Warfighters with Neuromusculoskeletal Injuries Research Award (RESTORE)
  • 2021 - 2024
  • Closed-loop recording-stimulation system for accelerating recovery after musculoskeletal injury.
• NIH R03
  • 2020 - 2022
  • Robust biomimetic models of human legs to solve high-dimensional real-time control problems.

Completed Grants and Contracts

PI unless otherwise stated

• NSF STTR Phase 1 for Neurowired. 19-555, 2020 - 2021
  • Contractor
  • Biomimetic solution for gesture-based human machine interactions.
• ​Byars-Tarney Program Endowment, 2020 - 2021
  • Corticospinal mechanisms of locomotor control in young and elderly adults.
• DARPA, BAA-14-30 HAPTIX, 2015 - 2019
  • Director, Gaunt; Co-Director, Boninger; Site PI Yakovenko
  • Spinal root sensory feedback for intramuscular myolectric prostheses; biomimetic prosthesis control. The goal of this grant was to develop reliable peripheral interfaces and advanced robotic devices for transradial amputee veterans.
• NIH/NIGMS, West Virginia Stroke CoBRE (P20 GM109098), 2014 - 2017
  • PI Simpkins, Project PI Yakovenko
  • Corticospinal control of sensorimotor synergies in health or disease. The goal of this grant was to study the reorganization of corticospinal mechanisms of gross and precise movement control in rodents before and after stroke. 
• NIH/NIGMS, IDeA CTR U54GM104942, 2014 - 2017
  • Quantitative measurement of spinal and cortical impairments and interventions to restore neuromuscular gait control. The major goal of this grant was to document reorganization of the CNS after partial spinal cord injury. 
• Pilot Grant, WVU, 2016 - 2017
  • PI Gritsenko, Co-PI Yakovenko
  • Biomimetic integrated system for targeted neuroplasticity and neuroprosthesis. The project's goal was to develop a flexible electrode grid and algorithms for manipulation of neural signals. 
• Pilot Grant, WV Clinical and Translational Science Institute, 2014 - 2016
  • Quantitative measurement of spinal and cortical impairments and interventions to restore neuromuscular gait control. The goal of this proposal was 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, 2015 - 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 was to develop methodology for measuring individual anatomic parameters from structural MRI and DTI for subject-specific musculoskeletal models. 
• DARPA BAA11-08, 2011 - 2013
  • PI Weber, Co-Investigator Yakovenko
  • Reliable spinal nerve interfaces. My contribution was to the TA3 section on Reliable Decoding Algorithms and Neuromuscular Models.