Optimization framework for the model-based estimation of in vivo α-motoneuron properties in the intact human

R. Ornelas Kobayashi; A. Gogeascoechea; J. Buitenweg; U. Yavuz; M. Sartori

doi:10.1109/EMBC46164.2021.9630260

Optimization framework for the model-based estimation of in vivo α-motoneuron properties in the intact human

R. Ornelas Kobayashi, A. Gogeascoechea, J. Buitenweg, U. Yavuz, M. Sartori

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

3 Citations (Scopus)

121 Downloads (Pure)

Abstract

The in vivo estimation of α-motoneuron (MN) properties in humans is crucial to characterize the effect that neurorehabilitation technologies may elicit over the composite neuro-musculoskeletal system. Here, we combine biophysical neuronal modelling, high-density electromyography and convolutive blind-source separation along with numerical optimization to estimate geometrical and electrophysiological properties of in vivo decoded human MNs. The proposed methodology implements multi-objective optimization to automatically tune ionic channels conductance and soma size of MN models for minimizing the error between several features of simulated and in vivo decoded MN spike trains. This approach will open new avenues for the closed-loop control of motor restorative technologies such as wearable robots and neuromodulation devices.Clinical Relevance- This work proposes a non-invasive framework for the in vivo estimation of person-specific α-motoneuron properties. This will enable predicting neuronal adaptations in response to neurorehabilitation therapies in the intact human.

Original language	English
Title of host publication	2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Place of Publication	Picataway, NJ
Publisher	IEEE
Pages	6126-6129
Number of pages	4
ISBN (Electronic)	978-1-7281-1179-7, 978-1-7281-1178-0 (USB)
ISBN (Print)	978-1-7281-1180-3
DOIs	https://doi.org/10.1109/EMBC46164.2021.9630260
Publication status	Published - 9 Dec 2021
Event	43rd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2021: Changing Global Health Care in the Twenty-First Century - Virtual Duration: 1 Nov 2021 → 5 Nov 2021 Conference number: 43

Conference

Conference	43rd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
Abbreviated title	EMBC
Period	1/11/21 → 5/11/21

Keywords

2022 OA procedure

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10.1109/EMBC46164.2021.9630260

ArticleFinal published version, 1.38 MBLicence: Taverne

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Kobayashi, R. O., Gogeascoechea, A., Buitenweg, J., Yavuz, U., & Sartori, M. (2021). Optimization framework for the model-based estimation of in vivo α-motoneuron properties in the intact human. In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 6126-6129). IEEE. https://doi.org/10.1109/EMBC46164.2021.9630260

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abstract = "The in vivo estimation of α-motoneuron (MN) properties in humans is crucial to characterize the effect that neurorehabilitation technologies may elicit over the composite neuro-musculoskeletal system. Here, we combine biophysical neuronal modelling, high-density electromyography and convolutive blind-source separation along with numerical optimization to estimate geometrical and electrophysiological properties of in vivo decoded human MNs. The proposed methodology implements multi-objective optimization to automatically tune ionic channels conductance and soma size of MN models for minimizing the error between several features of simulated and in vivo decoded MN spike trains. This approach will open new avenues for the closed-loop control of motor restorative technologies such as wearable robots and neuromodulation devices.Clinical Relevance- This work proposes a non-invasive framework for the in vivo estimation of person-specific α-motoneuron properties. This will enable predicting neuronal adaptations in response to neurorehabilitation therapies in the intact human.",

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author = "Kobayashi, {R. Ornelas} and A. Gogeascoechea and J. Buitenweg and U. Yavuz and M. Sartori",

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Kobayashi, RO, Gogeascoechea, A, Buitenweg, J , Yavuz, U & Sartori, M 2021, Optimization framework for the model-based estimation of in vivo α-motoneuron properties in the intact human. in 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, Picataway, NJ, pp. 6126-6129, 43rd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2021, 1/11/21. https://doi.org/10.1109/EMBC46164.2021.9630260

Optimization framework for the model-based estimation of in vivo α-motoneuron properties in the intact human. / Kobayashi, R. Ornelas; Gogeascoechea, A.; Buitenweg, J. et al.
2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Picataway, NJ: IEEE, 2021. p. 6126-6129.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Optimization framework for the model-based estimation of in vivo α-motoneuron properties in the intact human

AU - Kobayashi, R. Ornelas

AU - Gogeascoechea, A.

AU - Buitenweg, J.

AU - Yavuz, U.

AU - Sartori, M.

N1 - Conference code: 43

PY - 2021/12/9

Y1 - 2021/12/9

N2 - The in vivo estimation of α-motoneuron (MN) properties in humans is crucial to characterize the effect that neurorehabilitation technologies may elicit over the composite neuro-musculoskeletal system. Here, we combine biophysical neuronal modelling, high-density electromyography and convolutive blind-source separation along with numerical optimization to estimate geometrical and electrophysiological properties of in vivo decoded human MNs. The proposed methodology implements multi-objective optimization to automatically tune ionic channels conductance and soma size of MN models for minimizing the error between several features of simulated and in vivo decoded MN spike trains. This approach will open new avenues for the closed-loop control of motor restorative technologies such as wearable robots and neuromodulation devices.Clinical Relevance- This work proposes a non-invasive framework for the in vivo estimation of person-specific α-motoneuron properties. This will enable predicting neuronal adaptations in response to neurorehabilitation therapies in the intact human.

AB - The in vivo estimation of α-motoneuron (MN) properties in humans is crucial to characterize the effect that neurorehabilitation technologies may elicit over the composite neuro-musculoskeletal system. Here, we combine biophysical neuronal modelling, high-density electromyography and convolutive blind-source separation along with numerical optimization to estimate geometrical and electrophysiological properties of in vivo decoded human MNs. The proposed methodology implements multi-objective optimization to automatically tune ionic channels conductance and soma size of MN models for minimizing the error between several features of simulated and in vivo decoded MN spike trains. This approach will open new avenues for the closed-loop control of motor restorative technologies such as wearable robots and neuromodulation devices.Clinical Relevance- This work proposes a non-invasive framework for the in vivo estimation of person-specific α-motoneuron properties. This will enable predicting neuronal adaptations in response to neurorehabilitation therapies in the intact human.

KW - 2022 OA procedure

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M3 - Conference contribution

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BT - 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

PB - IEEE

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T2 - 43rd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2021

Y2 - 1 November 2021 through 5 November 2021

ER -

Optimization framework for the model-based estimation of in vivo α-motoneuron properties in the intact human

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