Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson's disease

Autores
Merk, Timon; Peterson, Victoria; Lipski, Witold J.; Blankertz, Benjamin; Turner, Robert S.; Li, Ningfei; Horn, Andreas; Richardson, Robert Mark; Neumann, Wolf-Julian
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Brain signal decoding promises significant advances in the development of clinical brain computer interfaces (BCI). In Parkinson’s disease (PD), first bidirectional BCI implants for adaptive deep brain stimulation (DBS) are now available. Brain signal decoding can extend the clinical utility of adaptive DBS but the impact of neural source, computational methods and PD pathophysiology on decoding performance are unknown. This represents an unmet need for the development of future neurotechnology. To address this, we developed an invasive brain-signal decoding approach based on intraoperative sensorimotor electrocorticography (ECoG) and subthalamic LFP to predict grip-force, a representative movement decoding application, in 11 PD patients undergoing DBS. We demonstrate that ECoG is superior to subthalamic LFP for accurate grip-force decoding. Gradient boosted decision trees (XGBOOST) outperformed other model architectures. ECoG based decoding performance negatively correlated with motor impairment, which could be attributed to subthalamic beta bursts in the motor preparation and movement period. This highlights the impact of PD pathophysiology on the neural capacity to encode movement vigor. Finally, we developed a connectomic analysis that could predict grip-force decoding performance of individual ECoG channels across patients by using their connectomic fingerprints. Our study provides a neurophysiological and computational framework for invasive brain signal decoding to aid the development of an individualized precision-medicine approach to intelligent adaptive DBS.
Fil: Merk, Timon. Charité – Universitätsmedizin Berlin; Alemania
Fil: Peterson, Victoria. Harvard Medical School; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentina
Fil: Lipski, Witold J.. University of Pittsburgh; Estados Unidos. University of Pittsburgh at Johnstown; Estados Unidos
Fil: Blankertz, Benjamin. Technische Universität Berln; Alemania
Fil: Turner, Robert S.. University of Pittsburgh; Estados Unidos. University of Pittsburgh at Johnstown; Estados Unidos
Fil: Li, Ningfei. Charité Universitätsmedizin Berlin; Alemania
Fil: Horn, Andreas. Charité Universitätsmedizin Berlin; Alemania
Fil: Richardson, Robert Mark. Harvard Medical School; Estados Unidos
Fil: Neumann, Wolf-Julian. Charité Universitätsmedizin Berlin; Alemania
Materia
BASAL GANGLIA
COMPUTATIONAL BIOLOGY
DEEP BRAIN STIMULATION
HUMAN
MACHINE LEARNING
NEUROMODULATION
NEUROSCIENCE
SYSTEMS BIOLOGY
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/214465
Acceder
id CONICETDig_5cc55fd404ad2383d31ac856c282651b
oai_identifier_str oai:ri.conicet.gov.ar:11336/214465
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson's diseaseMerk, TimonPeterson, VictoriaLipski, Witold J.Blankertz, BenjaminTurner, Robert S.Li, NingfeiHorn, AndreasRichardson, Robert MarkNeumann, Wolf-JulianBASAL GANGLIACOMPUTATIONAL BIOLOGYDEEP BRAIN STIMULATIONHUMANMACHINE LEARNINGNEUROMODULATIONNEUROSCIENCESYSTEMS BIOLOGYhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Brain signal decoding promises significant advances in the development of clinical brain computer interfaces (BCI). In Parkinson’s disease (PD), first bidirectional BCI implants for adaptive deep brain stimulation (DBS) are now available. Brain signal decoding can extend the clinical utility of adaptive DBS but the impact of neural source, computational methods and PD pathophysiology on decoding performance are unknown. This represents an unmet need for the development of future neurotechnology. To address this, we developed an invasive brain-signal decoding approach based on intraoperative sensorimotor electrocorticography (ECoG) and subthalamic LFP to predict grip-force, a representative movement decoding application, in 11 PD patients undergoing DBS. We demonstrate that ECoG is superior to subthalamic LFP for accurate grip-force decoding. Gradient boosted decision trees (XGBOOST) outperformed other model architectures. ECoG based decoding performance negatively correlated with motor impairment, which could be attributed to subthalamic beta bursts in the motor preparation and movement period. This highlights the impact of PD pathophysiology on the neural capacity to encode movement vigor. Finally, we developed a connectomic analysis that could predict grip-force decoding performance of individual ECoG channels across patients by using their connectomic fingerprints. Our study provides a neurophysiological and computational framework for invasive brain signal decoding to aid the development of an individualized precision-medicine approach to intelligent adaptive DBS.Fil: Merk, Timon. Charité – Universitätsmedizin Berlin; AlemaniaFil: Peterson, Victoria. Harvard Medical School; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; ArgentinaFil: Lipski, Witold J.. University of Pittsburgh; Estados Unidos. University of Pittsburgh at Johnstown; Estados UnidosFil: Blankertz, Benjamin. Technische Universität Berln; AlemaniaFil: Turner, Robert S.. University of Pittsburgh; Estados Unidos. University of Pittsburgh at Johnstown; Estados UnidosFil: Li, Ningfei. Charité Universitätsmedizin Berlin; AlemaniaFil: Horn, Andreas. Charité Universitätsmedizin Berlin; AlemaniaFil: Richardson, Robert Mark. Harvard Medical School; Estados UnidosFil: Neumann, Wolf-Julian. Charité Universitätsmedizin Berlin; AlemaniaeLife Sciences Publications2022-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/214465Merk, Timon; Peterson, Victoria; Lipski, Witold J.; Blankertz, Benjamin; Turner, Robert S.; et al.; Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson's disease; eLife Sciences Publications; eLife; 11; 5-2022; 1-272050-084XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.7554/eLife.75126info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:33:35Zoai:ri.conicet.gov.ar:11336/214465instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 15:33:36.252CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson's disease
title Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson's disease
spellingShingle Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson's disease
Merk, Timon
BASAL GANGLIA
COMPUTATIONAL BIOLOGY
DEEP BRAIN STIMULATION
HUMAN
MACHINE LEARNING
NEUROMODULATION
NEUROSCIENCE
SYSTEMS BIOLOGY
title_short Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson's disease
title_full Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson's disease
title_fullStr Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson's disease
title_full_unstemmed Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson's disease
title_sort Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson's disease
dc.creator.none.fl_str_mv Merk, Timon
Peterson, Victoria
Lipski, Witold J.
Blankertz, Benjamin
Turner, Robert S.
Li, Ningfei
Horn, Andreas
Richardson, Robert Mark
Neumann, Wolf-Julian
author Merk, Timon
author_facet Merk, Timon
Peterson, Victoria
Lipski, Witold J.
Blankertz, Benjamin
Turner, Robert S.
Li, Ningfei
Horn, Andreas
Richardson, Robert Mark
Neumann, Wolf-Julian
author_role author
author2 Peterson, Victoria
Lipski, Witold J.
Blankertz, Benjamin
Turner, Robert S.
Li, Ningfei
Horn, Andreas
Richardson, Robert Mark
Neumann, Wolf-Julian
author2_role author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv BASAL GANGLIA
COMPUTATIONAL BIOLOGY
DEEP BRAIN STIMULATION
HUMAN
MACHINE LEARNING
NEUROMODULATION
NEUROSCIENCE
SYSTEMS BIOLOGY
topic BASAL GANGLIA
COMPUTATIONAL BIOLOGY
DEEP BRAIN STIMULATION
HUMAN
MACHINE LEARNING
NEUROMODULATION
NEUROSCIENCE
SYSTEMS BIOLOGY
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Brain signal decoding promises significant advances in the development of clinical brain computer interfaces (BCI). In Parkinson’s disease (PD), first bidirectional BCI implants for adaptive deep brain stimulation (DBS) are now available. Brain signal decoding can extend the clinical utility of adaptive DBS but the impact of neural source, computational methods and PD pathophysiology on decoding performance are unknown. This represents an unmet need for the development of future neurotechnology. To address this, we developed an invasive brain-signal decoding approach based on intraoperative sensorimotor electrocorticography (ECoG) and subthalamic LFP to predict grip-force, a representative movement decoding application, in 11 PD patients undergoing DBS. We demonstrate that ECoG is superior to subthalamic LFP for accurate grip-force decoding. Gradient boosted decision trees (XGBOOST) outperformed other model architectures. ECoG based decoding performance negatively correlated with motor impairment, which could be attributed to subthalamic beta bursts in the motor preparation and movement period. This highlights the impact of PD pathophysiology on the neural capacity to encode movement vigor. Finally, we developed a connectomic analysis that could predict grip-force decoding performance of individual ECoG channels across patients by using their connectomic fingerprints. Our study provides a neurophysiological and computational framework for invasive brain signal decoding to aid the development of an individualized precision-medicine approach to intelligent adaptive DBS.
Fil: Merk, Timon. Charité – Universitätsmedizin Berlin; Alemania
Fil: Peterson, Victoria. Harvard Medical School; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentina
Fil: Lipski, Witold J.. University of Pittsburgh; Estados Unidos. University of Pittsburgh at Johnstown; Estados Unidos
Fil: Blankertz, Benjamin. Technische Universität Berln; Alemania
Fil: Turner, Robert S.. University of Pittsburgh; Estados Unidos. University of Pittsburgh at Johnstown; Estados Unidos
Fil: Li, Ningfei. Charité Universitätsmedizin Berlin; Alemania
Fil: Horn, Andreas. Charité Universitätsmedizin Berlin; Alemania
Fil: Richardson, Robert Mark. Harvard Medical School; Estados Unidos
Fil: Neumann, Wolf-Julian. Charité Universitätsmedizin Berlin; Alemania
description Brain signal decoding promises significant advances in the development of clinical brain computer interfaces (BCI). In Parkinson’s disease (PD), first bidirectional BCI implants for adaptive deep brain stimulation (DBS) are now available. Brain signal decoding can extend the clinical utility of adaptive DBS but the impact of neural source, computational methods and PD pathophysiology on decoding performance are unknown. This represents an unmet need for the development of future neurotechnology. To address this, we developed an invasive brain-signal decoding approach based on intraoperative sensorimotor electrocorticography (ECoG) and subthalamic LFP to predict grip-force, a representative movement decoding application, in 11 PD patients undergoing DBS. We demonstrate that ECoG is superior to subthalamic LFP for accurate grip-force decoding. Gradient boosted decision trees (XGBOOST) outperformed other model architectures. ECoG based decoding performance negatively correlated with motor impairment, which could be attributed to subthalamic beta bursts in the motor preparation and movement period. This highlights the impact of PD pathophysiology on the neural capacity to encode movement vigor. Finally, we developed a connectomic analysis that could predict grip-force decoding performance of individual ECoG channels across patients by using their connectomic fingerprints. Our study provides a neurophysiological and computational framework for invasive brain signal decoding to aid the development of an individualized precision-medicine approach to intelligent adaptive DBS.
publishDate 2022
dc.date.none.fl_str_mv 2022-05
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/214465
Merk, Timon; Peterson, Victoria; Lipski, Witold J.; Blankertz, Benjamin; Turner, Robert S.; et al.; Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson's disease; eLife Sciences Publications; eLife; 11; 5-2022; 1-27
2050-084X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/214465
identifier_str_mv Merk, Timon; Peterson, Victoria; Lipski, Witold J.; Blankertz, Benjamin; Turner, Robert S.; et al.; Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson's disease; eLife Sciences Publications; eLife; 11; 5-2022; 1-27
2050-084X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.7554/eLife.75126
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv eLife Sciences Publications
publisher.none.fl_str_mv eLife Sciences Publications
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1846083465840164864
score 13.22299

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