Changes in network connectivity during motor imagery and execution

Recent studies of functional or effective connectivity in the brain have reported that motor-related brain regions were activated during motor execution and motor imagery, but the relationship between motor and cognitive areas has not yet been completely understood. The objectives of our study were...

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Published inPloS one Vol. 13; no. 1; p. e0190715
Main Authors Kim, Yun Kwan, Park, Eunhee, Lee, Ahee, Im, Chang-Hwan, Kim, Yun-Hee
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 11.01.2018
Public Library of Science (PLoS)
Subjects
Bayes Theorem
Bayesian analysis
Biology and Life Sciences
Brain
Brain Mapping - methods
Brain research
Clinical trials
Cognitive ability
Cortex (motor)
Cortex (premotor)
Coupling
EEG
Electroencephalography
Health sciences
Humans
Imagery
Medical imaging
Medicine and Health Sciences
Mental task performance
Modelling
Motor Cortex - physiology
Motor task performance
Neural networks
Neural Pathways - physiology
Neuroimaging
NMR
Nuclear magnetic resonance
Planning
Prefrontal cortex
Rehabilitation
Research and Analysis Methods
Software
Studies
Supplementary motor area
Task Performance and Analysis
Online AccessGet full text
ISSN1932-6203
1932-6203
DOI10.1371/journal.pone.0190715

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Abstract Recent studies of functional or effective connectivity in the brain have reported that motor-related brain regions were activated during motor execution and motor imagery, but the relationship between motor and cognitive areas has not yet been completely understood. The objectives of our study were to analyze the effective connectivity between motor and cognitive networks in order to define network dynamics during motor execution and motor imagery in healthy individuals. Second, we analyzed the differences in effective connectivity between correct and incorrect responses during motor execution and imagery using dynamic causal modeling (DCM) of electroencephalography (EEG) data. Twenty healthy subjects performed a sequence of finger tapping trials using either motor execution or motor imagery, and the performances were recorded. Changes in effective connectivity between the primary motor cortex (M1), supplementary motor area (SMA), premotor cortex (PMC), and dorsolateral prefrontal cortex (DLPFC) were estimated using dynamic causal modeling. Bayesian model averaging with family-level inference and fixed-effects analysis was applied to determine the most likely connectivity model for these regions. Motor execution and imagery showed inputs to distinct brain regions, the premotor cortex and the supplementary motor area, respectively. During motor execution, the coupling strength of a feedforward network from the DLPFC to the PMC was greater than that during motor imagery. During motor imagery, the coupling strengths of a feedforward network from the PMC to the SMA and of a feedback network from M1 to the PMC were higher than that during motor execution. In imagined movement, although there were connectivity differences between correct and incorrect task responses, each motor imagery task that included correct and incorrect responses showed similar network connectivity characteristics. Correct motor imagery responses showed connectivity from the PMC to the DLPFC, while the incorrect responses had characteristic connectivity from the SMA to the DLPFC. These findings provide an understanding of effective connectivity between motor and cognitive areas during motor execution and imagery as well as the basis for future connectivity studies for patients with stroke.
AbstractList Recent studies of functional or effective connectivity in the brain have reported that motor-related brain regions were activated during motor execution and motor imagery, but the relationship between motor and cognitive areas has not yet been completely understood. The objectives of our study were to analyze the effective connectivity between motor and cognitive networks in order to define network dynamics during motor execution and motor imagery in healthy individuals. Second, we analyzed the differences in effective connectivity between correct and incorrect responses during motor execution and imagery using dynamic causal modeling (DCM) of electroencephalography (EEG) data.Twenty healthy subjects performed a sequence of finger tapping trials using either motor execution or motor imagery, and the performances were recorded. Changes in effective connectivity between the primary motor cortex (M1), supplementary motor area (SMA), premotor cortex (PMC), and dorsolateral prefrontal cortex (DLPFC) were estimated using dynamic causal modeling. Bayesian model averaging with family-level inference and fixed-effects analysis was applied to determine the most likely connectivity model for these regions.Motor execution and imagery showed inputs to distinct brain regions, the premotor cortex and the supplementary motor area, respectively. During motor execution, the coupling strength of a feedforward network from the DLPFC to the PMC was greater than that during motor imagery. During motor imagery, the coupling strengths of a feedforward network from the PMC to the SMA and of a feedback network from M1 to the PMC were higher than that during motor execution. In imagined movement, although there were connectivity differences between correct and incorrect task responses, each motor imagery task that included correct and incorrect responses showed similar network connectivity characteristics. Correct motor imagery responses showed connectivity from the PMC to the DLPFC, while the incorrect responses had characteristic connectivity from the SMA to the DLPFC.These findings provide an understanding of effective connectivity between motor and cognitive areas during motor execution and imagery as well as the basis for future connectivity studies for patients with stroke.
Background Recent studies of functional or effective connectivity in the brain have reported that motor-related brain regions were activated during motor execution and motor imagery, but the relationship between motor and cognitive areas has not yet been completely understood. The objectives of our study were to analyze the effective connectivity between motor and cognitive networks in order to define network dynamics during motor execution and motor imagery in healthy individuals. Second, we analyzed the differences in effective connectivity between correct and incorrect responses during motor execution and imagery using dynamic causal modeling (DCM) of electroencephalography (EEG) data. Method Twenty healthy subjects performed a sequence of finger tapping trials using either motor execution or motor imagery, and the performances were recorded. Changes in effective connectivity between the primary motor cortex (M1), supplementary motor area (SMA), premotor cortex (PMC), and dorsolateral prefrontal cortex (DLPFC) were estimated using dynamic causal modeling. Bayesian model averaging with family-level inference and fixed-effects analysis was applied to determine the most likely connectivity model for these regions. Results Motor execution and imagery showed inputs to distinct brain regions, the premotor cortex and the supplementary motor area, respectively. During motor execution, the coupling strength of a feedforward network from the DLPFC to the PMC was greater than that during motor imagery. During motor imagery, the coupling strengths of a feedforward network from the PMC to the SMA and of a feedback network from M1 to the PMC were higher than that during motor execution. In imagined movement, although there were connectivity differences between correct and incorrect task responses, each motor imagery task that included correct and incorrect responses showed similar network connectivity characteristics. Correct motor imagery responses showed connectivity from the PMC to the DLPFC, while the incorrect responses had characteristic connectivity from the SMA to the DLPFC. Conclusions These findings provide an understanding of effective connectivity between motor and cognitive areas during motor execution and imagery as well as the basis for future connectivity studies for patients with stroke.
Recent studies of functional or effective connectivity in the brain have reported that motor-related brain regions were activated during motor execution and motor imagery, but the relationship between motor and cognitive areas has not yet been completely understood. The objectives of our study were to analyze the effective connectivity between motor and cognitive networks in order to define network dynamics during motor execution and motor imagery in healthy individuals. Second, we analyzed the differences in effective connectivity between correct and incorrect responses during motor execution and imagery using dynamic causal modeling (DCM) of electroencephalography (EEG) data. Twenty healthy subjects performed a sequence of finger tapping trials using either motor execution or motor imagery, and the performances were recorded. Changes in effective connectivity between the primary motor cortex (M1), supplementary motor area (SMA), premotor cortex (PMC), and dorsolateral prefrontal cortex (DLPFC) were estimated using dynamic causal modeling. Bayesian model averaging with family-level inference and fixed-effects analysis was applied to determine the most likely connectivity model for these regions. Motor execution and imagery showed inputs to distinct brain regions, the premotor cortex and the supplementary motor area, respectively. During motor execution, the coupling strength of a feedforward network from the DLPFC to the PMC was greater than that during motor imagery. During motor imagery, the coupling strengths of a feedforward network from the PMC to the SMA and of a feedback network from M1 to the PMC were higher than that during motor execution. In imagined movement, although there were connectivity differences between correct and incorrect task responses, each motor imagery task that included correct and incorrect responses showed similar network connectivity characteristics. Correct motor imagery responses showed connectivity from the PMC to the DLPFC, while the incorrect responses had characteristic connectivity from the SMA to the DLPFC. These findings provide an understanding of effective connectivity between motor and cognitive areas during motor execution and imagery as well as the basis for future connectivity studies for patients with stroke.
Background Recent studies of functional or effective connectivity in the brain have reported that motor-related brain regions were activated during motor execution and motor imagery, but the relationship between motor and cognitive areas has not yet been completely understood. The objectives of our study were to analyze the effective connectivity between motor and cognitive networks in order to define network dynamics during motor execution and motor imagery in healthy individuals. Second, we analyzed the differences in effective connectivity between correct and incorrect responses during motor execution and imagery using dynamic causal modeling (DCM) of electroencephalography (EEG) data. Method Twenty healthy subjects performed a sequence of finger tapping trials using either motor execution or motor imagery, and the performances were recorded. Changes in effective connectivity between the primary motor cortex (M1), supplementary motor area (SMA), premotor cortex (PMC), and dorsolateral prefrontal cortex (DLPFC) were estimated using dynamic causal modeling. Bayesian model averaging with family-level inference and fixed-effects analysis was applied to determine the most likely connectivity model for these regions. Results Motor execution and imagery showed inputs to distinct brain regions, the premotor cortex and the supplementary motor area, respectively. During motor execution, the coupling strength of a feedforward network from the DLPFC to the PMC was greater than that during motor imagery. During motor imagery, the coupling strengths of a feedforward network from the PMC to the SMA and of a feedback network from M1 to the PMC were higher than that during motor execution. In imagined movement, although there were connectivity differences between correct and incorrect task responses, each motor imagery task that included correct and incorrect responses showed similar network connectivity characteristics. Correct motor imagery responses showed connectivity from the PMC to the DLPFC, while the incorrect responses had characteristic connectivity from the SMA to the DLPFC. Conclusions These findings provide an understanding of effective connectivity between motor and cognitive areas during motor execution and imagery as well as the basis for future connectivity studies for patients with stroke.
Recent studies of functional or effective connectivity in the brain have reported that motor-related brain regions were activated during motor execution and motor imagery, but the relationship between motor and cognitive areas has not yet been completely understood. The objectives of our study were to analyze the effective connectivity between motor and cognitive networks in order to define network dynamics during motor execution and motor imagery in healthy individuals. Second, we analyzed the differences in effective connectivity between correct and incorrect responses during motor execution and imagery using dynamic causal modeling (DCM) of electroencephalography (EEG) data.BACKGROUNDRecent studies of functional or effective connectivity in the brain have reported that motor-related brain regions were activated during motor execution and motor imagery, but the relationship between motor and cognitive areas has not yet been completely understood. The objectives of our study were to analyze the effective connectivity between motor and cognitive networks in order to define network dynamics during motor execution and motor imagery in healthy individuals. Second, we analyzed the differences in effective connectivity between correct and incorrect responses during motor execution and imagery using dynamic causal modeling (DCM) of electroencephalography (EEG) data.Twenty healthy subjects performed a sequence of finger tapping trials using either motor execution or motor imagery, and the performances were recorded. Changes in effective connectivity between the primary motor cortex (M1), supplementary motor area (SMA), premotor cortex (PMC), and dorsolateral prefrontal cortex (DLPFC) were estimated using dynamic causal modeling. Bayesian model averaging with family-level inference and fixed-effects analysis was applied to determine the most likely connectivity model for these regions.METHODTwenty healthy subjects performed a sequence of finger tapping trials using either motor execution or motor imagery, and the performances were recorded. Changes in effective connectivity between the primary motor cortex (M1), supplementary motor area (SMA), premotor cortex (PMC), and dorsolateral prefrontal cortex (DLPFC) were estimated using dynamic causal modeling. Bayesian model averaging with family-level inference and fixed-effects analysis was applied to determine the most likely connectivity model for these regions.Motor execution and imagery showed inputs to distinct brain regions, the premotor cortex and the supplementary motor area, respectively. During motor execution, the coupling strength of a feedforward network from the DLPFC to the PMC was greater than that during motor imagery. During motor imagery, the coupling strengths of a feedforward network from the PMC to the SMA and of a feedback network from M1 to the PMC were higher than that during motor execution. In imagined movement, although there were connectivity differences between correct and incorrect task responses, each motor imagery task that included correct and incorrect responses showed similar network connectivity characteristics. Correct motor imagery responses showed connectivity from the PMC to the DLPFC, while the incorrect responses had characteristic connectivity from the SMA to the DLPFC.RESULTSMotor execution and imagery showed inputs to distinct brain regions, the premotor cortex and the supplementary motor area, respectively. During motor execution, the coupling strength of a feedforward network from the DLPFC to the PMC was greater than that during motor imagery. During motor imagery, the coupling strengths of a feedforward network from the PMC to the SMA and of a feedback network from M1 to the PMC were higher than that during motor execution. In imagined movement, although there were connectivity differences between correct and incorrect task responses, each motor imagery task that included correct and incorrect responses showed similar network connectivity characteristics. Correct motor imagery responses showed connectivity from the PMC to the DLPFC, while the incorrect responses had characteristic connectivity from the SMA to the DLPFC.These findings provide an understanding of effective connectivity between motor and cognitive areas during motor execution and imagery as well as the basis for future connectivity studies for patients with stroke.CONCLUSIONSThese findings provide an understanding of effective connectivity between motor and cognitive areas during motor execution and imagery as well as the basis for future connectivity studies for patients with stroke.
Audience Academic
Author Kim, Yun Kwan
Park, Eunhee
Im, Chang-Hwan
Kim, Yun-Hee
Lee, Ahee
AuthorAffiliation 1 Sungkyunkwan University School of Cognitive Science, Seoul, Republic of Korea
4 Department of Biomedical Engineering, Hanyang University, Seoul, Republic of Korea
2 Department of Physical and Rehabilitation Medicine, Kyungpook National University Chilgok Hospital, Daegu, Republic of Korea
5 Department of Physical and Rehabilitation Medicine, Center for Prevention and Rehabilitation, Heart Vascular Stroke Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
3 Department of Health Sciences and Technology, Samsung Advanced Institute for Health Science and Technology, Sungkyunkwan University, Seoul, Republic of Korea
University of Minnesota, UNITED STATES
AuthorAffiliation_xml – name: 3 Department of Health Sciences and Technology, Samsung Advanced Institute for Health Science and Technology, Sungkyunkwan University, Seoul, Republic of Korea
– name: 2 Department of Physical and Rehabilitation Medicine, Kyungpook National University Chilgok Hospital, Daegu, Republic of Korea
– name: 5 Department of Physical and Rehabilitation Medicine, Center for Prevention and Rehabilitation, Heart Vascular Stroke Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
– name: 1 Sungkyunkwan University School of Cognitive Science, Seoul, Republic of Korea
– name: 4 Department of Biomedical Engineering, Hanyang University, Seoul, Republic of Korea
– name: University of Minnesota, UNITED STATES
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/29324886$$D View this record in MEDLINE/PubMed
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ContentType Journal Article
Copyright COPYRIGHT 2018 Public Library of Science
2018 Kim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
2018 Kim et al 2018 Kim et al
Copyright_xml – notice: COPYRIGHT 2018 Public Library of Science
– notice: 2018 Kim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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Snippet Recent studies of functional or effective connectivity in the brain have reported that motor-related brain regions were activated during motor execution and...
Background Recent studies of functional or effective connectivity in the brain have reported that motor-related brain regions were activated during motor...
Background Recent studies of functional or effective connectivity in the brain have reported that motor-related brain regions were activated during motor...
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StartPage e0190715
SubjectTerms Bayes Theorem
Bayesian analysis
Biology and Life Sciences
Brain
Brain Mapping - methods
Brain research
Clinical trials
Cognitive ability
Cortex (motor)
Cortex (premotor)
Coupling
EEG
Electroencephalography
Health sciences
Humans
Imagery
Medical imaging
Medicine and Health Sciences
Mental task performance
Modelling
Motor Cortex - physiology
Motor task performance
Neural networks
Neural Pathways - physiology
Neuroimaging
NMR
Nuclear magnetic resonance
Planning
Prefrontal cortex
Rehabilitation
Research and Analysis Methods
Software
Studies
Supplementary motor area
Task Performance and Analysis
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Title Changes in network connectivity during motor imagery and execution
URI https://www.ncbi.nlm.nih.gov/pubmed/29324886
https://www.proquest.com/docview/1986672657
https://www.proquest.com/docview/1989599799
https://pubmed.ncbi.nlm.nih.gov/PMC5764263
https://doaj.org/article/8c4aa06e1bc8408987ca92fbf9fd546c
http://dx.doi.org/10.1371/journal.pone.0190715
Volume 13
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