A Comparison of Independent Event-Related Desynchronization Responses in Motor-Related Brain Areas to Movement Execution, Movement Imagery, and Movement Observation

Duann, Jeng Ren; Chiou, Jin Chern

doi:10.1371/journal.pone.0162546

Cited by 48 publications

(38 citation statements)

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“…18.0 (IBM, Chicago, IL). We respectively, examined the effects of group, brain area, test condition, and time-phase on the ERD areas (%) in the mu band (8-12 Hz) and beta band (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25). Four-way repeated-measures analyses of variance (ANOVA) was used to assess the interactions and the main effects among four variables (group, brain area, condition, and time-phase).…”

Section: Statistical Analysesmentioning

confidence: 99%

“…MT provides visual feedback impressions created by observing the image of the less-affected upper extremity projected over the affected limb. The mechanisms underlying the efficacy of MT include its facilitation of motor learning, promotion of interhemispheric communication and balance between the motor cortices, and increased cortical activation after stroke (3,6,12,15,16). Likewise, mirror visual feedback is thought to restore cortical reorganization, and thereby to improve motor recovery in weak or paralyzed limbs.…”

Section: Introductionmentioning

confidence: 99%

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Alternative Motor Task-Based Pattern Training With a Digital Mirror Therapy System Enhances Sensorimotor Signal Rhythms Post-stroke

Chang

Chen

et al. 2019

Front. Neurol.

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Mirror therapy (MT) facilitates motor learning and induces cortical reorganization and motor recovery from stroke. We applied the new digital mirror therapy (DMT) system to compare the cortical activation under the three visual feedback conditions: (1) no mirror visual feedback (NoMVF), (2) bilateral synchronized task-based mirror visual feedback training (BMVF), and (3) reciprocal task-based mirror visual feedback training (RMVF). During DMT, EEG recordings, including time-dependent event-related desynchronization (ERD) signal amplitude in both mu and beta bands, were obtained from the standard C3 (ispilesional hemisphere, IH), C4 (contralesional hemisphere, CH), and Cz scalp sites (supplementary motor area, SMA). The entire ERD curve was separated into three time-phases: P0 (−2 to 0 s), P1 (0 to 2 s), and P2 (2 to 4 s). Four-way and subsequent repeated-measures analyses of variance were used to examine the effects of group (stroke vs. control group), test condition (NoMVF, BMVF, and RMVF), time-phase (P0, P1, and P2), and brain area (IH, CH, SMA) on the ERD areas (%) in mu and beta bands. For the mu band, generally, ERD areas (%) were larger in the control than in the stroke group. The ERD areas (%) were largest under the RMVF condition, followed by BMVF and NoMVF conditions. Similar results were found in the beta bands. The main effects of group, time-phase, and test condition on the ERD areas (%) were significant for the three brain areas, except the main effect of group in the SMA (Cz) and CH (C4) brain area. The ERD areas (%) were larger in the control than in the stroke group. The ERD area (%) was significantly larger during P1 than during P0 and P2 (ps < 0.02), and during P2 than during P0 (ps < 0.01). The ERD area (%) under the RMVF condition was significantly larger than that under the BMVF condition and NoMVF condition (ps < 0.05). The present study suggests that cortical activation particularly in the SMA (Cz) of the brain increases in the RMVF condition in both healthy subjects and stroke patients. This result supports the hypothesis that stroke patients may benefit from RMVF training.

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Section: Statistical Analysesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Alternative Motor Task-Based Pattern Training With a Digital Mirror Therapy System Enhances Sensorimotor Signal Rhythms Post-stroke

Chang

Chen

et al. 2019

Front. Neurol.

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“…The effect of top-down aspect of instruction, which relates to the incorporation of motor imagery (MI; i.e., imagining the execution of an action without physically performing it; Jeannerod, 1994;Hardwick et al, 2018) during AO on the subsequent motor performance has not been fully resolved. From a neural point of view, a partial neural activity overlap was found between AO, MI, and movement execution (Burianová et al, 2013;Kraeutner et al, 2014;Duann and Chiou, 2016;Hardwick et al, 2018;Solomon et al, 2019). According to a recent meta-analysis (Hardwick et al, 2018), AO and MI recruited similar premotor-parietal cortical networks.…”

Section: Introductionmentioning

confidence: 99%

“…They showed that MI exclusively activated areas-the bilateral occipital gyrus, left inferior parietal lobule (IPL), parahippocampus, right superior temporal gyrus, and superior frontal gyrus-that are part of a circuitry important for visuospatial imagery, the processing and remembering of visual scenes, and the representation of three-dimensional space (Epstein and Kanwisher, 1998;Mullaly and Maguire, 2011). Also, EEG studies showed partially resembled event-related desynchronization patterns in movement execution compared to MI and AO (Kraeutner et al, 2014;Duann and Chiou, 2016;Solomon et al, 2019). For example, the magnitude of event-related desynchronization in sensorimotor regions was significantly greater in movement execution than MI during both the preparatory and performance phases .…”

Section: Introductionmentioning

confidence: 99%

The Effects of Instruction Manipulation on Motor Performance Following Action Observation

Frenkel‐Toledo

Einat

Kozol

2020

Front. Hum. Neurosci.

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The effects of action observation (AO) on motor performance can be modulated by instruction. The effects of two top-down aspects of the instruction on motor performance have not been fully resolved: those related to attention to the observed task and the incorporation of motor imagery (MI) during AO. In addition, the immediate vs. 24-h retention test effects of those instruction's aspects are yet to be elucidated. Fortyeight healthy subjects were randomly instructed to: (1) observe reaching movement (RM) sequences toward five lighted units with the intention of reproducing the same sequence as fast and as accurate as possible (Intentional + Attentional group; AO+At);(2) observe the RMs sequence with the intention of reproducing the same sequence as fast and as accurate as possible and simultaneously to the observation to imagine performing the RMs (Intentional + attentional + MI group; AO+At+MI); and (3) observe the RMs sequence (Passive AO group). Subjects' performance was tested before and immediately after the AO and retested after 24 h. During each of the pretest, posttest, and retest, the subject performed RMs toward the units that were activated in the same order as the observed sequence. Occasionally, the sequence order was changed by beginning the sequence with a different activated unit. The outcome measures were: averaged response time of the RMs during the sequences, difference between the response time of the unexpected and expected RMs and percent of failures to reach the target within 1 s. The averaged response time and the difference between the response time of the unexpected and expected RMs were improved in all groups at posttest compared to pretest, regardless of instruction. Averaged response time was improved in the retest compared to the posttest only in the Passive AO group. The percent of failures across groups was higher in pretest compared to retest. Our findings suggest that manipulating top-down aspects of instruction by adding attention and MI to AO in an RM sequence task does not improve subsequent performance more than passive observation. Off-line learning of the sequence in the retention test was improved in comparison to posttest following passive observation only.

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“…For instance, motor-related brain 18 activity is manifested in the brain as a specific scenario of neural activity with 19 well-defined frequency and spatial localization. Particularly, it is characterized by event 20 related desynchronization (ERD) in alpha/mu-and beta-bands [9]. The same features 21 are observed during motor imagery in specially trained subjects [10,11].…”

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confidence: 79%

Human personality reflects spatio-temporal and time-frequency EEG structure

Pisarchik

Runnova

Maksimenko

et al. 2018

Preprint

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The brain controls all physiological processes in the organism and regulates its interaction with the external environment. The way the brain solves mental tasks is determined by individual human features, which are reflected in neuronal network dynamics, and therefore can be detected in neurophysiological data. Every human action is associated with a unique brain activity (motor-related, cognitive, etc.) represented by a specific oscillatory pattern in a multichannel electroencephalogram (EEG). The connection between neurophysiological processes and personal mental characteristics is manifested when using simple psycho-diagnostic tests (Schulte tables) in order to study the attention span. The analysis of spatio-temporal and time-frequency structures of the multichannel EEG using the Schulte tables allows us to divide subjects into three groups depending on their neural activity. The personality multi-factor profile of every participant can be individually described based on both the Sixteen Personality Factor Questionnaire (16PF) and a personal interview with an experienced psychologist. The correlation of the EEG-based personality classification with individual multi-factor profiles provides a possibility to identify human personality by analyzing electrical brain activity. The obtained results are of great interest for testing human personality and creating automatized intelligent programs that employ simple tests and EEG measurements for an objective estimation of human personality features. 4 event-related response of the neuronal brain network, in particular, a decrease in 5 alpha-wave (8-12 Hz) and an increase in beta-wave (15-30 Hz) activities. Such a 6 behavior reflects different cognitive functions, namely, the alpha-wave suppression is 7 associated with visual [1] or auditory [2] attention, while the beta-wave activation 8 relates to information processing [3] and an alerted state [4, 5]. Similar cognitive activity 9 was observed in the group of motivation-dependent volunteers [6]. 10 PLOS 1/15Universal EEG patterns were also identified in patients with pathological brain 11 activity, e.g., epileptic seizures. This type of activity is closely related to global 12 synchronization in the brain's neuronal network [7], which is manifested as 13 high-amplitude oscillations with a specific waveform (series of well-pronounced spikes 14 and waves) and frequency [8]. Such spike-and-wave oscillations have similar properties 15 for different patients and are only determined by the type of epilepsy. 16Different physiological and psychological states (e.g., sleep stages, arousal, etc.) are 17 known to possess specific properties of neural activity. For instance, motor-related brain 18 activity is manifested in the brain as a specific scenario of neural activity with 19 well-defined frequency and spatial localization. Particularly, it is characterized by event 20 related desynchronization (ERD) in alpha/mu-and beta-bands [9]. The same features 21 are observed during motor imagery in specially trained subjects [10...

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A Comparison of Independent Event-Related Desynchronization Responses in Motor-Related Brain Areas to Movement Execution, Movement Imagery, and Movement Observation

Cited by 48 publications

References 40 publications

Alternative Motor Task-Based Pattern Training With a Digital Mirror Therapy System Enhances Sensorimotor Signal Rhythms Post-stroke

Alternative Motor Task-Based Pattern Training With a Digital Mirror Therapy System Enhances Sensorimotor Signal Rhythms Post-stroke

The Effects of Instruction Manipulation on Motor Performance Following Action Observation

Human personality reflects spatio-temporal and time-frequency EEG structure

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