Attenuated NoGo-related beta desynchronisation and synchronisation in Parkinson’s disease revealed by magnetoencephalographic recording

Wu, Hsiang Ling; Hsiao, Fu Jung; Chen, Rou Shayn; Shan, Din E.; Hsu, Wan Yu; Chiang, Ming‐Chang; Lin, Yung Yang

doi:10.1038/s41598-019-43762-x

Cited by 21 publications

(40 citation statements)

References 83 publications

(111 reference statements)

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“…As in the beta ERD, the beta ERS appears not only after actual motor execution, but also after a motor imagery and/or after NoGo response in Go/NoGo tasks (Pfurtscheller and Lopes da Silva 1999;Wu et al 2019). Moreover, the beta ERS in PD patients is attenuated (Wu et al 2019). Those evidences suggest that the beta ERS represents a processing of afferent sensory information (Jurkiewicz et al 2006) and a decision-making process (Alegre et al 2004;Wu et al 2019) in combination with the beta ERD.…”

Section: Long-lasting High-beta Reboundmentioning

confidence: 91%

“…Moreover, ischemia in the peripheral afferent reduces the power of beta rebound (Cassim et al 2001). As in the beta ERD, the beta ERS appears not only after actual motor execution, but also after a motor imagery and/or after NoGo response in Go/NoGo tasks (Pfurtscheller and Lopes da Silva 1999;Wu et al 2019). Moreover, the beta ERS in PD patients is attenuated (Wu et al 2019).…”

Section: Long-lasting High-beta Reboundmentioning

confidence: 98%

“…A recent study showed that a smooth execution of a voluntary movement requires a decrease in the power of beta band (Heinrichs-Graham and Wilson 2016), suggesting that beta ERDs are associated with the preparation of actions and related information processing to facilitate a motor execution. Note that beta ERDs appear even in passive (non-voluntary) motor tasks (Vinding et al 2019) and also during the Go/NoGo tasks regardless of the motor selection, i.e., either Go or NoGo responses (Alegre et al 2004;Wu et al 2019). That is, beta ERD (and ERS) is not necessarily accompanied with actual motor executions, but it represents information processing of afferent sensory signals as well as the decisionmaking process (Jurkiewicz et al 2006;Alegre et al 2004;Wu et al 2019).…”

Section: Desynchronization Of Cortical Activities At the End Of The Imentioning

confidence: 99%

“…Note that beta ERDs appear even in passive (non-voluntary) motor tasks (Vinding et al 2019) and also during the Go/NoGo tasks regardless of the motor selection, i.e., either Go or NoGo responses (Alegre et al 2004;Wu et al 2019). That is, beta ERD (and ERS) is not necessarily accompanied with actual motor executions, but it represents information processing of afferent sensory signals as well as the decisionmaking process (Jurkiewicz et al 2006;Alegre et al 2004;Wu et al 2019). The high-beta ERD in this study appeared when the CoP recovering toward the upright position caught and surpassed the CoM, after which CoP and CoM became close to each other.…”

Section: Desynchronization Of Cortical Activities At the End Of The Imentioning

confidence: 99%

“…Since beta ERS for the NoGo response is not accompanied with an actual motor execution, it may represent motor-related decision-making processing in the supraspinal networks, in addition to information processing of sensory feedback signals. Therefore, considering attenuated beta ERS responses reported for the Go/NoGo task in PD patients (Wu et al 2019), the beta ERD and the subsequent beta ERS (beta rebound) represent information processing performed by the cortico-basal ganglia motor loop.…”

Section: Introductionmentioning

confidence: 99%

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Long-lasting event-related beta synchronizations of electroencephalographic activity in response to support-surface perturbations during upright stance

Nakamura

Suzuki

Milosevic

et al. 2020

Preprint

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word count: 299 words Abstract Movement related beta band cortical oscillations, including post-movement beta rebound and desynchronization/synchronization observed during Go/NoGo tasks, have drawn attention in motor control literature, particularly during movements of upper extremities. However, fewer study focused on beta band oscillations during postural control in upright stance. Here, we examined beta rebound and other components of electroencephalogram (EEG) activity during perturbed upright stance to investigate supraspinal contributions to postural stabilization. Particularly, we aimed to clarify the timing and duration of the beta rebound and other components within a non-sustained, but long-lasting, postural recovery process following perturbed stance that occurs much more slowly compared to upper extremities due to a larger time scale of mechanical dynamics. To this end, EEG signals were acquired from nine healthy young adults in response to an impulsive support-surface perturbation, together with the center of pressure (CoP), center of mass (CoM) and electromyogram (EMG) activities of ankle muscles. Event-related potentials (ERPs) and event-related spectral perturbations (ERSPs) were computed using the perturbation onset as a triggering event. Our results indicate that, after shortlatency (<0.3 s) ERPs, powers in high-beta and alpha bands decreased (event-related desynchronizations: ERDs) in the 0.3-0.6 s and 0.3-1.5 s time-intervals after the perturbation onset, respectively. This was followed by long-lasting theta band decrease (ERD) and high-beta increase (event-related synchronization: ERS), implying beta rebound. Specifically, the beta rebound sustained for approximately three seconds from 1.0 to 4.0 s. EMGs of the ankle muscles and joint torques of the ankle and hip were almost completely relaxed in the first half period of the beta rebound. In the remaining period, the CoP and CoM were in their final approach to the equilibrium with amplitudes comparable to those during quiet stance. Possible mechanisms of beta rebound and long-lasting theta-ERD may be related to underlying intermittent control strategy for upright stance.where we assumed that the foot-link is fixed on the moving support surface with no translational and rotational movement (̈F = , ̈F = 0, ̈f = 0). Moreover, we assumed that ( F − ANK ) = 0.02 m,

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Section: Long-lasting High-beta Reboundmentioning

confidence: 91%

Section: Long-lasting High-beta Reboundmentioning

confidence: 98%

Section: Desynchronization Of Cortical Activities At the End Of The Imentioning

confidence: 99%

Section: Desynchronization Of Cortical Activities At the End Of The Imentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Long-lasting event-related beta synchronizations of electroencephalographic activity in response to support-surface perturbations during upright stance

Nakamura

Suzuki

Milosevic

et al. 2020

Preprint

View full text Add to dashboard Cite

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Functional Neuroimaging and Rehabilitation

Tatti,

Cacciola

2024

Translational Neurorehabilitation

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Feature engineering of EEG applied to mental disorders: a systematic mapping study

García-Ponsoda,

García-Carrasco,

Teruel

et al. 2023

Appl Intell

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Around a third of the total population of Europe suffers from mental disorders. The use of electroencephalography (EEG) together with Machine Learning (ML) algorithms to diagnose mental disorders has recently been shown to be a prominent research area, as exposed by several reviews focused on the field. Nevertheless, previous to the application of ML algorithms, EEG data should be correctly preprocessed and prepared via Feature Engineering (FE). In fact, the choice of FE techniques can make the difference between an unusable ML model and a simple, effective model. In other words, it can be said that FE is crucial, especially when using complex, non-stationary data such as EEG. To this aim, in this paper we present a Systematic Mapping Study (SMS) focused on FE from EEG data used to identify mental disorders. Our SMS covers more than 900 papers, making it one of the most comprehensive to date, to the best of our knowledge. We gathered the mental disorder addressed, all the FE techniques used, and the Artificial Intelligence (AI) algorithm applied for classification from each paper. Our main contributions are: (i) we offer a starting point for new researchers on these topics, (ii) we extract the most used FE techniques to classify mental disorders, (iii) we show several graphical distributions of all used techniques, and (iv) we provide critical conclusions for detecting mental disorders. To provide a better overview of existing techniques, the FE process is divided into three parts: (i) signal transformation, (ii) feature extraction, and (iii) feature selection. Moreover, we classify and analyze the distribution of existing papers according to the mental disorder they treat, the FE processes used, and the ML techniques applied. As a result, we provide a valuable reference for the scientific community to identify which techniques have been proven and tested and where the gaps are located in the current state of the art. Graphical Abstract

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Attenuated NoGo-related beta desynchronisation and synchronisation in Parkinson’s disease revealed by magnetoencephalographic recording

Cited by 21 publications

References 83 publications

Long-lasting event-related beta synchronizations of electroencephalographic activity in response to support-surface perturbations during upright stance

Long-lasting event-related beta synchronizations of electroencephalographic activity in response to support-surface perturbations during upright stance

Functional Neuroimaging and Rehabilitation

Feature engineering of EEG applied to mental disorders: a systematic mapping study

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