Characteristic differences between the brain networks of high-level shooting athletes and non-athletes calculated using the phase-locking value algorithm

Gong, Anmin; Liu, Jianping; Lü, Ling; Wu, Gengrui; Jiang, Cuiping; Fu, Yunfa

doi:10.1016/j.bspc.2019.02.009

Cited by 25 publications

(21 citation statements)

References 46 publications

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“…However, Mikicin et al (2015) provided a better demonstration of the multiple dimensional measures taken in the SP–NFT, including participant attention response, Kraepelin work curves, and other indicators, and found that SP–NFT can also improve cognitive function ( Mikicin et al, 2015 ). Other studies have reported changes in other aspects such as EEG characteristics during feedback training or resting state, indicating the neuroplasticity of SP–NFT ( Gong et al, 2019 ). The more diverse the data reported, the more helpful it is to fully reveal the relevant mechanisms of SP–NFT.…”

Section: Some Issues In Sp–nft Developmentmentioning

confidence: 95%

“…These varying results may be because the inter-individual differences in motor level led to large differences in the state of the brain nerve when they obtained their best performance, so that the effective SP–NFT for expert shooters is not applicable for amateur shooters ( Hatfield et al, 1984 ; Bertollo et al, 2016 ; Gong et al, 2019 ). As a result, the simulated SP–NFT requires researchers to confirm whether the feedback characteristics match the brain state during the athletes’ best performance before training.…”

Section: Classification Of Sp–nft Based On User Experiencementioning

confidence: 99%

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A Review of Neurofeedback Training for Improving Sport Performance From the Perspective of User Experience

Gong

Nan

et al. 2021

Front. Neurosci.

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Neurofeedback training (NFT) is a non-invasive, safe, and effective method of regulating the nerve state of the brain. Presently, NFT is widely used to prevent and rehabilitate brain diseases and improve an individual’s external performance. Among the various NFT methods, NFT to improve sport performance (SP-NFT) has become an important research and application focus worldwide. Several studies have shown that the method is effective in improving brain function and motor control performance. However, appropriate reviews and prospective directions for this technology are lacking. This paper proposes an SP-NFT classification method based on user experience, classifies and discusses various SP-NFT research schemes reported in the existing literature, and reviews the technical principles, application scenarios, and usage characteristics of different SP-NFT schemes. Several key issues in SP-NFT development, including the factors involved in neural mechanisms, scheme selection, learning basis, and experimental implementation, are discussed. Finally, directions for the future development of SP-NFT, including SP-NFT based on other electroencephalograph characteristics, SP-NFT integrated with other technologies, and SP-NFT commercialization, are suggested. These discussions are expected to provide some valuable ideas to researchers in related fields.

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Section: Some Issues In Sp–nft Developmentmentioning

confidence: 95%

Section: Classification Of Sp–nft Based On User Experiencementioning

confidence: 99%

A Review of Neurofeedback Training for Improving Sport Performance From the Perspective of User Experience

Gong

Nan

et al. 2021

Front. Neurosci.

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“…PLVs result in normalized synchronization values ranging from 0 to 1, and thus no further modification is required before applying them to the weighted network analysis. PLV has been known for the fine performance with weighted minimum norm estimation (45) and has been widely used in the network analysis (46)(47)(48).…”

Section: Connectivity and Network Analysismentioning

confidence: 99%

Altered Cortical Functional Networks in Patients With Schizophrenia and Bipolar Disorder: A Resting-State Electroencephalographic Study

Kim

Shim

et al. 2020

Front. Psychiatry

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Background: Pathologies of schizophrenia and bipolar disorder have been poorly understood. Brain network analysis could help understand brain mechanisms of schizophrenia and bipolar disorder. This study investigates the source-level brain cortical networks using resting-state electroencephalography (EEG) in patients with schizophrenia and bipolar disorder. Methods: Resting-state EEG was measured in 38 patients with schizophrenia, 34 patients with bipolar disorder type I, and 30 healthy controls. Graph theory based source-level weighted functional networks were evaluated: strength, clustering coefficient (CC), path length (PL), and efficiency in six frequency bands. Results: At the global level, patients with schizophrenia or bipolar disorder showed higher strength, CC, and efficiency, and lower PL in the theta band, compared to healthy controls. At the nodal level, patients with schizophrenia or bipolar disorder showed higher CCs, mostly in the frontal lobe for the theta band. Particularly, patients with schizophrenia showed higher nodal CCs in the left inferior frontal cortex and the left ascending ramus of the lateral sulcus compared to patients with bipolar disorder. In addition, the nodal-level theta band CC of the superior frontal gyrus and sulcus (cognition-related region) correlated with positive symptoms and social and occupational functioning scale (SOFAS) scores in the schizophrenia group, while that of the middle frontal gyrus (emotion-related region) correlated with SOFAS scores in the bipolar disorder group. Conclusions: Altered cortical networks were revealed and these alterations were significantly correlated with core pathological symptoms of schizophrenia and bipolar disorder. These source-level cortical network indices could be promising biomarkers to evaluate patients with schizophrenia and bipolar disorder.

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“…Recent research identified neuronal coupling as underlying electrophysiological correlate of neural efficiency and expertise. Network interactions were found to be stronger in expert brains at rest (Gong et al, 2019) and during tasks related to their field of expertise (Bhattacharya and Petsche, 2005). In this view, α-band coupling thus reflects general intelligence and expertise.…”

Section: Discussionmentioning

confidence: 84%

Spontaneous Network Coupling Enables Efficient Task Performance without Local Task-Induced Activations

et al. 2020

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Neurobehavioral studies in humans have long concentrated on changes in local activity levels during repetitive executions of a task. Spontaneous neural coupling within extended networks has latterly been found to also influence performance. Here, we intend to uncover the underlying mechanisms and the interaction with task-induced activations. We demonstrate that high performers in visual perception and motor sequence tasks present an absence of classical task-induced activations, but, instead, strong spontaneous network coupling. Activations were thus a compensation mechanism needed only in subjects with lower spontaneous network interactions. This challenges classical models of neural processing and calls for new strategies in attempts to train and enhance performance.

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Characteristic differences between the brain networks of high-level shooting athletes and non-athletes calculated using the phase-locking value algorithm

Cited by 25 publications

References 46 publications

A Review of Neurofeedback Training for Improving Sport Performance From the Perspective of User Experience

A Review of Neurofeedback Training for Improving Sport Performance From the Perspective of User Experience

Altered Cortical Functional Networks in Patients With Schizophrenia and Bipolar Disorder: A Resting-State Electroencephalographic Study

Spontaneous Network Coupling Enables Efficient Task Performance without Local Task-Induced Activations

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