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      Rifle Shooting Performance Correlates with Electroencephalogram Beta Rhythm Network Activity during Aiming

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          Abstract

          To study the relationship between brain network and shooting performance during shooting aiming, we collected electroencephalogram (EEG) signals from 40 skilled shooters during rifle shooting and calculated the EEG functional coupling, functional brain network topology, and correlation coefficients between these EEG characteristics and shooting performance. Our result shows a significant negative correlation between shooting performance and functional coupling between the prefrontal, frontal, and temporal regions of the right brain in the Beta1 and Beta2 frequency bands. Global and local brain network topology characteristics were also significantly correlated with shooting performance. These findings indicate that under these experimental conditions, shooters with higher shooting performances exhibit lower functional coupling, higher global, and lower local information integration efficiency during shooting. These conclusions may provide a theoretical basis of the EEG brain network for studying the mental status of shooters while shooting.

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          Electrophysiological signatures of resting state networks in the human brain.

          D. Mantini, M G Perrucci, C Del Gratta … (2007)
          Functional neuroimaging and electrophysiological studies have documented a dynamic baseline of intrinsic (not stimulus- or task-evoked) brain activity during resting wakefulness. This baseline is characterized by slow (<0.1 Hz) fluctuations of functional imaging signals that are topographically organized in discrete brain networks, and by much faster (1-80 Hz) electrical oscillations. To investigate the relationship between hemodynamic and electrical oscillations, we have adopted a completely data-driven approach that combines information from simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Using independent component analysis on the fMRI data, we identified six widely distributed resting state networks. The blood oxygenation level-dependent signal fluctuations associated with each network were correlated with the EEG power variations of delta, theta, alpha, beta, and gamma rhythms. Each functional network was characterized by a specific electrophysiological signature that involved the combination of different brain rhythms. Moreover, the joint EEG/fMRI analysis afforded a finer physiological fractionation of brain networks in the resting human brain. This result supports for the first time in humans the coalescence of several brain rhythms within large-scale brain networks as suggested by biophysical studies.
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            Efficient Behavior of Small-World Networks

            Massimo Marchiori, Vito Latora (2001)
            We introduce the concept of efficiency of a network, measuring how efficiently it exchanges information. By using this simple measure small-world networks are seen as systems that are both globally and locally efficient. This allows to give a clear physical meaning to the concept of small-world, and also to perform a precise quantitative a nalysis of both weighted and unweighted networks. We study neural networks and man-made communication and transportation systems and we show that the underlying general principle of their construction is in fact a small-world principle of high efficiency.
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              Measuring phase synchrony in brain signals

              Francisco J. Varela, Eugenio Rodriguez, Jean-Philippe Lachaux … (1999)
              This article presents, for the first time, a practical method for the direct quantification of frequency‐specific synchronization (i.e., transient phase‐locking) between two neuroelectric signals. The motivation for its development is to be able to examine the role of neural synchronies as a putative mechanism for long‐range neural integration during cognitive tasks. The method, called phase‐locking statistics (PLS), measures the significance of the phase covariance between two signals with a reasonable time‐resolution (<100 ms). Unlike the more traditional method of spectral coherence, PLS separates the phase and amplitude components and can be directly interpreted in the framework of neural integration. To validate synchrony values against background fluctuations, PLS uses surrogate data and thus makes no a priori assumptions on the nature of the experimental data. We also apply PLS to investigate intracortical recordings from an epileptic patient performing a visual discrimination task. We find large‐scale synchronies in the gamma band (45 Hz), e.g., between hippocampus and frontal gyrus, and local synchronies, within a limbic region, a few cm apart. We argue that whereas long‐scale effects do reflect cognitive processing, short‐scale synchronies are likely to be due to volume conduction. We discuss ways to separate such conduction effects from true signal synchrony. Hum Brain Mapping 8:194–208, 1999. © 1999 Wiley‐Liss, Inc.
              Article 0 comments Cited 480 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Yunfa Fu:
                ORCID: http://orcid.org/0000-0002-4820-6337
                Journal
                Journal ID (nlm-ta): Comput Intell Neurosci
                Journal ID (iso-abbrev): Comput Intell Neurosci
                Journal ID (publisher-id): CIN
                Title: Computational Intelligence and Neuroscience
                Publisher: Hindawi
                ISSN (Print): 1687-5265
                ISSN (Electronic): 1687-5273
                Publication date Collection: 2018
                Publication date (Electronic): 11 November 2018
                Volume: 2018
                Electronic Location Identifier: 4097561
                Affiliations
                1School of Information Engineering, Engineering University of Armed Police Force, Xi'an, China
                2Key Laboratory of Sports Performance Evaluation and Technical Analysis, Capital Institute of Physical Education, Beijing, China
                3School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
                Author notes

                Academic Editor: Laura Marzetti

                Author information
                http://orcid.org/0000-0002-2715-2296
                http://orcid.org/0000-0002-4820-6337
                Article
                DOI: 10.1155/2018/4097561
                PMC ID: 6252210
                SO-VID: 6d985db2-6ffe-481c-becf-b40b5252959a
                Copyright © Copyright © 2018 Anmin Gong et al.
                License:

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 12 June 2018
                Date revision received : 3 September 2018
                Date accepted : 16 September 2018
                Funding
                Funded by: National Natural Science Foundation of China
                Award ID: 81470084
                Award ID: 61463024
                Award ID: 81771926
                Award ID: 61763022,
                Award ID: 31771244
                Funded by: State General Administration for Sports Scientific Research
                Award ID: 2015B040
                Funded by: Beijing Research Institute of Sports Science
                Categories
                Subject: Research Article

                ScienceOpen disciplines: Neurosciences
                Data availability:
                ScienceOpen disciplines: Neurosciences

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