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Using a Novel Functional Brain Network Approach to Locate Important Nodes for Working Memory Tasks

Author

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  • Weiwei Ding

    (College of Electronic and Optical Engineering & College of Microelectronics, Nanjing University of Posts and Telecommunications, Nanjing 210023, China)

  • Yuhong Zhang

    (College of Automation and Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China)

  • Liya Huang

    (College of Electronic and Optical Engineering & College of Microelectronics, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
    National and Local Joint Engineering Laboratory of RF Integration and Micro-Assembly Technology, Nanjing 210003, China)

Abstract

Working Memory (WM) is a short-term memory for processing and storing information. When investigating WM mechanisms using Electroencephalogram (EEG), its rhythmic synchronization properties inevitably become one of the focal features. To further leverage these features for better improve WM task performance, this paper uses a novel algorithm: Weight K-order propagation number (WKPN) to locate important brain nodes and their coupling characteristic in different frequency bands while subjects are proceeding French word retaining tasks, which is an intriguing but original experiment paradigm. Based on this approach, we investigated the node importance of PLV brain networks under different memory loads and found that the connectivity between frontal and parieto-occipital lobes in theta and beta frequency bands enhanced with increasing memory load. We used the node importance of the brain network as a feature vector of the SVM to classify different memory load states, and the highest classification accuracy of 95% is obtained in the beta band. Compared to the Weight degree centrality (WDC) and Weight Page Rank (WPR) algorithm, the SVM with the node importance of the brain network as the feature vector calculated by the WKPN algorithm has higher classification accuracy and shorter running time. It is concluded that the algorithm can effectively spot active central hubs so that researchers can later put more energy to study these areas where active hubs lie in such as placing Transcranial alternating current stimulation (tACS).

Suggested Citation

  • Weiwei Ding & Yuhong Zhang & Liya Huang, 2022. "Using a Novel Functional Brain Network Approach to Locate Important Nodes for Working Memory Tasks," IJERPH, MDPI, vol. 19(6), pages 1-14, March.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:6:p:3564-:d:773181
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    References listed on IDEAS

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    1. Ueli Rutishauser & Ian B. Ross & Adam N. Mamelak & Erin M. Schuman, 2010. "Human memory strength is predicted by theta-frequency phase-locking of single neurons," Nature, Nature, vol. 464(7290), pages 903-907, April.
    2. Joachims, Thorsten, 1998. "Making large-scale SVM learning practical," Technical Reports 1998,28, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
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