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

Weiwei Ding1, Yuhong Zhang2, Liya Huang1,3

  • 1College of Electronic and Optical Engineering & College of Microelectronics, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.

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|March 25, 2022
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A new algorithm, Weight K-order propagation number (WKPN), identifies key brain regions for working memory (WM) tasks. This method enhances brain network analysis for improved WM performance and targeted stimulation research.

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K-order propagation number algorithmfunctional brain networkworking memory

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Brain-Computer Interfaces

Background:

  • Working Memory (WM) relies on short-term information processing and storage.
  • Electroencephalogram (EEG) studies often focus on rhythmic synchronization for WM mechanisms.
  • Understanding brain network dynamics is crucial for enhancing WM performance.

Purpose of the Study:

  • To introduce and validate the Weight K-order propagation number (WKPN) algorithm for identifying important brain nodes in WM tasks.
  • To investigate how memory load affects brain network connectivity in different frequency bands.
  • To assess the efficacy of WKPN-derived features for classifying memory load states.

Main Methods:

  • Utilized a novel algorithm, Weight K-order propagation number (WKPN), to analyze brain networks during a French word retention task.
  • Examined Phase Locking Value (PLV) brain networks under varying memory loads.
  • Employed Support Vector Machine (SVM) with WKPN-calculated node importance as feature vectors for memory load classification.

Main Results:

  • Connectivity between frontal and parieto-occipital lobes increased in theta and beta bands with higher memory loads.
  • Achieved 95% classification accuracy for memory load states using beta band node importance.
  • WKPN algorithm demonstrated higher classification accuracy and faster computation compared to Weight Degree Centrality (WDC) and Weight Page Rank (WPR).

Conclusions:

  • The WKPN algorithm effectively identifies crucial brain hubs involved in working memory.
  • This method offers a more efficient and accurate approach to analyzing brain network dynamics.
  • Findings support targeted research and interventions, such as Transcranial alternating current stimulation (tACS), on identified active hubs.