IEEE Transactions on Neural Systems and Rehabilitation Engineering (Jan 2024)

Identification of Optimal and Most Significant Event Related Brain Functional Network

  • Venkateswarlu Gonuguntla,
  • A. T. Adebisi,
  • Kalyana C. Veluvolu

DOI
https://doi.org/10.1109/TNSRE.2024.3399308
Journal volume & issue
Vol. 32
pp. 1906 – 1915

Abstract

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Advancements in network science have facilitated the study of brain communication networks. Existing techniques for identifying event-related brain functional networks (BFNs) often result in fully connected networks. However, determining the optimal and most significant network representation for event-related BFNs is crucial for understanding complex brain networks. The presence of both false and genuine connections in the fully connected network requires network thresholding to eliminate false connections. However, a generalized framework for thresholding in network neuroscience is currently lacking. To address this, we propose four novel methods that leverage network properties, energy, and efficiency to select a generalized threshold level. This threshold serves as the basis for identifying the optimal and most significant event-related BFN. We validate our methods on an openly available emotion dataset and demonstrate their effectiveness in identifying multiple events. Our proposed approach can serve as a versatile thresholding technique to represent the fully connected network as an event-related BFN.

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