Identification of Optimal and Most Significant Event Related Brain Functional Network.
IEEE Trans Neural Syst Rehabil Eng
; 32: 1906-1915, 2024.
Article
em En
| MEDLINE
| ID: mdl-38722721
ABSTRACT
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Encéfalo
/
Eletroencefalografia
/
Emoções
/
Rede Nervosa
Limite:
Adult
/
Female
/
Humans
/
Male
Idioma:
En
Revista:
IEEE Trans Neural Syst Rehabil Eng
Ano de publicação:
2024
Tipo de documento:
Article