Analysis of task-related MEG functional brain networks using dynamic mode decomposition - Université de Rennes Accéder directement au contenu
Article Dans Une Revue Journal of Neural Engineering Année : 2023

Analysis of task-related MEG functional brain networks using dynamic mode decomposition

Résumé

Objective.Functional connectivity networks explain the different brain states during the diverse motor, cognitive, and sensory functions. Extracting connectivity network configurations and their temporal evolution is crucial for understanding brain function during diverse behavioral tasks.Approach.In this study, we introduce the use of dynamic mode decomposition (DMD) to extract the dynamics of brain networks. We compared DMD with principal component analysis (PCA) using real magnetoencephalography data during motor and memory tasks.Main results.The framework generates dominant connectivity brain networks and their time dynamics during simple tasks, such as button press and left-hand movement, as well as more complex tasks, such as picture naming and memory tasks. Our findings show that the proposed methodology with both the PCA-based and DMD-based approaches extracts similar dominant connectivity networks and their corresponding temporal dynamics.Significance.We believe that the proposed methodology with both the PCA and the DMD approaches has a very high potential for deciphering the spatiotemporal dynamics of electrophysiological brain network states during tasks.
Fichier principal
Vignette du fichier
Partamian et al - 2023 - Analysis of task-related MEG functional brain networks.pdf (2.93 Mo) Télécharger le fichier
Supplementary Material Analysis of Task Related.pdf (1.99 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04098994 , version 1 (16-05-2023)

Licence

Paternité - Pas d'utilisation commerciale

Identifiants

Citer

Hmayag Partamian, Judie Tabbal, Mahmoud Hassan, Fadi Karameh. Analysis of task-related MEG functional brain networks using dynamic mode decomposition. Journal of Neural Engineering, 2023, 20 (1), pp.016011. ⟨10.1088/1741-2552/acad28⟩. ⟨hal-04098994⟩
37 Consultations
17 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More