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Título
Connectivity-based Meta-Bands: A new approach for automatic frequency band identification in connectivity analyses
Autor
Año del Documento
2023
Editorial
Elsevier
Descripción
Producción Científica
Documento Fuente
NeuroImage, 2023, vol. 280, 120332
Resumen
The majority of electroencephalographic (EEG) and magnetoencephalographic (MEG) studies filter and analyse neural signals in specific frequency ranges, known as “canonical” frequency bands. However, this segmentation, is not exempt from limitations, mainly due to the lack of adaptation to the neural idiosyncrasies of each individual. In this study, we introduce a new data-driven method to automatically identify frequency ranges based on the topological similarity of the frequency-dependent functional neural network. The resting-state neural activity of 195 cognitively healthy subjects from three different databases (MEG: 123 subjects; EEG1: 27 subjects; EEG2: 45 subjects) was analysed. In a first step, MEG and EEG signals were filtered with a narrow-band filter bank (1 Hz bandwidth) from 1 to 70 Hz with a 0.5 Hz step. Next, the connectivity in each of these filtered signals was estimated using the orthogonalized version of the amplitude envelope correlation to obtain the frequency-dependent functional neural network. Finally, a community detection algorithm was used to identify communities in the frequency domain showing a similar network topology. We have called this approach the “Connectivity-based Meta-Bands” (CMB) algorithm. Additionally, two types of synthetic signals were used to configure the hyper-parameters of the CMB algorithm. We observed that the classical approaches to band segmentation are partially aligned with the underlying network topologies at group level for the MEG signals, but they are missing individual idiosyncrasies that may be biasing previous studies, as revealed by our methodology. On the other hand, the sensitivity of EEG signals to reflect this underlying frequency-dependent network structure is limited, revealing a simpler frequency parcellation, not aligned with that defined by the “canonical” frequency bands. To the best of our knowledge, this is the first study that proposes an unsupervised band segmentation method based on the topological similarity of functional neural network across frequencies. This methodology fully accounts for subject-specific patterns, providing more robust and personalized analyses, and paving the way for new studies focused on exploring the frequency-dependent structure of brain connectivity.
Materias (normalizadas)
Neurosciences
Electrical engineering
Materias Unesco
32 Ciencias Médicas
3205.07 Neurología
Palabras Clave
Functional network
Frequency bands
Louvain community detection
Red funcional
Bandas de frecuencia
Detección de la comunidad de Lovaina
ISSN
1053-8119
Revisión por pares
SI
Patrocinador
P. Núñez fue financiado por el proyecto ERA-Net FLAG-ERA JTC2021
Propietario de los Derechos
© 2023 The Authors
Idioma
eng
Tipo de versión
info:eu-repo/semantics/publishedVersion
Derechos
openAccess
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