RT info:eu-repo/semantics/article
T1 Connectivity-based Meta-Bands: A new approach for automatic frequency band identification in connectivity analyses
A1 Rodríguez González, Víctor
A1 Núñez Novo, Pablo
A1 Gómez Peña, Carlos
A1 Shigihara, Yoshihito
A1 Hoshi, Hideyuki
A1 Tola Arribas, Miguel Ángel
A1 Cano, Mónica
A1 Guerrero Peral, Angel Luis
A1 García Azorín, David
A1 Hornero Sánchez, Roberto
A1 Poza Crespo, Jesús
K1 Neurosciences
K1 Electrical engineering
K1 Functional network
K1 Frequency bands
K1 Louvain community detection
K1 Red funcional
K1 Bandas de frecuencia
K1 Detección de la comunidad de Lovaina
K1 32 Ciencias Médicas
K1 3205.07 Neurología
AB 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.
PB Elsevier
SN 1053-8119
YR 2023
FD 2023
LK https://uvadoc.uva.es/handle/10324/61503
UL https://uvadoc.uva.es/handle/10324/61503
LA eng
NO NeuroImage, 2023, vol. 280, 120332
NO Producción Científica
DS UVaDOC
RD 05-feb-2025