Harmoni: A method for eliminating spurious interactions due to the harmonic components in neuronal data.


Journal

NeuroImage
ISSN: 1095-9572
Titre abrégé: Neuroimage
Pays: United States
ID NLM: 9215515

Informations de publication

Date de publication:
15 05 2022
Historique:
received: 18 10 2021
revised: 09 02 2022
accepted: 01 03 2022
pubmed: 6 3 2022
medline: 15 4 2022
entrez: 5 3 2022
Statut: ppublish

Résumé

Cross-frequency synchronization (CFS) has been proposed as a mechanism for integrating spatially and spectrally distributed information in the brain. However, investigating CFS in Magneto- and Electroencephalography (MEG/EEG) is hampered by the presence of spurious neuronal interactions due to the non-sinusoidal waveshape of brain oscillations. Such waveshape gives rise to the presence of oscillatory harmonics mimicking genuine neuronal oscillations. Until recently, however, there has been no methodology for removing these harmonics from neuronal data. In order to address this long-standing challenge, we introduce a novel method (called HARMOnic miNImization - Harmoni) that removes the signal components which can be harmonics of a non-sinusoidal signal. Harmoni's working principle is based on the presence of CFS between harmonic components and the fundamental component of a non-sinusoidal signal. We extensively tested Harmoni in realistic EEG simulations. The simulated couplings between the source signals represented genuine and spurious CFS and within-frequency phase synchronization. Using diverse evaluation criteria, including ROC analyses, we showed that the within- and cross-frequency spurious interactions are suppressed significantly, while the genuine activities are not affected. Additionally, we applied Harmoni to real resting-state EEG data revealing intricate remote connectivity patterns which are usually masked by the spurious connections. Given the ubiquity of non-sinusoidal neuronal oscillations in electrophysiological recordings, Harmoni is expected to facilitate novel insights into genuine neuronal interactions in various research fields, and can also serve as a steppingstone towards the development of further signal processing methods aiming at refining within- and cross-frequency synchronization in electrophysiological recordings.

Identifiants

pubmed: 35247548
pii: S1053-8119(22)00182-3
doi: 10.1016/j.neuroimage.2022.119053
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

119053

Informations de copyright

Copyright © 2022. Published by Elsevier Inc.

Déclaration de conflit d'intérêts

Declaration of Competing Interest We declare no competing interests.

Auteurs

Mina Jamshidi Idaji (MJ)

Neurology Department, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; International Max Planck Research School NeuroCom, Leipzig, Germany; Machine Learning Group, Technical University of Berlin, Berlin, Germany. Electronic address: jamshidi@cbs.mpg.de.

Juanli Zhang (J)

Neurology Department, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany. Electronic address: juanlizhang@cbs.mpg.de.

Tilman Stephani (T)

Neurology Department, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; International Max Planck Research School NeuroCom, Leipzig, Germany. Electronic address: stephani@cbs.mpg.de.

Guido Nolte (G)

Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.

Klaus-Robert Müller (KR)

Machine Learning Group, Technical University of Berlin, Berlin, Germany; Department of Artificial Intelligence, Korea University, Anam-dong, Seongbuk-gu, Seoul, Republic of Korea; Max Planck Institute for Informatics, Saarbrücken, Germany; Google Research, Brain Team, USA.

Arno Villringer (A)

Neurology Department, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Department of Cognitive Neurology, University Hospital Leipzig, Leipzig, Germany.

Vadim V Nikulin (VV)

Neurology Department, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Centre for Cognition and Decision Making, Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia; Neurophysics Group, Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany. Electronic address: nikulin@cbs.mpg.de.

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