English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

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

Jamshidi Idaji, M., Zhang, J., Stephani, T., Nolte, G., Müller, K.-R., Villringer, A., et al. (2022). Harmoni: A method for eliminating spurious interactions due to the harmonic components in neuronal data. NeuroImage, 119053. doi:10.1016/j.neuroimage.2022.119053.

Item is

Basic

show hide
Genre: Journal Article

Files

show Files
hide Files
:
Jamshidi_pre.pdf (Preprint), 15MB
Name:
Jamshidi_pre.pdf
Description:
-
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show

Creators

show
hide
 Creators:
Jamshidi Idaji, Mina1, 2, 3, Author              
Zhang, Juanli1, 4, Author
Stephani, Tilman1, 2, Author              
Nolte, Guido5, Author
Müller, Klaus-Robert3, 6, 7, 8, Author
Villringer, Arno1, 9, Author              
Nikulin, Vadim V.1, 10, 11, Author              
Affiliations:
1Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
2International Max Planck Research School on Neuroscience of Communication: Function, Structure, and Plasticity, MPI for Human Cognitive and Brain Sciences, Max Planck Society, Leipzig, DE, ou_2616696              
3Machine Learning Group, Faculty of Electrical Engineering and Computer Science, TU Berlin, Germany, ou_persistent22              
4Department of Neurology, Charité University Medicine Berlin, Germany, ou_persistent22              
5Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Germany, ou_persistent22              
6Center for Artificial Intelligence, Korea University, Seoul, Republic of Korea, ou_persistent22              
7Max Planck Institute for Informatics, Saarbrücken, Germany, ou_persistent22              
8Google Research, Brain Team, Mountain View, CA, USA, ou_persistent22              
9Clinic for Cognitive Neurology, University of Leipzig, Germany, ou_persistent22              
10Centre for Cognition and Decision Making, National Research University Higher School of Economics, Moscow, Russia, ou_persistent22              
11Neurophysics Group, Department of Neurology, Charité University Medicine Berlin, Germany, ou_persistent22              

Content

show
hide
Free keywords: -
 Abstract: 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.

Details

show
hide
Language(s): eng - English
 Dates: 2022-02-092021-10-182022-03-012022-03-02
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1016/j.neuroimage.2022.119053
 Degree: -

Event

show

Legal Case

show

Project information

show hide
Project name : -
Grant ID : 01IS14013A-E, 01GQ1115, 01GQ0850
Funding program : -
Funding organization : German Ministry for Education and Research (BMBF)
Project name : -
Grant ID : 2017-0-00451, 2019-0-00079
Funding program : -
Funding organization : Korea Government

Source 1

show
hide
Title: NeuroImage
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Orlando, FL : Academic Press
Pages: - Volume / Issue: - Sequence Number: 119053 Start / End Page: - Identifier: ISSN: 1053-8119
CoNE: https://pure.mpg.de/cone/journals/resource/954922650166