Harmoni: A method for eliminating spurious interactions due to the harmonic components in neuronal data
Mina Jamshidi Idaji,
Juanli Zhang,
Tilman Stephani,
Guido Nolte,
Klaus-Robert Müller,
Arno Villringer,
Vadim V. Nikulin
Affiliations
Mina Jamshidi Idaji
Corresponding author at: Neurology Department, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.; 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
Juanli Zhang
Neurology Department, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
Tilman Stephani
Neurology Department, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; International Max Planck Research School NeuroCom, Leipzig, Germany
Guido Nolte
Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
Klaus-Robert Müller
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
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
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; Corresponding author at: Neurology Department, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
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.
