Finding brain oscillations with power dependencies in neuroimaging data.
Phase synchronization among neuronal oscillations within the same frequency band has been hypothesized to be a major mechanism for communication between different brain areas. On the other hand, cross-frequency communications are more flexible allowing interactions between oscillations with different frequencies. Among such cross-frequency interactions amplitude-to-amplitude interactions are of a special interest as they show how the strength of spatial synchronization in different neuronal populations relates to each other during a given task. While, previously, amplitude-to-amplitude correlations were studied primarily on the sensor level, we present a source separation approach using spatial filters which maximize the correlation between the envelopes of brain oscillations recorded with electro-/magnetoencephalography (EEG/MEG) or intracranial multichannel recordings. Our approach, which is called canonical source power correlation analysis (cSPoC), is thereby capable of extracting genuine brain oscillations solely based on their assumed coupling behavior even when the signal-to-noise ratio of the signals is low. In addition to using cSPoC for the analysis of cross-frequency interactions in the same subject, we show that it can also be utilized for studying amplitude dynamics of neuronal oscillations across subjects. We assess the performance of cSPoC in simulations as well as in three distinctively different analysis scenarios of real EEG data, each involving several subjects. In the simulations, cSPoC outperforms unsupervised state-of-the-art approaches. In the analysis of real EEG recordings, we demonstrate excellent unsupervised discovery of meaningful power-to-power couplings, within as well as across subjects and frequency bands.
Machine Learning Group, Department of Computer Science, Berlin Institute of Technology, Berlin, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany. Electronic address: sven.daehne@tu-berlin.de.
Sven
S
Dähne
Neurophysics Group, Department of Neurology, Campus Benjamin Franklin, Charité University Medicine Berlin, Berlin, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany; Centre for Cognition and Decision Making, National Research University Higher School of Economics, Moscow, Russia. Electronic address: vadim.nikulin@charite.de.
Vadim V
VV
Nikulin
Signal and System Theory Group, Department of Electrical Engineering and Information Technology, Universität Paderborn, Paderborn, Germany.
David
D
Ramírez
Signal and System Theory Group, Department of Electrical Engineering and Information Technology, Universität Paderborn, Paderborn, Germany.
Peter J
PJ
Schreier
Machine Learning Group, Department of Computer Science, Berlin Institute of Technology, Berlin, Germany; Bernstein Focus Neurotechnology, Berlin, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany; Department of Brain and Cognitive Engineering, Korea University, Anam-dong, Seongbuk-gu, Seoul Republic of Korea. Electronic address: klaus-robert.mueller@tu-berlin.de.
Klaus-Robert
KR
Müller
Machine Learning Group, Department of Computer Science, Berlin Institute of Technology, Berlin, Germany; Bernstein Focus Neurotechnology, Berlin, Germany; Neural Engineering Group, Department of Biomedical Engineering, The City College of New York, New York City, USA. Electronic address: stefan.haufe@tu-berlin.de.
Stefan
S
Haufe
D000465:Algorithms; D001683:Biological Clocks / Q000502:physiology*; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D058256:Brain Waves / Q000502:physiology*; D003627:Data Interpretation, Statistical; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D015225:Magnetoencephalography / Q000379:methods; D009415:Nerve Net / Q000502:physiology*; D009991:Oscillometry / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted
Cross-frequency coupling; ECoG; EEG; LFP; MEG; Neural oscillations; Power-to-power coupling; SPoC; cSPoC