title: A General Approach for Quantifying Nonlinear Connectivity in the Nervous System Based on Phase Coupling.
abstract: Interaction between distant neuronal populations is essential for communication within the nervous system and can occur as a highly nonlinear process. To better understand the functional role of neural interactions, it is important to quantify the nonlinear connectivity in the nervous system. We introduce a general approach to measure nonlinear connectivity through phase coupling: the multi-spectral phase coherence (MSPC). Using simulated data, we compare MSPC with existing phase coupling measures, namely n : m synchronization index and bi-phase locking value. MSPC provides a system description, including (i) the order of the nonlinearity, (ii) the direction of interaction, (iii) the time delay in the system, and both (iv) harmonic and (v) intermodulation coupling beyond the second order; which are only partly revealed by other methods. We apply MSPC to analyze data from a motor control experiment, where subjects performed isotonic wrist flexions while receiving movement perturbations. MSPC between the perturbation, EEG and EMG was calculated. Our results reveal directional nonlinear connectivity in the afferent and efferent pathways, as well as the time delay (43 ± 8 ms) between the perturbation and the brain response. In conclusion, MSPC is a novel approach capable to assess high-order nonlinear interaction and timing in the nervous system.
authors.affiliation: 1 Department of Biomechanical Engineering, Delft University of Technology, Delft 2628 CD, The Netherlands.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Yang
authors.affiliation: 1 Department of Biomechanical Engineering, Delft University of Technology, Delft 2628 CD, The Netherlands.
authors.forename: Teodoro
authors.initials: T
authors.lastname: Solis-Escalante
authors.affiliation: 2 Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA.
authors.forename: Jun
authors.initials: J
authors.lastname: Yao
authors.affiliation: 3 Faculty of Human Movement Sciences, VU University Amsterdam, Amsterdam, 1081 BT, The Netherlands.
authors.forename: Andreas
authors.initials: A
authors.lastname: Daffertshofer
authors.affiliation: 1 Department of Biomechanical Engineering, Delft University of Technology, Delft 2628 CD, The Netherlands.|4 MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, 7500 AE, The Netherlands.
authors.forename: Alfred C
authors.initials: AC
authors.lastname: Schouten
authors.affiliation: 1 Department of Biomechanical Engineering, Delft University of Technology, Delft 2628 CD, The Netherlands.
authors.forename: Frans C T
authors.initials: FC
authors.lastname: van der Helm
mesh_terms: D000328:Adult; D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D004569:Electroencephalography; D004576:Electromyography; D005260:Female; D006801:Humans; D008297:Male; D008959:Models, Neurological; D009132:Muscles / Q000502:physiology; D009434:Neural Pathways / Q000502:physiology*; D017711:Nonlinear Dynamics; D066191:Sensorimotor Cortex / Q000502:physiology; D014953:Wrist / Q000502:physiology; D055815:Young Adult
keywords: EEG; EMG; Phase coupling; motor control; nonlinear interaction; time delay