Estimation of nonlinear neural source interactions via sliced bicoherence

Date

2016-09-01

Author

Özkurt, Tolga Esat

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

142
views

0
downloads

Neural oscillations and their spatiotemporal interactions are of interest for the description of brain mechanisms. This study offers a novel third order spectral coupling measure named "sliced bicoherence". It is the diagonal slice of cross-bicoherence allowing an efficient quantification of the nonlinear interactions between neural sources. Our methodology comprises an indirect estimation method, a parametric confidence level formula and a subtracted version for robustness to volume conduction. The methodology provides an efficient estimation of third-order nonlinear cross relations reducing the complexity to the same order of second-order coherence computation. Unlike other bispectral measures, the suggested measure solely holds terms related to cross relations between channel sources and omits the possible strong autobispectral relations. Feasibility and robustness of the methodology are demonstrated both on simulated and publicly available MEG data. The latter were collected for a motor task and an eyes-open resting state. Analytical confidence level marked the non-significant couplings. Simulations confirmed that the subtracted bicoherence enabled robustness to volume conduction by avoiding the spurious nearby channel couplings. Central regions were shown to be coupled with muscular activity by sliced bicoherence. Couplings for spontaneous data occurred particularly at theta and alpha bands. Volume-conduction related bicoherence values originated especially from the low frequencies below 5 Hz. The suggested nonlinear measure is promising to be a part of the rich collection of the multichannel electrophysiological brain connectivity metrics.

Subject Keywords

Bicoherence, Connectivity, Cross-frequency coupling, Higher-order spectrum, Neural oscillations, Synchronization

URI

https://hdl.handle.net/11511/30732

Journal

BIOMEDICAL SIGNAL PROCESSING AND CONTROL

DOI

https://doi.org/10.1016/j.bspc.2016.05.001

Collections

Graduate School of Informatics, Article

Suggestions

OpenMETU
Core

Computational analysis of network activity and spatial reach of sharp wave-ripples Canakci, Sadullah; Toy, Muhammed Faruk; Inci, Ahmet Fatih; Liu, Xin; Kuzum, Duygu (Public Library of Science (PLoS), 2017-9-15) Network oscillations of different frequencies, durations and amplitudes are hypothesized to coordinate information processing and transfer across brain areas. Among these oscillations, hippocampal sharp wave-ripple complexes (SPW-Rs) are one of the most prominent. SPW-Rs occurring in the hippocampus are suggested to play essential roles in memory consolidation as well as information transfer to the neocortex. To-date, most of the knowledge about SPW-Rs comes from experimental studies averaging responses fro...
Neural networks with piecewise constant argument and impact activation Yılmaz, Enes; Akhmet, Marat; Department of Scientific Computing (2011) This dissertation addresses the new models in mathematical neuroscience: artificial neural networks, which have many similarities with the structure of human brain and the functions of cells by electronic circuits. The networks have been investigated due to their extensive applications in classification of patterns, associative memories, image processing, artificial intelligence, signal processing and optimization problems. These applications depend crucially on the dynamical behaviors of the networks. In t...
Parallel implementation of the boundary element method for electromagnetic source imaging of the human brain Ataseven, Yoldaş; Gençer, Nevzat Güneri; Department of Electrical and Electronics Engineering (2005) Human brain functions are based on the electrochemical activity and interaction of the neurons constituting the brain. Some brain diseases are characterized by abnormalities of this activity. Detection of the location and orientation of this electrical activity is called electro-magnetic source imaging (EMSI) and is of signi cant importance since it promises to serve as a powerful tool for neuroscience. Boundary Element Method (BEM) is a method applicable for EMSI on realistic head geometries that generates...
Representation of Cognitive Processes Using the Minimum Spanning Tree of Local Meshes Firat, Orhan; Ozay, Mete; Onal, Itir; GİLLAM, İLKE; Yarman Vural, Fatoş Tunay (2013-07-07) A new graphical model called Cognitive Process Graph (CPG) is proposed, for classifying cognitive processes based on neural activation patterns which are acquired via functional Magnetic Resonance Imaging (fMRI) in brain. In the CPG, first local meshes are formed around each voxel. Second, the relationships between a voxel and its neighbors in a local mesh, which are estimated by using a linear regression model, are used to form the edges among the voxels (graph nodes) in the CPG. Then, a minimum spanning t...
Engineering nonlinear response of nanomaterials using Fano resonances Turkpence, Deniz; Akguc, Gursoy B.; Bek, Alpan; Taşgın, Mehmet Emre (IOP Publishing, 2014-10-01) We show that nonlinear optical processes of nanoparticles can be controlled by the presence of interactions with a molecule or a quantum dot. By choosing the appropriate level spacing for the quantum emitter, one can either suppress or enhance the nonlinear frequency conversion. We reveal the underlying mechanism for this effect, which is already observed in recent experiments: (i) suppression occurs simply because transparency induced by Fano resonance does not allow an excitation at the converted frequenc...

Citation Formats

T. E. Özkurt, “Estimation of nonlinear neural source interactions via sliced bicoherence,” BIOMEDICAL SIGNAL PROCESSING AND CONTROL, pp. 43–52, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/30732.