Özkurt, Tolga Esat
Hirschmann, Jan
Schnitzler, Alfons
Neural oscillations in various distinct frequency bands and their interrelations yield high temporal resolution signatures of the human brain activity. This study demonstrates solutions to some of the common challenges in the analysis of neurophysiological data by means of subthalamic local field potentials (LFP) acquired form patients with Parkinson's Disease (PD) undergoing deep brain stimulation therapy. Multivariate empirical mode decomposition (MEMD), being a data-driven method suitable for multichannel data, is employed. This method allows identification of oscillatory bands without the requirement of fixed a priori basis functions. Our study focuses on two issues: (i) Determination of data specific frequency bands and revealing the weak inconspicuous high frequency components in the data and (ii) validation of the biological meaningfulness of the MEMD oscillatory components via phase-amplitude coupling as previously shown to be inherent in subcortical PD LFP data.


Estimation of nonlinear neural source interactions via sliced bicoherence
Özkurt, Tolga Esat (2016-09-01)
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 methodo...
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...
A Sparse Temporal Mesh Model for Brain Decoding
Afrasiyabi, Arman; Onal, Itir; Yarman Vural, Fatoş Tunay (2016-08-23)
One of the major drawbacks of brain decoding from the functional magnetic resonance images (fMRI) is the very high dimension of feature space which consists of thousands of voxels in sequence of brain volumes, recorded during a cognitive stimulus. In this study, we propose a new architecture, called Sparse Temporal Mesh Model (STMM), which reduces the dimension of the feature space by combining the voxel selection methods with the mesh learning method. We, first, select the "most discriminative" voxels usin...
A critical note on the definition of phase-amplitude cross-frequency coupling
Özkurt, Tolga Esat; Schnitzler, Alfons (2011-10-01)
Recent studies have observed the ubiquity of phase-amplitude coupling (PAC) phenomenon in human and animal brain recordings. While various methods were performed to quantify it, a rigorous analytical definition of PAC is lacking. This paper yields an analytical definition and accordingly offers theoretical insights into some of the current methods. A direct PAC estimator based on the given definition is presented and shown theoretically to be superior to some of the previous methods such as general linear m...
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...
Citation Formats
T. E. Özkurt, J. Hirschmann, and A. Schnitzler, “ADAPTIVE IDENTIFICATION OF OSCILLATORY BANDS FROM SUBCORTICAL NEURAL DATA,” 2015, Accessed: 00, 2020. [Online]. Available: