Identification of nonlinear features in cortical and subcortical signals of Parkinson's Disease patients via a novel efficient measure

Özkurt, Tolga Esat
Zrinzo, Ludvic
Limousin, Patricia
Foltynie, Tom
Oswal, Ashwini
Litvak, Vladimir
This study offers a novel and efficient measure based on a higher order version of autocorrelative signal memory that can identify nonlinearities in a single time series. The suggested method was applied to simultaneously recorded subthalamic nucleus (STN) local field potentials (LFP) and magnetoencephalography (MEG) from fourteen Parkinson's Disease (PD) patients who underwent surgery for deep brain stimulation. Recordings were obtained during rest for both OFF and ON dopaminergic medication states. We analyzed the bilateral LFP channels that had the maximum beta power in the OFF state and the cortical sources that had the maximum coherence with the selected LFP channels in the alpha band. Our findings revealed the inherent nonlinearity in the PD data as subcortical high beta


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Three-dimensional gradient echo (GRE) is the main workhorse sequence used for susceptibility weighted imaging (SWI), quantitative susceptibility mapping (QSM), and susceptibility tensor imaging (STI). Achieving optimal phase signal-to-noise ratio requires late echo times, thus necessitating a long repetition time (TR). Combined with the large encoding burden of whole-brain coverage with high resolution, this leads to increased scan time. Further, the dipole kernel relating the tissue phase to the underlying...
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In this study, a new method for type and parametric identification of a non-linear element in an otherwise linear structure is introduced. This work is an extension of a previous study in which a method was developed to localize non-linearity in multi-degree of freedom systems and to identify type and parameters of the non-linear element when it is located at a ground connection of the system. The method uses a describing function approach for representing the non-linearity in the structure. The describing ...
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Turhan, Hasan Ihsan; Demirekler, Mübeccel (2016-05-19)
This study proposes a multi-dimensional Hough transform algorithm that is improved from [1] by detecting weak targets in the high clutter. In the proposed algorithm execution time is reduced by eliminating the measurements considering speed and SNR values before the Hough transform. The skor-based track confirmation algorithm proposed in [1] is improved and tracks that belong to same target are eliminated. The proposed algorithm is tested with real data and results are presented.
Citation Formats
T. E. Özkurt, L. Zrinzo, P. Limousin, T. Foltynie, A. Oswal, and V. Litvak, “Identification of nonlinear features in cortical and subcortical signals of Parkinson’s Disease patients via a novel efficient measure,” NEUROIMAGE, pp. 0–0, 2020, Accessed: 00, 2020. [Online]. Available: