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

Date

2020-12-01

Author

Özkurt, Tolga Esat
Zrinzo, Ludvic
Limousin, Patricia
Foltynie, Tom
Oswal, Ashwini
Litvak, Vladimir

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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

Subject Keywords

Cognitive Neuroscience, Neurology

URI

https://hdl.handle.net/11511/69502

Journal

NEUROIMAGE

DOI

https://doi.org/10.1016/j.neuroimage.2020.117356

Collections

Graduate School of Informatics, Article

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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: https://hdl.handle.net/11511/69502.