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


Ozkurt T. E. , Akram H., Zrinzo L., Limousin P., Foltynie T., Oswal A., ...More

NEUROIMAGE, vol.223, 2020 (Peer-Reviewed Journal) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 223
  • Publication Date: 2020
  • Doi Number: 10.1016/j.neuroimage.2020.117356
  • Journal Name: NEUROIMAGE
  • Journal Indexes: Science Citation Index Expanded, Scopus, Academic Search Premier, PASCAL, Aerospace Database, BIOSIS, Biotechnology Research Abstracts, Communication Abstracts, EMBASE, MEDLINE, Metadex, Psycinfo, Public Affairs Index, Civil Engineering Abstracts
  • Keywords: Deep brain stimulation, Dopamine, Levodopa, Local field potentials, Neural oscillations, Nonlinearity, DEEP BRAIN-STIMULATION, FIELD POTENTIAL RECORDINGS, MOVEMENT-RELATED CHANGES, HIGHER-ORDER STATISTICS, SUBTHALAMIC NUCLEUS, BETA OSCILLATIONS, DYNAMICS, SYNCHRONIZATION, MODULATION, CONNECTIVITY

Abstract

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