Identification of nonlinear features in cortical and subcortical signals of Parkinson's Disease patients via a novel efficient measure
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 magnetoenc...
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doaj-234d03d12f4243718cb46c4f407938a82020-11-25T03:05:39ZengElsevierNeuroImage1095-95722020-12-01223117356Identification of nonlinear features in cortical and subcortical signals of Parkinson's Disease patients via a novel efficient measureTolga Esat Özkurt0Harith Akram1Ludvic Zrinzo2Patricia Limousin3Tom Foltynie4Ashwini Oswal5Vladimir Litvak6Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London, UK; Middle East Technical University, Department of Health Informatics, Graduate School of Informatics, Ankara, Turkey; Corresponding author at: Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London, UK.Department of Clinical and Movement Neurosciences, UCL Institute of Neurology and The National Hospital for Neurology and Neurosurgery, Queen Square, London, UKDepartment of Clinical and Movement Neurosciences, UCL Institute of Neurology and The National Hospital for Neurology and Neurosurgery, Queen Square, London, UKDepartment of Clinical and Movement Neurosciences, UCL Institute of Neurology and The National Hospital for Neurology and Neurosurgery, Queen Square, London, UKDepartment of Clinical and Movement Neurosciences, UCL Institute of Neurology and The National Hospital for Neurology and Neurosurgery, Queen Square, London, UKWellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London, UK; Department of Clinical Neurology, John Radcliffe Hospital, Oxford, UKWellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London, UKThis 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(20–30 Hz) band and cortical alpha (8–12 Hz) band activities. While the former was discernible without medication (p=0.015), the latter was induced upon the dopaminergic medication (p<6.10−4). The degree of subthalamic nonlinearity was correlated with contralateral tremor severity (r=0.45, p=0.02). Conversely, for the cortical signals nonlinearity was present for the ON medication state with a peak in the alpha band and correlated with contralateral akinesia and rigidity (r=0.46, p=0.02). This correlation appeared to be independent from that of alpha power and the two measures combined explained 34 % of the variance in contralateral akinesia scores. Our findings suggest that particular frequency bands and brain regions display nonlinear features closely associated with distinct motor symptoms and functions.http://www.sciencedirect.com/science/article/pii/S1053811920308429Deep brain stimulationDopamineLevodopaLocal field potentialsNeural oscillationsNonlinearity |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Tolga Esat Özkurt Harith Akram Ludvic Zrinzo Patricia Limousin Tom Foltynie Ashwini Oswal Vladimir Litvak |
spellingShingle |
Tolga Esat Özkurt Harith Akram Ludvic Zrinzo Patricia Limousin Tom Foltynie Ashwini Oswal Vladimir Litvak Identification of nonlinear features in cortical and subcortical signals of Parkinson's Disease patients via a novel efficient measure NeuroImage Deep brain stimulation Dopamine Levodopa Local field potentials Neural oscillations Nonlinearity |
author_facet |
Tolga Esat Özkurt Harith Akram Ludvic Zrinzo Patricia Limousin Tom Foltynie Ashwini Oswal Vladimir Litvak |
author_sort |
Tolga Esat Özkurt |
title |
Identification of nonlinear features in cortical and subcortical signals of Parkinson's Disease patients via a novel efficient measure |
title_short |
Identification of nonlinear features in cortical and subcortical signals of Parkinson's Disease patients via a novel efficient measure |
title_full |
Identification of nonlinear features in cortical and subcortical signals of Parkinson's Disease patients via a novel efficient measure |
title_fullStr |
Identification of nonlinear features in cortical and subcortical signals of Parkinson's Disease patients via a novel efficient measure |
title_full_unstemmed |
Identification of nonlinear features in cortical and subcortical signals of Parkinson's Disease patients via a novel efficient measure |
title_sort |
identification of nonlinear features in cortical and subcortical signals of parkinson's disease patients via a novel efficient measure |
publisher |
Elsevier |
series |
NeuroImage |
issn |
1095-9572 |
publishDate |
2020-12-01 |
description |
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(20–30 Hz) band and cortical alpha (8–12 Hz) band activities. While the former was discernible without medication (p=0.015), the latter was induced upon the dopaminergic medication (p<6.10−4). The degree of subthalamic nonlinearity was correlated with contralateral tremor severity (r=0.45, p=0.02). Conversely, for the cortical signals nonlinearity was present for the ON medication state with a peak in the alpha band and correlated with contralateral akinesia and rigidity (r=0.46, p=0.02). This correlation appeared to be independent from that of alpha power and the two measures combined explained 34 % of the variance in contralateral akinesia scores. Our findings suggest that particular frequency bands and brain regions display nonlinear features closely associated with distinct motor symptoms and functions. |
topic |
Deep brain stimulation Dopamine Levodopa Local field potentials Neural oscillations Nonlinearity |
url |
http://www.sciencedirect.com/science/article/pii/S1053811920308429 |
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