Phase-coherence classification: A new wavelet-based method to separate local field potentials into local (in)coherent and volume-conducted components.
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| Title: | Phase-coherence classification: A new wavelet-based method to separate local field potentials into local (in)coherent and volume-conducted components. |
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| Authors: | von Papen, M.1,2 m.von.papen@fz-juelich.de, Dafsari, H.3,4, Florin, E.3,5, Gerick, F.2, Timmermann, L.3,6, Saur, J.2 |
| Source: | Journal of Neuroscience Methods. Nov2017, Vol. 291, p198-212. 15p. |
| Subjects: | Neurons, Electrophysiology, Parkinson's disease, Wavelets (Mathematics), Electrodes |
| Abstract: | Background Local field potentials (LFP) reflect the integrated electrophysiological activity of large neuron populations and may thus reflect the dynamics of spatially and functionally different networks. New method We introduce the wavelet-based phase-coherence classification (PCC), which separates LFP into volume-conducted, local incoherent and local coherent components. It allows to compute power spectral densities for each component associated with local or remote electrophysiological activity. Results We use synthetic time series to estimate optimal parameters for the application to LFP from within the subthalamic nucleus of eight Parkinson patients. With PCC we identify multiple local tremor clusters and quantify the relative power of local and volume-conducted components. We analyze the electrophysiological response to an apomorphine injection during rest and hold. Here we show medication-induced significant decrease of incoherent activity in the low beta band and increase of coherent activity in the high beta band. On medication significant movement-induced changes occur in the high beta band of the local coherent signal. It increases during isometric hold tasks and decreases during phasic wrist movement. Comparison with existing methods The power spectra of local PCC components is compared to bipolar recordings. In contrast to bipolar recordings PCC can distinguish local incoherent and coherent signals. We further compare our results with classification based on the imaginary part of coherency and the weighted phase lag index. Conclusions The low and high beta band are more susceptible to medication- and movement-related changes reflected by incoherent and local coherent activity, respectively. PCC components may thus reflect functionally different networks. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Neuroscience Methods is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 125253253 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Phase-coherence classification: A new wavelet-based method to separate local field potentials into local (in)coherent and volume-conducted components. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22von+Papen%2C+M%2E%22">von Papen, M.</searchLink><relatesTo>1,2</relatesTo><i> m.von.papen@fz-juelich.de</i><br /><searchLink fieldCode="AR" term="%22Dafsari%2C+H%2E%22">Dafsari, H.</searchLink><relatesTo>3,4</relatesTo><br /><searchLink fieldCode="AR" term="%22Florin%2C+E%2E%22">Florin, E.</searchLink><relatesTo>3,5</relatesTo><br /><searchLink fieldCode="AR" term="%22Gerick%2C+F%2E%22">Gerick, F.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Timmermann%2C+L%2E%22">Timmermann, L.</searchLink><relatesTo>3,6</relatesTo><br /><searchLink fieldCode="AR" term="%22Saur%2C+J%2E%22">Saur, J.</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Neuroscience+Methods%22">Journal of Neuroscience Methods</searchLink>. Nov2017, Vol. 291, p198-212. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Neurons%22">Neurons</searchLink><br /><searchLink fieldCode="DE" term="%22Electrophysiology%22">Electrophysiology</searchLink><br /><searchLink fieldCode="DE" term="%22Parkinson's+disease%22">Parkinson's disease</searchLink><br /><searchLink fieldCode="DE" term="%22Wavelets+%28Mathematics%29%22">Wavelets (Mathematics)</searchLink><br /><searchLink fieldCode="DE" term="%22Electrodes%22">Electrodes</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Background Local field potentials (LFP) reflect the integrated electrophysiological activity of large neuron populations and may thus reflect the dynamics of spatially and functionally different networks. New method We introduce the wavelet-based phase-coherence classification (PCC), which separates LFP into volume-conducted, local incoherent and local coherent components. It allows to compute power spectral densities for each component associated with local or remote electrophysiological activity. Results We use synthetic time series to estimate optimal parameters for the application to LFP from within the subthalamic nucleus of eight Parkinson patients. With PCC we identify multiple local tremor clusters and quantify the relative power of local and volume-conducted components. We analyze the electrophysiological response to an apomorphine injection during rest and hold. Here we show medication-induced significant decrease of incoherent activity in the low beta band and increase of coherent activity in the high beta band. On medication significant movement-induced changes occur in the high beta band of the local coherent signal. It increases during isometric hold tasks and decreases during phasic wrist movement. Comparison with existing methods The power spectra of local PCC components is compared to bipolar recordings. In contrast to bipolar recordings PCC can distinguish local incoherent and coherent signals. We further compare our results with classification based on the imaginary part of coherency and the weighted phase lag index. Conclusions The low and high beta band are more susceptible to medication- and movement-related changes reflected by incoherent and local coherent activity, respectively. PCC components may thus reflect functionally different networks. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Neuroscience Methods is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.jneumeth.2017.08.021 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 198 Subjects: – SubjectFull: Neurons Type: general – SubjectFull: Electrophysiology Type: general – SubjectFull: Parkinson's disease Type: general – SubjectFull: Wavelets (Mathematics) Type: general – SubjectFull: Electrodes Type: general Titles: – TitleFull: Phase-coherence classification: A new wavelet-based method to separate local field potentials into local (in)coherent and volume-conducted components. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: von Papen, M. – PersonEntity: Name: NameFull: Dafsari, H. – PersonEntity: Name: NameFull: Florin, E. – PersonEntity: Name: NameFull: Gerick, F. – PersonEntity: Name: NameFull: Timmermann, L. – PersonEntity: Name: NameFull: Saur, J. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2017 Type: published Y: 2017 Identifiers: – Type: issn-print Value: 01650270 Numbering: – Type: volume Value: 291 Titles: – TitleFull: Journal of Neuroscience Methods Type: main |
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