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Wavelet-Based Bracketing, Time–Frequency Beta Burst Detection: New Insights in Parkinson’s Disease

Studies have shown that beta band activity is not tonically elevated but comprises exaggerated phasic bursts of varying durations and magnitudes, for Parkinson’s disease (PD) patients. Current methods for detecting beta bursts target a single frequency peak in beta band, potentially ignoring bursts...

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Autores principales: Sil, Tanmoy, Hanafi, Ibrahem, Eldebakey, Hazem, Palmisano, Chiara, Volkmann, Jens, Muthuraman, Muthuraman, Reich, Martin M., Peach, Robert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10684463/
https://www.ncbi.nlm.nih.gov/pubmed/37819489
http://dx.doi.org/10.1007/s13311-023-01447-4
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author Sil, Tanmoy
Hanafi, Ibrahem
Eldebakey, Hazem
Palmisano, Chiara
Volkmann, Jens
Muthuraman, Muthuraman
Reich, Martin M.
Peach, Robert
author_facet Sil, Tanmoy
Hanafi, Ibrahem
Eldebakey, Hazem
Palmisano, Chiara
Volkmann, Jens
Muthuraman, Muthuraman
Reich, Martin M.
Peach, Robert
author_sort Sil, Tanmoy
collection PubMed
description Studies have shown that beta band activity is not tonically elevated but comprises exaggerated phasic bursts of varying durations and magnitudes, for Parkinson’s disease (PD) patients. Current methods for detecting beta bursts target a single frequency peak in beta band, potentially ignoring bursts in the wider beta band. In this study, we propose a new robust framework for beta burst identification across wide frequency ranges. Chronic local field potential at-rest recordings were obtained from seven PD patients implanted with Medtronic SenSight™ deep brain stimulation (DBS) electrodes. The proposed method uses wavelet decomposition to compute the time–frequency spectrum and identifies bursts spanning multiple frequency bins by thresholding, offering an additional burst measure, ∆f, that captures the width of a burst in the frequency domain. Analysis included calculating burst duration, magnitude, and ∆f and evaluating the distribution and likelihood of bursts between the low beta (13–20 Hz) and high beta (21–35 Hz). Finally, the results of the analysis were correlated to motor impairment (MDS-UPDRS III) med off scores. We found that low beta bursts with longer durations and larger width in the frequency domain (∆f) were positively correlated, while high beta bursts with longer durations and larger ∆f were negatively correlated with motor impairment. The proposed method, finding clear differences between bursting behavior in high and low beta bands, has clearly demonstrated the importance of considering wide frequency bands for beta burst behavior with implications for closed-loop DBS paradigms. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13311-023-01447-4.
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spelling pubmed-106844632023-11-30 Wavelet-Based Bracketing, Time–Frequency Beta Burst Detection: New Insights in Parkinson’s Disease Sil, Tanmoy Hanafi, Ibrahem Eldebakey, Hazem Palmisano, Chiara Volkmann, Jens Muthuraman, Muthuraman Reich, Martin M. Peach, Robert Neurotherapeutics Original Article Studies have shown that beta band activity is not tonically elevated but comprises exaggerated phasic bursts of varying durations and magnitudes, for Parkinson’s disease (PD) patients. Current methods for detecting beta bursts target a single frequency peak in beta band, potentially ignoring bursts in the wider beta band. In this study, we propose a new robust framework for beta burst identification across wide frequency ranges. Chronic local field potential at-rest recordings were obtained from seven PD patients implanted with Medtronic SenSight™ deep brain stimulation (DBS) electrodes. The proposed method uses wavelet decomposition to compute the time–frequency spectrum and identifies bursts spanning multiple frequency bins by thresholding, offering an additional burst measure, ∆f, that captures the width of a burst in the frequency domain. Analysis included calculating burst duration, magnitude, and ∆f and evaluating the distribution and likelihood of bursts between the low beta (13–20 Hz) and high beta (21–35 Hz). Finally, the results of the analysis were correlated to motor impairment (MDS-UPDRS III) med off scores. We found that low beta bursts with longer durations and larger width in the frequency domain (∆f) were positively correlated, while high beta bursts with longer durations and larger ∆f were negatively correlated with motor impairment. The proposed method, finding clear differences between bursting behavior in high and low beta bands, has clearly demonstrated the importance of considering wide frequency bands for beta burst behavior with implications for closed-loop DBS paradigms. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13311-023-01447-4. Springer International Publishing 2023-10-11 2023-10 /pmc/articles/PMC10684463/ /pubmed/37819489 http://dx.doi.org/10.1007/s13311-023-01447-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Article
Sil, Tanmoy
Hanafi, Ibrahem
Eldebakey, Hazem
Palmisano, Chiara
Volkmann, Jens
Muthuraman, Muthuraman
Reich, Martin M.
Peach, Robert
Wavelet-Based Bracketing, Time–Frequency Beta Burst Detection: New Insights in Parkinson’s Disease
title Wavelet-Based Bracketing, Time–Frequency Beta Burst Detection: New Insights in Parkinson’s Disease
title_full Wavelet-Based Bracketing, Time–Frequency Beta Burst Detection: New Insights in Parkinson’s Disease
title_fullStr Wavelet-Based Bracketing, Time–Frequency Beta Burst Detection: New Insights in Parkinson’s Disease
title_full_unstemmed Wavelet-Based Bracketing, Time–Frequency Beta Burst Detection: New Insights in Parkinson’s Disease
title_short Wavelet-Based Bracketing, Time–Frequency Beta Burst Detection: New Insights in Parkinson’s Disease
title_sort wavelet-based bracketing, time–frequency beta burst detection: new insights in parkinson’s disease
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10684463/
https://www.ncbi.nlm.nih.gov/pubmed/37819489
http://dx.doi.org/10.1007/s13311-023-01447-4
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