Detecting phase-amplitude coupling with high frequency resolution using adaptive decompositions

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题名:	Detecting phase-amplitude coupling with high frequency resolution using adaptive decompositions
作者:	羅孟宗;Pittman-Polletta, Benjamin;Hsieh, Wan-Hsin;Kaur, Satvinder;Lo, Men-Tzung;Hu, Kun
贡献者:	生醫理工學院生醫科學與工程學系
关键词:	Animals;Brain - physiology;Computer Simulation;Electroencephalography - methods;Empirical mode decomposition;Fourier Analysis;Hilbert Huang transform;Mice;Models, Neurological;Multiscale interactions;Neurophysiological signal processing;Nonstationarity;Phase-amplitude coupling;Polysomnography - methods;Signal Processing, Computer-Assisted;Sleep, REM - physiology;Time Factors
日期:	2014-04-15
上传时间:	2026-04-23 11:15:40 (UTC+8)
出版者:	Netherlands: Elsevier B.V
摘要:	摘要： •Narrowband filtering can lead to poor frequency resolution, incorrect frequency assignment, and false negatives in PAC assessment.•Accurate PAC assessment can be obtained with adaptive and broadband decompositions, but these decompositions give little frequency information.•Coupling adaptive, broadband decompositions with time-local frequency assessment allows for PAC assessment that is both accurate and highly frequency resolved. Phase-amplitude coupling (PAC) – the dependence of the amplitude of one rhythm on the phase of another, lower-frequency rhythm – has recently been used to illuminate cross-frequency coordination in neurophysiological activity. An essential step in measuring PAC is decomposing data to obtain rhythmic components of interest. Current methods of PAC assessment employ narrowband Fourier-based filters, which assume that biological rhythms are stationary, harmonic oscillations. However, biological signals frequently contain irregular and nonstationary features, which may contaminate rhythms of interest and complicate comodulogram interpretation, especially when frequency resolution is limited by short data segments. To better account for nonstationarities while maintaining sharp frequency resolution in PAC measurement, even for short data segments, we introduce a new method of PAC assessment which utilizes adaptive and more generally broadband decomposition techniques – such as the empirical mode decomposition (EMD). To obtain high frequency resolution PAC measurements, our method distributes the PAC associated with pairs of broadband oscillations over frequency space according to the time-local frequencies of these oscillations. We compare our novel adaptive approach to a narrowband comodulogram approach on a variety of simulated signals of short duration, studying systematically how different types of nonstationarities affect these methods, as well as on EEG data. Our results show: (1) narrowband filtering can lead to poor PAC frequency resolution, and inaccuracy and false negatives in PAC assessment; (2) our adaptive approach attains better PAC frequency resolution and is more resistant to nonstationarities and artifacts than traditional comodulograms. 其他題名： J Neurosci Methods 出版者： Netherlands: Elsevier B.V 出版日期： 2014-04-15 出處： Journal of neuroscience methods, 2014-04, Vol.226, p.15-32 版權： 2014 Elsevier B.V. 版權： Copyright © 2014 Elsevier B.V. All rights reserved. 版權： 2014 Elsevier B.V. All rights reserved. 2014 識別號： ISSN: 0165-0270 識別號： ISSN: 1872-678X 識別號： EISSN: 1872-678X 識別號： DOI: 10.1016/j.jneumeth.2014.01.006 識別號： PMID: 24452055
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