Measuring transient phase-amplitude coupling using local mutual information

Martínez-Cancino, Ramón; Heng, Joseph; Delorme, Arnaud; Kreutz-Delgado, Kenneth; Sotero, Roberto C.; Makeig, Scott

doi:10.1016/j.neuroimage.2018.10.034

Cited by 48 publications

(53 citation statements)

References 68 publications

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“…Some of the most widely used phase-amplitude coupling measures today are the PLV (Mormann et al, 2005), also called synchronization index (SI) by Cohen (2008), the MVL (Canolty et al, 2006), the MI (Tort et al, 2008), the envelope-to-signal correlation (ESC) (Bruns and Eckhorn, 2004), the generalized linear modeling (GLM) method (Penny et al, 2008; Kramer and Eden, 2013), phase binning combined with analysis of variance (ANOVA) (BA) (Lakatos et al, 2005), and the weighted phase locking factor (wPLF) (Maris et al, 2011). Recent approaches (Sotero, 2016; Martínez-Cancino et al, 2019) use mutual information in order to compute phase-amplitude coupling. The computation of mutual information is sensitive to the amount of data and noise, but advantageous when handling non-linear relationships (Cohen, 2014).…”

Section: Introductionmentioning

confidence: 99%

Quantification of Phase-Amplitude Coupling in Neuronal Oscillations: Comparison of Phase-Locking Value, Mean Vector Length, Modulation Index, and Generalized-Linear-Modeling-Cross-Frequency-Coupling

2019

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Phase-amplitude coupling is a promising construct to study cognitive processes in electroencephalography (EEG) and magnetencephalography (MEG). Due to the novelty of the concept, various measures are used in the literature to calculate phase-amplitude coupling. Here, performance of the three most widely used phase-amplitude coupling measures – phase-locking value (PLV), mean vector length (MVL), and modulation index (MI) – and of the generalized linear modeling cross-frequency coupling (GLM-CFC) method is thoroughly compared with the help of simulated data. We combine advantages of previous reviews and use a realistic data simulation, examine moderators and provide inferential statistics for the comparison of all four indices of phase-amplitude coupling. Our analyses show that all four indices successfully differentiate coupling strength and coupling width when monophasic coupling is present. While the MVL was most sensitive to modulations in coupling strengths and width, only the MI and GLM-CFC can detect biphasic coupling. Coupling values of all four indices were influenced by moderators including data length, signal-to-noise-ratio, and sampling rate when approaching Nyquist frequencies. The MI was most robust against confounding influences of these moderators. Based on our analyses, we recommend the MI for noisy and short data epochs with unknown forms of coupling. For high quality and long data epochs with monophasic coupling and a high signal-to-noise ratio, the use of the MVL is recommended. Ideally, both indices are reported simultaneously for one data set.

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Section: Introductionmentioning

confidence: 99%

Quantification of Phase-Amplitude Coupling in Neuronal Oscillations: Comparison of Phase-Locking Value, Mean Vector Length, Modulation Index, and Generalized-Linear-Modeling-Cross-Frequency-Coupling

2019

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“…This idea was inspired by the modified version of mutual information: local mutual information ( 62 ). For this study, four tasks were analyzed (VP, VI, ME, and MI), but all tasks were used as the index for calculating the expSampEn.…”

Section: Methodsmentioning

confidence: 99%

Neural Decoding of Multi-Modal Imagery Behavior Focusing on Temporal Complexity

et al. 2020

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Mental imagery behaviors of various modalities include visual, auditory, and motor behaviors. Their alterations are pathologically involved in various psychiatric disorders. Results of earlier studies suggest that imagery behaviors are correlated with the modulated activities of the respective modality-specific regions and the additional activities of supramodal imagery-related regions. Additionally, despite the availability of complexity analysis in the neuroimaging field, it has not been used for neural decoding approaches. Therefore, we sought to characterize neural oscillation related to multimodal imagery through complexity-based neural decoding. For this study, we modified existing complexity measures to characterize the time evolution of temporal complexity. We took magnetoencephalography (MEG) data of eight healthy subjects as they performed multimodal imagery and non-imagery tasks. The MEG data were decomposed into amplitude and phase of sub-band frequencies by Hilbert-Huang transform. Subsequently, we calculated the complexity values of each reconstructed time series, along with raw data and band power for comparison, and applied these results as inputs to decode visual perception (VP), visual imagery (VI), motor execution (ME), and motor imagery (MI) functions. Consequently, intra-subject decoding with the complexity yielded a characteristic sensitivity map for each task with high decoding accuracy. The map is inverted in the occipital regions between VP and VI and in the central regions between ME and MI. Additionally, replacement of the labels into two classes as imagery and nonimagery also yielded better classification performance and characteristic sensitivity with the complexity. It is particularly interesting that some subjects showed characteristic sensitivities not only in modality-specific regions, but also in supramodal regions. These analyses indicate that two-class and four-class classifications each provided better performance when using complexity than when using raw data or band power as input. When inter-subject decoding was used with the same model, characteristic sensitivity maps were also obtained, although their decoding performance was lower. Results of this study underscore the availability of complexity measures in neural decoding

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“…Whilst Chapter 2 focussed on four of the most common PAC algorithms, there has been recent progress in the development of new PAC metrics (e.g. Cheng, Li, Wang, Wang, & Zhang, 2018;La Tour et al, 2017;Martínez-Cancino et al, 2019;Nadalin et al, 2019).…”

Section: A Robust Pipeline For Phase Amplitude Coupling (Pac)mentioning

confidence: 99%

Atypical cortical connectivity in autism spectrum disorder (ASD) as measured by magnetoencephalography (MEG)

Seymour¹,

Sowman²,

Kessler³

2019

Preprint

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Autism Spectrum Disorder (ASD) is a neurodevelopmental condition, characterised by impairments in social interaction and communication, the presence of repetitive behaviours, and multisensory hyper- and hypo-sensitives. This thesis utilised magnetoencephalography, in combination with robust analysis techniques, to investigate the neural basis of ASD. Based on previous research, it was hypothesised that cortical activity in ASD would be associated with disruptions to oscillatory synchronisation during sensory processing, as well as during high-level perspective-taking. More specifically, a novel framework was introduced, based on local gamma-band dysregulation, global hypoconnectivity and deficient predictive-coding. To test this framework, data were collected from adolescents diagnosed with ASD and age-matched controls.Using a visual grating stimulus, it was found that in primary visual cortex, ASD participants had reduced coupling between the phase of alpha oscillations and the amplitude of gamma oscillations (i.e. phase amplitude coupling), suggesting dysregulated visual gamma in ASD. These findings were based on a robust analysis pipeline outlined in Chapter 2. Next, directed connectivity in the visual system was quantified using Granger causality. Compared with controls, ASD participants showed reductions in feedback connectivity, mediated by alpha oscillations, but no differences in inter-regional feedforward connectivity, mediated by gamma oscillations. In the auditory domain, it was found that ASD participants had reduced steady-state responses at 40Hz, in terms of oscillatory power and inter-trial coherence, again suggesting dysregulated gamma. Investigating predictive-coding theories of ASD using an auditory oddball paradigm, it was found that evoked responses to the omission of an expected tone were reduced for ASD participants. Finally, we found reductions in theta-band oscillatory power and connectivity for ASD participants, during embodied perspective-taking. Overall, these findings fit the proposed framework, and demonstrate that cortical activity in ASD is characterised by disruptions to oscillatory synchronisation, at the local and global scales, during both sensory processing and higher-level perspective-taking.Keywords: Autism Spectrum Disorder; Magnetoencephalography; Oscillations; Phase Amplitude Coupling; Connectivity.

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Measuring transient phase-amplitude coupling using local mutual information

Cited by 48 publications

References 68 publications

Quantification of Phase-Amplitude Coupling in Neuronal Oscillations: Comparison of Phase-Locking Value, Mean Vector Length, Modulation Index, and Generalized-Linear-Modeling-Cross-Frequency-Coupling

Quantification of Phase-Amplitude Coupling in Neuronal Oscillations: Comparison of Phase-Locking Value, Mean Vector Length, Modulation Index, and Generalized-Linear-Modeling-Cross-Frequency-Coupling

Neural Decoding of Multi-Modal Imagery Behavior Focusing on Temporal Complexity

Atypical cortical connectivity in autism spectrum disorder (ASD) as measured by magnetoencephalography (MEG)

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