scholarly journals A statistical framework to assess cross-frequency coupling while accounting for confounding analysis effects

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Jessica K Nadalin ◽  
Louis-Emmanuel Martinet ◽  
Ethan B Blackwood ◽  
Meng-Chen Lo ◽  
Alik S Widge ◽  
...  

Cross frequency coupling (CFC) is emerging as a fundamental feature of brain activity, correlated with brain function and dysfunction. Many different types of CFC have been identified through application of numerous data analysis methods, each developed to characterize a specific CFC type. Choosing an inappropriate method weakens statistical power and introduces opportunities for confounding effects. To address this, we propose a statistical modeling framework to estimate high frequency amplitude as a function of both the low frequency amplitude and low frequency phase; the result is a measure of phase-amplitude coupling that accounts for changes in the low frequency amplitude. We show in simulations that the proposed method successfully detects CFC between the low frequency phase or amplitude and the high frequency amplitude, and outperforms an existing method in biologically-motivated examples. Applying the method to in vivo data, we illustrate examples of CFC during a seizure and in response to electrical stimuli.

2019 ◽  
Author(s):  
Jessica Nadalin ◽  
Louis-Emmanuel Martinet ◽  
Ethan Blackwood ◽  
Meng-Chen Lo ◽  
Alik S. Widge ◽  
...  

AbstractCross frequency coupling (CFC) is emerging as a fundamental feature of brain activity, correlated with brain function and dysfunction. Many different types of CFC have been identified through application of numerous data analysis methods, each developed to characterize a specific CFC type. Choosing an inappropriate method weakens statistical power and introduces opportunities for confounding effects. To address this, we propose a statistical modeling framework to estimate high frequency amplitude as a function of both the low frequency amplitude and low frequency phase; the result is a measure of phase-amplitude coupling that accounts for changes in the low frequency amplitude. We show in simulations that the proposed method successfully detects CFC between the low frequency phase or amplitude and the high frequency amplitude, and outperforms an existing method in biologically-motivated examples. Applying the method to in vivo data, we illustrate how CFC evolves during seizures and is affected by electrical stimuli.


2018 ◽  
Author(s):  
Juan L.P. Soto ◽  
Felipe V.D. Prado ◽  
Etienne Combrisson ◽  
Karim Jerbi

AbstractMany functional connectivity studies based on electrophysiological measurements, such as electro- and magnetoencephalography (EEG/MEG), start their investigations by extracting a narrowband representation of brain activity time series, and then computing their envelope amplitudes and instantaneous phases, which serve as inputs to subsequent data processing. The two most popular approaches for obtaining these narrowband amplitudes and phases are: bandpass filtering followed by Hilbert transform (we call this the Hilbert approach); and convolution with wavelet kernels (the wavelet approach). In this work, we investigate how these two approaches perform in detecting the phenomenon of phase-amplitude coupling (PAC), whereby the amplitude of a high-frequency signal is driven by the phase of a low-frequency signal. The comparison of both approaches is carried out by means of simulated brain activity, from which we run receiver operating characteristic (ROC) analyses, and of experimental MEG data from a visuomotor coordination study. The ROC analyses show that both approaches have comparable accuracy, except in the presence of interfering signals with frequencies near the high-frequency band. As for the visuomotor data, the most noticeable impact of the choice of approach was observed when evaluating task-based changes in PAC between the delta (2-5 Hz) and the high-gamma (60-90 Hz) frequency bands, as we were able to identify widespread brain areas with statistically significant effects only with the Hilbert approach. These results provide preliminary evidence of the advantages of the Hilbert approach over the wavelet approach, at least in the context of PAC estimates.


Author(s):  
Hiroaki Hashimoto ◽  
Hui Ming Khoo ◽  
Takufumi Yanagisawa ◽  
Naoki Tani ◽  
Satoru Oshino ◽  
...  

AbstractObjectiveHigh-frequency activities (HFAs) and phase-amplitude coupling (PAC) are gaining attention as key neurophysiological biomarkers for studying human epilepsy. We aimed to clarify and visualize how HFAs are modulated by the phase of low-frequency bands during seizures.MethodsWe used intracranial electrodes to record seizures of symptomatic focal epilepsy (15 seizures in seven patients). Ripples (80–250 Hz), as representative of HFAs, were evaluated along with PAC. The synchronization index (SI), representing PAC, was used to analyze the coupling between the amplitude of ripples and the phase of lower frequencies. We created a video in which the intracranial electrode contacts were represented by circles that were scaled linearly to the power changes of ripple.ResultsThe main low frequency band modulating ictal-ripple activities was the θ band (4–8 Hz), and after completion of ictal-ripple burst, δ (1–4 Hz)-ripple PAC occurred. The video showed that fluctuation of the diameter of these circles indicated the rhythmic changes during significant high values of θ-ripple PAC.ConclusionsWe inferred that ripple activities occurring during seizure evolution were modulated by θ rhythm. In addition, we concluded that rhythmic circles’ fluctuation presented in the video represents the PAC phenomenon. Our video is thus a useful tool for understanding how ripple activity is modulated by the low-frequency phase in relation with PAC.


Author(s):  
Coen S. Zandvoort ◽  
Guido Nolte

AbstractTwo measures of cross-frequency coupling (CFC) are Phase-Amplitude Coupling (PAC) and bicoherence. The estimation of PAC with meaningful bandwidth for the high frequency amplitude is crucial in order to avoid misinterpretations. While recommendations on the bandwidth of PAC’s amplitude component exist, there is no consensus yet. Here, we show that the earlier recommendations on filter settings lead to estimates which are smeared in the frequency domain, which makes it difficult to distinguish higher harmonics from other types of CFC. We also show that smearing can be avoided with a different choice of filter settings by theoretically relating PAC to bicoherence. To illustrate this, PAC estimates of simulations and empirical data are compared to bispectral analyses. We used simulations replicated from an earlier study and empirical data from human electro-encephalography and rat local field potentials. PAC’s amplitude component was estimated using a bandwidth with a ratio of (1) 2:1, (2) 1:1, or (3) 0.5:1 relative to the frequency of the phase component. For both simulated and empirical data, PAC was smeared over a broad frequency range and not present when the estimates comprised a 2:1- and 0.5:1-ratio, respectively. In contrast, the 1:1-ratio accurately avoids smearing and results in clear signals of CFC. Bicoherence estimates, which do not smear across frequencies by construction, were found to be essentially identical to PAC calculated with the recommended frequency setting.


2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Kyle Q. Lepage ◽  
Sujith Vijayan

Stochastic processes that exhibit cross-frequency coupling (CFC) are introduced. The ability of these processes to model observed CFC in neural recordings is investigated by comparison with published spectra. One of the proposed models, based on multiplying a pulsatile function of a low-frequency oscillation (θ) with an unobserved and high-frequency component, yields a process with a spectrum that is consistent with observation. Other models, such as those employing a biphasic pulsatile function of a low-frequency oscillation, are demonstrated to be less suitable. We introduce the full stochastic process time series model as a summation of three component weak-sense stationary (WSS) processes, namely,θ,γ, andη, withηa1/fαnoise process. Theγprocess is constructed as a product of a latent and unobserved high-frequency processxwith a function of the lagged, low-frequency oscillatory component (θ). After demonstrating that the model process is WSS, an appropriate method of simulation is introduced based upon the WSS property. This work may be of interest to researchers seeking to connect inhibitory and excitatory dynamics directly to observation in a model that accounts for known temporal dependence or to researchers seeking to examine what can occur in a multiplicative time-domain CFC mechanism.


2018 ◽  
Author(s):  
Christian D. Márton ◽  
Makoto Fukushima ◽  
Corrie R. Camalier ◽  
Simon R. Schultz ◽  
Bruno B. Averbeck

AbstractPredictive coding is a theoretical framework that provides a functional interpretation of top-down and bottom up interactions in sensory processing. The theory has suggested that specific frequency bands relay bottom-up and top-down information (e.g. “γ up, β down”). But it remains unclear whether this notion generalizes to cross-frequency interactions. Furthermore, most of the evidence so far comes from visual pathways. Here we examined cross-frequency coupling across four sectors of the auditory hierarchy in the macaque. We computed two measures of cross-frequency coupling, phase-amplitude coupling (PAC) and amplitude-amplitude coupling (AAC). Our findings revealed distinct patterns for bottom-up and top-down information processing among cross-frequency interactions. Both top-down and bottom-up made prominent use of low frequencies: low-to-low frequency (θ, α, β) and low frequency-to-high γ couplings were predominant top-down, while low frequency-to-low γ couplings were predominant bottom-up. These patterns were largely preserved across coupling types (PAC and AAC) and across stimulus types (natural and synthetic auditory stimuli), suggesting they are a general feature of information processing in auditory cortex. Moreover, our findings showed that low-frequency PAC alternated between predominantly top-down or bottom-up over time. Altogether, this suggests sensory information need not be propagated along separate frequencies upwards and downwards. Rather, information can be unmixed by having low frequencies couple to distinct frequency ranges in the target region, and by alternating top-down and bottom-up processing over time.1SignificanceThe brain consists of highly interconnected cortical areas, yet the patterns in directional cortical communication are not fully understood, in particular with regards to interactions between different signal components across frequencies. We employed a a unified, computationally advantageous Granger-causal framework to examine bi-directional cross-frequency interactions across four sectors of the auditory cortical hierarchy in macaques. Our findings extend the view of cross-frequency interactions in auditory cortex, suggesting they also play a prominent role in top-down processing. Our findings also suggest information need not be propagated along separate channels up and down the cortical hierarchy, with important implications for theories of information processing in the brain such as predictive coding.


2020 ◽  
Vol 9 (23) ◽  
Author(s):  
Woohyeun Kim ◽  
Jin Oh Na ◽  
Robert J. Thomas ◽  
Won Young Jang ◽  
Dong Oh Kang ◽  
...  

Background Sleep fragmentation and sleep apnea are common in patients with atrial fibrillation (AF). We investigated the impact of radio‐frequency catheter ablation (RFCA) on sleep quality in patients with paroxysmal AF and the effect of a change in sleep quality on recurrence of AF. Methods and Results Of 445 patients who underwent RFCA for paroxysmal AF between October 2007 and January 2017, we analyzed 225 patients who had a 24‐hour Holter test within 6 months before RFCA. Sleep quality was assessed by cardiopulmonary coupling analysis using 24‐hour Holter data. We compared cardiopulmonary coupling parameters (high‐frequency coupling, low‐frequency coupling, very‐low‐frequency coupling) before and after RFCA. Six months after RFCA, the high‐frequency coupling (marker of stable sleep) and very‐low‐frequency coupling (rapid eye movement/wake marker) was significantly increased (29.84%–36.15%; P <0.001; and 26.20%–28.76%; P =0.002, respectively) while low‐frequency coupling (unstable sleep marker) was decreased (41.25%–32.13%; P <0.001). We divided patients into 3 tertiles according to sleep quality before RFCA, and the risk of AF recurrence in each group was compared. The second tertile was used as a reference; patients with unstable sleep (Tertile 3) had a significantly lower risk of AF recurrence (hazard ratio [HR], 0.32; 95% CI, 0.12–0.83 for high‐frequency coupling; and HR, 0.22; 95% CI, 0.09–0.58 for low‐frequency coupling). Conclusions Sleep quality improved after RFCA in patients with paroxysmal AF. The recurrence rate was significantly lower in patients who had unstable sleep before RFCA. These results suggest that RFCA can influence sleep quality, and sleep quality assessment before RFCA may provide a risk marker for recurrence after RFCA in patients with paroxysmal AF.


2012 ◽  
Vol 59 (1) ◽  
pp. 8-11 ◽  
Author(s):  
R. T. Canolty ◽  
C. F. Cadieu ◽  
K. Koepsell ◽  
R. T. Knight ◽  
J. M. Carmena

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