Resting State Dynamics in People with Varying Degrees of Anxiety and Mindfulness: A Nonlinear and Nonstationary Perspective.

Anxiety and mindfulness are two inversely linked traits shown to be involved in various physiological domains. The current study used resting state electroencephalography (EEG) to explore differences between people with low mindfulness-high anxiety (LMHA) (n = 29) and high mindfulness-low anxiety (HMLA) (n = 27). The resting EEG was collected for a total of 6 min, with a randomized sequence of eyes closed and eyes opened conditions. Two advanced EEG analysis methods, Holo-Hilbert Spectral Analysis and Holo-Hilbert cross-frequency phase clustering (HHCFPC) were employed to estimate the power-based amplitude modulation of carrier frequencies, and cross-frequency coupling between low and high frequencies, respectively. The presence of higher oscillation power across the delta and theta frequencies in the LMHA group than the HMLA group might have been due to the similarity between the resting state and situations of uncertainty, which reportedly triggers motivational and emotional arousal. Although these two groups were formed based on their trait anxiety and trait mindfulness scores, it was anxiety that was found to be significant predictor of the EEG power, not mindfulness. It led us to conclude that it might be anxiety, not mindfulness, which might have contributed to higher electrophysiological arousal. Additionally, a higher δ-ß and δ-γ CFC in LMHA suggested greater local-global neural integration, consequently a greater functional association between cortex and limbic system than in the HMLA group. The present cross-sectional study may guide future longitudinal studies on anxiety aiming with interventions such as mindfulness to characterize the individuals based on their resting state physiology.

Prevalence, characteristics, mapping, and catheter ablation of potential rotors in nonparoxysmal atrial fibrillation.

BACKGROUND: Identification of critical atrial substrates in patients with nonparoxysmal atrial fibrillation (AF) failing to respond to pulmonary vein isolation is important. This study investigated the signal characteristics, substrate nature, and ablation results of rotors during AF. METHODS AND RESULTS: In total, 53 patients (age=55±8), 31 with persistent AF and 22 with long-lasting AF, underwent pulmonary vein isolation and substrate modification of complex fractionated atrial electrograms. Small-radius-reentrant rotors were identified from signal analyses of the dominant frequency and fractionation interval and nonlinear analyses (newly developed, beat-to-beat nonlinear measurement of the repetitiveness of the electrogram morphology>6 seconds). In 15% of the patients, activation maps demonstrated occurrences of rotor-like small-radius reentrant circuits (n=9; 1.1 per patient; cycle length=110±21 ms; diameter=11±6 mm) with fibrillation occurring outside these areas. Rotors were identified by conventional point-by-point mapping and signal analyses and were subsequently eradicated by catheter ablation in these patients. Persistent AF for <1 year, a smaller left atrial size, substrates with higher mean voltages and shorter total activation durations predicted a higher incidence of rotors (all P<0.05). In the multivariable model, areas of reentrant circuits exhibited a higher dominant frequency, kurtosis, and higher degree of a beat-to-beat electrogram similarity than areas without or outside the rotors (all P<0.05). CONCLUSIONS: Rotor-like re-entry with fibrillatory conduction was found in a limited number of patients with nonparoxysmal AF after pulmonary vein isolation. Those areas were characterized by rapid repetitive activity with a high degree of electrogram similarity.

Nonlinear analysis of fibrillatory electrogram similarity to optimize the detection of complex fractionated electrograms during persistent atrial fibrillation.

INTRODUCTION: Currently, the identification of complex fractionated atrial electrograms (CFEs) in the substrate modification is mostly based on cycle length-derived algorithms. The characteristics of the fibrillation electrogram morphology and their consistency over time are not clear. The aim of this study was to optimize the detection algorithm of crucial CFEs by using nonlinear measure electrogram similarity. METHODS AND RESULTS: One hundred persistent atrial fibrillation patients that underwent catheter ablation were included. In patients who required CFE ablation (79%), the time-domain fibrillation signals (6 seconds) were acquired for a linear analysis (mean fractionation interval and dominant frequency [DF]) and nonlinear-based waveform similarity analysis of the local electrograms, termed the similarity index (SI). Continuous CFEs were targeted with an endpoint of termination. Predictors of the various signal characteristics on the termination and clinical outcome were investigated. Procedural termination was observed in 39% and long-term sinus rhythm maintenance in 67% of the patients. The targeted CFEs didn't differ based on the linear analysis modalities between the patients who responded and did not respond to CFE ablation. In contrast, the average SI of the targeted CFEs was higher in termination patients, and they had a better outcome. Multivariate regression analysis showed that a higher SI independently predicted sites of termination (≥ 0.57; OR = 4.9; 95% CI = 1.33-18.0; P = 0.017). CONCLUSIONS: In persistent AF patients, a cycle length-based linear analysis could not differentiate culprit CFEs from bystanders. This study suggested that sites with a high level of fibrillation electrogram similarity at the CFE sites were important for AF maintenance.

Novel assessment of temporal variation in fractionated electrograms using histogram analysis of local fractionation interval in patients with persistent atrial fibrillation.

BACKGROUND: The characteristics of atrial electrograms associated with atrial fibrillation (AF) termination are controversial. We investigated the electrogram characteristics that indicate procedural AF termination during continuous complex fractionated electrogram ablation. METHODS AND RESULTS: Fifty-two consecutive patients with persistent AF (47 men; aged 54 ± 9 years), who underwent electrogram-based catheter ablation in the left atrium and coronary sinus after pulmonary vein isolation, were enrolled. The intracardiac bipolar atrial electrogram recordings were characterized by (1) fractionation interval (FI) analysis (>6 seconds), (2) kurtosis (shape of the FI histogram), and (3) skewness (asymmetry of the FI histogram). Sites showing complex, fractionated electrograms (mean FI ≤ 60 ms) were targeted, and AF was terminated in 20 patients (38%) after the pulmonary vein isolation. The conventional complex fractionated electrogram sites (mean ≤ 120 ms) in patients with AF termination exhibited higher median kurtosis (2.69 [interquartile range, 2.03-3.46] versus 2.35 [interquartile range, 1.79-2.48]; P=0.024) and higher complex fractionated electrogram-mean interval (102.7 ± 19.8 versus 87.7 ± 15.0; P=0.008) than patients without AF termination. Furthermore, AF termination sites had higher median kurtosis than targeted sites without AF termination (5.13 [interquartile range, 3.51-6.47] versus 4.18 [interquartile range, 2.91-5.34]; P<0.01) in patients with procedural termination. In addition, patients with AF termination had a higher sinus rhythm maintenance rate after a single procedure than patients without AF termination (log-rank test, P=0.007). CONCLUSIONS: A kurtosis analysis using the FI histogram may be a useful tool in identifying the critical substrate for persistent AF and potential responders to catheter ablation.