Genome-wide circadian rhythm detection methods: systematic evaluations and practical guidelines.

Circadian rhythms are oscillations of behavior, physiology and metabolism in many organisms. Recent advancements in omics technology make it possible for genome-wide profiling of circadian rhythms. Here, we conducted a comprehensive analysis of seven existing algorithms commonly used for circadian rhythm detection. Using gold-standard circadian and non-circadian genes, we systematically evaluated the accuracy and reproducibility of the algorithms on empirical datasets generated from various omics platforms under different experimental designs. We also carried out extensive simulation studies to test each algorithm's robustness to key variables, including sampling patterns, replicates, waveforms, signal-to-noise ratios, uneven samplings and missing values. Furthermore, we examined the distributions of the nominal $P$-values under the null and raised issues with multiple testing corrections using traditional approaches. With our assessment, we provide method selection guidelines for circadian rhythm detection, which are applicable to different types of high-throughput omics data.

Single-trial characterization of neural rhythms: Potential and challenges.

The average power of rhythmic neural responses as captured by MEG/EEG/LFP recordings is a prevalent index of human brain function. Increasing evidence questions the utility of trial-/group averaged power estimates however, as seemingly sustained activity patterns may be brought about by time-varying transient signals in each single trial. Hence, it is crucial to accurately describe the duration and power of rhythmic and arrhythmic neural responses on the single trial-level. However, it is less clear how well this can be achieved in empirical MEG/EEG/LFP recordings. Here, we extend an existing rhythm detection algorithm (extended Better OSCillation detection: "eBOSC"; cf. Whitten et al., 2011) to systematically investigate boundary conditions for estimating neural rhythms at the single-trial level. Using simulations as well as resting and task-based EEG recordings from a micro-longitudinal assessment, we show that alpha rhythms can be successfully captured in single trials with high specificity, but that the quality of single-trial estimates varies greatly between subjects. Despite those signal-to-noise-based limitations, we highlight the utility and potential of rhythm detection with multiple proof-of-concept examples, and discuss implications for single-trial analyses of neural rhythms in electrophysiological recordings. Using an applied example of working memory retention, rhythm detection indicated load-related increases in the duration of frontal theta and posterior alpha rhythms, in addition to a frequency decrease of frontal theta rhythms that was observed exclusively through amplification of rhythmic amplitudes.

Palpitations in puerperium-a self-recorded smart watch ECG gives the hint to hormone-induced ventricular arrhythmia: case report.

Background: Gender-related aspects in cardiac arrhythmias have gained increasing attention, still the understanding of peripartum electrical disorders remains vague. Case summary: A 28-year-old woman developed palpitations and presyncopes during the post-partum period after her second pregnancy. Palpitations remained unclear until a self-recorded single-lead smartwatch ECG revealed a complete episode of a fast broad complex tachycardia (260 b.p.m.) that led to hospital admission. Echocardiography, cardiac magnetic resonance imaging, and exercise testing, showed no relevant abnormalities. Recording the tachycardia in a 12-lead-ECG could eventually be achieved revealing an inferior axis and positive concordance in the precordial leads. Episodes of ventricular tachycardia (VT) could be provoked by breast feeding and mental stress, but not induced in two electrophysiological studies. Genetic testing was normal. The patient continued to experience repeated, self-terminating VT episodes, lasting between 10 and 40 s, leading to presyncopes and a syncope with a fall. The beginning of symptoms subsequent to child birth and frequent premature ventricular contractions in her first pregnancy made hormone-induced arrhythmia a tentative diagnosis. Heart rate-corrected QT (QTc) intervals showed significant variability corresponding to the frequency of episodes in a retrospective evaluation. The cessation of breastfeeding led to a termination of arrhythmias. The patient was temporarily equipped with a wearable cardioverter defibrillator vest, an implantable cardioverter defibrillator (ICD) was not implanted. Discussion: The case report highlights the potential of self-recorded, patient-activated ECG monitoring in diagnosing recurrent palpitations, and the dilemma of timing for implanting ICDs in young patients with ventricular arrythmias (VTs). Additionally, it underlines the role of post-partum hormones in the susceptibility to ventricular arrhythmias, calling for further research of gender-specific, and pregnancy-associated arrhythmias.

Electrophysiological indicators of sleep-associated memory consolidation in 5- to 6-year-old children.

In adults, the synchronized interplay of sleep spindles (SP) and slow oscillations (SO) supports memory consolidation. Given tremendous developmental changes in SP and SO morphology, it remains elusive whether across childhood the same mechanisms as identified in adults are functional. Based on topography and frequency, we characterize slow and fast SPs and their temporal coupling to SOs in 24 pre-school children. Further, we ask whether slow and fast SPs and their modulation during SOs are associated with behavioral indicators of declarative memory consolidation as suggested by the literature on adults. Employing an individually tailored approach, we reliably identify an inherent, development-specific fast centro-parietal SP type, nested in the adult-like slow SP frequency range, along with a dominant slow frontal SP type. Further, we provide evidence that the modulation of fast centro-parietal SPs during SOs is already present in pre-school children. However, the temporal coordination between fast centro-parietal SPs and SOs is weaker and less precise than expected from research on adults. While we do not find evidence for a critical contribution of SP-SO coupling for memory consolidation, crucially, slow frontal and fast centro-parietal SPs are each differentially related to sleep-associated consolidation of items of varying quality. Whereas a higher number of slow frontal SPs is associated with stronger maintenance of medium-quality memories, a higher number of fast centro-parietal SPs is linked to a greater gain of low-quality items. Our results demonstrate two functionally relevant inherent SP types in pre-school children although SP-SO coupling is not yet fully mature.

General population screening for atrial fibrillation with an automated rhythm-detection blood pressure device.

BACKGROUND: Screening strategies to diagnose previously undetected atrial fibrillation (AF), especially silent AF (SAF), in at-risk populations may help reduce the number of strokes. We prospectively assessed the incidence rate of AF, including SAF, using an automated AF-detection capable sphygmomanometer in the General Practitioner (GP) setting. METHODS: This was a population-based prospective study of unselected general population of ≥65 years without prior AF. Participating GPs were requested, in the period February 2018-April 2019, to record all AF diagnoses including those derived from the AF-detection capable sphygmomanometer and confirmed by 12­lead ECG or ECG Holter in asymptomatic patients. RESULTS: Overall, 14,987 patients assisted by 76 GPs accumulated 16,838 patient-years of follow up. The incidence rate of AF was 2.25% patient-years (95%CI 2.03-2.48). AF was more frequently detected in male, older, overweight, and patients with prior stroke, congestive heart failure, and chronic kidney disease. One in four patients had device-detected SAF (0.56% patient-years, 95%CI 0.46-0.69). Age, overweight, and the number of annual visits, were independent predictors of both SAF and AF. In addition, congestive heart failure, mitral valve disease were independent predictors of AF. Due to the interaction between blood pressure and age the risk of AF increased exponentially after 75 years of age in patients with higher systolic blood pressure values. CONCLUSION: We found a higher than previously reported incidence rate of AF possibly by capturing SAF. Our simple protocol might be feasible in large-scale screening for AF and SAF in routine GP care.

TimeTrial: An Interactive Application for Optimizing the Design and Analysis of Transcriptomic Time-Series Data in Circadian Biology Research.

The circadian rhythm drives the oscillatory expression of thousands of genes across all tissues, coordinating physiological processes. The effect of this rhythm on health has generated increasing interest in discovering genes under circadian control by searching for periodic patterns in transcriptomic time-series experiments. While algorithms for detecting cycling transcripts have advanced, there remains little guidance quantifying the effect of experimental design and analysis choices on cycling detection accuracy. We present TimeTrial, a user-friendly benchmarking framework using both real and synthetic data to investigate cycle detection algorithms' performance and improve circadian experimental design. Results show that the optimal choice of analysis method depends on the sampling scheme, noise level, and shape of the waveform of interest and provides guidance on the impact of sampling frequency and duration on cycling detection accuracy. The TimeTrial software is freely available for download and may also be accessed through a web interface. By supplying a tool to vary and optimize experimental design considerations, TimeTrial will enhance circadian transcriptomics studies.

Bootstrapping and Empirical Bayes Methods Improve Rhythm Detection in Sparsely Sampled Data.

There is much interest in using genome-wide expression time series to identify circadian genes. However, the cost and effort of such measurements often limit data collection. Consequently, it is difficult to assess the experimental uncertainty in the measurements and, in turn, to detect periodic patterns with statistical confidence. We show that parametric bootstrapping and empirical Bayes methods for variance shrinkage can improve rhythm detection in genome-wide expression time series. We demonstrate these approaches by building on the empirical JTK_CYCLE method (eJTK) to formulate a method that we term BooteJTK. Our procedure rapidly and accurately detects cycling time series by combining information about measurement uncertainty with information about the rank order of the time series values. We exploit a publicly available genome-wide data set with high time resolution to show that BooteJTK provides more consistent rhythm detection than existing methods at typical sampling frequencies. Then, we apply BooteJTK to genome-wide expression time series from multiple tissues and show that it reveals biologically sensible tissue relationships that eJTK misses. BooteJTK is implemented in Python and is freely available on GitHub at https://github.com/alanlhutchison/BooteJTK .