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Numerous physiological processes are cyclical, but sampling these processes densely enough to perform frequency decomposition and subsequent analyses can be challenging. Mathematical approaches for decomposition and reconstruction of sparsely and irregularly sampled signals are well established but have been under-utilized in physiological applications. We developed a basis pursuit denoising with polynomial detrending (BPWP) model that recovers oscillations and trends from sparse and irregularly sampled timeseries. We validated this model on a unique dataset of long-term inter-ictal epileptiform discharge (IED) rates from human hippocampus recorded with a novel investigational device with continuous local field potential sensing. IED rates have well established circadian and multiday cycles related to sleep, wakefulness, and seizure clusters. Given sparse and irregular samples of IED rates from multi-month intracranial EEG recordings from ambulatory humans, we used BPWP to compute narrowband spectral power and polynomial trend coefficients and identify IED rate cycles in three subjects. In select cases, we propose that random and irregular sampling may be leveraged for frequency decomposition of physiological signals.Author SummaryCircadian and multiday cycles are an important part of many long-term neuro-behavioral phenomena such as pathological inter-ictal epileptiform discharges (IEDs) in epilepsy. Long-term, ambulatory, neuro-behavioral monitoring in human patients involves complex recording systems that can be subject to intermittent, irregular data loss and storage limitations, resulting in sparse, irregularly sampled data. Cycle identification in sparse data or irregular data using traditional frequency decomposition techniques typically requires interpolation to create a regular timeseries. Using unique, long-term recordings of pathological brain activity in patients with epilepsy implanted with an investigational device, we developed a method to identify cycles in sparse, irregular neuro-behavioral data without interpolation. We anticipate this approach will enable retrospective cycle identification in sparse neuro-behavioral timeseries and support prospective sparse sampling in monitoring systems to enable long-term monitoring of patients and to extend storage capacity in a variety of ambulatory monitoring applications.

BackgroundTime series analysis is critical for understanding brain signals and their relationship to behavior and cognition. Cluster-based permutation tests (CBPT) are commonly used to analyze a variety of electrophysiological signals including EEG, MEG, ECoG, and sEEG data withouta prioriassumptions about specific temporal effects. However, two major limitations of CBPT include the inability to directly analyze experiments with multiple fixed effects and the inability to account for random effects (e.g. variability across subjects). Here, we propose a flexible multi-step hypothesis testing strategy using CBPT with Linear Mixed Effects Models (LMEs) and Generalized Linear Mixed Effects Models (GLMEs) that can be applied to a wide range of experimental designs and data types.MethodsWe first evaluate the statistical robustness of LMEs and GLMEs using simulated data distributions. Second, we apply a multi-step hypothesis testing strategy to analyze ERPs and broadband power signals extracted from human ECoG recordings collected during a simple image viewing experiment with image category and novelty as fixed effects. Third, we assess the statistical power differences between analyzing signals with CBPT using LMEs compared to CBPT using separate t-tests run on each fixed effect through simulations that emulate broadband power signals. Finally, we apply CBPT using GLMEs to high-gamma burst data to demonstrate the extension of the proposed method to the analysis of nonlinear data.ResultsFirst, we found that LMEs and GLMEs are robust statistical models. In simple simulations LMEs produced highly congruent results with other appropriately applied linear statistical models, but LMEs outperformed many linear statistical models in the analysis of “suboptimal” data and maintained power better than analyzing individual fixed effects with separate t-tests. GLMEs also performed similarly to other nonlinear statistical models. Second, in real world human ECoG data, LMEs performed at least as well as separate t-tests when applied to predefined time windows or when used in conjunction with CBPT. Additionally, fixed effects time courses extracted with CBPT using LMEs from group-level models of pseudo-populations replicated latency effects found in individual category-selective channels. Third, analysis of simulated broadband power signals demonstrated that CBPT using LMEs was superior to CBPT using separate t-tests in identifying time windows with significant fixed effects especially for small effect sizes. Lastly, the analysis of high-gamma burst data using CBPT with GLMEs produced results consistent with CBPT using LMEs applied to broadband power data.ConclusionsWe propose a general approach for statistical analysis of electrophysiological data using CBPT in conjunction with LMEs and GLMEs. We demonstrate that this method is robust for experiments with multiple fixed effects and applicable to the analysis of linearandnonlinear data. Our methodology maximizes the statistical power available in a dataset across multiple experimental variables while accounting for hierarchical random effects and controlling FWER across fixed effects. This approach substantially improves power and accuracy leading to better reproducibility. Additionally, CBPT using LMEs and GLMEs can be used to analyze individual channels or pseudo-population data for the comparison of functional or anatomical groups of data.Highlights● Combining CBPT with GLMEs allows statistical analysis to match experimental design.● CBPT with GLMEs accounts for subject variability and hierarchical random effects.● The proposed method maintains control of type I error, type II error, and FWER.● CBPT with GLMEs can be applied to individual channels and pseudo-population data.

We report the genome sequences of 24 newly discovered bacteriophages that infect Rhodobacter capsulatus , a model for photosynthesis and horizontal gene transfer studies. All have substantial relatedness to previously reported siphovirus bacteriophages. Most are categorized in known clusters (RcB, RcC, RcD, and RcF), with one forming a new cluster, RcG.

BACKGROUND: Drug resistant epilepsy (DRE) patients not amenable to epilepsy surgery can benefit from neurostimulation. Few data compare different neuromodulation strategies. OBJECTIVE: Compare five invasive neuromodulation strategies for treatment of DRE: anterior thalamic nuclei deep brain stimulation (ANT-DBS), centromedian thalamic nuclei DBS (CM-DBS), responsive neurostimulation (RNS), chronic subthreshold stimulation (CSS), and vagus nerve stimulation (VNS). METHODS: Single center retrospective review and phone survey for patients implanted with invasive neuromodulation for 2004–2021. RESULTS: N=159 (ANT-DBS=38, CM-DBS=19, RNS=30, CSS=32, VNS=40). Total median seizure reduction (MSR) was 61% for the entire cohort (IQR 5–90) and in descending order: CSS (85%), CM-DBS (63%), ANT-DBS (52%), RNS (50%), and VNS (50%); p=0.07. Responder rate was 60% after median follow-up time of 26 months. Seizure severity, life satisfaction and quality of sleep were improved. Cortical stimulation (RNS and CSS) was associated with improved seizure reduction compared to subcortical stimulation (ANT-DBS, CM-DBS, and VNS) (67% vs. 52%). Effectiveness was similar for focal epilepsy vs generalized epilepsy, closed loop vs open loop stimulation, pediatric vs. adult cases, and high frequency (>100 Hz) vs low frequency (