Power Metrics Research Articles

Tunable spring balanced magnetic energy harvester for low frequencies and small displacements

In this paper we present a novel concept to efficiently harvest vibrational energy at low frequencies and very small displacement. We describe and evaluate an electromagnetic energy harvester which generates power from a magnetic circuit with motion induced variations of an air gap. External vibrations induce oscillations of the gap length around an equilibrium point, due to a linear spring counteracting the magnetic force. The relative position of the spring can be adjusted to optimize the harvester output for excitation amplitude and frequency. A simulation model is built in COMSOL and verified by comparison with lab measurements. The simulation model is used to determine the potential performance of the proposed concept under both harmonic and non-harmonic excitation. Under harmonic excitation, we achieve a simulated RMS load power of 26.5 μW at 22 Hz and 0.028 g acceleration amplitude. From a set of comparable EH we achieve the highest theoretical power metric of 1712.2 µW/cm3/g2 while maintaining the largest relative bandwidth of 81.8%. Using measured non-harmonic vibration data, with a mean acceleration of 0.039 g, resulted in a mean power of 52 μW. Moreover, the simplicity and robustness of our design makes it a competitive alternative for use in practical situations.

Changes in EEG Activity Following Live Z-Score Training Predict Changes in Persistent Post-concussive Symptoms: An Exploratory Analysis.

A specific variant of neurofeedback therapy (NFT), Live Z-Score Training (LZT), can be configured to not target specific EEG frequencies, networks, or regions of the brain, thereby permitting implicit and flexible modulation of EEG activity. In this exploratory analysis, the relationship between post-LZT changes in EEG activity and self-reported symptom reduction is evaluated in a sample of patients with persistent post-concussive symptoms (PPCS). Penalized regressions were used to identify EEG metrics associated with changes in physical, cognitive, and affective symptoms; the predictive capacity of EEG variables selected by the penalized regressions were subsequently validated using linear regression models. Post-treatment changes in theta/alpha ratio predicted reduction in pain intensity and cognitive symptoms and changes in beta-related power metrics predicted improvements in affective symptoms. No EEG changes were associated with changes in a majority of physical symptoms. These data highlight the potential for NFT to target specific EEG patterns to provide greater treatment precision for PPCS patients. This exploratory analysis is intended to promote the refinement of NFT treatment protocols to improve outcomes for patients with PPCS.

Gait as a quantitative translational outcome measure in Angelman syndrome

Angelman syndrome (AS) is a genetic neurodevelopmental disorder characterized by developmental delay, lack of speech, seizures, intellectual disability, hypotonia, and motor coordination deficits. Motor abilities are an important outcome measure in AS as they comprise a broad repertoire of metrics including ataxia, hypotonia, delayed ambulation, crouched gait, and poor posture, and motor dysfunction affects nearly every individual with AS. Guided by collaborative work with AS clinicians studying gait, the goal of this study was to perform an in‐depth gait analysis using the automated treadmill assay, DigiGait. Our hypothesis is that gait presents a strong opportunity for a reliable, quantitative, and translational metric that can serve to evaluate novel pharmacological, dietary, and genetic therapies. In this study, we used an automated gait analysis system, in addition to standard motor behavioral assays, to evaluate components of motor, exploration, coordination, balance, and gait impairments across the lifespan in an AS mouse model. Our study demonstrated marked global motoric deficits in AS mice, corroborating previous reports. Uniquely, this is the first report of nuanced aberrations in quantitative spatial and temporal components of gait in AS mice compared to sex‐ and age‐matched wildtype littermates followed longitudinally using metrics that are analogous in AS individuals. Our findings contribute evidence toward the use of nuanced motor outcomes (i.e., gait) as valuable and translationally powerful metrics for therapeutic development for AS, as well as other genetic neurodevelopmental syndromes.Lay SummaryMovement disorders affect nearly every individual with Angelman Syndrome (AS). The most common motor problems include spasticity, ataxia of gait (observed in the majority of ambulatory individuals), tremor, and muscle weakness. This report focused on quantifying various spatial and temporal aspects of gait as a reliable, translatable outcome measure in a preclinical AS model longitudinally across development. By increasing the number of translational, reliable, functional outcome measures in our wheelhouse, we will create more opportunities for identifying and advancing successful medical interventions.

Artificial Neural Networks Oriented Testbed for Multiantenna Wireless Application

This research article provides viable research solutions by using Artificial Neural Networks (ANN) for Multiantenna wireless applications with less computational complexity. Artificial Neural Networks Oriented Testbed (ANNOT) is proposed where intelligence through artificial neurons are exploited for training multiantenna wireless application. A feedforward backpropagation network is trained with the required input parameters using training algorithms and its convergence for iterations for target parameters are simulated and developed. The ANNOT intelligently provides the required outputs from the trained values when validated for the tested output parameter in Multiantenna wireless application such as data transmission. Testbed input target parameters are bandwidth, signal power, channel statistics, noise power and output parameters metrics are capacity, probability of error which are executed in matrix laboratory (MATLAB). Obtained results are analyzed in gradient based algorithms and variants of neural networks for mean square error (MSE) against number of iterations/epochs which provide optimized results from ANNOT with less computational complexity. Validation results are also obtained for capacity and probability of error for data transmission multiantenna wireless application.

Population Genomics Approaches for Genetic Characterization of SARS-CoV-2 Lineages.

The genome of the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), the pathogen that causes coronavirus disease 2019 (COVID-19), has been sequenced at an unprecedented scale leading to a tremendous amount of viral genome sequencing data. To assist in tracing infection pathways and design preventive strategies, a deep understanding of the viral genetic diversity landscape is needed. We present here a set of genomic surveillance tools from population genetics which can be used to better understand the evolution of this virus in humans. To illustrate the utility of this toolbox, we detail an in depth analysis of the genetic diversity of SARS-CoV-2 in first year of the COVID-19 pandemic. We analyzed 329,854 high-quality consensus sequences published in the GISAID database during the pre-vaccination phase. We demonstrate that, compared to standard phylogenetic approaches, haplotype networks can be computed efficiently on much larger datasets. This approach enables real-time lineage identification, a clear description of the relationship between variants of concern, and efficient detection of recurrent mutations. Furthermore, time series change of Tajima's D by haplotype provides a powerful metric of lineage expansion. Finally, principal component analysis (PCA) highlights key steps in variant emergence and facilitates the visualization of genomic variation in the context of SARS-CoV-2 diversity. The computational framework presented here is simple to implement and insightful for real-time genomic surveillance of SARS-CoV-2 and could be applied to any pathogen that threatens the health of populations of humans and other organisms.

An Efficient Subcycle Alternative to Instantaneous Power Metrics

In this article, we discuss the relationship between subcycle and instantaneous power metrics in polyphase ac systems. While both approaches have very low computational cost, which makes them suitable for real-time control of industrial processes, the two-sample subcycle scheme offers several distinct advantages over the purely-instantaneous approach. In particular, we show that: a) the level of steady-state fluctuation of subcycle power metrics that use only two time-domain samples is significantly lower than that of purely-instantaneous metrics, and b) the subcycle scheme generates several additional power metrics, which can be used to detect onset of faults, and accurately determine their duration. We illustrate our points using both synthetic and (real-life) industrial examples. In addition, we examine the connection between the two-sample subcycle scheme and other near-instantaneous approaches, based on derivatives of currents and voltages. We again observe a significantly lower numerical sensitivity for the two-sample subcycle quantities. Furthermore, two-sample quantities have a unique feature that they can naturally be extended to more samples, with concomitant reduction in fluctuations, better accuracy and with only moderate increase in computational cost. We posit that the subcycle approach has promise in detection, control, and classification applications.

An Intelligent Radiomic Approach for Lung Cancer Screening

The efficiency of lung cancer screening for reducing mortality is hindered by the high rate of false positives. Artificial intelligence applied to radiomics could help to early discard benign cases from the analysis of CT scans. The available amount of data and the fact that benign cases are a minority, constitutes a main challenge for the successful use of state of the art methods (like deep learning), which can be biased, over-fitted and lack of clinical reproducibility. We present an hybrid approach combining the potential of radiomic features to characterize nodules in CT scans and the generalization of the feed forward networks. In order to obtain maximal reproducibility with minimal training data, we propose an embedding of nodules based on the statistical significance of radiomic features for malignancy detection. This representation space of lesions is the input to a feed forward network, which architecture and hyperparameters are optimized using own-defined metrics of the diagnostic power of the whole system. Results of the best model on an independent set of patients achieve 100% of sensitivity and 83% of specificity (AUC = 0.94) for malignancy detection.

An Artificial Neural Network Model to Predict Efficiency and Emissions of a Gasoline Engine

With global warming, and internal combustion engine emissions as the main global non-industrial emissions, how to further optimize the power performance and emissions of internal combustion engines (ICEs) has become a top priority. Since the internal combustion engine is a complex nonlinear system, it is often difficult to optimize engine performance from a certain factor of the internal combustion engine, and the various parameters of the internal combustion engine are coupled with each other and affect each other. Moreover, traditional experimental methods including 3D simulation or bench testing are very time consuming or expensive, which largely affects the development of engines and the speed of product updates. Machine learning algorithms are currently receiving a lot of attention in various fields, including the internal combustion engine field. In this study, an artificial neural network (ANN) model was built to predict three types of indicators (power, emissions, and combustion phasing) together, including 50% combustion crank angle (CA50), carbon monoxide (CO), unburned hydrocarbons (UHC), nitrogen oxides (NOx), indicated mean effective pressure (IMEP), and indicated thermal efficiency (ITE). The goal of this work was to verify that only one machine learning model can combine power, emissions, and phase metrics together for prediction. The predicted results showed that all coefficients of determination (R2) were larger than 0.97 with a relatively small RMSE, indicating that it is possible to build a predictive model with three types of parameters (power, emissions, phase) as outputs based on only one ANN model. Most importantly, when optimizing the powertrain control strategy of a hybrid vehicle, only a surrogate model can help establish the relationship between the input and output parameters of the whole engine, which is the need of the future research. Overall, this study demonstrated that it is feasible to integrate three types of combustion-related parameters in a single machine learning model.

Polarization memory rate as a metric to differentiate benign and malignant tissues.

Non-invasive optical methods for cancer diagnostics, such as microscopy, spectroscopy, and polarimetry, are rapidly advancing. In this respect, finding new and powerful optical metrics is an indispensable task. Here we introduce polarization memory rate (PMR) as a sensitive metric for optical cancer diagnostics. PMR characterizes the preservation of circularly polarized light relative to linearly polarized light as light propagates in a medium. We hypothesize that because of well-known indicators associated with the morphological changes of cancer cells, like an enlarged nucleus size and higher chromatin density, PMR should be greater for cancerous than for the non-cancerous tissues. A thorough literature review reveals how this difference arises from the anomalous depolarization behaviour of many biological tissues. In physical terms, though most biological tissue primarily exhibits Mie scattering, it typically exhibits Rayleigh depolarization. However, in cancerous tissue the Mie depolarization regime becomes more prominent than Rayleigh. Experimental evidence of this metric is found in a preliminary clinical study using a novel Stokes polarimetry probe. We conducted in vivo measurements of 20 benign, 28 malignant and 59 normal skin sites with a 660 nm laser diode. The median PMR values for cancer vs non-cancer are significantly higher for cancer which supports our hypothesis. The reported fundamental differences in depolarization may persist for other types of cancer and create a conceptual basis for further developments in polarimetry applications for cancer detection.

Relationship between aerobic fitness and metabolic power metrics in elite male soccer players.

The aim was to assess the relationship between aerobic fitness and metabolic power metrics in elite male soccer players, and the possible differences that playing positions might impose during match play over new metabolic power metrics. Sixty-two elite professional male soccer players (13 central backs, 13 side backs, 22 midfielders, and 14 forwards) took part in the study. Players were monitored during eleven months of full training (including pre-season and in-season) and over all official matches (Serie A matches, Italy Cup matches). Aerobic fitness tests were conducted one week after the start of the preseason, and 8, 24 and 36 weeks after the beginning of the Championship. Players’ aerobic fitness and metabolic power metrics were considered as the mean of all seasonal testing and of pooling data of 38 championship matches and 3 or 6 Italy Cup matches for all the calculations respectively. The velocity at 4 mmol·L-1 (VL4) was significantly related to metabolic power metrics match variables with correlation ranging from trivial to very large (r = 0.32 to r = 0.89). Receiver-operating-characteristic (ROC) analysis showed that speed at VL4 was sensitive in detecting high metabolic power distance (HMPD) changes in all but central back players as revealed by area under the curve (central back .78, 95%CI .47 to .95; full back .93, 95%CI .64 to 0.99; midfielder .88, 95%CI .67 to 0.98; forward .90, 95%CI .62 to 0.99). This study’s findings provide further evidence for the ecological validity of aerobic fitness in elite male soccer players. Players having a HMPD cut-off equal to or higher than > 1450 m for central backs, > 1990 m for full backs, > 2170 m for midfielders and > 1670 m for forwards may be considered as possessing superior aerobic fitness status. In light of this study’s findings, the VL4 test may be considered a valid test to evaluate meaningful information for direct generic aerobic training in soccer players.

Deep Reinforcement Learning from Self-Play in No-limit Texas Hold'em Poker

Imperfect information games describe many practical applications found in the real world as the information space is rarely fully available. This particular set of problems is challenging due to the random factor that makes even adaptive methods fail to correctly model the problem and find the best solution. Neural Fictitious Self Play (NFSP) is a powerful algorithm for learning approximate Nash equilibrium of imperfect information games from self-play. However, it uses only crude data as input and its most successful experiment was on the in-limit version of Texas Hold’em Poker. In this paper, we develop a new variant of NFSP that combines the established fictitious self-play with neural gradient play in an attempt to improve the performance on large-scale zero-sum imperfect information games and to solve the more complex no-limit version of Texas Hold’em Poker using powerful handcrafted metrics and heuristics alongside crude, raw data. When applied to no-limit Hold’em Poker, the agents trained through self-play outperformed the ones that used fictitious play with a normal-form single-step approach to the game. Moreover, we showed that our algorithm converges close to a Nash equilibrium within the limited training process of our agents with very limited hardware. Finally, our best self-play-based agent learnt a strategy that rivals expert human level.

Strategies for the Development of Conjugated Polymer Molecular Dynamics Force Fields Validated with Neutron and X-ray Scattering.

Conjugated polymers (CPs) enable a wide range of lightweight, lower cost, and flexible organic electronic devices, but a thorough understanding of relationships between molecular structure and dynamics and electronic performance is critical for improved device efficiencies and for new technologies. Molecular dynamics (MD) simulations offer in silico insight into this relationship, but their accuracy relies on the approach used to develop the model's parameters or force field (FF). In this Perspective, we first review current FFs for CPs and find that most of the models implement an arduous reparameterization of inter-ring torsion potentials and partial charges of classical FFs. However, there are few FFs outside of simple CP molecules, e.g., polythiophenes, that have been developed over the last two decades. There is also limited reparameterization of other parameters, such as nonbonded Lennard-Jones interactions, which we find to be directly influenced by conjugation in these materials. We further provide a discussion on experimental validation of MD FFs, with emphasis on neutron and X-ray scattering. We define multiple ways in which various scattering methods can be directly compared to results of MD simulations, providing a powerful experimental validation metric of local structure and dynamics at relevant length and time scales to charge transport mechanisms in CPs. Finally, we offer a perspective on the use of neutron scattering with machine learning to enable high-throughput parametrization of accurate and experimentally validated CP FFs enabled not only by the ongoing advancements in computational chemistry, data science, and high-performance computing but also using oligomers as proxies for longer polymer chains during FF development.

Roza: a new and comprehensive metric for evaluating classification systems

Many metrics such as accuracy rate (ACC), area under curve (AUC), Jaccard index (JI), and Cohen’s kappa coefficient are available to measure the success of the system in pattern recognition and machine/deep learning systems. However, the superiority of one system to one other cannot be determined based on the mentioned metrics. This is because such a system can be successful using one metric, but not the other ones. Moreover, such metrics are insufficient when the number of samples in the classes is unequal (imbalanced data). In this case, naturally, by using these metrics, a sensible comparison cannot be made between two given systems. In the present study, the comprehensive, fair, and accurate Roza (Roza means rose in Persian. When different permutations of the metrics used are superimposed in a polygon format, it looks like a flower, so we named it Roza.) metric is introduced for evaluating classification systems. This metric, which facilitates the comparison of systems, expresses the summary of many metrics with a single value. To verify the stability and validity of the metric and to conduct a comprehensive, fair, and accurate comparison between the systems, the Roza metric of the systems tested under the same conditions are calculated and comparisons are made. For this, systems tested with three different strategies on three different datasets are considered. The results show that the performance of the system can be summarized by a single value and the Roza metric can be used in all systems that include classification processes, as a powerful metric.

Chinese tourism diplomacy: a chinese–style modernity review

ABSTRACT Viewed from Chinese-style modernity, this article explores Chinese tourism diplomacy in relation to the development of Chinese outbound tourism. Using public policy analysis, the article examines the role of modern Chinese statecraft in the outbound tourism industry. It identifies four main characteristics in the nature of tourism diplomacy: the economic power of outbound tourism; outbound tourism as sanctionable or rewarding forces; the relatively low political sensitivity and high flexibility of outbound tourism; culture and tourism integration to enhance international relations. The article contributes to the literature by illuminating the interconnectivity between diplomacy and tourism via investigating through China-style modernity. It contributes to a repositioning of the nation-state power metrics of China’s outbound tourism industry.

Closed-Loop Neural Prostheses With On-Chip Intelligence: A Review and a Low-Latency Machine Learning Model for Brain State Detection.

The application of closed-loop approaches in systems neuroscience and therapeutic stimulation holds great promise for revolutionizing our understanding of the brain and for developing novel neuromodulation therapies to restore lost functions. Neural prostheses capable of multi-channel neural recording, on-site signal processing, rapid symptom detection, and closed-loop stimulation are critical to enabling such novel treatments. However, the existing closed-loop neuromodulation devices are too simplistic and lack sufficient on-chip processing and intelligence. In this paper, we first discuss both commercial and investigational closed-loop neuromodulation devices for brain disorders. Next, we review state-of-the-art neural prostheses with on-chip machine learning, focusing on application-specific integrated circuits (ASIC). System requirements, performance and hardware comparisons, design trade-offs, and hardware optimization techniques are discussed. To facilitate a fair comparison and guide design choices among various on-chip classifiers, we propose a new energy-area (E-A) efficiency figure of merit that evaluates hardware efficiency and multi-channel scalability. Finally, we present several techniques to improve the key design metrics of tree-based on-chip classifiers, both in the context of ensemble methods and oblique structures. A novel Depth-Variant Tree Ensemble (DVTE) is proposed to reduce processing latency (e.g., by 2.5× on seizure detection task). We further develop a cost-aware learning approach to jointly optimize the power and latency metrics. We show that algorithm-hardware co-design enables the energy- and memory-optimized design of tree-based models, while preserving a high accuracy and low latency. Furthermore, we show that our proposed tree-based models feature a highly interpretable decision process that is essential for safety-critical applications such as closed-loop stimulation.

Oxygenation Defects, Ventilatory Ratio, and Mechanical Power During Severe Pediatric Acute Respiratory Distress Syndrome: Longitudinal Time Sequence Analyses in a Single-Center Retrospective Cohort.

Our understanding of pediatric acute respiratory distress syndrome is based on information from studies reporting intermittent, serial respiratory data. We have analyzed a high-resolution, longitudinal dataset that incorporates measures of hypoxemia severity, metrics of lung mechanics, ventilatory ratio, and mechanical power and examined associations with survival after the onset of pediatric acute respiratory distress syndrome. Single-center retrospective cohort, 2013-2018. Tertiary surgical/medical PICU. Seventy-six cases of severe pediatric acute respiratory distress syndrome, determined according to the Pediatric Acute Lung Injury Consensus Conference criteria. None. The high-resolution database included continuous monitoring of ventilatory data (0.03 Hz) for up to 14 days after the diagnosis of pediatric acute respiratory distress syndrome or until extubation or death (n = 26). In the 12,128 hours of data during conventional mechanical ventilation, we used generalized estimating equations to compare groups, accounting for any effect of time. We identified an association between survival and faster rate of improvement in delta pressure (peak inspiratory pressure minus positive end-expiratory pressure; p = 0.028). Nonsurvival was associated with higher daily Pediatric Logistic Organ Dysfunction-2 scores (p = 0.005) and more severe hypoxemia metrics (p = 0.005). Mortality was also associated with the following respiratory/pulmonary metrics (mean difference [95% CI]): positive end-expiratory pressure level (+2.0 cm H2O [0.8-3.2 cm H2O]; p = 0.001), peak inspiratory pressure level (+3.0 cm H2O [0.5-5.5 cm H2O]; p = 0.022), respiratory rate (z scores +2.2 [0.9-3.6]; p = 0.003], ventilatory ratio (+0.41 [0.28-0.55]; p = 0.0001], and mechanical power (+5 Joules/min [1-10 Joules/min]; p = 0.013). Based on generalized linear mixed modeling, mechanical power remained associated with mortality after adjustment for normal respiratory rate, age, and daily Pediatric Logistic Organ Dysfunction-2 score (+3 Joules/breath [1-6 Joules/breath]; p = 0.009). Mortality after severe pediatric acute respiratory distress syndrome is associated with the severity of organ dysfunction, oxygenation defects, and pulmonary metrics including dead space and theoretical mechanical energy load.

The effects of strength vs. plyometric training on sprinting kinetics in post peak height velocity (PHV) female athletes

ABSTRACT Speed is a crucial factor for overall athletic development. While researchers have shown strength and plyometric training to improve sprinting speed in some adult and youth populations, no studies have compared the effects of strength and plyometric training on sprinting speed in young females. Fifty-two young females were divided into three groups and trained for 7 weeks, twice a week; strength training (n = 16, age 13.36 ± 0.84), plyometric training (n = 21, age 13.38 ± 0.75) and a physical education class as a control group (n = 15, age 13.95 ± 0.54). Participants were tested for sprinting performance and horizontal force (Fo), maximum velocity (Vmax) and maximum horizontal power (Pmax) metrics over 30 m distance, isometric strength and unilateral horizontal jump distance before and after the intervention. Both the strength and plyometric groups significantly improved all performance variables (p < 0.05). The strength group significantly improved 10 m split time (6.76%; Hedge’s g = 0.65) and Fo (18.98%; g = 0.67), whereas the plyometric group significantly improved Vmax (4.91%; g = 0.50) and Pmax (7.91%; g = 0.31). The findings of this study suggest that both strength and plyometric training can improve sprinting kinetics, jumping performance and overall strength in young females.

Antigen and checkpoint receptor engagement recalibrates T cell receptor signal strength

SummaryHow T cell receptor (TCR) signal strength modulates T cell function and to what extent this is modified by immune checkpoint blockade (ICB) are key questions in immunology. Using Nr4a3-Tocky mice, we characterized early quantitative and qualitative changes that occur in CD4+ T cells in relation to TCR signaling strength. We captured how dose- and time-dependent programming of distinct co-inhibitory receptors rapidly recalibrates T cell activation thresholds and visualized the immediate effects of ICB on T cell re-activation. Our findings reveal that anti-PD1 immunotherapy leads to an increased TCR signal strength. We defined a strong TCR signal metric of five genes upregulated by anti-PD1 in T cells (TCR.strong), which was superior to a canonical T cell activation gene signature in stratifying melanoma patient outcomes to anti-PD1 therapy. Our study therefore reveals how analysis of TCR signal strength—and its manipulation—can provide powerful metrics for monitoring outcomes to immunotherapy.

A Unified Framework of adaptive sidelobe canceller design by antenna/subarray selection

This paper proposes a unified framework of adaptive sidelobe canceller (SLC) design, specifically the optimization of auxiliary array configuration in terms of two commonly-used metrics under both structures of antenna reuse and array splitting via antenna/subarray selection. Concerning the cancellation of desired signal caused by conventional SLCs, we first propose two new methods to design the beamforming weights for SLCs. For the first metric of minimum output power, a regularization penalty on signal cancellation is added. For the second metric of beampattern matching, additional zero-forcing constraint of auxiliary beampattern is imposed. Both modified methods aim to maximize the output signal to interference-plus-noise ratio (MaxSINR) under certain constraints. To further enhance the performance of these two methods, we optimize the auxiliary array configuration in terms of MaxSINR. We conduct the optimal SLC design under two structures of antenna reuse and array splitting, where both antenna and subarray selection are leveraged for auxiliary array construction. Furthermore, the optimum SLC design problem is formulated into convex optimization, which in turn can be readily solved using off-the-shelf commercial software or toolbox. Comparative simulation results demonstrate the optimality and superiority of the proposed adaptive SLC.

The Dynamic Relationship Between Alpha and Beta Power and Next-Day Suicidal Ideation in Individuals With Treatment-Resistant Depression

BackgroundNocturnal wakefulness has emerged as a potential predictor of short-term suicide risk. This analysis used dynamic temporal patterns in alpha and beta power and global sleep metrics to explore the possible link between next-day suicidal ideation (NDSI) and wakefulness measures in unmedicated participants with treatment-resistant depression. MethodsThirty-three medication-free participants with treatment-resistant depression completed overnight polysomnography. Alpha and beta spectral power as functions over time were used to represent arousal-related components of the dynamic sleep process. A functional data analytic approach (multilevel functional principal component analysis [MFPCA]) was used to preserve the oscillatory nature of the data; MFPCA PC scores were then associated with NDSI. Associations between NDSI and polysomnography-defined wakefulness after sleep onset, sleep efficiency, and total sleep time were also evaluated. ResultsNDSI had the strongest relationship with the second beta PC score (slope = 0.09 [90% credible interval, 0.03 to 0.14]), which represented an oscillating pattern that reflected disturbed sleep. The first PCs from both alpha and beta MFPCAs represented the overall magnitude of power and were most closely associated with traditional polysomnography metrics but were not related to NDSI. Results were equivocal for wakefulness after sleep onset with NDSI and did not support a relationship between NDSI and either sleep efficiency or total sleep time, highlighting the value of information contained in oscillating electroencephalogram patterns for identifying physiological links between nocturnal wakefulness and NDSI. ConclusionsThis study leveraged the dynamic nature of wakefulness-related electroencephalogram frequencies and provides a potential electrophysiological link between suicidal ideation and wakefulness during sleep in individuals with treatment-resistant depression.