Switching state-space modeling of neural signal dynamics.

Linear parametric state-space models are a ubiquitous tool for analyzing neural time series data, providing a way to characterize the underlying brain dynamics with much greater statistical efficiency than non-parametric data analysis approaches. However, neural time series data are frequently time-varying, exhibiting rapid changes in dynamics, with transient activity that is often the key feature of interest in the data. Stationary methods can be adapted to time-varying scenarios by employing fixed-duration windows under an assumption of quasi-stationarity. But time-varying dynamics can be explicitly modeled by switching state-space models, i.e., by using a pool of state-space models with different dynamics selected by a probabilistic switching process. Unfortunately, exact solutions for state inference and parameter learning with switching state-space models are intractable. Here we revisit a switching state-space model inference approach first proposed by Ghahramani and Hinton. We provide explicit derivations for solving the inference problem iteratively after applying a variational approximation on the joint posterior of the hidden states and the switching process. We introduce a novel initialization procedure using an efficient leave-one-out strategy to compare among candidate models, which significantly improves performance compared to the existing method that relies on deterministic annealing. We then utilize this state inference solution within a generalized expectation-maximization algorithm to estimate model parameters of the switching process and the linear state-space models with dynamics potentially shared among candidate models. We perform extensive simulations under different settings to benchmark performance against existing switching inference methods and further validate the robustness of our switching inference solution outside the generative switching model class. Finally, we demonstrate the utility of our method for sleep spindle detection in real recordings, showing how switching state-space models can be used to detect and extract transient spindles from human sleep electroencephalograms in an unsupervised manner.

Nanoparticle manipulation based on chiral plasmon effects.

Chiral plasmonic structures have garnered increasing attention owing to their distinctive chiroptical response. Localized surface plasmon resonance can significantly enhance the circular dichroism and local electromagnetic field of chiral plasmonic structures, resulting in enhanced electromagnetic forces acting on surrounding nanoparticles. Moreover, the circular dichroism response of chiral structures provides an effective means for macroscopic adjustment of microscopic electromagnetic fields. However, chiral plasmon effects are naturally related to angular momentum, and particle control studies of chirality usually focus on angular momentum. This paper proposes a particle manipulation method utilizing chiral light-matter interactions. Through optimization of the optical response of the chiral structure, the direction of electromagnetic forces exerted on surrounding polystyrene particles reverses upon a change in the incident light's handedness. According to this characteristic, the movement direction control of polystyrene particles with a diameter of 100 nm was achieved. By altering the handedness of a single circularly polarized light, more than 94% high-precision particle manipulation was achieved, reducing the complexity of particle manipulation. This microfluidic method has significant implications for advancing microfluidic research and chiral applications.

Photonic thermal switch via metamaterials made of vanadium dioxide-coated nanoparticles.

In this work, a photonic thermal switch is proposed based on the phase-change material vanadium dioxide (VO2) within the framework of near-field radiative heat transfer (NFRHT). The switch consists of two metamaterials filled with core-shell nanoparticles, with the shell made of VO2. Compared to traditional VO2 slabs, the proposed switch exhibits a more than two times increase in the switching ratio, reaching as high as 90.29% with a 100 nm vacuum gap. The improved switching effect is attributed to the capability of the VO2 shell to couple with the core, greatly enhancing heat transfer with the insulating VO2, while blocking the motivation of the core in the metallic state of VO2. The proposed switch opens pathways for active control of NFRHT and holds practical significance for developing thermal photon-based logic circuits.

Performance improvement of three-body radiative diodes driven by graphene surface plasmon polaritons.

As an analogue to an electrical diode, a radiative thermal diode allows radiation to transfer more efficiently in one direction than in the opposite direction by operating in a contactless mode. In this study, we demonstrated that within the framework of three-body photon thermal tunneling, the rectification performance of a three-body radiative diode can be greatly improved by bringing graphene into the system. The system is composed of three parallel slabs, with the hot and cold terminals of the diode coated with graphene films and the intermediate body made of vanadium dioxide (VO2). The rectification factor of the proposed radiative thermal diode reaches 300% with a 350 nm separation distance between the hot and cold terminals of the diode. With the help of graphene, the rectification performance of the radiative thermal diode can be improved by over 11 times. By analyzing the spectral heat flux and energy transmission coefficients, it was found that the improved performance is primarily attributed to the surface plasmon polaritons (SPPs) of graphene. They excite the modes of insulating VO2 in the forward-biased scenario by forming strongly coupled modes between graphene and VO2 and thus dramatically enhance the heat flux. However, for the reverse-biased scenario, the VO2 is at its metallic state, and thus, graphene SPPs cannot work by three-body photon thermal tunneling. Furthermore, the improvement was also investigated for different chemical potentials of graphene and geometric parameters of the three-body system. Our findings demonstrate the feasibility of using thermal-photon-based logical circuits, creating radiation-based communication technology and implementing thermal management approaches at the nanoscale.

Age-Dependent Electroencephalogram Features in Infants Under Spinal Anesthesia Appear to Mirror Physiologic Sleep in the Developing Brain: A Prospective Observational Study.

BACKGROUND: Infants under spinal anesthesia appear to be sedated despite the absence of systemic sedative medications. In this prospective observational study, we investigated the electroencephalogram (EEG) of infants under spinal anesthesia and hypothesized that we would observe EEG features similar to those seen during sleep. METHODS: We computed the EEG power spectra and spectrograms of 34 infants undergoing infraumbilical surgeries under spinal anesthesia (median age 11.5 weeks postmenstrual age, range 38-65 weeks postmenstrual age). Spectrograms were visually scored for episodes of EEG discontinuity or spindle activity. We characterized the relationship between EEG discontinuity or spindles and gestational age, postmenstrual age, or chronological age using logistic regression analyses. RESULTS: The predominant EEG patterns observed in infants under spinal anesthesia were slow oscillations, spindles, and EEG discontinuities. The presence of spindles, observed starting at about 49 weeks postmenstrual age, was best described by postmenstrual age ( P =.002) and was more likely with increasing postmenstrual age. The presence of EEG discontinuities, best described by gestational age ( P = .015), was more likely with decreasing gestational age. These age-related changes in the presence of spindles and EEG discontinuities in infants under spinal anesthesia generally corresponded to developmental changes in the sleep EEG. CONCLUSIONS: This work illustrates 2 separate key age-dependent transitions in EEG dynamics during infant spinal anesthesia that may reflect the maturation of underlying brain circuits: (1) diminishing discontinuities with increasing gestational age and (2) the appearance of spindles with increasing postmenstrual age. The similarity of these age-dependent transitions under spinal anesthesia with transitions in the developing brain during physiological sleep supports a sleep-related mechanism for the apparent sedation observed during infant spinal anesthesia.

Nanoparticle manipulation using plasmonic optical tweezers based on particle sizes and refractive indices.

As an effective tool for micro/nano-scale particle manipulation, plasmonic optical tweezers can be used to manipulate cells, DNA, and macromolecules. Related research is of great significance to the development of nanoscience. In this work, we investigated a sub-wavelength particle manipulation technique based on plasmonic optical tweezers. When the local plasmonic resonance is excited on the gold nanostructure arrays, the local electromagnetic field will be enhanced to generate a strong gradient force acting on nanoparticles, which could achieve particle sorting in sub-wavelength scale. On this basis, we explored the plasmonic enhancement effect of the sorting device and the corresponding optical force and optical potential well distributions. Additionally, the sorting effect of the sorting device was investigated in statistical methods, which showed that the sorting device could effectively sort particles of different diameters and refractive indices.

Influence of the temperature-dependent dielectric constant on the photoacoustic effect of gold nanospheres.

Photoacoustic imaging techniques with gold nanoparticles as contrast agents have received a great deal of attention. The photoacoustic response of gold nanoparticles strongly depends on the far-field optical properties, which essentially depend on the dielectric constant of the material. The dielectric constant of gold not only varies with wavelength but is also affected by temperature. However, the effect of the temperature dependence of the dielectric constant on gold nanoparticles' photoacoustic response has not been fully investigated. In this work, the Drude-Lorentz model and Mie theory are used to calculate the dielectric constant and absorption efficiency of gold nanospheres in aqueous solution, respectively. Then, the finite element method is used to simulate the heat transfer process of gold nanospheres and surrounding water. Finally, the one-dimensional velocity-stress equation is solved by the finite-difference time-domain method to obtain the photoacoustic response of gold nanospheres. The results show that under the irradiation of a high-fluence nanosecond pulse laser, ignoring the temperature dependence of the dielectric constant will lead to large errors in the photothermal response and the nonlinear photoacoustic signals (it can even exceed 20% and 30%). The relative error of the photothermal and photoacoustic response caused by ignoring the temperature-dependent dielectric constant is determined from both the temperature dependence of absorption efficiency and the maximum temperature increase of gold nanospheres. This work provides a new perspective for the photothermal and photoacoustic effects of gold nanospheres, which is meaningful for the development of high-resolution photoacoustic detectors and nano/microscale temperature measurement techniques.

Radiative thermal switch driven by anisotropic black phosphorus plasmons.

Black phosphorus (BP), as a two-dimensional material, has exhibited unique optoelectronic properties due to its anisotropic plasmons. In the present work, we theoretically propose a radiative thermal switch (RTS) composed of BP gratings in the context of near-field radiative heat transfer. The simply mechanical rotation between the gratings enables considerable modulation of radiative heat flux, especially when combined with the use of non-identical parameters, i.e., filling factors and electron densities of BP. Among all the cases including asymmetric BP gratings, symmetric BP gratings, and BP films, we find that the asymmetric BP gratings possess the most excellent switching performance. The optimized switching factors can be as high as 90% with the vacuum separation d=50 nm and higher than 70% even in the far-field regime d=1 µm. The high-performance switching is basically attributed to the rotatable-tunable anisotropic BP plasmons between the asymmetric gratings. Moreover, due to the twisting principle, the RTS can work at a wide range of temperature, which has great advantage over the phase change materials-based RTS. The proposed switching scheme has great significance for the applications in optoelectronic devices and thermal circuits.

Magnetoplasmon-surface phonon polaritons' coupling effects in radiative heat transfer.

In this work, the coupling of magnetoplasmon polaritons (MPP) to surface phonon polaritons (SPhPs) in near-field radiative heat transfer is theoretically investigated. The system is composed of two parallel graphene-coated SiO2 substrates. By applying an external magnetic field, the separated branches of MPPs can couple with SPhPs to form tunable modes. The behavior remolds the energy transport of the system. The relative thermal magnetoresistance ratio can reach values of up to 160% for a magnetic field of 8 T. In addition, the thermal stealthy for the coated graphene is realized by tuning the intensity of fields. This work has substantial importance to graphene-based magneto-optical devices.

Active control of near-field radiative heat transfer by a graphene-gratings coating-twisting method.

In this Letter, active control of near-field radiative heat transfer (NFRHT) between two isotropic materials is realized by a coating-twisting method. The two slabs are coated with graphene gratings, and then the NFRHT can be not only enhanced but also weakened, by tuning the twisted angle between the two gratings. The physical mechanism is attributed to the modes coupled by the graphene gratings and the isotropic material, which can vary with the twisted angle. The proposed method is also applicable for other kinds of anisotropic films and may provide a way to realize high-precision nanoscale thermal management, nimble thermal communications, and thermal switch.

Near-field radiative heat transfer in multilayered graphene system considering equilibrium temperature distribution.

In the present work, the near-field radiative heat transfer of a multilayered graphene system is investigated within the framework of the many-body theory. For the first time, the temperature distribution corresponding to the steady state of the system is investigated. Unique temperature steps are observed near both boundaries of the system, especially in the strong near-field regime. By utilizing the effective radiative thermal conductance, the thermal freedom of heat flux in different regions of the system is analyzed quantitatively, and the cause of various temperature distributions is explained accordingly. To characterize the heat transfer ability of the whole system, we evaluate the system with two heat transfer coefficients (HTC), transient heat transfer coefficient (THTC), and steady heat transfer coefficient (SHTC). A unique many-body enhancement is observed, which causes a red-shift of resonance peak corresponding to graphene surface plasmon polaritons. Furthermore, a three-body enhancement of SHTC emerges thanks to the relay effect and the complexity of the system. The regime of heat transport can be tuned by changing the chemical potentials of graphene and undergoes a transition from diffusive to quasi-ballistic transport in the strong near-field regime.

A dynamic generative model can extract interpretable oscillatory components from multichannel neurophysiological recordings.

Modern neurophysiological recordings are performed using multichannel sensor arrays that are able to record activity in an increasingly high number of channels numbering in the 100's to 1000's. Often, underlying lower-dimensional patterns of activity are responsible for the observed dynamics, but these representations are difficult to reliably identify using existing methods that attempt to summarize multivariate relationships in a post-hoc manner from univariate analyses, or using current blind source separation methods. While such methods can reveal appealing patterns of activity, determining the number of components to include, assessing their statistical significance, and interpreting them requires extensive manual intervention and subjective judgement in practice. These difficulties with component selection and interpretation occur in large part because these methods lack a generative model for the underlying spatio-temporal dynamics. Here we describe a novel component analysis method anchored by a generative model where each source is described by a bio-physically inspired state space representation. The parameters governing this representation readily capture the oscillatory temporal dynamics of the components, so we refer to it as Oscillation Component Analysis (OCA). These parameters - the oscillatory properties, the component mixing weights at the sensors, and the number of oscillations - all are inferred in a data-driven fashion within a Bayesian framework employing an instance of the expectation maximization algorithm. We analyze high-dimensional electroencephalography and magnetoencephalography recordings from human studies to illustrate the potential utility of this method for neuroscience data.

A dynamic generative model can extract interpretable oscillatory components from multichannel neurophysiological recordings.

Modern neurophysiological recordings are performed using multichannel sensor arrays that are able to record activity in an increasingly high number of channels numbering in the 100s to 1000s. Often, underlying lower-dimensional patterns of activity are responsible for the observed dynamics, but these representations are difficult to reliably identify using existing methods that attempt to summarize multivariate relationships in a post hoc manner from univariate analyses or using current blind source separation methods. While such methods can reveal appealing patterns of activity, determining the number of components to include, assessing their statistical significance, and interpreting them requires extensive manual intervention and subjective judgment in practice. These difficulties with component selection and interpretation occur in large part because these methods lack a generative model for the underlying spatio-temporal dynamics. Here, we describe a novel component analysis method anchored by a generative model where each source is described by a bio-physically inspired state-space representation. The parameters governing this representation readily capture the oscillatory temporal dynamics of the components, so we refer to it as oscillation component analysis. These parameters - the oscillatory properties, the component mixing weights at the sensors, and the number of oscillations - all are inferred in a data-driven fashion within a Bayesian framework employing an instance of the expectation maximization algorithm. We analyze high-dimensional electroencephalography and magnetoencephalography recordings from human studies to illustrate the potential utility of this method for neuroscience data.

Simulation Analysis on the Characteristics of Aerosol Particles to Inhibit the Infrared Radiation of Exhaust Plumes.

Aerosol infrared stealth technology is a highly effective method to reduce the intensity of infrared radiation by releasing aerosol particles around the hot exhaust plume. This paper uses a Computational Fluid Dynamics (CFD) two-phase flow model to simulate the exhaust plume fields of three kinds of engine nozzles containing aerosol particles. The Planck-weighted narrow spectral band gas model and the Reverse Monte Carlo method are used for infrared radiation transfer calculations to analyze the influencing factors and laws for the suppression of the infrared radiation properties of exhaust plumes by four typical aerosol particles. The simulation calculation results show that the radiation suppression efficiency of aerosol particles on the exhaust plume reaches its maximum value at a detection angle (ϕ) of 0° and decreases with increasing ϕ, reaching its minimum value at 90°. Reducing the aerosol particle size and increasing the aerosol mass flux can enhance the suppression effect. In the exhaust plume studied in this paper, the radiation suppression effect is best when the particle size is 1 µm and the mass flux is 0.08 kg/s. In addition, the inhibition of aerosol particles varies among different materials, with graphite having the best inhibition effect, followed by H2O, MgO, and SiO2. Solid particles will increase the radiation intensity and change the spectral radiation characteristics of the exhaust plume at detection angles close to the vertical nozzle axis due to the scattering effect. Finally, this paper analyzed the suppression effects of three standard nozzle configurations under the same aerosol particle condition and found that the S-bend nozzle provides better suppression.

Effects of aging on externally cued and internally driven uncertainty representations.

OBJECTIVE: The Hick-Hyman law states that response time (RT) increases linearly with increasing information uncertainty. The effects of aging on uncertainty representations in choice RT paradigms remain unclear, including whether aging differentially affects processes mediating externally cued versus internally driven uncertainty. This study sought to characterize age-related differences in uncertainty representations using a card-sorting task. METHOD: The task separately manipulated internally driven uncertainty (i.e., probability of each stimulus type with fixed number of response piles) and externally cued uncertainty (i.e., number of response piles with fixed probability of each stimulus type). RESULTS: Older adults (OA) showed greater RT slowing than younger adults in response to uncertainty load, an effect that was stronger in the externally cued than internally driven condition. While both age groups showed lower accuracy and greater RTs in response to unexpected (surprising) stimuli in the internally driven condition at low uncertainty loads, OA were unable to distinguish between expected and nonexpected stimuli at higher uncertainty loads when the probability of each stimulus type was close to equal. Among OA, better performance on the internally driven, but not externally cued, condition was associated with better global cognitive performance and verbal fluency. CONCLUSIONS: Collectively, these findings provide behavioral evidence of age-related disruptions to bottom-up (externally cued) and top-down (supporting internally driven mental representations) resources to process uncertainty and coordinate task-relevant action. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Individualized temporal patterns dominate cortical upstate and sleep depth in driving human sleep spindle timing.

Sleep spindles are critical for memory consolidation and strongly linked to neurological disease and aging. Despite their significance, the relative influences of factors like sleep depth, cortical up/down states, and spindle temporal patterns on individual spindle production remain poorly understood. Moreover, spindle temporal patterns are typically ignored in favor of an average spindle rate. Here, we analyze spindle dynamics in 1008 participants from the Multi-Ethnic Study of Atherosclerosis using a point process framework. Results reveal fingerprint-like temporal patterns, characterized by a refractory period followed by a period of increased spindle activity, which are highly individualized yet consistent night-to-night. We observe increased timing variability with age and distinct gender/age differences. Strikingly, and in contrast to the prevailing notion, individualized spindle patterns are the dominant determinant of spindle timing, accounting for over 70% of the statistical deviance explained by all of the factors we assessed, surpassing the contribution of slow oscillation (SO) phase (~14%) and sleep depth (~16%). Furthermore, we show spindle/SO coupling dynamics with sleep depth are preserved across age, with a global negative shift towards the SO rising slope. These findings offer novel mechanistic insights into spindle dynamics with direct experimental implications and applications to individualized electroencephalography biomarker identification.

Transient oscillation dynamics during sleep provide a robust basis for electroencephalographic phenotyping and biomarker identification.

Transient oscillatory events in the sleep electroencephalogram represent short-term coordinated network activity. Of particular importance, sleep spindles are transient oscillatory events associated with memory consolidation, which are altered in aging and in several psychiatric and neurodegenerative disorders. Spindle identification, however, currently contains implicit assumptions derived from what waveforms were historically easiest to discern by eye, and has recently been shown to select only a high-amplitude subset of transient events. Moreover, spindle activity is typically averaged across a sleep stage, collapsing continuous dynamics into discrete states. What information can be gained by expanding our view of transient oscillatory events and their dynamics? In this paper, we develop a novel approach to electroencephalographic phenotyping, characterizing a generalized class of transient time-frequency events across a wide frequency range using continuous dynamics. We demonstrate that the complex temporal evolution of transient events during sleep is highly stereotyped when viewed as a function of slow oscillation power (an objective, continuous metric of depth-of-sleep) and phase (a correlate of cortical up/down states). This two-fold power-phase representation has large intersubject variability-even within healthy controls-yet strong night-to-night stability for individuals, suggesting a robust basis for phenotyping. As a clinical application, we then analyze patients with schizophrenia, confirming established spindle (12-15 Hz) deficits as well as identifying novel differences in transient non-rapid eye movement events in low-alpha (7-10 Hz) and theta (4-6 Hz) ranges. Overall, these results offer an expanded view of transient activity, describing a broad class of events with properties varying continuously across spatial, temporal, and phase-coupling dimensions.

Age-related differences in prefrontal glutamate are associated with increased working memory decay that gives the appearance of learning deficits.

The ability to use past experience to effectively guide decision-making declines in older adulthood. Such declines have been theorized to emerge from either impairments of striatal reinforcement learning systems (RL) or impairments of recurrent networks in prefrontal and parietal cortex that support working memory (WM). Distinguishing between these hypotheses has been challenging because either RL or WM could be used to facilitate successful decision-making in typical laboratory tasks. Here we investigated the neurocomputational correlates of age-related decision-making deficits using an RL-WM task to disentangle these mechanisms, a computational model to quantify them, and magnetic resonance spectroscopy to link them to their molecular bases. Our results reveal that task performance is worse in older age, in a manner best explained by working memory deficits, as might be expected if cortical recurrent networks were unable to sustain persistent activity across multiple trials. Consistent with this, we show that older adults had lower levels of prefrontal glutamate, the excitatory neurotransmitter thought to support persistent activity, compared to younger adults. Individuals with the lowest prefrontal glutamate levels displayed the greatest impairments in working memory after controlling for other anatomical and metabolic factors. Together, our results suggest that lower levels of prefrontal glutamate may contribute to failures of working memory systems and impaired decision-making in older adulthood.

Teleportation of hybrid entangled states with continuous-variable entanglement.

Hybrid entanglement between discrete-variable (DV) and continuous-variable (CV) quantum systems is an essential resource for heterogeneous quantum networks. Our previous work showed that in lossy channels the teleportation of DV qubits, via CV-entangled states, can be significantly improved by a new protocol defined by a modified Bell state measurement at the sender. This work explores whether a new, similarly modified, CV-based teleportation protocol can lead to improvement in the transfer of hybrid entangled states. To set the scene, we first determine the performance of such a modified protocol in teleporting CV-only qubits, showing that significant improvement over traditional CV-based teleportation is obtained. We then explore similar modifications in the teleportation of a specific hybrid entangled state showing that significant improvement over traditional CV-based teleportation is again found. For a given channel loss, we find teleporting the DV qubit of the hybrid entangled state can always achieve higher fidelity than teleporting the CV qubit. We then explore the use of various non-Gaussian operations in our modified teleportation protocol, finding that, at a cost of lower success probability, quantum scissors provides the most improvement in the loss tolerance. Our new results emphasize that in lossy conditions, the quantum measurements undertaken at the sender can have a surprising and dramatic impact on CV-based teleportation.

Time perception in film is modulated by sensory modality and arousal.

Considerable research has shown that the perception of time can be distorted subjectively, but little empirical work has examined what factors affect time perception in film, a naturalistic multimodal stimulus. Here, we explore the effect of sensory modality, arousal, and valence on how participants estimate durations in film. Using behavioral ratings combined with pupillometry in a within-participants design, we analyzed responses to and duration estimates of film clips in three experimental conditions: audiovisual (containing music and sound effects), visual (without music and sound effects), and auditory (music and sound effects without a visual scene). Participants viewed clips from little-known nature documentaries, fiction, animation, and experimental films. They were asked to judge clip duration and to report subjective arousal and valence, as their pupil sizes were recorded. Data were analyzed using linear mixed-effects models. Results reveal duration estimates varied between experimental conditions. Clip durations were judged to be shorter than actual durations in all three conditions, with visual-only clips perceived as longer (i.e., less distorted in time) than auditory-only and audiovisual clips. High levels of Composite Arousal (an average of self-reported arousal and pupil size changes) were correlated with longer (more accurate) estimates of duration, particularly in the audiovisual modality. This effect may reflect stimulus complexity or greater cognitive engagement. Increased ratings of valence were correlated with longer estimates of duration. The use of naturalistic, complex stimuli such as film can enhance our understanding of the psychology of time perception.