A state space modeling approach to real-time phase estimation

Brain rhythms have been proposed to facilitate brain function, with an especially important role attributed to the phase of low frequency rhythms. Understanding the role of phase in neural function requires interventions that perturb neural activity at a target phase, necessitating estimation of phase in real-time. Current methods for real-time phase estimation rely on bandpass filtering, which assumes narrowband signals and couples the signal and noise in the phase estimate, adding noise to the phase and impairing detections of relationships between phase and behavior. To address this, we propose a state space phase estimator for real-time tracking of phase. By tracking the analytic signal as a latent state, this framework avoids the requirement of bandpass filtering, separately models the signal and the noise, accounts for rhythmic confounds, and provides credible intervals for the phase estimate. We demonstrate in simulations that the state space phase estimator outperforms current state-of-the-art real-time methods in the contexts of common confounds such as broadband rhythms, phase resets and co-occurring rhythms. Finally, we show applications of this approach to in vivo data. The method is available as a ready-to-use plug-in for the OpenEphys acquisition system, making it widely available for use in experiments.

245Comparison of methods for classifying heterogeneity in gestational diabetes (GDM) and association with outcomes

Background Clinical experience suggests that diverse clinical subtypes exist within the broader diagnosis of GDM. Analysis from a single centre recently outlined heterogeneity in GDM with respect to insulin secretion and sensitivity, defining four GDM subtypes: 1) GDMsecr (<25th centile HOMA-β (Hb) for non-GDM); 2) GDMsens (<25th centile Matsuda Index for non-GDM); 3) GDMmixed (both GDMsecr and GDMsens); 4) GDMND, no defect (neither GDMsecr and GDMsens). Classification using these subtypes is associated with adverse outcomes. Methods Following similar methodology, women with GDM were classified into four subtypes including comparison of Hb, insulinogenic index (II) and Stumvoll first-phase estimate (SV) for defining GDMsecr. Analyses compared neonatal outcomes with non-GDM women and between GDM groups using c2 tests and regression analyses adjusted for multiple confounders including maternal age, BMI and HAPO study centre. Results Hb, II and SV gave divergent results for GDMsecr, with only 19% concordance. In all analyses, GDMND (10% by Hb, 6% by II, 6% by SV) showed outcome frequencies similar to those of non-GDM women; groups 1-3 showed higher risks (p < 0.01 vs non GDM). These results persisted in the fully adjusted model (aOR generally >2.0). Conclusions Different clinical subtypes in GDM are associated with differing risks of adverse outcome. Key messages Determination of GDM subtype can assist in assessing GDM women at higher risk of adverse clinical outcome and help guide clinical practice.

A State Space Modeling Approach to Real-Time Phase Estimation

Brain rhythms have been proposed to facilitate brain function, with an especially important role attributed to the phase of low frequency rhythms. Understanding the role of phase in neural function requires interventions that perturb neural activity at a target phase, necessitating estimation of phase in real-time. Current methods for real-time phase estimation rely on bandpass filtering, which assumes narrowband signals and couples the signal and noise in the phase estimate, adding noise to the phase and impairing detections of relationships between phase and behavior. To address this, we propose a state space phase estimator for real-time tracking of phase. By tracking the analytic signal as a latent state, this framework avoids the requirement of bandpass filtering, separately models the signal and the noise, accounts for rhythmic confounds, and provides credible intervals for the phase estimate. We demonstrate in simulations that the state space phase estimator outperforms current state-of-the-art real-time methods in the contexts of common confounds such as broadband rhythms, phase resets and co-occurring rhythms. Finally, we show applications of this approach to in vivo data. The method is available as a ready-to-use plug-in for the OpenEphys acquisition system, making it widely available for use in experiments.

Phase-dependent closed-loop modulation of neural oscillations in vivo

Normal brain function is associated with an assortment of oscillations of various frequencies, each reflecting the timing of separate computational processes and levels of synchronization within and between brain areas. Stimulation accurately delivered on a specified phase of a given oscillation provides the opportunity to target individual aspects of brain function. To achieve this, we have developed a highly responsive system to produce a continuous online phase-estimate. In addition to stable oscillations, the system accurately tracks the early cycles of short, transient oscillations and can operate across the frequency range of most established neuronal oscillations (4 to 250 Hz). Here we demonstrate bidirectional modulation of the pathologically elevated parkinsonian beta-band oscillation (around 35 Hz) in 6-OHDA hemi-lesioned rats. Beta phase, monitored using a single channel electrocorticogram above secondary motor cortex, was used to drive electrical stimulation of the globus pallidus on one of eight phases spanning the oscillation cycle. Stimulation of the early ascending phase suppressed the oscillation whereas stimulation of the early descending phase was amplifying. By implementing a rule that prevented stimulation when the phase estimate was unstable, we achieved a system that could adapt stimulation rate and pattern to respond to the changes produced in the target oscillation. This allowed the electronic system to create and maintain a state of equilibrium with the biological system resulting in continuous stable modulation of the target oscillation over time. These results demonstrate the feasibility of phase locked stimulation as a more refined strategy for remediation of pathological beta oscillations in the treatment of the motor symptoms of Parkinson’s disease. Furthermore, they establish the utility of our algorithm and allow for the potential to assess the contribution of rhythmic activity in neuronal computation across a number of brain systems.

Adaptive Structured Light with Scatter Correction for High-Precision Underwater 3D Measurements

High-precision underwater 3D cameras are required to automate many of the traditional subsea inspection, maintenance and repair (IMR) operations. In this paper we introduce a novel multi-frequency phase stepping (structured light) method for high-precision 3D estimation even in turbid water. We introduce an adaptive phase-unwrapping procedure which uses the phase-uncertainty to determine the highest frequency that can be reliably unwrapped. Light scattering adversely affects the phase estimate. We propose to remove the effect of forward scatter with an unsharp filter and a model-based method to remove the backscatter effect. Tests in varying turbidity show that the scatter correction removes the adverse effect of scatter on the phase estimates. The adaptive frequency unwrapping with scatter correction results in images with higher accuracy and precision and less phase unwrap errors than the Gray-Code Phase Stepping (GCPS) approach.

Phase estimation of coherent states with a noiseless linear amplifier

Amplification of quantum states is inevitably accompanied with the introduction of noise at the output. For protocols that are probabilistic with heralded success, noiseless linear amplification in theory may still be possible. When the protocol is successful, it can lead to an output that is a noiselessly amplified copy of the input. When the protocol is unsuccessful, the output state is degraded and is usually discarded. Probabilistic protocols may improve the performance of some quantum information protocols, but not for metrology if the whole statistics is taken into consideration. We calculate the precision limits on estimating the phase of coherent states using a noiseless linear amplifier by computing its quantum Fisher information and we show that on average, the noiseless linear amplifier does not improve the phase estimate. We also discuss the case where abstention from measurement can reduce the cost for estimation.