There is growing evidence in favour of the temporal-coding hypothesis that temporal correlation of neuronal discharges may serve to bind distributed neuronal activity into unique representations and, in particular, that $θ$ (3.5-7.5 Hz) and $δ$ ($0.5<$3.5 Hz) oscillations facilitate information coding. The $θ$ and $δ$ rhythms are shown to be involved in various sleep stages, and during anæsthesia, and they undergo changes with the depth of anæsthesia. We introduce a thalamocortical model of interacting neuronal ensembles to describe phase relationships between $θ$ and $δ$ oscillations, especially during deep and light anæsthesia. Asymmetric and long range interactions among the thalamocortical neuronal oscillators are taken into account. The model results are compared with the experimental observations of Musizza et al. {\it J. Physiol. (London)} 2007 580:315-326. The $δ$ and $θ$ activities are found to be separately generated and are governed by the thalamus and cortex respectively. Changes in the degree of intra--ensemble and inter--ensemble synchrony imply that the neuronal ensembles inhibit information coding during deep anæsthesia and facilitate it during light anæsthesia.
Electrical activity of fungus \emph{Pleurotus ostreatus} is characterised by slow (hours) irregular waves of baseline potential drift and fast (minutes) action potential likes spikes of the electrical potential. An exposure of the mycelium colonised substrate to a chloroform vapour lead to several fold decrease of the baseline potential waves and increase of their duration. The chloroform vapour also causes either complete cessation of spiking activity or substantial reduction of the spiking frequency. Removal of the chloroform vapour from the growth containers leads to a gradual restoration of the mycelium electrical activity.
We propose uncommon self-knowledge (USK) as a candidate criterion for consciousness: synergistic information a system carries about itself that exists only in the joint of its subsystems and is destroyed by decomposition. Drawing on Gottwald's partition-lattice grounding of Partial Information Decomposition (PID), where redundancy corresponds to Aumann's common knowledge and synergy to the gap between separate and joint observation, we propose the synergistic component of self-directed information as a candidate formal signature for conscious processing. If correct, the framework would (1) offer a clean separation between consciousness and metacognition (synergistic vs. redundant self-knowledge), (2) provide principled resolutions to counterexamples that challenge IIT, GWT, and HOT, (3) be operationalizable via Partial Information Rate Decomposition (PIRD) with self-targeting, and (4) generate distinctive empirical predictions, the strongest being a GWT timing dissociation (consciousness correlates with pre-broadcast synergy formation, not broadcast itself) and a specific dissociation between self-report disruption and task-performance disruption under middle-layer perturbation in
Background: Accurate prediction of surgical case duration underpins operating room (OR) scheduling, yet existing models often depend on site- or surgeon-specific inputs and rarely undergo external validation, limiting generalisability. Methods: We undertook a retrospective multicentre study using routinely collected perioperative data from two general hospitals in Japan (development: 1 January 2021-31 December 2023; temporal test: 1 January-31 December 2024). Elective weekday procedures with American Society of Anesthesiologists (ASA) Physical Status 1-4 were included. Pre-specified preoperative predictors comprised surgical context (year, month, weekday, scheduled duration, general anaesthesia indicator, body position) and patient factors (sex, age, body mass index, allergy, infection, comorbidity, ASA). Missing data were addressed by multiple imputation by chained equations. Four learners (elastic-net, generalised additive models, random forest, gradient-boosted trees) were tuned within internal-external cross-validation (IECV; leave-one-cluster-out by centre-year) and combined by stacked generalisation to predict log-transformed duration. Results: We analysed 63,206 procedures (
During deep sleep and under anaesthesia spontaneous patterns of cortical activation frequently take the form of slow travelling waves. Slow wave sleep is an important cognitive state especially because of its relevance for memory consolidation. However, despite extensive research the exact mechanisms are still ill-understood. Novel methods such as high speed widefield imaging of GCamP activity offer new potentials. Here we show how data recorded from transgenic mice under anesthesia can be processed to analyze sources, sinks and patterns of flow. To make the best possible use of the data novel means of data processing are necessary. Therefore, we (1) give a an brief account on processes that play a role in generating slow waves and demonstrate (2) a novel approach to characterize its patterns in GCamP recordings. While slow waves are highly variable, it shows that some are surprisingly similar. To enable quantitative means of analysis and examine the structure of such prototypical events we propose a novel approach for the characterization of slow waves: The Helmholtz-Decomposition of gradient-based Dense Optical Flow of the pixeldense GCamP contrast (df/f). It allows to detect the
Electroencephalogram (EEG) signals reflect brain activity across different brain states, characterized by distinct frequency distributions. Through multifractal analysis tools, we investigate the scaling behaviour of different classes of EEG signals and artifacts. We show that brain states associated to sleep and general anaesthesia are not in general characterized by scale invariance. The lack of scale invariance motivates the development of artifact removal algorithms capable of operating independently at each scale. We examine here the properties of the wavelet quantile normalization algorithm, a recently introduced adaptive method for real-time correction of transient artifacts in EEG signals. We establish general results regarding the regularization properties of the WQN algorithm, showing how it can eliminate singularities introduced by artefacts, and we compare it to traditional thresholding algorithms. Furthermore, we show that the algorithm performance is independent of the wavelet basis. We finally examine its continuity and boundedness properties and illustrate its distinctive non-local action on the wavelet coefficients through pathological examples.
The analysis of survey data is a frequently arising issue in clinical trials, particularly when capturing quantities which are difficult to measure. Typical examples are questionnaires about patient's well-being, pain, or consent to an intervention. In these, data is captured on a discrete scale containing only a limited number of possible answers, from which the respondent has to pick the answer which fits best his/her personal opinion. This data is generally located on an ordinal scale as answers can usually be arranged in an ascending order, e.g., "bad", "neutral", "good" for well-being. Since responses are usually stored numerically for data processing purposes, analysis of survey data using ordinary linear regression models are commonly applied. However, assumptions of these models are often not met as linear regression requires a constant variability of the response variable and can yield predictions out of the range of response categories. By using linear models, one only gains insights about the mean response which may affect representativeness. In contrast, ordinal regression models can provide probability estimates for all response categories and yield information about t
This work presents an RL-based agent for outpatient hysteroscopy training. Hysteroscopy is a gynecological procedure for examination of the uterine cavity. Recent advancements enabled performing this type of intervention in the outpatient setup without anaesthesia. While being beneficial to the patient, this approach introduces new challenges for clinicians, who should take additional measures to maintain the level of patient comfort and prevent tissue damage. Our prior work has presented a platform for hysteroscopic training with the focus on the passage of the cervical canal. With this work, we aim to extend the functionality of the platform by designing a subsystem that autonomously performs the task of the passage of the cervical canal. This feature can later be used as a virtual instructor to provide educational cues for trainees and assess their performance. The developed algorithm is based on the soft actor critic approach to smooth the learning curve of the agent and ensure uniform exploration of the workspace. The designed algorithm was tested against the performance of five clinicians. Overall, the algorithm demonstrated high efficiency and reliability, succeeding in 98%
Needle positioning is essential for various medical applications such as epidural anaesthesia. Physicians rely on their instincts while navigating the needle in epidural spaces. Thereby, identifying the tissue structures may be helpful to the physician as they can provide additional feedback in the needle insertion process. To this end, we propose a deep neural network that classifies the tissues from the phase and intensity data of complex OCT signals acquired at the needle tip. We investigate the performance of the deep neural network in a limited labelled dataset scenario and propose a novel contrastive pretraining strategy that learns invariant representation for phase and intensity data. We show that with 10% of the training set, our proposed pretraining strategy helps the model achieve an F1 score of 0.84 whereas the model achieves an F1 score of 0.60 without it. Further, we analyse the importance of phase and intensity individually towards tissue classification.
Background: Electroencephalographic (EEG) indicators for anaesthesia depth are being developed. Here, we propose novel EEG parameters created by using two kinds of waveforms discriminated by voltage thresholds from a new perspective. Methods: Six young-adult beagles and 6 senior beagles were anaesthetised with end-tidal sevoflurane (SEV) of 2.0%-4.0% in 0.5% intervals, and 2-channel EEG (256 Hz) was recorded. Two events were discriminated: a consecutive part (τ) with a peak-to-peak potential difference (Vpp) within 1-6, 8, 12, or 20 mcrV, and another part (burst) with Vpp outside the threshold. Number of τ (Nτ), mean τ duration (Mτ), total percentage of τ (SRτ), mean burst duration (Mbst), and amplitude of burst (Abst) were evaluated as anaesthesia depth indicators using Pearsons correlation coefficients (r). Results: When Vpp was near the suppression wave threshold, Nτ had the highest correlation with SEV in both groups. As SEV was increased until onset of burst suppression, Nτ decreased, Mτ remained unchanged, and Mbst and Abst increased. In the young-adult group, mean |r| exceeded 0.95 for Nτ with Vpp of 4-6 mcrV, for Mbst with Vpp of 8-12 mcrV, and for Abst with Vpp of 4-12 mcr
Recurrent networks of dynamic elements frequently exhibit emergent collective oscillations, which can display substantial regularity even when the individual elements are considerably noisy. How noise-induced dynamics at the local level coexists with regular oscillations at the global level is still unclear. Here we show that a combination of stochastic recurrence-based initiation with deterministic refractoriness in an excitable network can reconcile these two features, leading to maximum collective coherence for an intermediate noise level. We report this behavior in the slow oscillation regime exhibited by a cerebral cortex network under dynamical conditions resembling slow-wave sleep and anaesthesia. Computational analysis of a biologically realistic network model reveals that an intermediate level of background noise leads to quasi-regular dynamics. We verify this prediction experimentally in cortical slices subject to varying amounts of extracellular potassium, which modulates neuronal excitability and thus synaptic noise. The model also predicts that this effectively regular state should exhibit noise-induced memory of the spatial propagation profile of the collective oscill
We present a non-linear dynamical system for modelling the effect of drug infusions on the vital signs of patients admitted in Intensive Care Units (ICUs). More specifically we are interested in modelling the effect of a widely used anaesthetic drug (Propofol) on a patient's monitored depth of anaesthesia and haemodynamics. We compare our approach with one from the Pharmacokinetics/Pharmacodynamics (PK/PD) literature and show that we can provide significant improvements in performance without requiring the incorporation of expert physiological knowledge in our system.
An upgrade to the ATLAS silicon tracker cooling control system may require a change from C3F8 (octafluoro-propane) to a blend containing 10-30% of C2F6 (hexafluoro-ethane) to reduce the evaporation temperature and better protect the silicon from cumulative radiation damage with increasing LHC luminosity. Central to this upgrade is a new acoustic instrument for the real-time measurement of the C3F8/C2F6 mixture ratio and flow. The instrument and its Supervisory, Control and Data Acquisition (SCADA) software are described in this paper. The instrument has demonstrated a resolution of 3.10-3 for C3F8/C2F6 mixtures with ~20%C2F6, and flow resolution of 2% of full scale for mass flows up to 30gs-1. In mixtures of widely-differing molecular weight (mw), higher mixture precision is possible: a sensitivity of < 5.10-4 to leaks of C3F8 into the ATLAS pixel detector nitrogen envelope (mw difference 160) has been seen. The instrument has many potential applications, including the analysis of mixtures of hydrocarbons, vapours for semi-conductor manufacture and anaesthesia.
Mechanical ventilation is one of the most widely used therapies in the ICU. However, despite broad application from anaesthesia to COVID-related life support, many injurious challenges remain. We frame these as a control problem: ventilators must let air in and out of the patient's lungs according to a prescribed trajectory of airway pressure. Industry-standard controllers, based on the PID method, are neither optimal nor robust. Our data-driven approach learns to control an invasive ventilator by training on a simulator itself trained on data collected from the ventilator. This method outperforms popular reinforcement learning algorithms and even controls the physical ventilator more accurately and robustly than PID. These results underscore how effective data-driven methodologies can be for invasive ventilation and suggest that more general forms of ventilation (e.g., non-invasive, adaptive) may also be amenable.
This report describes technical adaptations of a traumatic brain injury (TBI) model-largely inspired by Marmarou-in order to monitor microdialysis data and PtiO2 (brain tissue oxygen) before, during and after injury. We particularly focalize on our model requirements which allows us to re-create some drastic pathological characteristics experienced by severely head-injured patients: impact on a closed skull, no ventilation immediately after impact, presence of diffuse axonal injuries and secondary brain insults from systemic origin...We notably give priority to minimize anaesthesia duration in order to tend to banish any neuroprotection. Our new model will henceforth allow a better understanding of neurochemical and biochemical alterations resulting from traumatic brain injury, using microdialysis and PtiO2 techniques already monitored in our Intensive Care Unit. Studies on efficiency and therapeutic window of neuroprotective pharmacological molecules are now conceivable to ameliorate severe head-injury treatment.
For more than a century, pianists and music teachers have argued over whether a performer’s touch can actually change the tone color of a piano note — and now scientists say the answer is yes。 Using a cutting-edge sensor system that tracked piano key movements at 1,000 frames per second, researchers discovered that elite pianists subtly manipulate
Scientists used nanoscale gold metamaterials to supercharge heat transfer across tiny gaps, achieving up to four times more energy flow than similar conventional systems。 The breakthrough could lead to better chip cooling, more efficient energy technologies, and a new era of precision heat engineering
Researchers have finally resolved a key problem in a 100-year-old theory of color, showing that the qualities we perceive in colors are intrinsic to the mathematics of color space itself。 The discovery sharpens our understanding of human vision and could lead to more precise color technologies and visualizations
As traditional chip miniaturization slows, researchers have found a way to pack more computing power into the same space by stacking silicon circuits in multiple layers。 The new process uses ultra-thin silicon membranes and low-temperature manufacturing techniques to overcome a major obstacle that has long blocked the production of true 3D chips
NotebookLM is getting a big upgrade, but it's only for AI Ultra and enterprise accounts right now