Behavioral and neural measures of confidence using a novel auditory pitch identification task.

Observers can discriminate between correct versus incorrect perceptual decisions with feelings of confidence. The centro-parietal positivity build-up rate (CPP slope) has been suggested as a likely neural signature of accumulated evidence, which may guide both perceptual performance and confidence. However, CPP slope also covaries with reaction time, which also covaries with confidence in previous studies, and performance and confidence typically covary; thus, CPP slope may index signatures of perceptual performance rather than confidence per se. Moreover, perceptual metacognition-including neural correlates-has largely been studied in vision, with few exceptions. Thus, we lack understanding of domain-general neural signatures of perceptual metacognition outside vision. Here we designed a novel auditory pitch identification task and collected behavior with simultaneous 32-channel EEG in healthy adults. Participants saw two tone labels which varied in tonal distance on each trial (e.g., C vs D, C vs F), then heard a single auditory tone; they identified which label was correct and rated confidence. We found that pitch identification confidence varied with tonal distance, but performance, metacognitive sensitivity (trial-by-trial covariation of confidence with accuracy), and reaction time did not. Interestingly, however, while CPP slope covaried with performance and reaction time, it did not significantly covary with confidence. We interpret these results to mean that CPP slope is likely a signature of first-order perceptual processing and not confidence-specific signals or computations in auditory tasks. Our novel pitch identification task offers a valuable method to examine the neural correlates of auditory and domain-general perceptual confidence.

Secretory factors released from high dose radiation-activated osteoclasts increase the expression level of pain-associated neuropeptides in sensory neuronal cultures.

Stereotactic Body Radiation Therapy for lung tumors near the chest wall often causes significant chest wall pain (CWP), negatively impacting patients' quality of life. The mechanisms behind SBRT-induced CWP remain unclear and may involve multiple factors. We investigated the potential crosstalk between radiation-activated osteoclasts and sensory neurons, focusing on osteoclast-derived factors in CWP. Using the murine pre-osteoclast cell line Raw264.7, we induced differentiation with RANKL, followed by 10Gy gamma-irradiation. Conditioned media from these irradiated osteoclasts was used to treat sensory neuronal cultures from mouse dorsal root ganglia. Neuronal cultures were also directly exposed to 10Gy radiation, with and without osteoclast co-culture. Analysis of osteoclast markers and pain-associated neuropeptides was conducted using RT-qPCR and histochemical staining. Osteoclast differentiation and activity were inhibited using Osteoprotegerin and risedronate. Results showed that high-dose radiation significantly increased osteoclast size, resorption pit size, and activity biomarkers. Neurons treated with CM from irradiated osteoclasts showed increased expression of pain-associated neuropeptides CGRP and Substance P, which was mitigated by osteoprotegerin and risedronate. This study suggests that high-dose radiation enhances osteoclast activity, upregulating pain-associated neuropeptides in sensory neurons, and that inhibitors like osteoprotegerin and risedronate may offer therapeutic strategies for managing radiation-induced pain.

Unconscious Perception of Vernier Offsets.

The comparison between conscious and unconscious perception is a cornerstone of consciousness science. However, most studies reporting above-chance discrimination of unseen stimuli do not control for criterion biases when assessing awareness. We tested whether observers can discriminate subjectively invisible offsets of Vernier stimuli when visibility is probed using a bias-free task. To reduce visibility, stimuli were either backward masked or presented for very brief durations (1-3 milliseconds) using a modern-day Tachistoscope. We found some behavioral indicators of perception without awareness, and yet, no conclusive evidence thereof. To seek more decisive proof, we simulated a series of Bayesian observer models, including some that produce visibility judgements alongside type-1 judgements. Our data are best accounted for by observers with slightly suboptimal conscious access to sensory evidence. Overall, the stimuli and visibility manipulations employed here induced mild instances of blindsight-like behavior, making them attractive candidates for future investigation of this phenomenon.

A predictor-informed multi-subject bayesian approach for dynamic functional connectivity.

Dynamic functional connectivity investigates how the interactions among brain regions vary over the course of an fMRI experiment. Such transitions between different individual connectivity states can be modulated by changes in underlying physiological mechanisms that drive functional network dynamics, e.g., changes in attention or cognitive effort. In this paper, we develop a multi-subject Bayesian framework where the estimation of dynamic functional networks is informed by time-varying exogenous physiological covariates that are simultaneously recorded in each subject during the fMRI experiment. More specifically, we consider a dynamic Gaussian graphical model approach where a non-homogeneous hidden Markov model is employed to classify the fMRI time series into latent neurological states. We assume the state-transition probabilities to vary over time and across subjects as a function of the underlying covariates, allowing for the estimation of recurrent connectivity patterns and the sharing of networks among the subjects. We further assume sparsity in the network structures via shrinkage priors, and achieve edge selection in the estimated graph structures by introducing a multi-comparison procedure for shrinkage-based inferences with Bayesian false discovery rate control. We evaluate the performances of our method vs alternative approaches on synthetic data. We apply our modeling framework on a resting-state experiment where fMRI data have been collected concurrently with pupillometry measurements, as a proxy of cognitive processing, and assess the heterogeneity of the effects of changes in pupil dilation on the subjects' propensity to change connectivity states. The heterogeneity of state occupancy across subjects provides an understanding of the relationship between increased pupil dilation and transitions toward different cognitive states.

Tests for consciousness in humans and beyond.

Which systems/organisms are conscious? New tests for consciousness ('C-tests') are urgently needed. There is persisting uncertainty about when consciousness arises in human development, when it is lost due to neurological disorders and brain injury, and how it is distributed in nonhuman species. This need is amplified by recent and rapid developments in artificial intelligence (AI), neural organoids, and xenobot technology. Although a number of C-tests have been proposed in recent years, most are of limited use, and currently we have no C-tests for many of the populations for which they are most critical. Here, we identify challenges facing any attempt to develop C-tests, propose a multidimensional classification of such tests, and identify strategies that might be used to validate them.

Development of infection prevention program standards for critical access hospitals.

Although critical access hospitals are small, the expected infection prevention activities remain extensive. Program standards, aligned with the Association for Professionals in Infection Control and Epidemiology infection prevention competency model domains, were developed and implemented in a midwestern health care system. Time estimates for completion of each activity were assigned and then extrapolated to offer guidance on necessary full-time equivalents for adequate staffing.

Toward 'Computational-Rationality' Approaches to Arbitrating Models of Cognition: A Case Study Using Perceptual Metacognition.

Perceptual confidence results from a metacognitive process which evaluates how likely our percepts are to be correct. Many competing models of perceptual metacognition enjoy strong empirical support. Arbitrating these models traditionally proceeds via researchers conducting experiments and then fitting several models to the data collected. However, such a process often includes conditions or paradigms that may not best arbitrate competing models: Many models make similar predictions under typical experimental conditions. Consequently, many experiments are needed, collectively (sub-optimally) sampling the space of conditions to compare models. Here, instead, we introduce a variant of optimal experimental design which we call a computational-rationality approach to generative models of cognition, using perceptual metacognition as a case study. Instead of designing experiments and post-hoc specifying models, we began with comprehensive model comparison among four competing generative models for perceptual metacognition, drawn from literature. By simulating a simple experiment under each model, we identified conditions where these models made maximally diverging predictions for confidence. We then presented these conditions to human observers, and compared the models' capacity to predict choices and confidence. Results revealed two surprising findings: (1) two models previously reported to differently predict confidence to different degrees, with one predicting better than the other, appeared to predict confidence in a direction opposite to previous findings; and (2) two other models previously reported to equivalently predict confidence showed stark differences in the conditions tested here. Although preliminary with regards to which model is actually 'correct' for perceptual metacognition, our findings reveal the promise of this computational-rationality approach to maximizing experimental utility in model arbitration while minimizing the number of experiments necessary to reveal the winning model, both for perceptual metacognition and in other domains.

Consciousness beyond the human case.

There is growing interest in the relationship been AI and consciousness. Joseph LeDoux and Jonathan Birch thought it would be a good moment to put some of the big questions in this area to some leading experts. The challenge of addressing the questions they raised was taken up by Kristin Andrews, Nicky Clayton, Nathaniel Daw, Chris Frith, Hakwan Lau, Megan Peters, Susan Schneider, Anil Seth, Thomas Suddendorf, and Marie Vanderkerckhoeve.

Locus coeruleus integrity predicts ease of attaining and maintaining neural states of high attentiveness.

The locus coeruleus (LC), a small subcortical structure in the brainstem, is the brain's principal source of norepinephrine. It plays a primary role in regulating stress, the sleep-wake cycle, and attention, and its degradation is associated with aging and neurodegenerative diseases associated with cognitive deficits (e.g., Parkinson's, Alzheimer's). Yet precisely how norepinephrine drives brain networks to support healthy cognitive function remains poorly understood - partly because LC's small size makes it difficult to study noninvasively in humans. Here, we characterized LC's influence on brain dynamics using a hidden Markov model fitted to functional neuroimaging data from healthy young adults across four attention-related brain networks and LC. We modulated LC activity using a behavioral paradigm and measured individual differences in LC magnetization transfer contrast. The model revealed five hidden states, including a stable state dominated by salience-network activity that occurred when subjects actively engaged with the task. LC magnetization transfer contrast correlated with this state's stability across experimental manipulations and with subjects' propensity to enter into and remain in this state. These results provide new insight into LC's role in driving spatiotemporal neural patterns associated with attention, and demonstrate that variation in LC integrity can explain individual differences in these patterns even in healthy young adults.

Pediatric Sedation Unit: A Safe, Effective, and Satisfactory Alternative to Outpatient Pediatric Urological Procedures Under Anesthesia.

INTRODUCTION: The operating room is an increasingly expensive and limited resource. The aim of this study was to evaluate the efficacy, safety, cost, and parental satisfaction of transitioning minor pediatric urology procedures from an operating room setting to a pediatric sedation unit. METHODS: Minor urological procedures were transitioned from the operating room to the pediatric sedation unit if they could be completed in 20 minutes using minimal instrumentation. Information regarding patient demographics, procedure characteristics, rates of success and complications, and cost were collected from urology procedures performed in the pediatric sedation unit between August 2019 and September 2021. Patient demographics and cost data from the most common urology procedures performed in the pediatric sedation unit were compared to data from historical controls of cases occurring in the operating room. Parent surveys were performed following the completion of procedures in the pediatric sedation unit. RESULTS: A total of 103 patients, ranging from 6-207 months old (mean 72 months), underwent procedures in the pediatric sedation unit. The most common procedures were lysis of adhesions and meatotomy. All procedures were successfully completed with procedural sedation, and no procedure was complicated by serious sedation adverse events. The cost reduction for lysis of adhesions in the pediatric sedation unit was 53.5% compared to the operating room, and meatotomy was 27.9%, leading to approximately $57,000 cost savings per year. Fifty families completed a follow-up satisfaction survey, and 83% of parents were satisfied with the care their family received. CONCLUSIONS: The pediatric sedation unit can provide a successful and cost-efficient alternative to the operating room while preserving safety and high rates of parental satisfaction.

An exploratory study of distractions during the induction phase of pediatric procedural sedation with propofol.

INTRODUCTION: Distractions are a leading cause of disturbance to workflow during medical care. Distractions affecting the anesthetic team in the operating room are frequent and have a negative impact on patient care one-fifth of the time. The objective of this study was to evaluate the frequency, source, target, and impact of distractions during the induction phase of pediatric procedural sedation outside the operating room. METHODS: Distractions were analyzed during propofol induction for oncology procedures from 45 video recordings. Distraction was defined as any event that disturbs or has potential to disturb the sedation team from performing their primary tasks. The type of distraction was cataloged into communication, coordination, extraneous events, equipment, layout, and usability. A five-point Likert scale was used to quantify the impact on the sedation team or its members. RESULTS: All patients had a diagnosis of acute lymphocytic leukemia and had a mean age of 8.4 years. Five hundred and sixty-seven distractions occurred and averaged 12.6 events (±5.6) per induction (mean induction time 3 min 12 s). Extraneous events were most common, accounting for 55% (312/567) of all distractions. Most distractions had an impact on the sedation team's workflow, resulting in multitasking (46%, n = 262), and in either brief or complete disruption from a primary task (17%). Sedation nurses were impacted most often, 62% of the time. Coordination and usability issues resulted in the greatest negative impact, mean ± SD, 3.7 ± 1.0 and 3.5 ± 0.9, respectively. There was no significant association between distractions and adverse events or induction length. DISCUSSION: Distractions are common during procedural sedation, with extraneous events being most frequent. Coordination issues within the team and usability problems had the greatest negative impact on sedation team workflow. Nurses were the most frequent target. CONCLUSION: Distractions impacted sedation team workflow but had no association with patient outcomes.

Serious Infusion Reactions in Two Adolescents Receiving Bebtelovimab.

There is scant literature available for pediatric prescribers regarding safety and efficacy of monoclonal antibody formulations against coronavirus disease 2019 (COVID-19). Here, we present 2 cases of serious infusion reactions in adolescent patients receiving the monoclonal antibody bebtelovimab and a succinct review of available antiviral medications for pediatric patients with mild or moderate COVID-19.

A Novel Hidden Markov Approach to Studying Dynamic Functional Connectivity States in Human Neuroimaging.

Introduction: Hidden Markov models (HMMs) are a popular choice to extract and examine recurring patterns of activity or functional connectivity in neuroimaging data, both in terms of spatial patterns and their temporal progression. Although many diverse HMMs have been applied to neuroimaging data, most have defined states based on activity levels (intensity-based [IB] states) rather than patterns of functional connectivity between brain areas (connectivity-based states), which is problematic if we want to understand connectivity dynamics: IB states are unlikely to provide comprehensive information about dynamic connectivity patterns. Methods: We addressed this problem by introducing a new HMM that defines states based on full functional connectivity (FFC) profiles among brain regions. We empirically explored the behavior of this new model in comparison to existing approaches based on IB or summed functional connectivity states using the Human Connectome Project unrelated 100 functional magnetic resonance imaging "resting-state" dataset. Results: Our FFC model discovered connectivity states with more distinguishable (i.e., unique and separable from each other) patterns than previous approaches, and recovered simulated connectivity-based states more faithfully than the other models tested. Discussion: Thus, if our goal is to extract and interpret connectivity states in neuroimaging data, our new model outperforms previous methods, which miss crucial information about the evolution of functional connectivity in the brain. Impact statement Hidden Markov models (HMMs) can be used to investigate brain states noninvasively. Previous models "recover" connectivity from intensity-based hidden states, or from connectivity "summed" across nodes. In this study, we introduce a novel connectivity-based HMM and show how it can reveal true connectivity hidden states under minimal assumptions.

Comparing random dot motion in MATLAB vs. Inquisit Millisecond.

Random Dot Motion (RDM) displays refer to clouds of independently moving dots that can be parametrically manipulated to provide a perception of the overall cloud moving coherently in a specified direction of motion. As a well-studied probe of motion perception, RDMs have been widely employed to understand underlying neural mechanisms of motion perception, perceptual decision-making, and perceptual learning, among other processes. Despite their wide use, RDM stimuli implementation is highly dependent on the parameters and the generation algorithm of the stimuli; both can greatly influence behavioral performance on RDM tasks. With the advent of the COVID pandemic and an increased need for more accessible platforms, we aimed to validate a novel RDM paradigm on Inquisit Millisecond, a platform for the online administration of cognitive and neuropsychological tests and assessments. We directly compared, in the same participants using the same display, a novel RDM paradigm on both Inquisit Millisecond and MATLAB with Psychtoolbox. We found that psychometric functions of Coherence largely match between Inquisit Millisecond and MATLAB, as do the effects of Duration. These data demonstrate that the Millisecond RDM provides data largely consistent with those previously found in laboratory-based systems, and the present findings can serve as a reference point for expected thresholds for when these procedures are used remotely on different platforms.

Recent Advances at the Interface of Neuroscience and Artificial Neural Networks.

Biological neural networks adapt and learn in diverse behavioral contexts. Artificial neural networks (ANNs) have exploited biological properties to solve complex problems. However, despite their effectiveness for specific tasks, ANNs are yet to realize the flexibility and adaptability of biological cognition. This review highlights recent advances in computational and experimental research to advance our understanding of biological and artificial intelligence. In particular, we discuss critical mechanisms from the cellular, systems, and cognitive neuroscience fields that have contributed to refining the architecture and training algorithms of ANNs. Additionally, we discuss how recent work used ANNs to understand complex neuronal correlates of cognition and to process high throughput behavioral data.

Towards characterizing the canonical computations generating phenomenal experience.

Science and philosophy have long struggled with how to even begin studying the neural or computational basis of qualitative experience. Here I review psychological, neuroscience, and philosophical literature to reveal how perceptual metacognition possesses five unique properties that provide a powerful opportunity for studying the neural and computational correlates of subjective experience: (1) Metacognition leads to subjective experiences (we "feel" confident); (2) Metacognition is "about" internal representations, formalizing introspection; (3) Metacognitive computations are "recursive" (applying to meta-cognition and meta-meta-cognition), so we might discover "canonical computations" preserved across processing levels and implementations; (4) Metacognition is anchored to observable behavior; and (5) Metacognitive computations are unobservable yet hierarchically dependent, requiring development of sensitive, specific models. Given these properties, computational models of metacognition provide an empirically-tractable early step in characterizing the generative process that constructs qualitative experience. I also present practical ways to make progress in this vein, applying decades of developments in nearby fields to perceptual metacognition to reveal new and exciting insights about how the brain constructs subjective conscious experiences.

The Nature, Frequency, and Timing of Pediatric Sedation Adverse Events.

OBJECTIVES: The nature and frequency of pediatric sedation adverse events (AEs) have been well described. However, the timing of specific AEs in induction, procedure, and recovery phase of sedation remains unknown. The objective was to describe the nature, frequency, and timing of AEs. We hypothesized that most AEs would start at the induction phase. METHODS: We examined prospectively collected data of sedation encounters of children 3 months to 18 years of age, characterized by at least 1 AE, from January 1, 2013 to December 31, 2020. Patient characteristics, primary diagnosis, procedure type, nature, frequency, and timing of AEs were reported. RESULTS: Of 12 012 sedation encounters, the mean age was 7.6 (SD = 4.9) years, most (89%) were American Society of Anesthesiologists II risk, the most common diagnosis was hematology/oncology (27.3%) and the most common procedure radiologic (47.8%). At least 1 AE occurred during 765 (6.4%) encounters. Respiratory AEs were most common (n = 645, 5.4% of all encounters) and started more often during induction (64.5% of respiratory AEs). Partial upper airway obstruction was the most common respiratory AE (2.8% of all encounters). Partial (59.4%) and complete (77.3%) upper airway obstruction and apnea (84%) all began more often during induction. Laryngospasm (48.4% vs 46.8%) and hypoxemia (59.3% vs 39%) were similarly distributed between induction and procedure, respectively, though they were rare during recovery. CONCLUSIONS: Most respiratory events in this cohort started during the induction or procedure phases. The sedation team should be especially prepared to administer rescue maneuvers and allocate staff/resources during these phases.

Consensus Goals in the Field of Visual Metacognition.

Despite the tangible progress in psychological and cognitive sciences over the last several years, these disciplines still trail other more mature sciences in identifying the most important questions that need to be solved. Reaching such consensus could lead to greater synergy across different laboratories, faster progress, and increased focus on solving important problems rather than pursuing isolated, niche efforts. Here, 26 researchers from the field of visual metacognition reached consensus on four long-term and two medium-term common goals. We describe the process that we followed, the goals themselves, and our plans for accomplishing these goals. If this effort proves successful within the next few years, such consensus building around common goals could be adopted more widely in psychological science.

Interaction of prior category knowledge and novel statistical patterns during visual search for real-world objects.

Two aspects of real-world visual search are typically studied in parallel: category knowledge (e.g., searching for food) and visual patterns (e.g., predicting an upcoming street sign from prior street signs). Previous visual search studies have shown that prior category knowledge hinders search when targets and distractors are from the same category. Other studies have shown that task-irrelevant patterns of non-target objects can enhance search when targets appear in locations that previously contained these irrelevant patterns. Combining EEG (N2pc ERP component, a neural marker of target selection) and behavioral measures, the present study investigated how search efficiency is simultaneously affected by prior knowledge of real-world objects (food and toys) and irrelevant visual patterns (sequences of runic symbols) within the same paradigm. We did not observe behavioral differences between locating items in patterned versus random locations. However, the N2pc components emerged sooner when search items appeared in the patterned location, compared to the random location, with a stronger effect when search items were targets, as opposed to non-targets categorically related to the target. A multivariate pattern analysis revealed that neural responses during search trials in the same time window reflected where the visual patterns appeared. Our finding contributes to our understanding of how knowledge acquired prior to the search task (e.g., category knowledge) interacts with new content within the search task.

Anticholinergics and serious adverse events in pediatric procedural sedation: A report of the pediatric sedation research consortiums.

BACKGROUND: Pediatric sedation is a clinical activity with potential for serious but rare airway adverse events, particularly laryngospasm. Anticholinergic drugs, atropine and glycopyrrolate, are frequently used with the intention to improve sedation safety by virtue of their antisialagogue effects. AIMS: The objective of this study is to describe the current practice of anticholinergic use in pediatric sedation and to compare the frequency of serious sedation-related adverse events in patients who received anticholinergics to those who did not. METHODS: We examined prospectively collected data from the Pediatric Sedation Research Consortium database. Patient characteristics, procedure type, sedation provider, sedatives, location of sedation, anticholinergic administered, adverse events, and airway interventions were reported. Propensity score matching and multivariable logistic regression were used to test whether any association exists between anticholinergic use and serious sedation-related adverse events. RESULTS: Anticholinergics were administered in 7.1% (n = 18 707) of all cases (n = 263 883) reported between November 2011 and October 2017. When anticholinergics were used, atropine was used in 22% (n = 4111) and glycopyrrolate in 78.1% (n = 14 601) of sedations. Use of anticholinergics was more common in patients with well-described risk factors for airway adverse events: active/history of upper respiratory infection, history of reactive airway disease/asthma, and exposure to smoke. However, infants and ASA 3 patients were not associated with higher rate of anticholinergic use. Anticholinergic use was independently associated with an increase in the odds of serious adverse events, OR 1.8 (95% CI 1.6-2.1), especially airway adverse events. CONCLUSIONS: In this large Pediatric Sedation Research Consortium study, we found the use of anticholinergic adjuvants independently associated with greater odds of serious adverse events, especially airway adverse events, after adjusting for well-known sedation risk factors using propensity score matching and multivariate analysis.