Subcortical origin of nonlinear sound encoding in auditory cortex.

A major challenge in neuroscience is to understand how neural representations of sensory information are transformed by the network of ascending and descending connections in each sensory system. By recording from neurons at several levels of the auditory pathway, we show that much of the nonlinear encoding of complex sounds in auditory cortex can be explained by transformations in the midbrain and thalamus. Modeling cortical neurons in terms of their inputs across these subcortical populations enables their responses to be predicted with unprecedented accuracy. By contrast, subcortical responses cannot be predicted from descending cortical inputs, indicating that ascending transformations are irreversible, resulting in increasingly lossy, higher-order representations across the auditory pathway. Rather, auditory cortex selectively modulates the nonlinear aspects of thalamic auditory responses and the functional coupling between subcortical neurons without affecting the linear encoding of sound. These findings reveal the fundamental role of subcortical transformations in shaping cortical responses.

Automatic Population of the Case Report Forms for an International Multifactorial Adaptive Platform Trial Amid the COVID-19 Pandemic.

OBJECTIVES: To automatically populate the case report forms (CRFs) for an international, pragmatic, multifactorial, response-adaptive, Bayesian COVID-19 platform trial. METHODS: The locations of focus included 27 hospitals and 2 large electronic health record (EHR) instances (1 Cerner Millennium and 1 Epic) that are part of the same health system in the United States. This paper describes our efforts to use EHR data to automatically populate four of the trial's forms: baseline, daily, discharge, and response-adaptive randomization. RESULTS: Between April 2020 and May 2022, 417 patients from the UPMC health system were enrolled in the trial. A MySQL-based extract, transform, and load pipeline automatically populated 499 of 526 CRF variables. The populated forms were statistically and manually reviewed and then reported to the trial's international data coordinating center. CONCLUSIONS: We accomplished automatic population of CRFs in a large platform trial and made recommendations for improving this process for future trials.

Individual and ecological heterogeneity promote complex communication in social vertebrate group decisions.

To receive the benefits of social living, individuals must make effective group decisions that enable them to achieve behavioural coordination and maintain cohesion. However, heterogeneity in the physical and social environments surrounding group decision-making contexts can increase the level of difficulty social organisms face in making decisions. Groups that live in variable physical environments (high ecological heterogeneity) can experience barriers to information transfer and increased levels of ecological uncertainty. In addition, in groups with large phenotypic variation (high individual heterogeneity), individuals can have substantial conflicts of interest regarding the timing and nature of activities, making it difficult for them to coordinate their behaviours or reach a consensus. In such cases, active communication can increase individuals' abilities to achieve coordination, such as by facilitating the transfer and aggregation of information about the environment or individual behavioural preferences. Here, we review the role of communication in vertebrate group decision-making and its relationship to heterogeneity in the ecological and social environment surrounding group decision-making contexts. We propose that complex communication has evolved to facilitate decision-making in specific socio-ecological contexts, and we provide a framework for studying this topic and testing related hypotheses as part of future research in this area. This article is part of the theme issue 'The power of sound: unravelling how acoustic communication shapes group dynamics'.

Neural processing in the primary auditory cortex following cholinergic lesions of the basal forebrain in ferrets.

Cortical acetylcholine (ACh) release has been linked to various cognitive functions, including perceptual learning. We have previously shown that cortical cholinergic innervation is necessary for accurate sound localization in ferrets, as well as for their ability to adapt with training to altered spatial cues. To explore whether these behavioral deficits are associated with changes in the response properties of cortical neurons, we recorded neural activity in the primary auditory cortex (A1) of anesthetized ferrets in which cholinergic inputs had been reduced by making bilateral injections of the immunotoxin ME20.4-SAP in the nucleus basalis (NB) prior to training the animals. The pattern of spontaneous activity of A1 units recorded in the ferrets with cholinergic lesions (NB ACh-) was similar to that in controls, although the proportion of burst-type units was significantly lower. Depletion of ACh also resulted in more synchronous activity in A1. No changes in thresholds, frequency tuning or in the distribution of characteristic frequencies were found in these animals. When tested with normal acoustic inputs, the spatial sensitivity of A1 neurons in the NB ACh- ferrets and the distribution of their preferred interaural level differences also closely resembled those found in control animals, indicating that these properties had not been altered by sound localization training with one ear occluded. Simulating the animals' previous experience with a virtual earplug in one ear reduced the contralateral preference of A1 units in both groups, but caused azimuth sensitivity to change in slightly different ways, which may reflect the modest adaptation observed in the NB ACh- group. These results show that while ACh is required for behavioral adaptation to altered spatial cues, it is not required for maintenance of the spectral and spatial response properties of A1 neurons.

Predicting Homelessness Among Transitioning U.S. Army Soldiers.

INTRODUCTION: This study develops a practical method to triage Army transitioning service members (TSMs) at highest risk of homelessness to target a preventive intervention. METHODS: The sample included 4,790 soldiers from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS) who participated in 1 of 3 Army STARRS 2011-2014 baseline surveys followed by the third wave of the STARRS-LS online panel surveys (2020-2022). Two machine learning models were trained: a Stage-1 model that used administrative predictors and geospatial data available for all TSMs at discharge to identify high-risk TSMs for initial outreach; and a Stage-2 model estimated in the high-risk subsample that used self-reported survey data to help determine highest risk based on additional information collected from high-risk TSMs once they are contacted. The outcome in both models was homelessness within 12 months after leaving active service. RESULTS: Twelve-month prevalence of post-transition homelessness was 5.0% (SE=0.5). The Stage-1 model identified 30% of high-risk TSMs who accounted for 52% of homelessness. The Stage-2 model identified 10% of all TSMs (i.e., 33% of high-risk TSMs) who accounted for 35% of all homelessness (i.e., 63% of the homeless among high-risk TSMs). CONCLUSIONS: Machine learning can help target outreach and assessment of TSMs for homeless prevention interventions.

Toward personalized care for insomnia in the US Army: a machine learning model to predict response to cognitive behavioral therapy for insomnia.

STUDY OBJECTIVES: The standard of care for military personnel with insomnia is cognitive behavioral therapy for insomnia (CBT-I). However, only a minority seeking insomnia treatment receive CBT-I, and little reliable guidance exists to identify those most likely to respond. As a step toward personalized care, we present results of a machine learning (ML) model to predict CBT-I response. METHODS: Administrative data were examined for n = 1,449 nondeployed US Army soldiers treated for insomnia with CBT-I who had moderate-severe baseline Insomnia Severity Index (ISI) scores and completed 1 or more follow-up ISIs 6-12 weeks after baseline. An ensemble ML model was developed in a 70% training sample to predict clinically significant ISI improvement (reduction of at least 2 standard deviations on the baseline ISI distribution). Predictors included a wide range of military administrative and baseline clinical variables. Model accuracy was evaluated in the remaining 30% test sample. RESULTS: 19.8% of patients had clinically significant ISI improvement. Model area under the receiver operating characteristic curve (standard error) was 0.60 (0.03). The 20% of test-sample patients with the highest probabilities of improvement were twice as likely to have clinically significant improvement compared with the remaining 80% (36.5% vs 15.7%; χ21 = 9.2, P = .002). Nearly 85% of prediction accuracy was due to 10 variables, the most important of which were baseline insomnia severity and baseline suicidal ideation. CONCLUSIONS: Pending replication, the model could be used as part of a patient-centered decision-making process for insomnia treatment. Parallel models will be needed for alternative treatments before such a system is of optimal value. CITATION: Gabbay FH, Wynn GH, Georg MW, et al. Toward personalized care for insomnia in the US Army: a machine learning model to predict response to cognitive behavioral therapy for insomnia. J Clin Sleep Med. 2024;20(6):921-931.

NHE3 inhibitor tenapanor maintains intestinal barrier function, decreases visceral hypersensitivity, and attenuates TRPV1 signaling in colonic sensory neurons.

The pathogenesis of irritable bowel syndrome (IBS) is multifactorial, characterized in part by increased intestinal permeability, and visceral hypersensitivity. Increased permeability is associated with IBS severity and abdominal pain. Tenapanor is FDA-approved for the treatment of IBS with constipation (IBS-C) and has demonstrated improvements in bowel motility and a reduction in IBS-related pain; however, the mechanism by which tenapanor mediates these functions remains unclear. Here, the effects of tenapanor on colonic pain signaling and intestinal permeability were assessed through behavioral, electrophysiological, and cell culture experiments. Intestinal motility studies in rats and humans demonstrated that tenapanor increased luminal sodium and water retention and gastrointestinal transit versus placebo. A significantly reduced visceral motor reflex (VMR) to colonic distension was observed with tenapanor treatment versus vehicle in two rat models of visceral hypersensitivity (neonatal acetic acid sensitization and partial restraint stress; both P < 0.05), returning VMR responses to that of nonsensitized controls. Whole cell voltage patch-clamp recordings of retrogradely labeled colonic dorsal root ganglia (DRG) neurons from sensitized rats found that tenapanor significantly reduced DRG neuron hyperexcitability to capsaicin versus vehicle (P < 0.05), an effect not mediated by epithelial cell secretions. Tenapanor also attenuated increases in intestinal permeability in human colon monolayer cultures caused by incubation with proinflammatory cytokines (P < 0.001) or fecal supernatants from patients with IBS-C (P < 0.005). These results support a model in which tenapanor reduces IBS-related pain by strengthening the intestinal barrier, thereby decreasing permeability to macromolecules and antigens and reducing DRG-mediated pain signaling.NEW & NOTEWORTHY A series of nonclinical experiments support the theory that tenapanor inhibits IBS-C-related pain by strengthening the intestinal barrier. Tenapanor treatment reduced visceral motor responses to nonsensitized levels in two rat models of hypersensitivity and reduced responses to capsaicin in sensitized colonic nociceptive dorsal root ganglia neurons. Intestinal permeability experiments in human colon monolayer cultures found that tenapanor attenuates increases in permeability induced by either inflammatory cytokines or fecal supernatants from patients with IBS-C.

The impact of COVID-19 vaccine reactions on secondary vaccine hesitancy.

BACKGROUND: Primary and booster vaccinations are critical for mitigating COVID-19 transmission, morbidity, and mortality. Future booster vaccine campaigns rely on an increased understanding of vaccine hesitancy. OBJECTIVE: To evaluate self-reported allergic and skin vaccine reactions as factors potentially associated with vaccine hesitancy in a nationwide vaccine allergy registry. METHODS: Responses to survey questions concerning COVID-19 vaccine perceptions, coded from free text by 2 independent reviewers. Multivariable logistic regression models were used to determine the association between changed negative perception and respondent demographics, vaccination history, and reaction characteristics. RESULTS: A total of 993 individuals (median of 46 years [IQR, 36-59], 88% female, 82% White) self-reported reactions to COVID-19 vaccination. Reactions included the following: delayed large local skin reaction (40%), hives/urticaria (32%), immediate large local skin reaction (3%), swelling (3%), anaphylaxis (2%), and other or unspecified (20%). Most respondents were initially unconcerned about the safety of COVID-19 vaccines (56%). After reactions, 401 of 993 (40%) report negative change in perception of vaccination, with more than half of these respondents (n = 211, 53%) citing their reasoning as a negative experience with adverse effects. Of 102 individuals asked about future vaccination, 79 (77%) indicated that they were unlikely or very unlikely to receive future COVID-19 vaccinations. Increased negative perception after reaction was associated with younger age, later COVID-19 vaccination dose number, and reaction type. CONCLUSION: Our findings reveal that an individual's experience with allergic or cutaneous adverse effects after COVID-19 vaccination affects attitudes and decision-making regarding future vaccination, even in initially non-hesitant individuals. Further investigation of secondary vaccine hesitancy is necessary for adapting public health messaging to this important population.

Machine learning-based prediction of low-value care for hospitalized patients.

Objective: Low-value care (i.e., costly health care treatments that provide little or no benefit) is an ongoing problem in United States hospitals. Traditional strategies for reducing low-value care are only moderately successful. Informed by behavioral science principles, we sought to use machine learning to inform a targeted prompting system that suggests preferred alternative treatments at the point of care but before clinicians have made a decision. Methods: We used intravenous administration of albumin for fluid resuscitation in intensive care unit (ICU) patients as an exemplar of low-value care practice, identified using the electronic health record of a multi-hospital health system. We divided all ICU episodes into 4-h periods and defined a set of relevant clinical features at the period level. We then developed two machine learning models: a single-stage model that directly predicts if a patient will receive albumin in the next period; and a two-stage model that first predicts if any resuscitation fluid will be administered and then predicts albumin only among the patients with a high probability of fluid use. Results: We examined 87,489 ICU episodes divided into approximately 1.5 million 4-h periods. The area under the receiver operating characteristic curve was 0.86 for both prediction models. The positive predictive value was 0.21 (95% confidence interval: 0.20, 0.23) for the single-stage model and 0.22 (0.20, 0.23) for the two-stage model. Applying either model in a targeted prompting system could prevent 10% of albumin administrations, with an attending physician receiving one prompt every 4.2 days of ICU service. Conclusion: Prediction of low-value care is feasible and could enable a point-of-care, targeted prompting system that offers suggestions ahead of the moment of need before clinicians have already decided. A two-stage approach does not improve performance but does interject new levers for the calibration of such a system.

Influence of Metal Identity and Complex Nuclearity in Kumada Cross-Coupling Polymerizations with a Pyridine Diimine-Based Ligand Scaffold.

Cross-coupling polymerizations have fundamentally changed the field of conjugated polymers (CPs) by expanding the scope of accessible materials. Despite the prevalence of cross-coupling in CP synthesis, almost all polymerizations rely on mononuclear Ni or Pd catalysts. Here, we report a systematic exploration of mono- and dinuclear Fe and Ni precatalysts with a pyridine diimine ligand scaffold for Kumada cross-coupling polymerization of a donor thiophene and an acceptor benzotriazole monomers. We observe that variation of the metal identity from Ni to Fe produces contrasting polymerization mechanisms, while complex nuclearity has a minimal impact on reactivity. Specifically, Fe complexes appear to catalyze step-growth Kumada polymerizations and can readily access both Csp2-Csp3 and Csp2-Csp2 cross-couplings, while Ni complexes catalyze chain-growth polymerizations and predominantly Csp2-Csp2 cross-couplings. Thus, our work sheds light on important design parameters for transition metal complexes used in cross-coupling polymerizations, demonstrates the viability of iron catalysis in Kumada polymerization, and opens the door to novel polymer compositions.

Smoking status among cancer patients by specialty: A U.S. nationwide representative analysis.

BACKGROUND: Persistence in tobacco use among cancer survivors has been associated with a multitude of clinicodemographic factors. However, there is a paucity of understanding regarding the role the healthcare professional's specialty plays in tobacco cessation in tobacco-related cancer survivors. METHODS: We conducted a cross-sectional analysis of data from cancer survivors with a smoking history using the Behavioral Risk Factor Surveillance System (BRFSS) database to examine differences in the proportion of patients continuing tobacco use among patients with a diagnosis of cancer segregated by cancer site specialty over the 2016-2020 period. We accounted for complex survey design and used sampling weights to obtain a nationwide representative sample. We employed modified Poisson regression adjusting for age, gender, education, income, race, marital status, and medical specialty. RESULTS: We analyzed 19,855 cancer survivors with a current or past history of tobacco use, of whom 5222 (26,3%) self-reported to be current smokers. Patients with urological and gynecological tobacco-related malignancies had a higher relative risk (RR) of being current smokers with a RR of 1.30 (95% confidence interval, 1.12-1.51) and 1.25 (95% confidence interval, 1.12-1.39) respectively. Malignant Hematology had the lowest RR of smoking status among all other specialties RR 0.85 (95% confidence interval, 0.59-1.21). CONCLUSIONS: Continuing smoking rates among tobacco-related cancer survivors were different between specialties. One in four cancer survivors were current smokers; this emphasizes health professionals' paramount role in tobacco cessation counseling.

A practical risk calculator for suicidal behavior among transitioning U.S. Army soldiers: results from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS).

BACKGROUND: Risk of suicide-related behaviors is elevated among military personnel transitioning to civilian life. An earlier report showed that high-risk U.S. Army soldiers could be identified shortly before this transition with a machine learning model that included predictors from administrative systems, self-report surveys, and geospatial data. Based on this result, a Veterans Affairs and Army initiative was launched to evaluate a suicide-prevention intervention for high-risk transitioning soldiers. To make targeting practical, though, a streamlined model and risk calculator were needed that used only a short series of self-report survey questions. METHODS: We revised the original model in a sample of n = 8335 observations from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS) who participated in one of three Army STARRS 2011-2014 baseline surveys while in service and in one or more subsequent panel surveys (LS1: 2016-2018, LS2: 2018-2019) after leaving service. We trained ensemble machine learning models with constrained numbers of item-level survey predictors in a 70% training sample. The outcome was self-reported post-transition suicide attempts (SA). The models were validated in the 30% test sample. RESULTS: Twelve-month post-transition SA prevalence was 1.0% (s.e. = 0.1). The best constrained model, with only 17 predictors, had a test sample ROC-AUC of 0.85 (s.e. = 0.03). The 10-30% of respondents with the highest predicted risk included 44.9-92.5% of 12-month SAs. CONCLUSIONS: An accurate SA risk calculator based on a short self-report survey can target transitioning soldiers shortly before leaving service for intervention to prevent post-transition SA.

Hierarchical temporal prediction captures motion processing along the visual pathway.

Visual neurons respond selectively to features that become increasingly complex from the eyes to the cortex. Retinal neurons prefer flashing spots of light, primary visual cortical (V1) neurons prefer moving bars, and those in higher cortical areas favor complex features like moving textures. Previously, we showed that V1 simple cell tuning can be accounted for by a basic model implementing temporal prediction - representing features that predict future sensory input from past input (Singer et al., 2018). Here, we show that hierarchical application of temporal prediction can capture how tuning properties change across at least two levels of the visual system. This suggests that the brain does not efficiently represent all incoming information; instead, it selectively represents sensory inputs that help in predicting the future. When applied hierarchically, temporal prediction extracts time-varying features that depend on increasingly high-level statistics of the sensory input.

Evidence of personality-dependent plasticity in dairy calf movement behaviours derived from automated data collection.

Individual consistency in behaviour, known as animal personality, and behavioural plasticity in response to environmental changes are important factors shaping individual behaviour. Correlations between them, called personality-dependent plasticity, indicate that personality can affect individual reactions to the environment. In farm animals this could impact the response to management changes or stressors but has not yet been investigated. Here we use ultra-wideband location sensors to measure personality and plasticity in the movement of 90 dairy calves for up to 56 days starting in small pair-housing enclosures, and subsequently moved to larger social housings. For the first time calves were shown to differ in personality and plasticity of movement when changing housing. There were significant correlations between personality and plasticity for distance travelled (0.57), meaning that individuals that travelled the furthest in the pair housing increased their movement more in the social groups, and for residence time (- 0.65) as those that stayed in the same area more decreased more with the change in housing, demonstrating personality-dependent plasticity. Additionally, calves conformed to their pen-mate's behaviour in pairs, but this did not continue in the groups. Therefore, personality, plasticity and social effects impact how farm animals respond to changes and can inform management decisions.

A voice-based digital assistant for intelligent prompting of evidence-based practices during ICU rounds.

OBJECTIVE: To evaluate the technical feasibility and potential value of a digital assistant that prompts intensive care unit (ICU) rounding teams to use evidence-based practices based on analysis of their real-time discussions. METHODS: We evaluated a novel voice-based digital assistant which audio records and processes the ICU care team's rounding discussions to determine which evidence-based practices are applicable to the patient but have yet to be addressed by the team. The system would then prompt the team to consider indicated but not yet delivered practices, thereby reducing cognitive burden compared to traditional rigid rounding checklists. In a retrospective analysis, we applied automatic transcription, natural language processing, and a rule-based expert system to generate personalized prompts for each patient in 106 audio-recorded ICU rounding discussions. To assess technical feasibility, we compared the system's prompts to those created by experienced critical care nurses who directly observed rounds. To assess potential value, we also compared the system's prompts to a hypothetical paper checklist containing all evidence-based practices. RESULTS: The positive predictive value, negative predictive value, true positive rate, and true negative rate of the system's prompts were 0.45 ± 0.06, 0.83 ± 0.04, 0.68 ± 0.07, and 0.66 ± 0.04, respectively. If implemented in lieu of a paper checklist, the system would generate 56% fewer prompts per patient, with 50%±17% greater precision. CONCLUSION: A voice-based digital assistant can reduce prompts per patient compared to traditional approaches for improving evidence uptake on ICU rounds. Additional work is needed to evaluate field performance and team acceptance.

The Role of Data Science in Closing the Implementation Gap.

Data science has the potential to greatly enhance efforts to translate evidence into practice in critical care. The intensive care unit is a data-rich environment enabling insight into both patient-level care patterns and clinician-level treatment patterns. By applying artificial intelligence to these novel data sources, implementation strategies can be tailored to individual patients, individual clinicians, and individual situations, revealing when evidence-based practices are missed and facilitating context-sensitive clinical decision support. To achieve these goals, technology developers should work closely with clinicians to create unbiased applications that are integrated into the clinical workflow.

Designing and Implementing "Living and Breathing" Clinical Trials: An Overview and Lessons Learned from the COVID-19 Pandemic.

The practice of medicine is characterized by uncertainty, and the findings of randomized clinical trials (RCTs) are meant to help curb that uncertainty. Traditional RCTs, however, have many limitations. To overcome some of these limitations, new trial paradigms rooted in the origins of evidence-based medicine are beginning to disrupt the traditional mold. These new designs recognize uncertainty permeates medical decision making and aim to capitalize on modern health system infrastructure to integrate investigation as a component of care delivery. This article provides an overview of "living, breathing" trials, including current state, anticipated developments, and areas of controversy.