Search

Background: The early detection of respiratory infections could improve responses against outbreaks. Wearable devices can provide insights into health and well-being using longitudinal physiological signals., Objective: The purpose of this study was to prospectively evaluate the performance of a consumer wearable physiology-based respiratory infection detection algorithm in health care workers., Methods: In this study, we evaluated the performance of a previously developed system to predict the presence of COVID-19 or other upper respiratory infections. The system generates real-time alerts using physiological signals recorded from a smartwatch. Resting heart rate, respiratory rate, and heart rate variability measured during the sleeping period were used for prediction. After baseline recordings, when participants received a notification from the system, they were required to undergo testing at a Northwell Health System site. Participants were asked to self-report any positive tests during the study. The accuracy of model prediction was evaluated using respiratory infection results (laboratory results or self-reports), and postnotification surveys were used to evaluate potential confounding factors., Results: A total of 577 participants from Northwell Health in New York were enrolled in the study between January 6, 2022, and July 20, 2022. Of these, 470 successfully completed the study, 89 did not provide sufficient physiological data to receive any prediction from the model, and 18 dropped out. Out of the 470 participants who completed the study and wore the smartwatch as required for the 16-week study duration, the algorithm generated 665 positive alerts, of which 153 (23.0%) were not acted upon to undergo testing for respiratory viruses. Across the 512 instances of positive alerts that involved a respiratory viral panel test, 63 had confirmed respiratory infection results (ie, COVID-19 or other respiratory infections detected using a polymerase chain reaction or home test) and the remaining 449 had negative upper respiratory infection test results. Across all cases, the estimated false-positive rate based on predictions per day was 2%, and the positive-predictive value ranged from 4% to 10% in this specific population, with an observed incidence rate of 198 cases per week per 100,000. Detailed examination of questionnaires filled out after receiving a positive alert revealed that physical or emotional stress events, such as intense exercise, poor sleep, stress, and excessive alcohol consumption, could cause a false-positive result., Conclusions: The real-time alerting system provides advance warning on respiratory viral infections as well as other physical or emotional stress events that could lead to physiological signal changes. This study showed the potential of wearables with embedded alerting systems to provide information on wellness measures., (©Zeinab Esmaeilpour, Aravind Natarajan, Hao-Wei Su, Anthony Faranesh, Ciaran Friel, Theodoros P Zanos, Stefani D’Angelo, Conor Heneghan. Originally published in JMIR Formative Research (https://formative.jmir.org), 17.07.2024.)

Patients with autoimmune diseases are at higher risk for severe infection due to their underlying disease and immunosuppressive treatments. In this real-world observational study of 463 patients with autoimmune diseases, we examined risk factors for poor B and T cell responses to SARS-CoV-2 vaccination. We show a high frequency of inadequate anti-spike IgG responses to vaccination and boosting in the autoimmune population but minimal suppression of T cell responses. Low IgG responses in B cell-depleted patients with multiple sclerosis (MS) were associated with higher CD8 T cell responses. By contrast, patients taking mycophenolate mofetil (MMF) exhibited concordant suppression of B and T cell responses. Treatments with highest risk for low anti-spike IgG response included B cell depletion within the last year, fingolimod, and combination treatment with MMF and belimumab. Our data show that the mRNA-1273 (Moderna) vaccine is the most effective vaccine in the autoimmune population. There was minimal induction of either disease flares or autoantibodies by vaccination and no significant effect of preexisting anti-type I IFN antibodies on either vaccine response or breakthrough infections. The low frequency of breakthrough infections and lack of SARS-CoV-2-related deaths suggest that T cell immunity contributes to protection in autoimmune disease.

Background: COVID-19 is a challenging disease to characterize given its wide-ranging heterogeneous symptomatology. Several studies have attempted to extract clinical phenotypes but often relied on data from small patient cohorts, usually limited to only one viral variant and utilizing a static snapshot of patient data., Objective: This study aimed to identify clinical phenotypes of hospitalized COVID-19 patients and investigate their longitudinal dynamics throughout the pandemic, with the goal to relate these phenotypes to clinical outcomes and treatment strategies., Methods: We utilized routinely collected demographic and clinical data throughout the hospitalization of 38,077 patients admitted between 3/2020 to 5/2022, in 12 New York hospitals. Uniform Manifold Approximation and Projection and agglomerative hierarchical clustering were used to derive the clusters, followed by exploratory data analysis to compare the prevalence of comorbidities and treatments per cluster., Results: 4 distinct clinical phenotypes remained robust in multi-site validation and were associated with different mortality rates. The temporal progression of these phenotypes throughout the COVID-19 pandemic demonstrated increased variability across the waves of the three dominant viral variants (alpha, delta, omicron). Longitudinal analysis evaluating changes in clinical phenotypes of each patient throughout the course of a 4-week hospital stay exemplified the dynamic nature of the disease progression. Factors such as sex, race/ethnicity and specific treatment modalities revealed significant and clinically relevant differences between the observed phenotypes., Conclusions: Our proposed methodology has the potential of enabling clinicians and policy makers to draw evidence-based conclusions for guiding treatment modalities in a dynamic fashion., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

The emerging field of bioelectronic medicine (BEM) is poised to make a significant impact on the treatment of several neurological and inflammatory disorders. With several BEM therapies being recently approved for clinical use and others in late-phase clinical trials, the 2022 BEM summit was a timely scientific meeting convening a wide range of experts to discuss the latest developments in the field. The BEM Summit was held over two days in New York with more than thirty-five invited speakers and panelists comprised of researchers and experts from both academia and industry. The goal of the meeting was to bring international leaders together to discuss advances and cultivate collaborations in this emerging field that incorporates aspects of neuroscience, physiology, molecular medicine, engineering, and technology. This Meeting Report recaps the latest findings discussed at the Meeting and summarizes the main developments in this rapidly advancing interdisciplinary field. Our hope is that this Meeting Report will encourage researchers from academia and industry to push the field forward and generate new multidisciplinary collaborations that will form the basis of new discoveries that we can discuss at the next BEM Summit., (© 2023. Feinstein Institute for Medical Research.)

Objectives: We aimed to correlate lung disease burden on presentation chest radiographs (CXR), quantified at the time of study interpretation, with clinical presentation in patients hospitalized with coronavirus disease 2019 (COVID-19)., Material and Methods: This retrospective cross-sectional study included 5833 consecutive adult patients, aged 18 and older, hospitalized with a diagnosis of COVID-19 with a CXR quantified in real-time while hospitalized in 1 of 12 acute care hospitals across a multihospital integrated healthcare network between March 24, 2020, and May 22, 2020. Lung disease burden was quantified in real-time by 118 radiologists on 5833 CXR at the time of exam interpretation with each lung annotated by the degree of lung opacity as clear (0%), mild (1-33%), moderate (34-66%), or severe (67-100%). CXR findings were classified as (1) clear versus disease, (2) unilateral versus bilateral, (3) symmetric versus asymmetric, or (4) not severe versus severe. Lung disease burden was characterized on initial presentation by patient demographics, co-morbidities, vital signs, and lab results with chi-square used for univariate analysis and logistic regression for multivariable analysis., Results: Patients with severe lung disease were more likely to have oxygen impairment, an elevated respiratory rate, low albumin, high lactate dehydrogenase, and high ferritin compared to non-severe lung disease. A lack of opacities in COVID-19 was associated with a low estimated glomerular filtration rate, hypernatremia, and hypoglycemia., Conclusions: COVID-19 lung disease burden quantified in real-time on presentation CXR was characterized by demographics, comorbidities, emergency severity index, Charlson Comorbidity Index, vital signs, and lab results on 5833 patients. This novel approach to real-time quantified chest radiograph lung disease burden by radiologists needs further research to understand how this information can be incorporated to improve clinical care for pulmonary-related diseases.. An absence of opacities in COVID-19 may be associated with poor oral intake and a prerenal state as evidenced by the association of clear CXRs with a low eGFR, hypernatremia, and hypoglycemia., Competing Interests: Declaration of competing interest None., (Copyright © 2023. Published by Elsevier Inc.)

Background: Non-arrivals to scheduled ambulatory visits are common and lead to a discontinuity of care, poor health outcomes, and increased subsequent healthcare utilization. Reducing non-arrivals is important given their association with poorer health outcomes and cost to health systems., Objective: To develop and validate a prediction model for ambulatory non-arrivals., Design: Retrospective cohort study., Patients or Subjects: Patients at an integrated health system who had an outpatient visit scheduled from January 1, 2020, to February 28, 2022., Main Measures: Non-arrivals to scheduled appointments., Key Results: There were over 4.3 million ambulatory appointments from 1.2 million adult patients. Patients with appointment non-arrivals were more likely to be single, racial/ethnic minorities, and not having an established primary care provider compared to those who arrived at their appointments. A prediction model using the XGBoost machine learning algorithm had the highest AUC value (0.768 [0.767-0.770]). Using SHAP values, the most impactful features in the model include rescheduled appointments, lead time (number of days from scheduled to appointment date), appointment provider, number of days since last appointment with the same department, and a patient's prior appointment status within the same department. Scheduling visits close to an appointment date is predicted to be less likely to result in a non-arrival. Overall, the prediction model calibrated well for each department, especially over the operationally relevant probability range of 0 to 40%. Departments with fewer observations and lower non-arrival rates generally had a worse calibration., Conclusions: Using a machine learning algorithm, we developed a prediction model for non-arrivals to scheduled ambulatory appointments usable for all medical specialties. The proposed prediction model can be deployed within an electronic health system or integrated into other dashboards to reduce non-arrivals. Future work will focus on the implementation and application of the model to reduce non-arrivals., (© 2023. The Author(s), under exclusive licence to Society of General Internal Medicine.)

Objectives: Neonatal early onset sepsis (EOS), bacterial infection during the first seven days of life, is difficult to diagnose because presenting signs are non-specific, but early diagnosis before birth can direct life-saving treatment for mother and baby. Specifically, maternal fever during labor from placental infection is the strongest predictor of EOS. Alterations in maternal heart rate variability (HRV) may precede development of intrapartum fever, enabling incipient EOS detection. The objective of this work was to build a predictive model for intrapartum fever., Methods: Continuously measured temperature, heart rate, and beat-to-beat RR intervals were obtained from wireless sensors on women ( n  = 141) in labor; traditional manual vital signs were taken every 3-6 hours. Validated measures of HRV were calculated in moving 5-minute windows of RR intervals: standard deviation of normal-to-normal intervals (SDNN) and root mean square of successive differences (RMSSD) between normal heartbeats., Results: Fever (>38.0 °C) was detected by manual or continuous measurements in 48 women. Compared to afebrile mothers, average SDNN and RMSSD in febrile mothers decreased significantly ( p  < 0.001) at 2 and 3 hours before fever onset, respectively. This observed HRV divergence and raw recorded vitals were applied to a logistic regression model at various time horizons, up to 4-5 hours before fever onset. Model performance increased with decreasing time horizons, and a model built using continuous vital signs as input variables consistently outperformed a model built from episodic vital signs., Conclusions: HRV-based predictive models could identify mothers at risk for fever and infants at risk for EOS, guiding maternal antibiotic prophylaxis and neonatal monitoring., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2023.)

Introduction: Clinical prediction models (CPMs) for coronavirus disease 2019 (COVID-19) may support clinical decision making, treatment, and communication. However, attitudes about using CPMs for COVID-19 decision making are unknown., Methods: Online focus groups and interviews were conducted among health care providers, survivors of COVID-19, and surrogates (i.e., loved ones/surrogate decision makers) in the United States and the Netherlands. Semistructured questions explored experiences about clinical decision making in COVID-19 care and facilitators and barriers for implementing CPMs., Results: In the United States, we conducted 4 online focus groups with 1) providers and 2) surrogates and survivors of COVID-19 between January 2021 and July 2021. In the Netherlands, we conducted 3 focus groups and 4 individual interviews with 1) providers and 2) surrogates and survivors of COVID-19 between May 2021 and July 2021. Providers expressed concern about CPM validity and the belief that patients may interpret CPM predictions as absolute. They described CPMs as potentially useful for resource allocation, triaging, education, and research. Several surrogates and people who had COVID-19 were not given prognostic estimates but believed this information would have supported and influenced their decision making. A limited number of participants felt the data would not have applied to them and that they or their loved ones may not have survived, as poor prognosis may have suggested withdrawal of treatment., Conclusions: Many providers had reservations about using CPMs for people with COVID-19 due to concerns about CPM validity and patient-level interpretation of the outcome predictions. However, several people who survived COVID-19 and their surrogates indicated that they would have found this information useful for decision making. Therefore, information provision may be needed to improve provider-level comfort and patient and surrogate understanding of CPMs., Highlights: While clinical prediction models (CPMs) may provide an objective means of assessing COVID-19 prognosis, provider concerns about CPM validity and the interpretation of CPM predictions may limit their clinical use.Providers felt that CPMs may be most useful for resource allocation, triage, research, or educational purposes for COVID-19.Several survivors of COVID-19 and their surrogates felt that CPMs would have been informative and may have aided them in making COVID-19 treatment decisions, while others felt the data would not have applied to them.

Objective. Sensory nerves of the peripheral nervous system (PNS) transmit afferent signals from the body to the brain. These peripheral nerves are composed of distinct subsets of fibers and associated cell bodies, which reside in peripheral ganglia distributed throughout the viscera and along the spinal cord. The vagus nerve (cranial nerve X) is a complex polymodal nerve that transmits a wide array of sensory information, including signals related to mechanical, chemical, and noxious stimuli. To understand how stimuli applied to the vagus nerve are encoded by vagal sensory neurons in the jugular-nodose ganglia, we developed a framework for micro-endoscopic calcium imaging and analysis. Approach. We developed novel methods for in vivo imaging of the intact jugular-nodose ganglion using a miniature microscope (Miniscope) in transgenic mice with the genetically-encoded calcium indicator GCaMP6f. We adapted the Python-based analysis package Calcium Imaging Analysis (CaImAn) to process the resulting one-photon fluorescence data into calcium transients for subsequent analysis. Random forest classification was then used to identify specific types of neuronal responders. Results. We demonstrate that recordings from the jugular-nodose ganglia can be accomplished through careful surgical dissection and ganglia stabilization. Using a customized acquisition and analysis pipeline, we show that subsets of vagal sensory neurons respond to different chemical stimuli applied to the vagus nerve. Successful classification of the responses with a random forest model indicates that certain calcium transient features, such as amplitude and duration, are important for encoding these stimuli by sensory neurons. Significance. This experimental approach presents a new framework for investigating how individual vagal sensory neurons encode various stimuli on the vagus nerve. Our surgical and analytical approach can be applied to other PNS ganglia in rodents and other small animal species to elucidate previously unexplored roles for peripheral neurons in a diverse set of physiological functions., (Creative Commons Attribution license.)

Vagal fibers travel inside fascicles and form branches to innervate organs and regulate organ functions. Existing vagus nerve stimulation (VNS) therapies activate vagal fibers non-selectively, often resulting in reduced efficacy and side effects from non-targeted organs. The transverse and longitudinal arrangement of fibers inside the vagal trunk with respect to the functions they mediate and organs they innervate is unknown, however it is crucial for selective VNS. Using micro-computed tomography imaging, we tracked fascicular trajectories and found that, in swine, sensory and motor fascicles are spatially separated cephalad, close to the nodose ganglion, and merge caudad, towards the lower cervical and upper thoracic region; larynx-, heart- and lung-specific fascicles are separated caudad and progressively merge cephalad. Using quantified immunohistochemistry at single fiber level, we identified and characterized all vagal fibers and found that fibers of different morphological types are differentially distributed in fascicles: myelinated afferents and efferents occupy separate fascicles, myelinated and unmyelinated efferents also occupy separate fascicles, and small unmyelinated afferents are widely distributed within most fascicles. We developed a multi-contact cuff electrode to accommodate the fascicular structure of the vagal trunk and used it to deliver fascicle-selective cervical VNS in anesthetized and awake swine. Compound action potentials from distinct fiber types, and physiological responses from different organs, including laryngeal muscle, cough, breathing, and heart rate responses are elicited in a radially asymmetric manner, with consistent angular separations that agree with the documented fascicular organization. These results indicate that fibers in the trunk of the vagus nerve are anatomically organized according to functions they mediate and organs they innervate and can be asymmetrically activated by fascicular cervical VNS., Competing Interests: Declaration of competing of interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Amyotrophic lateral sclerosis (ALS) is a rare progressive neurodegenerative disease that affects upper and lower motor neurons. As the molecular basis of the disease is still elusive, the development of high-throughput sequencing technologies, combined with data mining techniques and machine learning methods, could provide remarkable results in identifying pathogenetic mechanisms. High dimensionality is a major problem when applying machine learning techniques in biomedical data analysis, since a huge number of features is available for a limited number of samples. The aim of this study was to develop a methodology for training interpretable machine learning models in the classification of ALS and ALS-subtypes samples, using gene expression datasets., Methods: We performed dimensionality reduction in gene expression data using a semi-automated preprocessing systematic gene selection procedure using Statistically Equivalent Signature (SES), a causality-based feature selection algorithm, followed by Boosted Regression Trees (XGBoost) and Random Forest to train the machine learning classifiers. The SHapley Additive exPlanations (SHAP values) were used for interpretation of the machine learning classifiers. The methodology was developed and tested using two distinct publicly available ALS RNA-seq datasets. We evaluated the performance of SES as a dimensionality reduction method against: (a) Least Absolute Shrinkage and Selection Operator (LASSO), and (b) Local Outlier Factor (LOF)., Results: The proposed methodology achieved 85.18% accuracy for the classification of cerebellum or frontal cortex samples as C9orf72-related familial ALS, sporadic ALS or healthy samples. Importantly, the genes identified as the most determinative have also been reported as disease-associated in ALS literature. When tested in the evaluation dataset, the methodology achieved 88.89% accuracy for the classification of sporadic ALS motor neuron samples. When LASSO was used as feature selection method instead of SES, the accuracy of the machine learning classifiers ranged from 74.07 to 96.30%, depending on tissue assessed, while LOF underperformed significantly (77.78% accuracy for the classification of pooled cerebellum and frontal cortex samples)., Conclusions: Using SES, we addressed the challenge of high dimensionality in gene expression data analysis, and we trained accurate machine learning ALS classifiers, specific for the gene expression patterns of different disease subtypes and tissue samples, while identifying disease-associated genes., (© 2023. The Author(s).)

Chest radiographs (CXRs) are the most widely available radiographic imaging modality used to detect respiratory diseases that result in lung opacities. CXR reports often use non-standardized language that result in subjective, qualitative, and non-reproducible opacity estimates. Our goal was to develop a robust deep transfer learning framework and adapt it to estimate the degree of lung opacity from CXRs. Following CXR data selection based on exclusion criteria, segmentation schemes were used for ROI (Region Of Interest) extraction, and all combinations of segmentation, data balancing, and classification methods were tested to pick the top performing models. Multifold cross validation was used to determine the best model from the initial selected top models, based on appropriate performance metrics, as well as a novel Macro-Averaged Heatmap Concordance Score (MA HCS). Performance of the best model is compared against that of expert physician annotators, and heatmaps were produced. Finally, model performance sensitivity analysis across patient populations of interest was performed. The proposed framework was adapted to the specific use case of estimation of degree of CXR lung opacity using ordinal multiclass classification. Acquired between March 24, 2020, and May 22, 2020, 38,365 prospectively annotated CXRs from 17,418 patients were used. We tested three neural network architectures (ResNet-50, VGG-16, and ChexNet), three segmentation schemes (no segmentation, lung segmentation, and lateral segmentation based on spine detection), and three data balancing strategies (undersampling, double-stage sampling, and synthetic minority oversampling) using 38,079 CXR images for training, and validation with 286 images as the out-of-the-box dataset that underwent expert radiologist adjudication. Based on the results of these experiments, the ResNet-50 model with undersampling and no ROI segmentation is recommended for lung opacity classification, based on optimal values for the MAE metric and HCS (Heatmap Concordance Score). The degree of agreement between the opacity scores predicted by this model with respect to the two sets of radiologist scores (OR or Original Reader and OOBTR or Out Of Box Reader) in terms of performance metrics is superior to the inter-radiologist opacity score agreement., (© 2022. The Author(s).)

Background: Supporting decisions for patients who present to the emergency department (ED) with COVID-19 requires accurate prognostication. We aimed to evaluate prognostic models for predicting outcomes in hospitalized patients with COVID-19, in different locations and across time., Methods: We included patients who presented to the ED with suspected COVID-19 and were admitted to 12 hospitals in the New York City (NYC) area and 4 large Dutch hospitals. We used second-wave patients who presented between September and December 2020 (2137 and 3252 in NYC and the Netherlands, respectively) to evaluate models that were developed on first-wave patients who presented between March and August 2020 (12,163 and 5831). We evaluated two prognostic models for in-hospital death: The Northwell COVID-19 Survival (NOCOS) model was developed on NYC data and the COVID Outcome Prediction in the Emergency Department (COPE) model was developed on Dutch data. These models were validated on subsequent second-wave data at the same site (temporal validation) and at the other site (geographic validation). We assessed model performance by the Area Under the receiver operating characteristic Curve (AUC), by the E-statistic, and by net benefit., Results: Twenty-eight-day mortality was considerably higher in the NYC first-wave data (21.0%), compared to the second-wave (10.1%) and the Dutch data (first wave 10.8%; second wave 10.0%). COPE discriminated well at temporal validation (AUC 0.82), with excellent calibration (E-statistic 0.8%). At geographic validation, discrimination was satisfactory (AUC 0.78), but with moderate over-prediction of mortality risk, particularly in higher-risk patients (E-statistic 2.9%). While discrimination was adequate when NOCOS was tested on second-wave NYC data (AUC 0.77), NOCOS systematically overestimated the mortality risk (E-statistic 5.1%). Discrimination in the Dutch data was good (AUC 0.81), but with over-prediction of risk, particularly in lower-risk patients (E-statistic 4.0%). Recalibration of COPE and NOCOS led to limited net benefit improvement in Dutch data, but to substantial net benefit improvement in NYC data., Conclusions: NOCOS performed moderately worse than COPE, probably reflecting unique aspects of the early pandemic in NYC. Frequent updating of prognostic models is likely to be required for transportability over time and space during a dynamic pandemic., (© 2022. The Author(s).)

Clinical prognostic models can assist patient care decisions. However, their performance can drift over time and location, necessitating model monitoring and updating. Despite rapid and significant changes during the pandemic, prognostic models for COVID-19 patients do not currently account for these drifts. We develop a framework for continuously monitoring and updating prognostic models and apply it to predict 28-day survival in COVID-19 patients. We use demographic, laboratory, and clinical data from electronic health records of 34912 hospitalized COVID-19 patients from March 2020 until May 2022 and compare three modeling methods. Model calibration performance drift is immediately detected with minor fluctuations in discrimination. The overall calibration on the prospective validation cohort is significantly improved when comparing the dynamically updated models against their static counterparts. Our findings suggest that, using this framework, models remain accurate and well-calibrated across various waves, variants, race and sex and yield positive net-benefits., (© 2022. The Author(s).)

We assessed the frequency and correlates of COVID-19 vaccine hesitancy before Canada's vaccine rollout. A cross-sectional vaccine hesitancy survey was completed by consecutive patients/family members/staff who received the influenza vaccine at McGill University affiliated hospitals. Based on the self-reported likelihood of receiving a future vaccine (scale 0-10), the following three groups were defined: non-hesitant (score 10), mildly hesitant (7.1-9.9), and significantly hesitant (0-7). Factors associated with vaccine hesitancy were assessed with multivariate logistic regression analyses and binomial logistic regression machine learning modelling. The survey was completed by 1793 people. Thirty-seven percent of participants (n = 669) were hesitant (mildly: 315 (17.6%); significantly: 354 (19.7%)). Lower education levels, opposition and uncertainty about vaccines being mandatory, feelings of not receiving enough information about COVID-19 prevention, perceived social pressure to get a future vaccine, vaccine safety concerns, uncertainty regarding the vaccine risk-benefit ratio, and distrust towards pharmaceutical companies were factors associated with vaccine hesitancy. Vaccine safety concerns and opposition to mandatory vaccinations were the strongest correlates of vaccine hesitancy in both the logistic regressions and the machine learning model. In conclusion, in this study, over a third of people immunized for influenza before the COVID-19 vaccine rollout expressed some degree of vaccine hesitancy. Effectively addressing COVID-19 vaccine safety concerns may enhance vaccine uptake.

Visual plasticity declines sharply after the critical period, yet we easily learn to recognize new faces and places, even as adults. Such learning is often characterized by a "moment of insight," an abrupt and dramatic improvement in recognition. The mechanisms that support abrupt learning are unknown, but one hypothesis is that they involve changes in synchronization between brain regions. To test this hypothesis, we used a behavioral task in which non-human primates rapidly learned to recognize novel images and to associate them with specific responses. Simultaneous recordings from inferotemporal and prefrontal cortices revealed a transient synchronization of neural activity between these areas that peaked around the moment of insight. Synchronization was strongest between inferotemporal sites that encoded images and reward-sensitive prefrontal sites. Moreover, its magnitude intensified gradually over image exposures, suggesting that abrupt learning is the culmination of a search for informative signals within a circuit linking sensory information to task demands., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Posttraumatic stress disorder (PTSD) remains one of the most prevalent diagnoses of World Trade Center (WTC) 9/11 responders. Transcutaneous auricular vagus nerve stimulation (taVNS) is a potential treatment for PTSD, as it can downregulate activity in the brain, which is known to be related to stress responses and hyperarousal. To understand barriers and facilitators to engagement in mental health care and the feasibility and acceptability of using the taVNS device as a treatment for PTSD symptoms, a focus group was conducted among patients from the Queens WTC Health Program who had elevated symptoms of PTSD. The focus group discussion was recorded, transcribed, and analyzed. Three themes and subthemes emerged: (1) the continued prevalence of mental health difficulties and systematic challenges to accessing care; (2) positive reception toward the taVNS device as a potential treatment option, including a discussion of how to increase usability; and (3) feedback on increasing the feasibility and acceptance of the research methodology associated with testing the device in a pilot clinical trial. The findings highlight the need for additional treatment options to reduce PTSD symptoms in this population and provide key formative phase input for the pilot clinical trial of taVNS.

Novel research in the field of bioelectronic medicine requires neuromodulation systems that pair high-performance neurostimulation and bio-signal acquisition hardware with advanced signal processing and control algorithms. Although mice are the most commonly used animal in medical research, the size, weight, and power requirements of such bioelectronic systems either preclude use in mice or impose significant constraints on experimental design. Here, a fully-implantable recording and stimulation neuromodulation system suitable for use in mice is presented, measuring 2.2 cm 3 and weighing 2.8 g. The bidirectional wireless interface allows simultaneous readout of multiple physiological signals and complete control over stimulation parameters, and a wirelessly rechargeable battery provides a lifetime of up to 5 days on a single charge. The device was implanted to deliver vagus nerve stimulation (n = 12 animals) and a functional neural interface (capable of inducing acute bradycardia) was demonstrated with lifetimes exceeding three weeks. The design utilizes only commercially-available electrical components and 3D-printed packaging, with the goal of facilitating widespread adoption and accelerating discovery and translation of future bioelectronic therapeutics., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Neonatal early onset sepsis (EOS) occurs in 0.5-0.8/1000 live births and is a major cause of morbidity and mortality. Its presenting signs in newborns are non-specific, so risk assessment before birth is essential. Maternal fever during labor is the strongest predictor of EOS, but the current standard is for infrequent manual determinations of temperature. We aimed to determine whether continuous measurement of temperature during labor is feasible, accurate, and more effective than manual measurements for detecting fever. Women were recruited on admission in labor at > 35 weeks gestational age, with < 6 cm cervical dilation. Sensors were affixed in the axilla, which transmitted every 4 minutes by Bluetooth to a dedicated tablet. Conventional temperature measurements were taken every 3-6 hours per routine. Of 336 subjects recruited, 155 had both > 4 hours of continuous data and > 2 manual temperature measurements and were included for analysis. Continuous recordings were feasible and correlated well with manual measurements independent of mean temperature. Of 15 episodes of fever > 38 °C detected by both methods, 13 were detected earlier by continuous (9 of those more than 1 hour earlier). Manual measurements missed 32 fevers > 38 °C and 13 fevers > 38.5 °C that were identified by continuous. Continuous measurement of maternal temperature for the duration of labor is practical and accurate. It may be more sensitive for identifying infants at risk for EOS than the current practice, enabling earlier and more effective targeted treatment of affected infants., (© 2021. The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature.)

Background: The autonomic nervous system (ANS) maintains physiological homeostasis in various organ systems via parasympathetic and sympathetic branches. ANS function is altered in common diffuse and focal conditions and heralds the beginning of environmental and disease stresses. Reliable, sensitive, and quantitative biomarkers, first defined in healthy participants, could discriminate among clinically useful changes in ANS function. This framework combines controlled autonomic testing with feature extraction during physiological responses., Methods: Twenty-one individuals were assessed in two morning and two afternoon sessions over two weeks. Each session included five standard clinical tests probing autonomic function: squat test, cold pressor test, diving reflex test, deep breathing, and Valsalva maneuver. Noninvasive sensors captured continuous electrocardiography, blood pressure, breathing, electrodermal activity, and pupil diameter. Heart rate, heart rate variability, mean arterial pressure, electrodermal activity, and pupil diameter responses to the perturbations were extracted, and averages across participants were computed. A template matching algorithm calculated scaling and stretching features that optimally fit the average to an individual response. These features were grouped based on test and modality to derive sympathetic and parasympathetic indices for this healthy population., Results: A significant positive correlation (p = 0.000377) was found between sympathetic amplitude response and body mass index. Additionally, longer duration and larger amplitude sympathetic and longer duration parasympathetic responses occurred in afternoon testing sessions; larger amplitude parasympathetic responses occurred in morning sessions., Conclusions: These results demonstrate the robustness and sensitivity of an algorithmic approach to extract multimodal responses from standard tests. This novel method of quantifying ANS function can be used for early diagnosis, measurement of disease progression, or treatment evaluation., Trial Registration: This study registered with Clinicaltrials.gov , identifier NCT04100486 . Registered September 24, 2019, https://www.clinicaltrials.gov/ct2/show/NCT04100486 ., (© 2021. The Author(s).)

Vagus nerve stimulation (VNS) suppresses inflammation and autoimmune diseases in preclinical and clinical studies. The underlying molecular, neurological, and anatomical mechanisms have been well characterized using acute electrophysiological stimulation of the vagus. However, there are several unanswered mechanistic questions about the effects of chronic VNS, which require solving numerous technical challenges for a long-term interface with the vagus in mice. Here, we describe a scalable model for long-term VNS in mice developed and validated in four research laboratories. We observed significant heart rate responses for at least 4 weeks in 60-90% of animals. Device implantation did not impair vagus-mediated reflexes. VNS using this implant significantly suppressed TNF levels in endotoxemia. Histological examination of implanted nerves revealed fibrotic encapsulation without axonal pathology. This model may be useful to study the physiology of the vagus and provides a tool to systematically investigate long-term VNS as therapy for chronic diseases modeled in mice., Competing Interests: IM, JH, NJ, DT, UA, EP, YC, AA, TD, EC, TZ, SL, RF, CW, YA, SZ No competing interests declared, KT holds patents broadly related to this work. He has assigned all rights to the Feinstein Institutes for Medical Research., (© 2021, Mughrabi et al.)

Background: Predicting early respiratory failure due to COVID-19 can help triage patients to higher levels of care, allocate scarce resources, and reduce morbidity and mortality by appropriately monitoring and treating the patients at greatest risk for deterioration. Given the complexity of COVID-19, machine learning approaches may support clinical decision making for patients with this disease., Objective: Our objective is to derive a machine learning model that predicts respiratory failure within 48 hours of admission based on data from the emergency department., Methods: Data were collected from patients with COVID-19 who were admitted to Northwell Health acute care hospitals and were discharged, died, or spent a minimum of 48 hours in the hospital between March 1 and May 11, 2020. Of 11,525 patients, 933 (8.1%) were placed on invasive mechanical ventilation within 48 hours of admission. Variables used by the models included clinical and laboratory data commonly collected in the emergency department. We trained and validated three predictive models (two based on XGBoost and one that used logistic regression) using cross-hospital validation. We compared model performance among all three models as well as an established early warning score (Modified Early Warning Score) using receiver operating characteristic curves, precision-recall curves, and other metrics., Results: The XGBoost model had the highest mean accuracy (0.919; area under the curve=0.77), outperforming the other two models as well as the Modified Early Warning Score. Important predictor variables included the type of oxygen delivery used in the emergency department, patient age, Emergency Severity Index level, respiratory rate, serum lactate, and demographic characteristics., Conclusions: The XGBoost model had high predictive accuracy, outperforming other early warning scores. The clinical plausibility and predictive ability of XGBoost suggest that the model could be used to predict 48-hour respiratory failure in admitted patients with COVID-19., (©Siavash Bolourani, Max Brenner, Ping Wang, Thomas McGinn, Jamie S Hirsch, Douglas Barnaby, Theodoros P Zanos, Northwell COVID-19 Research Consortium. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 10.02.2021.)

Objectives: Musculoskeletal pain and fatigue are common features in systemic lupus erythematosus (SLE). The cholinergic anti-inflammatory pathway is a physiological mechanism diminishing inflammation, engaged by stimulating the vagus nerve. We evaluated the effects of non-invasive vagus nerve stimulation in patients with SLE and with musculoskeletal pain., Methods: 18 patients with SLE and with musculoskeletal pain ≥4 on a 10 cm Visual Analogue Scale were randomised (2:1) in this double-blind study to receive transcutaneous auricular vagus nerve stimulation (taVNS) or sham stimulation (SS) for 4 consecutive days. Evaluations at baseline, day 5 and day 12 included patient assessments of pain, disease activity (PtGA) and fatigue. Tender and swollen joint counts and the Physician Global Assessment (PGA) were completed by a physician blinded to the patient's therapy. Potential biomarkers were evaluated., Results: taVNS and SS were well tolerated. Subjects receiving taVNS had a significant decrease in pain and fatigue compared with SS and were more likely (OR=25, p=0.02) to experience a clinically significant reduction in pain. PtGA, joint counts and PGA also improved. Pain reduction and improvement of fatigue correlated with the cumulative current received. In general, responses were maintained through day 12. Plasma levels of substance P were significantly reduced at day 5 compared with baseline following taVNS but other neuropeptides, serum and whole blood-stimulated inflammatory mediators, and kynurenine metabolites showed no significant change at days 5 or 12 compared with baseline., Conclusion: taVNS resulted in significantly reduced pain, fatigue and joint scores in SLE. Additional studies evaluating this intervention and its mechanisms are warranted., Competing Interests: Competing interests: KJT reports a financial relationship with Set Point Medical and My String; Prof. CB and Assistant Professors TPZ and Datta-Chaudhuri have a provisional patent application: "Auricular stimulation device, system and methods of use"., (© Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.)

Impaired sleep for hospital patients is an all too common reality. Sleep disruptions due to unnecessary overnight vital sign monitoring are associated with delirium, cognitive impairment, weakened immunity, hypertension, increased stress, and mortality. It is also one of the most common complaints of hospital patients while imposing additional burdens on healthcare providers. Previous efforts to forgo overnight vital sign measurements and improve patient sleep used providers' subjective stability assessment or utilized an expanded, thus harder to retrieve, set of vitals and laboratory results to predict overnight clinical risk. Here, we present a model that incorporates past values of a small set of vital signs and predicts overnight stability for any given patient-night. Using data obtained from a multi-hospital health system between 2012 and 2019, a recurrent deep neural network was trained and evaluated using ~2.3 million admissions and 26 million vital sign assessments. The algorithm is agnostic to patient location, condition, and demographics, and relies only on sequences of five vital sign measurements, a calculated Modified Early Warning Score, and patient age. We achieved an area under the receiver operating characteristic curve of 0.966 (95% confidence interval [CI] 0.956-0.967) on the retrospective testing set, and 0.971 (95% CI 0.965-0.974) on the prospective set to predict overnight patient stability. The model enables safe avoidance of overnight monitoring for ~50% of patient-nights, while only misclassifying 2 out of 10,000 patient-nights as stable. Our approach is straightforward to deploy, only requires regularly obtained vital signs, and delivers easily actionable clinical predictions for a peaceful sleep in hospitals.

Background: Cervical vagus nerve stimulation (VNS) is an emerging bioelectronic treatment for brain, metabolic, cardiovascular and immune disorders. Its desired and off-target effects are mediated by different nerve fiber populations and knowledge of their engagement could guide calibration and monitoring of VNS therapies., Objective: Stimulus-evoked compound action potentials (eCAPs) directly provide fiber engagement information but are currently not feasible in humans. A method to estimate fiber engagement through common, noninvasive physiological readouts could be used in place of eCAP measurements., Methods: In anesthetized rats, we recorded eCAPs while registering acute physiological response markers to VNS: cervical electromyography (EMG), changes in heart rate (ΔHR) and breathing interval (ΔBI). Quantitative models were established to capture the relationship between A-, B- and C-fiber type activation and those markers, and to quantitatively estimate fiber activation from physiological markers and stimulation parameters., Results: In bivariate analyses, we found that EMG correlates with A-fiber, ΔHR with B-fiber and ΔBI with C-fiber activation, in agreement with known physiological functions of the vagus. We compiled multivariate models for quantitative estimation of fiber engagement from these markers and stimulation parameters. Finally, we compiled frequency gain models that allow estimation of fiber engagement at a wide range of VNS frequencies. Our models, after calibration in humans, could provide noninvasive estimation of fiber engagement in current and future therapeutic applications of VNS., Competing Interests: Declaration of competing interest SZ and YCC have a pending patent application with United Therapeutics Corporation that includes aspects of the research presented in this paper. The other authors declare no conflict of interest., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: The number of cases from the coronavirus disease 2019 (COVID-19) global pandemic has overwhelmed existing medical facilities and forced clinicians, patients, and families to make pivotal decisions with limited time and information., Main Body: While machine learning (ML) methods have been previously used to augment clinical decisions, there is now a demand for "Emergency ML." Throughout the patient care pathway, there are opportunities for ML-supported decisions based on collected vitals, laboratory results, medication orders, and comorbidities. With rapidly growing datasets, there also remain important considerations when developing and validating ML models., Conclusion: This perspective highlights the utility of evidence-based prediction tools in a number of clinical settings, and how similar models can be deployed during the COVID-19 pandemic to guide hospital frontlines and healthcare administrators to make informed decisions about patient care and managing hospital volume., Competing Interests: Competing interestsThe authors declare that they have no competing interests., (© The Author(s) 2020.)

Quantitative descriptions of the morphology and structure of peripheral nerves is central in the development of bioelectronic devices interfacing the nerves. While histological procedures and microscopy techniques yield high-resolution detailed images of individual axons, automated methods to extract relevant information at the single-axon level are not widely available. We implemented a segmentation algorithm that allows for subsequent feature extraction in immunohistochemistry (IHC) images of peripheral nerves at the single fiber scale. These features include short and long cross-sectional diameters, area, perimeter, thickness of surrounding myelin and polar coordinates of single axons within a nerve or nerve fascicle. We evaluated the performance of our algorithm using manually annotated IHC images of 27 fascicles of the swine cervical vagus; the accuracy of single-axon detection was 82%, and of the classification of fiber myelination was 89%.

Vagus nerve stimulation (VNS) is a bioelectronic therapy for disorders of the brain and peripheral organs, and a tool to study the physiology of autonomic circuits. Selective activation of afferent or efferent vagal fibers can maximize efficacy and minimize off-target effects of VNS. Anodal block (ABL) has been used to achieve directional fiber activation in nerve stimulation. However, evidence for directional VNS with ABL has been scarce and inconsistent, and it is unknown whether ABL permits directional fiber activation with respect to functional effects of VNS. Through a series of vagotomies, we established physiological markers for afferent and efferent fiber activation by VNS: stimulus-elicited change in breathing rate (ΔBR) and heart rate (ΔHR), respectively. Bipolar VNS trains of both polarities elicited mixed ΔHR and ΔBR responses. Cathode cephalad polarity caused an afferent pattern of responses (relatively stronger ΔBR) whereas cathode caudad caused an efferent pattern (stronger ΔHR). Additionally, left VNS elicited a greater afferent and right VNS a greater efferent response. By analyzing stimulus-evoked compound nerve potentials, we confirmed that such polarity differences in functional responses to VNS can be explained by ABL of A- and B-fiber activation. We conclude that ABL is a mechanism that can be leveraged for directional VNS.

Importance: There is limited information describing the presenting characteristics and outcomes of US patients requiring hospitalization for coronavirus disease 2019 (COVID-19)., Objective: To describe the clinical characteristics and outcomes of patients with COVID-19 hospitalized in a US health care system., Design, Setting, and Participants: Case series of patients with COVID-19 admitted to 12 hospitals in New York City, Long Island, and Westchester County, New York, within the Northwell Health system. The study included all sequentially hospitalized patients between March 1, 2020, and April 4, 2020, inclusive of these dates., Exposures: Confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection by positive result on polymerase chain reaction testing of a nasopharyngeal sample among patients requiring admission., Main Outcomes and Measures: Clinical outcomes during hospitalization, such as invasive mechanical ventilation, kidney replacement therapy, and death. Demographics, baseline comorbidities, presenting vital signs, and test results were also collected., Results: A total of 5700 patients were included (median age, 63 years [interquartile range {IQR}, 52-75; range, 0-107 years]; 39.7% female). The most common comorbidities were hypertension (3026; 56.6%), obesity (1737; 41.7%), and diabetes (1808; 33.8%). At triage, 30.7% of patients were febrile, 17.3% had a respiratory rate greater than 24 breaths/min, and 27.8% received supplemental oxygen. The rate of respiratory virus co-infection was 2.1%. Outcomes were assessed for 2634 patients who were discharged or had died at the study end point. During hospitalization, 373 patients (14.2%) (median age, 68 years [IQR, 56-78]; 33.5% female) were treated in the intensive care unit care, 320 (12.2%) received invasive mechanical ventilation, 81 (3.2%) were treated with kidney replacement therapy, and 553 (21%) died. As of April 4, 2020, for patients requiring mechanical ventilation (n = 1151, 20.2%), 38 (3.3%) were discharged alive, 282 (24.5%) died, and 831 (72.2%) remained in hospital. The median postdischarge follow-up time was 4.4 days (IQR, 2.2-9.3). A total of 45 patients (2.2%) were readmitted during the study period. The median time to readmission was 3 days (IQR, 1.0-4.5) for readmitted patients. Among the 3066 patients who remained hospitalized at the final study follow-up date (median age, 65 years [IQR, 54-75]), the median follow-up at time of censoring was 4.5 days (IQR, 2.4-8.1)., Conclusions and Relevance: This case series provides characteristics and early outcomes of sequentially hospitalized patients with confirmed COVID-19 in the New York City area.

Objective: There is a growing interest in the personalization of chest compressions to increase blood flow during cardiopulmonary resuscitation (CPR), but there has been very little systematic work to test the feasibility of a closed loop mechanical CPR system. The purpose of this study is to determine if it is possible to model the response of the carotid blood flow to different chest compression waveforms as a function of time during resuscitation from cardiac arrest. This work tests several approaches to predict the carotid blood flow generated by the next chest compression based on knowledge of the duration of resuscitation, the chest compression rate, and the last compression's carotid blood flow., Methods: Using an existing physiological database from swine cardiac arrest studies, we computed the features of CPR epoch, compression index, compression rate, and the previous carotid blood flow and used them as the inputs to our model in order to predict carotid blood flow using a Random Forest algorithm. We tested animal specific (estimated with data from a single animal) and global (estimated with data from all but one animals) models for effectiveness., Results: Animal specific models did not generalize when applied to the rest of the animals. The global model performed reasonably well when trained on six animals and tested on the 7th, resulting in errors of 40-160 μL per compression, compared to an average of approximately 400 μL net carotid blood flow per compression in early compressions. In addition, the global model highlighted the inter-animal variability in carotid blood flow generated by identical chest compression waveforms. Generation of probability distribution functions of carotid blood flows suggested at least three different distribution profiles in seven animals., Conclusion: A single physiological metric, carotid blood flow, combined with information about the duration of resuscitation and the compression rate was sufficient to model and predict carotid blood flow in the next compression., Significance: This demonstrates that the physiological response to chest compression can be predicted from a relatively modest data set. This suggests that closed loop mechanical CPR is a viable medical device target.

Background: The peripheral nervous system is involved in a multitude of physiological functions. Recording neural signals provides information that can be used by diagnostic bioelectronic medicine devices, closed-loop neuromodulation therapies and other neuroprosthetic applications. The ability to accurately record these signals is challenging, due to the presence of various biological and instrument-related interference sources., New Method: We developed a common-mode interference rejection algorithm based on an impedance matching approach for bipolar cuff electrodes. Two unipolar channels were recorded from the two electrode contacts of a bipolar cuff. The impedance mismatch was estimated and used to correct one of the two channels., Results: When applied to electrocardiographic (ECG) artifacts collected from three mice using CorTec electrodes, the algorithm reduced the interference to noise ratio (INR) over simple subtraction by 12 dB on average. The algorithm also reduced the INR of stimulation artifacts in recordings from three rats collected using flexible electrodes by an additional 2.4 dB. In the same experiments evoked electromyographic (EMG) interference was suppressed by 1.3 dB., Comparison With Existing Methods: Simple subtraction is the common approach for reducing common-mode interference in bipolar recordings, however impedance mismatches that exist or emerge compromise its efficiency., Conclusions: The algorithm significantly reduced the common-mode interference from ECG artifacts, stimulation artifacts, and evoked EMG interference, while retaining neural signals, in two animal models and two recording setups. This approach can be used in a variety of different neurophysiological setups to remove common-mode interference from a variety of sources., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Our bodies have built-in neural reflexes that continuously monitor organ function and maintain physiological homeostasis. Whereas the field of bioelectronic medicine has mainly focused on the stimulation of neural circuits to treat various conditions, recent studies have started to investigate the possibility of leveraging the sensory arm of these reflexes to diagnose disease states. To accomplish this, neural signals emanating from the body's built-in biosensors and propagating through peripheral nerves must be recorded and decoded to identify the presence or levels of relevant biomarkers of disease. The process of acquiring these signals poses several technical challenges related to the neural interfaces, surgical techniques, and data-processing framework needed to record and analyze them. However, these challenges can be addressed with a rigorous experimental approach and new advances in implantable electrodes, signal processing, and machine learning methods. Outlined in this review are studies decoding vagus nerve activity as it related to inflammatory, metabolic, and cardiopulmonary biomarkers. Successfully decoding peripheral nerve activity related to disease states will not only enable the development of real-time diagnostic devices, but also help advancing truly closed-loop neuromodulation technologies., (Copyright © 2019 Cold Spring Harbor Laboratory Press; all rights reserved.)

Background: Glucose is a crucial energy source. In humans, it is the primary sugar for high energy demanding cells in brain, muscle and peripheral neurons. Deviations of blood glucose levels from normal levels for an extended period of time is dangerous or even fatal, so regulation of blood glucose levels is a biological imperative. The vagus nerve, comprised of sensory and motor fibres, provides a major anatomical substrate for regulating metabolism. While prior studies have implicated the vagus nerve in the neurometabolic interface, its specific role in either the afferent or efferent arc of this reflex remains elusive., Methods: Here we use recently developed methods to isolate and decode specific neural signals acquired from the surface of the vagus nerve in BALB/c wild type mice to identify those that respond robustly to hypoglycemia. We also attempted to decode neural signals related to hyperglycemia. In addition to wild type mice, we analyzed the responses to acute hypo- and hyperglycemia in transient receptor potential cation channel subfamily V member 1 (TRPV1) cell depleted mice. The decoding algorithm uses neural signals as input and reconstructs blood glucose levels., Results: Our algorithm was able to reconstruct the blood glucose levels with high accuracy (median error 18.6 mg/dl). Hyperglycemia did not induce robust vagus nerve responses, and deletion of TRPV1 nociceptors attenuated the hypoglycemia-dependent vagus nerve signals., Conclusion: These results provide insight to the sensory vagal signaling that encodes hypoglycemic states and suggest a method to measure blood glucose levels by decoding nerve signals., Trial Registration: Not applicable., Competing Interests: Competing interestsThe authors declare that they have no competing interests., (© The Author(s) 2019.)

The nervous system maintains physiological homeostasis through reflex pathways that modulate organ function. This process begins when changes in the internal milieu (e.g., blood pressure, temperature, or pH) activate visceral sensory neurons that transmit action potentials along the vagus nerve to the brainstem. IL-1β and TNF, inflammatory cytokines produced by immune cells during infection and injury, and other inflammatory mediators have been implicated in activating sensory action potentials in the vagus nerve. However, it remains unclear whether neural responses encode cytokine-specific information. Here we develop methods to isolate and decode specific neural signals to discriminate between two different cytokines. Nerve impulses recorded from the vagus nerve of mice exposed to IL-1β and TNF were sorted into groups based on their shape and amplitude, and their respective firing rates were computed. This revealed sensory neural groups responding specifically to TNF and IL-1β in a dose-dependent manner. These cytokine-mediated responses were subsequently decoded using a Naive Bayes algorithm that discriminated between no exposure and exposures to IL-1β and TNF (mean successful identification rate 82.9 ± 17.8%, chance level 33%). Recordings obtained in IL-1 receptor-KO mice were devoid of IL-1β-related signals but retained their responses to TNF. Genetic ablation of TRPV1 neurons attenuated the vagus neural signals mediated by IL-1β, and distal lidocaine nerve block attenuated all vagus neural signals recorded. The results obtained in this study using the methodological framework suggest that cytokine-specific information is present in sensory neural signals within the vagus nerve., Competing Interests: The authors declare no conflict of interest., (Copyright © 2018 the Author(s). Published by PNAS.)

There has been growing interest in transcranial direct current stimulation (tDCS), a non-invasive technique purported to modulate neural activity via weak, externally applied electric fields. Although some promising preliminary data have been reported for applications ranging from stroke rehabilitation to cognitive enhancement, little is known about how tDCS affects the human brain, and some studies have concluded that it may have no effect at all. Here, we describe a macaque model of tDCS that allows us to simultaneously examine the effects of tDCS on brain activity and behavior. We find that applying tDCS to right prefrontal cortex improves monkeys' performance on an associative learning task. While firing rates do not change within the targeted area, tDCS does induce large low-frequency oscillations in the underlying tissue. These oscillations alter functional connectivity, both locally and between distant brain areas, and these long-range changes correlate with tDCS's effects on behavior. Together, these results are consistent with the idea that tDCS leads to widespread changes in brain activity and suggest that it may be a valuable method for cheaply and non-invasively altering functional connectivity in humans., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Unlabelled: Psychophysical studies have shown that subjects are often unaware of visual stimuli presented around the time of an eye movement. This saccadic suppression is thought to be a mechanism for maintaining perceptual stability. The brain might accomplish saccadic suppression by reducing the gain of visual responses to specific stimuli or by simply suppressing firing uniformly for all stimuli. Moreover, the suppression might be identical across the visual field or concentrated at specific points. To evaluate these possibilities, we recorded from individual neurons in cortical area V4 of nonhuman primates trained to execute saccadic eye movements. We found that both modes of suppression were evident in the visual responses of these neurons and that the two modes showed different spatial and temporal profiles: while gain changes started earlier and were more widely distributed across visual space, nonspecific suppression was found more often in the peripheral visual field, after the completion of the saccade. Peripheral suppression was also associated with increased noise correlations and stronger local field potential oscillations in the α frequency band. This pattern of results suggests that saccadic suppression shares some of the circuitry responsible for allocating voluntary attention., Significance Statement: We explore our surroundings by looking at things, but each eye movement that we make causes an abrupt shift of the visual input. Why doesn't the world look like a film recorded on a shaky camera? The answer in part is a brain mechanism called saccadic suppression, which reduces the responses of visual neurons around the time of each eye movement. Here we reveal several new properties of the underlying mechanisms. First, the suppression operates differently in the central and peripheral visual fields. Second, it appears to be controlled by oscillations in the local field potentials at frequencies traditionally associated with attention. These results suggest that saccadic suppression shares the brain circuits responsible for actively ignoring irrelevant stimuli., (Copyright © 2016 the authors 0270-6474/16/369227-13$15.00/0.)

Transcranial electrical stimulation (tES) can be optimized to achieve maximal current flow at desired brain regions. The aim of this study was to characterize electric field magnitudes generated by tES optimization and to compare them to experimentally induced values as determined by data from intracranial electrodes. Local field potentials were recorded from two monkeys with implanted multi-site intracranial Utah arrays during transcranial direct current stimulation (tDCS), and the neural effect predictions obtained from optimized electrode placement were assessed. Comparative data between the two sites of intracranial recordings during tDCS partially validated the predictions of our tES optimization algorithms.

Traveling waves of neural activity are frequently observed to occur in concert with the presentation of a sensory stimulus or the execution of a movement. Although such waves have been studied for decades, little is known about their function. Here we show that traveling waves in the primate extrastriate visual cortex provide a means of integrating sensory and motor signals. Specifically, we describe a traveling wave of local field potential (LFP) activity in cortical area V4 of macaque monkeys that is triggered by the execution of saccadic eye movements. These waves sweep across the V4 retinotopic map, following a consistent path from the foveal to the peripheral representations of space; their amplitudes correlate with the direction and size of each saccade. Moreover, these waves are associated with a reorganization of the postsaccadic neuronal firing patterns, which follow a similar retinotopic progression, potentially prioritizing the processing of behaviorally relevant stimuli., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Local field potentials (LFP) reflect the properties of neuronal circuits or columns recorded in a volume around a microelectrode (Buzsáki et al., 2012). The extent of this integration volume has been a subject of some debate, with estimates ranging from a few hundred microns (Katzner et al., 2009; Xing et al., 2009) to several millimeters (Kreiman et al., 2006). We estimated receptive fields (RFs) of multi-unit activity (MUA) and LFPs at an intermediate level of visual processing, in area V4 of two macaques. The spatial structure of LFP receptive fields varied greatly as a function of time lag following stimulus onset, with the retinotopy of LFPs matching that of MUAs at a restricted set of time lags. A model-based analysis of the LFPs allowed us to recover two distinct stimulus-triggered components: an MUA-like retinotopic component that originated in a small volume around the microelectrodes (~350 μm), and a second component that was shared across the entire V4 region; this second component had tuning properties unrelated to those of the MUAs. Our results suggest that the LFP reflects neural activity across multiple spatial scales, which both complicates its interpretation and offers new opportunities for investigating the large-scale structure of network processing.

Intracortical recordings comprise both fast events, action potentials (APs), and slower events, known as local field potentials (LFPs). Although it is believed that LFPs mostly reflect local synaptic activity, it is unclear which of their signal components are most closely related to synaptic potentials and would therefore be causally related to the occurrence of individual APs. This issue is complicated by the significant contribution from AP waveforms, especially at higher LFP frequencies. In recordings of single-cell activity and LFPs from the human temporal cortex, we computed quantitative, nonlinear, causal dynamic models for the prediction of AP timing from LFPs, at millisecond resolution, before and after removing AP contributions to the LFP. In many cases, the timing of a significant number of single APs could be predicted from spike-free LFPs at different frequencies. Not surprisingly, model performance was superior when spikes were not removed. Cells whose activity was predicted by the spike-free LFP models generally fell into one of two groups: in the first group, neuronal spike activity was associated with specific phases of low LFP frequencies, lower spike activity at high LFP frequencies, and a stronger linear component in the spike-LFP model; in the second group, neuronal spike activity was associated with larger amplitude of high LFP frequencies, less frequent phase locking, and a stronger nonlinear model component. Spike timing in the first group was better predicted by the sign and level of the LFP preceding the spike, whereas spike timing in the second group was better predicted by LFP power during a certain time window before the spike.

Surround suppression is a common feature of sensory neurons. For neurons of the visual cortex, it occurs when a visual stimulus extends beyond a neuron's classical receptive field, reducing the neuron's firing rate. While several studies have been attributing the suppression effect on horizontal, long-range lateral or feedback connections, the underlying circuitry for surround modulation remain unidentified. Since most of these models have been relying on single neuron recordings, the contribution of lateral connections can only be suggested from the surround field properties. A more straightforward approach would be to detect these connections and their dynamics using simultaneous recordings from multiple neurons in one or more visual areas. We have developed a method for estimating these connections and we analyzed data obtained from 100-electrode Utah arrays chronically implanted into area V4 of the macaque monkey. Using a method based on the nonlinear Volterra modeling approach, we computed estimates of the strength and statistical reliability of connections among neurons, including nonlinear interactions and excitatory and inhibitory connections. Our results thus far reveal a pattern of connectivity within V4 that conforms to the results of previous anatomical work: Excitatory connections are far more common than inhibitory connections (∼65%), stronger connections are found among neurons that are physically near one another, and connections are stronger among neurons with similar receptive field properties. However, this connectivity is capable of reorganizing on short time scales according to the stimulus: Stimuli that evoke strong suppression at the single-unit level introduce stronger inhibition among V4 neurons, identifying recurrent connectivity as the source of the suppression. Overall, these results provide insight into the dynamic nature of neuronal organization within V4 and its contribution to surround suppression.

Single neurons carry out important sensory and motor functions related to the larger networks in which they are embedded. Understanding the relationships between single-neuron spiking and network activity is therefore of great importance and the latter can be readily estimated from low-frequency brain signals known as local field potentials (LFPs). In this work we examine a number of issues related to the estimation of spike and LFP signals. We show that spike trains and individual spikes contain power at the frequencies that are typically thought to be exclusively related to LFPs, such that simple frequency-domain filtering cannot be effectively used to separate the two signals. Ground-truth simulations indicate that the commonly used method of estimating the LFP signal by low-pass filtering the raw voltage signal leads to artifactual correlations between spikes and LFPs and that these correlations exert a powerful influence on popular metrics of spike-LFP synchronization. Similar artifactual results were seen in data obtained from electrophysiological recordings in macaque visual cortex, when low-pass filtering was used to estimate LFP signals. In contrast LFP tuning curves in response to sensory stimuli do not appear to be affected by spike contamination, either in simulations or in real data. To address the issue of spike contamination, we devised a novel Bayesian spike removal algorithm and confirmed its effectiveness in simulations and by applying it to the electrophysiological data. The algorithm, based on a rigorous mathematical framework, outperforms other methods of spike removal on most metrics of spike-LFP correlations. Following application of this spike removal algorithm, many of our electrophysiological recordings continued to exhibit spike-LFP correlations, confirming previous reports that such relationships are a genuine aspect of neuronal activity. Overall, these results show that careful preprocessing is necessary to remove spikes from LFP signals, but that when effective spike removal is used, spike-LFP correlations can potentially yield novel insights about brain function.

This paper presents a pilot application of the Boolean-Volterra modeling methodology presented in the companion paper (Part I) that is suitable for the analysis of systems with point-process inputs and outputs (e.g., recordings of the activity of neuronal ensembles). This application seeks to discover the causal links between two neuronal ensembles in the hippocampus of a behaving rat. The experimental data come from multi-unit recordings in the CA3 and CA1 regions of the hippocampus in the form of sequences of action potentials-treated mathematically as point-processes and computationally as spike-trains-that are collected in vivo during two behavioral tasks. The modeling objective is to identify and quantify the causal links among the neurons generating the recorded activity, using Boolean-Volterra models estimated directly from the data according to the methodological framework presented in the companion paper. The obtained models demonstrate the feasibility of the proposed approach using short data-records and provide some insights into the functional properties of the system (e.g., regarding the presence of rhythmic characteristics in the neuronal dynamics of these ensembles), making the proposed methodology an attractive tool for the analysis and modeling of multi-unit recordings from neuronal systems in a practical context.

This paper presents a new modeling approach for neural systems with point-process (spike) inputs and outputs that utilizes Boolean operators (i.e. modulo 2 multiplication and addition that correspond to the logical AND and OR operations respectively, as well as the AND&#95;NOT logical operation representing inhibitory effects). The form of the employed mathematical models is akin to a "Boolean-Volterra" model that contains the product terms of all relevant input lags in a hierarchical order, where terms of order higher than first represent nonlinear interactions among the various lagged values of each input point-process or among lagged values of various inputs (if multiple inputs exist) as they reflect on the output. The coefficients of this Boolean-Volterra model are also binary variables that indicate the presence or absence of the respective term in each specific model/system. Simulations are used to explore the properties of such models and the feasibility of their accurate estimation from short data-records in the presence of noise (i.e. spurious spikes). The results demonstrate the feasibility of obtaining reliable estimates of such models, with excitatory and inhibitory terms, in the presence of considerable noise (spurious spikes) in the outputs and/or the inputs in a computationally efficient manner. A pilot application of this approach to an actual neural system is presented in the companion paper (Part II).

The increasing availability of multiunit recordings gives new urgency to the need for effective analysis of "multidimensional" time-series data that are derived from the recorded activity of neuronal ensembles in the form of multiple sequences of action potentials--treated mathematically as point-processes and computationally as spike-trains. Whether in conditions of spontaneous activity or under conditions of external stimulation, the objective is the identification and quantification of possible causal links among the neurons generating the observed binary signals. A multiple-input/multiple-output (MIMO) modeling methodology is presented that can be used to quantify the neuronal dynamics of causal interrelationships in neuronal ensembles using spike-train data recorded from individual neurons. These causal interrelationships are modeled as transformations of spike-trains recorded from a set of neurons designated as the "inputs" into spike-trains recorded from another set of neurons designated as the "outputs." The MIMO model is composed of a set of multiinput/single-output (MISO) modules, one for each output. Each module is the cascade of a MISO Volterra model and a threshold operator generating the output spikes. The Laguerre expansion approach is used to estimate the Volterra kernels of each MISO module from the respective input-output data using the least-squares method. The predictive performance of the model is evaluated with the use of the receiver operating characteristic (ROC) curve, from which the optimum threshold is also selected. The Mann-Whitney statistic is used to select the significant inputs for each output by examining the statistical significance of improvements in the predictive accuracy of the model when the respective inputs is included. Illustrative examples are presented for a simulated system and for an actual application using multiunit data recordings from the hippocampus of a behaving rat.

Implementation of neuroprosthetic devices requires a reliable and accurate quantitative representation of the input-output transformations performed by the involved neuronal populations. Nonparametric, data driven models with predictive capabilities are excellent candidates for these purposes. When modeling input-output relations in multi-input neuronal systems, it is important to select the subset of inputs that are functionally and causally related to the output. Inputs that do not convey information about the actual transformation not only increase the computational burden but also affect the generalization of the model. Moreover, a reliable functional connectivity measure can provide patterns of information flow that can be linked to physiological and anatomical properties of the system. We propose a method based on the Volterra modeling approach that selects distinct subsets of inputs for each output based on the prediction of the respective models and its statistical evaluation. The algorithm builds successive models with increasing number of inputs and examines whether the inclusion of additional inputs benefits the predictive accuracy of the overall model. It also explores possible second-order (inter-modulatory) interactions among the inputs. The method was applied to multi-unit recordings from the CA3 (input) and CA1 (output) regions of the hippocampus in behaving rats, in order to reveal spatiotemporal connectivity maps of the input-output transformation taking place in the CA3-CA1 synapse.

This paper presents a novel modeling approach for neural systems with point-process inputs and outputs (binary time-series of 0's and 1's) that utilizes Boolean operators of modulo-2 multiplication and addition, corresponding to the logical AND and OR operations respectively. The form of the employed mathematical model is akin to a "Boolean-Volterra" model that contains the product terms of all relevant input lags in a hierarchical order, where terms of order higher than first represent nonlinear interactions among the various lagged values of each input point-process or among lagged values of various inputs (if multiple inputs exist) as they reflect on the output. The coefficients of this Boolean model are also binary variables that indicate the presence or absence of the respective term in each specific model/system. Simulations are used to explore the properties of such models and the feasibility of accurate estimation of such models from short data-records in the presence of noise (i.e. spurious spikes). The results demonstrate the feasibility of obtaining reliable estimates of such models, even in the presence of considerable noise in the input and/or output, thus making the proposed approach an attractive candidate for modeling neural systems in a practical context.

A multi-input modeling approach is introduced to quantify hippocampal neural dynamics. It is based on the Volterra modeling approach extended to multiple inputs. The computed Volterra kernels allow quantitative description of hippocampal transformations and define a predictive model that can produce responses to arbitrary input patterns. Electrophysiological data from several CA3 and CA1 cells in behaving rats were recorded simultaneously using an array of penetrating electrodes. This activity was used to compute kernels up to third order for single and multiple input cases. Representative sets of kernels illustrate the variability of the dynamics of the CA3-CA1 transformations. Our model's predictive accuracy was evaluated using ROC curves.

A new modeling approach of hippocampal nonlinear dynamics is presented. It is based on Principal Dynamics Modes (PDMs) derived from the Volterra kernels quantifying the hippocampal transformations. The approach is illustrated using data obtained from acute hippocampal slice preparations and from behaving rats performs a memory task. The resulting PDM models are comparable in performance to the Volterra models and require significantly less representational and implementational overhead.