Machine learning based DNA melt curve profiling enables automated novel genotype detection

Surveillance for genetic variation of microbial pathogens, both within and among species, plays an important role in informing research, diagnostic, prevention, and treatment activities for disease control. However, large-scale systematic screening for novel genotypes remains challenging in part due to technological limitations. Towards addressing this challenge, we present an advancement in universal microbial high resolution melting (HRM) analysis that is capable of accomplishing both known genotype identification and novel genotype detection. Specifically, this novel surveillance functionality is achieved through time-series modeling of sequence-defined HRM curves, which is uniquely enabled by the large-scale melt curve datasets generated using our high-throughput digital HRM platform. Taking the detection of bacterial genotypes as a model application, we demonstrate that our algorithms accomplish an overall classification accuracy over 99.7% and perform novelty detection with a sensitivity of 0.96, specificity of 0.96 and Youden index of 0.92. Since HRM-based DNA profiling is an inexpensive and rapid technique, our results add support for the feasibility of its use in surveillance applications.

Emerging wearable technologies for multisystem monitoring and treatment of Parkinson's disease: a narrative review

Parkinson's disease (PD) is a chronic movement disorder characterized by a variety of motor and nonmotor comorbidities, including cognitive impairment, gastrointestinal (GI) dysfunction, and autonomic/sleep disturbances. Symptoms typically fluctuate with different settings and environmental factors and thus need to be consistently monitored. Current methods, however, rely on infrequent rating scales performed in clinic. The advent of wearable technologies presents a new avenue to track objective measures of PD comorbidities longitudinally and more frequently. This narrative review discusses and proposes emerging wearable technologies that can monitor manifestations of motor, cognitive, GI, and autonomic/sleep comorbidities throughout the daily lives of PD individuals. This can provide more wholistic insight into real-time physiological versus pathological function with the potential to better assess treatments during clinical trials and allow physicians to optimize treatment regimens. Additionally, this narrative review briefly examines novel applications of wearables as therapy for PD patients.

Exploring the Gut-Brain Connection in Gastroparesis With Autonomic and Gastric Myoelectric Monitoring

OBJECTIVE

The goal of this study was to identify autonomic and gastric myoelectric biomarkers from throughout the day that differentiate patients with gastroparesis, diabetics without gastroparesis, and healthy controls, while providing insight into etiology.

METHODS

We collected 19 24-hour recordings of electrocardiogram (ECG) and electrogastrogram (EGG) data from healthy controls and patients with diabetic or idiopathic gastroparesis. We used physiologically and statistically rigorous models to extract autonomic and gastric myoelectric information from the ECG and EGG data, respectively. From these, we constructed quantitative indices which differentiated the distinct groups and demonstrated their application in automatic classification paradigms and as quantitative summary scores.

RESULTS

We identified several differentiators that separate healthy controls from gastroparetic patient groups, specifically around sleep and meals. We also demonstrated the downstream utility of these differentiators in automatic classification and quantitative scoring paradigms. Even with this small pilot dataset, automated classifiers achieved an accuracy of 79% separating autonomic phenotypes and 65% separating gastrointestinal phenotypes. We also achieved 89% accuracy separating controls from gastroparetic patients in general and 90% accuracy separating diabetics with and without gastroparesis. These differentiators also suggested varying etiologies for different phenotypes.

CONCLUSION

The differentiators we identified were able to successfully distinguish between several autonomic and gastrointestinal (GI) phenotypes using data collected while at-home with non-invasive sensors.

SIGNIFICANCE

Autonomic and gastric myoelectric differentiators, obtained using at-home recording of fully non-invasive signals, can be the first step towards dynamic quantitative markers to track severity, disease progression, and treatment response for combined autonomic and GI phenotypes.

Differentiation of Bolus Texture During Deglutition via High-Density Surface Electromyography: A Pilot Study

OBJECTIVE

Swallowing is a complex neuromuscular task. There is limited spatiotemporal data on normative surface electromyographic signal during swallow, particularly across standard textures. We hypothesize the pattern of electromyographic signal of the anterior neck varies cranio-caudally, that laterality can be evaluated, and categorization of bolus texture can be differentiated by high-density surface electromyography (HDsEMG) through signal analysis.

METHODS

An HDsEMG grid of 20 electrodes captured electromyographic activity in eight healthy adult subjects across 240 total swallows. Participants swallowed five standard textures: saliva, thin liquid, puree, mixed consistency, and dry solid. Data were bandpass filtered, underwent functional alignment of signal, and then placed into binary classifier receiver operating characteristic (ROC) curves. Muscular activity was visualized by creating two-dimensional EMG heat maps.

RESULTS

Signal analysis results demonstrated a positive correlation between signal amplitude and bolus texture. Greater differences of amplitude in the cranial most region of the array when compared to the caudal most region were noted in all subjects. Lateral comparison of the array revealed symmetric power levels across all subjects and textures. ROC curves demonstrated the ability to correctly classify textures within subjects in 6 of 10 texture comparisons.

CONCLUSION

This pilot study suggests that utilizing HDsEMG during deglutition can noninvasively differentiate swallows of varying texture noninvasively. This may prove useful in future diagnostic and behavioral swallow applications.

LEVEL OF EVIDENCE

4 Laryngoscope, 133:2695-2703, 2023.

Spontaneous enteric nervous system activity precedes maturation of gastrointestinal motility

Spontaneous neuronal network activity is essential in development of central and peripheral circuits, yet whether this is a feature of enteric nervous system development has yet to be established. Using ex vivo gastrointestinal (GI) motility assays with unbiased computational analyses, we identify a previously unknown pattern of spontaneous neurogenic GI motility. We further show that this motility is driven by cholinergic signaling, which may inform GI pharmacology for preterm patients.

Exploring the aperiodic nature of parasympathetic activity during sleep in idiopathic gastroparesis

The parasympathetic nervous system is necessary to regulate both sleep and digestion. Investigating abnormalities during the controlled setting of sleep can shed light on digestion, specifically for patients with idiopathic gastroparesis. In this study, we specifically investigate heartbeat-derived parasympathetic activity during sleep at very low frequencies, relevant to sleep cycle regulation. To do this, we adapt a method that extracts both periodic and aperiodic information from the power spectral density and recognize that the aperiodic activity may contain information relevant to very low frequencies. After testing on both synthetic noise data (pink and white) and overnight data from seven healthy controls and idiopathic gastroparetics, we find that the healthy controls' low-frequency aperiodic activity reflects pink noise structure, while the majority of the patients' aperiodic activity reflects white noise structure. At these low frequencies, these differences suggest differences in autonomic sleep cycle regulation.Clinical Relevance- This methodology can be optimized to track the health of the parasympathetic nervous system and suggest whether individual disease etiology is autonomic-related.

Simultaneous Gut-Brain Electrophysiology Shows Cognition and Satiety Specific Coupling

Recent studies, using high resolution magnetoencephalography (MEG) and electrogastrography (EGG), have shown that during resting state, rhythmic gastric physiological signals are linked with cortical brain oscillations. Yet, gut-brain coupling has not been investigated with electroencephalography (EEG) during cognitive brain engagement or during hunger-related gut engagement. In this study in 14 young adults (7 females, mean ± SD age 25.71 ± 8.32 years), we study gut-brain coupling using simultaneous EEG and EGG during hunger and satiety states measured in separate visits, and compare responses both while resting as well as during a cognitively demanding working memory task. We find that EGG-EEG phase-amplitude coupling (PAC) differs based on both satiety state and cognitive effort, with greater PAC modulation observed in the resting state relative to working memory. We find a significant interaction between gut satiation levels and cognitive states in the left fronto-central brain region, with larger cognitive demand based differences in the hunger state. Furthermore, strength of PAC correlated with behavioral performance during the working memory task. Altogether, these results highlight the role of gut-brain interactions in cognition and demonstrate the feasibility of these recordings using scalable sensors.

Wireless Heart Sensor for Capturing Cardiac Orienting Response for Prediction of Neurodevelopmental Delay in Infants

Early identification of infants at risk of neurodevelopmental delay is an essential public health aim. Such a diagnosis allows early interventions for infants that maximally take advantage of the neural plasticity in the developing brain. Using standardized physiological developmental tests, such as the assessment of neurophysiological response to environmental events using cardiac orienting responses (CORs), is a promising and effective approach for early recognition of neurodevelopmental delay. Previous CORs have been collected on children using large bulky equipment that would not be feasible for widespread screening in routine clinical visits. We developed a portable wireless electrocardiogram (ECG) system along with a custom application for IOS tablets that, in tandem, can extract CORs with sufficient physiologic and timing accuracy to reflect the well-characterized ECG response to both auditory and visual stimuli. The sensor described here serves as an initial step in determining the extent to which COR tools are cost-effective for the early screening of children to determine who is at risk of developing neurocognitive deficits and may benefit from early interventions. We demonstrated that our approach, based on a wireless heartbeat sensor system and a custom mobile application for stimulus display and data recording, is sufficient to capture CORs from infants. The COR monitoring approach described here with mobile technology is an example of a desired standardized physiologic assessment that is a cost-and-time efficient, scalable method for early recognition of neurodevelopmental delay.

Robust Regression and Optimal Transport Methods to Predict Gastrointestinal Disease Etiology From High Resolution EGG and Symptom Severity

OBJECTIVE

Gastric functional and motility disorders are highly prevalent, with gastroparesis (GP) and functional dyspepsia (FD), affecting 1.5-3% and 10% of the population, respectively. Multiple disease etiologies with overlapping symptoms, such as antral hypomotility, pylorospasm, autonomic dysfunction, and gastric myoelectric dysfunction underlie GP and FD. There is an unmet need to differentiate these etiologies non-invasively to tailor treatment strategies and predict treatment response.

METHODS

We performed cutaneous high-resolution electrogastrogram (HR-EGG) recordings on 32 human subjects (controls, GP, and FD) and computed gastric slow wave propagation patterns. We implemented robust regression and clustering methods to identify one group of patients with symptoms well explained by spatial slow wave features and another with symptom severity significantly exceeding predictions from spatial slow wave features. Five patients were re-assessed with validated symptom questionnaires after pyloric and prokinetic interventions.

RESULTS

A group of seven patients was identified whose spatial slow wave features lie within the same range as control subjects but whose symptom severity significantly exceeded what is predicted from spatial slow wave features. We hypothesize that gastric myoelectric dysfunction is not a prominent disease etiology in this group. A highly accurate regression holds in the other group of patients (r=0.8). Of the patients with repeat questionnaires, patients with symptom severity exceeding the regression line reported symptom improvement, whereas patients with symptoms in close proximity to the regression line experienced no improvement.

CONCLUSION

These findings suggest that patients with symptom severity significantly exceeding the robust regression line have symptoms that cannot be explained by gastric myoelectric dysfunction alone, and vice versa.

SIGNIFICANCE

This methodology may provide clinicians with an opportunity to screen patients to determine when existing interventions will be effective, and on the flipside, when slow wave restoration interventions, such as gastric neuromodulation, may be most effective in improving symptoms and quality of life.

Modeling relationships between rhythmic processes and neuronal spike timing

Neurons are embedded in complex networks, where they participate in repetitive, coordinated interactions with other neurons. Neuronal spike timing is thus predictably constrained by a range of ionic currents that shape activity at both short (milliseconds) and longer (tens to hundreds of milliseconds) timescales, but we lack analytical tools to rigorously identify these relationships. Here, we innovate a modeling approach to test the relationship between oscillations in the local field potential (LFP) and neuronal spike timing. We use kernel density estimation to relate single neuron spike timing and the phase of LFP rhythms (in simulated and hippocampal CA1 neuronal spike trains). We then combine phase and short (3 ms) spike history information within a logistic regression framework ("phaseSH models"), and show that models that leverage refractory constraints and oscillatory phase information can effectively test whether-and the degree to which-rhythmic currents (as measured from the LFP) reliably explain variance in neuronal spike trains. This approach allows researchers to systematically test the relationship between oscillatory activity and neuronal spiking dynamics as they unfold over time and as they shift to adapt to distinct behavioral conditions.

A Mutual Information Measure of Phase-Amplitude Coupling using High Dimensional Sparse Models

Cross frequency coupling (CFC) between electrophysiological signals in the brain has been observed and it's abnormalities have been observed in conditions such as Parkinson's disease and epilepsy. More recently, CFC has been observed in stomach-brain electrophysiologic studies and thus becomes an enticing possible target for diseases involving aberrations of the gut-brain axis. However, current methods of detecting coupling do not attempt to capture the underlying statistical relationships that give rise to this coupling. In this paper, we demonstrate a new method of calculating phase amplitude coupling by estimating the mutual information between phase and amplitude, using a flexible parametric modeling approach. Specifically, we develop an exponential generalized linear model (GLM) to model amplitude given phase, using a high dimensional basis of von-Mises function regressors and l₁ regularized model selection. Using synthetically generated gut-brain coupled signals, we demonstrate that our method outperforms the existing gold-standard methods for detectable low-levels of phase amplitude coupling through receiver operating characteristic (ROC) curve analysis.

Automated classification of sleep and wake from single day triaxial accelerometer data

Actigraphy allows for the remote monitoring of subjects' activity for clinical and research purposes. However, most standard methods are built for proprietary measures from specific devices that are not widely used. In this study, we develop an algorithm for classifying sleep and awake using a single day of triaxial accelerometer data, which can be acquired from all smart devices. This algorithm consists of two stages, clustering and hidden Markov modeling, and outperforms standard algorithms in sensitivity (94%), specificity (93 %), and overall accuracy (93%) across seven subjects. This method can help automate actigraphy analyses at scale using widely available technology using even a single day's worth of data. Clinical Relevance- Automated monitoring of patients' activity at home can help track recovery trajectories after surgery and injury, disease progression, treatment response.

Passive longitudinal weight and cardiopulmonary monitoring in the home bed

Home health monitoring has the potential to improve outpatient management of chronic cardiopulmonary diseases such as heart failure. However, it is often limited by the need for adherence to self-measurement, charging and self-application of wearables, or usage of apps. Here, we describe a non-contact, adherence-independent sensor, that when placed beneath the legs of a patient's home bed, longitudinally monitors total body weight, detailed respiratory signals, and ballistocardiograms for months, without requiring any active patient participation. Accompanying algorithms separate weight and respiratory signals when the bed is shared by a partner or a pet. Validation studies demonstrate quantitative equivalence to commercial sensors during overnight sleep studies. The feasibility of detecting obstructive and central apneas, cardiopulmonary coupling, and the hemodynamic consequences of non-sustained ventricular tachycardia is also established. Real-world durability is demonstrated by 3 months of in-home monitoring in an example patient with heart failure and ischemic cardiomyopathy as he recovers from coronary artery bypass grafting surgery. BedScales is the first sensor to measure adherence-independent total body weight as well as longitudinal cardiopulmonary physiology. As such, it has the potential to create a multidimensional picture of chronic disease, learn signatures of impending hospitalization, and enable optimization of care in the home.

Macrophage calcium reporter mice reveal immune cell communication in vitro and in vivo

Cell communication underlies emergent functions in diverse cell types and tissues. Recent evidence suggests that macrophages are organized in communicating networks, but new tools are needed to quantitatively characterize the resulting cellular conversations. Here, we infer cell communication from spatiotemporal correlations of intracellular calcium dynamics that are non-destructively imaged across cell populations expressing genetically encoded calcium indicators. We describe a hematopoietic calcium reporter mouse (Csf1rCreGCaMP5fl) and a computational analysis pipeline for inferring communication between reporter cells based on "excess synchrony." We observed signals suggestive of cell communication in macrophages treated with immune-stimulatory DNA in vitro and tumor-associated immune cells imaged in a dorsal window chamber model in vivo. Together, the methods described here expand the toolkit for discovery of cell communication events in macrophages and other immune cells.

Statistical uncertainty quantification to augment clinical decision support: a first implementation in sleep medicine

Machine learning has the potential to change the practice of medicine, particularly in areas that require pattern recognition (e.g. radiology). Although automated classification is unlikely to be perfect, few modern machine learning tools have the ability to assess their own classification confidence to recognize uncertainty that might need human review. Using automated single-channel sleep staging as a first implementation, we demonstrated that uncertainty information (as quantified using Shannon entropy) can be utilized in a "human in the loop" methodology to promote targeted review of uncertain sleep stage classifications on an epoch-by-epoch basis. Across 20 sleep studies, this feedback methodology proved capable of improving scoring agreement with the gold standard over automated scoring alone (average improvement in Cohen's Kappa of 0.28), in a fraction of the scoring time compared to full manual review (60% reduction). In summary, our uncertainty-based clinician-in-the-loop framework promotes the improvement of medical classification accuracy/confidence in a cost-effective and economically resourceful manner.

Miniaturized wireless gastric pacing via inductive power transfer with non-invasive monitoring using cutaneous Electrogastrography

BACKGROUND

Gastroparesis is a debilitating disease that is often refractory to pharmacotherapy. While gastric electrical stimulation has been studied as a potential treatment, current devices are limited by surgical complications and an incomplete understanding of the mechanism by which electrical stimulation affects physiology.

METHODS

A leadless inductively-powered pacemaker was implanted on the gastric serosa in an anesthetized pig. Wireless pacing was performed at transmitter-to-receiver distances up to 20 mm, frequency of 0.05 Hz, and pulse width of 400 ms. Electrogastrogram (EGG) recordings using cutaneous and serosal electrode arrays were analyzed to compute spectral and spatial statistical parameters associated with the slow wave.

RESULTS

Our data demonstrated evident change in EGG signal patterns upon initiation of pacing. A buffer period was noted before a pattern of entrainment appeared with consistent and low variability in slow wave direction. A spectral power increase in the EGG frequency band during entrainment also suggested that pacing increased strength of the slow wave.

CONCLUSION

Our preliminary in vivo study using wireless pacing and concurrent EGG recording established the foundations for a minimally invasive approach to understand and optimize the effect of pacing on gastric motor activity as a means to treat conditions of gastric dysmotility.

Building a Simple and Versatile Illumination System for Optogenetic Experiments

Controlling biological processes using light has increased the accuracy and speed with which researchers can manipulate many biological processes. Optical control allows for an unprecedented ability to dissect function and holds the potential for enabling novel genetic therapies. However, optogenetic experiments require adequate light sources with spatial, temporal, or intensity control, often a bottleneck for researchers. Here we detail how to build a low-cost and versatile LED illumination system that is easily customizable for different available optogenetic tools. This system is configurable for manual or computer control with adjustable LED intensity. We provide an illustrated step-by-step guide for building the circuit, making it computer-controlled, and constructing the LEDs. To facilitate the assembly of this device, we also discuss some basic soldering techniques and explain the circuitry used to control the LEDs. Using our open-source user interface, users can automate precise timing and pulsing of light on a personal computer (PC) or an inexpensive tablet. This automation makes the system useful for experiments that use LEDs to control genes, signaling pathways, and other cellular activities that span large time scales. For this protocol, no prior expertise in electronics is required to build all the parts needed or to use the illumination system to perform optogenetic experiments.

Data-driven noise modeling of digital DNA melting analysis enables prediction of sequence discriminating power

MOTIVATION

The need to rapidly screen complex samples for a wide range of nucleic acid targets, like infectious diseases, remains unmet. Digital High-Resolution Melt (dHRM) is an emerging technology with potential to meet this need by accomplishing broad-based, rapid nucleic acid sequence identification. Here, we set out to develop a computational framework for estimating the resolving power of dHRM technology for defined sequence profiling tasks. By deriving noise models from experimentally generated dHRM datasets and applying these to in silico predicted melt curves, we enable the production of synthetic dHRM datasets that faithfully recapitulate real-world variations arising from sample and machine variables. We then use these datasets to identify the most challenging melt curve classification tasks likely to arise for a given application and test the performance of benchmark classifiers.

RESULTS

This toolbox enables the in silico design and testing of broad-based dHRM screening assays and the selection of optimal classifiers. For an example application of screening common human bacterial pathogens, we show that human pathogens having the most similar sequences and melt curves are still reliably identifiable in the presence of experimental noise. Further, we find that ensemble methods outperform whole series classifiers for this task and are in some cases able to resolve melt curves with single-nucleotide resolution.

AVAILABILITY

Data and code available on https://github.com/lenlan/dHRM-noise-modeling.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Robust Methods to Detect Abnormal Initiation in the Gastric Slow Wave from Cutaneous Recordings

Upper gastrointestinal (GI) disorders are highly prevalent, with gastroparesis (GP) and functional dyspepsia (FD) affecting 3% and 10% of the US population, respectively. Despite overlapping symptoms, differing etiologies of GP and FD have distinct optimal treatments, thus making their management a challenge. One such cause, that of gastric slow wave abnormalities, affects the electromechanical coordination of pacemaker cells and smooth muscle cells in propelling food through the GI tract. Abnormalities in gastric slow wave initiation location and propagation patterns can be treated with novel pacing technologies but are challenging to identify with traditional spectral analyses from cutaneous recordings due to their occurrence at the normal slow wave frequency. This work advances our previous work in developing a 3D convolutional neural network to process multi-electrode cutaneous recordings and successfully classify, in silico, normal versus abnormal slow wave location and propagation patterns. Here, we use transfer learning to build a method that is robust to heterogeneity in both the location of the abnormal initiation on the stomach surface as well as the recording start times with respect to slow wave cycles. We find that by starting with training lowest-complexity models and building complexity in training sets, transfer learning one model to the next, the final network exhibits, on average, 80% classification accuracy in all but the most challenging spatial abnormality location, and below 5% Type-I error probabilities across all locations.

Smart Electronic Eyedrop Bottle for Unobtrusive Monitoring of Glaucoma Medication Adherence

Glaucoma, the leading cause of irreversible blindness, affects >70 million people worldwide. Lowering intraocular pressure via topical administration of eye drops is the most common first-line therapy for glaucoma. This treatment paradigm has notoriously high non-adherence rates: ranging from 30% to 80%. The advent of smart phone enabled technologies creates promise for improving eyedrop adherence. However, previous eyedrop electronic monitoring solutions had awkward medication bottle adjuncts and crude software for monitoring the administration of a drop that adversely affected their ability to foster sustainable improvements in adherence. The current work begins to address this unmet need for wireless technology by creating a “smart drop” bottle. This medication bottle is instrumented with sensing electronics that enable detection of each eyedrop administered while maintaining the shape and size of the bottle. This is achieved by a thin electronic force sensor wrapped around the bottle and underneath the label, interfaced with a thin electronic circuit underneath the bottle that allows for detection and wireless transmission to a smart-phone application. We demonstrate 100% success rate of wireless communication over 75 feet with <1% false positive and false negative rates of single drop deliveries, thus providing a viable solution for eyedrop monitoring for glaucoma patients.

Spatial Patterns From High-Resolution Electrogastrography Correlate With Severity of Symptoms in Patients With Functional Dyspepsia and Gastroparesis

BACKGROUND & AIMS

Invasive gastric electrical mapping has revealed spatial abnormalities of the slow wave in subjects with gastroparesis and functional gastrointestinal disorders. Cutaneous high-resolution electrogastrography (HR-EGG) is a non-invasive method that can detect spatial features of the gastric slow wave. We performed HR-EGG in subjects with active foregut symptoms to evaluate associations between gastric myoelectric abnormalities, symptoms (based on a validated questionnaire), and gastric emptying.

METHODS

We performed a case-control study of 32 subjects, including 7 healthy individuals (controls), 7 subjects with functional dyspepsia and normal gastric emptying, and 18 subjects with gastroparesis, from a tertiary care program. All subjects were assessed by computed tomography imaging of the abdomen and HR-EGG and completed the PAGI-SYM questionnaire on foregut symptoms, which includes the gastroparesis cardinal symptom index. We performed volume reconstruction of the torso and stomach from computed tomography images to guide accurate placement of the HR-EGG array.

RESULTS

Spatial slow-wave abnormalities were detected in 44% of subjects with foregut symptoms. Moreover, subjects with a higher percentage of slow waves with aberrant propagation direction had a higher total gastroparesis cardinal symptom index score (r = 0.56; P < .001) and more severe abdominal pain (r = 0.46; P = .009). We found no correlation between symptoms and traditional EGG parameters.

CONCLUSIONS

In case-control study, we found that the genesis of symptoms of functional dyspepsia and gastroparesis is likely multifactorial, including possible contribution from gastric myoelectric dysfunction. Abnormal spatial parameters, detected by cutaneous HR-EGG, correlated with severity of upper gastrointestinal symptoms, regardless of gastric emptying. This noninvasive, repeatable approach might be used to identify patients for whom gastric myoelectric dysfunction contributes to functional dyspepsia and gastroparesis.

Bayesian inverse methods for spatiotemporal characterization of gastric electrical activity from cutaneous multi-electrode recordings

Gastrointestinal (GI) problems give rise to 10 percent of initial patient visits to their physician. Although blockages and infections are easy to diagnose, more than half of GI disorders involve abnormal functioning of the GI tract, where diagnosis entails subjective symptom-based questionnaires or objective but invasive, intermittent procedures in specialized centers. Although common procedures capture motor aspects of gastric function, which do not correlate with symptoms or treatment response, recent findings with invasive electrical recordings show that spatiotemporal patterns of the gastric slow wave are associated with diagnosis, symptoms, and treatment response. We here consider developing non-invasive approaches to extract this information. Using CT scans from human subjects, we simulate normative and disordered gastric surface electrical activity along with associated abdominal activity. We employ Bayesian inference to solve the ill-posed inverse problem of estimating gastric surface activity from cutaneous recordings. We utilize a prior distribution on the spatiotemporal activity pertaining to sparsity in the number of wavefronts on the stomach surface, and smooth evolution of these wavefronts across time. We implement an efficient procedure to construct the Bayes optimal estimate and demonstrate its superiority compared to other commonly used inverse methods, for both normal and disordered gastric activity. Region-specific wave direction information is calculated and consistent with the simulated normative and disordered cases. We apply these methods to cutaneous multi-electrode recordings of two human subjects with the same clinical description of motor function, but different diagnosis of underlying cause. Our method finds statistically significant wave propagation in all stomach regions for both subjects, anterograde activity throughout for the subject with diabetic gastroparesis, and retrograde activity in some regions for the subject with idiopathic gastroparesis. These findings provide a further step towards towards non-invasive phenotyping of gastric function and indicate the long-term potential for enabling population health opportunities with objective GI assessment.

A Deep Convolutional Neural Network Approach to Classify Normal and Abnormal Gastric Slow Wave Initiation From the High Resolution Electrogastrogram

OBJECTIVE

Gastric slow wave abnormalities have been associated with gastric motility disorders. Invasive studies in humans have described normal and abnormal propagation of the slow wave. This study aims to disambiguate the abnormally functioning wave from one of normalcy using multi-electrode abdominal waveforms of the electrogastrogram (EGG).

METHODS

Human stomach and abdominal models are extracted from computed tomography scans. Normal and abnormal slow waves are simulated along stomach surfaces. Current dipoles at the stomachs surface are propagated to virtual electrodes on the abdomen with a forward model. We establish a deep convolutional neural network (CNN) framework to classify normal and abnormal slow waves from the multi-electrode waveforms. We investigate the effects of non-idealized measurements on performance, including shifted electrode array positioning, smaller array sizes, high body mass index (BMI), and low signal-to-noise ratio (SNR). We compare the performance of our deep CNN to a linear discriminant classifier using wave propagation spatial features.

RESULTS

A deep CNN framework demonstrated robust classification, with accuracy above 90% for all SNR above 0 dB, horizontal shifts within 3 cm, vertical shifts within 6 cm, and abdominal tissue depth within 6 cm. The linear discriminant classifier was much more vulnerable to SNR, electrode placement, and BMI.

CONCLUSION

This is the first study to attempt and, moreover, succeed in using a deep CNN to disambiguate normal and abnormal gastric slow wave patterns from high-resolution EGG data.

SIGNIFICANCE

These findings suggest that multi-electrode cutaneous abdominal recordings have the potential to serve as widely deployable clinical screening tools for gastrointestinal foregut disorders.

A Distributed Framework for the Construction of Transport Maps

The need to reason about uncertainty in large, complex, and multimodal data sets has become increasingly common across modern scientific environments. The ability to transform samples from one distribution <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>P</mml:mi></mml:math> to another distribution <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>Q</mml:mi></mml:math> enables the solution to many problems in machine learning (e.g., Bayesian inference, generative modeling) and has been actively pursued from theoretical, computational, and application perspectives across the fields of information theory, computer science, and biology. Performing such transformations in general still leads to computational difficulties, especially in high dimensions. Here, we consider the problem of computing such "measure transport maps" with efficient and parallelizable methods. Under the mild assumptions that <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>P</mml:mi></mml:math> need not be known but can be sampled from and that the density of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>Q</mml:mi></mml:math> is known up to a proportionality constant, and that <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>Q</mml:mi></mml:math> is log-concave, we provide in this work a convex optimization problem pertaining to relative entropy minimization. We show how an empirical minimization formulation and polynomial chaos map parameterization can allow for learning a transport map between <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>P</mml:mi></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>Q</mml:mi></mml:math> with distributed and scalable methods. We also leverage findings from nonequilibrium thermodynamics to represent the transport map as a composition of simpler maps, each of which is learned sequentially with a transport cost regularized version of the aforementioned problem formulation. We provide examples of our framework within the context of Bayesian inference for the Boston housing data set and generative modeling for handwritten digit images from the MNIST data set.

High-density surface electromyography: A visualization method of laryngeal muscle activity

OBJECTIVES/HYPOTHESIS

Laryngeal muscle activation is a complex and dynamic process. Current evaluation methods include needle and surface electromyography (sEMG). Limitations of needle electromyography include patient discomfort, interpretive complexity, and limited duration of recording. sEMG demonstrates interpretive challenges given loss of spatial selectivity. Application of high-density sEMG (HD sEMG) arrays were evaluated for potential to compensate for spatial selectivity loss while retaining benefits of noninvasive monitoring.

STUDY DESIGN

Basic science.

METHODS

Ten adults performed phonatory tasks while a 20-channel array recorded spatiotemporal data of the anterior neck. Data were processed to provide average spectral power of each electrode. Comparison was made between rest, low-, and high-pitch phonation. Two-dimensional (2D) spectral energy maps were created to evaluate use in gross identification of muscle location.

RESULTS

Three phonatory tasks yielded spectral power measures across the HD sEMG array. Each electrode within the array demonstrated unique power values across all subjects (P < .001). Comparison of each electrode to itself across phonatory tasks yielded differences in all subjects during rest versus low versus high, rest versus low, and rest versus high and in 9/10 subjects (P < .001) for low versus high phonation. Symmetry of HD sEMG signal was noted. Review of 2D coronal energy maps allowed for gross identification of cricothyroid muscle amidst anterior strap musculature.

CONCLUSIONS

HD sEMG can be used to identify differences in anterior neck muscle activity between rest, low-, and high-pitch phonation. HD sEMG of the anterior neck holds potential to enhance diagnostic and therapeutic monitoring for pathologies of laryngeal function.

LEVEL OF EVIDENCE

NA Laryngoscope, 129:2347-2353, 2019.

A flexible likelihood approach for predicting neural spiking activity from oscillatory phase

Background:

The synchronous ionic currents that give rise to neural oscillations have complex influences on neuronal spiking activity that are challenging to characterize.

New method:

Here we present a method to estimate probabilistic relationships between neural spiking activity and the phase of field oscillations using a generalized linear model (GLM) with an overcomplete basis of circular functions. We first use an L1-regularized maximum likelihood procedure to select an active set of regressors from the overcomplete set and perform model fitting using standard maximum likelihood estimation. An information theoretic model selection procedure is then used to identify an optimal subset of regressors and associated coefficients that minimize overfitting. To assess goodness of fit, we apply the time-rescaling theorem and compare model predictions to original data using quantile-quantile plots.

Results:

Spike-phase relationships in synthetic data were robustly characterized. When applied to in vivo hippocampal data from an awake behaving rat, our method captured a multimodal relationship between the spiking activity of a CA1 interneuron, a theta (5–10 Hz) rhythm, and a nested high gamma (65–135 Hz) rhythm.

Comparison with existing methods:

Previous methods for characterizing spike-phase relationships are often only suitable for unimodal relationships, impose specific relationship shapes, or have limited ability to assess the accuracy or fit of their characterizations.

Conclusions:

This method advances the way spike-phase relationships are visualized and quantified, and captures multimodal spike-phase relationships, including relationships with multiple nested rhythms. Overall, our method is a powerful tool for revealing a wide range of neural circuit interactions.

The activity of discrete sets of neurons in the posterior insula correlates with the behavioral expression and extinction of conditioned fear

The interoceptive insular cortex is known to be involved in the perception of bodily states and emotions. Increasing evidence points to an additional role for the insula in the storage of fear memories. However, the activity of the insula during fear expression has not been studied. We addressed this issue by recording single units from the posterior insular cortex (pIC) of awake behaving rats expressing conditioned fear during its extinction. We found a set of pIC units showing either significant increase or decrease in activity during high fear expression to the auditory cue ("freezing units"). Firing rate of freezing units showed high correlation with freezing and outlasted the duration of the auditory cue. In turn, a different set of units showed either significant increase or decrease in activity during low fear state ("extinction units"). These findings show that expression of conditioned freezing is accompanied with changes in pIC neural activity and suggest that the pIC is important to regulate the behavioral expression of fear memory. NEW & NOTEWORTHY Here, we show novel single-unit data from the interoceptive insula underlying the behavioral expression of fear. We show that different populations of neurons in the insula codify expression and extinction of conditioned fear. Our data add further support for the insula as an important player in the regulation of emotions.

In-Home Sleep Recordings in Military Veterans With Posttraumatic Stress Disorder Reveal Less REM and Deep Sleep <1 Hz

Veterans with posttraumatic stress disorder (PTSD) often report suboptimal sleep quality, often described as lack of restfulness for unknown reasons. These experiences are sometimes difficult to objectively quantify in sleep lab assessments. Here, we used a streamlined sleep assessment tool to record in-home 2-channel electroencephalogram (EEG) with concurrent collection of electrodermal activity (EDA) and acceleration. Data from a single forehead channel were transformed into a whole-night spectrogram, and sleep stages were classified using a fully automated algorithm. For this study, 71 control subjects and 60 military-related PTSD subjects were analyzed for percentage of time spent in Light, Hi Deep (1-3 Hz), Lo Deep (<1 Hz), and rapid eye movement (REM) sleep stages, as well as sleep efficiency and fragmentation. The results showed a significant tendency for PTSD sleepers to spend a smaller percentage of the night in REM (p < 0.0001) and Lo Deep (p = 0.001) sleep, while spending a larger percentage of the night in Hi Deep (p < 0.0001) sleep. The percentage of combined Hi+Lo Deep sleep did not differ between groups. All sleepers usually showed EDA peaks during Lo, but not Hi, Deep sleep; however, PTSD sleepers were more likely to lack EDA peaks altogether, which usually coincided with a lack of Lo Deep sleep. Linear regressions with all subjects showed that a decreased percentage of REM sleep in PTSD sleepers was accounted for by age, prazosin, SSRIs and SNRIs (p < 0.02), while decreased Lo Deep and increased Hi Deep in the PTSD group could not be accounted for by any factor in this study (p < 0.005). Linear regression models with only the PTSD group showed that decreased REM correlated with self-reported depression, as measured with the Depression, Anxiety, and Stress Scales (DASS; p < 0.00001). DASS anxiety was associated with increased REM time (p < 0.0001). This study shows altered sleep patterns in sleepers with PTSD that can be partially accounted for by age and medication use; however, differences in deep sleep related to PTSD could not be linked to any known factor. With several medications [prazosin, selective serotonin reuptake inhibitors (SSRIs), serotonin-norepinephrine reuptake inhibitors (SNRIs); p < 0.03], as well as SSRIs were associated with less sleep efficiency (b = -3.3 ± 0.95; p = 0.0005) and more sleep fragmentation (b = -1.7 ± 0.51; p = 0.0009). Anti-psychotics were associated with less sleep efficiency (b = -4.9 ± 1.4; p = 0.0004). Sleep efficiency was negatively impacted by SSRIs, antipsychotic medications, and depression (p < 0.008). Increased sleep fragmentation was associated with SSRIs, SNRIs, and anxiety (p < 0.009), while prazosin and antipsychotic medications correlated with decreased sleep fragmentation (p < 0.05).

A High-Resolution Digital DNA Melting Platform for Robust Sequence Profiling and Enhanced Genotype Discrimination

DNA melting analysis provides a rapid method for genotyping a target amplicon directly after PCR amplification. To transform melt genotyping into a broad-based profiling approach for heterogeneous samples, we previously proposed the integration of universal PCR and melt analysis with digital PCR. Here, we advanced this concept by developing a high-resolution digital melt platform with precise thermal control to accomplish reliable, high-throughput heat ramping of microfluidic chip digital PCR reactions. Using synthetic DNA oligos with defined melting temperatures, we characterized sources of melting variability and minimized run-to-run variations. Within-run comparisons throughout a 20,000-reaction chip revealed that high-melting-temperature sequences were significantly less prone to melt variation. Further optimization using bacterial 16S amplicons revealed a strong dependence of the number of melting transitions on the heating rate during curve generation. These studies show that reliable high-resolution melt curve genotyping can be achieved in digital, picoliter-scale reactions and demonstrate that rate-dependent melt signatures may be useful for enhancing automated melt genotyping.

Emerging Technologies for Molecular Diagnosis of Sepsis

Rapid and accurate profiling of infection-causing pathogens remains a significant challenge in modern health care. Despite advances in molecular diagnostic techniques, blood culture analysis remains the gold standard for diagnosing sepsis. However, this method is too slow and cumbersome to significantly influence the initial management of patients. The swift initiation of precise and targeted antibiotic therapies depends on the ability of a sepsis diagnostic test to capture clinically relevant organisms along with antimicrobial resistance within 1 to 3 h. The administration of appropriate, narrow-spectrum antibiotics demands that such a test be extremely sensitive with a high negative predictive value. In addition, it should utilize small sample volumes and detect polymicrobial infections and contaminants. All of this must be accomplished with a platform that is easily integrated into the clinical workflow. In this review, we outline the limitations of routine blood culture testing and discuss how emerging sepsis technologies are converging on the characteristics of the ideal sepsis diagnostic test. We include seven molecular technologies that have been validated on clinical blood specimens or mock samples using human blood. In addition, we discuss advances in machine learning technologies that use electronic medical record data to provide contextual evaluation support for clinical decision-making.

Biosynthesis of Orthogonal Molecules Using Ferredoxin and Ferredoxin-NADP+ Reductase Systems Enables Genetically Encoded PhyB Optogenetics

Transplanting metabolic reactions from one species into another has many uses as a research tool with applications ranging from optogenetics to crop production. Ferredoxin (Fd), the enzyme that most often supplies electrons to these reactions, is often overlooked when transplanting enzymes from one species to another because most cells already contain endogenous Fd. However, we have shown that the production of chromophores used in Phytochrome B (PhyB) optogenetics is greatly enhanced in mammalian cells by expressing bacterial and plant Fds with ferredoxin-NADP+ reductases (FNR). We delineated the rate limiting factors and found that the main metabolic precursor, heme, was not the primary limiting factor for producing either the cyanobacterial or plant chromophores, phycocyanobilin or phytochromobilin, respectively. In fact, Fd is limiting, followed by Fd+FNR and finally heme. Using these findings, we optimized the PCB production system and combined it with a tissue penetrating red/far-red sensing PhyB optogenetic gene switch in animal cells. We further characterized this system in several mammalian cell lines using red and far-red light. Importantly, we found that the light-switchable gene system remains active for several hours upon illumination, even with a short light pulse, and requires very small amounts of light for maximal activation. Boosting chromophore production by matching metabolic pathways with specific ferredoxin systems will enable the unparalleled use of the many PhyB optogenetic tools and has broader implications for optimizing synthetic metabolic pathways.

Scalable Manufacturing of Solderable and Stretchable Physiologic Sensing Systems

Methods for microfabrication of solderable and stretchable sensing systems (S4s) and a scaled production of adhesive-integrated active S4s for health monitoring are presented. S4s' excellent solderability is achieved by the sputter-deposited nickel-vanadium and gold pad metal layers and copper interconnection. The donor substrate, which is modified with "PI islands" to become selectively adhesive for the S4s, allows the heterogeneous devices to be integrated with large-area adhesives for packaging. The feasibility for S4-based health monitoring is demonstrated by developing an S4 integrated with a strain gauge and an onboard optical indication circuit. Owing to S4s' compatibility with the standard printed circuit board assembly processes, a variety of commercially available surface mount chip components, such as the wafer level chip scale packages, chip resistors, and light-emitting diodes, can be reflow-soldered onto S4s without modifications, demonstrating the versatile and modular nature of S4s. Tegaderm-integrated S4 respiration sensors are tested for robustness for cyclic deformation, maximum stretchability, durability, and biocompatibility for multiday wear time. The results of the tests and demonstration of the respiration sensing indicate that the adhesive-integrated S4s can provide end users a way for unobtrusive health monitoring.

Epidermal Electrode Technology for Detecting Ultrasonic Perturbation of Sensory Brain Activity

OBJECTIVE

We aim to demonstrate the in vivo capability of a wearable sensor technology to detect localized perturbations of sensory-evoked brain activity.

METHODS

Cortical somatosensory evoked potentials (SSEPs) were recorded in mice via wearable, flexible epidermal electrode arrays. We then utilized the sensors to explore the effects of transcranial focused ultrasound, which noninvasively induced neural perturbation. SSEPs recorded with flexible epidermal sensors were quantified and benchmarked against those recorded with invasive epidural electrodes.

RESULTS

We found that cortical SSEPs recorded by flexible epidermal sensors were stimulus frequency dependent. Immediately following controlled, focal ultrasound perturbation, the sensors detected significant SSEP modulation, which consisted of dynamic amplitude decreases and altered stimulus-frequency dependence. These modifications were also dependent on the ultrasound perturbation dosage. The effects were consistent with those recorded with invasive electrodes, albeit with roughly one order of magnitude lower signal-to-noise ratio.

CONCLUSION

We found that flexible epidermal sensors reported multiple SSEP parameters that were sensitive to focused ultrasound. This work therefore 1) establishes that epidermal electrodes are appropriate for monitoring the integrity of major CNS functionalities through SSEP; and 2) leveraged this technology to explore ultrasound-induced neuromodulation. The sensor technology is well suited for this application because the sensor electrical properties are uninfluenced by direct exposure to ultrasound irradiation.

SIGNIFICANCE

The sensors and experimental paradigm we present involve standard, safe clinical neurological assessment methods and are thus applicable to a wide range of future translational studies in humans with any manner of health condition.

A State Space and Density Estimation Framework for Sleep Staging in Obstructive Sleep Apnea

OBJECTIVE

Although the importance of sleep is increasingly recognized, the lack of robust and efficient algorithms hinders scalable sleep assessment in healthy persons and those with sleep disorders. Polysomnography (PSG) and visual/manual scoring remain the gold standard in sleep evaluation, but more efficient/automated systems are needed. Most previous works have demonstrated algorithms in high agreement with the gold standard in healthy/normal (HN) individuals-not those with sleep disorders.

METHODS

This paper presents a statistical framework that automatically estimates whole-night sleep architecture in patients with obstructive sleep apnea (OSA)-the most common sleep disorder. Single-channel frontal electroencephalography was extracted from 65 HN/OSA sleep studies, and decomposed into 11 spectral features in 60 903 30 s sleep epochs. The algorithm leveraged kernel density estimation to generate stage-specific likelihoods, and a 5-state hidden Markov model to estimate per-night sleep architecture.

RESULTS

Comparisons to full PSG expert scoring revealed the algorithm was in fair agreement with the gold standard (median Cohen's kappa = 0.53). Further, analysis revealed modest decreases in median scoring agreement as OSA severity increased from HN (kappa = 0.63) to severe (kappa = 0.47). A separate implementation on HN data from the Physionet Sleep-EDF Database resulted in a median kappa = 0.65, further indicating the algorithm's broad applicability.

CONCLUSION

Results of this work indicate the proposed single-channel framework can emulate expert-level scoring of sleep architecture in OSA.

SIGNIFICANCE

Algorithms constructed to more accurately model physiological variability during sleep may help advance automated sleep assessment, for practical and general use in sleep medicine.

Visualization of Whole-Night Sleep EEG From 2-Channel Mobile Recording Device Reveals Distinct Deep Sleep Stages with Differential Electrodermal Activity

Brain activity during sleep is a powerful marker of overall health, but sleep lab testing is prohibitively expensive and only indicated for major sleep disorders. This report demonstrates that mobile 2-channel in-home electroencephalogram (EEG) recording devices provided sufficient information to detect and visualize sleep EEG. Displaying whole-night sleep EEG in a spectral display allowed for quick assessment of general sleep stability, cycle lengths, stage lengths, dominant frequencies and other indices of sleep quality. By visualizing spectral data down to 0.1 Hz, a differentiation emerged between slow-wave sleep with dominant frequency between 0.1–1 Hz or 1–3 Hz, but rarely both. Thus, we present here the new designations, Hi and Lo Deep sleep, according to the frequency range with dominant power. Simultaneously recorded electrodermal activity (EDA) was primarily associated with Lo Deep and very rarely with Hi Deep or any other stage. Therefore, Hi and Lo Deep sleep appear to be physiologically distinct states that may serve unique functions during sleep. We developed an algorithm to classify five stages (Awake, Light, Hi Deep, Lo Deep and rapid eye movement (REM)) using a Hidden Markov Model (HMM), model fitting with the expectation-maximization (EM) algorithm, and estimation of the most likely sleep state sequence by the Viterbi algorithm. The resulting automatically generated sleep hypnogram can help clinicians interpret the spectral display and help researchers computationally quantify sleep stages across participants. In conclusion, this study demonstrates the feasibility of in-home sleep EEG collection, a rapid and informative sleep report format, and novel deep sleep designations accounting for spectral and physiological differences.

The Use of Cardiac Orienting Responses as an Early and Scalable Biomarker of Alcohol-Related Neurodevelopmental Impairment

BACKGROUND

Considered the leading cause of developmental disabilities worldwide, fetal alcohol spectrum disorders (FASD) are a global health problem. To take advantage of neural plasticity, early identification of affected infants is critical. The cardiac orienting response (COR) has been shown to be sensitive to the effects of prenatal alcohol exposure and is an inexpensive, easy to administer assessment tool. The purpose of this study was to evaluate the COR effectiveness in assessing individual risk of developmental delay.

METHODS

As part of an ongoing longitudinal cohort study in Ukraine, live-born infants of women with some to heavy amounts of alcohol consumption in pregnancy were recruited and compared to infants of women who consumed low or no alcohol. At 6 and 12 months, infants were evaluated with the Bayley Scales of Infant Development-II. CORs were also collected during a habituation/dishabituation learning paradigm. Using a supervised logistic regression classifier, we compared the predictive utility of the COR indices to that of the 6-month Bayley scores for identification of developmental delay based on 12-month Bayley scores. Heart rate collected at each second (Standard COR) was compared to key features (Key COR) extracted from the response.

RESULTS

Negative predictive values (NPV) were 85% for Standard COR, 82% for Key COR, and 77% for the Bayley, and positive predictive values (PPV) were 66% for Standard COR, 62% for Key COR, and 43% for the Bayley.

CONCLUSIONS

Predictive analysis based on the COR resulted in better NPV and PPV than the 6-month Bayley score. As the resources required to obtain a Bayley score are substantially more than in a COR-based paradigm, the findings are suggestive of its utility as an early scalable screening tool based on the COR. Further work is needed to test its long-term predictive accuracy.

High-Resolution Electrogastrogram: A Novel, Noninvasive Method for Determining Gastric Slow-Wave Direction and Speed

Despite its simplicity and noninvasiveness, the use of the electrogastrogram (EGG) remains limited in clinical practice for assessing gastric disorders. Recent studies have characterized the occurrence of spatial gastric myoelectric abnormalities that are ignored by typical approaches relying on time-frequency analysis of single channels. In this paper we present the highresolution (HR) EGG, which utilizes an array of electrodes to estimate the direction and speed of gastric slow-waves. The approach was verified on a forward electrophysiology model of the stomach, demonstrating that an accurate assessment of slow-wave propagation can be made. Furthermore, we tested the methodology on eight healthy adults and calculated propagation directions (181 ± 29 degrees) and speeds (3.7 ± 0.5 mm/s) that are consistent with serosal recordings of slow-waves described in the literature. By overcoming the limitations of current methods, HR-EGG is a fully automated tool that may unveil new classes of gastric abnormalities. This could lead to a better diagnosis of diseases and inspire novel drugs and therapies, ultimately improving clinical outcomes.

Scalable Microfabrication Procedures for Adhesive-Integrated Flexible and Stretchable Electronic Sensors

New classes of ultrathin flexible and stretchable devices have changed the way modern electronics are designed to interact with their target systems. Though more and more novel technologies surface and steer the way we think about future electronics, there exists an unmet need in regards to optimizing the fabrication procedures for these devices so that large-scale industrial translation is realistic. This article presents an unconventional approach for facile microfabrication and processing of adhesive-peeled (AP) flexible sensors. By assembling AP sensors on a weakly-adhering substrate in an inverted fashion, we demonstrate a procedure with 50% reduced end-to-end processing time that achieves greater levels of fabrication yield. The methodology is used to demonstrate the fabrication of electrical and mechanical flexible and stretchable AP sensors that are peeled-off their carrier substrates by consumer adhesives. In using this approach, we outline the manner by which adhesion is maintained and buckling is reduced for gold film processing on polydimethylsiloxane substrates. In addition, we demonstrate the compatibility of our methodology with large-scale post-processing using a roll-to-roll approach.

Sensing Nerve Regeneration with Implantable Flexible Electronics

Objectives:

(1) Translate epidermal electronic systems (EES) into an implantable electrode system (IES) with the potential to detect electric activity from growing axons distal to the site of nerve repair. (2) Demonstrate baseline and stimulated activity in intact nerves using IES. (3) Demonstrate nerve activity using IES following nerve regeneration across a surgical repair site.

Methods:

EES consists of thin flexible electronic circuitry that is capable of sensing and wirelessly transmitting multiple modalities including electrical activity, temperature, and stretch following transfer to skin. We have modified EES technology into an implantable electrode system consisting of biocompatible components that can detect nerve activity in vivo in a rodent model. We obtained recordings of intact sciatic nerve activity following pulsed stimulation applied via the proximal portion of the electrode array. Future experiments will focus on detection of activity following nerve transection and repair.

Results:

A first-generation IES sensor was tested on intact mouse sciatic nerves. Proximal electrodes were used to stimulate the nerve, and distal electrodes recorded the stimulus. Action potentials were successfully recorded. Recorded activity was abolished following nerve transection.

Conclusions:

We demonstrate the adaptation of EES technology with the ability to stimulate and record nerve activity in a rodent model. An implantable device capable of sensing nerve regeneration would provide crucial information on the status of nerve repair and at much earlier time points allowed by clinical exam and electromyography. This technology has the potential to improve clinical decision-making and patient outcomes.

Granger causality analysis of functional connectivity of spiking neurons in orofacial motor cortex during chewing and swallowing

Primate feeding behavior is characterized by a series of jaw movement cycles of different types making it ideal for investigating the role of motor cortex in controlling transitions between different kinematic states. We recorded spiking activity in populations of neurons in the orofacial portion of primary motor cortex (MIo) of a macaque monkey and, using a Granger causality model, estimated their functional connectivity during transitions between chewing cycles and from chewing to swallowing cycles. We found that during rhythmic chewing, the network was dominated by excitatory connections and exhibited a few "out degree" hub neurons, while during transitions from rhythmic chews to swallows, the numbers of excitatory and inhibitory connections became comparable, and more "in degree" hub neurons emerged. These results suggest that networks of neurons in MIo change their operative states with changes in kinematically defined behavioral states.

Circadian rhythm of redox state regulates excitability in suprachiasmatic nucleus neurons

Daily rhythms of mammalian physiology, metabolism, and behavior parallel the day-night cycle. They are orchestrated by a central circadian clock in the brain, the suprachiasmatic nucleus (SCN). Transcription of clock genes is sensitive to metabolic changes in reduction and oxidation (redox); however, circadian cycles in protein oxidation have been reported in anucleate cells, where no transcription occurs. We investigated whether the SCN also expresses redox cycles and how such metabolic oscillations might affect neuronal physiology. We detected self-sustained circadian rhythms of SCN redox state that required the molecular clockwork. The redox oscillation could determine the excitability of SCN neurons through nontranscriptional modulation of multiple potassium (K(+)) channels. Thus, dynamic regulation of SCN excitability appears to be closely tied to metabolism that engages the clockwork machinery.

Information transfer between neurons in the motor cortex triggered by visual cues

It was previously shown that beta oscillations of local field potentials in the arm area of the primary motor cortex (MI) of nonhuman primates propagate as travelling waves across MI of monkeys during movement preparation and execution and are believed to subserve cortical information transfer. To investigate the information transfer and its change over time at the single-cell level, we analyzed simultaneously recorded multiple MI neural spike trains of a monkey using a Granger causality measure for point process models before and after visual cues instructing the onset of reaching movements. In this analysis, we found that more pairs of neurons showed information transfer between them after appearances of upcoming movement targets than before, and the directions of the information transfer across neurons in MI were coincident with the directions of the propagating waves. These results suggest that the neuron pairs identified in the current study are the candidates of neurons that travel with spatiotemporal dynamics of beta oscillations in the MI.

A stochastic control approach to optimally designing hierarchical flash sets in P300 communication prostheses

The P300-based speller is a well-established brain-computer interface for communication. It displays a matrix of objects on the computer screen, flashes each object in sequence, and looks for a P300 response induced by flashing the desired object. Most existing P300 spellers uses a fixed set of flash objects. We demonstrate that performance can be significantly improved by sequential selections from a hierarchy of flash sets containing variable number of objects. Theoretically, the optimal hierarchy of flash sets--with respect to a given statistical language model--can be found by solving a stochastic control problem of low computational complexity. Experimentally, statistical analysis demonstrates that the average time per output character at 85% accuracy is reduced by over 50% using our variable-flash-set approach as compared to traditional fixed-flash-set spellers.

A computationally efficient method for nonparametric modeling of neural spiking activity with point processes

Point-process models have been shown to be useful in characterizing neural spiking activity as a function of extrinsic and intrinsic factors. Most point-process models of neural activity are parametric, as they are often efficiently computable. However, if the actual point process does not lie in the assumed parametric class of functions, misleading inferences can arise. Nonparametric methods are attractive due to fewer assumptions, but computation in general grows with the size of the data. We propose a computationally efficient method for nonparametric maximum likelihood estimation when the conditional intensity function, which characterizes the point process in its entirety, is assumed to be a Lipschitz continuous function but otherwise arbitrary. We show that by exploiting much structure, the problem becomes efficiently solvable. We next demonstrate a model selection procedure to estimate the Lipshitz parameter from data, akin to the minimum description length principle and demonstrate consistency of our estimator under appropriate assumptions. Finally, we illustrate the effectiveness of our method with simulated neural spiking data, goldfish retinal ganglion neural data, and activity recorded in CA1 hippocampal neurons from an awake behaving rat. For the simulated data set, our method uncovers a more compact representation of the conditional intensity function when it exists. For the goldfish and rat neural data sets, we show that our nonparametric method gives a superior absolute goodness-of-fit measure used for point processes than the most common parametric and splines-based approaches.

Estimating the directed information to infer causal relationships in ensemble neural spike train recordings

Advances in recording technologies have given neuroscience researchers access to large amounts of data, in particular, simultaneous, individual recordings of large groups of neurons in different parts of the brain. A variety of quantitative techniques have been utilized to analyze the spiking activities of the neurons to elucidate the functional connectivity of the recorded neurons. In the past, researchers have used correlative measures. More recently, to better capture the dynamic, complex relationships present in the data, neuroscientists have employed causal measures-most of which are variants of Granger causality-with limited success. This paper motivates the directed information, an information and control theoretic concept, as a modality-independent embodiment of Granger's original notion of causality. Key properties include: (a) it is nonzero if and only if one process causally influences another, and (b) its specific value can be interpreted as the strength of a causal relationship. We next describe how the causally conditioned directed information between two processes given knowledge of others provides a network version of causality: it is nonzero if and only if, in the presence of the present and past of other processes, one process causally influences another. This notion is shown to be able to differentiate between true direct causal influences, common inputs, and cascade effects in more two processes. We next describe a procedure to estimate the directed information on neural spike trains using point process generalized linear models, maximum likelihood estimation and information-theoretic model order selection. We demonstrate that on a simulated network of neurons, it (a) correctly identifies all pairwise causal relationships and (b) correctly identifies network causal relationships. This procedure is then used to analyze ensemble spike train recordings in primary motor cortex of an awake monkey while performing target reaching tasks, uncovering causal relationships whose directionality are consistent with predictions made from the wave propagation of simultaneously recorded local field potentials.

A dynamical point process model of auditory nerve spiking in response to complex sounds

In this paper, we develop a dynamical point process model for how complex sounds are represented by neural spiking in auditory nerve fibers. Although many models have been proposed, our point process model is the first to capture elements of spontaneous rate, refractory effects, frequency selectivity, phase locking at low frequencies, and short-term adaptation, all within a compact parametric approach. Using a generalized linear model for the point process conditional intensity, driven by extrinsic covariates, previous spiking, and an input-dependent charging/discharging capacitor model, our approach robustly captures the aforementioned features on datasets taken at the auditory nerve of chinchilla in response to speech inputs. We confirm the goodness of fit of our approach using the Time-Rescaling Theorem for point processes.