Todd P. Coleman is an Associate Professor in the Department of Bioengineering, and by courtesy, Electrical Engineering at Stanford University. He received B.S. degrees in electrical engineering (summa cum laude), as well as computer engineering (summa cum laude) from the University of Michigan. He received M.S. and Ph.D. degrees from MIT in electrical engineering and computer science. He did postdoctoral studies at MIT and Mass General Hospital in quantitative neuroscience. He previously was a faculty member in the Departments of Electrical & Computer Engineering and Bioengineering at the University of Illinois, Urbana-Champaign, and the University of California, San Diego, respectively. Dr. Coleman’s research is very multi-disciplinary, using tools from applied probability, physiology, and bioelectronics. Examples include, for instance, optimal transport methods in high-dimensional uncertainty quantification and developing technologies and algorithms to monitor and modulate physiology of the nervous systems in the brain and visceral organs. He has served as a Principal Investigator on grants from the NSF, NIH, Department of Defense, and multiple private foundations. Dr. Coleman is an inventor on 10 granted US patents. He has been selected as a Gilbreth Lecturer for the National Academy of Engineering, a TEDMED speaker, and a Fellow of the American Institute for Medical and Biological Engineering. He is currently the Chair of the National Academies Standing Committee Biotechnology Capabilities and National Security Needs.
Executive Committee Member, Wu Tsai Human Performance Alliance, Stanford University (2021 - Present)
Honors & Awards
Fellow, American Institute for Medical and Biological Engineering (2019)
Gilbreth Lecturer, National Academy of Engineering (2015)
Boards, Advisory Committees, Professional Organizations
Chair, National Academies Standing Committee Biotechnology Capabilities and National Security Needs (2021 - Present)
Advisory Committee Member, Burroughs-Wellcome Fund, Career Award at the Scientific Interface (2019 - Present)
Statistical uncertainty quantification to augment clinical decision support: a first implementation in sleep medicine.
NPJ digital medicine
2021; 4 (1): 142
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.
View details for DOI 10.1038/s41746-021-00515-3
View details for PubMedID 34593972
Miniaturized wireless gastric pacing via inductive power transfer with non-invasive monitoring using cutaneous Electrogastrography.
2021; 7 (1): 12
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 20mm, frequency of 0.05Hz, and pulse width of 400ms. 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.
View details for DOI 10.1186/s42234-021-00074-8
View details for PubMedID 34425917
- An Adhesive-Integrated Stretchable Silver-Silver Chloride Electrode Array for Unobtrusive Monitoring of Gastric Neuromuscular Activity ADVANCED MATERIALS TECHNOLOGIES 2021; 6 (5)
Building a Simple and Versatile Illumination System for Optogenetic Experiments
JOVE-JOURNAL OF VISUALIZED 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.
View details for DOI 10.3791/61914
View details for Web of Science ID 000646171700033
View details for PubMedID 33522514
Interoceptive insular cortex participates in sensory processing of gastrointestinal malaise and associated behaviors
2020; 10 (1): 21642
The insular cortex plays a central role in the perception and regulation of bodily needs and emotions. Its modular arrangement, corresponding with different sensory modalities, denotes a complex organization, and reveals it to be a hub that is able to coordinate autonomic and behavioral responses to many types of stimuli. Yet, little is known about the dynamics of its electrical activity at the neuronal level. We recorded single neurons in behaving rats from the posterior insula cortex (pIC), a subdivision considered as a primary interoceptive cortex, during gastrointestinal (GI) malaise, a state akin to the emotion of disgust in humans. We found that a large proportion of pIC neurons were modulated during the rodent compensatory behaviors of lying on belly (LOB) and Pica. Furthermore, we demonstrated that LOB was correlated with low-frequency oscillations in the field potentials and spikes at the theta (8 Hz) band, and that low-frequency electrical microstimulation of pIC elicited LOB and Pica. These findings demonstrate that pIC neurons play a critical role in GI malaise perception, and that the pIC influences the expression of behaviors that alleviate GI malaise. Our model provides an accessible approach at the single cell level to study innate emotional behaviors, currently elusive in humans.
View details for DOI 10.1038/s41598-020-78200-w
View details for Web of Science ID 000609190900002
View details for PubMedID 33303809
View details for PubMedCentralID PMC7730439
Data-driven noise modeling of digital DNA melting analysis enables prediction of sequence discriminating power
2020; 36 (22-23): 5337-5343
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.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.Data and code available on https://github.com/lenlan/dHRM-noise-modeling.Supplementary data are available at Bioinformatics online.
View details for DOI 10.1093/bioinformatics/btaa1053
View details for Web of Science ID 000661074700011
View details for PubMedID 33355665
View details for PubMedCentralID PMC8016452
Direct and Indirect Effects-An Information Theoretic Perspective
2020; 22 (8)
Information theoretic (IT) approaches to quantifying causal influences have experienced some popularity in the literature, in both theoretical and applied (e.g., neuroscience and climate science) domains. While these causal measures are desirable in that they are model agnostic and can capture non-linear interactions, they are fundamentally different from common statistical notions of causal influence in that they (1) compare distributions over the effect rather than values of the effect and (2) are defined with respect to random variables representing a cause rather than specific values of a cause. We here present IT measures of direct, indirect, and total causal effects. The proposed measures are unlike existing IT techniques in that they enable measuring causal effects that are defined with respect to specific values of a cause while still offering the flexibility and general applicability of IT techniques. We provide an identifiability result and demonstrate application of the proposed measures in estimating the causal effect of the El Niño-Southern Oscillation on temperature anomalies in the North American Pacific Northwest.
View details for DOI 10.3390/e22080854
View details for Web of Science ID 000564152800001
View details for PubMedID 33286625
View details for PubMedCentralID PMC7517455
- Measuring Sample Path Causal Influences With Relative Entropy IEEE TRANSACTIONS ON INFORMATION THEORY 2020; 66 (5): 2777-2798
Smart Electronic Eyedrop Bottle for Unobtrusive Monitoring of Glaucoma Medication Adherence
2020; 20 (9)
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.
View details for DOI 10.3390/s20092570
View details for Web of Science ID 000537106200134
View details for PubMedID 32366013
View details for PubMedCentralID PMC7248824
A Deep Convolutional Neural Network Approach to Classify Normal and Abnormal Gastric Slow Wave Initiation From the High Resolution Electrogastrogram
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
2020; 67 (3): 854-867
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).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.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.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.These findings suggest that multi-electrode cutaneous abdominal recordings have the potential to serve as widely deployable clinical screening tools for gastrointestinal foregut disorders.
View details for DOI 10.1109/TBME.2019.2922235
View details for Web of Science ID 000526271800020
View details for PubMedID 31199249
Spatial Patterns From High-Resolution Electrogastrography Correlate With Severity of Symptoms in Patients With Functional Dyspepsia and Gastroparesis
CLINICAL GASTROENTEROLOGY AND HEPATOLOGY
2019; 17 (13): 2668-2677
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.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.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.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.
View details for DOI 10.1016/j.cgh.2019.04.039
View details for Web of Science ID 000497972200016
View details for PubMedID 31009794
Bayesian inverse methods for spatiotemporal characterization of gastric electrical activity from cutaneous multi-electrode recordings
2019; 14 (10): e0220315
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.
View details for DOI 10.1371/journal.pone.0220315
View details for Web of Science ID 000532565400002
View details for PubMedID 31609972
View details for PubMedCentralID PMC6791545
High-density surface electromyography: A visualization method of laryngeal muscle activity
WILEY. 2019: 2347-2353
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.Basic science.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.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.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.NA Laryngoscope, 129:2347-2353, 2019.
View details for DOI 10.1002/lary.27784
View details for Web of Science ID 000488184200039
View details for PubMedID 30663053
A Distributed Framework for the Construction of Transport Maps.
2019; 31 (4): 613–52
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 to another distribution 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 need not be known but can be sampled from and that the density of is known up to a proportionality constant, and that 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 and 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.
View details for PubMedID 30764740
A flexible likelihood approach for predicting neural spiking activity from oscillatory phase
JOURNAL OF NEUROSCIENCE METHODS
2019; 311: 307-317
The synchronous ionic currents that give rise to neural oscillations have complex influences on neuronal spiking activity that are challenging to characterize.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.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.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.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.
View details for DOI 10.1016/j.jneumeth.2018.10.028
View details for Web of Science ID 000452935000036
View details for PubMedID 30367887
View details for PubMedCentralID PMC6387742
A High-Resolution Digital DNA Melting Platform for Robust Sequence Profiling and Enhanced Genotype Discrimination
2018; 23 (6): 580-591
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.
View details for DOI 10.1177/2472630318769846
View details for Web of Science ID 000452285600009
View details for PubMedID 29652558
The activity of discrete sets of neurons in the posterior insula correlates with the behavioral expression and extinction of conditioned fear
JOURNAL OF NEUROPHYSIOLOGY
2018; 120 (4): 1906-1913
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.
View details for DOI 10.1152/jn.00318.2018
View details for Web of Science ID 000451350100039
View details for PubMedID 30133379
View details for PubMedCentralID PMC6230801
- A Modularized Efficient Framework for Non-Markov Time Series Estimation IEEE TRANSACTIONS ON SIGNAL PROCESSING 2018; 66 (12): 3140-3154
A State Space and Density Estimation Framework for Sleep Staging in Obstructive Sleep Apnea
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
2018; 65 (6): 1201-1212
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.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.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.Results of this work indicate the proposed single-channel framework can emulate expert-level scoring of sleep architecture in OSA.Algorithms constructed to more accurately model physiological variability during sleep may help advance automated sleep assessment, for practical and general use in sleep medicine.
View details for DOI 10.1109/TBME.2017.2702123
View details for Web of Science ID 000432982200003
View details for PubMedID 28499990
View details for PubMedCentralID PMC5677582
Epidermal Electrode Technology for Detecting Ultrasonic Perturbation of Sensory Brain Activity
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
2018; 65 (6): 1272-1280
We aim to demonstrate the in vivo capability of a wearable sensor technology to detect localized perturbations of sensory-evoked brain activity.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.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.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.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.
View details for DOI 10.1109/TBME.2017.2713647
View details for Web of Science ID 000432982200010
View details for PubMedID 28858781
In-Home Sleep Recordings in Military Veterans With Posttraumatic Stress Disorder Reveal Less REM and Deep Sleep < 1 Hz
FRONTIERS IN HUMAN NEUROSCIENCE
2018; 12: 196
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).
View details for DOI 10.3389/fnhum.2018.00196
View details for Web of Science ID 000431896300001
View details for PubMedID 29867419
View details for PubMedCentralID PMC5958207
Emerging Technologies for Molecular Diagnosis of Sepsis
CLINICAL MICROBIOLOGY REVIEWS
2018; 31 (2)
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.
View details for DOI 10.1128/CMR.00089-17
View details for Web of Science ID 000427924600007
View details for PubMedID 29490932
View details for PubMedCentralID PMC5967692
Artifact Rejection Methodology Enables Continuous, Noninvasive Measurement of Gastric Myoelectric Activity in Ambulatory Subjects
2018; 8: 5019
The increasing prevalence of functional and motility gastrointestinal (GI) disorders is at odds with bottlenecks in their diagnosis, treatment, and follow-up. Lack of noninvasive approaches means that only specialized centers can perform objective assessment procedures. Abnormal GI muscular activity, which is coordinated by electrical slow-waves, may play a key role in symptoms. As such, the electrogastrogram (EGG), a noninvasive means to continuously monitor gastric electrical activity, can be used to inform diagnoses over broader populations. However, it is seldom used due to technical issues: inconsistent results from single-channel measurements and signal artifacts that make interpretation difficult and limit prolonged monitoring. Here, we overcome these limitations with a wearable multi-channel system and artifact removal signal processing methods. Our approach yields an increase of 0.56 in the mean correlation coefficient between EGG and the clinical "gold standard", gastric manometry, across 11 subjects (p < 0.001). We also demonstrate this system's usage for ambulatory monitoring, which reveals myoelectric dynamics in response to meals akin to gastric emptying patterns and circadian-related oscillations. Our approach is noninvasive, easy to administer, and has promise to widen the scope of populations with GI disorders for which clinicians can screen patients, diagnose disorders, and refine treatments objectively.
View details for DOI 10.1038/s41598-018-23302-9
View details for Web of Science ID 000428032400004
View details for PubMedID 29568042
View details for PubMedCentralID PMC5864836
Biosynthesis of Orthogonal Molecules Using Ferredoxin and Ferredoxin-NADP(+) Reductase Systems Enables Genetically Encoded PhyB Optogenetics
ACS SYNTHETIC BIOLOGY
2018; 7 (2): 706-717
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.
View details for DOI 10.1021/acssynbio.7b00413
View details for Web of Science ID 000426012600042
View details for PubMedID 29301067
View details for PubMedCentralID PMC5820651
Scalable Manufacturing of Solderable and Stretchable Physiologic Sensing Systems
2017; 29 (39)
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.
View details for DOI 10.1002/adma.201701312
View details for Web of Science ID 000412925600006
View details for PubMedID 28837756
High-Resolution Electrogastrogram: A Novel, Noninvasive Method for Determining Gastric Slow-Wave Direction and Speed
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
2017; 64 (4): 807-815
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.
View details for DOI 10.1109/TBME.2016.2579310
View details for Web of Science ID 000398738300008
View details for PubMedID 27305668
View details for PubMedCentralID PMC5474202
- An Information and Control Framework for Optimizing User-Compliant Human-Computer Interfaces PROCEEDINGS OF THE IEEE 2017; 105 (2): 273-285
The Use of Cardiac Orienting Responses as an Early and Scalable Biomarker of Alcohol-Related Neurodevelopmental Impairment
ALCOHOLISM-CLINICAL AND EXPERIMENTAL RESEARCH
2017; 41 (1): 128-138
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.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.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.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.
View details for DOI 10.1111/acer.13261
View details for Web of Science ID 000393890700014
View details for PubMedID 27883195
View details for PubMedCentralID PMC5205554
Visualization of Whole-Night Sleep EEG From 2-Channel Mobile Recording Device Reveals Distinct Deep Sleep Stages with Differential Electrodermal Activity
FRONTIERS IN HUMAN NEUROSCIENCE
2016; 10: 605
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.
View details for DOI 10.3389/fnhum.2016.00605
View details for Web of Science ID 000388762000001
View details for PubMedID 27965558
View details for PubMedCentralID PMC5126123
Learning Minimal Latent Directed Information Polytrees
2016; 28 (9): 1723-1768
We propose an approach for learning latent directed polytrees as long as there exists an appropriately defined discrepancy measure between the observed nodes. Specifically, we use our approach for learning directed information polytrees where samples are available from only a subset of processes. Directed information trees are a new type of probabilistic graphical models that represent the causal dynamics among a set of random processes in a stochastic system. We prove that the approach is consistent for learning minimal latent directed trees. We analyze the sample complexity of the learning task when the empirical estimator of mutual information is used as the discrepancy measure.
View details for DOI 10.1162/NECO_a_00874
View details for Web of Science ID 000383599800001
View details for PubMedID 27391682
- Directed Information Graphs IEEE TRANSACTIONS ON INFORMATION THEORY 2015; 61 (12): 6887-6909
Scalable Microfabrication Procedures for Adhesive-Integrated Flexible and Stretchable Electronic Sensors
2015; 15 (9): 23459-23476
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.
View details for DOI 10.3390/s150923459
View details for Web of Science ID 000362512200125
View details for PubMedID 26389915
View details for PubMedCentralID PMC4610501
Large-scale spatiotemporal spike patterning consistent with wave propagation in motor cortex
2015; 6: 7169
Aggregate signals in cortex are known to be spatiotemporally organized as propagating waves across the cortical surface, but it remains unclear whether the same is true for spiking activity in individual neurons. Furthermore, the functional interactions between cortical neurons are well documented but their spatial arrangement on the cortical surface has been largely ignored. Here we use a functional network analysis to demonstrate that a subset of motor cortical neurons in non-human primates spatially coordinate their spiking activity in a manner that closely matches wave propagation measured in the beta oscillatory band of the local field potential. We also demonstrate that sequential spiking of pairs of neuron contains task-relevant information that peaks when the neurons are spatially oriented along the wave axis. We hypothesize that the spatial anisotropy of spike patterning may reflect the underlying organization of motor cortex and may be a general property shared by other cortical areas.
View details for DOI 10.1038/ncomms8169
View details for Web of Science ID 000355534400006
View details for PubMedID 25994554
View details for PubMedCentralID PMC4443713
- An Optimizer's Approach to Stochastic Control Problems With Nonclassical Information Structures IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2015; 60 (4): 937-949
A Scalable Framework to Transform Samples from One Continuous Distribution to Another
IEEE. 2015: 676-680
View details for Web of Science ID 000380904700135
EEG Gamma Band Oscillations Differentiate the Planning of Spatially Directed Movements of the Arm Versus Eye: Multivariate Empirical Mode Decomposition Analysis
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
2014; 22 (5): 1083-1096
The neural dynamics underlying the coordination of spatially-directed limb and eye movements in humans is not well understood. Part of the difficulty has been a lack of signal processing tools suitable for the analysis of nonstationary electroencephalographic (EEG) signals. Here, we use multivariate empirical mode decomposition (MEMD), a data-driven approach that does not employ predefined basis functions. High-density EEG, and arm and eye movements were synchronously recorded in 10 subjects performing time-constrained reaching and/or eye movements. Subjects were allowed to move both the hand and the eyes, only the hand, or only the eyes following a 500-700 ms delay interval where the hand and gaze remained on a central fixation cross. An additional condition involved a nonspatially-directed "lift" movement of the hand. The neural activity during a 500 ms delay interval was decomposed into intrinsic mode functions (IMFs) using MEMD. Classification analysis revealed that gamma band (30 Hz) IMFs produced more classifiable features differentiating the EEG according to the different upcoming movements. A benchmark test using conventional algorithms demonstrated that MEMD was the best algorithm for extracting oscillatory bands from EEG, yielding the best classification of the different movement conditions. The gamma rhythm decomposed using MEMD showed a higher correlation with the eventual movement accuracy than any other band rhythm and than any other algorithm.
View details for DOI 10.1109/TNSRE.2014.2332450
View details for Web of Science ID 000342080400020
View details for PubMedID 25014959
- Dynamic and Succinct Statistical Analysis of Neuroscience Data PROCEEDINGS OF THE IEEE 2014; 102 (5): 683-698
Fractal design concepts for stretchable electronics
Stretchable electronics provide a foundation for applications that exceed the scope of conventional wafer and circuit board technologies due to their unique capacity to integrate with soft materials and curvilinear surfaces. The range of possibilities is predicated on the development of device architectures that simultaneously offer advanced electronic function and compliant mechanics. Here we report that thin films of hard electronic materials patterned in deterministic fractal motifs and bonded to elastomers enable unusual mechanics with important implications in stretchable device design. In particular, we demonstrate the utility of Peano, Greek cross, Vicsek and other fractal constructs to yield space-filling structures of electronic materials, including monocrystalline silicon, for electrophysiological sensors, precision monitors and actuators, and radio frequency antennas. These devices support conformal mounting on the skin and have unique properties such as invisibility under magnetic resonance imaging. The results suggest that fractal-based layouts represent important strategies for hard-soft materials integration.
View details for DOI 10.1038/ncomms4266
View details for Web of Science ID 000332667600013
View details for PubMedID 24509865
Grand Challenges in Mapping the Human Brain: NSF Workshop Report
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
2013; 60 (11): 2983-2992
This report summarizes the outcomes of the NSF Workshop on Mapping and Engineering the Brain, held at Arlington, VA, during August 13-14, 2013. Three grand challenges were identified, including high spatiotemporal resolution neuroimaging, perturbation-based neuroimaging, and neuroimaging in naturalistic environments. It was highlighted that each grand challenge requires groundbreaking discoveries, enabling technologies, appropriate knowledge transfer, and multi- and transdisciplinary education and training for success.
View details for DOI 10.1109/TBME.2013.2283970
View details for Web of Science ID 000325974500001
View details for PubMedID 24108705
- Efficient Methods to Compute Optimal Tree Approximations of Directed Information Graphs IEEE TRANSACTIONS ON SIGNAL PROCESSING 2013; 61 (12): 3173-3182
- A Timing Channel Spyware for the CSMA/CA Protocol IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2013; 8 (3): 477-487
- Bit-Wise Unequal Error Protection for Variable-Length Block Codes With Feedback IEEE TRANSACTIONS ON INFORMATION THEORY 2013; 59 (3): 1475-1504
Robust Directed Tree Approximations for Networks of Stochastic Processes
IEEE. 2013: 2254-2258
View details for Web of Science ID 000348913402077
Circadian Rhythm of Redox State Regulates Excitability in Suprachiasmatic Nucleus Neurons
2012; 337 (6096): 839-842
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.
View details for DOI 10.1126/science.1222826
View details for Web of Science ID 000307535600042
View details for PubMedID 22859819
View details for PubMedCentralID PMC3490628
- Characterizing the Efficacy of the NRL Network Pump in Mitigating Covert Timing Channels IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2012; 7 (1): 64-75
An Optimizer's Approach to Stochastic Control Problems with Nonclassical Information Structures
IEEE. 2012: 154-159
View details for Web of Science ID 000327200400026
A Stochastic Control Approach to Optimally Designing Hierarchical Flash Sets in P300 Communication Prostheses
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
2012; 20 (1): 102-112
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.
View details for DOI 10.1109/TNSRE.2011.2179560
View details for Web of Science ID 000299512300013
View details for PubMedID 22203722
- A Message-Passing Approach to Combating Desynchronization Attacks IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2011; 6 (3): 894-905
2011; 333 (6044): 838-843
We report classes of electronic systems that achieve thicknesses, effective elastic moduli, bending stiffnesses, and areal mass densities matched to the epidermis. Unlike traditional wafer-based technologies, laminating such devices onto the skin leads to conformal contact and adequate adhesion based on van der Waals interactions alone, in a manner that is mechanically invisible to the user. We describe systems incorporating electrophysiological, temperature, and strain sensors, as well as transistors, light-emitting diodes, photodetectors, radio frequency inductors, capacitors, oscillators, and rectifying diodes. Solar cells and wireless coils provide options for power supply. We used this type of technology to measure electrical activity produced by the heart, brain, and skeletal muscles and show that the resulting data contain sufficient information for an unusual type of computer game controller.
View details for DOI 10.1126/science.1206157
View details for Web of Science ID 000293785400031
View details for PubMedID 21836009
Estimating the directed information to infer causal relationships in ensemble neural spike train recordings
JOURNAL OF COMPUTATIONAL NEUROSCIENCE
2011; 30 (1): 17-44
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.
View details for DOI 10.1007/s10827-010-0247-2
View details for Web of Science ID 000287458400003
View details for PubMedID 20582566
View details for PubMedCentralID PMC3171872
- A Feedback Information-Theoretic Approach to the Design of Brain-Computer Interfaces INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION 2011; 27 (1): 5-23
A Computationally Efficient Method for Nonparametric Modeling of Neural Spiking Activity with Point Processes
2010; 22 (8): 2002-2030
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.
View details for DOI 10.1162/NECO_a_00001-Coleman
View details for Web of Science ID 000279109600003
View details for PubMedID 20438334
A dynamical point process model of auditory nerve spiking in response to complex sounds
JOURNAL OF COMPUTATIONAL NEUROSCIENCE
2010; 29 (1-2): 193-201
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.
View details for DOI 10.1007/s10827-009-0146-6
View details for Web of Science ID 000281563500015
View details for PubMedID 19353258
View details for PubMedCentralID PMC4138954
- Introduction to the Special Issue on Information Theory in Molecular Biology and Neuroscience IEEE TRANSACTIONS ON INFORMATION THEORY 2010; 56 (2): 649-652
- Joint Source-Channel Coding for Transmitting Correlated Sources Over Broadcast Networks IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. 2009: 3864-3868
- A Low-Complexity Universal Scheme for Rate-Constrained Distributed Regression Using a Wireless Sensor Network IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. 2009: 1731-1744
- Low-complexity approaches to Slepian-Wolf near-lossless distributed data compression IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. 2006: 3546-3561
- A distributed scheme for achieving energy-delay tradeoffs with multiple service classes over a dynamically varying network IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2004; 22 (5): 929-941
- Capacity of time-slotted ALOHA packetized multiple-access systems over the AWGN channel IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2004; 3 (2): 486-499