RESUMEN

Previously, we demonstrated that prebiotics may provide a complementary strategy for increasing calcium (Ca) absorption in adolescents which may improve long-term bone health. However, not all children responded to prebiotic intervention. We determine if certain baseline characteristics of gut microbiome composition predict prebiotic responsiveness. In this secondary analysis, we compared differences in relative microbiota taxa abundance between responders (greater than or equal to 3% increase in Ca absorption) and non-responders (less than 3% increase). Dual stable isotope methodologies were used to assess fractional Ca absorption at the end of crossover treatments with placebo, 10, and 20 g/day of soluble corn fiber (SCF). Microbial DNA was obtained from stool samples collected before and after each intervention. Sequencing of the 16S rRNA gene was used to taxonomically characterize the gut microbiome. Machine learning techniques were used to build a predictive model for identifying responders based on baseline relative taxa abundances. Model output was used to infer which features contributed most to prediction accuracy. We identified 19 microbial features out of the 221 observed that predicted responsiveness with 96.0% average accuracy. The results suggest a simplified prescreening can be performed to determine if a subject's bone health may benefit from a prebiotic. Additionally, the findings provide insight and prompt further investigation into the metabolic and genetic underpinnings affecting calcium absorption during pubertal bone development.

Asunto(s)

RESUMEN

In this article, we introduce a novel use of depth camera to extract cardiac pulse signal from human chest area, in which the depth information is obtained from a near infrared sensor using time-of-flight technology. We successfully isolate weak chest motion due to heartbeat by processing a sequence of depth images without raising privacy concern. We discuss motion sensitivity in depth video with examples from actuator simulation and human chest motion. Compared to other imaging modalities, the depth image intensity can be directly used for micromotion reconstruction. To deal with the challenges of recovering heartbeat from the chest area, we develop a set of coherent processing techniques to suppress the unwanted motion interference from breathing motion and involuntary body motion and eventually obtain clean cardiac pulse signal. We, thus, derive inter-beat-interval, showing high consistency to the contact photoplethysmography. Additionally, we develop a graphical interpretation of the most and the less pulsatile principal components in eigen space. For validation, we test our method on ten healthy human subjects with different resting heart rates. More importantly, we conduct a set of experiments to study the robustness and weakness of our methods, including extended range, multi-subject, thickness of clothes and generation to other measurement site.

Asunto(s)

RESUMEN

Serial dependence is an attractive pull that recent perceptual history exerts on current judgments. Theory suggests that this bias is due to a form of short-term plasticity prevalent specifically in the frontal lobe. We sought to test the importance of the frontal lobe to serial dependence by disrupting neural activity along its lateral surface during two tasks with distinct perceptual and motor demands. In our first experiment, stimulation of the lateral prefrontal cortex (LPFC) during an oculomotor delayed response task decreased serial dependence only in the first saccade to the target, whereas stimulation posterior to the LPFC decreased serial dependence only in adjustments to eye position after the first saccade. In our second experiment, which used an orientation discrimination task, stimulation anterior to, in, and posterior to the LPFC all caused equivalent decreases in serial dependence. In this experiment, serial dependence occurred only between stimuli at the same location; an alternation bias was observed across hemifields. Frontal stimulation had no effect on the alternation bias. Transcranial magnetic stimulation to parietal cortex had no effect on serial dependence in either experiment. In summary, our experiments provide evidence for both functional differentiation (Experiment 1) and redundancy (Experiment 2) in frontal cortex with respect to serial dependence.

Asunto(s)

RESUMEN

Contactless vital sign measurement technologies have the potential to greatly improve patient experiences and practitioner safety while creating the opportunity for comfortable continuous monitoring. We introduce a contactless alternative for measuring human heart sounds. We leverage millimeter wave frequency-modulated continuous wave radar and multi-input multi-output beamforming techniques to capture fine skin vibrations that result from the cardiac movements that cause heart sounds. We discuss contact-based heart sound measurement techniques and directly compare the radar heart sound technique with these contact-based approaches. We present experimental cases to test the strengths and limitations of both the contact-based measurement techniques and the contactless radar measurement. We demonstrate that the radar measurement technique is a viable and potentially superior method for capturing human heart sounds in many practical settings.

RESUMEN

Spectral congestion and modern consumer applications motivate radio technologies that efficiently cooperate with nearby users and provide several services simultaneously. We designed and implemented a joint positioning-communications system that simultaneously enables network communications, timing synchronization, and localization to a variety of airborne and ground-based platforms. This Communications and High-Precision Positioning (CHP2) system simultaneously performs communications and precise ranging (<10 cm) with a narrow band waveform (10 MHz) at a carrier frequency of 915 MHz (US ISM) or 783 MHz (EU Licensed). The ranging capability may be extended to estimate the relative position and orientation by leveraging the spatial diversity of the multiple-input, multiple-output (MIMO) platforms. CHP2 also digitally synchronizes distributed platforms with sub-nanosecond precision without support from external systems (GNSS, GPS, etc.). This performance is enabled by leveraging precise time-of-arrival (ToA) estimation techniques, a network synchronization algorithm, and the intrinsic cooperation in the joint processing chain that executes these tasks simultaneously. In this manuscript, we describe the CHP2 system architecture, hardware implementation, and in-lab and over-the-air experimental validation.

RESUMEN

This study presents findings in the terahertz (THz) frequency spectrum for non-contact cardiac sensing applications. Cardiac pulse information is simultaneously extracted using THz waves based on the established principles in electronics and optics. The first fundamental principle is micro-Doppler motion effect. This motion based method, primarily using coherent phase information from the radar receiver, has been widely exploited in microwave frequency bands and has recently found popularity in millimeter waves (mmWave) for breathe rate and heart rate detection. The second fundamental principle is reflectance based optical measurement using infrared or visible light. The variation in the light reflection is proportional to the volumetric change of the heart, often referred as photoplethysmography (PPG). Herein, we introduce the concept of terahertz-wave-plethysmography (TPG), which detects blood volume changes in the upper dermis tissue layer by measuring the reflectance of THz waves, similar to the existing remote PPG (rPPG) principle. The TPG principle is justified by scientific deduction, electromagnetic wave simulations and carefully designed experimental demonstrations. Additionally, pulse measurements from various peripheral body parts of interest (BOI), palm, inner elbow, temple, fingertip and forehead, are demonstrated using a wideband THz sensing system developed by the Terahertz Electronics Lab at Arizona State University, Tempe. Among the BOIs under test, it is found that the measurements from forehead BOI gives the best accuracy with mean heart rate (HR) estimation error 1.51 beats per minute (BPM) and standard deviation 1.08 BPM. The results validate the feasibility of TPG for direct pulse monitoring. A comparative study on pulse sensitivity is conducted between TPG and rPPG. The results indicate that the TPG contains more pulsatile information from the forehead BOI than that in the rPPG signals in regular office lighting condition and thus generate better heart rate estimation statistic in the form of empirical cumulative distribution function of HR estimation error. Last but not least, TPG penetrability test for covered skin is demonstrated using two types of garment materials commonly used in daily life.

Asunto(s)

RESUMEN

The study of human reaction time (RT) is invaluable not only to understand the sensory-motor functions but also to translate brain signals into machine comprehensible commands that can facilitate augmentative and alternative communication using brain-computer interfaces (BCI). Recent developments in sensor technologies, hardware computational capabilities, and neural network models have significantly helped advance biomedical signal processing research. This study is an attempt to utilize state-of-the-art resources to explore the relationship between human behavioral responses during perceptual decision-making and corresponding brain signals in the form of electroencephalograms (EEG). In this paper, a generalized 3D convolutional neural network (CNN) architecture is introduced to estimate RT for a simple visual task using single-trial multi-channel EEG. Earlier comparable studies have also employed a number of machine learning and deep learning-based models, but none of them considered inter-channel relationships while estimating RT. On the contrary, the use of 3D convolutional layers enabled us to consider the spatial relationship among adjacent channels while simultaneously utilizing spectral information from individual channels. Our model can predict RT with a root mean square error of 91.5 ms and a correlation coefficient of 0.83. These results surpass all the previous results attained from different studies.Clinical relevance Novel approaches to decode brain signals can facilitate research on brain-computer interfaces (BCIs), psychology, and neuroscience, enabling people to utilize assistive devices by root-causing psychological or neuromuscular disorders.

Asunto(s)

RESUMEN

PURPOSE: We explore the potential use of radar technology for fiducial marker tracking for monitoring of respiratory tumor motion during radiotherapy. Historically microwave radar technology has been widely deployed in various military and civil aviation applications to provide detection, position, and tracking of single or multiples objects from far away and even through barriers. Recently, due to many advantages of the microwave technology, it has been successfully demonstrated to detect breast tumor, and to monitor vital signs in real time such as breathing signals or heart rates. We demonstrate a proof-of-concept for radar-based fiducial marker tracking through the synthetic human tissue phantom. METHODS: We performed a series of experiments with the vector network analyzer (VNA) and wideband directional horn antenna. We considered the frequency range from 2.0 to 6.0 GHz, with a maximum power of 3 dBm. A horn antenna, transmitting and receiving radar pulses, was connected to the vector network analyzer to probe a gold fiducial marker through a customized synthetic human tissue phantom, consisting of 1-mm thickness of skin, 5-mm fat, and 25-mm muscle layers. A 1.2 × 10-mm gold fiducial marker was exploited as a motion surrogate, which was placed behind the phantom and statically positioned with an increment of 12.7 mm to simulate different marker displacements. The returned signals from the marker were acquired and analyzed to evaluate the localization accuracy as a function of the marker position. RESULTS: The fiducial marker was successfully localized at various measurement positions through a simplified phantom study. The averaged localization accuracy across measurements was 3.5 ± 1.3 mm, with a minimum error of 1.9 mm at the closest measurement location and a maximum error of 4.9 mm at the largest measurement location. CONCLUSIONS: We demonstrated that the 2-6 GHz radar can penetrate through the attenuating tissues and localize a fiducial marker. This successful feasibility study establishes a foundation for further investigation of radar technology as a non-ionizing tumor localization device for radiotherapy.

Asunto(s)

RESUMEN

Dopamine is required for working memory, but how it modulates the large-scale cortex is unknown. Here, we report that dopamine receptor density per neuron, measured by autoradiography, displays a macroscopic gradient along the macaque cortical hierarchy. This gradient is incorporated in a connectome-based large-scale cortex model endowed with multiple neuron types. The model captures an inverted U-shaped dependence of working memory on dopamine and spatial patterns of persistent activity observed in over 90 experimental studies. Moreover, we show that dopamine is crucial for filtering out irrelevant stimuli by enhancing inhibition from dendrite-targeting interneurons. Our model revealed that an activity-silent memory trace can be realized by facilitation of inter-areal connections and that adjusting cortical dopamine induces a switch from this internal memory state to distributed persistent activity. Our work represents a cross-level understanding from molecules and cell types to recurrent circuit dynamics underlying a core cognitive function distributed across the primate cortex.

Asunto(s)

RESUMEN

Though it is often taken as a truism that communication contributes to organizational productivity, there are surprisingly few empirical studies documenting a relationship between observable interaction and productivity. This is because comprehensive, direct observation of communication in organizational settings is notoriously difficult. In this paper, we report a method for extracting network and speech characteristics data from audio recordings of participants talking with each other in real time. We use this method to analyze communication and productivity data from seventy-nine employees working within a software engineering organization who had their speech recorded during working hours for a period of approximately 3 years. From the speech data, we infer when any two individuals are talking to each other and use this information to construct a communication graph for the organization for each week. We use the spectral and temporal characteristics of the produced speech and the structure of the resultant communication graphs to predict the productivity of the group, as measured by the number of lines of code produced. The results indicate that the most important speech and network features for predicting productivity include those that measure the number of unique people interacting within the organization, the frequency of interactions, and the topology of the communication network.

Asunto(s)

RESUMEN

Microwave radar technology is very attractive for ubiquitous short-range health monitoring due to its non-contact, see-through, privacy-preserving and safe features compared to the competing remote technologies such as optics. The possibility of radar-based approaches for breathing and cardiac sensing was demonstrated a few decades ago. However, investigation regarding the robustness of radar-based vital-sign monitoring (VSM) is not available in the current radar literature. In this paper, we aim to close this gap by presenting an extensive experimental study of vital-sign radar approach. We consider diversity in test subjects, fitness levels, poses/postures, and, more importantly, random body movement (RBM) in the study. We discuss some new insights that lead to robust radar heart-rate (HR) measurements. A novel active motion cancellation signal-processing technique is introduced, exploiting dual ultra-wideband (UWB) radar system for motion-tolerant HR measurements. Additionally, we propose a spectral pruning routine to enhance HR estimation performance. We validate the proposed method theoretically and experimentally. Totally, we record and analyze about 3500 seconds of radar measurements from multiple human subjects.

Asunto(s)

RESUMEN

Multiplexed deep neural networks (DNN) have engendered high-performance predictive models gaining popularity for decoding brain waves, extensively collected in the form of electroencephalogram (EEG) signals. In this paper, to the best of our knowledge, we introduce a first-ever DNN-based generalized approach to estimate reaction time (RT) using the periodogram representation of single-trial EEG in a visual stimulus-response experiment with 48 participants. We have designed a Fully Connected Neural Network (FCNN) and a Convolutional Neural Network (CNN) to predict and classify RTs for each trial. Though deep neural networks are widely known for classification applications, cascading FCNN/CNN with the Random Forest model, we designed a robust regression-based estimator to predict RT. With the FCNN model, the accuracies obtained for binary and 3-class classification were 93% and 76%, respectively, which further improved with the use of CNN (94% and 78%, respectively). The regression-based approach predicted RTs with correlation coefficients (CC) of 0.78 and 0.80 for FCNN and CNN, respectively. Investigating further, we found that the left central as well as parietal and occipital lobes were crucial for predicting RT, with significant activities in the theta and alpha frequency bands.

Asunto(s)

RESUMEN

The estimation of the visual stimulus-based reaction time (RT) using subtle and complex information from the brain signals is still a challenge, as the behavioral response during perceptual decision making varies inordinately across trials. Several investigations have tried to formulate the estimation based on electroencephalogram (EEG) signals. However, these studies are subject-specific and limited to regression-based analysis. In this paper, for the first time to our knowledge, a generalized model is introduced to estimate RT using single-trial EEG features for a simple visual reaction task, considering both regression and classification-based approaches. With the regression-based approach, we could predict RT with a root mean square error of 111.2 ms and a correlation coefficient of 0.74. A binary and a 3-class classifier model were trained, based on the magnitude of RT, for the classification approach. Accuracy of 79% and 72% were achieved for the binary and the 3-class classification, respectively. Limiting our study to only high and low RT groups, the model classified the two groups with an accuracy of 95%. Relevant EEG channels were evaluated to localize the part of the brain significantly responsible for RT estimation, followed by the isolation of important features.Clinical relevance- Electroencephalogram (EEG) signals can be used in Brain-computer interfaces (BCIs), enabling people with neuromuscular disorders like brainstem stroke, amyotrophic lateral sclerosis, and spinal cord injury to communicate with assistive devices. However, advancements regarding EEG signal analysis and interpretation are far from adequate, and this study is a step forward.

Asunto(s)

RESUMEN

OBJECTIVE: This study develops an electro-encephalography-based method for predicting postoperative delirium early during cardiac surgeries involving deep hypothermia circulatory arrest (DHCA), potentially providing an opportunity to intervene and minimize poor surgical outcome. DHCA is a surgical technique used during cardiac surgeries to facilitate repairs. Deep hypothermia is induced and supplemented by perfusion techniques to protect the brain during circulatory arrest, but concern for cerebral injury still remains. METHODS: This research studies whether or not monitoring burst suppression, an electrophysiological phenomenon observed during patient cooling and warming, helps in predicting postoperative delirium, a correlate of poor prognosis. A metric called the burst suppression duty cycle (BSDC), akin to burst suppression ratio, is formulated to characterize this electrophysiological activity. RESULTS: Assuming no complications occur prior to circulatory arrest, delirium diagnoses are correlated with the time elapsed until suppression activity ceases since resuming cardiopulmonary bypass. By comparing against a benchmark the times when BSDC reaches 100%, 15 of 16 cases can be correctly predicted. Similar accuracy can be achieved when querying BSDC progress earlier during warming. CONCLUSION: Our results show that our BSDC metric is a promising candidate for early detection of postoperative delirium, and motivates further analysis of the causal relationship between postoperative delirium and the procedure transitioning out of circulatory arrest. SIGNIFICANCE: The developed methodology anticipates incidences of postoperative delirium during rewarming, which potentially provides an opportunity to intervene and avert it.

Asunto(s)

RESUMEN

The purpose of this study was to classify, and model various physical activities performed by a diverse group of participants in a supervised lab-based protocol and utilize the model to identify physical activity in a free-living setting. Wrist-worn accelerometer data were collected from ( N = 152 ) adult participants; age 18⁻64 years, and processed the data to identify and model unique physical activities performed by the participants in controlled settings. The Gaussian mixture model (GMM) and the hidden Markov model (HMM) algorithms were used to model the physical activities with time and frequency-based accelerometer features. An overall model accuracy of 92.7% and 94.7% were achieved to classify 24 physical activities using GMM and HMM, respectively. The most accurate model was then used to identify physical activities performed by 20 participants, each recorded for two free-living sessions of approximately six hours each. The free-living activity intensities were estimated with 80% accuracy and showed the dominance of stationary and light intensity activities in 36 out of 40 recorded sessions. This work proposes a novel activity recognition process to identify unsupervised free-living activities using lab-based classification models. In summary, this study contributes to the use of wearable sensors to identify physical activities and estimate energy expenditure in free-living settings.

Asunto(s)

RESUMEN

Bounds are developed on the maximum communications rate between a transmitter and a fusion node aided by a cluster of distributed receivers with limited resources for cooperation, all in the presence of an additive Gaussian interferer. The receivers cannot communicate with one another and can only convey processed versions of their observations to the fusion center through a Local Array Network (LAN) with limited total throughput. The effectiveness of each bound's approach for mitigating a strong interferer is assessed over a wide range of channels. It is seen that, if resources are shared effectively, even a simple quantize-and-forward strategy can mitigate an interferer 20 dB stronger than the signal in a diverse range of spatially Ricean channels. Monte-Carlo experiments for the bounds reveal that, while achievable rates are stable when varying the receiver's observed scattered-path to line-of-sight signal power, the receivers must adapt how they share resources in response to this change. The bounds analyzed are proven to be achievable and are seen to be tight with capacity when LAN resources are either ample or limited.

RESUMEN

The intrinsic white matter connections of the frontal cortex are highly complex, and the organization of these connections is not fully understood. Quantitative graph-theoretical methods, which are not solely reliant on human observation and interpretation, can be powerful tools for describing the organizing network principles of frontal cortex. Here, we examined the network structure of frontal cortical subregions by applying graph-theoretical community detection analyses to a graph of frontal cortex compiled from over 400+ macaque white-matter tracing studies. We find evidence that the lateral frontal cortex can be partitioned into distinct modules roughly organized along the dorsoventral and rostrocaudal axis.

Asunto(s)

RESUMEN

Recent work has established that visual working memory is subject to serial dependence: current information in memory blends with that from the recent past as a function of their similarity. This tuned temporal smoothing likely promotes the stability of memory in the face of noise and occlusion. Serial dependence accumulates over several seconds in memory and deteriorates with increased separation between trials. While this phenomenon has been extensively characterized in behavior, its neural mechanism is unknown. In the present study, we investigate the circuit-level origins of serial dependence in a biophysical model of cortex. We explore two distinct kinds of mechanisms: stable persistent activity during the memory delay period and dynamic "activity-silent" synaptic plasticity. We find that networks endowed with both strong reverberation to support persistent activity and dynamic synapses can closely reproduce behavioral serial dependence. Specifically, elevated activity drives synaptic augmentation, which biases activity on the subsequent trial, giving rise to a spatiotemporally tuned shift in the population response. Our hybrid neural model is a theoretical advance beyond abstract mathematical characterizations, offers testable hypotheses for physiological research, and demonstrates the power of biological insights to provide a quantitative explanation of human behavior.

Asunto(s)

RESUMEN

Recent experiments have shown that visual cognition blends current input with that from the recent past to guide ongoing decision making. This serial dependence appears to exploit the temporal autocorrelation normally present in visual scenes to promote perceptual stability. While this benefit has been assumed, evidence that serial dependence directly alters stimulus perception has been limited. In the present study, we parametrically vary the delay between stimulus and response in a spatial delayed response task to explore the trajectory of serial dependence from the moment of perception into post-perceptual visual working memory. We find that behavioral responses made immediately after viewing a stimulus show evidence of adaptation, but not attractive serial dependence. Only as the memory period lengthens is a blending of past and present information apparent in behavior, reaching its maximum with a delay of six seconds. These results dovetail with other recent findings to bolster the interpretation that serial dependence is a phenomenon of mnemonic rather than perceptual processes. However, even while this pattern of effects in group-averaged data has now been found consistently, we show that the relative strengths of adaptation and serial dependence vary widely across individuals. Finally, we demonstrate that when leading mathematical models of working memory are adjusted to account for these trial-history effects, their fit to behavioral data is substantially improved.

Asunto(s)

RESUMEN

Information that has been recently perceived or remembered can bias current processing. This has been viewed as both a corrupting (e.g., proactive interference in short-term memory) and stabilizing (e.g., serial dependence in perception) phenomenon. We hypothesize that this bias is a generally adaptive aspect of brain function that leads to occasionally maladaptive outcomes.

Asunto(s)