Future of service member monitoring: the intersection of biology, wearables and artificial intelligence.

While substantial investment has been made in the early identification of mental and behavioural health disorders in service members, rates of depression, substance abuse and suicidality continue to climb. Objective and persistent measures are needed for early identification and treatment of these rising health issues. Considerable potential lies at the intersection of biology, wearables and artificial intelligence to provide high accuracy, objective monitoring of mental and behavioural health in training, operations and healthcare settings. While the current generation of wearable devices has predominantly targeted non-military use cases, military agencies have demonstrated successes in monitoring and diagnosis via off-label uses. Combined with context-aware and individualised algorithms, the integration of wearable data with artificial intelligence allows for a deeper understanding of individual-level and group-level mental and behavioural health at scale. Emerging digital phenotyping approaches which leverage ubiquitous sensing technology can provide monitoring at a greater scale, lower price point and lower individual burden by removing the need for additional body-worn technology. The intersection of this technology will enable individualised strategies to promote service member mental and physical health, reduce injury, and improve long-term well-being and deployability.

Automatic detection of pain using machine learning.

Pain is one of the most common symptoms reported by individuals presenting to hospitals and clinics and is associated with significant disability and economic impacts; however, the ability to quantify and monitor pain is modest and typically accomplished through subjective self-report. Since pain is associated with stereotypical physiological alterations, there is potential for non-invasive, objective pain measurements through biosensors coupled with machine learning algorithms. In the current study, a physiological dataset associated with acute pain induction in healthy adults was leveraged to develop an algorithm capable of detecting pain in real-time and in natural field environments. Forty-one human subjects were exposed to acute pain through the cold pressor test while being monitored using electrocardiography. A series of respiratory and heart rate variability features in the time, frequency, and nonlinear domains were calculated and used to develop logistic regression classifiers of pain for two scenarios: (1) laboratory/clinical use with an F1 score of 81.9% and (2) field/ambulatory use with an F1 score of 79.4%. The resulting pain algorithms could be leveraged to quantify acute pain using data from a range of sources, such as ECG data in clinical settings or pulse plethysmography data in a growing number of consumer wearables. Given the high prevalence of pain worldwide and the lack of objective methods to quantify it, this approach has the potential to identify and better mitigate individual pain.

Automated stress detection using mobile application and wearable sensors improves symptoms of mental health disorders in military personnel.

Leading causes in global health-related burden include stress, depression, anger, fatigue, insomnia, substance abuse, and increased suicidality. While all individuals are at risk, certain career fields such as military service are at an elevated risk. Cognitive behavioral therapy (CBT) is highly effective at treating mental health disorders but suffers from low compliance and high dropout rates in military environments. The current study conducted a randomized controlled trial with military personnel to assess outcomes for an asymptomatic group (n = 10) not receiving mental health treatment, a symptomatic group (n = 10) using a mHealth application capable of monitoring physiological stress via a commercial wearable alerting users to the presence of stress, guiding them through stress reduction techniques, and communicating information to providers, and a symptomatic control group (n = 10) of military personnel undergoing CBT. Fifty percent of symptomatic controls dropped out of CBT early and the group maintained baseline symptoms. In contrast, those who used the mHealth application completed therapy and showed a significant reduction in symptoms of depression, anxiety, stress, and anger. The results from this study demonstrate the feasibility of pairing data-driven mobile applications with CBT in vulnerable populations, leading to an improvement in therapy compliance and a reduction in symptoms compared to CBT treatment alone. Future work is focused on the inclusion of passive sensing modalities and the integration of additional data sources to provide better insights and inform clinical decisions to improve personalized support.

Mobile, Game-Based Training for Myoelectric Prosthesis Control.

Myoelectric prostheses provide upper limb amputees with hand and arm movement control using muscle activity of the residual limb, but require intensive training to effectively operate. The result is that many amputees abandon their prosthesis before mastering control of their device. In the present study, we examine a novel, mobile, game-based approach to myoelectric prosthesis training. Using the non-dominant limb in a group of able-bodied participants to model amputee pre-prosthetic training, a significant improvement in factors underlying successful myoelectric prosthesis use, including muscle control, sequencing, and isolation were observed. Participants also reported high levels of usability, and motivation with the game-based approach to training. Given fiscal or geographic constraints that limit pre-prosthetic amputee care, mobile myosite training, as described in the current study, has the potential to improve rehabilitation success rates by providing myosite training outside of the clinical environment. Future research should include longitudinal studies in amputee populations to evaluate the impact of pre-prosthetic training methods on prosthesis acceptance, wear time, abandonment, functional outcomes, quality of life, and return to work.

Improved Mental Acuity Forecasting with an Individualized Quantitative Sleep Model.

Sleep impairment significantly alters human brain structure and cognitive function, but available evidence suggests that adults in developed nations are sleeping less. A growing body of research has sought to use sleep to forecast cognitive performance by modeling the relationship between the two, but has generally focused on vigilance rather than other cognitive constructs affected by sleep, such as reaction time, executive function, and working memory. Previous modeling efforts have also utilized subjective, self-reported sleep durations and were restricted to laboratory environments. In the current effort, we addressed these limitations by employing wearable systems and mobile applications to gather objective sleep information, assess multi-construct cognitive performance, and model/predict changes to mental acuity. Thirty participants were recruited for participation in the study, which lasted 1 week. Using the Fitbit Charge HR and a mobile version of the automated neuropsychological assessment metric called CogGauge, we gathered a series of features and utilized the unified model of performance to predict mental acuity based on sleep records. Our results suggest that individuals poorly rate their sleep duration, supporting the need for objective sleep metrics to model circadian changes to mental acuity. Participant compliance in using the wearable throughout the week and responding to the CogGauge assessments was 80%. Specific biases were identified in temporal metrics across mobile devices and operating systems and were excluded from the mental acuity metric development. Individualized prediction of mental acuity consistently outperformed group modeling. This effort indicates the feasibility of creating an individualized, mobile assessment and prediction of mental acuity, compatible with the majority of current mobile devices.

Development and Clinical Evaluation of an mHealth Application for Stress Management.

A large number of individuals experience mental health disorders, with cognitive behavioral therapy (CBT) emerging as a standard practice for reduction in psychiatric symptoms, including stress, anger, anxiety, and depression. However, CBT is associated with significant patient dropout and lacks the means to provide objective data regarding a patient's experience and symptoms between sessions. Emerging wearables and mobile health (mHealth) applications represent an approach that may provide objective data to the patient and provider between CBT sessions. Here, we describe the development of a classifier of real-time physiological stress in a healthy population (n = 35) and apply it in a controlled clinical evaluation for armed forces veterans undergoing CBT for stress and anger management (n = 16). Using cardiovascular and electrodermal inputs from a wearable device, the classifier was able to detect physiological stress in a non-clinical sample with accuracy greater than 90%. In a small clinical sample, patients who used the classifier and an associated mHealth application were less likely to discontinue therapy (p = 0.016, d = 1.34) and significantly improved on measures of stress (p = 0.032, d = 1.61), anxiety (p = 0.050, d = 1.26), and anger (p = 0.046, d = 1.41) compared to controls undergoing CBT alone. Given the large number of individuals that experience mental health disorders and the unmet need for treatment, especially in developing nations, such mHealth approaches have the potential to provide or augment treatment at low cost in the absence of in-person care.

Identification of resilient individuals and those at risk for performance deficits under stress.

Human task performance is affected by exposure to physiological and psychological stress. The ability to measure the physiological response to stressors and correlate that to task performance could be used to identify resilient individuals or those at risk for stress-related performance decrements. Accomplishing this prior to performance under severe stress or the development of clinical stress disorders could facilitate focused preparation such as tailoring training to individual needs. Here we measure the effects of stress on physiological response and performance through behavior, physiological sensors, and subjective ratings, and identify which individuals are at risk for stress-related performance decrements. Participants performed military-relevant training tasks under stress in a virtual environment, with autonomic and hypothalamic-pituitary-adrenal axis (HPA) reactivity analyzed. Self-reported stress, as well as physiological indices of stress, increased in the group pre-exposed to socioevaluative stress. Stress response was effectively captured via electrodermal and cardiovascular measures of heart rate and skin conductance level. A resilience classification algorithm was developed based upon physiological reactivity, which correlated with baseline unstressed physiological and self-reported stress values. Outliers were identified in the experimental group that had a significant mismatch between self-reported stress and salivary cortisol. Baseline stress measurements were predictive of individual resilience to stress, including the impact stress had on physiological reactivity and performance. Such an approach may have utility in identifying individuals at risk for problems performing under severe stress. Continuing work has focused on adapting this method for military personnel, and assessing the utility of various coping and decision-making strategies on performance and physiological stress.

A mesoscale connectome of the mouse brain.

Comprehensive knowledge of the brain's wiring diagram is fundamental for understanding how the nervous system processes information at both local and global scales. However, with the singular exception of the C. elegans microscale connectome, there are no complete connectivity data sets in other species. Here we report a brain-wide, cellular-level, mesoscale connectome for the mouse. The Allen Mouse Brain Connectivity Atlas uses enhanced green fluorescent protein (EGFP)-expressing adeno-associated viral vectors to trace axonal projections from defined regions and cell types, and high-throughput serial two-photon tomography to image the EGFP-labelled axons throughout the brain. This systematic and standardized approach allows spatial registration of individual experiments into a common three dimensional (3D) reference space, resulting in a whole-brain connectivity matrix. A computational model yields insights into connectional strength distribution, symmetry and other network properties. Virtual tractography illustrates 3D topography among interconnected regions. Cortico-thalamic pathway analysis demonstrates segregation and integration of parallel pathways. The Allen Mouse Brain Connectivity Atlas is a freely available, foundational resource for structural and functional investigations into the neural circuits that support behavioural and cognitive processes in health and disease.

A 0.5 cm(3) four-channel 1.1 mW wireless biosignal interface with 20 m range.

This paper presents a self-contained, single-chip biosignal monitoring system with wireless programmability and telemetry interface suitable for mainstream healthcare applications. The system consists of low-noise front end amplifiers, ADC, MICS/ISM transmitter and infrared programming capability to configure the state of the chip. An on-chip packetizer ensures easy pairing with standard off-the-shelf receivers. The chip is realized in the IBM 130 nm CMOS process with an area of 2×2 mm(2). The entire system consumes 1.07 mW from a 1.2 V supply. It weighs 0.6 g including a zinc-air battery. The system has been extensively tested in in vivo biological experiments and requires minimal human interaction or calibration.

The challenge of integrating devices into the central nervous system.

Implanted biomedical devices are playing an increasingly important role in the treatment of central nervous system disorders. While devices such as deep brain stimulation electrodes and drug delivery systems have shown clinical success in chronic applications, other devices such as nerve guidance substrates and recording electrodes that operate over a very short length scale have not had the same kind of clinical impact. By reviewing what is currently known about the brain tissue response to implanted electrodes, the authors propose that the foreign-body response, which changes the tissue structure immediately surrounding implanted devices, may be the reason near-function devices are stalled in preclinical development. The article concludes by reviewing recent efforts to reduce the foreign body response, which shows promise to accelerate the clinical development of this new generation of biomedical devices.

Biocompatibility of adhesive complex coacervates modeled after the sandcastle glue of Phragmatopoma californica for craniofacial reconstruction.

Craniofacial reconstruction would benefit from a degradable adhesive capable of holding bone fragments in three-dimensional alignment and gradually being replaced by new bone without loss of alignment or volume changes. Modeled after a natural adhesive secreted by the sandcastle worm, we studied the biocompatibility of adhesive complex coacervates in vitro and in vivo with two different rat calvarial models. We found that the adhesive was non-cytotoxic and supported the attachment, spreading, and migration of a commonly used osteoblastic cell line over the course of several days. In animal studies we found that the adhesive was capable of maintaining three-dimensional bone alignment in freely moving rats over a 12 week indwelling period. Histological evidence indicated that the adhesive was gradually resorbed and replaced by new bone that became lamellar across the defect without loss of alignment, changes in volume, or changes in the adjacent uninjured bone. The presence of inflammatory cells was consistent with what has been reported with other craniofacial fixation methods including metal plates, screws, tacks, calcium phosphate cements and cyanoacrylate adhesives. Collectively, the results suggest that the new bioadhesive formulation is degradable, osteoconductive and appears suitable for use in the reconstruction of craniofacial fractures.

A comparison of the tissue response to chronically implanted Parylene-C-coated and uncoated planar silicon microelectrode arrays in rat cortex.

In this study we employed a quantitative immunohistochemical approach to compare the brain tissue response to planar silicon microelectrode arrays that were conformally coated with Parylene-C to uncoated controls at 2, 4, and 12 weeks following implantation into the cortex of adult male Sprague-Dawley rats. We did not find any difference in the relative intensity or the spatial distribution of neuronal or glial markers over the indwelling period, even though Parylene-C-coated substrates supported significantly less cell attachment, indicating that the foreign body response to planar silicon microelectrode arrays has little to do with the composition or decomposition of the silicon electrode. Moreover, our results suggest that changes in microelectrode surface chemistry do not have a strong influence on the cytoarchitectural changes that accompany the brain foreign body response to planar silicon microelectrode arrays. Our quantitative comparison over the indwelling period does not support progressive increases in astrocyte encapsulation and/or progressive neuronal loss in the recording zone as dominant failure mechanisms of the type of chronic recording device. Finally, we found evidence of two potentially new failure mechanisms that were associated with CD68 immunoreactivity including demyelination of adjacent neurons and BBB breakdown surrounding implanted electrodes at long indwelling times.

Quantitative analysis of the tissue response to chronically implanted microwire electrodes in rat cortex.

Several hypotheses have been proposed to explain how the brain tissue reaction to single unit recording electrodes influences biocompatibility including progressive changes in the spatial distribution of reactive astrocytes, and the loss of neurons over the indwelling period. To examine these hypotheses, the spatial distribution of biomarkers associated with the foreign body response to insulated microwires placed in rat cerebral cortex was analyzed 2, 4, and 12 weeks after implantation using quantitative methods. We observed a stereotypical tissue response that was similar in some aspects to that previously reported for penetrating planar silicon microelectrode arrays with some specific differences including an overall lower degree of cortical tissue reactivity. While we found no evidence that reactive gliosis increases over time or that neuronal loss is progressive, we did find evidence of persistent inflammation and enhanced BBB permeability at the electrode brain tissue interface that extended over the 3 month indwelling period and that exhibited more animal to animal variability at 3 months than at 2 and 4 weeks.