Highlights •Extrasynaptic levels of dopamine in mouse cortex exhibit spontaneous impulses •Impulses are broadly distributed in amplitude and time, with a rate of about 0.01/s •Feedback was used to train mice to volitionally control their spontaneous impulses •Mice learned to reliably modulate dopamine impulses in order to receive a reward Summary In their pioneering study on dopamine release, Romo and Schultz speculated “...that the amount of dopamine released by unmodulated spontaneous impulse activity exerts a tonic, permissive influence on neuronal processes more actively engaged in preparation of self-initiated movements....”1 Motivated by the suggestion of “spontaneous impulses,” as well as by the “ramp up” of dopaminergic neuronal activity that occurs when rodents navigate to a reward,2, 3, 4, 5 we asked two questions. First, are there spontaneous impulses of dopamine that are released in cortex? Using cell-based optical sensors of extrasynaptic dopamine, [DA]ex,6 we found that spontaneous dopamine impulses in cortex of naive mice occur at a rate of ∼0.01 per second. Next, can mice be trained to change the amplitude and/or timing of dopamine events triggered by internal brain dynamics, much as they can change the amplitude and timing of dopamine impulses based on an external cue?7, 8, 9 Using a reinforcement learning paradigm based solely on rewards that were gated by feedback from real-time measurements of [DA]ex, we found that mice can volitionally modulate their spontaneous [DA]ex. In particular, by only the second session of daily, hour-long training, mice increased the rate of impulses of [DA]ex, increased the amplitude of the impulses, and increased their tonic level of [DA]ex for a reward. Critically, mice learned to reliably elicit [DA]ex impulses prior to receiving a reward. These effects reversed when the reward was removed. We posit that spontaneous dopamine impulses may serve as a salient cognitive event in behavioral planning.
Motor imagery offers an excellent opportunity as a stimulus-free paradigm for brain–machine interfaces. Conventional electroencephalography (EEG) for motor imagery requires a hair cap with multiple wired electrodes and messy gels, causing motion artifacts. Here, a wireless scalp electronic system with virtual reality for real-time, continuous classification of motor imagery brain signals is introduced. This low-profile, portable system integrates imperceptible microneedle electrodes and soft wireless circuits. Virtual reality addresses subject variance in detectable EEG response to motor imagery by providing clear, consistent visuals and instant biofeedback. The wearable soft system offers advantageous contact surface area and reduced electrode impedance density, resulting in significantly enhanced EEG signals and classification accuracy. The combination with convolutional neural network-machine learning provides a real-time, continuous motor imagery-based brain–machine interface. With four human subjects, the scalp electronic system offers a high classification accuracy (93.22 ± 1.33% for four classes), allowing wireless, real-time control of a virtual reality game.
Brain–computer interfaces (BCIs) provide bidirectional communication between the brain and output devices that translate user intent into function. Among the different brain imaging techniques used to operate BCIs, electroencephalography (EEG) constitutes the preferred method of choice, owing to its relative low cost, ease of use, high temporal resolution, and noninvasiveness. In recent years, significant progress in wearable technologies and computational intelligence has greatly enhanced the performance and capabilities of EEG-based BCIs (eBCIs) and propelled their migration out of the laboratory and into real-world environments. This rapid translation constitutes a paradigm shift in human–machine interaction that will deeply transform different industries in the near future, including healthcare and wellbeing, entertainment, security, education, and marketing. In this contribution, the state-of-the-art in wearable biosensing is reviewed, focusing on the development of novel electrode interfaces for long term and noninvasive EEG monitoring. Commercially available EEG platforms are surveyed, and a comparative analysis is presented based on the benefits and limitations they provide for eBCI development. Emerging applications in neuroscientific research and future trends related to the widespread implementation of eBCIs for medical and nonmedical uses are discussed. Finally, a commentary on the ethical, social, and legal concerns associated with this increasingly ubiquitous technology is provided, as well as general recommendations to address key issues related to mainstream consumer adoption.
BACKGROUND Technology to restore the ability to communicate in paralyzed persons who cannot speak has the potential to improve autonomy and quality of life. An approach that decodes words and sentences directly from the cerebral cortical activity of such patients may represent an advancement over existing methods for assisted communication. METHODS We implanted a subdural, high-density, multielectrode array over the area of the sensorimotor cortex that controls speech in a person with anarthria (the loss of the ability to articulate speech) and spastic quadriparesis caused by a brain-stem stroke. Over the course of 48 sessions, we recorded 22 hours of cortical activity while the participant attempted to say individual words from a vocabulary set of 50 words. We used deep-learning algorithms to create computational models for the detection and classification of words from patterns in the recorded cortical activity. We applied these computational models, as well as a natural-language model that yielded next-word probabilities given the preceding words in a sequence, to decode full sentences as the participant attempted to say them. RESULTS We decoded sentences from the participant’s cortical activity in real time at a median rate of 15.2 words per minute, with a median word error rate of 25.6%. In post hoc analyses, we detected 98% of the attempts by the participant to produce individual words, and we classified words with 47.1% accuracy using cortical signals that were stable throughout the 81-week study period. CONCLUSIONS In a person with anarthria and spastic quadriparesis caused by a brain-stem stroke, words and sentences were decoded directly from cortical activity during attempted speech with the use of deep-learning models and a natural-language model. (Funded by Facebook and others; ClinicalTrials.gov number, NCT03698149. opens in new tab.)
Brain-machine interfaces (BMIs) provide a promising information channel between the biological brain and external devices and are applied in building brain-to-device control. Prior studies have explored the feasibility of establishing a brain-brain interface (BBI) across various brains via the combination of BMIs. However, using BBI to realize the efficient multidegree control of a living creature, such as a rat, to complete a navigation task in a complex environment has yet to be shown. In this study, we developed a BBI from the human brain to a rat implanted with microelectrodes (i.e., rat cyborg), which integrated electroencephalogram-based motor imagery and brain stimulation to realize human mind control of the rat’s continuous locomotion. Control instructions were transferred from continuous motor imagery decoding results with the proposed control models and were wirelessly sent to the rat cyborg through brain micro-electrical stimulation. The results showed that rat cyborgs could be smoothly and successfully navigated by the human mind to complete a navigation task in a complex maze. Our experiments indicated that the cooperation through transmitting multidimensional information between two brains by computer-assisted BBI is promising.
Importance: A head computed tomography (CT) with positive results for acute intracranial hemorrhage is the gold-standard diagnostic biomarker for acute traumatic brain injury (TBI). In moderate to severe TBI (Glasgow Coma Scale [GCS] scores 3-12), some CT features have been shown to be associated with outcomes. In mild TBI (mTBI; GCS scores 13-15), distribution and co-occurrence of pathological CT features and their prognostic importance are not well understood. Objective: To identify pathological CT features associated with adverse outcomes after mTBI. Design, Setting, and Participants: The longitudinal, observational Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study enrolled patients with TBI, including those 17 years and older with GCS scores of 13 to 15 who presented to emergency departments at 18 US level 1 trauma centers between February 26, 2014, and August 8, 2018, and underwent head CT imaging within 24 hours of TBI. Evaluations of CT imaging used TBI Common Data Elements. Glasgow Outcome Scale-Extended (GOSE) scores were assessed at 2 weeks and 3, 6, and 12 months postinjury. External validation of results was performed via the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study. Data analyses were completed from February 2020 to February 2021. Exposures: Acute nonpenetrating head trauma. Main Outcomes and Measures: Frequency, co-occurrence, and clustering of CT features; incomplete recovery (GOSE scores <8 vs 8); and an unfavorable outcome (GOSE scores <5 vs ≥5) at 2 weeks and 3, 6, and 12 months. Results: In 1935 patients with mTBI (mean [SD] age, 41.5 [17.6] years; 1286 men [66.5%]) in the TRACK-TBI cohort and 2594 patients with mTBI (mean [SD] age, 51.8 [20.3] years; 1658 men [63.9%]) in an external validation cohort, hierarchical cluster analysis identified 3 major clusters of CT features: contusion, subarachnoid hemorrhage, and/or subdural hematoma; intraventricular and/or petechial hemorrhage; and epidural hematoma. Contusion, subarachnoid hemorrhage, and/or subdural hematoma features were associated with incomplete recovery (odds ratios [ORs] for GOSE scores <8 at 1 year: TRACK-TBI, 1.80 [95% CI, 1.39-2.33]; CENTER-TBI, 2.73 [95% CI, 2.18-3.41]) and greater degrees of unfavorable outcomes (ORs for GOSE scores <5 at 1 year: TRACK-TBI, 3.23 [95% CI, 1.59-6.58]; CENTER-TBI, 1.68 [95% CI, 1.13-2.49]) out to 12 months after injury, but epidural hematoma was not. Intraventricular and/or petechial hemorrhage was associated with greater degrees of unfavorable outcomes up to 12 months after injury (eg, OR for GOSE scores <5 at 1 year in TRACK-TBI: 3.47 [95% CI, 1.66-7.26]). Some CT features were more strongly associated with outcomes than previously validated variables (eg, ORs for GOSE scores <5 at 1 year in TRACK-TBI: neuropsychiatric history, 1.43 [95% CI .98-2.10] vs contusion, subarachnoid hemorrhage, and/or subdural hematoma, 3.23 [95% CI 1.59-6.58]). Findings were externally validated in 2594 patients with mTBI enrolled in the CENTER-TBI study. Conclusions and Relevance: In this study, pathological CT features carried different prognostic implications after mTBI to 1 year postinjury. Some patterns of injury were associated with worse outcomes than others. These results support that patients with mTBI and these CT features need TBI-specific education and systematic follow-up.
Given the prominent role that smartphones have in everyday life, research in the field has proliferated. From a theoretical perspective, problematic smartphone use (PSPU) is described as a multi-faceted phenomenon entailing a variety of dysfunctional manifestations (e.g., addictive, antisocial and dangerous use). To date, however, there is still a lack of empirical evidence supporting the identification of PSPU as a potential behavioural addiction. Driven by theory, the aim of the present study was to provide an empirically validated model by testing the contribution of specific factors leading to PSPU. Relationships among individual characteristics (internalised psychopathology, impulsivity and personality traits) and PSPU uses (addictive, antisocial and dangerous) were investigated according to the updated version of the theoretical framework provided by the Pathway Model of problematic smartphone use (Billieux et al., 2015). An online survey was administered to a convenience sample (N = 511) of smartphone users in order to examine their daily engagement, problematic usage patterns and related psychological correlates. Path analysis revealed important information about different PSPU components and results are discussed in light of the available literature. Recommendations for future research are proposed to further investigate the problematic behaviour, including the study of additional variables, such as the fear of missing out (FoMO), nomophobia and excessive social media use.
Background: Contact tracing apps are potentially useful tools for supporting national COVID-19 containment strategies. Various national apps with different technical design features have been commissioned and issued by governments worldwide. Objective: Our goal was to develop and propose an item set that was suitable for describing and monitoring nationally issued COVID-19 contact tracing apps. This item set could provide a framework for describing the key technical features of such apps and monitoring their use based on widely available information. Methods: We used an open-source intelligence approach (OSINT) to access a multitude of publicly available sources and collect data and information regarding the development and use of contact tracing apps in different countries over several months (from June 2020 to January 2021). The collected documents were then iteratively analyzed via content analysis methods. During this process, an initial set of subject areas were refined into categories for evaluation (ie, coherent topics), which were then examined for individual features. These features were paraphrased as items in the form of questions and applied to information materials from a sample of countries (ie, Brazil, China, Finland, France, Germany, Italy, Singapore, South Korea, Spain, and the United Kingdom [England and Wales]). This sample was purposefully selected; our intention was to include the apps of different countries from around the world and to propose a valid item set that can be relatively easily applied by using an OSINT approach. Results: Our OSINT approach and subsequent analysis of the collected documents resulted in the definition of the following five main categories and associated subcategories: (1) background information (open-source code, public information, and collaborators); (2) purpose and workflow (secondary data use and warning process design); (3) technical information (protocol, tracing technology, exposure notification system, and interoperability); (4) privacy protection (the entity of trust and anonymity); and (5) availability and use (release date and the number of downloads). Based on this structure, a set of items that constituted the evaluation framework were specified. The application of these items to the 10 selected countries revealed differences, especially with regard to the centralization of the entity of trust and the overall transparency of the apps’ technical makeup. Conclusions: We provide a set of criteria for monitoring and evaluating COVID-19 tracing apps that can be easily applied to publicly issued information. The application of these criteria might help governments to identify design features that promote the successful, widespread adoption of COVID-19 tracing apps among target populations and across national boundaries.
Currently, all space missions rely on propellants produced on Earth. In order to make space exploration and extraterrestrial habitation sustainable, technologies must be developed to enable propellant manufacturing off-planet. Extraterrestrial propellant production will drive down costs, increasing access to locations near and far from Earth. However, even though there is agreement that off-planet propellant manufacture is an important early step in driving down space mission costs, no minimum viable product (MVP) for in-situ propellant exists. This is primarily due to the difficulty of storage of typical in-situ derived propellants. The Moon is a near-term source of resources, and frozen polar water is one of the most available. Hydrolox is a high specific impulse fuel that can be produced from lunar water. However, it is challenging to handle because hydrogen and oxygen must be stored at cryogenic temperatures for use as a liquid propellent. While work is being undertaken to build a system capable of managing cryogenic propellant, such systems are likely to have mass and capital requirements and development timelines of more than a decade that exclude cryogens from upcoming lunar missions. Storable propellant options exist, but most require carbon or nitrogen, which are not widely available on the lunar surface. Furthermore, many, such as hydrazine, are highly toxic. Orbit Fab has undertaken a trade study of various propulsion systems proposed to date. One nontoxic, storable monopropellant stands out for near-term in-situ resource utilization (ISRU): high-concentration hydrogen peroxide (HTP), which can be made directly from pure water. HTP decomposes catalytically into oxygen and water vapor and provides sufficient specific impulse to power a lunar ascent vehicle. Few suppliers of HTP exist today, and those that do are centralized and expensive, stifling development and making it difficult to rely on as a propellant, even on Earth. Orbit Fab is developing a system that radically simplifies the production of hydrogen peroxide while increasing its availability. Such a system could enable in-situ HTP use within a few years. This study discusses the needs and requirements for ISRU propellant architectures. Emphasis is placed on supporting lunar missions in this decade. This work specifically focuses on a trade study of various ISRU propulsion architecture options which are presented and ranked on several criteria: complexity, ability to provide lunar ascent thrust, solar requirements, ISRU feasibility, and storability. Consideration is given not only to the scientific value of the propellant architecture, but also its potential for market viability. The benefits of storability in upcoming ISRU propellant architectures are explored, and this study concludes by considering R&D needs and expected requirements for a storable propellant MVP.
Mechanical stimuli play important roles on the growth, development, and behavior of tissue. A simple and novel paper-based in vitro tissue chip was developed that can deliver two types of mechanical stimuli—local compression and shear flow—in a programmed manner. Rat vascular endothelial cells (RVECs) were patterned on collagen-coated nitrocellulose paper to create a tissue chip. Localized compression and shear flow were introduced by simply tapping and bending the paper chip in a programmed manner, utilizing an inexpensive servo motor controlled by an Arduino microcontroller and powered by batteries. All electrical compartments and a paper-based tissue chip were enclosed in a single 3D-printed enclosure, allowing the whole device to be independently placed within an incubator. This simple device effectively simulated in vivo conditions and induced successful RVEC migration in as early as 5 h. The developed device provides an inexpensive and flexible alternative for delivering mechanical stimuli to other in vitro tissue models.