Special Issue-Biosensors and Neuroscience: Is Biosensors Engineering Ready to Embrace Design Principles from Neuroscience?

In partnership with the Air Force Office of Scientific Research (AFOSR), the National Science Foundation's (NSF) Emerging Frontiers and Multidisciplinary Activities (EFMA) office of the Directorate for Engineering (ENG) launched an Emerging Frontiers in Research and Innovation (EFRI) topic for the fiscal years FY22 and FY23 entitled "Brain-inspired Dynamics for Engineering Energy-Efficient Circuits and Artificial Intelligence" (BRAID) [...].

NSF DARE-transforming modeling in neurorehabilitation: perspectives and opportunities from US funding agencies.

In recognition of the importance and timeliness of computational models for accelerating progress in neurorehabilitation, the U.S. National Science Foundation (NSF) and the National Institutes of Health (NIH) sponsored a conference in March 2023 at the University of Southern California that drew global participation from engineers, scientists, clinicians, and trainees. This commentary highlights promising applications of computational models to understand neurorehabilitation ("Using computational models to understand complex mechanisms in neurorehabilitation" section), improve rehabilitation care in the context of digital twin frameworks ("Using computational models to improve delivery and implementation of rehabilitation care" section), and empower future interdisciplinary workforces to deliver higher-quality clinical care using computational models ("Using computational models in neurorehabilitation requires an interdisciplinary workforce" section). The authors describe near-term gaps and opportunities, all of which encourage interdisciplinary team science. Four major opportunities were identified including (1) deciphering the relationship between engineering figures of merit-a term commonly used by engineers to objectively quantify the performance of a device, system, method, or material relative to existing state of the art-and clinical outcome measures, (2) validating computational models from engineering and patient perspectives, (3) creating and curating datasets that are made publicly accessible, and (4) developing new transdisciplinary frameworks, theories, and models that incorporate the complexities of the nervous and musculoskeletal systems. This commentary summarizes U.S. funding opportunities by two Federal agencies that support computational research in neurorehabilitation. The NSF has funding programs that support high-risk/high-reward research proposals on computational methods in neurorehabilitation informed by theory- and data-driven approaches. The NIH supports the development of new interventions and therapies for a wide range of nervous system injuries and impairments informed by the field of computational modeling. The conference materials can be found at https://dare2023.usc.edu/ .

Transcranial Low-Intensity Focused Ultrasound Stimulation of the Visual Thalamus Produces Long-Term Depression of Thalamocortical Synapses in the Adult Visual Cortex.

Transcranial focused ultrasound stimulation (tFUS) is a noninvasive neuromodulation technique, which can penetrate deeper and modulate neural activity with a greater spatial resolution (on the order of millimeters) than currently available noninvasive brain stimulation methods, such as transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS). While there are several studies demonstrating the ability of tFUS to modulate neuronal activity, it is unclear whether it can be used for producing long-term plasticity as needed to modify circuit function, especially in adult brain circuits with limited plasticity such as the thalamocortical synapses. Here we demonstrate that transcranial low-intensity focused ultrasound (LIFU) stimulation of the visual thalamus (dorsal lateral geniculate nucleus, dLGN), a deep brain structure, leads to NMDA receptor (NMDAR)-dependent long-term depression of its synaptic transmission onto layer 4 neurons in the primary visual cortex (V1) of adult mice of both sexes. This change is not accompanied by large increases in neuronal activity, as visualized using the cFos Targeted Recombination in Active Populations (cFosTRAP2) mouse line, or activation of microglia, which was assessed with IBA-1 staining. Using a model (SONIC) based on the neuronal intramembrane cavitation excitation (NICE) theory of ultrasound neuromodulation, we find that the predicted activity pattern of dLGN neurons upon sonication is state-dependent with a range of activity that falls within the parameter space conducive for inducing long-term synaptic depression. Our results suggest that noninvasive transcranial LIFU stimulation has a potential for recovering long-term plasticity of thalamocortical synapses in the postcritical period adult brain.

Bayesian optimization of distributed neurodynamical controller models for spatial navigation.

Dynamical systems models for controlling multi-agent swarms have demonstrated advances toward resilient, decentralized navigation algorithms. We previously introduced the NeuroSwarms controller, in which agent-based interactions were modeled by analogy to neuronal network interactions, including attractor dynamics and phase synchrony, that have been theorized to operate within hippocampal place-cell circuits in navigating rodents. This complexity precludes linear analyses of stability, controllability, and performance typically used to study conventional swarm models. Further, tuning dynamical controllers by manual or grid-based search is often inadequate due to the complexity of objectives, dimensionality of model parameters, and computational costs of simulation-based sampling. Here, we present a framework for tuning dynamical controller models of autonomous multi-agent systems with Bayesian optimization. Our approach utilizes a task-dependent objective function to train Gaussian process surrogate models to achieve adaptive and efficient exploration of a dynamical controller model's parameter space. We demonstrate this approach by studying an objective function selecting for NeuroSwarms behaviors that cooperatively localize and capture spatially distributed rewards under time pressure. We generalized task performance across environments by combining scores for simulations in multiple mazes with distinct geometries. To validate search performance, we compared high-dimensional clustering for high- vs. low-likelihood parameter points by visualizing sample trajectories in 2-dimensional embeddings. Our findings show that adaptive, sample-efficient evaluation of the self-organizing behavioral capacities of complex systems, including dynamical swarm controllers, can accelerate the translation of neuroscientific theory to applied domains.

Online learning for orientation estimation during translation in an insect ring attractor network.

Insect neural systems are a promising source of inspiration for new navigation algorithms, especially on low size, weight, and power platforms. There have been unprecedented recent neuroscience breakthroughs with Drosophila in behavioral and neural imaging experiments as well as the mapping of detailed connectivity of neural structures. General mechanisms for learning orientation in the central complex (CX) of Drosophila have been investigated previously; however, it is unclear how these underlying mechanisms extend to cases where there is translation through an environment (beyond only rotation), which is critical for navigation in robotic systems. Here, we develop a CX neural connectivity-constrained model that performs sensor fusion, as well as unsupervised learning of visual features for path integration; we demonstrate the viability of this circuit for use in robotic systems in simulated and physical environments. Furthermore, we propose a theoretical understanding of how distributed online unsupervised network weight modification can be leveraged for learning in a trajectory through an environment by minimizing orientation estimation error. Overall, our results may enable a new class of CX-derived low power robotic navigation algorithms and lead to testable predictions to inform future neuroscience experiments.

Cognitive swarming in complex environments with attractor dynamics and oscillatory computing.

Neurobiological theories of spatial cognition developed with respect to recording data from relatively small and/or simplistic environments compared to animals' natural habitats. It has been unclear how to extend theoretical models to large or complex spaces. Complementarily, in autonomous systems technology, applications have been growing for distributed control methods that scale to large numbers of low-footprint mobile platforms. Animals and many-robot groups must solve common problems of navigating complex and uncertain environments. Here, we introduce the NeuroSwarms control framework to investigate whether adaptive, autonomous swarm control of minimal artificial agents can be achieved by direct analogy to neural circuits of rodent spatial cognition. NeuroSwarms analogizes agents to neurons and swarming groups to recurrent networks. We implemented neuron-like agent interactions in which mutually visible agents operate as if they were reciprocally connected place cells in an attractor network. We attributed a phase state to agents to enable patterns of oscillatory synchronization similar to hippocampal models of theta-rhythmic (5-12 Hz) sequence generation. We demonstrate that multi-agent swarming and reward-approach dynamics can be expressed as a mobile form of Hebbian learning and that NeuroSwarms supports a single-entity paradigm that directly informs theoretical models of animal cognition. We present emergent behaviors including phase-organized rings and trajectory sequences that interact with environmental cues and geometry in large, fragmented mazes. Thus, NeuroSwarms is a model artificial spatial system that integrates autonomous control and theoretical neuroscience to potentially uncover common principles to advance both domains.

Ultrasonic modulation of neural circuit activity.

Ultrasound (US) is recognized for its use in medical imaging as a diagnostic tool. As an acoustic energy source, US has become increasingly appreciated over the past decade for its ability to non-invasively modulate cellular activity including neuronal activity. Data obtained from a host of experimental models has shown that low-intensity US can reversibly modulate the physiological activity of neurons in peripheral nerves, spinal cord, and intact brain circuits. Experimental evidence indicates that acoustic pressures exerted by US act, in part, on mechanosensitive ion channels to modulate activity. While the precise mechanisms of action enabling US to both stimulate and suppress neuronal activity remain to be clarified, there are several advantages conferred by the physics of US that make it an appealing option for neuromodulation. For example, it can be focused with millimeter spatial resolutions through skull bone to deep-brain regions. By increasing our engineering capability to leverage such physical advantages while growing our understanding of how US affects neuronal function, the development of a new generation of non-invasive neurotechnology can be developed using ultrasonic methods.

Plasmonic coupled nanotorch structures leading to uniform surface enhanced Raman scattering detection.

Nanocrescent-like structures have become important surface enhanced Raman scattering structures because instead of relying on a few nanometer gap inter-particle plasmonic coupling to achieve local field enhancement, intra-particle plasmonic coupling between the cavity modes and the tip edges are utilized to achieve high local field enhancement at the tips. Our fabrication approach creates 'nanotorch' structures with controllable cavity rim opening and deterministic orientation to yield uniform Raman measurements, consisting of three-dimensional upright-oriented nanocrescent structures resting on nanopillars. Each structure serves as a single SERS substrate. We demonstrate that a nanotorch with a smaller rim opening results in a higher enhancement factor compare to one with a larger opening. More importantly, the uniformity of all the analysed Raman modes of adsorbed benzenethiol is better than 80% due to the consistent, upright orientation of each single nanotorch SERS substrate, paving the way for practical implementation of SERS detection.

Collection of aerosolized human cytokines using Teflon® filters.

BACKGROUND: Collection of exhaled breath samples for the analysis of inflammatory biomarkers is an important area of research aimed at improving our ability to diagnose, treat and understand the mechanisms of chronic pulmonary disease. Current collection methods based on condensation of water vapor from exhaled breath yield biomarker levels at or near the detection limits of immunoassays contributing to problems with reproducibility and validity of biomarker measurements. In this study, we compare the collection efficiency of two aerosol-to-liquid sampling devices to a filter-based collection method for recovery of dilute laboratory generated aerosols of human cytokines so as to identify potential alternatives to exhaled breath condensate collection. METHODOLOGY/PRINCIPAL FINDINGS: Two aerosol-to-liquid sampling devices, the SKC® Biosampler and Omni 3000™, as well as Teflon® filters were used to collect aerosols of human cytokines generated using a HEART nebulizer and single-pass aerosol chamber setup in order to compare the collection efficiencies of these sampling methods. Additionally, methods for the use of Teflon® filters to collect and measure cytokines recovered from aerosols were developed and evaluated through use of a high-sensitivity multiplex immunoassay. Our results show successful collection of cytokines from pg/m(3) aerosol concentrations using Teflon® filters and measurement of cytokine levels in the sub-picogram/mL concentration range using a multiplex immunoassay with sampling times less than 30 minutes. Significant degradation of cytokines was observed due to storage of cytokines in concentrated filter extract solutions as compared to storage of dry filters. CONCLUSIONS: Use of filter collection methods resulted in significantly higher efficiency of collection than the two aerosol-to-liquid samplers evaluated in our study. The results of this study provide the foundation for a potential new technique to evaluate biomarkers of inflammation in exhaled breath samples.

A model-based tool to predict the propagation of infectious disease via airports.

Epidemics of novel or re-emerging infectious diseases have quickly spread globally via air travel, as highlighted by pandemic H1N1 influenza in 2009 (pH1N1). Federal, state, and local public health responders must be able to plan for and respond to these events at aviation points of entry. The emergence of a novel influenza virus and its spread to the United States were simulated for February 2009 from 55 international metropolitan areas using three basic reproduction numbers (R(0)): 1.53, 1.70, and 1.90. Empirical data from the pH1N1 virus were used to validate our SEIR model. Time to entry to the U.S. during the early stages of a prototypical novel communicable disease was predicted based on the aviation network patterns and the epidemiology of the disease. For example, approximately 96% of origins (R(0) of 1.53) propagated a disease into the U.S. in under 75 days, 90% of these origins propagated a disease in under 50 days. An R(0) of 1.53 reproduced the pH1NI observations. The ability to anticipate the rate and location of disease introduction into the U.S. provides greater opportunity to plan responses based on the scenario as it is unfolding. This simulation tool can aid public health officials to assess risk and leverage resources efficiently.

An analysis on the detection of biological contaminants aboard aircraft.

The spread of infectious disease via commercial airliner travel is a significant and realistic threat. To shed some light on the feasibility of detecting airborne pathogens, a sensor integration study has been conducted and computational investigations of contaminant transport in an aircraft cabin have been performed. Our study took into consideration sensor sensitivity as well as the time-to-answer, size, weight and the power of best available commercial off-the-shelf (COTS) devices. We conducted computational fluid dynamics simulations to investigate three types of scenarios: (1) nominal breathing (up to 20 breaths per minute) and coughing (20 times per hour); (2) nominal breathing and sneezing (4 times per hour); and (3) nominal breathing only. Each scenario was implemented with one or seven infectious passengers expelling air and sneezes or coughs at the stated frequencies. Scenario 2 was implemented with two additional cases in which one infectious passenger expelled 20 and 50 sneezes per hour, respectively. All computations were based on 90 minutes of sampling using specifications from a COTS aerosol collector and biosensor. Only biosensors that could provide an answer in under 20 minutes without any manual preparation steps were included. The principal finding was that the steady-state bacteria concentrations in aircraft would be high enough to be detected in the case where seven infectious passengers are exhaling under scenarios 1 and 2 and where one infectious passenger is actively exhaling in scenario 2. Breathing alone failed to generate sufficient bacterial particles for detection, and none of the scenarios generated sufficient viral particles for detection to be feasible. These results suggest that more sensitive sensors than the COTS devices currently available and/or sampling of individual passengers would be needed for the detection of bacteria and viruses in aircraft.

Recent advances in peptide probe-based biosensors for detection of infectious agents.

Recent biological terrorism threats and outbreaks of microbial pathogens clearly emphasize the need for biosensors that can quickly and accurately identify infectious agents. The majority of rapid biosensors generate detectable signals when a molecular probe in the detector interacts with an analyte of interest. Analytes may be whole bacterial or fungal cells, virus particles, or specific molecules, such as chemicals or protein toxins, produced by the infectious agent. Peptides and nucleic acids are most commonly used as probes in biosensors because of their versatility in forming various tertiary structures. The interaction between the probe and the analyte can be detected by various sensor platforms, including quartz crystal microbalances, surface acoustical waves, surface plasmon resonance, amperometrics, and magnetoelastics. The field of biosensors is constantly evolving to develop devices that have higher sensitivity and specificity, and are smaller, portable, and cost-effective. This mini review discusses recent advances in peptide-dependent rapid biosensors and their applications as well as relative advantages and disadvantages of each technology.

Characterizing the ERP Old-New effect in a short-term memory task.

The early and late components of the event-related potential (ERP) Old-New effect are well characterized with respect to long-term memory, and have been associated with processes of familiarity and recollection, respectively. Now, using a short-term memory paradigm with verbal and nonverbal stimuli, we explored the way that these two components respond to variation in recency and stimulus type. We found that the amplitude of the early component (or frontal N400, FN400) showed Old-New effects only for verbal stimuli and increased with recency. In contrast, the later component (or late positive component, LPC) showed Old-New effects across a range of stimulus types and did not scale with recency. These results are consistent with the way that these same ERP components have been characterized in long-term memory, supporting the idea that some of the same processes underlie long- and short-term item recognition.