Adaptation and learning as strategies to maximize reward in neurofeedback tasks.

Introduction: Adaptation and learning have been observed to contribute to the acquisition of new motor skills and are used as strategies to cope with changing environments. However, it is hard to determine the relative contribution of each when executing goal directed motor tasks. This study explores the dynamics of neural activity during a center-out reaching task with continuous visual feedback under the influence of rotational perturbations. Methods: Results for a brain-computer interface (BCI) task performed by two non-human primate (NHP) subjects are compared to simulations from a reinforcement learning agent performing an analogous task. We characterized baseline activity and compared it to the activity after rotational perturbations of different magnitudes were introduced. We employed principal component analysis (PCA) to analyze the spiking activity driving the cursor in the NHP BCI task as well as the activation of the neural network of the reinforcement learning agent. Results and discussion: Our analyses reveal that both for the NHPs and the reinforcement learning agent, the task-relevant neural manifold is isomorphic with the task. However, for the NHPs the manifold is largely preserved for all rotational perturbations explored and adaptation of neural activity occurs within this manifold as rotations are compensated by reassignment of regions of the neural space in an angular pattern that cancels said rotations. In contrast, retraining the reinforcement learning agent to reach the targets after rotation results in substantial modifications of the underlying neural manifold. Our findings demonstrate that NHPs adapt their existing neural dynamic repertoire in a quantitatively precise manner to account for perturbations of different magnitudes and they do so in a way that obviates the need for extensive learning.

A low-energy emulsification platform based on a Diet Coke-Mentos reaction-derived bubbly flow for formulating various emulsions as drug carriers.

The formulation of a drug using high-energy emulsification commonly causes drug deterioration. Exploiting the well-known Diet Coke-Mentos reaction (DCMR), a U-shaped tube reactor that can generate an eruption of bubbly flow that can serve as a low-energy emulsification platform, is proposed. The liquid in the U-tube reactor is a supersaturated solution of aqueous CO2, which mimics Diet Coke. When glass beads with rough surfaces, mimicking Mentos, are dropped into the carbonated water, an eruptive bubbly flow is spontaneously created, mediating effective emulsification at a compound water-oil interface. Experimental results demonstrate that DCMR-mediated bubbly flow may provide a versatile platform for the production of "oil-in-water" or "water-in-oil" droplets and Pickering emulsion composite particles as drug carriers. The DCMR-derived bubbly flow is generated without significant temperature elevation, so the activity of the drug to be emulsified is unaffected. In vivo results reveal the feasibility of using this low-energy emulsification platform to formulate an emulsion system that contains catalase, a temperature-sensitive oxidoreductase, to mitigate an experimentally formed paw inflammation in mice. The as-proposed emulsification platform is attractive for formulating numerous drug delivery systems on a small-scale in a customized manner to meet the needs of each individual for personalized medicine.

Short-training Algorithm for Online Brain-machine Interfaces Using One-photon Microendoscopic Calcium Imaging.

Calcium imaging has great potential to be applied to online brain-machine interfaces (BMIs). As opposed to two-photon imaging settings, a one-photon microendoscopic imaging device can be chronically implanted and is subject to little motion artifacts. Traditionally, one-photon microendoscopic calcium imaging data are processed using the constrained nonnegative matrix factorization (CNMFe) algorithm, but this batched processing algorithm cannot be applied in real-time. An online analysis of calcium imaging data algorithm (or OnACIDe) has been proposed, but OnACIDe updates the neural components by repeatedly performing neuron identification frame-by-frame, which may decelerate the update speed if applying to online BMIs. For BMI applications, the ability to track a stable population of neurons in real-time has a higher priority over accurately identifying all the neurons in the field of view. By leveraging the fact that 1) microendoscopic recordings are rather stable with little motion artifacts and 2) the number of neurons identified in a short training period is sufficient for potential online BMI tasks such as cursor movements, we proposed the short-training CNMFe algorithm (stCNMFe) that skips motion correction and neuron identification processes to enable a more efficient BMI training program in a one-photon microendoscopic setting.

Multi-scale neural decoding and analysis.

Objective. Complex spatiotemporal neural activity encodes rich information related to behavior and cognition. Conventional research has focused on neural activity acquired using one of many different measurement modalities, each of which provides useful but incomplete assessment of the neural code. Multi-modal techniques can overcome tradeoffs in the spatial and temporal resolution of a single modality to reveal deeper and more comprehensive understanding of system-level neural mechanisms. Uncovering multi-scale dynamics is essential for a mechanistic understanding of brain function and for harnessing neuroscientific insights to develop more effective clinical treatment.Approach. We discuss conventional methodologies used for characterizing neural activity at different scales and review contemporary examples of how these approaches have been combined. Then we present our case for integrating activity across multiple scales to benefit from the combined strengths of each approach and elucidate a more holistic understanding of neural processes.Main results. We examine various combinations of neural activity at different scales and analytical techniques that can be used to integrate or illuminate information across scales, as well the technologies that enable such exciting studies. We conclude with challenges facing future multi-scale studies, and a discussion of the power and potential of these approaches.Significance. This roadmap will lead the readers toward a broad range of multi-scale neural decoding techniques and their benefits over single-modality analyses. This Review article highlights the importance of multi-scale analyses for systematically interrogating complex spatiotemporal mechanisms underlying cognition and behavior.

In Situ Self-Assembling Micellar Depots that Can Actively Trap and Passively Release NO with Long-Lasting Activity to Reverse Osteoporosis.

Treatment with exogenous nitric oxide (NO) donors is regarded as being effective against osteoporosis. However, NO has a short half-life, limiting its clinical usefulness. To overcome this limitation, an injectable microparticle (MP) system is developed that consists of phase-change materials capric acid (CA) and octadecane, and encapsulates a NO donor. The therapeutic efficacy of the MPs is evaluated in ovariectomized (OVX) rats with osteoporosis. Upon subcutaneous administration, the MPs undergo a phase transition, leaching out the NO donor and generating NO bubbles that are instantly covered by a layer of tightly packed CA surfactant molecules, forming micellar depots. The in situ self-assembling micellar depots can actively protect the NO bubbles, prolonging their half-life, while the entrapped NO may passively diffuse through the micellar depots over time, performing a long-lasting therapeutic function, reversing the OVX-induced osteoporosis. It is possible to use the concept of in situ self-assembling micellar depots developed herein to expand the therapeutic effect of NO in its diverse range of clinical applications.