Ab initio molecular dynamics of solvation effects on reactivity at electrified interfaces.

Using ab initio molecular dynamics as implemented in periodic, self-consistent (generalized gradient approximation Perdew-Burke-Ernzerhof) density functional theory, we investigated the mechanism of methanol electrooxidation on Pt(111). We investigated the role of water solvation and electrode potential on the energetics of the first proton transfer step, methanol electrooxidation to methoxy (CH3O) or hydroxymethyl (CH2OH). The results show that solvation weakens the adsorption of methoxy to uncharged Pt(111), whereas the binding energies of methanol and hydroxymethyl are not significantly affected. The free energies of activation for breaking the C-H and O-H bonds in methanol were calculated through a Blue Moon Ensemble using constrained ab initio molecular dynamics. Calculated barriers for these elementary steps on unsolvated, uncharged Pt(111) are similar to results for climbing-image nudged elastic band calculations from the literature. Water solvation reduces the barriers for both C-H and O-H bond activation steps with respect to their vapor-phase values, although the effect is more pronounced for C-H bond activation, due to less disruption of the hydrogen bond network. The calculated activation energy barriers show that breaking the C-H bond of methanol is more facile than the O-H bond on solvated negatively biased or uncharged Pt(111). However, with positive bias, O-H bond activation is enhanced, becoming slightly more facile than C-H bond activation.

Atomic layer-by-layer deposition of Pt on Pd nanocubes for catalysts with enhanced activity and durability toward oxygen reduction.

An effective strategy for reducing the Pt content while retaining the activity of a Pt-based catalyst is to deposit the Pt atoms as ultrathin skins of only a few atomic layers thick on nanoscale substrates made of another metal. During deposition, however, the Pt atoms often take an island growth mode because of a strong bonding between Pt atoms. Here we report a versatile route to the conformal deposition of Pt as uniform, ultrathin shells on Pd nanocubes in a solution phase. The introduction of the Pt precursor at a relatively slow rate and high temperature allowed the deposited Pt atoms to spread across the entire surface of a Pd nanocube to generate a uniform shell. The thickness of the Pt shell could be controlled from one to six atomic layers by varying the amount of Pt precursor added into the system. Compared to a commercial Pt/C catalyst, the Pd@PtnL (n = 1-6) core-shell nanocubes showed enhancements in specific activity and durability toward the oxygen reduction reaction (ORR). Density functional theory (DFT) calculations on model (100) surfaces suggest that the enhancement in specific activity can be attributed to the weakening of OH binding through ligand and strain effects, which, in turn, increases the rate of OH hydrogenation. A volcano-type relationship between the ORR specific activity and the number of Pt atomic layers was derived, in good agreement with the experimental results. Both theoretical and experimental studies indicate that the ORR specific activity was maximized for the catalysts based on Pd@Pt2-3L nanocubes. Because of the reduction in Pt content used and the enhancement in specific activity, the Pd@Pt1L nanocubes showed a Pt mass activity with almost three-fold enhancement relative to the Pt/C catalyst.

Facile synthesis of palladium right bipyramids and their use as seeds for overgrowth and as catalysts for formic acid oxidation.

Controlling the shape and thus facets of metal nanocrystals is an effective way to enhance their performance in catalytic reactions. While Pd nanocrystals with a myriad of shapes have been successfully prepared with good uniformity and in high yield, Pd right bipyramids (RBPs) that have a singly twinned structure have been elusive. We report a facile route based on polyol reduction for the synthesis of Pd RBPs with purity >90% and sizes controlled in the range 5-15 nm. The success of our synthesis relies on the use of iodide ions to manipulate the strength of an oxidative etchant and selectively cap the Pd{100} facets. The as-prepared RBPs could serve as seeds to generate a set of Pd nanocrystals with novel shapes and structures. The RBPs also exhibited enhanced catalytic activity toward formic acid oxidation, with a current density 2.5 and 7.1 times higher than those of the single-crystal Pd nanocubes (which were also mainly covered by {100} facets) and commercial Pd black, respectively.

Artifact characterization and mitigation techniques during concurrent sensing and stimulation using bidirectional deep brain stimulation platforms.

Bidirectional deep brain stimulation (DBS) platforms have enabled a surge in hours of recordings in naturalistic environments, allowing further insight into neurological and psychiatric disease states. However, high amplitude, high frequency stimulation generates artifacts that contaminate neural signals and hinder our ability to interpret the data. This is especially true in psychiatric disorders, for which high amplitude stimulation is commonly applied to deep brain structures where the native neural activity is miniscule in comparison. Here, we characterized artifact sources in recordings from a bidirectional DBS platform, the Medtronic Summit RC + S, with the goal of optimizing recording configurations to improve signal to noise ratio (SNR). Data were collected from three subjects in a clinical trial of DBS for obsessive-compulsive disorder. Stimulation was provided bilaterally to the ventral capsule/ventral striatum (VC/VS) using two independent implantable neurostimulators. We first manipulated DBS amplitude within safe limits (2-5.3 mA) to characterize the impact of stimulation artifacts on neural recordings. We found that high amplitude stimulation produces slew overflow, defined as exceeding the rate of change that the analog to digital converter can accurately measure. Overflow led to expanded spectral distortion of the stimulation artifact, with a six fold increase in the bandwidth of the 150.6 Hz stimulation artifact from 147-153 to 140-180 Hz. By increasing sense blank values during high amplitude stimulation, we reduced overflow by as much as 30% and improved artifact distortion, reducing the bandwidth from 140-180 Hz artifact to 147-153 Hz. We also identified artifacts that shifted in frequency through modulation of telemetry parameters. We found that telemetry ratio changes led to predictable shifts in the center-frequencies of the associated artifacts, allowing us to proactively shift the artifacts outside of our frequency range of interest. Overall, the artifact characterization methods and results described here enable increased data interpretability and unconstrained biomarker exploration using data collected from bidirectional DBS devices.

Robustness of Beta Desynchronization from Chronically Implanted Cortical Electrodes on Multiple Time Scales.

Adaptive deep brain stimulation (aDBS) promises a significant improvement in patient outcomes, compared to existing deep brain stimulation devices. Fully implanted systems represent the next step to the clinical adoption of aDBS. We take advantage of a unique longitudinal data set formed as part of an effort to investigate aDBS for essential tremor to verify the long term reliability of electrocorticography strips over the motor cortex as a source of bio-markers for control of adaptive stimulation. We show that beta band event related de-synchronization, a promising bio-marker for movement, is robust even when used to trigger aDBS. Over the course of several months we show a minor increase in beta band event related de-synchronization in patients with active deep brain stimulation confirming that it could be used in chronically implanted systems.Clinical relevance - We show the promise and practicality of cortical electrocorticography strips for use in fully implanted, clinically translatable, aDBS systems.

Ramp Rate Evaluation and Configuration for Safe and Tolerable Closed-Loop Deep Brain Stimulation.

Closed-loop deep brain stimulation is a novel form of therapy that has shown benefit in preliminary studies and may be clinically available in the near future. Initial closed-loop studies have primarily focused on responding to sensed biomarkers with adjustments to stimulation amplitude, which is often perceptible to study participants depending on the slew or "ramp" rate of the amplitude changes. These subjective responses to stimulation ramping can result in transient side effects, illustrating that ramp rate is a unique safety parameter for closed-loop neural systems. This presents a challenge to the future of closed-loop neuromodulation systems: depending on the goal of the control policy, clinicians will need to balance ramp rates to avoid side effects and keep the stimulation therapeutic by responding in time to affect neural dynamics. In this paper, we demonstrate the results of an initial investigation into methodology for finding safe and tolerable ramp rates in four people with Parkinson's disease (PD). Results suggest that optimal ramp rates were found more accurately during varying stimulation when compared to simply toggling between maximal and minimal intensity levels. Additionally, switching frequency instantaneously was tolerable at therapeutic levels of stimulation. Future work should focus on including optimization techniques to find ramp rates.

Intraoperative Characterization of Subthalamic Nucleus-to-Cortex Evoked Potentials in Parkinson's Disease Deep Brain Stimulation.

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is a clinically effective tool for treating medically refractory Parkinson's disease (PD), but its neural mechanisms remain debated. Previous work has demonstrated that STN DBS results in evoked potentials (EPs) in the primary motor cortex (M1), suggesting that modulation of cortical physiology may be involved in its therapeutic effects. Due to technical challenges presented by high-amplitude DBS artifacts, these EPs are often measured in response to low-frequency stimulation, which is generally ineffective at PD symptom management. This study aims to characterize STN-to-cortex EPs seen during clinically relevant high-frequency STN DBS for PD. Intraoperatively, we applied STN DBS to 6 PD patients while recording electrocorticography (ECoG) from an electrode strip over the ipsilateral central sulcus. Using recently published techniques, we removed large stimulation artifacts to enable quantification of STN-to-cortex EPs. Two cortical EPs were observed - one synchronized with DBS onset and persisting during ongoing stimulation, and one immediately following DBS offset, here termed the "start" and the "end" EPs respectively. The start EP is, to our knowledge, the first long-latency cortical EP reported during ongoing high-frequency DBS. The start and end EPs differ in magnitude (p < 0.05) and latency (p < 0.001), and the end, but not the start, EP magnitude has a significant relationship (p < 0.001, adjusted for random effects of subject) to ongoing high gamma (80-150 Hz) power during the EP. These contrasts may suggest mechanistic or circuit differences in EP production during the two time periods. This represents a potential framework for relating DBS clinical efficacy to the effects of a variety of stimulation parameters on EPs.

Closing the Loop With Cortical Sensing: The Development of Adaptive Deep Brain Stimulation for Essential Tremor Using the Activa PC+S.

Deep Brain Stimulation (DBS) is an important tool in the treatment of pharmacologically resistant neurological movement disorders such as essential tremor (ET) and Parkinson's disease (PD). However, the open-loop design of current systems may be holding back the true potential of invasive neuromodulation. In the last decade we have seen an explosion of activity in the use of feedback to "close the loop" on neuromodulation in the form of adaptive DBS (aDBS) systems that can respond to the patient's therapeutic needs. In this paper we summarize the accomplishments of a 5-year study at the University of Washington in the use of neural feedback from an electrocorticography strip placed over the sensorimotor cortex. We document our progress from an initial proof of hardware all the way to a fully implanted adaptive stimulation system that leverages machine-learning approaches to simplify the programming process. In certain cases, our systems out-performed current open-loop approaches in both power consumption and symptom suppression. Throughout this effort, we collaborated with neuroethicists to capture patient experiences and take them into account whilst developing ethical aDBS approaches. Based on our results we identify several key areas for future work. "Graded" aDBS will allow the system to smoothly tune the stimulation level to symptom severity, and frequent automatic calibration of the algorithm will allow aDBS to adapt to the time-varying dynamics of the disease without additional input from a clinician. Additionally, robust computational models of the pathophysiology of ET will allow stimulation to be optimized to the nuances of an individual patient's symptoms. We also outline the unique advantages of using cortical electrodes for control and the remaining hardware limitations that need to be overcome to facilitate further development in this field. Over the course of this study we have verified the potential of fully-implanted, cortically driven aDBS as a feasibly translatable treatment for pharmacologically resistant ET.

Epilepsy Personal Assistant Device-A Mobile Platform for Brain State, Dense Behavioral and Physiology Tracking and Controlling Adaptive Stimulation.

Epilepsy is one of the most common neurological disorders, and it affects almost 1% of the population worldwide. Many people living with epilepsy continue to have seizures despite anti-epileptic medication therapy, surgical treatments, and neuromodulation therapy. The unpredictability of seizures is one of the most disabling aspects of epilepsy. Furthermore, epilepsy is associated with sleep, cognitive, and psychiatric comorbidities, which significantly impact the quality of life. Seizure predictions could potentially be used to adjust neuromodulation therapy to prevent the onset of a seizure and empower patients to avoid sensitive activities during high-risk periods. Long-term objective data is needed to provide a clearer view of brain electrical activity and an objective measure of the efficacy of therapeutic measures for optimal epilepsy care. While neuromodulation devices offer the potential for acquiring long-term data, available devices provide very little information regarding brain activity and therapy effectiveness. Also, seizure diaries kept by patients or caregivers are subjective and have been shown to be unreliable, in particular for patients with memory-impairing seizures. This paper describes the design, architecture, and development of the Mayo Epilepsy Personal Assistant Device (EPAD). The EPAD has bi-directional connectivity to the implanted investigational Medtronic Summit RC+STM device to implement intracranial EEG and physiological monitoring, processing, and control of the overall system and wearable devices streaming physiological time-series signals. In order to mitigate risk and comply with regulatory requirements, we developed a Quality Management System (QMS) to define the development process of the EPAD system, including Risk Analysis, Verification, Validation, and protocol mitigations. Extensive verification and validation testing were performed on thirteen canines and benchtop systems. The system is now under a first-in-human trial as part of the US FDA Investigational Device Exemption given in 2018 to study modulated responsive and predictive stimulation using the Mayo EPAD system and investigational Medtronic Summit RC+STM in ten patients with non-resectable dominant or bilateral mesial temporal lobe epilepsy. The EPAD system coupled with an implanted device capable of EEG telemetry represents a next-generation solution to optimizing neuromodulation therapy.

Demonstration of Kinematic-Based Closed-loop Deep Brain Stimulation for Mitigating Freezing of Gait in People with Parkinson's Disease.

Impaired gait in Parkinson's disease is marked by slow, arrhythmic stepping, and often includes freezing of gait episodes where alternating stepping halts completely. Wearable inertial sensors offer a way to detect these gait changes and novel deep brain stimulation (DBS) systems can respond with clinical therapy in a real-time, closed-loop fashion. In this paper, we present two novel closed-loop DBS algorithms, one using gait arrhythmicity and one using a logistic-regression model of freezing of gait detection as control signals. Benchtop validation results demonstrate the feasibility of running these algorithms in conjunction with a closed-loop DBS system by responding to real-time human subject kinematic data and pre-recorded data from leg-worn inertial sensors from a participant with Parkinson's disease. We also present a novel control policy algorithm that changes neurostimulator frequency in response to the kinematic inputs. These results provide a foundation for further development, iteration, and testing in a clinical trial for the first closed-loop DBS algorithms using kinematic signals to therapeutically improve and understand the pathophysiological mechanisms of gait impairment in Parkinson's disease.

A Closed-loop Deep Brain Stimulation Approach for Mitigating Burst Durations in People with Parkinson's Disease.

Increased beta band synchrony has been demonstrated to be a biomarker of Parkinson's disease (PD). This abnormal synchrony can often be prolonged in long bursts of beta activity, which may interfere with normal sensorimotor processing. Previous closed loop deep brain stimulation (DBS) algorithms used averaged beta power to drive neurostimulation, which were indiscriminate to physiological (short) versus pathological (long) beta burst durations. We present a closed-loop DBS algorithm using beta burst duration as the control signal. Benchtop validation results demonstrate the feasibility of the algorithm in real-time by responding to pre-recorded STN data from a PD participant. These results provide the basis for future improved closed-loop algorithms focused on burst durations for in mitigating symptoms of PD.

Rebound effect in deep brain stimulation for essential tremor and symptom severity estimation from neural data.

Deep brain stimulation (DBS) is a safe and established treatment for essential tremor (ET). However, there remains considerable room for improvement due to concerns associated with the initial implant surgery, semi-regular revision surgeries for battery replacements, and side effects including paresthesia, gait ataxia, and emotional disinhibition that have been associated with continuous, or conventional, DBS (cDBS) treatment. Adaptive DBS (aDBS) seeks to ameliorate some of these concerns by using feedback from either an external wearable or implanted sensor to modulate stimulation parameters as needed. aDBS has been demonstrated to be as or more effective than cDBS, but the purely binary control system most commonly deployed by aDBS systems likely still provides sub-optimal treatment and may introduce new issues. One example of these issues is rebound effect, in which the tremor symptoms of an ET patient receiving DBS therapy temporarily worsen after cessation of stimulation before leveling out to a steady state. Here is presented a quantitative analysis of rebound effect in 3 patients receiving DBS for ET. Rebound was evident in all 3 patients by both clinical assessment and inertial measurement unit data, peaking by the latter at Tp = 6.65 minutes after cessation of stimulation. Using features extracted from neural data, linear regression was applied to predict tremor severity, with $R_{avg{\text{ }}}^2 = 0.82$. These results strongly suggest that rebound effect and the additional information made available by rebound effect should be considered and exploited when designing novel aDBS systems.

A tablet- and mobile-based application for remote diagnosis and analysis of movement disorder symptoms.

One significant hindrance to effective diagnosis of movement disorders (MDs) and analysis of their progression is the requirement for patients to conduct tests in the presence of a clinician. Here is presented a pilot study for diagnosis of essential tremor (ET), the world's most common MD, through analysis of a tablet- or mobile-based drawing task that may be selected at will, with the spiral- and line-drawing tasks of the Fahn-Tolosa-Marin tremor rating scale serving as our task in this work. This system replaces the need for pen-and-paper drawing tests while permitting advanced quantitative analysis of drawing smoothness, pressure applied, and other measures. Data is securely recorded and stored in the cloud, from which all analysis was conducted remotely. This will enable longitudinal analysis of patient disease progression without the need for excessive clinical visits. Several features were extracted and recursive feature elimination applied to rank the features' individual contribution to our classifier. Maximum cross-validated classification accuracy on a preliminary sample set was 98.3%. Future work will involve collecting healthy subject data from an age-controlled population and extending this diagnostic application to additional conditions, as well as incorporating regression-based symptom severity analysis. This highly promising new technology has the potential to substantially alleviate the demands placed on both clinicians and patients by bringing MD treatment more into line with the era of personalized medicine.

Integrating Brain Implants With Local and Distributed Computing Devices: A Next Generation Epilepsy Management System.

Brain stimulation has emerged as an effective treatment for a wide range of neurological and psychiatric diseases. Parkinson's disease, epilepsy, and essential tremor have FDA indications for electrical brain stimulation using intracranially implanted electrodes. Interfacing implantable brain devices with local and cloud computing resources have the potential to improve electrical stimulation efficacy, disease tracking, and management. Epilepsy, in particular, is a neurological disease that might benefit from the integration of brain implants with off-the-body computing for tracking disease and therapy. Recent clinical trials have demonstrated seizure forecasting, seizure detection, and therapeutic electrical stimulation in patients with drug-resistant focal epilepsy. In this paper, we describe a next-generation epilepsy management system that integrates local handheld and cloud-computing resources wirelessly coupled to an implanted device with embedded payloads (sensors, intracranial EEG telemetry, electrical stimulation, classifiers, and control policy implementation). The handheld device and cloud computing resources can provide a seamless interface between patients and physicians, and realtime intracranial EEG can be used to classify brain state (wake/sleep, preseizure, and seizure), implement control policies for electrical stimulation, and track patient health. This system creates a flexible platform in which low demand analytics requiring fast response times are embedded in the implanted device and more complex algorithms are implemented in offthebody local and distributed cloud computing environments. The system enables tracking and management of epileptic neural networks operating over time scales ranging from milliseconds to months.

Cortical Brain-Computer Interface for Closed-Loop Deep Brain Stimulation.

Essential tremor is the most common neurological movement disorder. This progressive disease causes uncontrollable rhythmic motions-most often affecting the patient'sdominant upper extremity-thatoccur during volitional movement and make it difficult for the patient to perform everyday tasks. Medication may also become ineffective as the disorder progresses. For many patients, deep brain stimulation (DBS) of the thalamus is an effective means of treating this condition when medication fails. In current use, however, clinicians set the patient's stimulator to apply stimulation at all times-whether it is needed or not. This practice leads to excess power use, and more rapid depletion of batteries that require surgical replacement. In this paper, for the first time, neural sensing of movement (using chronically implanted cortical electrodes) is used to enable or disable stimulation for tremor. Therapeutic stimulation is delivered onlywhen the patient is actively using their effected limb, thereby reducing the total stimulation applied, and potentially extending the lifetime of surgically implanted batteries. This paper, which involves both implanted and external subsystems, paves the way for fully-implanted closed-loop DBS in the future.

Chronic electrocorticography for sensing movement intention and closed-loop deep brain stimulation with wearable sensors in an essential tremor patient.

Deep brain stimulation (DBS) has become a widespread and valuable treatment for patients with movement disorders such as essential tremor (ET). However, current DBS treatment constantly delivers stimulation in an open loop, which can be inefficient. Closing the loop with sensors to provide feedback may increase power efficiency and reduce side effects for patients. New implantable neuromodulation platforms, such as the Medtronic Activa PC+S DBS system, offer important data sources by providing chronic neural sensing capabilities and a means of investigating dynamic stimulation based on symptom measurements. The authors implanted in a single patient with ET an Activa PC+S system, a cortical strip of electrodes on the hand sensorimotor cortex, and therapeutic electrodes in the ventral intermediate nucleus of the thalamus. In this paper they describe the effectiveness of the platform when sensing cortical movement intentions while the patient actually performed and imagined performing movements. Additionally, they demonstrate dynamic closed-loop DBS based on several wearable sensor measurements of tremor intensity.

Towards first-principles molecular design of liquid crystal-based chemoresponsive systems.

Nematic liquid crystals make promising chemoresponsive systems, but their development is currently limited by extensive experimental screening. Here we report a computational model to understand and predict orientational changes of surface-anchored nematic liquid crystals in response to chemical stimuli. In particular, we use first-principles calculations to evaluate the binding energies of benzonitrile, a model for 4'-pentyl-4-biphenylcarbonitrile, and dimethyl methylphosphonate to metal cation models representing the substrate chemical sensing surface. We find a correlation between these quantities and the experimental response time useful for predicting the response time of cation-liquid crystal combinations. Consideration of charge donation from chemical species in the surface environment is critical for obtaining agreement between theory and experiment. Our model may be extended to the design of improved chemoresponsive liquid crystals for selectively detecting other chemicals of practical interest by choosing appropriate combinations of metal cations with liquid crystals of suitable molecular structure.

Atomic layer-by-layer deposition of platinum on palladium octahedra for enhanced catalysts toward the oxygen reduction reaction.

We systematically evaluated two different approaches to the syntheses of Pd@PtnL (n = 2-5) core-shell octahedra. We initially prepared the core-shell octahedra using a polyol-based route by titrating a Pt(IV) precursor into the growth solution containing Pd octahedral seeds at 200 °C through the use of a syringe pump. The number of Pt atomic layers could be precisely controlled from two to five by increasing the volume of the precursor solution while fixing the amount of seeds. We then demonstrated the synthesis of Pd@PtnL octahedra using a water-based route at 95 °C through the one-shot injection of a Pt(II) precursor. Due to the large difference in reaction temperature, the Pd@PtnL octahedra obtained via the water-based route showed sharper corners than their counterparts obtained through the polyol-based route. When compared to a commercial Pt/C catalyst based upon 3.2 nm Pt particles, the Pd@PtnL octahedra prepared using both methods showed similar remarkable enhancement in terms of activity (both specific and mass) and durability toward the oxygen reduction reaction. Calculations based upon periodic, self-consistent density functional theory suggested that the enhancement in specific activity for the Pd@PtnL octahedra could be attributed to the destabilization of OH on their PtnL*/Pd(111) surface relative to the {111} and {100} facets exposed on the surface of Pt/C. The destabilization of OH facilitates its hydrogenation, which was found to be the rate-limiting step of the oxygen reduction reaction on all these surfaces.