A blended neurostimulation protocol to delineate cortico-muscular and spino-muscular dynamics following neuroplastic adaptation.

In this paper we propose a novel neurostimulation protocol that provides an intervention-based assessment to distinguish the contributions of different motor control networks in the cortico-spinal system. Specifically, we use a combination of non-invasive brain stimulation and neuromuscular stimulation to probe neuromuscular system behavior with targeted impulse-response system identification. In this protocol, we use an in-house developed human-machine interface (HMI) for an isotonic wrist movement task, where the user controls a cursor on-screen. During the task, we generate unique motor evoked potentials based on triggered cortical or spinal level perturbations. Externally applied brain-level perturbations are triggered through TMS to cause wrist flexion/extension during the volitional task. The resultant contraction output and related reflex responses are measured by the HMI. These movements also include neuromodulation in the excitability of the brain-muscle pathway via transcranial direct current stimulation. Colloquially, spinal-level perturbations are triggered through skin-surface neuromuscular stimulation of the wrist muscles. The resultant brain-muscle and spinal-muscle pathways perturbed by the TMS and NMES, respectively, demonstrate temporal and spatial differences as manifested through the human-machine interface. This then provides a template to measure the specific neural outcomes of the movement tasks, and in decoding differences in the contribution of cortical- (long-latency) and spinal-level (short-latency) motor control. This protocol is part of the development of a diagnostic tool that can be used to better understand how interaction between cortical and spinal motor centers changes with learning, or injury such as that experienced following stroke.

Rate of change in longitudinal EMG indicates time course of an individual's neuromuscular adaptation in resistance-based muscle training.

An individual's long-term neuromuscular adaptation can be measured through time-domain analyses of surface electromyograms (EMG) in regular resistance-based training. The perceived changes in recruitment, such as those measured during muscle fatigue, can subsequently prolong the recovery time in rehabilitation applications. Thus, by developing quantifiable methods for measuring neuromuscular adaptation, adjuvant treatments applied during neurorehabilitation can be improved to reduce recovery times and to increase patient quality of care. This study demonstrates a novel time-domain analysis of long-term changes in EMG captured neuromuscular activity that we aim to use to develop a quantified performance metric for muscle-based intervention training and optimization of an individual. We measure EMG of endurance and hypertrophy-based resistance exercises of healthy participants over 100 days to identify trends in long-term neuromuscular adaptation. Particularly, we show that the rate of EMG amplitude increase (motor recruitment) is dependent on the training modality of an individual. Particularly, EMG decreases over time with repetitive training - but the rate of decrease is different in hypertrophy, endurance, and control exercises. We found that the EMG peak contraction decreases across all subjects, on average, by 8.23 dB during hypertrophy exercise and 10.09 dB for endurance exercises over 100 days of training, while control participants showed negligible change. This represents approximately 2 dB difference EMG activity when comparing endurance and hypertrophy exercises, and >8 dB change when comparing to our control cases. As such, we show that the slope of the long-term EMG activity is related to the resistance-based exercise. We believe this can be used to identify person-specific performance metrics, and to create optimized interventions using a measured performance baseline of an individual.

Neural Net Modeling of Checkpoint Inhibitor Related Myocarditis and Steroid Response.

Background: Serious but rare side effects associated with immunotherapy pose a difficult problem for regulators and practitioners. Immune checkpoint inhibitors (ICIs) have come into widespread use in oncology in recent years and are associated with rare cardiotoxicity, including potentially fatal myocarditis. To date, no comprehensive model of myocarditis progression and outcomes integrating time-series based laboratory and clinical signals has been constructed. In this paper, we describe a time-series neural net (NN) model of ICI-related myocarditis derived using supervised machine learning. Methods: We extracted and modeled data from electronic medical records of ICI-treated patients who had an elevation in their troponin. All data collection was performed using an electronic case report form, with approximately 300 variables collected on as many occasions as available, yielding 6000 data elements per patient over their clinical course. Key variables were scored 0-5 and sequential assessments were used to construct the model. The NN model was developed in MatLab and applied to analyze the time course and outcomes of treatments. Results: We identified 23 patients who had troponin elevations related to their ICI therapy, 15 of whom had ICI-related myocarditis, while the remaining 8 patients on ICIs had other causes for troponin elevation, such as myocardial infarction. Our model showed that troponin was the most predictive biomarker of myocarditis, in line with prior studies. Our model also identified early and aggressive use of steroid treatment as a major determinant of survival for cases of grade 3 or 4 ICI-related myocarditis. Conclusion: Our study shows that a supervised learning NN can be used to model rare events such as ICI-related myocarditis and thus provide clinical insight into drivers of progression and treatment outcomes. These findings direct attention to early detection biomarkers and clinical symptoms as the best means of implementing early and potentially life-saving steroid treatment.

Clinical characteristics, time course, treatment and outcomes of patients with immune checkpoint inhibitor-associated myocarditis.

BACKGROUND: Immune checkpoint inhibitors (ICI) have emerged as a front-line therapy for a variety of solid tumors. With the widespread use of these agents, immune-associated toxicities are increasingly being recognized, including fatal myocarditis. There are limited data on the outcomes and prognostic utility of biomarkers associated with ICI-associated myocarditis. Our objective was to examine the associations between clinical biomarkers of cardiomyocyte damage and mortality in patients with cancer treated with ICIs. METHODS: We retrospectively studied 23 patients who developed symptomatic and asymptomatic troponin elevations while receiving ICI therapy at a National Cancer Institute-designated comprehensive cancer center. We obtained serial ECGs, troponin I, and creatine kinase-MD (CK-MB), in addition to other conventional clinical biomarkers, and compared covariates between survivors and non-survivors. RESULTS: Among patients with myocarditis, higher troponin I (p=0.037) and CK-MB (p=0.034) levels on presentation correlated with progression to severe myocarditis. Higher troponin I (p=0.016), CK (p=0.013), and CK-MB (p=0.034) levels were associated with increased mortality, while the presence of advanced atrioventricular block on presentation (p=0.088) trended toward increased mortality. Weekly troponin monitoring lead to earlier hospitalization for potential myocarditis (p=0.022) and was associated with decreased time to steroid initiation (p=0.053) and improved outcomes. CONCLUSIONS: Routine troponin surveillance may be helpful in predicting mortality in ICI-treated patients with cancer in the early phase of ICI therapy initiation. Early detection of troponin elevation is associated with earlier intervention and improved outcomes in ICI-associated myocarditis. The recommended assessment and diagnostic studies guiding treatment decisions are presented.

Systems Analysis of Human Visuo-Myoelectric Control Facilitated by Anodal Transcranial Direct Current Stimulation in Healthy Humans.

Induction of neuroplasticity by transcranial direct current stimulation (tDCS) applied to the primary motor cortex facilitates motor learning of the upper extremities in healthy humans. The impact of tDCS on lower limb functions has not been studied extensively so far. In this study, we applied a system identification approach to investigate the impact of anodal transcranial direct current stimulation of the leg area of the motor cortex via the human visuo-myoelectric controller. The visuo-myoelectric reaching task (VMT) involves ballistic muscle contraction after a visual cue. We applied a black box approach using a linear ARX (Auto-regressive with eXogenous input) model for a visuomotor myoelectric reaching task. We found that a 20th order finite impulse response (FIR) model captured the TARGET (single input)-CURSOR (single output) dynamics during a VMT. The 20th order FIR model was investigated based on gain/phase margin analysis, which showed a significant (p < 0.01) effect of anodal tDCS on the gain margin of the VMT system. Also, response latency and the corticomuscular coherence (CMC) time delay were affected (p < 0.05) by anodal tDCS when compared to sham tDCS. Furthermore, gray box simulation results from a Simplified Spinal-Like Controller (SSLC) model demonstrated that the input-output function for motor evoked potentials (MEP) played an essential role in increasing muscle activation levels and response time improvement post-tDCS when compared to pre-tDCS baseline performance. This computational approach can be used to simulate the behavior of the neuromuscular controller during VMT to elucidate the effects of adjuvant treatment with tDCS.

An integrated epidemiological and neural net model of the warfarin effect in managed care patients.

INTRODUCTION: Risk assessment tools are utilized to estimate the risk for stroke and need of anticoagulation therapy for patients with atrial fibrillation (AF). These risk stratification scores are limited by the information inputted into them and a reliance on time-independent variables. The objective of this study was to develop a time-dependent neural net model to identify AF populations at high risk of poor clinical outcomes and evaluate the discriminatory ability of the model in a managed care population. METHODS: We performed a longitudinal, cohort study within a health-maintenance organization from 1997 to 2008. Participants were identified with incident AF irrespective of warfarin status and followed through their duration within the database. Three clinical outcome measures were evaluated including stroke, myocardial infarction, and hemorrhage. A neural net model was developed to identify patients at high risk of clinical events and defined to be an "enriched" patient. The model defines the enrichment based on the top 10 minimum mean square error output parameters that describe the three clinical outcomes. Cox proportional hazard models were utilized to evaluate the outcome measures. RESULTS: Among 285 patients, the mean age was 74±12 years with a mean follow-up of 4.3±2.6 years, and 154 (54%) were treated with warfarin. After propensity score adjustment, warfarin use was associated with a slightly increased risk of adverse outcomes (including stroke, myocardial infarction, and hemorrhage), though it did not attain statistical significance (adjusted hazard ratio [aHR] =1.22; 95% confidence interval [CI] 0.75-1.97; p=0.42). Within the neural net model, subjects at high risk of adverse outcomes were identified and labeled as "enriched." Following propensity score adjustment, enriched subjects were associated with an 81% higher risk of adverse outcomes as compared to nonenriched subjects (aHR=1.81; 95% CI, 1.15-2.88; p=0.01). CONCLUSION: Enrichment methodology improves the statistical discrimination of meaningful endpoints when used in a health records-based analysis.

Efferent Feedback in a Spinal-Like Controller: Reaching With Perturbations.

We use simulations of a controller that adopts a spinal-like network topology for goal-oriented reaching and assess its sensitivity to the dynamics of internal elements that allow context-independent performance. Such internal elements are often referred to as inverse or forward models of the periphery dynamics, depending on the proposed controller theory. Here, the "models" are used in a forward implementation, and we evaluate how the controller's performance would be affected by the nature of the model. For each point-to-point reaching motion experiment, we use forms of internal "efference models" (e.g., full mathematical representations of peripheral dynamics, simple spindle feedback, etc.) driven by motor reafference, then compare hand trajectories and hand path speeds in the presence or absence of external perturbations. It is demonstrated that a simple velocity-based model reduced the effects of dynamic perturbations by as much as 66%. In addition, the 2D hand trajectories varied from a biological reference by only 0.05 cm. Thus, the controller facilitated biological like motions while providing response to dynamic events which are omitted in earlier biomimetic controllers. This research suggests that these spinal-like systems are robust and tunable via gain-fields without the need of context dependent pre-planning.

An adaptive spinal-like controller: tunable biomimetic behavior for a robotic limb.

BACKGROUND: Spinal-like regulators have recently been shown to support complex behavioral patterns during volitional goal-oriented reaching paradigms. We use an interpretation of the adaptive spinal-like controller as inspiration for the development of a controller for a robotic limb. It will be demonstrated that a simulated robot arm with linear actuators can achieve biological-like limb movements. In addition, it will be shown that programmability in the regulator enables independent spatial and temporal changes to be defined for movement tasks, downstream of central commands using sensory stimuli. The adaptive spinal-like controller is the first to demonstrate such behavior for complex motor behaviors in multi-joint limb movements. METHODS: The controller is evaluated using a simulated robotic apparatus and three goal-oriented reaching paradigms: 1) shaping of trajectory profiles during reaching; 2) sensitivity of trajectories to sudden perturbations; 3) reaching to a moving target. The experiments were designed to highlight complex motor tasks that are omitted in earlier studies, and important for the development of improved artificial limb control. RESULTS: In all three cases the controller was able to reach the targets without a priori planning of end-point or segmental motor trajectories. Instead, trajectory spatio-temporal dynamics evolve from properties of the controller architecture using the spatial error (vector distance to goal). Results show that curvature amplitude in hand trajectory paths are reduced by as much as 98% using simple gain scaling techniques, while adaptive network behavior allows the regulator to successfully adapt to perturbations and track a moving target. An important observation for this study is that all motions resemble human-like movements with non-linear muscles and complex joint mechanics. CONCLUSIONS: The controller shows that it can adapt to various behavioral contexts which are not included in previous biomimetic studies. The research supplements an earlier study by examining the tunability of the spinal-like controller for complex reaching tasks. This work is a step toward building more robust controllers for powered artificial limbs.

A Simplified Spinal-Like Controller Facilitates Muscle Synergies and Robust Reaching Motions.

We develop an adaptive controller for multi-joint, multi-muscle arm movements based on simplified spinal-like circuits found in the periphery, muscle synergies, and interpretations of gain-field projections from reach related neurons in the Superior Colliculus. The resulting innovation provides a highly robust sensory based controller that can be adapted to systems which require multi-muscle co-ordination. It provides human-like responses during perturbations elicited either internally or by the environment and for simple point-to-point reaching. We simulate limb motion and EMGs in Simulink using Virtual Muscle models and a variety of paradigms, including motion with external perturbations, and varying levels of antagonist muscle co-contractions. The results show that the system can exhibit smooth coordinated motions, without explicit kinematic or dynamic planning even in the presence of perturbations. In addition, we show by varying the level of muscle co-contractions from 0% to 40%, that the effects of external perturbations on joint trajectories can be reduced by up to 42%. The improved controller design is novel providing robust behavior during dynamic events and an automatic adaptive response from sensory-integration.

Control of the lower leg during walking: a versatile model of the foot.

An improved biomechanical model has been implemented for use in gait simulations and functional electrical stimulation (FES). The novelty includes longitudinal bending of the foot which implements geometrical changes that appear "healthy-like" during the stance phase of gait. The simulation uses optimal control which minimizes the activation of flexor and extensor muscles, as well as the tracking error. Correspondingly, the results of the bending foot model, contrasted against a rigid foot biomechanical model, show that torques in the knee during foot contact were as much as 36.9 Nm (46.1%) lower, while muscle excitation was on average 6.1% lower. The simulation also shows that the shank angle of the bending foot model was virtually identical to that of the rigid foot model. However, this model's worth is most prevalent in its use for stance phase control in individuals who use multichannel FES. Notably, it can also be used for simulating the gait of individuals who lack ankle articulation and use an active transfemoral prosthesis.

Control aspects of motor neural prosthesis: sensory interface.

A neural prosthesis (NP) has two applications: permanent assistance of function, and temporary assistance that contributes to long-term recovery of function. Here, we address control issues for a therapeutic NP which uses surface electrodes. We suggest that the effective NP for therapy needs to implement rule-based control. Rule-based control relies on the triggering of preprogrammed sequences of electrical stimulation by the sensory signals. The sensory system in the therapeutic NP needs to be simple for installation, allow self-calibration, it must be robust, and sufficiently redundant in order to guarantee safe operation. The sensory signals need to generate control signals; hence, sensory fusion is needed. MEMS technology today provides sensors that fulfill the technical requirements (accelerometers, gyroscopes, force sensing resistors). Therefore, the task was to design a sensory signal processing method from the mentioned solid state sensors that would recognize phases during the gait cycle. This is necessary for the control of multi channel electrical stimulation. The sensory fusion consists of the following two phases: 1) estimation of vertical and horizontal components of the ground reaction force, center of pressure, and joint angles from the solid-state sensors, and 2) fusion of the estimated signals into a sequence of command signals. The first phase was realized by the use of artificial neural networks and adaptive neuro-fuzzy inference systems, while the second by the use of inductive learning described in our earlier work [1].