The effects of model composition design choices on high-fidelity simulations of motoneuron recruitment and firing behaviors

Motoneuron excitability dysfunction in ALS: Pseudo-mystery or authentic conundrum?

In amyotrophic lateral sclerosis (ALS), abnormalities in motoneuronal excitability are seen in early pathogenesis and throughout disease progression. Fully understanding motoneuron excitability dysfunction may lead to more effective treatments. Yet decades of research have not produced consensus on the nature, role or underlying mechanisms of motoneuron excitability dysfunction in ALS. For example, contrary to Ca excitotoxicity theory, predictions of motoneuronal hyper-excitability, normal and hypo-excitability have also been seen at various disease stages and in multiple ALS lines. Accordingly, motoneuron excitability dysfunction in ALS is a disputed topic in the field. Specifically, the form (hyper, hypo or unchanged) and what role excitability dysfunction plays in the disease (pathogenic or downstream of other pathologies; neuroprotective or detrimental) are currently unclear. Although several motoneuron properties that determine cellular excitability change in the disease, some of these changes are pro-excitable, whereas others are anti-excitable, making dynamic fluctuations in overall 'net' excitability highly probable. Because various studies assess excitability via differing methods and at differing disease stages, the conflicting reports in the literature are not surprising. Hence, the overarching process of excitability degradation and motoneuron degeneration is not fully understood. Consequently, the discrepancies on motoneuron excitability dysfunction in the literature represent a substantial barrier to our understanding of the disease. Emerging studies suggest that biological variables, variations in experimental protocols, issues of rigor and sampling/analysis strategies are key factors that may underlie conflicting data in the literature. This review highlights potential confounding factors for researchers to consider and also offers ideas on avoiding pitfalls and improving robustness of data.

In-silico development and assessment of a Kalman filter motor decoder for prosthetic hand control

Up to 50% of amputees abandon their prostheses, partly due to rapid degradation of the control systems, which require frequent recalibration. The goal of this study was to develop a Kalman filter-based approach to decoding motoneuron activity to identify movement kinematics and thereby provide stable, long-term, accurate, real-time decoding. The Kalman filter-based decoder was examined via biologically varied datasets generated from a high-fidelity computational model of the spinal motoneuron pool. The estimated movement kinematics controlled a simulated MuJoCo prosthetic hand. This clear-box approach showed successful estimation of hand movements under eight varied physiological conditions with no retraining. The mean correlation coefficient of 0.98 and mean normalized root mean square error of 0.06 over these eight datasets provide proof of concept that this decoder would improve long-term integrity of performance while performing new, untrained movements. Additionally, the decoder operated in real-time (~0.3 ms). Further results include robust performance of the Kalman filter when re-trained to more severe post-amputation limitations in the type and number of motoneurons remaining. An additional analysis shows that the decoder achieves better accuracy when using the firing of individual motoneurons as input, compared to using aggregate pool firing. Moreover, the decoder demonstrated robustness to noise affecting both the trained decoder parameters and the decoded motoneuron activity. These results demonstrate the utility of a proof of concept Kalman filter decoder that can support prosthetics' control systems to maintain accurate and stable real-time movement performance.

Adaptive Neural Decoder for Prosthetic Hand Control

The overarching goal was to resolve a major barrier to real-life prosthesis usability-the rapid degradation of prosthesis control systems, which require frequent recalibrations. Specifically, we sought to develop and test a motor decoder that provides (1) highly accurate, real-time movement response, and (2) unprecedented adaptability to dynamic changes in the amputee's biological state, thereby supporting long-term integrity of control performance with few recalibrations. To achieve that, an adaptive motor decoder was designed to auto-switch between algorithms in real-time. The decoder detects the initial aggregate motoneuron spiking activity from the motor pool, then engages the optimal parameter settings for decoding the motoneuron spiking activity in that particular state. "Clear-box" testing of decoder performance under varied physiological conditions and post-amputation complications was conducted by comparing the movement output of a simulated prosthetic hand as driven by the decoded signal vs. as driven by the actual signal. Pearson's correlation coefficient and Normalized Root Mean Square Error were used to quantify the accuracy of the decoder's output. Our results show that the decoder algorithm extracted the features of the intended movement and drove the simulated prosthetic hand accurately with real-time performance (<10 ms) (Pearson's correlation coefficient >0.98 to >0.99 and Normalized Root Mean Square Error <13-5%). Further, the decoder robustly decoded the spiking activity of multi-speed inputs, inputs generated from reversed motoneuron recruitment, and inputs reflecting substantial biological heterogeneity of motoneuron properties, also in real-time. As the amputee's neuromodulatory state changes throughout the day and the electrical properties and ratio of slower vs. faster motoneurons shift over time post-amputation, the motor decoder presented here adapts to such changes in real-time and is thus expected to greatly enhance and extend the usability of prostheses.

A Classification Approach to Recognize the Firing of Spinal Motoneurons in Amyotrophic Lateral Sclerosis

Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disease that affects the nervous system causing muscle weakness, paralysis, leading to death. Given that abnormalities in spinal motoneuron (MN) excitability begin long before symptoms manifest, developing an approach that could recognize fluctuations in MN firing could help in early diagnosis of ALS. This paper introduces a machine learning approach to discriminate between ALS and normal MN firing. The approach is based on two electrophysiological markers; namely, spiking latency and the spike-triggered average signal. The method is examined using data generated from a computational model under systematic variation of MN properties. Such variations mimic the differential dynamic changes in cellular properties that different MN types experience during ALS progression. Our results demonstrate the ability of the approach to accurately recognize ALS firing patterns across the spectrum of examined variations in MN properties.Clinical Relevance- These results represent a proof of concept for using the proposed machine-learning approach in early diagnosis of ALS.

Meta-analysis of biological variables' impact on spinal motoneuron electrophysiology data

Experimental, methodological, and biological variables must be accounted for statistically to maximize accuracy and comparability of published neuroscience data. However, accounting for all variables is nigh impossible. Thus we aimed to identify particularly influential variables within published neurological data, from cat, rat, and mouse studies, via a robust statistical process. Our goal was to develop tools to improve rigor in the collection and analysis of data. We strictly constrained experimental and methodological variables and then assessed four key biological variables within motoneuron research: species, age, sex, and cell type. We quantified intraexperimental and interexperimental variances in 11 commonly reported electrophysiological properties of spinal motoneurons. We first assessed variances without accounting for biological variables and then reassessed them while accounting for all four variables. We next assessed variances with all possible combinations of these four variables. We concluded that some motoneuron properties have low intraexperimental, but high interexperimental, variance; that individual motoneuron properties are impacted differently by biological variables; and that some unexplained variances still remain. We report here the optimal combinations of biological variables to reduce interexperimental variance for all 11 parameters. We also rank each parameter by intra- and interexperimental consistency. We expect these results to assist with design of experimental and analytical methods, and to support accuracy in simulations. Furthermore, although demonstrated on spinal motoneuron electrophysiology literature, our approach is applicable to biological data from all fields of neuroscience. This approach represents an important aid to experimental design, comparison of reported data, and reduction of unexplained variance in neuroscience data.NEW & NOTEWORTHY Our meta-analysis shows the impact of species, age, sex, and cell type on lumbosacral motoneuron electrophysiological properties by thoroughly quantifying variances across literature for the first time. We quantify the variances of 11 motoneuron properties with consideration of biological variables, thus providing specific insights for motoneuron modelers and experimenters, and providing a general methodological template for the quantification of variance in neurological data with the consideration of any experimental, methodological, or biological variables of interest.

Multiscale modeling of the neuromuscular system: Coupling neurophysiology and skeletal muscle mechanics

Mathematical models and computer simulations have the great potential to substantially increase our understanding of the biophysical behavior of the neuromuscular system. This, however, requires detailed multiscale, and multiphysics models. Once validated, such models allow systematic in silico investigations that are not necessarily feasible within experiments and, therefore, have the ability to provide valuable insights into the complex interrelations within the healthy system and for pathological conditions. Most of the existing models focus on individual parts of the neuromuscular system and do not consider the neuromuscular system as an integrated physiological system. Hence, the aim of this advanced review is to facilitate the prospective development of detailed biophysical models of the entire neuromuscular system. For this purpose, this review is subdivided into three parts. The first part introduces the key anatomical and physiological aspects of the healthy neuromuscular system necessary for modeling the neuromuscular system. The second part provides an overview on state-of-the-art modeling approaches representing all major components of the neuromuscular system on different time and length scales. Within the last part, a specific multiscale neuromuscular system model is introduced. The integrated system model combines existing models of the motor neuron pool, of the sensory system and of a multiscale model describing the mechanical behavior of skeletal muscles. Since many sub-models are based on strictly biophysical modeling approaches, it closely represents the underlying physiological system and thus could be employed as starting point for further improvements and future developments. This article is categorized under: Physiology > Mammalian Physiology in Health and Disease Analytical and Computational Methods > Computational Methods Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models.