Coupling of neural oscillations is essential for the transmission of cortical motor commands to motoneuron pools through direct and indirect descending motor pathways. Most studies focus on iso-frequency coupling between brain and muscle activities, i.e., cortico-muscular coherence, which is thought to reflect motor command transmission in the mono-synaptic corticospinal pathway. Compared to this direct pathway, indirect corticobulbospinal motor pathways involve multiple intermediate synaptic connections via spinal interneurons. Neuronal processing of synaptic inputs can lead to modulation of inter-spike intervals which produces cross-frequency coupling. This theoretical study aims to evaluate the effect of the number of synaptic layers in descending pathways on the expression of cross-frequency coupling between supraspinal input and the cumulative output of the motoneuron pool using a computer simulation. We simulated descending pathways as various layers of interneurons with a terminal motoneuron pool using Hogdkin-Huxley styled neuron models. Both cross-and iso-frequency coupling between the supraspinal input and the motorneuron pool output were computed using a novel generalized coherence measure, i.e., n:m coherence. We found that the iso-frequency coupling is only dominant in the mono-synaptic corticospinal tract, while the cross-frequency coupling is dominant in multi-synaptic indirect motor pathways. Furthermore, simulations incorporating both mono-synaptic direct and multi-synaptic indirect descending pathways showed that increased reliance on a multi-synaptic indirect pathway over a mono-synaptic direct pathway enhances the dominance of cross-frequency coupling between the supraspinal input and the motorneuron pool output. These results provide the theoretical basis for future human subject study quantitatively assessing motor command transmission in indirect vs. direct pathways and its changes after neurological disorders such as unilateral brain injury.

Background: Stroke-related sensory and motor deficits often steal away the independent mobility and balance from stroke survivors. Often, this compels the stroke survivors to rely heavily on their non-paretic leg during weight shifting to execute activities of daily living (ADL), with reduced usage of the paretic leg. Increased reliance on non-paretic leg often leads to learned nonuse of the paretic leg. Therefore, it is necessary to measure the contribution of individual legs toward one's overall balance. In turn, techniques can be developed to condition the usage of both the legs during one's balance training, thereby encouraging the hemiplegic patients for increased use of their paretic leg. The aim of this study is to (1) develop a virtual reality (VR)-based balance training platform that can estimate the contribution of each leg during VR-based weight-shifting tasks in an individualized manner and (2) understand the implication of operant conditioning paradigm during balance training on the overall balance of hemiplegic stroke patients. Result: Twenty-nine hemiplegic patients participated in a single session of VR-based balance training. The participants maneuvered virtual objects in the virtual environment using two Wii Balance Boards that measured displacement in the center of pressure (CoP) due to each leg when one performed weight-shifting tasks. For operant conditioning, the weight distribution across both the legs was conditioned (during normal trial) to reward participants for increased usage of the paretic leg during the weight-shifting task. The participants were offered multiple levels of normal trials with intermediate catch trial (with equal weight distribution between both legs) in an individualized manner. The effect of operant conditioning during the normal trials was measured in the following catch trials. The participants showed significantly improved performance in the final catch trial compared to their initial catch trial task. Also, the enhancement in CoP displacement of the paretic leg was significant in the final catch trial compared to the initial catch trial. Conclusion: The developed system was able to encourage participants for improved usage of their paretic leg during weight-shifting tasks. Such an approach has the potential to address the issue of learned nonuse of the paretic leg in stroke patients.

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One of the most common types of models that helps us to understand neuron behavior is based on the Hodgkin–Huxley ion channel formulation (HH model). A major challenge with inferring parameters in HH models is non-uniqueness: many different sets of ion channel parameter values produce similar outputs for the same input stimulus. Such phenomena result in an objective function that exhibits multiple modes (i.e., multiple local minima). This non-uniqueness of local optimality poses challenges for parameter estimation with many algorithmic optimization techniques. HH models additionally have severe non-linearities resulting in further challenges for inferring parameters in an algorithmic fashion. To address these challenges with a tractable method in high-dimensional parameter spaces, we propose using a particular Markov chain Monte Carlo (MCMC) algorithm, which has the advantage of inferring parameters in a Bayesian framework. The Bayesian approach is designed to be suitable for multimodal solutions to inverse problems. We introduce and demonstrate the method using a three-channel HH model. We then focus on the inference of nine parameters in an eight-channel HH model, which we analyze in detail. We explore how the MCMC algorithm can uncover complex relationships between inferred parameters using five injected current levels. The MCMC method provides as a result a nine-dimensional posterior distribution, which we analyze visually with solution maps or landscapes of the possible parameter sets. The visualized solution maps show new complex structures of the multimodal posteriors, and they allow for selection of locally and globally optimal value sets, and they visually expose parameter sensitivities and regions of higher model robustness. We envision these solution maps as enabling experimentalists to improve the design of future experiments, increase scientific productivity and improve on model structure and ideation when the MCMC algorithm is applied to experimental data.