Symbiotic electroneural and musculoskeletal framework to encode proprioception via neurostimulation: ProprioStim

Abstract

Peripheral nerve stimulation in amputees achieved the restoration of touch, but not proprioception, which is critical in locomotion. A plausible reason is the lack of means to artificially replicate the complex activity of proprioceptors. To uncover this, we coupled neuromuscular models from ten subjects and nerve histologies from two implanted amputees to develop ProprioStim: a framework to encode proprioception by electrical evoking neural activity in close agreement with natural proprioceptive activity. We demonstrated its feasibility through non-invasive stimulation on seven healthy subjects comparing it with standard linear charge encoding. Results showed that ProprioStim multichannel stimulation was felt more natural, and hold promises for increasing accuracy in knee angle tracking, especially in future implantable solutions. Additionally, we quantified the importance of realistic 3D-nerve models against extruded models previously adopted for further design and validation of novel neurostimulation encoding strategies. ProprioStim provides clear guidelines for the development of neurostimulation policies restoring natural proprioception.

Keywords: Behavioral neuroscience; Bioelectronics; Clinical neuroscience.

Graphical abstract

Figure 1

Graphical representation of the proposed methodologies pipeline (A) The Proprio-Neural Model (PNM) pipeline. Experimental data (joint angles, muscular activation, and microneurography) is used for the calibration of the modified Ia Prochazka muscle spindle transducer model on human data. Neuromusculoskeletal model (NMS) is used to estimate muscle fiber elongations and contraction velocities, that together with normalized EMG are used as input for the computation of the natural neural activity of Ia fibers through the calibrated PNM. (B) The Electro-Neural Model (ENM) pipeline. 3D Finite Element Method modeling is used to simulate electrical nerve implant in the sciatic nerve. 3D potential distribution generated by the active site electrical stimulation is calculated for the whole structure and interpolated on multiple Ia fibers path belonging to different population distributions in the nerve fascicles. Neuron compartmental models of the Ia fibers finally simulate the depolarization caused by extracellular stimulation generating electrically evoked neural activity. In red, the final encoding strategy for the proprioception encoding strategy. Minimization between the natural and the modeled electrically evoked Ia neural activity is used to select the active site, the injected charge level, and the frequency of the neurostimulation.

Figure 2

Experimental acquisition for Proprio-Neural Modeling simulations and Electro-Neural Modeling of the realistic nerve structures (A) Experimental set-up for motion acquisition during walking trials. The motion capture system acquired body markers on the subject walking on a sensorized treadmill. Computed joint kinematics from the motion capture data is then used through NMS modeling to predict muscle tendon unit kinematics. Finally, EMG muscle activity, muscle lengths, and elongation velocity are used to predict Ia afferent fiber natural activity from the modeled Ia muscle spindle transducers. (B) Histological images taken from three cross-sectional layers of the sciatic nerves from two transfemoral amputees and the corresponding nerve segmentations based on those cross-sections (shown on the right). On the bottom, the table shows the number of fascicles visible in the histological images. (C) A schematic view of the ENM. In Electrical modeling: a geometrical reconstruction of a curving fascicle and the complete nerve representation, based on the segmented cross-sections; the meshed nerve structure; the model of the nerve section with an implanted electrode and the electrical potentials map obtained during an active site stimulation. In Neural modeling: probability density function of fibers spatial density, based on fiber diameter and on subject age; an example of fiber distribution in the endo-fascicular space; the stimulation-driven activation of the Ia target population estimated through NEURON from the potentials produced by the FEM model, interpolated on the generated fiber paths.

Figure 3

Validation of PNM and ENM through experimental data (A) Experimental data from Vallbo’s research studies^,, on index flexion around the metacarpal-phalangeal joint is used for a reparameterization of Prochazka’s Ia fibers muscle spindle transducer model. The trials are subdivided randomly in training and validation sets. From the training set, joint angles are used to compute muscle fiber elongation and contraction velocities through NMS modeling; together with EMG activity they are used as input for the Prochazka model computation. RMSE minimization between the Ia predicted firing rate and experimentally recorded microneurography is performed to adapt model parameters to human data. Final validation of the modified model is performed on the validation set. See also Figure S1. (B) Four electrodes were implanted in each of the two amputees. The perceptual thresholds and location of the sensation within a month from implantation are hereby reported. (C) The modeled electrode placements on subject 1. (D) QQ-plot of experimental and model thresholds. (E) Histogram and boxplot of experimental and model thresholds. The range of injected charges associated with intrafascicular placements in the model is marked. (F) Boxplot of ratio of intrafascicular active sites in model and in experiment (estimated using the same charge range) and an example of the experimental results for one electrode applied on the best fitting modeled placement.

Figure 4

A comparison: 3D-nerve vs. extruded nerve model (A) Model computation information, with averaged values across three individual electrode positions for each subject. Computational times for the calculation of fiber recruitments were averaged across 1000 fibers. (B) Comparative representation of fascicles for the 3D-nerve and the extruded model (the former based on the three nerve level segmentations, the latter constructed by a simple extrusion from the middle cross-section) with a magnification of an exemplary fascicle for both subjects. (C) Visual representations of deviations of relative recruitments across 6 individual active site stimulations in one of the models. (D) On the left: the 3D-nerve and the extruded version of the nerve model of Subject 2 as an example, with 3 highlighted fascicles chosen for the electrode structure placements and the comparisons. On the right: boxplot of the mean absolute deviation (MAD) of relative recruitment at seven different cross-sectional levels (corresponding to the electrode position levels) averaged for both subjects.

Figure 5

Simulation of PNM during walking trials (A) Results of PNM simulations during two different walking velocities; the PNM is computed on medialis and lateralis gastrocnemius muscles of the right leg. The first two rows (Musculoskeletal Model Output) plot the NMS model estimations of muscles fiber lengths and fiber elongation velocity during a gait cycle. The third row displays the normalized EMG muscular activation (Experimental data) obtained from experimental recorded data. The final two rows (Muscle Spindle Output) instead show the predicted output of the natural Ia afferent fibers recruiting rate and mean firing rate for the two simulated muscles from the PNM. PNM generated fibers activity is reconducted to implemented fibers in the Electro-Neural model. (B) Multiple bundles are selected so that in each bundle two fascicles (one for each gastrocnemius muscles) respect appropriate dimensions and number of Ia fibers reported from literature. (C) For each combination of fascicles, fibers are divided in three groups based on fiber diameter; Primary fibers are subsequently divided in Ia, Ib, and alpha motor neurons (percentage according to literature findings). See also Figure S3. (D) Different dispositions of the fiber populations have been tested, the disposition of the populations can be clustered or random inside the 3D fascicular volume.

Figure 6

PNM-driven encoding stimulation in-silico simulations (A) A virtual subject is modeled after the previously acquired data of Subject 2. In each modeled electrode placement, stimulation of the most selective active sites for each identified potential gastrocnemius fascicle is encoded using two different types of strategies: standard linear encoding and novel PNM-driven encoding. Calibration is performed using linear interpolation between reported percived sensations and related stimulation amplitude: the obtained calibration curve is reported on the right pannel with standard deviation for repeated measures. (B) The recorded knee angle is used to modulate linearly the stimulation impulse charge between the minimal and maximal charge levels; stimulation impulse frequency is kept constant. The resulting time-varying charge encoding parameters are presented in the central block. On the right, Ia fiber stimulation-derived recruitments and mean firing rate are displayed and compared with the PNM estimated natural activity. (C) The PNM estimated natural Ia fibers recruitment is used to modulate linearly the stimulation impulse charge between the minimal and maximal charge levels; stimulation impulse frequency is instead obtained by dividing the estimated mean firing rate and the fibers recruitment. The resulting time-varying charge and frequency encoding parameters are presented in the central block. On the right, Ia fiber stimulation-derived recruitments and mean firing rate are displayed and compared with the PNM estimated natural activity. The presented plots and RMSE values refer to a single fascicle combination and fiber population disposition. Comprehensive results for all the simulations are displayed in Figure S2.

Figure 7

ProprioStim implemented via TENS (A) Seven subjects are set up with two channels TENS targeting the sciatic nerve on the popliteal fossa. The positions of the cathodes are tailored to each subject so to make the evoked sensation as much somatotopic as possible. Calibration curves are derived linearly interpolating the normalized perceived sensation and the related stimulation amplitude. (B and C) A knee ramp trajectory starting from flexed leg toward a defined target angle is designed and used to linearly modulate the stimulation charge while the frequency is kept constant at 20Hz. (C) A knee ramp trajectory starting from flexed leg toward a defined target angle is designed and is used as input trajectory for the neuromusculoskeletal model. The estimated fiber lengths and muscular activations are used to estimate with PNM recruitment and firing frequency of the Ia fibers of the lateral and medial gastrocnemius muscles. The dashed vertical lines in panels (B and C) mark the moment when the knee angle was measured. (D) Results of the force choice test (mean ± SD of percentage of preferences per subject) showed that the PNM-driven encoding was considered closer to the real sensation from the pool of subjects, and knee angle matching task showed that it resulted in a higher accuracy. See also Figures S4 and S5.

Figure 8

Future clinical trial overview for neuroprostheses in proprioceptive sensory restoration (A) Virtual subject wearing knee prosthesis with sensorized socket (EMG and IMU sensors) for real-time acquisition of physiological and kinematic data. (B) The data obtained from IMU and EMG sensors embedded in the prosthetic device allow the computation of a real-time NMS model, which predicts the MTU kinematics of muscles of interest (medial and lateral gastrocnemius). The acquired information is subsequently used as an input for the estimation of the natural Ia fibers recruitment and firing rate activity. This activity serves as a modulating variable for the subject-specific calibrated encoding strategy, which produces the real-time electrical stimulus parametrization. (C) The subject is implanted with multiple electrodes in the tibial branch of the sciatic nerve; IPG neural stimulator applies the appropriate stimulation to the relevant active site. (D) Biomimetic encoded electrical stimulation generates coherent Ia fibers activation with respect to natural fiber activity. Electrically driven Ia fibers activity is interpreted from the central nervous as proprioceptive feedback, allowing the subject to perceive the flexion/extension of the joint.