Interfacing the neural output of the spinal cord: robust and reliable longitudinal identification of motor neurons in humans

Fourteen healthy men volunteered to participate to this study, whose procedures and protocols were approved by the local Ethical Committee of the University of Rome 'Foro Italico' (approval no. 44 680) and complied with the standards set by the Declaration of Helsinki. Prior to their involvement, participants were thoroughly informed about the purpose of the study alongside with the experimental procedures and the potential side effects involved. Volunteers were non-smoking recreationally active individuals without any previous history of neuromuscular disorders and/or lower limb surgery. Participants' eligibility to the study and physical activity habits were assessed with a standard health survey and with the IPAQ (International Physical Activity Questionnaires) questionnaire [24]. Only volunteers with light to moderate aerobic physical activity up to a maximum of twice per week and not involved in any regular physical activity program were considered eligible to participate to the study. Because of personal reasons not pertaining to the study, two out of fourteen individuals withdrew, which resulted in a total of 12 participants (age: 25.1  ±  2.9 year; height: 1.78  ±  0.06 m; weight, 73.3  ±  8.0 kg; IPAQ: 2161  ±  1128 MET min · wk⁻¹) completing the study. A written informed consent was obtained by all participants prior to the beginning of the study.

The research was designed as a cross-sectional study characterized by three distinct laboratory visits over a period of seven weeks. Session one was dedicated to a familiarization with the experimental setup and testing protocol, involving the practice of maximal voluntary contractions (MVCs) and submaximal trapezoidal isometric contractions of ankle dorsiflexors. Session two, which was carried out three to five days after the familiarization, was dedicated to the main baseline assessment and included the concomitant recordings of ankle dorsiflexor forces exerted during maximal and submaximal isometric contractions and of high-density surface EMG (HDsEMG) signals from the tibialis anterior muscle of the dominant limb. Session three was held four weeks after session two and included the repetition of the same baseline measurements. Participants were thoroughly instructed to maintain their physical activity daily habits and to keep their diet unchanged throughout the four-week period of the study.

All the experimental sessions took place in the Integrated Exercise Physiology Laboratory (LIFE) of the Department of Movement, Human and Health Sciences of the University of Rome, Italy. All the experimental sessions (sessions 1-to-3) were conducted on the same customized ankle ergometer (see below).

Each testing session started with a brief standardized warm-up and was followed by the main task involving isometric maximal (MVCs) and submaximal (trapezoidal contractions) voluntary ankle dorsiflexions of the dominant foot (self-reported). Warm-up consisted of eight isometric dorsiflexions (4  ×  50%, 3  ×  70%, 1  ×  90% of self-perceived maximum) separated by 15–30 s of rest. After the warm-up, participants performed 3-to-4 maximal isometric voluntary contractions (MVCs) in order to determine the peak maximal voluntary force (MVF) of dorsiflexor muscles. Each MVC was separated by a recovery period of 30 s. During the MVC tasks, participants were instructed to 'push as hard as possible' for 3-to-5 s and were verbally encouraged to exert peak force within each trial. A feedback of the peak force achieved during each previous MVC was provided through a horizontal bar displayed on a monitor. After filtering the force with a 20 Hz low-pass filter, ankle dorsiflexors' MVF, corresponded to the highest force recorded among any MVC contractions, was used as a reference to establish the intensity of the submaximal contractions.

Five minutes after the completion of the MVCs, the participants performed a total of six submaximal isometric trapezoidal contractions at three force targets (2  ×  35%, 2  ×  50%, 2  ×  70% of MVF), with three to five min of rest in between. Each trapezoidal contraction was characterized by a linear increase in force up to a target level, maintenance of a steady state at the target force (10 s), and a linear decrease of force to the baseline values. The contraction speed during the ascending and descending phase of the trapezoidal contraction was kept constant at 5% MVF·s-1 in all contractions. As practiced in the familiarization session, participants had to match as precisely as possible a trapezoidal template displayed on a monitor placed at 1-m distance with a constant 88 x 88 px/%MVF visual gain. The muscle force expressed during each contraction was also provided on the monitor as visual biofeedback. These trials were randomized and for each participant, the same randomization order of the baseline measurement was maintained in the post assessment.

Participants were asked to avoid vigorous physical exercise (48 h) and caffeine consumption (24 h) before the main measurement sessions. Potential effect of diurnal variability in muscle contractility and hence force production were also taken into account by carrying out the two main measurement sessions at the same time of the day [25].

The experimental setup consisted of a custom stiff ankle ergometer (OT Bioelettronica, Turin, Italy), secured to an examination table by adjustable straps (see figure 1). The adjustability of the ergometer allowed a tailored regulation of its placement according to individual lower limb length. The individual experimental configuration was defined during the familiarization visit and re-adopted in the main experimental sessions. The volunteers were comfortably seated on the massage table, with the hips flexed at ~120° (180°  =  neutral position), the knees fully extended at ~180° (180°  =  neutral position) and the ankle of the dominant foot at ~100° of plantar flection (90°  =  neutral position). The ankle joint and the foot were tightly fastened to an adjustable foot plate by means of Velcro straps (~3 cm width) arranged on the distal third of metatarsals and on the foot dorsum, respectively. The foot plate was in turn connected in series with a calibrated force transducer (CCT Transducer s.a.s, Turin, Italy), which was positioned perpendicular to the plantar surface of the foot. In order to limit unnecessary movements, the dominant knee was also secured by a strap positioned above the patella. The contralateral leg (non-dominant) rested on the massage table.

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The analogue signal from the load cell was amplified (×200), sampled at 2048 Hz and converted to digital with an external 16-bit analogue-to-digital (A/D) converter (EMG-Quattrocento, OT Bioelettronica, Turin, Italy). The force signal was recorded with the software OTbiolab (OT Bioelettronica, Turin, Italy) and the force visual feedback was provided to the participants through a custom-made software (LabVIEW 8.0: National Instruments, Austin, TX, USA).

Surface EMG signals were detected from the dominant tibialis anterior muscle during submaximal isometric ankle-dorsiflexions. In particular, two high-density adhesive grids of 64 equally spaced electrodes (13 rows  ×  5 columns; gold-coated; diameter 1 mm; inter-electrode distance (IED) of 8 mm; OT Bioelettronica, Turin, Italy) were positioned on the muscle belly (1st grid) and on the distal portion (2nd) of TA muscle, respectively. The baseline noise in the EMG signals was set a threshold of 20 µV (average peak-to-peak amplitude over 6 s, monopolar signals).

Before placing the two high-density grids, an experienced operator identified through palpation the perimeter of the muscle and marked its profile with a surgical pen. A 16-electrodes dry array (silver bar 5 mm long; 1 mm thick; IED of 5 mm; OT Bioelettronica, Turin, Italy) was adopted to locate the distal innervation zone (IZ) of TA and to estimate the anatomical direction of muscle fibres. The linear array was moved over the muscle parallel to the lateral margin of the tibia and sensibly adjusted in order to clearly observe the point of inversion in propagation direction of action potentials (APs) along the column of electrodes [11, 22]. The estimation of anatomical direction of muscle fibres was assessed by an experienced operator who visually inspected a clear propagation of motor unit action potentials from the innervation zone to the tendon without significant changes of APs waveforms. Once identified, both the IZ and estimated direction of muscle fibres were marked with a surgical pen for optimal electrode placement and following this procedure, the skin was prepared (shaving, light skin abrasion and cleansing with 70% ethanol). In order to guarantee an optimal skin-electrode contact, the two adhesive grids were attached to the skin above the muscle with disposable bi-adhesive foam layers whose cavities, in correspondence with electrodes, were filled with conductive paste (SPES Medica, Salerno, Italy). The first grid was positioned over the distal portion of TA muscle with the first column of electrodes oriented to the direction of muscle fibres and the fourth row over the IZ. In order to cover most of TA proximal area, the second grid was positioned more proximally on the muscle belly. The boundaries of the HDsEMG grids was marked with a surgical pen. The participants were asked to mark these boundaries for the whole duration of the study.

Two reference electrodes, one for each grid, were placed on the tuberosity of the tibia (1st grid) and on the medial malleolus (2nd grid) of the dominant leg, respectively. The main ground electrode was placed on the styloid process of the ulna of the tested side.

The signals were recorded in monopolar derivation, amplified (×150), sampled at 2048 Hz, band-pass filtered (10–500 Hz) and converted to digital by means of a 16-bit multichannel amplifier (EMG-Quattrocento, OT Bioelettronica, Turin, Italy). HDsEMG signals were synchronized with force signal at source by the same acquisition system.

The recorded data were processed offline using custom-written Matlab programs (MATLAB R2018, MathWorks, Natick, MA, USA). Before being low-pass filtered with a fourth-order, zero-lag Butterworth filter (cut-off frequency 15 Hz), the force signal was converted to newtons (N) and the offset was removed by gravity compensation. Only one of the two ramp contractions at each force intensity was analyzed. The ramp contractions yielding the lowest deviation from the reference (force trajectory) signal was used for decomposition analysis. Monopolar HDsEMG signals were digitally band-pass filtered between 20–500 Hz using a second-order, zero-lag Butterworth filter, prior to signal decomposition. Then, they were decomposed into the activity of individual motor units with an algorithm based on convolutive blind source separation [23, 26]. Briefly, the HDsEMG signals consist of convolutive mixtures of motor neuron spike trains. The relative large dimension of the electrode grid allows for an accurate spatial sampling of the waveforms of the motor unit action potentials. It has been previously demonstrated that motor unit discharge times can be identified from these recordings for a broad range of forces [11, 17, 23]. The decomposition accuracy was first assessed using the pulse-to-noise ratio (PNR) and accordingly, only motor units with a PNR  >  30 dB and/or interspike intervals  <2 s were retained for further analysis [23, 27]. Therefore, all identified motor units were manually inspected by an experienced operator who only retained MUs characterized by a high PNR and a reliable discharge pattern. In some rare cases, the same motor unit was identified at two different force intensities. For these motor units, only the discharge timings of the motor unit identified at the lowest force level was maintained for further analysis. We then tested the decomposition accuracy with novel parameters, described in the following.

Since the decomposition algorithm identifies only motor unit discharge times, action potential waveforms for each identified motor unit (MUAPs) were estimated with the spike-triggered averaging technique [22, 28]. In particular, MUAP waveforms were extracted by averaging monopolar HDsEMG signals over 35 ms (MUAP duration) intervals, using the discharge times as triggers. The extracted 2D waveforms were used to track motor units across sessions. The tracking procedure the motor unit action potentials and then computes 2D cross-correlations between the spatial representations of the waveforms identified before and after the four weeks period of the study (figure 1).

The motor units with the highest 2D coefficient of correlation (R) were then visually inspected by an experienced investigator. The spike trigger average was performed in three distinct phases of the ramp contraction. Figure 1(E) shows one tracked motor unit discharge times with three distinct colors. These colors correspond to the ramp-up phase (red), plateau (blue), and ramp-down (green). The cross-correlation between 2D MUAPs extracted by spike triggered averaging in these three phases were compared across all possible conditions (figure 1(E)). Moreover, the MUAP was extracted also using random time instants corresponding to the discharge timings during the whole duration of the contraction (figure 1(E) purple dots) and for random time instants that did not correspond to the discharge timings identified by decomposition (figure 1(E) yellow diamonds). We then injected noise in the estimated motor unit discharge timings (figure 2(A)). A similar approach has been recently used for feline motor units [29]. The noise levels followed a gaussian distribution that had a standard deviation of 1, 3, 5, and 10 ms. It must be noted that these procedures are mainly related to the validation of the MUAP waveform and therefore physiological parameters such as rate coding, motor unit recruitment and derecruitment thresholds, are not taken into account in this analysis. We successively performed a physiological validation of the decomposition. For this purpose, we estimated the recruitment threshold of the motor neuron as the force value corresponding to the occurrence of the first MUAP. The de-recruitment threshold corresponded to the force value at the discharge of the last MUAP. The discharge rate was the inverse of the interspike interval and was estimated during the first ten MUAP at the plateau of the ramp contraction.

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We used the Shapiro-Wilk test to assess the normality of distribution of the data, before the comparisons of the MUAP waveforms and physiological parameters of the motor units. The assumption of sphericity was verified by the Mauchly's test and when the assumption was violated the Greenhouse-Geisser correction was applied. Most of the variables included in the study exhibited a normal distribution. Two-way repeated measure ANOVAs (contraction condition and noise level or motor unit location) were adopted to examine the effect of contraction intensity, noise level, and MUAPs during the different phases of the contraction. Non-parametric independent t-tests (Mann-Whitney U test) were used when the data was not normally distributed. The relationship before and after four weeks of the physiological motor unit variables was assessed by Pearson statistics at the individual subject level. The coefficient of determination (R) and mean standard deviation (±) is reported in the text. The P value was set at P  <  0.05.

We applied grids of electrodes that recorded the myoelectrical activity of the tibialis anterior muscle in two experimental sessions separated by four weeks. The contractions consisted of voluntary force modulation at varying intensities, from low (35% of the maximal voluntary contraction, MVC, force) to higher forces (70% MVC). The total number of identified motor neurons from the twelve subjects in the two experimental sessions was 1270 and ~30% of these motor neurons were tracked in both sessions (an average of 18.15  ±  3.14 motor units and 5.66  ±  1.72 for the tracked motor units per subject and contraction). The average number of identified motor units across subjects and for each contraction intensities for both the total and tracked motor units is shown in table 1.

Figure 1 shows an example of motor unit identification before and after four weeks (blue motor neuron spiking, figure 1(B)). In the two sessions, the EMG grids were placed approximately on the same position over the tibialis anterior and covered most of the surface of the muscle. The EMG decomposition identified motor unit action potentials by blind source separation [18, 26]. From the decoded discharge instants (figure 1(B)), we extracted the motor unit action potential waveforms by spike-triggered averaging [14]. The correlation (2D, row and column corresponding to the high-density grids) between all the combinations of the waveforms was used to track the same action potentials across sessions. Figure 1(C) shows an example of the discharge timings of eight (out of ~25) longitudinally tracked motor neurons over one month for a representative subject. In this example, the recruitment order, assessed by the timing of the first activation of the motor neurons, was the same before and after four weeks.

The analysis of accuracy was based on the extracted action potential waveforms. The assumption was that the decomposition identified the correct discharge times if they provided stable action potential waveforms when used as triggers for spike-triggered averaging the interference EMG. Therefore, we compared the estimated motor unit action potentials with those extracted by artificially perturbing the discharge times provided by decomposition. Figure 1(D) shows three motor unit action potentials in a 3D representation before and after four weeks. The 3D waveform corresponds to the shape of the action potential at a fixed time instant and it is possible to observe very small variance in the waveform pattern. The next step was to quantify the variability of these waveforms when changing the set of discharge timings used as trigger. For this purpose, we used a 2D cross-correlation function of the motor unit action potential. We compared the shape of the action potential during the ramp-up phase, the plateau, and the ramp-down phase of the contraction (red, blue, and green firings respectively in figure 1(E)). Moreover, we selected random motor unit discharge timings across the whole duration of the contraction for triggering the motor unit waveforms (purple dots figure 1(E(e)) and random instants of time within the average motor unit spike train window (yellow diamonds figure 1(E(e)). Successively, we performed 2D correlations between all possible combinations of the action potentials extracted in these five conditions. The motor units that showed a 2D correlation above 0.65 (when using the motor unit discharge timings across the whole contraction) were considered as similar. The confusion matrix for the individual phases of the ramp contractions are shown in figure 1(F). The 2D cross-correlation between the decomposed motor units showed high discriminability among the action potentials. This step confirms that each motor unit had a unique signature. Indeed, the average correlation for the tracked motor units was consistently higher than 0.6 for all subjects and contraction conditions (figures 1(G)–(M)). On the other hand, the average 2D correlation value was lower than 0.5 for all motor units, with an average of 0.34  ±  0.12 across subjects and contraction conditions. We then followed this procedure for all subjects and contraction conditions (figures 1(G)–(M)).

The random conditions consistently showed a 2D correlation that was eight-fold lower when compared with the spikes selected by the decomposition instead of randomly (figures 1(G)–(I)) and were independent of the location during the ramp contraction (P  >  0.05, figures 1(G)–(I) and of the force level (P  >  0.05 figures 1(G)–(I) and (M)).

We then added gaussian noise in the times of discharge of the tracked motor units at the different force levels (figures 1(J)–(L)). The noise standard deviation ranged from 1 ms to 10 ms, and the 2D cross-correlation was tested between all tracked motor units with different levels of noise. As little as 3 ms of standard deviation in the gaussian noise disrupted the correlation between the motor unit action potential before and after four weeks (figures 1(J)–(L)), and independently of the force level (figure 1(M)). We then tested the accuracy of decomposition by adding gaussian noise to all the pulse trains identified by decomposition and testing the changes in estimated motor unit action potential waveforms as a function of the added noise (figure 2). Across all motor units and subjects, with 5 ms standard deviation of the gaussian noise in the discharge times, the 3D action potential waveform was reduced in amplitude to the noise baseline of the EMG (figure 2(A)). These observations were consistent for all subjects and motor units (figures 2(A)–(D). There was an effect of contraction level in this 2D correlation analysis when all motor units were taken into account (figure 2(B), P  <  0.05), which is consistent with the increase in baseline activity (i.e. activity of motor units beside the triggered one) with increasing force. Figure 2(C) shows the confusion matrix for these five contraction conditions. Each motor unit action potential showed strong distinctiveness with the action potentials from other motor units. This observation was consistent across all subject and contraction intensities.

The above analysis indicates highly accurate decomposition of the EMG. We then further extended this validation by assessing the tracking over time of motor units. The longitudinal tracking revealed not only the possibility to follow the behavior of motor neurons in separate experimental sessions, but it also provided a further proof of validation of accuracy. Indeed, the likelihood of identifying the same motor unit action potentials in experimental sessions separated by a month, with independent decompositions of signals recorded with electrode replacement, is negligible unless the decomposition is highly accurate.