Electrical Somatosensory and Motor Thresholds across Pulse Widths: Characterizing Strength-Duration Properties and Nerve Excitability

Electrical Somatosensory and Motor Thresholds across Pulse Widths: Characterizing Strength-Duration Properties and Nerve Excitability | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Electrical Somatosensory and Motor Thresholds across Pulse Widths: Characterizing Strength-Duration Properties and Nerve Excitability Izarbe Ríos-Asín, Miguel Malo-Urriés, Jorge Pérez-Rey, Alejandro Lete, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9232031/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Quantitative assessment of peripheral sensorimotor function via surface electrical stimulation provides essential insights into axonal excitability; however, standardized stimulation parameters remain poorly defined. This study aimed to characterize the strength–duration (S–D) relationship of electrical sensory, motor, and pain thresholds (EST, EMT, and EPT) across a wide range of pulse widths to identify physiological stabilization plateaus and determine durations that optimize selective fiber recruitment. Thirty healthy volunteers underwent electrical threshold testing (ETT) on the forearm using a symmetrical biphasic current (100 Hz). Eleven pulse widths (20–650 µs) were evaluated in randomized order. Threshold transitions were modelled using a power function ( y = ax b ). Stabilization points were determined through consecutive pairwise comparisons, and discriminative capacity was assessed using effect-size analysis. All thresholds followed a characteristic hyperbolic decay consistent with fundamental axonal excitability properties. A clear stabilization toward the rheobase was identified: EST reached a physiological plateau at ∼150 µs, while EMT and EPT stabilized at ∼200 µs. Extreme pulse widths (20 and 650 µs) demonstrated the highest discriminative capacity between thresholds (η²ₚ = 0.936 and 0.921). These findings demonstrate that electrical thresholds follow a predictable neurophysiological pattern across pulse widths, and suggest that pulse width selection is critical for achieving target fiber selectivity, providing a reference framework for standardizing ETT protocols in both research and clinical diagnosis. Electrical threshold testing strength-duration electrical stimulation somatosensory function fiber recruitment quantitative sensory testing Figures Figure 1 INTRODUCTION Quantitative assessment of somatosensory and motor responses represents a fundamental approach to understanding the functioning of the peripheral nervous system. Conventional methods, such as Quantitative Sensory Testing (QST) and self-reported scales, primarily offer a subjective approach to assessing detection and pain sensitivity [ 1 , 2 ]. In contrast, objective instrumental electroneuromyography (ENMG) provides a more precise estimation of neural activity, minimizing examiner bias and contextual influences by directly recording both somatosensory and motor electrical responses of peripheral nerves and muscles [ 3 ]. In this context, transcutaneous electrical stimulation stands out as a pivotal and versatile tool, offering a non-invasive and reproducible method to evaluate quantitative indicators of neural function, encompassing both afferent and efferent pathways [ 4 ]. Electrical threshold testing (ETT) enables the precise configuration of various stimulation parameters, which directly influence the selectivity of nerve fiber recruitment based on their specific excitability characteristics [ 5 , 6 ], as well as muscle performance or fatigability [ 7 ], thus determining the resulting threshold values. Among these adjustable parameters, pulse width—the duration of the stimulation pulse measured in microseconds (µs)—is of particular relevance. According to the fundamental strength-duration relationship, shorter pulse widths require a higher current intensity to reach the given threshold level [ 6 , 8 ]. As pulse width increases, the required current intensity decreases and tends to approach the rheobase, defined as the minimum current needed to elicit a response with pulses of infinitely long duration [ 9 ]. Beyond these general properties, varying pulse widths might favour the depolarization of specific neural fiber types. Shorter pulse widths preferentially activate large-diameter, low-threshold axons (such as A-β fibers), whereas longer pulse widths tend to recruit smaller-diameter, higher-threshold fibers, including the A-δ fibers involved in nociception [ 10 , 11 ]. In contrast to this size-order recruitment, the activation of motor units by surface electrical stimulation appears to follow a non-selective and temporally synchrnous pattern [ 7 , 12 ], contrary to the previous idea that supported a recuitment order opposite to Henneman’s principle [ 13 ]. Nevertheless, it was indicated that greater pulse widths (> 600 µs) increased force production until the rheaobase was approached, while shorter pulses (< 100 µs) result in smaller M-waves and twitch forces [ 7 ]. Furthermore, the use of wide-pulse stimulation (up to 1 ms) may not only depolarize motor axons peripherally but also elicit the recruitment of spinal motoneurons via the sensory volley, thereby contributing to the development of central torque [ 14 ]. Despite the established theoretical framework of fiber selectivity, a lack of consensus persists regarding the optimal stimulation parameters required to standardize these measurements and ensure the most effective recruitment and specificity for each fiber type [ 7 , 15 ]. Previous research has highlighted that changes in responsiveness to electrical stimuli can be indicative of specific properties in neuromotor control and sensory processing [ 3 ]. Therefore, characterizing electrical thresholds across a wide range of pulse widths is essential to understand the underlying physiological mechanisms of axonal excitability and to improve the standarization of assessments with electrical stimulation [ 9 ]. Such characterization is not only of fundamental interest but also carries significant clinical weight, as it may enhance the early diagnosis of peripheral neuropathies and the monitoring of treatment effectiveness in conditions where sensorimotor function is altered [ 4 , 16 ]. Therefore, the main objective of this study is to compare and characterize somatosensory and motor electrical thresholds across a comprehensive range of pulse widths. Specifically, we aimed to identify the pulse width at which these thresholds reach a physiological plateau and to determine the duration that provides the greatest selectivity in nerve fiber activation. METHODS Study design . An observational, descriptive, cross-sectional, prospective study was designed. The study was approved by the Research Ethics Committee of X (X) (C.I. XXXX/XXX) on 29/05/2024 and received authorization for the processing of personal study data from the Data Protection Unit of the University of X (X), with reference number RAT XXXX-XXX. All participants signed an informed consent form in accordance with the Declaration of Helsinki [ 17 ]. Participants. The study consisted of volunteers with no history of systemic disease. To be eligible for the study, participants were required to be aged 18 years or older, to have the ability to communicate and to understand the tests performed and to provide written informed consent. Participants were excluded if they had a history of chronic disorders, including endocrine, neurological, psychiatric, urogenital, or musculoskeletal conditions, or had any other condition that could interfere with results. Sample Size Calculation . The sample size was estimated a priori using G*Power software (version 3.1, University of Düsseldorf, Düsseldorf, Germany). The calculation was based on the primary objective of comparing the electrical thresholds using a repeated-measures within-subjects ANOVA design. The following parameters were set: effect size f = 0.30 (considered a medium-to-large effect based on expected differences between thresholds), alpha error probability (α) = 0.05, desired power (1–β) = 0.90, number of measurements = 3, assumed correlation among repeated measures = 0.5, and nonsphericity correction ε = 1. Based on these parameters, the minimum required sample size was determined to be 25 participants. To account for potential dropouts, unforeseen data loss, and to ensure sufficient power for secondary analyses involving multiple comparisons across pulse widths, the final sample size was increased to 30 participants. Main Outcomes . A single assessment session was scheduled. Participants completed a questionnaire assessing demographic data. The main variables evaluated in this study were the electrical sensory threshold (EST) [ 4 , 18 ], defined as the minimum current intensity required to induce conscious sensory perception; the electrical motor threshold (EMT) [ 19 , 20 ], defined as the minimum intensity needed to elicit a visible muscle contraction; and the electrical pain threshold (EPT), defined as the lowest current intensity that produced a clearly perceptible painful sensation [ 20 , 21 ]. Electrical Threshold Testing . Thresholds were measured using a low-frequency symmetrical biphasic current with disposable, squared, 25 cm² electrodes. A GYMNA MYO 200 electrotherapy device was used for assessment. Throughout the procedure, participants remained in a supine position, with their dominant forearm in supination [ 22 ]. Measurements were taken on the dominant forearm as a representative site for sensory perception and muscle excitability. Electrodes were placed on the anterior surface, aligned along the longitudinal axis of the wrist flexor muscle. The distal electrode was positioned 4 cm proximal to the wrist joint line, and the proximal electrode was placed 4 cm from the elbow fold [ 23 ]. The primary researcher was responsible for applying and removing all electrodes to minimize inter-examiner bias and ensure consistency throughout the procedure. Different pulse widths were explored: 20, 35, 50, 75, 100, 150, 200, 250, 300, 500 and 650 µs. A stimulation frequency of 100 Hz was used, with a 90-second inter-trial interval to ensure adequate nerve recovery between measurements [ 24 ]. Once the electrodes were placed and the parameters fixed, the current intensity was gradually increased until the participant perceived the current (EST). The intensity was then further increased until a motor response was elicited (EMT). Finally, the EPT was determined when the participant reported the first sensation of pain induced by the electrical current (EPT) [ 18 , 21 ]. A single measurement of electrical thresholds was taken for each pulse width, with the order of pulse widths randomized. Statistical Analysis . Statistical analysis was conducted using IBM SPSS Statistics (version 29.0, IBM Corp., Armonk, NY, USA). Descriptive analyses were performed to characterise the demographic variables of the sample. Normality was assessed using the Shapiro–Wilk test and a combination of parametric and non-parametric approaches was applied to compare the three thresholds. Pairwise comparisons with Bonferroni adjustment were performed. To characterise the behaviour of thresholds, a graphic representation of the strength-duration (S-D) curve was made, and the data was adequately modelled using a power function ( y = ax b ). To determine the point at which the EST, EMT and EPT stabilized across increasing pulse widths, a statistical analysis was conducted to assess the presence of significant differences between consecutive pulse width values using a Welch’s t-test. The p-value was calculated based on the Welch’s t-statistic and its associated degrees of freedom, using the cumulative distribution function of the Student’s t-distribution. Stabilization of intensity was considered to have occurred when at least two consecutive pairs of pulse width values showed no statistically significant differences. To assess the discriminative capacity, effect-size analyses were conducted in two stages. First, for each pulse width, a repeated-measures ANOVA was performed to compare the EST, EMT and EPT. Partial eta squared (η²ₚ) was extracted as a measure of the global effect size. Subsequently, pairwise comparisons were carried out between each pair of thresholds using paired-samples t-tests. For each comparison, Cohen’s d z for paired data was calculated. These analyses were repeated for all pulse widths. A significance level of α = 0.05 was established for all statistical analyses. RESULTS Participants. The characteristics of participants (n = 30) were summarized in Table 1 . The mean age of the sample was 27.67 ± 12.68 years, with an average height of 164.20 ± 8.63 cm and an average weight of 62.45 ± 9.89 kg. Of the participants, 86.7% were female and 93.3% were right-handed. Table 1 Descriptive Characteristics of the Participants. Outcome Mean / AF SD / % Age (years) 27.67 12.68 Height (cm) 164.20 8.63 Weight (kg) 62.45 9.89 Sex (women) 26 86.70% Laterality (right) 28 93.30% Categorical variables are expressed as absolute frequencies (AF) and percentages (%) within each group. Quantitative variables are expressed as mean and standard deviation (SD) . Comparative Analysis between Thresholds . Table 2 presents the differences between somatosensory and motor thresholds across varying pulse widths. The analysis revealed statistically significant differences between all threshold types at each pulse width ( P < 0.001). The mean EST ranged from 45.73 ± 9.65 mA at 20 µs to 2.03 ± 0.72 mA at 650 µs. The mean EMT ranged from 69.20 ± 15.25 mA at 20 µs to 4.03 ± 1.10 mA at 650 µs. The mean EPT ranged from 92.10 ± 16.40 mA at 20 µs to 6.30 ± 1.54 mA at 650 µs. Table 2 Differences between thresholds across different pulse widths. Pulse Width (µs) EST (mA) EMT (mA) EPT (mA) P 20 45.73 ± 9.65 69.20 ± 15.25 92.10 ± 16.40 < 0.001 abc 35 24.40 ± 4.52 37.05 ± 5.42 52.25 ± 8.12 < 0.001 abc 50 16.37 ± 4.00 25.17 ± 3.99 33.67 ± 7.24 < 0.001 abc 75 11.13 ± 2.49 17.23 ± 2.80 23.17 ± 4.61 < 0.001 abc 100 8.70 ± 2.40 13.50 ± 2.43 17.73 ± 3.53 < 0.001 abc 150 6.23 ± 1.85 9.63 ± 2.48 13.23 ± 3.38 < 0.001 abc 200 4.37 ± 1.27 7.17 ± 1.29 10.30 ± 2.56 < 0.001 abc 250 3.77 ± 1.36 6.30 ± 1.75 9.17 ± 2.35 < 0.001 abc 300 3.23 ± 0.94 5.53 ± 1.63 8.30 ± 2.38 < 0.001 abc 500 2.37 ± 0.85 4.30 ± 1.32 6.60 ± 1.59 < 0.001 abc 650 2.03 ± 0.72 4.03 ± 1.10 6.30 ± 1.54 < 0.001 abc Values are expressed as mean ± standard deviation. a: differences between EST and EMT; b: differences between EST and EPT; c: differences between EMT and EPT. EMT: Electrical Motor Threshold; EPT: Electrical Pain Threshold; EST: Electrical Sensory Threshold . Strength-Duration Relationship . Figure 1 displays the S-D curve of EST, EMT and EPT, showing the mean intensity values as a function of pulse width. Each mean value is accompanied by an error bar representing the 95% confidence interval, providing a visual indication of the variability of the measurements at each point. Values are expressed as mean ± 95% confidence interval. EMT: Electrical Motor Threshold; EPT: Electrical Pain Threshold; EST: Electrical Sensory Threshold . As shown in Fig. 1 , the increase in pulse width produces a sharp decrease in EST, particularly between 20 and 75 µs, where the intensity decreases sharply from approximately 45 to 10 mA. Beyond this range, the trend begins to plateau, with intensity values reaching a rheobase of ∼2 mA at 650 µs. For EMT, a similarly pronounced decline is observed over the same interval (20 to 75 µs), with thresholds decreasing from around 70 mA to 17 mA. This reduction appears more substantial than that observed for EST. Beyond 75 µs, the rate of decline diminishes, with EMT values tending to a rheobase of ∼4 mA at 650 µs. EPT exhibits the most pronounced reduction in intensity across increasing pulse widths. Between 20 and 75 µs, threshold values fall steeply from approximately 90 mA to 20 mA. Following this sharp decrease, the curve levels off, with intensity values reaching a rheobase of ∼6.3 mA at 650 µs, showing minimal variation despite further increases in pulse width. Electrical Thresholds Stabilization . The dataset collected for different thresholds was well modelled by a power function. For EST, the fitted function accurately describes how intensity varies with pulse width, with an R 2 of 0.991. $$\:y=575.7·{x}^{-0.898}$$ Similarly, the mathematical model for EMT provided a strong fit to the observed data, with an R 2 of 0.983. $$\:y=672.6·{x}^{-0.829}$$ The EPT data were also well described by the fitted function, with an R 2 of 0.978. $$\:y=762.0·{x}^{-0.784}$$ where the dependent variable y represents the intensity (mA), and the independent variable x corresponds to the corresponding pulse width (µs). The results of the statistical analysis for each pair of pulse width values, along with the corresponding P obtained through the method described in Methods, are presented in Table 3 . Table 3 Statistical Analysis for Stabilization of EST, EMT and EPT. Interval (µs) EST P EMT P EPT P 20–35 < 0.001 < 0.001 < 0.001 35–50 < 0.001 < 0.001 < 0.001 50–75 < 0.001 < 0.001 < 0.001 75–100 0.012 < 0.001 < 0.001 100–150 0.002 < 0.001 < 0.001 150–200 0.795 0.001 0.015 200–250 0.794 0.523 0.783 250–300 0.790 0.800 0.938 300–500 0.056 0.071 0.067 500–650 0.858 0.999 1.000 EMT: Electrical Motor Threshold; EPT: Electrical Pain Threshold; EST: Electrical Sensory Threshold . For EST analysis, it can be observed that from a pulse width of 150 µs onwards, all consecutive pairs yield p-values > 0.05. This indicates that the differences between these pairs are not statistically significant. Therefore, it is considered that intensity stabilizes at this point, as further increases in pulse width no longer produce meaningful changes in the intensity. The statistical analysis results for EMT showed that, from a pulse width of 200 µs onwards, all consecutive pairs exhibit p-values > 0.05, indicating no statistically significant differences between them. This suggests that the intensity stabilizes beyond this point. Similarly, EPT analysis obtained p-values > 0.05 with a pulse with of 200 µs onwards, suggesting a stabilization up to this measure. The fixed model was used to calculate the chronaxie—defined as the time on such S-D curve for twice the rheobase—for each threshold. A chronaxie of ∼163.6 µs was obtained for EST; ∼210.5 µs for EMT and ∼104.5 µs for EPT. Selectivity for Electrical Thresholds . Across all pulse widths, the comparison between EST, EMT and EPT revealed consistently large effect sizes, with η²ₚ values exceeding 0.869. This indicates that a substantial proportion of the variance in ETT was attributable to the type of threshold, regardless of pulse duration. Notably, the highest discriminative capacity between threshold types was observed at a pulse width of 20 µs (η²ₚ = 0.936), followed closely by 650 µs (η²ₚ = 0.921) and by 35 µs (η²ₚ = 0.914), suggesting that these durations produced the most pronounced differences among EST, EMT and EPT. Table 4 presents the effect size in pairs between different types of thresholds. Pairwise comparisons between threshold types using Cohen’s d z revealed consistently large effect sizes across all pulse widths, further supporting a strong discriminative capacity. The comparison between the EST and both the EMT and EPT showed particularly strong effects at 75 µs (d z = − 2.529 for EST–EMT; −3.052 for EST–EPT), and at 650 µs (d z = − 2.296 for EST–EMT; −3.321 for EST–EPT). When focusing on comparisons involving EMT, the most discriminative pulse width appeared to be 650 µs, with Cohen’s d z values of − 2.296 (EST–EMT) and − 2.097 (EPT–EMT). In contrast, for comparisons involving EPT, the most specific pulse width was 20 µs, with extremely high effect sizes observed between EST and EPT (d z = − 3.573) and between EMT and EPT (d z = − 2.488). These results indicate that different pulse widths may optimize the differentiation between specific threshold pairs. Table 4 Cohen’s d z for Effect-Size Analysis between Pairs of Thresholds. Pulse Width (µs) EST-EMT EST-EPT EMT-EPT 20 -1,857 -3,573 -2,488 35 -1,713 -3,159 -1,809 50 -1,970 -2,859 -1,642 75 -2,529 -3,052 -1,864 100 -1,740 -2,891 -2,020 150 -1,480 -2,530 -1,631 200 -2,115 -2,825 -1,584 250 -1,636 -2,784 -1,969 300 -1,713 -2,580 -1,843 500 -1,691 -2,958 -2,055 650 -2,296 -3,321 -2,097 Values are expressed as Cohen’s dz for effect−size analysis. EMT: Electrical Motor Threshold; EPT: Electrical Pain Threshold; EST: Electrical Sensory Threshold . DISCUSSION The main aim of this study was to compare and characterize the EST, EMT, and EPT across varying pulse widths, and to determine the pulse width at which the greatest selectivity in excitability occurs. The results revealed statistically significant differences between the three thresholds at all pulse widths. The thresholds followed a consistent stimulation order, requiring the lowest intensity to activate non-nociceptive sensory fibers, followed by motor fibers, and the highest intensity for pain fibers. The S-D curves indicated that the thresholds responded similarly to increasing pulse widths, following a hyperbolic exponential decay that tends to flatten at longer pulse widths. Furthermore, it was found that extreme pulse widths (either very short or very long) resulted in greater selectivity for recruiting different fiber types, compared to intermediate pulse widths. The results aligned with well-known previous literature in order of recruitment fibres. As expected, fiber recruitment followed a progressive pattern, beginning with the perception of electrical stimulation, followed by visible muscle activation, and finally inducing pain response. Electrical thresholds and fiber activation are influenced, among other factors, by fiber diameter and anatomical location of fiber terminations [ 25 , 26 ]. Electrocutaneous current initially activates large-diameter, myelinated A-β fibers, which transmit light touch sensations [ 27 ] and predominantly terminate in the superficial dermis [ 26 ]. This is followed by the stimulation of motor fibers, although larger in diameter than A-β fibers, are located deeper within the tissue. Lastly, A-δ fibers and C fibers are recruited [ 26 ]. A-δ fibers are smaller in diameter than A-β and conduct signals related to sharp pain and deep touch, while C fibers are the smallest, unmyelinated and are associated with the transmission of dull, lingering pain [ 28 , 29 ]. Both fibers are mainly located in the dermis. Additionally, as demonstrated in this study, fiber depolarization also depends on the amount of electrical charge, the electrical properties of different fibers and tissues, as well as the duration of stimulation delivered [ 30 ]. Statistical differences between thresholds were found in every pulse width, and different chronaxies were reported. It has been previously stated that different neural structures have different chronaxies [ 9 ], suggesting that thresholds would show different values as they recruit different fiber types. Rheobase values were ∼2 mA for EST, ∼4 mA for EMT and ∼6.3 mA for EPT at 650 µs. The corresponding chronaxie values were ∼163.6 µs in EST, ∼210.5 µs in EMT and ∼104.5 µs in EPT. These results are comparable with those of Zhu et al. (2017), who applied biphasic current in the posterior forearm with 9 mm diameter electrodes on six subjects, and found a rheobase of ∼1.7 mA at 200 µs pulse width for EST [ 31 ]. Gaines et al. (2018) obtained a rheobase of ∼6 V and a chronaxie of 230 µs for a sensory axons, and a rheobase of ∼14.2 V and a chronaxie of 150 µs for motor axons in median nerve stimulation at the elbow [ 25 ]. In their study, the chronaxie for EMT was smaller than that for EST, which contrasts with the results of the model in this study. These differences may stem from the selectivity of axonal activation in their protocol, as they quantified the proportion of each axon type activated and recorded the specific electrical properties of the tissues assessed. Similar to the present findings, Guillen et al. (2025) found a rheobase of 1.75 mA at 495 µs and a chronaxie of 232 µs for EST with a single pulse of biphasic current in the median nerve in the forearm [ 32 ]. The curves S-D showed the same behaviour for the three thresholds: initially, there was a sharp decrease in intensity as pulse width increased, followed by a tendency to flatten and stabilize at the rheobase level for pulse widths up to 650 µs. This is consistent with previous studies. For instance, Alon et al. (1983) demonstrated that pain thresholds mediated by pain-conducting fibers in healthy subjects resulted in reducing thresholds (350 mA—30 mA) as pulse duration was increased (5 µs—1,000 µs) [ 33 ]. Kurz et al, (2022) studied motor activation in facial nerves and reported a decrease in intensity needed for sensory and motor fibers activation as pulse duration increases until reaching a plateau. However, as seen in their S-D curve, as pulse duration increases from 0.5 ms, the curves rise again [ 34 ]. Guillen et al, (2025) found the same behaviour in EST [ 32 ]. They also suggested that there is a linear relationship between the volume of tissue activated and pulse width, indicating that longer widths would lead to a deeper activation. In this context, thresholds with fixed models with an R 2 from 0.991 to 0.971 were represented. De Jesus et al. (2015) could represent EST and EMT by a regression curve with a R 2 of 0.95 and 0.88 respectively [ 22 ]. In the current study, it was possible to more accurately estimate the intensity needed to maintain the same level of neural excitation at different pulse durations. It was found that the selective activation of fibers better occurs, when comparing the three thresholds, with 20 and 650 µs. Consistently, it has been reported that isolated excitation of different nerve groups (motor, sensory, pain-conducting fibers) in adult cats may be easier with a short duration pulse [ 35 ]. Previous studies showed that shorter pulse widths increased the difference between fibers lying at differences distances of the electrode, and thus makes easier active close fibers without activating far ones [ 5 ]. This suggests that lower pulses widths provide a wide range of control between the minimum stimulus needed to activate the desired fibres and the ones non-desired (spillover) [ 33 ]. When selecting different fibers, it was found that larger-diameter A-β fibers more selectively activates with either short (75 µs) or long (650 µs) pulse widths. Motor fibers were more effectively recruited at longer pulse widths (650 µs), whereas small-diameter nociceptive A-δ fibers responded better to shorter pulse widths (20 µs). Alon et al. (1983) proposed optimal pulse width ranges for different stimulation targets. Consistent with the results of the current study, they suggested that pulse durations of 5 to 10 µs were favoured for eliciting pain responses. In contrast, they reported different ranges for sensory and motor thresholds: 20–100 µs for sensory perception and 20–200 µs for motor stimulation [ 33 ]. These discrepancies may be attributed to differences in stimulation parameters, as they used a monophasic current of 20 Hz in a small sample of six male participants. Current findings also contrast with those of Tigerholm et al. (2019), who stated that selective activation of small fibers (e.g., A-δ fibers) is increased with long pulse durations (> 0.4 ms) [ 4 ]. Due to methodological constraints, this study investigated pulse widths ranging from 20 to 650 µs, it was not possible to infer fiber behaviour beyond this range. Notably, at 650 µs, the selectivity for EPT was relatively strong, though less so than at 20 µs, which could align with this finding. In clinical practice, selective activation must be balanced with therapeutic goals and device performance. It has been reported that shorter pulse widths can prolong battery life without compromising therapeutic efficacy [ 30 ], highlighting the importance of optimizing stimulation parameters not only for physiological selectivity but also for practical device considerations. These findings might contribute to a better understanding of the basis underlying neurostimulation in clinical practice. Pulse width selection plays a critical role in optimizing fiber selectivity, treatment efficacy, and patient comfort. It is important to clearly define the therapeutic goals, consider the clinical context, and account for individual differences such as sensory characteristics, anatomical variability and functional status. In general, shorter pulse widths are more effective to stimulate specific fiber types while minimizing spillover to adjacent fibers, thereby enhancing patient comfort and increasing battery life. In contrast, longer pulse widths may be more feasible to stimulate short or denervated fibers that require higher-intensity charges of stimulation. Computational models allow for the prediction of patient responses to different stimulation protocols, enabling more precise adjustment of treatment parameters. Moreover, these models can support the monitoring of disease progression and therapeutic outcomes. This approach contributes to the development of more personalized and effective treatments through the application of individualized stimulation parameters. Future research should aim to evaluate the reliability of existing ETT protocols and work toward the development of a standardized, unified method that uses widely accessible electrodes and current types compatible with most electrotherapy devices. Establishing normative data across the general population is also essential to define reference ranges and to differentiate pathological conditions based on deviations above or below these thresholds. In accordance with this, ETT should be systematically explored in populations with somatosensory dysfunctions to ensure construct validity and to determine whether these measures can reliably distinguish between healthy individuals and those with pathology. Moreover, given the variability in neural composition across different anatomical regions, it is important to investigate how thresholds may vary depending on local tissue and fiber characteristics. Finally, studies should bridge the gap between computational modelling and clinical application by generating actionable recommendations that are directly translatable to practice. This study has several limitations that should be considered when interpreting the findings. First, the sample was homogeneous in terms of age and sex, with a mean age of 27.67 ± 12.68 years and 86.70% of female participants, which is not representative of the general population. Nevertheless, since the objective was to explore current thresholds in healthy individuals, this sample ensured the absence of underlying pathologies that could have interfered with the results. Another limitation to general applicability is that measurements were taken only at one anatomical location—the dominant forearm. However, this site is commonly used in literature to assess somatosensory function in non-pain-referred areas using QST [ 36 ], supporting our methodological choice. Furthermore, this study explored pulse width as a single independent variable, with a fixed frequency and waveform. Future research could benefit from exploring interactions between pulse width, frequency, and waveform to better understand their combined effects on current thresholds. Despite this limitation, current findings showed trends consistent with previous studies that used different stimulation parameters [ 32 ]. Lastly, due to logistical constraints, it was not possible to assess the distance from the electrodes to neural structures. A standardized protocol to measure QST in forearm was carefully followed, reducing variability between subjects [ 22 ]. Moreover, information of other potential confounding variables was collected, such as gender, age or weight, which have previously showed to affect perception and motor thresholds [ 19 , 37 ]. In conclusion, a consistent pattern across the EST, EMT and EPT was observed, with higher values at shorter pulse widths and a tendency to plateau as pulse width increased. The order of fiber recruitment remained consistent across all pulse widths. Lower pulse widths demonstrated greater discrimination between thresholds, which may be attributed to the selective recruitment of specific fiber types. More specifically, EST was best assessed at extreme pulse widths (either very short or very long), where the activation of other thresholds could be more easily avoided. In contrast, both EMT and EPT were most reliably captured at longer pulse widths, although a strong effect size was observed across all conditions. Future research should prioritize the development of standardized and unified assessment protocols, including the evaluation of reliability and normative values. Additionally, efforts should be made to translate theoretical and computational models into practical clinical applications. Declarations Competing Interests . The authors have no relevant financial or non-financial interests to disclose. Ethics Approval . This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Research Ethics Committee of the Autonomous Community of Aragón (CEICA) (C.I.PI24/248) on 29th of May of 2024, and received authorization for the processing of personal study data from the Data Protection Unit of the University of Zaragoza (CUSTOS), with reference number RAT 2024 − 142 on 26th of April of 2024. Consent to Participate . Informed consent was obtained from all individual participants included in the study. Funding. No funding was received for conducting this study. Author Contribution Conceptualization, I.R. and M.M.; Methodology, E.B.; Data collection, I.R. and J.P.; Data analysis, I.R. and A.L.; Writing—original draft preparation, M.M. and I.R.; Writing—review and editing, A.L., I.A. and E.B. All authors read and approved the final manuscript. Acknowledgement The authors would like to express their sincere gratitude to all the participants who generously contributed their time and effort to take part in this study. We also wish to thank the members of our research group for their valuable input and collaboration in the design and development of the study. Data Availability The data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request. References Treede R-D. The role of quantitative sensory testing in the prediction of chronic pain. Pain 2019;160 Suppl:S66–9. https://doi.org/10.1097/j.pain.0000000000001544 . Li R, Holley AL, Palermo TM, Ohls O, Edwards RR, Rabbitts JA. Feasibility and reliability of a quantitative sensory testing protocol in youth with acute musculoskeletal pain postsurgery or postinjury. Pain 2023;164:1627–38. https://doi.org/10.1097/j.pain.0000000000002865 . Dzheldubayeva ÉR, Chuyan EN, Bogdanova O V, Strizhak LA. Electroneuromyographic studies of pain sensitivity. Neurophysiology 2009;41:211–29. https://doi.org/10.1007/s11062-009-9091-2 . 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Lee SY, Kim JB, Lee JW, Woo AM, Kim CJ, Chung MY, et al. A Quantitative Measure of Pain with Current Perception Threshold, Pain Equivalent Current, and Quantified Pain Degree: A Retrospective Study. J Clin Med 2023;12. https://doi.org/10.3390/jcm12175476 . Roberto R, Guirro DJ, Caldeira E, Guirro DO. Sensory and Motor Thresholds of Transcutaneous Electrical Stimulation Are Influenced by Gender and Age. Phys Med Rehabil 2015;7:42–7. https://doi.org/10.1016/j.pmrj.2014.07.004 . Barbosa M de B, Guirro EC de O, Nunes FR. Evaluation of sensitivity, motor and pain thresholds across the menstrual cycle through medium-frequency transcutaneous electrical nerve stimulation. Clinics (Sao Paulo) 2013;68:901–8. https://doi.org/10.6061/clinics/2013(07)03 . Inoue T, Soshi S, Kubota M, Marumo K. Efficacy of Laminoplasty in Improving Sensory Disturbances in Patients with Cervical Spondylotic Myelopathy: A Prospective Study. World Neurosurg 2020;134:e581–8. https://doi.org/10.1016/j.wneu.2019.10.141 . Guirro RR de J, Guirro EC de O, de Sousa NTA. Sensory and motor thresholds of transcutaneous electrical stimulation are influenced by gender and age. PM R 2015;7:42–7. https://doi.org/10.1016/j.pmrj.2014.07.004 . Iacovides S, Avidon I, Baker FC. Women with dysmenorrhoea are hypersensitive to experimentally induced forearm ischaemia during painful menstruation and during the pain-free follicular phase. Eur J Pain 2015;19:797–804. https://doi.org/10.1002/ejp.604 . Ríos-asín I, Malo-urriés M, Pérez-rey J, Bueno-gracia E, García-díez M, Burgos-garlito L. Recovery Time of Electrical Sensory, Motor, and Pain Thresholds : A Pilot Study Towards Standardization of Quantitative Sensory Testing in Healthy Population 2025:1–11. https://doi.org/10.3390/healthcare13192492 . Gaines JL, Finn KE, Slopsema JP, Heyboer LA, Polasek KH. A model of motor and sensory axon activation in the median nerve using surface electrical stimulation. J Comput Neurosci 2018;45:29–43. https://doi.org/10.1007/s10827-018-0689-5 . Mørch CD, Hennings K, Andersen OK. Estimating nerve excitation thresholds to cutaneous electrical stimulation by finite element modeling combined with a stochastic branching nerve fiber model. Med Biol Eng Comput 2011;49:385–95. https://doi.org/10.1007/s11517-010-0725-8 . Lee GI, Neumeister MW. Pain: Pathways and Physiology. Clin Plast Surg 2020;47:173–80. https://doi.org/10.1016/j.cps.2019.11.001 . Handwerker HO, Kobal G. Psychophysiology of experimentally induced pain. Physiol Rev 1993;73:639–71. https://doi.org/10.1152/physrev.1993.73.3.639 . Sluka KA, Walsh D. Transcutaneous electrical nerve stimulation: basic science mechanisms and clinical effectiveness. J Pain 2003;4:109–21. https://doi.org/10.1054/jpai.2003.434 . Rueb J, Goldman HB, Vasavada S, Moore C, Rackley R, Gill BC. Effect of pulse width variations on sacral neuromodulation for overactive bladder symptoms: A prospective randomized crossover feasibility study. Neurourol Urodyn 2023;42:770–7. https://doi.org/10.1002/nau.25161 . Zhu K, Li L, Wei X, Sui X. A 3D Computational Model of Transcutaneous Electrical Nerve Stimulation for Estimating Aβ Tactile Nerve Fiber Excitability. Front Neurosci 2017;11:250. https://doi.org/10.3389/fnins.2017.00250 . Guillen A, Truong DQ, Cakmak YO, Li S, Datta A. The interplay between pulse width and activation depth in TENS: a computational study. Front Pain Res 2025;Volume 6. https://doi.org/10.3389/fpain.2025.1526277 . Alon G, Allin J, Inbar GF. Optimization of pulse duration and pulse charge during transcutaneous electrical nerve stimulation. Aust J Physiother 1983;29:195–201. https://doi.org/10.1016/S0004-9514(14)60670-X . Kurz A, Volk GF, Arnold D, Schneider-Stickler B, Mayr W, Guntinas-Lichius O. Selective Electrical Surface Stimulation to Support Functional Recovery in the Early Phase After Unilateral Acute Facial Nerve or Vocal Fold Paralysis. Front Neurol 2022;Volume 13. https://doi.org/10.3389/fneur.2022.869900 . Li CL, Bak A. Excitability characteristics of the A- and C-fibers in a peripheral nerve. Exp Neurol 1976;50:67–79. https://doi.org/10.1016/0014-4886(76)90236-3 . Powell-Roach KL, Yao Y, Rutherford JN, Schlaeger JM, Patil CL, Suarez ML, et al. Thermal and mechanical quantitative sensory testing values among healthy African American adults. J Pain Res 2019;12:2511–27. https://doi.org/10.2147/JPR.S211855 . Brodoehl S, Klingner C, Stieglitz K, Witte O. Age-related changes in the somatosensory processing of tactile stimulation-An fMRI study. Behav Brain Res 2012;238. https://doi.org/10.1016/j.bbr.2012.10.038 . Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9232031","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":615939685,"identity":"82a24334-aaac-4e4c-b32b-6e8d0d3b3851","order_by":0,"name":"Izarbe Ríos-Asín","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArUlEQVRIiWNgGAWjYBACNgbGBgaGChCDNC1nSNECBoxtpKjmkz7c+PDnvMN5fPyHn31gqKgjwmF8ic3GvNsOF7MxHDOewXDmMBFaeBjbpBm3HU5sY2wwBrrwAHFaJH/OAWphZv/MwPiPGIcBtUjwNgC1sPEAbWlgJkpLszHPsfTENh6eYoaEY0T4Rb6H/eHDHzXWifP7j29m+FBDhMNQQQKpGkbBKBgFo2AUYAcAS8Mv8XcGSrQAAAAASUVORK5CYII=","orcid":"","institution":"University of Zaragoza","correspondingAuthor":true,"prefix":"","firstName":"Izarbe","middleName":"","lastName":"Ríos-Asín","suffix":""},{"id":615939686,"identity":"62b69e69-64ee-4835-86fb-d32e379a232e","order_by":1,"name":"Miguel Malo-Urriés","email":"","orcid":"","institution":"University of Zaragoza","correspondingAuthor":false,"prefix":"","firstName":"Miguel","middleName":"","lastName":"Malo-Urriés","suffix":""},{"id":615939687,"identity":"505d01ce-3bcd-430c-bd10-e1ce4890328f","order_by":2,"name":"Jorge Pérez-Rey","email":"","orcid":"","institution":"University of Zaragoza","correspondingAuthor":false,"prefix":"","firstName":"Jorge","middleName":"","lastName":"Pérez-Rey","suffix":""},{"id":615939691,"identity":"47150d49-4607-41c5-b893-a1ae5dc83f6c","order_by":3,"name":"Alejandro Lete","email":"","orcid":"","institution":"University of Zaragoza","correspondingAuthor":false,"prefix":"","firstName":"Alejandro","middleName":"","lastName":"Lete","suffix":""},{"id":615939694,"identity":"51842dcc-7a6f-4874-a74d-fa6cecc808cc","order_by":4,"name":"Isabel Albarova-Corral","email":"","orcid":"","institution":"University of Zaragoza","correspondingAuthor":false,"prefix":"","firstName":"Isabel","middleName":"","lastName":"Albarova-Corral","suffix":""},{"id":615939695,"identity":"01762020-33f6-4b52-9d0e-cb5a5e6b837e","order_by":5,"name":"Elena Bueno-Gracia","email":"","orcid":"","institution":"University of Zaragoza","correspondingAuthor":false,"prefix":"","firstName":"Elena","middleName":"","lastName":"Bueno-Gracia","suffix":""}],"badges":[],"createdAt":"2026-03-26 09:23:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9232031/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9232031/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106255831,"identity":"64ff50e9-269e-4fa5-b4bb-fddaaf1c21a7","added_by":"auto","created_at":"2026-04-06 18:49:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":33289,"visible":true,"origin":"","legend":"\u003cp\u003eStrength-Duration Curve for EST, EMT and EPT\u003c/p\u003e\n\u003cp\u003eValues are expressed as mean ± 95% confidence interval. EMT: Electrical Motor Threshold; EPT: Electrical Pain Threshold; EST: Electrical Sensory Threshold.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9232031/v1/5b4de1dd959e2b590d57503c.png"},{"id":106403434,"identity":"4613cfd9-3436-4f96-ad56-6a8db87e2030","added_by":"auto","created_at":"2026-04-08 09:14:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":738731,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9232031/v1/901cb231-55eb-4942-afff-16582b8c7826.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eElectrical Somatosensory and Motor Thresholds across Pulse Widths: Characterizing Strength-Duration Properties and Nerve Excitability\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eQuantitative assessment of somatosensory and motor responses represents a fundamental approach to understanding the functioning of the peripheral nervous system. Conventional methods, such as Quantitative Sensory Testing (QST) and self-reported scales, primarily offer a subjective approach to assessing detection and pain sensitivity [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In contrast, objective instrumental electroneuromyography (ENMG) provides a more precise estimation of neural activity, minimizing examiner bias and contextual influences by directly recording both somatosensory and motor electrical responses of peripheral nerves and muscles [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In this context, transcutaneous electrical stimulation stands out as a pivotal and versatile tool, offering a non-invasive and reproducible method to evaluate quantitative indicators of neural function, encompassing both afferent and efferent pathways [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eElectrical threshold testing (ETT) enables the precise configuration of various stimulation parameters, which directly influence the selectivity of nerve fiber recruitment based on their specific excitability characteristics [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], as well as muscle performance or fatigability [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], thus determining the resulting threshold values. Among these adjustable parameters, pulse width\u0026mdash;the duration of the stimulation pulse measured in microseconds (\u0026micro;s)\u0026mdash;is of particular relevance. According to the fundamental strength-duration relationship, shorter pulse widths require a higher current intensity to reach the given threshold level [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. As pulse width increases, the required current intensity decreases and tends to approach the rheobase, defined as the minimum current needed to elicit a response with pulses of infinitely long duration [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBeyond these general properties, varying pulse widths might favour the depolarization of specific neural fiber types. Shorter pulse widths preferentially activate large-diameter, low-threshold axons (such as A-β fibers), whereas longer pulse widths tend to recruit smaller-diameter, higher-threshold fibers, including the A-δ fibers involved in nociception [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In contrast to this size-order recruitment, the activation of motor units by surface electrical stimulation appears to follow a non-selective and temporally synchrnous pattern [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], contrary to the previous idea that supported a recuitment order opposite to Henneman\u0026rsquo;s principle [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Nevertheless, it was indicated that greater pulse widths (\u0026gt;\u0026thinsp;600 \u0026micro;s) increased force production until the rheaobase was approached, while shorter pulses (\u0026lt;\u0026thinsp;100 \u0026micro;s) result in smaller M-waves and twitch forces [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Furthermore, the use of wide-pulse stimulation (up to 1 ms) may not only depolarize motor axons peripherally but also elicit the recruitment of spinal motoneurons via the sensory volley, thereby contributing to the development of central torque [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the established theoretical framework of fiber selectivity, a lack of consensus persists regarding the optimal stimulation parameters required to standardize these measurements and ensure the most effective recruitment and specificity for each fiber type [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Previous research has highlighted that changes in responsiveness to electrical stimuli can be indicative of specific properties in neuromotor control and sensory processing [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Therefore, characterizing electrical thresholds across a wide range of pulse widths is essential to understand the underlying physiological mechanisms of axonal excitability and to improve the standarization of assessments with electrical stimulation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Such characterization is not only of fundamental interest but also carries significant clinical weight, as it may enhance the early diagnosis of peripheral neuropathies and the monitoring of treatment effectiveness in conditions where sensorimotor function is altered [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, the main objective of this study is to compare and characterize somatosensory and motor electrical thresholds across a comprehensive range of pulse widths. Specifically, we aimed to identify the pulse width at which these thresholds reach a physiological plateau and to determine the duration that provides the greatest selectivity in nerve fiber activation.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cb\u003eStudy design\u003c/b\u003e. An observational, descriptive, cross-sectional, prospective study was designed. The study was approved by the Research Ethics Committee of X (X) (C.I. XXXX/XXX) on 29/05/2024 and received authorization for the processing of personal study data from the Data Protection Unit of the University of X (X), with reference number RAT XXXX-XXX. All participants signed an informed consent form in accordance with the Declaration of Helsinki [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eParticipants. The study consisted of volunteers with no history of systemic disease. To be eligible for the study, participants were required to be aged 18 years or older, to have the ability to communicate and to understand the tests performed and to provide written informed consent. Participants were excluded if they had a history of chronic disorders, including endocrine, neurological, psychiatric, urogenital, or musculoskeletal conditions, or had any other condition that could interfere with results.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSample Size Calculation\u003c/b\u003e. The sample size was estimated a priori using G*Power software (version 3.1, University of D\u0026uuml;sseldorf, D\u0026uuml;sseldorf, Germany). The calculation was based on the primary objective of comparing the electrical thresholds using a repeated-measures within-subjects ANOVA design. The following parameters were set: effect size f\u0026thinsp;=\u0026thinsp;0.30 (considered a medium-to-large effect based on expected differences between thresholds), alpha error probability (α)\u0026thinsp;=\u0026thinsp;0.05, desired power (1\u0026ndash;β)\u0026thinsp;=\u0026thinsp;0.90, number of measurements\u0026thinsp;=\u0026thinsp;3, assumed correlation among repeated measures\u0026thinsp;=\u0026thinsp;0.5, and nonsphericity correction ε\u0026thinsp;=\u0026thinsp;1. Based on these parameters, the minimum required sample size was determined to be 25 participants. To account for potential dropouts, unforeseen data loss, and to ensure sufficient power for secondary analyses involving multiple comparisons across pulse widths, the final sample size was increased to 30 participants.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMain Outcomes\u003c/b\u003e. A single assessment session was scheduled. Participants completed a questionnaire assessing demographic data. The main variables evaluated in this study were the electrical sensory threshold (EST) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], defined as the minimum current intensity required to induce conscious sensory perception; the electrical motor threshold (EMT) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], defined as the minimum intensity needed to elicit a visible muscle contraction; and the electrical pain threshold (EPT), defined as the lowest current intensity that produced a clearly perceptible painful sensation [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eElectrical Threshold Testing\u003c/b\u003e. Thresholds were measured using a low-frequency symmetrical biphasic current with disposable, squared, 25 cm\u0026sup2; electrodes. A GYMNA MYO 200 electrotherapy device was used for assessment. Throughout the procedure, participants remained in a supine position, with their dominant forearm in supination [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Measurements were taken on the dominant forearm as a representative site for sensory perception and muscle excitability. Electrodes were placed on the anterior surface, aligned along the longitudinal axis of the wrist flexor muscle. The distal electrode was positioned 4 cm proximal to the wrist joint line, and the proximal electrode was placed 4 cm from the elbow fold [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The primary researcher was responsible for applying and removing all electrodes to minimize inter-examiner bias and ensure consistency throughout the procedure. Different pulse widths were explored: 20, 35, 50, 75, 100, 150, 200, 250, 300, 500 and 650 \u0026micro;s. A stimulation frequency of 100 Hz was used, with a 90-second inter-trial interval to ensure adequate nerve recovery between measurements [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Once the electrodes were placed and the parameters fixed, the current intensity was gradually increased until the participant perceived the current (EST). The intensity was then further increased until a motor response was elicited (EMT). Finally, the EPT was determined when the participant reported the first sensation of pain induced by the electrical current (EPT) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. A single measurement of electrical thresholds was taken for each pulse width, with the order of pulse widths randomized.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistical Analysis\u003c/b\u003e. Statistical analysis was conducted using IBM SPSS Statistics (version 29.0, IBM Corp., Armonk, NY, USA). Descriptive analyses were performed to characterise the demographic variables of the sample. Normality was assessed using the Shapiro\u0026ndash;Wilk test and a combination of parametric and non-parametric approaches was applied to compare the three thresholds. Pairwise comparisons with Bonferroni adjustment were performed. To characterise the behaviour of thresholds, a graphic representation of the strength-duration (S-D) curve was made, and the data was adequately modelled using a power function (\u003cem\u003ey\u0026thinsp;=\u0026thinsp;ax\u003c/em\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e). To determine the point at which the EST, EMT and EPT stabilized across increasing pulse widths, a statistical analysis was conducted to assess the presence of significant differences between consecutive pulse width values using a Welch\u0026rsquo;s t-test. The p-value was calculated based on the Welch\u0026rsquo;s t-statistic and its associated degrees of freedom, using the cumulative distribution function of the Student\u0026rsquo;s t-distribution. Stabilization of intensity was considered to have occurred when at least two consecutive pairs of pulse width values showed no statistically significant differences. To assess the discriminative capacity, effect-size analyses were conducted in two stages. First, for each pulse width, a repeated-measures ANOVA was performed to compare the EST, EMT and EPT. Partial eta squared (η\u0026sup2;ₚ) was extracted as a measure of the global effect size. Subsequently, pairwise comparisons were carried out between each pair of thresholds using paired-samples t-tests. For each comparison, Cohen\u0026rsquo;s d\u003csub\u003ez\u003c/sub\u003e for paired data was calculated. These analyses were repeated for all pulse widths. A significance level of α\u0026thinsp;=\u0026thinsp;0.05 was established for all statistical analyses.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e \u003cb\u003eParticipants.\u003c/b\u003e The characteristics of participants (n\u0026thinsp;=\u0026thinsp;30) were summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The mean age of the sample was 27.67\u0026thinsp;\u0026plusmn;\u0026thinsp;12.68 years, with an average height of 164.20\u0026thinsp;\u0026plusmn;\u0026thinsp;8.63 cm and an average weight of 62.45\u0026thinsp;\u0026plusmn;\u0026thinsp;9.89 kg. Of the participants, 86.7% were female and 93.3% were right-handed.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Characteristics of the Participants.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean / AF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD / %\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e164.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (women)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86.70%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaterality (right)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93.30%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003eCategorical variables are expressed as absolute frequencies (AF) and percentages (%) within each group. Quantitative variables are expressed as mean and standard deviation (SD)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eComparative Analysis between Thresholds\u003c/b\u003e. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the differences between somatosensory and motor thresholds across varying pulse widths. The analysis revealed statistically significant differences between all threshold types at each pulse width (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The mean EST ranged from 45.73\u0026thinsp;\u0026plusmn;\u0026thinsp;9.65 mA at 20 \u0026micro;s to 2.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72 mA at 650 \u0026micro;s. The mean EMT ranged from 69.20\u0026thinsp;\u0026plusmn;\u0026thinsp;15.25 mA at 20 \u0026micro;s to 4.03\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10 mA at 650 \u0026micro;s. The mean EPT ranged from 92.10\u0026thinsp;\u0026plusmn;\u0026thinsp;16.40 mA at 20 \u0026micro;s to 6.30\u0026thinsp;\u0026plusmn;\u0026thinsp;1.54 mA at 650 \u0026micro;s.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferences between thresholds across different pulse widths.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulse Width (\u0026micro;s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEST (mA)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEMT (mA)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEPT (mA)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e45.73\u0026thinsp;\u0026plusmn;\u0026thinsp;9.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e69.20\u0026thinsp;\u0026plusmn;\u0026thinsp;15.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e92.10\u0026thinsp;\u0026plusmn;\u0026thinsp;16.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e24.40\u0026thinsp;\u0026plusmn;\u0026thinsp;4.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e37.05\u0026thinsp;\u0026plusmn;\u0026thinsp;5.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e52.25\u0026thinsp;\u0026plusmn;\u0026thinsp;8.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e16.37\u0026thinsp;\u0026plusmn;\u0026thinsp;4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e25.17\u0026thinsp;\u0026plusmn;\u0026thinsp;3.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e33.67\u0026thinsp;\u0026plusmn;\u0026thinsp;7.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e11.13\u0026thinsp;\u0026plusmn;\u0026thinsp;2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e17.23\u0026thinsp;\u0026plusmn;\u0026thinsp;2.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e23.17\u0026thinsp;\u0026plusmn;\u0026thinsp;4.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e8.70\u0026thinsp;\u0026plusmn;\u0026thinsp;2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e13.50\u0026thinsp;\u0026plusmn;\u0026thinsp;2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e17.73\u0026thinsp;\u0026plusmn;\u0026thinsp;3.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e6.23\u0026thinsp;\u0026plusmn;\u0026thinsp;1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e9.63\u0026thinsp;\u0026plusmn;\u0026thinsp;2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e13.23\u0026thinsp;\u0026plusmn;\u0026thinsp;3.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.37\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e10.30\u0026thinsp;\u0026plusmn;\u0026thinsp;2.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.77\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e6.30\u0026thinsp;\u0026plusmn;\u0026thinsp;1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e9.17\u0026thinsp;\u0026plusmn;\u0026thinsp;2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5.53\u0026thinsp;\u0026plusmn;\u0026thinsp;1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e8.30\u0026thinsp;\u0026plusmn;\u0026thinsp;2.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.30\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e6.60\u0026thinsp;\u0026plusmn;\u0026thinsp;1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.03\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e6.30\u0026thinsp;\u0026plusmn;\u0026thinsp;1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003eValues are expressed as mean \u0026plusmn; standard deviation. a: differences between EST and EMT; b: differences between EST and EPT; c: differences between EMT and EPT. EMT: Electrical Motor Threshold; EPT: Electrical Pain Threshold; EST: Electrical Sensory Threshold\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStrength-Duration Relationship\u003c/b\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e displays the S-D curve of EST, EMT and EPT, showing the mean intensity values as a function of pulse width. Each mean value is accompanied by an error bar representing the 95% confidence interval, providing a visual indication of the variability of the measurements at each point.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003eValues are expressed as mean \u0026plusmn; 95% confidence interval. EMT: Electrical Motor Threshold; EPT: Electrical Pain Threshold; EST: Electrical Sensory Threshold\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the increase in pulse width produces a sharp decrease in EST, particularly between 20 and 75 \u0026micro;s, where the intensity decreases sharply from approximately 45 to 10 mA. Beyond this range, the trend begins to plateau, with intensity values reaching a rheobase of \u0026sim;2 mA at 650 \u0026micro;s. For EMT, a similarly pronounced decline is observed over the same interval (20 to 75 \u0026micro;s), with thresholds decreasing from around 70 mA to 17 mA. This reduction appears more substantial than that observed for EST. Beyond 75 \u0026micro;s, the rate of decline diminishes, with EMT values tending to a rheobase of \u0026sim;4 mA at 650 \u0026micro;s. EPT exhibits the most pronounced reduction in intensity across increasing pulse widths. Between 20 and 75 \u0026micro;s, threshold values fall steeply from approximately 90 mA to 20 mA. Following this sharp decrease, the curve levels off, with intensity values reaching a rheobase of \u0026sim;6.3 mA at 650 \u0026micro;s, showing minimal variation despite further increases in pulse width.\u003c/p\u003e \u003cp\u003e \u003cb\u003eElectrical Thresholds Stabilization\u003c/b\u003e. The dataset collected for different thresholds was well modelled by a power function. For EST, the fitted function accurately describes how intensity varies with pulse width, with an R\u003csup\u003e2\u003c/sup\u003e of 0.991.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:y=575.7\u0026middot;{x}^{-0.898}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eSimilarly, the mathematical model for EMT provided a strong fit to the observed data, with an R\u003csup\u003e2\u003c/sup\u003e of 0.983.\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:y=672.6\u0026middot;{x}^{-0.829}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe EPT data were also well described by the fitted function, with an R\u003csup\u003e2\u003c/sup\u003e of 0.978.\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:y=762.0\u0026middot;{x}^{-0.784}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere the dependent variable \u003cem\u003ey\u003c/em\u003e represents the intensity (mA), and the independent variable \u003cem\u003ex\u003c/em\u003e corresponds to the corresponding pulse width (\u0026micro;s).\u003c/p\u003e \u003cp\u003eThe results of the statistical analysis for each pair of pulse width values, along with the corresponding \u003cem\u003eP\u003c/em\u003e obtained through the method described in Methods, are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistical Analysis for Stabilization of EST, EMT and EPT.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterval (\u0026micro;s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEST \u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEMT \u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEPT \u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100\u0026ndash;150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e150\u0026ndash;200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e200\u0026ndash;250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e250\u0026ndash;300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e300\u0026ndash;500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e500\u0026ndash;650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003eEMT: Electrical Motor Threshold; EPT: Electrical Pain Threshold; EST: Electrical Sensory Threshold\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFor EST analysis, it can be observed that from a pulse width of 150 \u0026micro;s onwards, all consecutive pairs yield p-values\u0026thinsp;\u0026gt;\u0026thinsp;0.05. This indicates that the differences between these pairs are not statistically significant. Therefore, it is considered that intensity stabilizes at this point, as further increases in pulse width no longer produce meaningful changes in the intensity. The statistical analysis results for EMT showed that, from a pulse width of 200 \u0026micro;s onwards, all consecutive pairs exhibit p-values\u0026thinsp;\u0026gt;\u0026thinsp;0.05, indicating no statistically significant differences between them. This suggests that the intensity stabilizes beyond this point. Similarly, EPT analysis obtained p-values\u0026thinsp;\u0026gt;\u0026thinsp;0.05 with a pulse with of 200 \u0026micro;s onwards, suggesting a stabilization up to this measure. The fixed model was used to calculate the chronaxie\u0026mdash;defined as the time on such S-D curve for twice the rheobase\u0026mdash;for each threshold. A chronaxie of \u0026sim;163.6 \u0026micro;s was obtained for EST; \u0026sim;210.5 \u0026micro;s for EMT and \u0026sim;104.5 \u0026micro;s for EPT.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSelectivity for Electrical Thresholds\u003c/b\u003e. Across all pulse widths, the comparison between EST, EMT and EPT revealed consistently large effect sizes, with η\u0026sup2;ₚ values exceeding 0.869. This indicates that a substantial proportion of the variance in ETT was attributable to the type of threshold, regardless of pulse duration. Notably, the highest discriminative capacity between threshold types was observed at a pulse width of 20 \u0026micro;s (η\u0026sup2;ₚ = 0.936), followed closely by 650 \u0026micro;s (η\u0026sup2;ₚ = 0.921) and by 35 \u0026micro;s (η\u0026sup2;ₚ = 0.914), suggesting that these durations produced the most pronounced differences among EST, EMT and EPT. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the effect size in pairs between different types of thresholds. Pairwise comparisons between threshold types using Cohen\u0026rsquo;s d\u003csub\u003ez\u003c/sub\u003e revealed consistently large effect sizes across all pulse widths, further supporting a strong discriminative capacity. The comparison between the EST and both the EMT and EPT showed particularly strong effects at 75 \u0026micro;s (d\u003csub\u003ez\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.529 for EST\u0026ndash;EMT; \u0026minus;3.052 for EST\u0026ndash;EPT), and at 650 \u0026micro;s (d\u003csub\u003ez\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.296 for EST\u0026ndash;EMT; \u0026minus;3.321 for EST\u0026ndash;EPT). When focusing on comparisons involving EMT, the most discriminative pulse width appeared to be 650 \u0026micro;s, with Cohen\u0026rsquo;s d\u003csub\u003ez\u003c/sub\u003e values of \u0026minus;\u0026thinsp;2.296 (EST\u0026ndash;EMT) and \u0026minus;\u0026thinsp;2.097 (EPT\u0026ndash;EMT). In contrast, for comparisons involving EPT, the most specific pulse width was 20 \u0026micro;s, with extremely high effect sizes observed between EST and EPT (d\u003csub\u003ez\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;3.573) and between EMT and EPT (d\u003csub\u003ez\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.488). These results indicate that different pulse widths may optimize the differentiation between specific threshold pairs.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCohen\u0026rsquo;s d\u003csub\u003ez\u003c/sub\u003e for Effect-Size Analysis between Pairs of Thresholds.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulse Width (\u0026micro;s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEST-EMT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEST-EPT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEMT-EPT\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1,857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3,573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2,488\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1,713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3,159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1,809\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1,970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2,859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1,642\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2,529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3,052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1,864\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1,740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2,891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2,020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1,480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2,530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1,631\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2,115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2,825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1,584\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1,636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2,784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1,969\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1,713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2,580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1,843\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1,691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2,958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2,055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2,296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3,321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2,097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003eValues are expressed as Cohen\u0026rsquo;s dz for effect\u0026minus;size analysis. EMT: Electrical Motor Threshold; EPT: Electrical Pain Threshold; EST: Electrical Sensory Threshold\u003c/sup\u003e.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe main aim of this study was to compare and characterize the EST, EMT, and EPT across varying pulse widths, and to determine the pulse width at which the greatest selectivity in excitability occurs. The results revealed statistically significant differences between the three thresholds at all pulse widths. The thresholds followed a consistent stimulation order, requiring the lowest intensity to activate non-nociceptive sensory fibers, followed by motor fibers, and the highest intensity for pain fibers. The S-D curves indicated that the thresholds responded similarly to increasing pulse widths, following a hyperbolic exponential decay that tends to flatten at longer pulse widths. Furthermore, it was found that extreme pulse widths (either very short or very long) resulted in greater selectivity for recruiting different fiber types, compared to intermediate pulse widths.\u003c/p\u003e \u003cp\u003eThe results aligned with well-known previous literature in order of recruitment fibres. As expected, fiber recruitment followed a progressive pattern, beginning with the perception of electrical stimulation, followed by visible muscle activation, and finally inducing pain response. Electrical thresholds and fiber activation are influenced, among other factors, by fiber diameter and anatomical location of fiber terminations [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Electrocutaneous current initially activates large-diameter, myelinated A-β fibers, which transmit light touch sensations [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and predominantly terminate in the superficial dermis [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This is followed by the stimulation of motor fibers, although larger in diameter than A-β fibers, are located deeper within the tissue. Lastly, A-δ fibers and C fibers are recruited [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. A-δ fibers are smaller in diameter than A-β and conduct signals related to sharp pain and deep touch, while C fibers are the smallest, unmyelinated and are associated with the transmission of dull, lingering pain [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Both fibers are mainly located in the dermis. Additionally, as demonstrated in this study, fiber depolarization also depends on the amount of electrical charge, the electrical properties of different fibers and tissues, as well as the duration of stimulation delivered [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStatistical differences between thresholds were found in every pulse width, and different chronaxies were reported. It has been previously stated that different neural structures have different chronaxies [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], suggesting that thresholds would show different values as they recruit different fiber types. Rheobase values were \u0026sim;2 mA for EST, \u0026sim;4 mA for EMT and \u0026sim;6.3 mA for EPT at 650 \u0026micro;s. The corresponding chronaxie values were \u0026sim;163.6 \u0026micro;s in EST, \u0026sim;210.5 \u0026micro;s in EMT and \u0026sim;104.5 \u0026micro;s in EPT. These results are comparable with those of Zhu et al. (2017), who applied biphasic current in the posterior forearm with 9 mm diameter electrodes on six subjects, and found a rheobase of \u0026sim;1.7 mA at 200 \u0026micro;s pulse width for EST [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Gaines et al. (2018) obtained a rheobase of \u0026sim;6 V and a chronaxie of 230 \u0026micro;s for a sensory axons, and a rheobase of \u0026sim;14.2 V and a chronaxie of 150 \u0026micro;s for motor axons in median nerve stimulation at the elbow [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In their study, the chronaxie for EMT was smaller than that for EST, which contrasts with the results of the model in this study. These differences may stem from the selectivity of axonal activation in their protocol, as they quantified the proportion of each axon type activated and recorded the specific electrical properties of the tissues assessed. Similar to the present findings, Guillen et al. (2025) found a rheobase of 1.75 mA at 495 \u0026micro;s and a chronaxie of 232 \u0026micro;s for EST with a single pulse of biphasic current in the median nerve in the forearm [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe curves S-D showed the same behaviour for the three thresholds: initially, there was a sharp decrease in intensity as pulse width increased, followed by a tendency to flatten and stabilize at the rheobase level for pulse widths up to 650 \u0026micro;s. This is consistent with previous studies. For instance, Alon et al. (1983) demonstrated that pain thresholds mediated by pain-conducting fibers in healthy subjects resulted in reducing thresholds (350 mA\u0026mdash;30 mA) as pulse duration was increased (5 \u0026micro;s\u0026mdash;1,000 \u0026micro;s) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Kurz et al, (2022) studied motor activation in facial nerves and reported a decrease in intensity needed for sensory and motor fibers activation as pulse duration increases until reaching a plateau. However, as seen in their S-D curve, as pulse duration increases from 0.5 ms, the curves rise again [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Guillen et al, (2025) found the same behaviour in EST [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. They also suggested that there is a linear relationship between the volume of tissue activated and pulse width, indicating that longer widths would lead to a deeper activation. In this context, thresholds with fixed models with an R\u003csup\u003e2\u003c/sup\u003e from 0.991 to 0.971 were represented. De Jesus et al. (2015) could represent EST and EMT by a regression curve with a R\u003csup\u003e2\u003c/sup\u003e of 0.95 and 0.88 respectively [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In the current study, it was possible to more accurately estimate the intensity needed to maintain the same level of neural excitation at different pulse durations.\u003c/p\u003e \u003cp\u003eIt was found that the selective activation of fibers better occurs, when comparing the three thresholds, with 20 and 650 \u0026micro;s. Consistently, it has been reported that isolated excitation of different nerve groups (motor, sensory, pain-conducting fibers) in adult cats may be easier with a short duration pulse [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Previous studies showed that shorter pulse widths increased the difference between fibers lying at differences distances of the electrode, and thus makes easier active close fibers without activating far ones [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This suggests that lower pulses widths provide a wide range of control between the minimum stimulus needed to activate the desired fibres and the ones non-desired (spillover) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. When selecting different fibers, it was found that larger-diameter A-β fibers more selectively activates with either short (75 \u0026micro;s) or long (650 \u0026micro;s) pulse widths. Motor fibers were more effectively recruited at longer pulse widths (650 \u0026micro;s), whereas small-diameter nociceptive A-δ fibers responded better to shorter pulse widths (20 \u0026micro;s). Alon et al. (1983) proposed optimal pulse width ranges for different stimulation targets. Consistent with the results of the current study, they suggested that pulse durations of 5 to 10 \u0026micro;s were favoured for eliciting pain responses. In contrast, they reported different ranges for sensory and motor thresholds: 20\u0026ndash;100 \u0026micro;s for sensory perception and 20\u0026ndash;200 \u0026micro;s for motor stimulation [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. These discrepancies may be attributed to differences in stimulation parameters, as they used a monophasic current of 20 Hz in a small sample of six male participants. Current findings also contrast with those of Tigerholm et al. (2019), who stated that selective activation of small fibers (e.g., A-δ fibers) is increased with long pulse durations (\u0026gt;\u0026thinsp;0.4 ms) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Due to methodological constraints, this study investigated pulse widths ranging from 20 to 650 \u0026micro;s, it was not possible to infer fiber behaviour beyond this range. Notably, at 650 \u0026micro;s, the selectivity for EPT was relatively strong, though less so than at 20 \u0026micro;s, which could align with this finding. In clinical practice, selective activation must be balanced with therapeutic goals and device performance. It has been reported that shorter pulse widths can prolong battery life without compromising therapeutic efficacy [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], highlighting the importance of optimizing stimulation parameters not only for physiological selectivity but also for practical device considerations.\u003c/p\u003e \u003cp\u003eThese findings might contribute to a better understanding of the basis underlying neurostimulation in clinical practice. Pulse width selection plays a critical role in optimizing fiber selectivity, treatment efficacy, and patient comfort. It is important to clearly define the therapeutic goals, consider the clinical context, and account for individual differences such as sensory characteristics, anatomical variability and functional status. In general, shorter pulse widths are more effective to stimulate specific fiber types while minimizing spillover to adjacent fibers, thereby enhancing patient comfort and increasing battery life. In contrast, longer pulse widths may be more feasible to stimulate short or denervated fibers that require higher-intensity charges of stimulation. Computational models allow for the prediction of patient responses to different stimulation protocols, enabling more precise adjustment of treatment parameters. Moreover, these models can support the monitoring of disease progression and therapeutic outcomes. This approach contributes to the development of more personalized and effective treatments through the application of individualized stimulation parameters.\u003c/p\u003e \u003cp\u003eFuture research should aim to evaluate the reliability of existing ETT protocols and work toward the development of a standardized, unified method that uses widely accessible electrodes and current types compatible with most electrotherapy devices. Establishing normative data across the general population is also essential to define reference ranges and to differentiate pathological conditions based on deviations above or below these thresholds. In accordance with this, ETT should be systematically explored in populations with somatosensory dysfunctions to ensure construct validity and to determine whether these measures can reliably distinguish between healthy individuals and those with pathology. Moreover, given the variability in neural composition across different anatomical regions, it is important to investigate how thresholds may vary depending on local tissue and fiber characteristics. Finally, studies should bridge the gap between computational modelling and clinical application by generating actionable recommendations that are directly translatable to practice.\u003c/p\u003e \u003cp\u003eThis study has several limitations that should be considered when interpreting the findings. First, the sample was homogeneous in terms of age and sex, with a mean age of 27.67\u0026thinsp;\u0026plusmn;\u0026thinsp;12.68 years and 86.70% of female participants, which is not representative of the general population. Nevertheless, since the objective was to explore current thresholds in healthy individuals, this sample ensured the absence of underlying pathologies that could have interfered with the results. Another limitation to general applicability is that measurements were taken only at one anatomical location\u0026mdash;the dominant forearm. However, this site is commonly used in literature to assess somatosensory function in non-pain-referred areas using QST [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], supporting our methodological choice. Furthermore, this study explored pulse width as a single independent variable, with a fixed frequency and waveform. Future research could benefit from exploring interactions between pulse width, frequency, and waveform to better understand their combined effects on current thresholds. Despite this limitation, current findings showed trends consistent with previous studies that used different stimulation parameters [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Lastly, due to logistical constraints, it was not possible to assess the distance from the electrodes to neural structures. A standardized protocol to measure QST in forearm was carefully followed, reducing variability between subjects [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Moreover, information of other potential confounding variables was collected, such as gender, age or weight, which have previously showed to affect perception and motor thresholds [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn conclusion, a consistent pattern across the EST, EMT and EPT was observed, with higher values at shorter pulse widths and a tendency to plateau as pulse width increased. The order of fiber recruitment remained consistent across all pulse widths. Lower pulse widths demonstrated greater discrimination between thresholds, which may be attributed to the selective recruitment of specific fiber types. More specifically, EST was best assessed at extreme pulse widths (either very short or very long), where the activation of other thresholds could be more easily avoided. In contrast, both EMT and EPT were most reliably captured at longer pulse widths, although a strong effect size was observed across all conditions. Future research should prioritize the development of standardized and unified assessment protocols, including the evaluation of reliability and normative values. Additionally, efforts should be made to translate theoretical and computational models into practical clinical applications.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003e \u003cb\u003eCompeting Interests\u003c/b\u003e.\u003c/h2\u003e \u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003e \u003cb\u003eEthics Approval\u003c/b\u003e.\u003c/h2\u003e \u003cp\u003e This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Research Ethics Committee of the Autonomous Community of Arag\u0026oacute;n (CEICA) (C.I.PI24/248) on 29th of May of 2024, and received authorization for the processing of personal study data from the Data Protection Unit of the University of Zaragoza (CUSTOS), with reference number RAT 2024\u0026thinsp;\u0026minus;\u0026thinsp;142 on 26th of April of 2024.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003e \u003cb\u003eConsent to Participate\u003c/b\u003e.\u003c/strong\u003e \u003cp\u003e Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding.\u003c/h2\u003e \u003cp\u003eNo funding was received for conducting this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, I.R. and M.M.; Methodology, E.B.; Data collection, I.R. and J.P.; Data analysis, I.R. and A.L.; Writing\u0026mdash;original draft preparation, M.M. and I.R.; Writing\u0026mdash;review and editing, A.L., I.A. and E.B. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to express their sincere gratitude to all the participants who generously contributed their time and effort to take part in this study. We also wish to thank the members of our research group for their valuable input and collaboration in the design and development of the study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTreede R-D. The role of quantitative sensory testing in the prediction of chronic pain. 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Behav Brain Res 2012;238. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.bbr.2012.10.038\u003c/span\u003e\u003cspan address=\"10.1016/j.bbr.2012.10.038\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"neurophysiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Neurophysiology](https://link.springer.com/journal/11062)","snPcode":"11062","submissionUrl":"https://submission.springernature.com/new-submission/11062/3","title":"Neurophysiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Electrical threshold testing, strength-duration, electrical stimulation, somatosensory function, fiber recruitment, quantitative sensory testing","lastPublishedDoi":"10.21203/rs.3.rs-9232031/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9232031/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eQuantitative assessment of peripheral sensorimotor function via surface electrical stimulation provides essential insights into axonal excitability; however, standardized stimulation parameters remain poorly defined. This study aimed to characterize the strength\u0026ndash;duration (S\u0026ndash;D) relationship of electrical sensory, motor, and pain thresholds (EST, EMT, and EPT) across a wide range of pulse widths to identify physiological stabilization plateaus and determine durations that optimize selective fiber recruitment. Thirty healthy volunteers underwent electrical threshold testing (ETT) on the forearm using a symmetrical biphasic current (100 Hz). Eleven pulse widths (20\u0026ndash;650 \u0026micro;s) were evaluated in randomized order. Threshold transitions were modelled using a power function (\u003cem\u003ey\u0026thinsp;=\u0026thinsp;ax\u003c/em\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e). Stabilization points were determined through consecutive pairwise comparisons, and discriminative capacity was assessed using effect-size analysis. All thresholds followed a characteristic hyperbolic decay consistent with fundamental axonal excitability properties. A clear stabilization toward the rheobase was identified: EST reached a physiological plateau at \u0026sim;150 \u0026micro;s, while EMT and EPT stabilized at \u0026sim;200 \u0026micro;s. Extreme pulse widths (20 and 650 \u0026micro;s) demonstrated the highest discriminative capacity between thresholds (η\u0026sup2;ₚ = 0.936 and 0.921). These findings demonstrate that electrical thresholds follow a predictable neurophysiological pattern across pulse widths, and suggest that pulse width selection is critical for achieving target fiber selectivity, providing a reference framework for standardizing ETT protocols in both research and clinical diagnosis.\u003c/p\u003e","manuscriptTitle":"Electrical Somatosensory and Motor Thresholds across Pulse Widths: Characterizing Strength-Duration Properties and Nerve Excitability","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-06 18:49:52","doi":"10.21203/rs.3.rs-9232031/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"122659195043770436839103139141483142409","date":"2026-05-15T18:27:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"160818228284972047581914329189917912533","date":"2026-05-15T16:55:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-12T21:08:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-08T12:30:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"306927318619010035510750229999632580050","date":"2026-03-31T08:04:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"188014065110204484441621972844106318087","date":"2026-03-30T06:22:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-28T18:17:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-28T01:58:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-28T01:58:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Neurophysiology","date":"2026-03-26T09:17:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"neurophysiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Neurophysiology](https://link.springer.com/journal/11062)","snPcode":"11062","submissionUrl":"https://submission.springernature.com/new-submission/11062/3","title":"Neurophysiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"36a92d7f-f3ce-49bb-83ef-93a94dcd761e","owner":[],"postedDate":"April 6th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"122659195043770436839103139141483142409","date":"2026-05-15T18:27:12+00:00","index":38,"fulltext":""},{"type":"reviewerAgreed","content":"160818228284972047581914329189917912533","date":"2026-05-15T16:55:37+00:00","index":37,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-06T18:49:52+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-06 18:49:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9232031","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9232031","identity":"rs-9232031","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}