Neck muscle biomechanics and neural control

Abstract

The mechanics, morphometry, and geometry of our joints, segments, and muscles are fundamental biomechanical properties intrinsic to human neural control. The goal of our study was to investigate whether the biomechanical actions of individual neck muscles predict their neural control. Specifically, we compared the moment direction and variability produced by electrical stimulation of a neck muscle (biomechanics) to the preferred activation direction and variability (neural control). Subjects sat upright with their head fixed to a six-axis load cell and their torso restrained. Indwelling wire electrodes were placed into the sternocleidomastoid (SCM), splenius capitis (SPL), and semispinalis capitis (SSC) muscles. The electrically stimulated direction was defined as the moment direction produced when a current (2–19 mA) was passed through each muscle’s electrodes. Preferred activation direction was defined as the vector sum of the spatial tuning curve built from root mean squared electromyogram when subjects produced isometric moments at 7.5% and 15% of their maximum voluntary contraction (MVC) in 26 three-dimensional directions. The spatial tuning curves at 15% MVC were well defined (unimodal, P < 0.05), and their preferred directions were 23°, 39°, and 21° different from their electrically stimulated directions for the SCM, SPL, and SSC, respectively (P < 0.05). Intrasubject variability was smaller in electrically stimulated moment directions compared with voluntary preferred directions, and intrasubject variability decreased with increased activation levels. Our findings show that the neural control of neck muscles is not based solely on optimizing individual muscle biomechanics but, as activation increases, biomechanical constraints in part dictate the activation of synergistic neck muscles.

NEW & NOTEWORTHY Biomechanics are an intrinsic part of human neural control. In this study, we found that the biomechanics of individual neck muscles cannot fully predict their neural control. Consequently, physiologically based computational neck muscle controllers cannot calculate muscle activation schemes based on the isolated biomechanics of muscles. Furthermore, by measuring biomechanics we showed that the intrasubject variability of the neural control was lower for electrical vs. voluntary activation of the neck muscles.

INTRODUCTION

The mechanics, morphometry, and geometry of our joints, segments, and muscles are fundamental biomechanical properties intrinsic to human neural control. This concept applies equally to simple and complex musculoskeletal systems within the body, but in the neck, which includes >25 muscle pairs with multijoint insertions and complex lines of action, the relationship between biomechanics and neural control has been difficult to unravel. The question we pose here is whether the biomechanics (e.g., line of action and variability) of individual neck muscles is a useful indicator of how we voluntarily activate them to generate forces/moments in different directions.

Spatial tuning curves have been used to explore how humans control and activate neck muscles. These tuning curves define a muscle’s preferred direction during voluntary isometric tasks (Blouin et al. 2007; Keshner et al. 1989; Vasavada et al. 2002) and reflexive activity during seated perturbations (Ólafsdóttir et al. 2015). Apart from the splenius capitis (SPL) muscle, consistent preferred directions were observed among volunteers for each muscle studied (Blouin et al. 2007; Keshner et al. 1989). This consistency is perhaps surprising given that there are more actuators, i.e., neck muscles, than there are degrees of freedom of head movement, possibly making the head-neck system a redundant system (Bernstein 1967). The previously reported consistency in preferred directions suggests a common (or similar) neural control strategy among participants for the active generation of neck forces and moments, which in turn suggests that voluntary and reflexive control of the neck muscles are based, at least in part, on biomechanical constraints.

In human volunteers, the biomechanical function of individual neck muscles is difficult to characterize because humans cannot voluntarily activate isolated neck muscles. Instead, muscle morphometric measurements in cadavers (Anderson et al. 2005; Kamibayashi and Richmond 1998) have been combined with a computational model of the head and neck (Vasavada et al. 1998) to infer the biomechanical function of neck muscles. A comparison of the biomechanical lines of action predicted by this computational model to the preferred directions derived from the spatial tuning curves of human participants revealed differences of at least 45° for the sternocleidomastoid (SCM), SPL, and semispinalis capitis (SSC) muscles and 15° for the trapezius muscle (Vasavada et al. 2002). These differences imply that individual neck muscles are not activated according to their biomechanics alone; however, the lines of action used for this analysis were inferred from a computational model and have not been validated in human volunteers. Muscle-induced intersegmental dynamics have proven very complex and difficult to unravel even with detailed computational models of the neck (Cox et al. 2014; Suderman et al. 2012). Differences between a muscle’s line of action and its neural control have also been reported in appendicular muscles (Buchanan et al. 1989; Hoffman and Strick 1999; Kurtzer et al. 2006; Nozaki et al. 2005; van Zuylen et al. 1988), but these studies relied upon textbook definitions of a muscle’s predicted line of action and did not attempt to measure the biomechanics of human muscles.

One way to overcome these limitations and characterize experimentally the biomechanics of individual human neck muscles is to measure the neck moment produced during intramuscular electrical stimulation. Intramuscular stimulation can selectively activate individual muscles (Popovic et al. 1991), and stimulation of individual muscles has been used to validate biomechanical models in the lower limbs (Hunter et al. 2009; Riener et al. 1996). Moreover, using the same electrodes to stimulate a muscle to establish its moment direction and to measure a muscle’s activity to establish its preferred directions provides an opportunity to determine how the biomechanics of a muscle shapes its neural control.

The goal of our study was to investigate whether the biomechanics of individual neck muscles can predict the neural control of these muscles when activated voluntarily. To achieve this goal, we first compared the neck moment direction generated by electrically stimulating a volunteer’s individual neck muscle, which measures the underlying biomechanics of the muscle, to the preferred direction derived from that volunteer’s spatial tuning curve, which represents the neural control of the muscle. In light of previous findings in the neck (Vasavada et al. 2002), we hypothesized that the electrically stimulated directions and voluntary preferred directions for each muscle would not align. Second, we compared the intrasubject variability for both the electrically stimulated responses and voluntary preferred directions. Jones et al. (2002) argued that motor variability was mostly of central origin because it increased with the magnitude of voluntary activation but not with the amplitude of muscle electrical activation. Accordingly, we hypothesized that variability in the voluntary preferred direction would be larger than variability of the electrically activated moment direction. This secondary analysis was possible because we measured the biomechanical action of each subject’s muscles rather than assuming or computing its action. The results of this study will help further our understanding of the complexity of the neural control of the human neck muscle system and provide valuable data for the design of neck muscle controllers for use in computational neck models.

METHODS

Participants.

Eight male participants (29 ± 4 yr, 179 ± 4 cm, 82 ± 9 kg) with no history of whiplash injury, neck/back pain, frequent/severe headaches, or neuromuscular injury participated in the study. Participants provided written informed consent before participating in the study, which was approved by the University of British Columbia Clinical Research Ethics Board and conformed to the Declaration of Helsinki.

Each participant had indwelling electrodes (0.076 mm 304 SS; California Fine Wire Company, Grover Beach, CA) inserted into the right SCM, SPL, and SSC under ultrasound guidance (MicroMaXX; Sonosite, Bothell, WA) (Fig. 1B). Two single wires were inserted into each muscle to achieve an ~20-mm electrode spacing along the muscle fiber direction. This spacing was used to increase the volume of muscle that was activated during electrical stimulation and recorded during voluntary activation of the muscles. Each wire had ~2 mm of insulation removed at the hooked tip, and the electrode pairs were placed near the center of each muscle’s horizontal cross section nominally spanning the C₃–C₅ levels (Fig. 1B). In the SCM, the wires always remained superficial to the readily identifiable cleidomastoid subvolume (Kamibayashi and Richmond 1998).

Fig. 1.

A: experimental set-up showing the subject seated with their torso constrained and their head fixed to a 6-axis load cell through a tightly fitting modified helmet. B: approximate locations of indwelling electrodes that were inserted with ultrasound guidance in the right sternocleidomastoid (top left), splenius capitis (top right), and semispinalis capitis (bottom) [adapted from Gray (1918), with permission of bartleby.com]. C: spatial tuning task contraction directions and direction cosines for 1 quadrant of the 26 contraction directions performed. M, moment; R, right; Axl, axial; Ext, extension; Lat. Bend., lateral bending. D: visual feedback provided to the subjects during the spatial tuning task. Subjects generated horizontal plane moment to move the dot into the circle and generated axial moments to rotate the dial into the wedge. MVC, maximum voluntary contraction.

Spatial tuning curve procedures.

Participants sat with their torso constrained to a vertical seatback and their head fixed to a six-axis load cell (model 45E15; JR3, Woodland, CA) through a tightly fitting helmet (Fig. 1A). At least a day before the main experiment, participants were familiarized with the setup and performed isometric maximum voluntary contractions (MVCs) with visual feedback in the six principal directions (flexion/extension, left/right lateral bending, and left/right axial rotation) with two trials per direction (Fice et al. 2014). The MVC magnitudes for each participant were defined as the peak moment measured in each direction over the two trials. The MVCs were done beforehand to avoid fatigue during the main experiment, and they were done without the wire electrodes.

For the main experiment, the spatial tuning task consisted of isometric contractions in 26 three-dimensional (3D) moment directions while neck muscle electromyogram (EMG) and head reaction forces and moments were recorded at 2 kHz (PXI-6221; National Instruments, Austin, TX). EMG signals were amplified (×2,000) and filtered with a Neurolog system (Digitimer, Welwyn Garden City, UK). The 26 target directions were defined in a normalized moment space (Fig. 1C; Fice et al. 2014) and then personalized for each subject by multiplying the components for each normalized direction by the subject’s corresponding principal-direction MVCs to calculate their target resultant moments (Eq. 1). Note that we define our moment directions by the direction of the axis about which a moment is generated, e.g., flexion has a moment direction toward the left ear, which is considered a moment in the horizontal plane. Eight of the target directions were in the horizontal plane (flexion, extension, left and right lateral bending, and combinations of each, all with no axial moment) and then repeated with both a left and a right axial moment to yield 24 target directions. Pure left and right axial moments brought the total to 26 target directions. Contractions in all target directions were performed at two magnitudes (7.5% or 15% MVC). Participants were given visual feedback of their 3D moments on a computer screen, which showed their axial rotation moment with a rotating needle indicator whose origin moved to indicate their horizontal plane moments (Fig. 1D). A contraction was considered successful if the resultant magnitude of the subject’s moment error (3D target moment minus 3D actual moment) remained less than 10% of the target resultant moment magnitude for 1 s. Contractions at each target direction were performed three times at both magnitudes, and the presentation order was randomized for direction within blocks of the same magnitude. Each block contained 26 trials (1 in each direction), and block order was randomized between subjects.

$${\overrightarrow{M}}_{\text{target}}=\left({\text{NT}}_{\text{Flex/Ext}}\times {\text{MVC}}_{\text{Flex/Ext}}\right)\overrightarrow{i}+\left({\text{NT}}_{\text{LatBend}}\times {\text{MVC}}_{\text{LatBend}}\right)\overrightarrow{j}+\left({\text{NT}}_{\text{AxlRot}}\times {\text{MVC}}_{\text{AxlRot}}\right)\overrightarrow{k}$$ (1)

where ${\overrightarrow{M}}_{\text{target}}$ is personalized target moment vector, NT_Flex/Ext, NT_LatBend, and NT_AxlRot are the components of the normalized target moment directions (Fig. 1C), MVC_Flex/Ext is maximum flexion or extension moment, MVC_LatBend is maximum left or right lateral bending moment, MVC_AxlRot is maximum left or right axial moment, and $\overrightarrow{i}$, $\overrightarrow{j}$, and $\overrightarrow{k}$ are unit vectors in the flexion/extension, lateral bending, and axial rotation directions, respectively.

Neck muscle stimulation procedures.

The indwelling electrodes were connected to a constant-current stimulator (model DS5; Digitimer) to electrically activate the neck muscles one at a time. The stimulation waveform consisted of 20 trains, with a random intertrain interval of 2–5 s. Each train consisted of three square-wave pulses with duration of 0.5 ms and interpulse interval of 10 ms. This waveform was selected to deliver a substantial contraction while reducing discomfort for the participant and was based on pilot work and prior studies (Binder-Macleod et al. 1995, 1998; Crago et al. 1980; Popovic et al. 1991). The neck moments induced by electrical stimulation are produced by the activation of a single muscle rather than by multiple muscles for the voluntary tuning task. Since we expected the moment generated by an individual muscle during electrical stimulation to be less than the net moment generated by multiple neck muscles during voluntary activation, we chose two levels of electrical stimulation that generated moments that were about half those used to quantify the voluntary preferred directions. The high stimulation amplitude for each participant was set to generate a target moment of 7.5% MVC and was compared to the muscle’s preferred direction for the voluntary task at 15% MVC. In some cases, the participant found this stimulation level too uncomfortable and their maximum tolerable amplitude was utilized as the high-level stimulation. The actual moment was <80% of the target moment in five subjects’ SPL muscle and one subject’s SSC muscle, with the lowest moment being 44% of the target moment. The low stimulation amplitude for each participant was set to generate a target moment of ~3.5–4% MVC. Across all subjects, the high-level electrical current amplitude varied from 2 to 5 mA for SCM, from 7 to 18 mA for SPL, and from 7 to 19 mA for SSC, and the low-level current amplitude was half of these values. Participants were instructed to remain relaxed while the stimulation was delivered. The 3D moments generated by each stimulated neck muscle were recorded by the load cell.

All moments were resolved to the C₇–T₁ joint axis by calculating the reaction moment required at C₇–T₁ to balance the moments and forces measured at the load cell. The location of the C₇–T₁ joint axis in each participant was determined as the midpoint of the line joining the sternal notch and the C₇ spinous process (Queisser et al. 1994) measured with a 3D localizer (Polaris Vicra; Northern Digital, Waterloo, ON, Canada).

Data analysis.

To calculate the voluntary preferred directions from the spatial tuning task, we selected from each trial the 500-ms window with the smallest standard deviation in the resultant moment from within the 1-s period when the participant was generating a moment within 10% of the target moment. The corresponding EMG data for each muscle were digitally filtered to remove movement artifacts (4th order, dual pass, Butterworth, 50 Hz high pass), and then the root mean squared (RMS) magnitude was calculated over the whole 500-ms window to define the amplitude of the muscle activity for each trial (Fig. 2). These amplitudes were averaged across the three repetitions for each direction and magnitude, and then the average was normalized by the largest average RMS EMG for each muscle across all directions within each contraction level, i.e., normalized RMS EMG varied between 0 and 1. Separate 3D spatial tuning curves were then created for each muscle and contraction magnitude for each subject using spherical coordinates, with each of the 26 contraction directions represented by a radial vector whose length was equal to the average normalized RMS across the three repetitions in that direction and whose 3D direction was defined by the vectorial average of the moment directions across the same three repetitions (Fisher et al. 1987). Each muscle’s preferred direction was then calculated from the vectorial sum of the 26 radial vectors, and its focus was calculated as the magnitude of the vectorial sum of the 26 radial vectors divided by the arithmetic sum of the magnitudes of the radial vectors. The spatial tuning curves were visually inspected for unimodality and tested to determine whether they were statistically different from a uniform distribution with a Rayleigh test on the focus (Batschelet 1981; n = 26, P < 0.05). We defined the moment axis of the voluntary preferred direction in spherical coordinates using an azimuth angle (φ) in the horizontal plane (0° at extension, positive 90° at right lateral bending) and an elevation angle (θ) above the horizontal plane (positive angles denote left axial rotation).

Fig. 2.

One subject’s filtered electromyogram (EMG) for 1 repetition in 26 target directions at 15% maximum voluntary contraction (MVC) for the sternocleidomastoid (SCM), splenius capitis (SPL), and semispinalis capitis (SSC) muscles. The shaded band spanning all 3 muscles shows the 500-ms window that was selected based on minimal moment variability. Data beyond this band are not shown. The relative height of the dark columns within each shaded band represents the magnitude of the root mean squared (RMS) EMG within that window. Positive moments are depicted as curved arrows about the moment axis (straight arrows).

To determine the electrically stimulated directions for both the high and low levels, the C₇–T₁ 3D moment components from the electrical stimulation trials of each muscle were aligned to the stimulus onset and averaged over the 20 stimulation trains. Before averaging of the moment components, the bias was removed by subtracting the mean of each component over the 25 ms before each electrical stimulation train. The resultant moment was then calculated from the averaged components, and the direction of the moment was calculated at the time of the peak resultant moment.

To test our first hypothesis, we performed a pairwise, i.e., within-subject, comparison of each muscle’s voluntary preferred direction and electrically stimulated direction for the high and low levels. We performed this comparison by first rotating both direction vectors of each muscle about an axis in the horizontal plane so that the electrically stimulated direction vector aligned with the pole (θ = 90°). These rotations maintained the 3D angular difference between each participant’s voluntary preferred direction and their electrically stimulated direction but aligned the electrically stimulated directions of all participants with the pole (θ = 90°). A 95th percentile confidence ellipse was then calculated for the rotated voluntary preferred directions of each muscle across all participants (Fisher et al. 1987; Kent 1982; Leong and Carlile 1998). If the 95th percentile confidence ellipse included the pole, then we concluded that there was no statistical difference between the voluntary preferred direction and the electrically stimulated direction for that muscle. The mean difference between the voluntary preferred and electrically stimulated directions was reported as the angle between the centroid of the 95th percentile confidence ellipse and the pole (θ = 90°). To give the reader a sense of the variability of these differences, the major and minor axes of the 95th percentile confidence ellipse are also reported.

To visualize the mean response across participants, voluntary preferred directions and electrically stimulated directions were vectorially averaged separately. The mean spatial tuning curve for each muscle was calculated by taking the vectorial average of each direction from each participant’s spatial tuning curves (Fisher et al. 1987). To estimate the variability across participants, we fit a Kent distribution to both the voluntary preferred directions and the electrically stimulated directions and then calculated the standard deviation ellipse (Fisher et al. 1987; Leong and Carlile 1998).

Intrasubject variability of the voluntary preferred direction was estimated by resampling the spatial tuning curves for each subject’s muscle 10,000 times by randomly selecting the RMS EMG from one of three repetitions for each of the 26 directions that made up the spatial tuning curve. The preferred direction from each tuning curve was then calculated with the same method described above. A Kent distribution (Kent 1982; Leong and Carlile 1998) was then fit to the 10,000 preferred directions. Finally, the radius of a circle with the same area as the standard deviation ellipse was used as a univariate estimate of each muscle’s variability. This equivalent radius (expressed in °) was calculated separately for both the 7.5% MVC and 15% MVC contraction levels for all three muscles and for all subjects. The intrasubject variability of the electrically stimulated directions was estimated by fitting a Kent distribution (Kent 1982; Leong and Carlile 1998) to the 20 moment vectors measured for the 20 stimulation trains performed for each stimulation intensity, muscle, and subject. Similarly, an equivalent radius was calculated for each standard deviation ellipse representing the intrasubject variability for each stimulation intensity, muscle, and subject.

To test our second hypothesis, i.e., to determine whether intrasubject variability is larger for the voluntary preferred directions compared with the electrically stimulated directions, we performed a repeated-measures two-way ANOVA on the equivalent radius (voluntary vs. electrical activation; low vs. high intensity). This analysis was performed separately for each muscle studied. Because the equivalent radii were not normally distributed (Shapiro-Wilk test; P > 0.05), a Box-Cox transformation was applied (λ = −0.65). No outliers were found in the transformed data, as determined by the studentized residuals being within ±3. Any interaction observed between terms in the ANOVA was decomposed with post hoc Tukey honestly significant difference test.

All data processing and statistical analyses were performed in MATLAB (MathWorks, Natick, MA) except for the repeated-measures ANOVA and its related Box-Cox transformation and post hoc testing, which were performed with Statistica (TIBCO Software, Palo Alto, CA). Statistical significance was set to P < 0.05.

RESULTS

The raw and RMS EMG data for the voluntary contractions revealed stereotypical patterns of activity for each muscle and contraction direction (Fig. 2). In SCM, the voluntary activation levels were larger for contraction directions that included flexion, ipsilateral bending, and contralateral axial rotation. In SPL the voluntary activation levels were larger for contraction directions that included extension and ipsilateral axial rotation, whereas in SSC the voluntary activation levels were larger for contraction directions that included an extension moment but no axial rotation.

The 24 spatial tuning curves (8 subjects × 3 muscles) generated from the voluntary contraction data had better-defined preferred directions at the 15% MVC activation level than at the 7.5% MVC activation level. At 15% MVC, 21 of the 24 spatial tuning curves were significantly different from a uniform distribution (focus = 0.35–0.71; P = 0.00–0.04). The three distributions that were not different from uniform were in SSC for two subjects and in SCM for a third subject (focus = 0.31–0.33; P = 0.06–0.08). At 7.5% MVC, only 16 of the 24 spatial tuning curves were significantly different from a uniform distribution (focus = 0.34–0.70; P = 0.00–0.05). The eight distributions that were not different from uniform included SCM and SPL in two subjects, SPL and SSC in another subject, and finally SCM and SPL in separate subjects (focus = 0.18–0.33; P = 0.06–0.42).

When the voluntary contraction data from all subjects were combined, the average spatial tuning curves for the three muscles were unimodal and significantly different from a uniform distribution at the 15% MVC level (focus = 0.44, 0.39, 0.52; P = 0.01, 0.02, 0.00 for SCM, SPL, and SSC) but not at the 7.5% MVC level (focus = 0.30, 0.30, 0.51; P = 0.09, 0.09, 0.00 for SCM, SPL, and SSC; Fig. 3). In light of these findings, we limited the analyses of the voluntary preferred directions to the 15% MVC data. At the 15% MVC contraction level, the average voluntary preferred direction for SCM consisted primarily of flexion and ipsilateral bending, with a component of contralateral axial rotation (Fig. 3), and this was consistent across participants. SPL’s average voluntary preferred direction was predominantly extension, with a small component of ipsilateral axial rotation and variable amounts of lateral bending: three participants had a contralateral bending component, three had a minimal lateral bending component, and two had an ipsilateral bending component. SSC’s average voluntary preferred direction was almost entirely extension, although one participant had some contralateral bending and another had a large ipsilateral bending component.

Fig. 3.

Spatial tuning plots for the 15% maximum voluntary contraction (MVC) contraction of the sternocleidomastoid (SCM), splenius capitis (SPL), and semispinalis capitis (SSC) muscles showing the voluntary preferred directions (black) and the electrically stimulated directions (gray) (n = 8 subjects). Three different views are shown for each muscle. Left: the mean directions and standard deviation (SD) ellipses projected into the horizontal plane. Center: the mean directions and SD ellipses projected in the sagittal plane. Right: a 3-dimensional view with the SD ellipses (found by fitting a Kent distribution) and individual subject’s voluntary preferred directions and electrically stimulated directions. Left and center columns also show the tuning plots (black dashed line) plus 1 SD (gray shading) for the directions that lie within the horizontal and sagittal planes, respectively. The mean voluntary preferred direction shown here is the vector sum of all points on the spatial tuning curve of all subjects. The electrically stimulated direction shown here is the direction of moment generation when the muscle was electrically stimulated. Both the mean voluntary preferred and electrically stimulated directions are shown with arcs to represent the SD elliptical cone across subjects.

Electrical stimulation generated well-defined moments for each muscle in all participants (see exemplar data from 1 subject in Fig. 4). For SCM the grouped high-level electrically stimulated direction was primarily lateral bending, with components in flexion and contralateral axial rotation (Fig. 3). In SPL the high-level electrically stimulated direction was primarily ipsilateral bending and extension, with a component of ipsilateral axial rotation, whereas in SSC the electrically stimulated direction was predominantly extension, with smaller components of ipsilateral axial rotation and lateral bending (Fig. 3).

Fig. 4.

Exemplar data from 1 subject showing the moments generated by electrical stimulation in sternocleidomastoid (SCM; top), splenius capitis (SPL; middle), and semispinalis capitis (SSC; bottom). The shaded area is the mean moment response ± 1 standard deviation. Positive values denote flexion, right lateral bending, and right axial moments.

The voluntary preferred directions and electrically stimulated directions were significantly different for all three muscles at the high level (95% confidence ellipse did not include the pole; Tables 1 and 2, Fig. 3). The high-level electrically stimulated direction for the SCM included more lateral bending and less axial rotation than its voluntary preferred direction. For SPL the high-level electrically stimulated direction included more lateral bending and slightly less axial rotation, whereas for SSC the high-level electrically stimulated direction included more lateral bending and axial rotation (Fig. 3, Table 1). The differences in average directions ranged from 21° for SSC to 39° for SPL.

Table 1.

Voluntary preferred and electrically stimulated directions

Muscle	Voluntary Preferred Direction			Muscle	Electrically Stimulated Direction
	Azimuth φ, °	Elevation θ, °	Focus		Azimuth φ, °	Elevation θ, °
7.5% MVC				Low Stim.
SCM	113.0	24.7	0.30 (P = 0.09)	SCM	101.1	13.4
SPL	−3.5	−41.3	0.30 (P = 0.09)	SPL	41.5	−35.0
SSC	8.1	−4.1	0.51 (P = 0.00)*	SSC	23.3	−9.2
15% MVC				High Stim.
SCM	130.8	20.4	0.44 (P = 0.01)*	SCM	105.1	9.8
SPL	−3.3	−35.8	0.39 (P = 0.02)*	SPL	42.2	−31.1
SSC	6.4	−3.4	0.52 (P = 0.00)*	SSC	26.0	−12.6

Average azimuth and elevation angles for the mean voluntary preferred directions at 7.5% and 15% maximum voluntary contraction (MVC) and electrically stimulated directions for right sternocleidomastoid (SCM), splenius capitis (SPL), and semispinalis capitis (SSC) muscles across all 8 subjects. The azimuth angle (φ) was defined in the horizontal plane, with 0° at extension, positive 90° denoting right lateral bending. The elevation angle (θ) denotes axial rotation, with positive angles to the left and negative angles to the right.

^*The spatial tuning curve for the voluntary preferred direction pooled across subjects was significantly different from a uniform distribution (P < 0.05). Tested using the focus calculated over the 26 contraction directions and a Rayleigh test; see methods for details.

Table 2.

Difference between voluntary preferred and electrically stimulated directions

Muscle	Difference Δ, °	95% CI Major, °	95% CI Minor, °
SCM	23.4*	9.3	6.9
SPL	38.6*	21.3	8.9
SSC	21.3*	18.2	4.1

Difference between the voluntary preferred directions at 15% maximum voluntary contraction (MVC) and the high-level electrically stimulated directions (n = 8 subjects). Also included are the major and minor axes of the 95th percentile confidence ellipses (CI) of the differences. At P < 0.05 there was a significant difference in the angular difference for all 3 muscles. SCM, sternocleidomastoid; SPL, splenius capitis; SSC, semispinalis capitis.

^*The voluntary preferred direction was significantly different from the electrically stimulated direction (P < 0.05). Tested by checking whether the 95% confidence ellipse included the pole; see methods for details.

For all muscles, the intrasubject variability (estimated with the equivalent radius) was largest for the voluntary preferred direction at the low activation intensity (Fig. 5). These observations were confirmed by the results of the repeated-measures ANOVA: for SPL and SSC, main effects revealed larger intrasubject variability for the voluntary activation than for electrical stimulation (SPL: 2.43°, F_1,7 = 23.8, P = 0.0018; SSC: 0.78°, F_1,7 = 6.2, P = 0.042) and larger intrasubject variability for low activation intensity than for high activation intensity (SPL: 0.86°, F_1,7 = 10.0, P = 0.016; SSC: 0.62°, F_1,7 = 20.9, P = 0.0026). For SCM, a significant interaction between activation type (electrical/voluntary) and intensity (F_1,7 = 9.8, P = 0.017) was observed. Post hoc testing of the interaction revealed that the intrasubject variability of the voluntary preferred directions was larger than the intrasubject variability of the electrically stimulated directions (0.55–3.66°, multiple P, all <0.016). Intrasubject variability also decreased from low to high voluntary activation intensity (3.1°, P = 0.0007), but the effect of activation intensity was not significant for the electrical activation of the SCM (0.37°, P = 0.06).

Fig. 5.

A: intrasubject variability of the electrically stimulated direction and voluntary preferred direction as shown by the standard deviation (SD) ellipse from a Kent distribution. Left: the low activation/stimulation level. Right: the high level. Top: sternocleidomastoid (SCM). Middle: splenius capitis (SPL). Bottom: semispinalis capitis (SSC). B: the radius with the equivalent area of the SD ellipses for each subject and muscle. Note that means and SDs were calculated on Box-Cox-transformed data (λ = −0.65), which were then transformed back.

DISCUSSION

Our goal was to determine whether the biomechanical actions of individual neck muscles are useful indicators of their neural control. To achieve this goal, we compared a volunteer’s preferred activation direction of three neck muscles during voluntary isometric contractions (representing their neural control) to the direction of the moment produced by the same muscles when electrically activated (representing their biomechanical action). We found that the average directions of voluntary activation and electrical stimulation differed by from a minimum of 21° (SSC) to a maximum of 39° (SPL) at the 15% MVC level. We also found that the intrasubject variability decreased for electrical vs. voluntary activation of the neck muscles as well as for larger activation intensity. These differences between the electrically stimulated direction and the voluntary preferred direction show that a neck muscle’s function is not based solely on its underlying biomechanics and that the neural system that controls neck muscles combines the biomechanics of numerous muscles to achieve its simultaneous goals of movement and stability.

In all three muscles, we observed larger components in the right lateral bending direction during electrical stimulation than during voluntary activation. This finding is perhaps not surprising given the isolated, unilateral nature of the electrical stimulus. A more unexpected finding was the reduced left axial rotation component for SCM during electrical stimulation compared with voluntary activation. The geometry of the right SCM suggests that the mastoid process would be pulled forward during isolated activation of this muscle (Kamibayashi and Richmond 1998), and thus we expected the electrical activation to have a larger left axial rotation component compared with voluntary activation. Another unexpected result was a right axial rotation component that increased more for SSC than SPL during the electrical stimulation compared with the voluntary activation. The line of action of SPL is more lateral and less vertical than the line of action of SSC (Kamibayashi and Richmond 1998), and therefore we expected isolated electrical activation to increase the right axial rotation component more in SPL than in SSC. Our expectations were based on an assumption that the neural control of the neck muscle system would attenuate rather than amplify the underlying biomechanics of individual muscles. However, these findings show that our assumption was incorrect and provide compelling evidence that the neural control can both attenuate and amplify, sometimes simultaneously, different components of a muscle’s underlying biomechanics.

The 21–39° differences between the voluntary preferred directions and the measured biomechanical directions are smaller than the 45–65° differences reported by Vasavada et al. (2002) for the same muscles. Vasavada et al. (2002) compared the voluntary preferred directions of various neck muscles to a computational model that predicted the direction of the muscle’s moment arm. The 3D directions of our electrically stimulated muscles closely matched the lines of action predicted by their computational model, and thus the primary differences between our findings and their findings reside in the voluntary preferred directions. The voluntary preferred directions in their work had larger axial rotation components for all three muscles, a larger flexion component for SCM, and smaller lateral bending components for SPL and SSC. Methodological differences in the voluntary contraction procedures, particularly our use of moments normalized to the volunteer’s MVC components compared with their use of fixed moments that ranged from 11% of the maximum extension moment to 80% of the maximum axial rotation moment, could explain these different results.

The differences in preferred direction that we observed between the average voluntary and electrically stimulated directions in human neck muscles are similar to those observed for human appendicular muscles, where the preferred activation direction does not align with assumed biomechanical function based on musculoskeletal anatomy (Buchanan et al. 1989; Hoffman and Strick 1999; Kurtzer et al. 2006; Nozaki et al. 2005; van Zuylen et al. 1988). Our findings also agree with observations in primate neck muscles, where natural head movements are generated by neck muscle activity that does not accord solely with the muscle’s assumed biomechanical function (Farshadmanesh et al. 2012a, 2012b). A major distinction from previous appendicular and cervical work, however, is that we measured the biomechanical line of action for each muscle with electrical activation instead of relying on the muscle’s line of action reported in anatomical textbooks or computational models. This is particularly important in the neck because >25 pairs of muscles act over several joints with multiple degrees of freedom, with some muscles not even attaching to the cervical vertebrae. All these factors render assumptions regarding the line of action of a neck muscle based on its insertion/attachment points and fiber orientation difficult.

Measuring the biomechanical action of a muscle also allowed us to determine that the intrasubject variability was 1) larger for the voluntary preferred direction than the electrically stimulated direction and 2) decreased as the level of activation increased. The generally larger intrasubject variability for voluntary preferred directions supports the notion that the nervous system is optimizing something other than individual muscle biomechanics. This “other” optimization strategy may be due to the neck’s large mobility, which constrains the size and line of action of the neck muscles, meaning that not enough muscles may be optimally aligned to perform a given task. For example, there are no large neck muscles devoted entirely to generating axial moments; muscles like the right SPL and left SCM will act agonistically to generate right axial rotation, but the moments they generate in other directions need to be cancelled by each other and/or other muscles. Indeed, many predominantly vertical muscles may be used in nonoptimal ways to generate axial moment (Ackland et al. 2011; Peterson et al. 2001; Vasavada et al. 1998) and could explain our findings that the voluntary preferred directions of SCM and SPL were more aligned with axial rotation than the electrically stimulated directions.

For the voluntary activations, the decreased intrasubject variability we observed with increased activation level may appear to contradict the concept of signal-dependent noise. This latter concept stipulates that, within a single muscle, the variability of force output increases as the voluntary activation increases (Harris and Wolpert 1998; Jones et al. 2002; Schmidt et al. 1979). However, to produce our voluntary preferred directions we did not measure the variability of a single neck muscle; rather, we measured the variability of a group of neck muscles working synergistically. For multiple muscles acting across a joint, there is only one combination of muscle activation that can achieve the MVC effort in any given direction (Valero-Cuevas 2000). As activation decreases (i.e., submaximal activation of the muscles), multiple combinations of muscle activation can achieve the target force direction, similar to the redundancy problem where more muscles can be activated across a joint than there are degrees of freedom for that joint (Bernstein 1967). Hence, the multiple combinations of neck muscles available to produce a submaximal effort in a given direction could explain the larger intrasubject variability in the preferred direction for lower activation levels. Potentially, the biomechanical constraints in the neck musculoskeletal system increasingly dictate the activation of muscles contributing to the net neck moment, resulting in lower intrasubject variability in preferred direction as activation level increases. It is also possible that subtle differences in posture and stability requirements of the unstable neck system between repetitions contribute to the variability in voluntary muscle activation to produce the target 3D moment.

Our findings have potentially important implications on musculoskeletal modeling of the human neck. The latest neck modeling efforts use feedback controllers for muscle activation (Iwamoto et al. 2012; Östh et al. 2015), but the neural motor controller cannot be a simple feedback controller because the neural delays and sensorimotor noise would make the system (i.e., our body) unstable (Franklin and Wolpert 2011). Thus, as we move toward more advanced physiologically based neck muscle controllers, it may be tempting to make simplifying assumptions about the neural control of neck muscles and its variability. One such assumption is that the neural controller will activate a neck muscle when generating or resisting a load on the head that is aligned with that muscle’s biomechanical line of action. This assumption would simplify a physiologically based muscle controller, although the results we present show that such a simplification is not valid. In the future, optimal feedback control (reviewed in Scott 2012) could potentially be used to generate muscle activation schemes in novel situations that would improve the biofidelity of neck models, and data from the results we present here could be used to build the muscle activity vs. moment relationships.

A limitation of our work is the number of contraction directions we examined. We attempted to balance the number of directions and repetitions against the risk of subject fatigue. Future work could eliminate the lower activation level and increase the number of contraction directions at a single activation level to better map the entire contraction direction space. Because the voluntary preferred direction data at 7.5% MVC were not significantly different from a uniform distribution, we only compared voluntary data at 15% MVC to electrically stimulated data that attempted to match the 7.5% MVC level. The similarity in preferred directions between the nonsignificant 7.5% MVC and significant 15% MVC distributions justifies this decision. It further suggests that the uniform distributions observed at the 7.5% MVC level were likely due to sample size. Also, our intrasubject variability analysis was based on electrical stimulation data at ∼3.75% and ∼7.5% MVC vs. voluntary data at 7.5% and 15% to match the expected decrement in moment from activating a single muscle during the electrical stimulation relative to activating multiple muscles during the voluntary tuning task. This 50% reduction may not accurately reflect the actual contribution of the other muscles during the voluntary task; however, given that variability increased with lower voluntary activation levels, we would expect the reported differences between voluntary and electrical activation to be larger if we had matched the voluntary activation and electrically stimulated levels of muscle activation. We also stimulated the muscles with a single pair of indwelling electrodes spaced ∼20 mm apart to increase the number of muscle fibers that were electrically activated. It is possible that the moment generated with this electrode spacing may not represent the biomechanics of the whole muscle but rather only the muscle fibers that were activated by the stimulation electrodes. Differences between local and whole muscle biomechanics could stem from functional compartmentalization in the neck muscles (Richmond et al. 1985; Wilson et al. 1983), although a preliminary study did not identify obvious compartments in SPL (Siegmund et al. 2011). Finally, our results are limited to three muscles tested during an isometric task in a single neck posture. There are >20 other muscle pairs in the neck, and further work is needed to examine their neural control in different postures and during different tasks.

In summary, the voluntary preferred directions of SCM, SPL, and SSC derived from an isometric spatial tuning task in the neutral head/neck posture did not align with the moment produced when electrically stimulating those muscles in the same posture. The intrasubject variability of the voluntary preferred direction was larger than the electrically stimulated direction and decreased as the level of activation increased. These findings show that the neural control of neck muscles is not solely optimized for their biomechanical function but, as activation increases, biomechanical constraints in part dictate the activation of synergistic neck muscles contributing the net moments in a target direction. The concept of biomechanical constraints dictating synergistic muscle activation with larger net forces/moment requires further validation in other multimuscle, multisegment musculoskeletal systems.

GRANTS

This work has been funded by the Natural Science and Engineering Research Council of Canada and the Canada Foundation for Innovation.

DISCLOSURES

G. P. Siegmund is an employee and a director of and owns shares in MEA Forensic Engineers & Scientists, a forensic consulting company, and he may benefit from being involved in this study. No conflicts of interest, financial or otherwise, are declared by the other authors.

AUTHOR CONTRIBUTIONS

J.B.F., G.P.S., and J.-S.B. conceived and designed research; J.B.F. performed experiments; J.B.F. analyzed data; J.B.F., G.P.S., and J.-S.B. interpreted results of experiments; J.B.F. prepared figures; J.B.F. drafted manuscript; J.B.F., G.P.S., and J.-S.B. edited and revised manuscript; J.B.F., G.P.S., and J.-S.B. approved final version of manuscript.