Edo de Bruijn
ISOMETRIC AND D
YNAMIC CONTROL OF NECK MUSCLES: REFLEXIVE CONTRIBUTIONS AND MUSCLE S
YNERGIES
EDO DE BRUIJN
ISOMETRIC
AND DYNAMIC
CONTROL OF
NECK MUSCLES
reflexive contributions
and muscle synergies
EDO DE BRUIJN
ISOMETRIC and d
ynamic control of neck muscles
reflexive contributions and
muscle
synergies
Thesis defense -
15 DECEMBER 2014 - 12H00 - SENT
AA
TSZAAL
AULA
- MEKEL
ISOMETRIC
AND
DYNAMIC
CONTROL OF NECK MUSCLES
reflexive contributions and muscle synergies
Cover page design: Marlot te Kiefte
The Torticollis project:
This research is supported by the Dutch Technology Foundation STW, which is part of the Netherlands Organisation for Scientific Research (NWO) and partly funded by the Ministry of Economic Affairs (Project number 10736)
ISOMETRIC
AND
DYNAMIC
CONTROL OF NECK MUSCLES
reflexive contributions and muscle synergies
Proefschrift
ter verkrijging van de graad van doctor aan de Technische Universiteit Delft;
op gezag van de Rector Magnificus prof. ir. K.C.A.M. Luyben; voorzitter van het College van Promoties
in het openbaar te verdedigen op 15 december om 12:30 uur
door
Ir. EDO DE BRUIJN geboren te Braine l'Alleud
Dit proefschrift is goedgekeurd door de promotoren:
Prof. F.C.T. van der Helm Prof. M.A.J. de Koning-Tijssen
Copromotor: Dr. R. Happee
Samenstelling promotiecommissie:
Rector Magnificus, Technische Universiteit Delft, voorzitter
Prof. dr. F.C.T. van der Helm, Technische Universiteit Delft, promotor
Prof. dr. M.A.J. de Koning-Tijssen, Universitair Medisch Centrum Groningen, promotor
Dr. ir. R. Happee, Technische Universiteit Delft, copromotor
Prof. dr. H.E.J. Veeger, Technische Universiteit Delft
Prof. dr. ir. N.M. Maurits Rijksuniversiteit Groningen
Prof. W. Skalli ParisTech • Laboratoire de Biomécanique LBM
Dr. C.G.M. Meskers VU Medisch Centrum, Amsterdam
CONTENTS
List of abbreviations 9
Chapter 1: Introduction
Reflex loops and muscle patterns ... 13
Cervical dystonia ... 16
Problem statement ... 18
Main objectives ... 18
Research approach... 19
Thesis outline ... 20
INVESTIGATION OF NEUROMUSCULAR CONTROL IN HEALTHY SUBJECTS Chapter 2: Dependency of human neck reflex responses on the bandwidth of pseudorandom anterior-posterior torso perturbations Introduction ... 27 Methods ... 28 Data analysis ... 30 Results ... 32 Discussion ... 40 Conclusions... 45
Chapter 3: Analysis of isometric cervical strength with a nonlinear musculoskeletal model with 48 degrees of freedom Introduction ... 49
Methods ... 50
Results ... 57
Discussion ... 66
Chapter 4: Vestibular reflex gains and cocontraction increase during high bandwidth perturbations: a modeling approach Introduction ... 75
Methods ... 76
Results ... 82
ISOMETRIC EVALUATION OF CERVICAL DYSTONIA
Chapter 5: Improved identification of dystonic cervical muscles via abnormal muscle activity during isometric contractions
Introduction ... 97
Subjects and Methods ... 97
Results ... 102
Discussion ... 108
Concluding remarks ... 109
Chapter 6: Dystonic neck muscles show a relative shift in autospectral power during isometric contractions Introduction ... 115 Methods ... 116 Results ... 120 Discussion ... 123 Chapter 7: Epilogue Conclusions... 130 Discussion ... 131 Future directions ... 136 Concluding remarks ... 137 References 139 Summary 155 Samenvatting 161 Acknowledgments 167 Curriculum Vitae 171
LIST OF ABBREVIATIONS
BoNT botulinum toxin
C control group
C0 skull
C1-C7 first to seventh cervical vertebrae CCR cervicocollic reflex
CD cervical dystonia
CDF cumulative distribution function CDF10 cumulative distribution at 10 Hz CNS central nervous system
CoG Center of Gravity
DOF degree-of-freedom
EC eyes closed
eFRF EMG frequency response function
EMG electromyography
EO eyes open
HUMOS Human Model for Safety project ICR instantaneous center of rotation kFRF kinematic frequency response function LFP local field potential
MVC maximum voluntary contraction
P patient group
PMHS post-mortem human subject PCSA physiological cross-sectional area
RC resist contraction
RMS root-mean-square
SCM sternocleidomastoidus
SD standard deviation
SEM standard error of the mean SNR signal-to-noise ratio SPL splenius capitis SS semispinalis capitis
sub-MVC submaximal voluntary contraction T1 first thoracic vertebra
TWSTRS Toronto Western Spasmodic Torticollis Scale VAF variance accounted for
Chapter 1
12
Humans have about 650 skeletal muscles(Poole, 1986) to enable movement in daily life and the skill to use them differs largely between individuals. Where one always finds the back of the net with a well placed kick of the ball, the other finds the neighbor’s window. We know that practice can lead to improved control of the muscles, but while we are aware that learning to play the Für Elise1 requires ceaseless practice, we strangely take other skills for granted. An example is the use of roughly 80 muscles(Borst et al. , 2011) in the neck to balance our head against the pull of gravity and the vagaries of disturbances around us (Figure 1). How does this happen without us giving it a second thought? Apparently, automatized systems within our subconscious self can keep our head upright. In search of these systems humans have performed anatomical studies on muscles since ancient Egypt(Loukas et al. , 2011), and specifically on neck muscles since the late 19th
century(Ducic et al. , 2010). Physiological studies towards mechanisms of reflexive neck muscle control have been performed since the seventies(Richmond and Abrahams, 1979, Viviani and Berthoz, 1975b). Since then it has been hypothesized that the central nervous system (CNS) is able to adapt the dynamic response of the neck to improve its response against external disturbances, or perturbations. By modulating the contribution of reflexive pathways and levels of cocontraction it can vary the stiffness and damping properties of the
neck(Keshner and Peterson, 1995a, Peng et al. , 1996). Also, the CNS does not control all muscles individually with an infinite number of possible muscle combinations. Instead, the CNS activates preprogrammed muscle sets, known as muscle synergies, to stabilize and move the head(Keshner et al. , 1989, Vasavada et al. , 2002).
Figure 1: Lateral view of neck muscles. By Elisa Schorn, circa 1900. From the anatomical literature and drawings collection at Heidelberg University.
Introduction
13
REFLEX LOOPS AND MUSCLE PATTERNS
Keeping the head upright requires constant muscle control to counter the destabilizing effects of gravity. The stiffness of the passive head and neck system is by itself not sufficient to compensate for the downward gravitational pull, necessitating other mechanisms to keep the head from dropping. Two reflex pathways primarily contribute to the stabilization of the head: the cervicocollic reflex (CCR) and the vestibulocollic reflex (VCR). The CCR exerts a response generated from proprioceptive inputs of the muscle spindles in the neck when the head moves with respect to the torso. The VCR compensates for head movements in space measured by the vestibular organ. In addition, another strategy of the CNS is to stiffen the spine by activating agonist and antagonist muscles, called cocontraction(Choi, 2003, Keshner, Campbell, 1989, Vasavada, Peterson, 2002). However, continuous muscle cocontraction is energy consuming and the CNS prefers to use the more efficient reflexive control(Keshner, 2009a). The general consensus is that the VCR, CCR, and cocontraction jointly stabilize the neck and that their contributions are modulated, or weighted, as a function of task and environmental loading condition to achieve the best performance(Keshner et al. , 1995a, Peterka, 2002b, Schouten et al. , 2008a). These neural signals activate the appropriate muscles(Peng et al. , 1999, Reynolds et al. , 2008), but how these components interact is still unclear.
14
Figure 2: Schematic figure of the major contributors to neural control of neck muscles. The reflexes from muscle spindles (CCR) and the vestibular organ (VCR) are modulated by the brain in response to external perturbations and the received sensory information. The figure is adapted from Figure 790 of Gray's Anatomy(Gray, 1918).
cervicocollic reflex (CCR)
The CCR generates a compensatory response to the stretch of a neck muscle, or stretch reflex, using
information from muscle spindles and Golgi tendon organs(Peterson et al. , 1985). Muscle spindles measure muscle length and lengthening velocity and Golgi tendon organs measure force in the tendon of the muscle. Neck muscles contain a very large density of muscle spindles(Liu et al. , 2003, Richmond and Abrahams, 1979)(Liu et al. , 2003, Richmond and Abrahams, 1979)(Liu et al. , 2003, Richmond and Abrahams, 1979)(Liu et al. , 2003, Richmond and Abrahams, 1979)(Liu et al. , 2003, Richmond and Abrahams, 1979)(Liu et al. , 2003, Richmond and Abrahams, 1979)(Liu et al. , 2003, Richmond and Abrahams, 1979)(Liu et al. , 2003,
CCR VCR motor command voluntary command reflex modulation sensory information vestibular organ brain spinal cord
Introduction
15
Richmond and Abrahams, 1979)(Liu et al. , 2003, Richmond and Abrahams, 1979)(Liu et al. , 2003, Richmond and Abrahams, 1979)(Liu et al. , 2003, Richmond and Abrahams, 1979)(Liu et al. , 2003, Richmond and Abrahams, 1979)(Liu et al. , 2003, Richmond and Abrahams, 1979)(Liu et al. , 2003, Richmond and Abrahams, 1979)(Liu et al. , 2003, Richmond and Abrahams, 1979)(Liu et al. , 2003, Richmond and Abrahams, 1979)(Liu et al. , 2003, Richmond and Abrahams, 1979)(Liu et al. , 2003, Richmond and Abrahams, 1979)(Liu et al. , 2003, Richmond and Abrahams, 1979)(Liu et al. , 2003, Richmond and Abrahams, 1979)(Liu et al. , 2003, Richmond and Abrahams, 1979)(Liu et al. , 2003, Richmond and Abrahams, 1979)(Liu, Thornell, 2003), which allows for swift postural corrective responses(Keshner, 2009a). The phase lead in the CCR caused by the sensitivity to muscle stretch velocity is necessary to compensate for the phase lag occurring when converting the neural signal to muscle force(Peterson, Goldberg, 1985).
vestibulocollic reflex (VCR)
The vestibular system is composed of the left and right vestibular organ, composed of three semicircular canals and one otolith, located by each inner ear and has a primary role in determining self-motion. The semicircular canals are approximately orthogonal to each other and respond to angular acceleration and velocity of the head. They will therefore generate a response when rotational movement of the head occurs, where they are most sensitive to yaw motion (axial rotation)(Fitzpatrick and Day, 2004). The otolith organ senses linear acceleration at the head in space resulting from the inertial forces at the head and from gravity, referred to as the specific gravitoinertial force. To estimate the linear acceleration at the head and head tilt the otolith organs are innervated by tonic (regular) and phasic (irregular) afferent fibers. The tonic afferents act as a low-pass filter to the gravitoinertial force and give an estimate of a constant acceleration field like gravity. Phasic
afferents respond to high frequency accelerations related to the motions of the head(Fitzpatrick and Day, 2004, Zupan et al. , 2002).
reflex gain modulation
There is substantial evidence(Keshner, Cromwell, 1995a, Keshner and Peterson, 1995a) that in order to cope with the highly variable conditions in which the head has to be kept upright, the CNS adapts its system dynamics to the task and the environment through reflexive gains and cocontraction. Reflex modulation dependent on the frequency content of the perturbation has been shown for the arm(Schouten, de Vlugt, 2008a) and in stance(Peterka, 2002b), but not yet for the neck. Reflex gains only appear to be marginally affected by an increase in amplitude of environmental perturbations(Keshner and Peterson, 1995a), but the influence of the perturbation content at frequency level is yet to be determined for the neck.
individual muscle activation
Next to integrating the sensory CCR and VCR responses the CNS chooses muscle sets to provide reflexive stabilizing forces tailored to loading directions. Monosynaptic pathways are found to play an important role in the CCR response(Peterson, Goldberg, 1985) and arguably do not directly innervate large muscle sets.
However, to generate a reflexive muscle response from a local head acceleration or angular velocity sensed by the vestibular organ requires a synchronized activation of many muscles. The muscle patterns that emerge to
16
generate a load at the head have been shown to be very reproducible. This suggests that a preprogrammed muscle synergy is involved(Keshner, Campbell, 1989, Siegmund et al. , 2007), where muscles were activated in a preferred direction of activation. Patterned muscle activity varies greatly depending on the loading
direction(Vasavada, Peterson, 2002). Even when moving to another fixed head position muscle synergies are adapted to balance the moments generated by muscles in different positions(Lecompte et al. , 2007). In fact, while these mappings are quite repeatable during loading conditions when the head is fixed there is very little known about individual muscle activity under dynamic conditions.
CERVICAL DYSTONIA
Despite the advances in understanding the control of neck muscles the past forty years, the modulation of reflex pathways and regulation of the individual neck muscles by the CNS is not fully understood. It is important to establish how modulation is achieved, because this will allow us to identify and localize
malfunctioning behavior in the neck. Movement disorders can disrupt the control of muscles and one specific to the neck is cervical dystonia (CD). CD is the most frequent form of dystonia with around 8000 patients in the Netherlands (Jankovic et al. , 2007, Warner et al. , 2000). It is a neurological movement disorder
characterized by involuntary activity of the neck muscles leading to debilitating abnormal postures, twisting movements and pain(Chan et al. , 1991, Fahn, 1988). Dystonic movements are typically patterned and twisting, and may be tremulous. Furthermore, CD can lead to abnormal forward (antecollis), rearward (retrocollis), lateral (laterocollis), or twisting (torticollis) postures of the head, and generally patients show a combination of these postures(Fahn, 1989). Dystonic movements also seem to be initiated or worsened by voluntary action(Albanese et al. , 2013).
Figure 3: Different head postures in cervical dystonia patients. Most patients show a combined posture, making identification of the dystonic muscles more difficult.
Dystonic muscles receive increased neuronal signals coming down from the CNS. Many studies have been performed on CD in the past fifteen years (Figure 4), showing functional and morphometric changes in different brain areas, such as the cerebral cortex(de Vries et al. , 2012, Draganski et al. , 2003, Egger et al. , 2007, Obermann et al. , 2007), superior colliculus(Holmes et al. , 2012), thalamus(Butterworth et al. , 2003, Chang et al. , 2002, Krauss et al. , 1999, Kupsch et al. , 2006, Vidailhet et al. , 2007), and cerebellum(LeDoux and Brady, 2003, Neychev et al. , 2008, Prudente et al. , 2013, Sadnicka et al. , 2012). The basal ganglia circuit, with an important role in motor control, is likely involved(Berardelli et al. , 1998, Brown and Marsden, 1998, Van Der Kamp et al. , 1995) and an abnormal low frequency common motor command found in dystonic
Introduction
17
muscles suggests a problem with the downward signal from the CNS(Tijssen et al. , 2000, Tijssen et al. , 2002, van der Meer et al. , 2010). However, the muscles are part of a reflexive network, in particular the CCR, sending sensory information back to the brain through spindles and Golgi tendon organs. Indeed, roughly 80% of the CD patients show a remarkable characteristic called a ‘sensory trick’ supporting the involvement of sensory feedback in CD(Muller et al. , 2001). With certain tactile stimuli, for instance when patients lightly touch their head with a finger, dystonic activity can be suppressed temporarily(Naumann et al. , 2000). These tricks can even be effective when patients imagine the stimulus(Schramm et al. , 2004) or when providing a visual stimulus(Lee et al. , 2012), even though the sensors and feedback pathways appear to be intact. Current evidence points towards changes in the neuronal circuitry involved in motor and somatosensory
functioning(Berardelli, Rothwell, 1998, Lehéricy et al. , 2013, Tinazzi et al. , 2003). Rhythmic brain interactions can be found in patients that receive deep brain stimulation by measuring local field potentials (LFP) of structures in the deep brain. Unusual rhythmic behavior in the basal ganglia, and in particular the Globus Pallidus, has been found between 3 to 30 Hz (Chen et al. , 2006, Liu et al. , 2008, Sharott et al. , 2008,
Silberstein et al. , 2003) and it is hypothesized that aberrance in these rhythms may relate to the hyperkinesia in CD patients(Weinberger et al. , 2012). Similar spectral changes can be found in the activity of the dystonic muscles measured through electromyography (EMG)(Tijssen, Marsden, 2000, Tijssen, Munchau, 2002, Van Gerpen et al. , 2000). CD can therefore be regarded as a movement disorder in which rhythmic interactions between brain regions are disrupted and the integration of sensory afferents is impaired. This causes aberrant motor signals which may be (partly) common in the dystonic muscles, leading to the complex movement characteristics of dystonia.
Patients are generally treated with botulinum toxin (BoNT), which provides a lasting relaxation for several months in muscle tone by inhibiting acetylcholine(Ramirez-Castaneda et al. , 2013). The treatment involves impairing dystonic muscles and is said to provide satisfactory improvements in over 85% of the
cases(Jankovic, 2004). The procedure in which dystonic muscles are identified and injected with BoNT is repeated approximately every three months when the toxin begins to lose its effect(Chapman et al. , 2007). During a clinical examination with a neurologist dystonic muscles are typically identified through muscle palpation and by analyzing the dystonic head posture(Dressler, 2000). However, finding the dystonic muscles is not trivial because of the biomechanical complexity of the many muscles in the neck, and because dystonic muscle activity cannot always be distinguished from healthy muscles acting in compensation(Benecke and Dressler, 2007, Tijssen, Munchau, 2002). There is a large variability in movement patterns and head positions between patients(Berardelli, Rothwell, 1998) and the current subjective procedure depends highly on the capabilities of the neurologist. To improve muscle selection polymyographic EMG (video EMG) had been proposed(Deuschl et al. , 1992, Nijmeijer et al. , 2013). Although improvements were found especially in complex cases of CD, the limitation of the EMG studies is that both dystonic and compensatory active muscles show increased muscle activity.
18
Figure 4: Publications on cervical dystonia have steadily increased since the nineties (source: Web of Science database).
PROBLEM STATEMENT
To stabilize, the CNS adapts properties of the head-neck system by modulating reflexive feedback pathways and cocontraction and activating individual muscles in synergy, but how this is achieved is not well
understood. Improved knowledge on head-neck stabilization in healthy subjects enables us to apply this knowledge to patients with abnormal neck movements, like patients with CD providing insight in the pathophysiology of dystonia. This could lead to improvements in treatment. In particular, new protocols for the identification of aberrant muscle activity could improve the selection of muscles to be treated with BoNT.
MAIN OBJECTIVES
This thesis aims to provide insight into the contribution of reflex loops and individual muscles to the postural control of the neck in healthy subjects and in patients with cervical dystonia. Also, we aim to learn more about the pathophysiology behind cervical dystonia and to introduce new methods for more accurate muscle
selection. Three main objectives are defined to achieve these aims:
I. To establish how individual neck muscles are activated by the CNS.
II. To define the contribution of VCR and CCR pathways and cocontraction and to comprehend how reflex gains modulate under different loading conditions to ensure head and neck stabilization. III. To understand the pathophysiology behind cervical dystonia by quantifying aberrance of individual
muscle activation in patients, and to improve dystonic muscle selection for possible diagnostic use. These objectives were pursued through isometric and dynamics experiments with healthy subjects and cervical dystonia patients, and a detailed musculoskeletal model capturing individual muscle activation and reflex modulation. 1995 1990 2000 2005 2010 200 160 120 80 40 publication year ye a rl y n u mb e r o f p u b lica ti o n s o n ce rvi ca l d yst o n ia
Introduction
19
RESEARCH APPROACH
This research is part of the Torticollis project in the neuroSIPE program (www.neuroSIPE.nl), in which in vivo parameters of neurophysiological systems are identified and estimated in an effort to develop diagnostic tools for neurological disorders. Besides this thesis the Torticollis project includes two complementary PhD programs performed by two PhD candidates. Bas Nijmeijer was responsible for the clinical application of the results and investigated a common low frequency neural drive in dystonic muscles using intermuscular coherence, aimed to improve diagnostics(Nijmeijer et al. , 2014 (Conditionally Accepted)). Patrick Forbes investigated head-neck stabilization in healthy subjects and the ability of CD patients to correctly modulate their reflexes in an effort to identify problems in the reflex pathways(Forbes, 2014).
experimentation
To study muscle control a variety of experiments can be performed. In isometric experiments subjects are fixed in one position. This ensures a very similar head position between subjects and enables the measurement of forces at the head, which can be used to standardize force tasks through visual feedback. In dynamic
experiments the head is left free and reflexive stabilization can be assessed by perturbing (shaking) the torso. Typically, the kinematic and electromyographic response is measured to identify parameters of an unknown closed-loop system such as the head-neck complex. From this data a model can be derived, which may then be used to address differences between subject groups due to environmental changes. To this end a perturbation platform was used on which subjects were seated and shaken. Kinematic motions of the head and responses of neck muscles were used to make estimates of the internal reflexive behavior of the subjects. To investigate aberrance of individual muscles in patients an isometric protocol was used. In search of a standardized protocol to better identify aberrant activity of individual muscles an isometric device was developed in which the head of subjects was locked. Loads generated by the subject at the head were measured along with muscle activation and visual feedback was used to ensure standardized task instructions.
modeling
Experimental studies are important to learn how humans behave under different circumstances, but often conclusions cannot be drawn from looking at data alone. Models are a useful tool to come to terms with what is seen and can assist in addressing implications related to the observations. There are many head-neck models to choose from that vary in morphological complexity and detail in neural control. Inverted pendulum models are simple and can be used to gain insight into the overall dynamics of the head and neck system(Fard et al. , 2004, Gillies et al. , 1998, Liang and Chiang, 2008). In one of its simplest forms the head and neck can be modelled as a single planar inverted pendulum with the head at the top and rotating about the first thoracic joint (T1) of the spine. These models however do not have sufficient detail to address the role of individual (dystonic) muscles during stabilization or to analyze individual muscle contractions. In this thesis a detailed musculoskeletal model was developed that could capture individual muscle activity in isometric and dynamic conditions. Initially, the goal was to use this model to analyze differences in individual muscle patterns and in reflexive behavior between healthy subjects and CD patients. However, the distribution of individual muscle
20
activity was not substantially different in the isometric experiments in Chapter 5. Likewise in a dynamic experiment, part of the Torticollis project, no differences in reflexive behavior between the two groups were observed (Forbes, 2014). These findings will be addressed in the epilogue of this thesis.
THESIS OUTLINE
In Part I neuromuscular control of healthy subjects is investigated through a dynamic experiment (Chapter 2) and modelling (Chapter 3 and 4). The aim of Chapter 2 is to understand how reflexes are modulated in healthy subjects during stabilization of the neck, when the frequency content of perturbations is varied.
Chapter 3 investigates the contribution of individual muscles during isometric contractions using an isometrically validated neuromuscular model of the neck. It also addresses the importance of mechanical equilibrium in all cervical joints when estimating cervical strength using a model. Chapter 4 uses the model developed in Chapter 3 to identify the contribution of cervicocollic and vestibulocollic reflex loops and cocontraction on human stabilization. The modulation of these loops with the frequency content at which the neck is being perturbed is evaluated using the experimental results from Chapter 2.
The two chapters in Part II focus on cervical dystonia and provide first steps towards a diagnostic tool for the improvement of dystonic muscle selection. An isometric setup is developed to ensure a standardized protocol between subjects. Chapter 5 aims to evaluate activity of individual muscles through standardized measures using and relating increased activity to dystonic muscles. Additionally, it addresses a possible worsening of dystonic responses due to an increase in voluntary muscle contractions. In Chapter 6 the hypothesis of an altered central drive to dystonic muscles is investigated by analyzing the distribution of spectral information in activity of cervical muscles during isometric contractions.
The epilogue (Chapter 7) will review findings of this thesis as well as other key studies in the Torticollis project.
Introduction
PART I:
INVESTIGATION OF
NEUROMUSCULAR CONTROL IN
HEALTHY SUBJECTS
Chapter 2
Dependency of human neck reflex responses on
the bandwidth of pseudorandom
anterior-posterior torso perturbations
P. A. FORBES, E. DE BRUIJN, AC SCHOUTEN, F. C. T. VAN DER HELM, R.
HAPPEE
Investigation of neuromuscular control in healthy subjects
26
ABSTRACT
The vestibulocollic (VCR) and cervicocollic (CCR) reflexes are essential to stabilize the head-neck system and to deal with unexpected disturbances. This study investigates how neck reflexes contribute to stabilization and modulate with perturbation properties. We hypothesized that VCR and CCR modulate with the bandwidth of the perturbation and that this modulation is maintained across amplitudes and influenced by the eyes being open or closed. Seated subjects were perturbed in an anterior-posterior direction. The perturbations varied in bandwidth from 0.3 Hz to a maximum of 1.2, 2.0, 4.0, and 8.0 Hz, at three amplitudes, and with eyes open and closed. Frequency response functions of head kinematics and neck muscle EMG demonstrated substantial changes with bandwidth and vision and minor changes with amplitude, which through closed-loop identification were attributed to neural (reflexive) modulation. Results suggest that both reflexes were attenuated when perturbations exceeded the system’s natural frequency, thereby shifting from a head-in-space to a head-on-trunk stabilization tendency. Additionally, results indicate that reflexive and mechanical stiffness marginally exceed the negative stiffness due to gravity; a stabilization strategy which minimizes effort. With eyes closed, reflexes were attenuated further, presumably due to a reduced ability to discriminate self-motion, driving the system to a head-on-trunk stabilization strategy at the highest bandwidth. We conclude that VCR and CCR modulate with perturbation bandwidth and visual feedback conditions to maintain head-upright posture, but are invariant across amplitude changes.
Chapter 2
27
INTRODUCTION
Upright head-neck posture is maintained through vestibular, muscle afferent, and visual feedback
mechanisms, as well as intrinsic muscular properties, including co-contraction. The vestibular and muscle afferent feedback mechanisms are considered the primary contributors to head-neck control (Keshner, 2009b) and elicit vestibulocollic reflex (VCR) and cervicocollic reflex (CCR) responses, respectively. The VCR acts to stabilize the head-in-space while the CCR acts to stabilize the head on the trunk; both generate compensatory neck muscle activity in response to vestibular and muscle afferent feedback. Given the complex and variable nature of human head kinematics (Grossman et al. , 1988, Hirasaki et al. , 1999, Menz et al. , 2003, Pozzo et al. , 1990), it is likely that these reflexes are adapted to the properties of external perturbations. The goal of this study was to investigate modulation of human neck reflexes during linear anterior-posterior torso
perturbations under varying conditions.
Substantial efforts have been made to reveal the functional characteristics of the VCR and CCR in the isolated human head-neck. Neck reflexes have been shown to modulate with head inertia (Goldberg and Peterson, 1986, Keshner et al. , 1999, Reynolds, Blum, 2008) and mental set (Keshner, 2000), while perturbation
amplitude has limited effects on system dynamics (Keshner et al. , 1995b). Most notably, reflexive stabilization provides damping mechanisms which dominate over a bandwidth of approximately 1–2 Hz (Keshner,
Cromwell, 1995b, Keshner, Hain, 1999, Keshner and Peterson, 1995b), just below the system natural frequency (2–3 Hz) which is most likely due to the inherent reflex time delays. However, reflex contributions above 2 Hz and their modulation as a function of perturbation bandwidth (i.e. frequency content) have not been
established.
Considering the substantial influence perturbation bandwidth is known to have on extremity postural control (Kearney et al. , 1997, Mirbagheri et al. , 2000, Mugge et al. , 2010, Schouten et al. , 2008b, van der Helm et al. , 2002), it is possible that the use of wide bandwidth perturbations (0–4 Hz) in the majority of head-neck studies limited the effectiveness of both the VCR and CCR. Because the frequency range of head motion during normal locomotor activities (i.e. walking, running, voluntary head motions) is ~1–3 Hz (Grossman, Leigh, 1988, Pozzo, Berthoz, 1990), these reflex contributions may have been underestimated using
perturbations with higher bandwidths.
In addition, visual feedback via the focus on stationary targets is thought to improve head-neck stabilization below the natural frequency (Goldberg and Cullen, 2011); however, the interaction of vision with neck reflexes remains uncertain. Collins and De Luca (1995) hypothesized that for closed-loop musculoskeletal systems, vision serves to decrease stiffness by decreasing reflex gains. However, simultaneous stimulation of vestibular otoliths and vision in cats (Borel and Lacour, 1992) results in increased neck reflex stabilization (i.e. increased reflex gains). Sensory integration research supports the latter observation, whereby vestibular and visual senses integrate to improve the perception of motion (Angelaki et al. , 2011). Either way, the effect of visual feedback on head-neck stabilization when varying perturbation bandwidth has yet to be studied.
Investigation of neuromuscular control in healthy subjects
28
In the present study, we systematically assessed head-neck kinematics and EMG responses to identify changes in system dynamics across perturbation (bandwidth and amplitude) and vision conditions. Our objective was to attribute their effects to either neural (reflexive) or mechanical (muscle co-contraction) modulation. It was hypothesized firstly that reflex contributions to head-neck stabilization modulate with the bandwidth of the perturbation, and secondly, that this modulation is maintained across amplitude of the perturbation and is influenced by the presence of visual feedback. Subjects were exposed to linear anterior-posterior perturbations in a seated position with the head free. Results demonstrate a high degree of neck reflex modulation with the frequency bandwidth of pseudorandom anterior-posterior torso perturbations not seen in previous research.
METHODS
Subjects
Twelve healthy subjects (nine men) with an age range of 22–26 years having no self-reported history of
neurological disorders or head-neck injuries participated in these experiments. The experimental protocol was in accordance with the Declaration of Helsinki and was approved by the Human Research Ethics Committee at the Delft University of Technology. All participants gave written informed consent.
Apparatus
Subjects were restrained by a five point harness to a rigid chair mounted on a hydraulically actuated six degree of freedom motion platform. The seat base was horizontally oriented 0.3 m above the platform, and the seat back was oriented slightly rearward from vertical providing a comfortable seating posture. The platform was position controlled through a dedicated computer system (dSpace, Paderborn, Germany) with a custom made controller (Matlab/Simulink, Mathworks Inc., Natick, MA, USA). A thin plastic helmet was tightly secured to the head to facilitate monitoring of head motion. The helmet had a mass of 180 g, which is negligible
compared to an average human head at approximately 4 kg (Yoganandan et al. , 2009).
Data collection
Three-dimensional (3D) kinematic data of the head, trunk, and platform were recorded at 200 Hz using an Oqus 6-camera motion capture system (Qualisys AB, Gothenburg, Sweden). Three markers were attached directly to the helmet on the front, side, and back. Another three were attached directly to the head on the mastoid process, immediately in front of the ear in line with the tragion, and on the lower border of the eye socket. Three markers were attached to the torso on the spinous process of the T1 vertebra, the incisura jugularis at the top of the sternum, and a point approximately mid-sternum. Four additional markers were attached to the chair – two on the seat base and two on the seat back – to track the motion of the platform and to confirm that the torso moved together with the chair on the platform.
EMG was recorded using differential surface electrodes (Delsys Bagnoli Systems, Delsys, Boston, USA) bilaterally from the sternocleidomastoid (SCM) and semispinalis capitis (SEMI). These were chosen as antagonist muscles known to contribute to the stabilization of the head-neck system in the anterior-posterior
Chapter 2
29
directions (Keshner, 2003a). EMG of the splenius capitis and the upper trapezius was also recorded, but analysis is not included here due to their low coherence to the perturbation. Before digitizing at 2 kHz, the EMG signals were preamplified and bandpass filtered (20–450 Hz).
Procedures
Voluntary force measurements
To facilitate EMG normalization as well as to confirm correct sensor placement, subjects performed maximum isometric trials in both the flexion and extension directions by pushing with the head against the hand of an experimenter. These measurements were taken twice before and twice after the main experiment.
Main experiments
Subjects placed their feet in front of them on the floor and their hands in their lap. Subjects were instructed to take on a comfortable upright seating position, maintaining the head comfortably above the torso while performing one of two vision conditions: eyes open (EO) or eyes closed (EC). In EO, subjects were instructed to maintain visual focus on a stationary target 3 meters in front of the platform. In EC, subjects were
blindfolded. During all trials, subjects listened to a science-based radio program to distract them from the stabilization process and minimize voluntary responses. Subjects could rest between trials for as long as necessary to prevent fatigue. The experiment consisted of 14 trials of 100 s resulting in a total experiment time of approximately 2 h.
Perturbations
Ten different multisine anterior-posterior perturbation signals were used that varied in frequency content and amplitude. For safety and comfort of the subjects, the maximum acceleration was kept below 1G. Multisine perturbation signals were used as they are deterministic and thereby avoid spectral leakage, which is an advantage over white noise stimuli (Pintelon and Schoukens, 2001). Power can be placed at selected frequency points to improve the signal-to-noise ratio (SNR), which is an advantage over pseudorandom binary noise. All signals were 20 s in length providing a frequency resolution of 0.05 Hz. Each test was 100 s long, comprised of 4 complete cycles of the perturbation, as well as a 10-s phase in and phase out to minimize transient effects and prevent abrupt transitions. To justify the use of linear approximations (see “System identification”), the deviations of the head relative to the torso were kept small within each condition (<15 mm). The perturbation variations are summarized in Table 1 and are described below.
Bandwidth: Four perturbations were included to investigate the effect of perturbation bandwidth on head-neck stabilization. These perturbation signals had flat power in velocity and a root-mean-square (RMS) velocity of 0.08 m/s (denoted further as amplitude A2). The lowest frequency was fixed at 0.3 Hz for all perturbations, and the highest frequency was defined at 1.2, 2, 4, and 8 Hz (V1, V2, V4, and V8, where the V denotes flat power in velocity). Between 0.3 and 2 Hz, excited frequencies were equidistantly spaced in clusters
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of four adjacent frequency points, while above 2 Hz excited frequencies were logarithmically spaced, resulting in three clusters for V1 and thirteen for V8 (see Figure 5, left column).
Amplitude: The lowest three bandwidth perturbations (V1, V2, V4) were scaled to 50 % (A1) and 150 % (A3) to assess the effects of amplitude on head-neck stabilization.
Table 1: Experimental matrix summarizing the perturbations (bandwidth and amplitude) and visual feedback conditions of the 14 trials.
Main condition Bandwidth (Hz) Frequency points (clusters)
Amplitude and visual feedback
A1 A2 A3 V1 0.3–1.2 12 (3) EO EO/EC* EO V2 0.3–2.0 20 (5) EO EO/EC* EO V4 0.3–4.0 36 (9) EO EO/EC* EO V8 0.3–8.0 52 (13) – EO/EC* – DATA ANALYSIS
The first and last 10 s of the trials, with the fading of the perturbation, were excluded. All six head/helmet markers were used to define a head rigid body providing translational and rotational data using Qualisys’ rigid body algorithms. The head local coordinate system origin was positioned at the head center of gravity, which was estimated relative to the tragion marker at a fixed distance (Yoganandan, Pintar, 2009) oriented along the Frankfurt plane. The three torso markers were used to define the torso rigid body local coordinate system having its origin at the T1 marker. The rigid body positions were low-pass filtered at 30 Hz with a second-order Butterworth filter. The x-position of the torso (XT1), head in global space (XGH), and head relative to the torso (XRH) as well as the y-rotation (pitch) of the head (θH) were differentiated to obtain velocities. The head kinematic velocities ɺXGH , ɺXRH and θɺ
H were used for further analysis in order to describe system dynamics in global and relative reference frames.
EMG data were rectified and normalized to the average EMG obtained from the voluntary force experiments in flexion (SCM) and extension (SEMI). An estimate of baseline muscle activity (eb) of a trial was calculated by low-pass filtering the separate EMG signals at 20 Hz (second-order Butterworth) and then taking the mode of the mean flexor signal (left/right SCM) and the mean extensor signal (left/right SEMI) separately. The mode represents the most common value in the rectified-filtered signal from each muscle pair (see Figure 6), where values are rounded to the nearest thousandth generating a bin size of 0.001, and represents the highest point in the EMG histogram (see Figure 6). We hypothesize that in these experiments, the mode represents a base-line activity counteracting gravity and possibly including co-contraction, while activity exceeding the mode represents reflex activity. Hence, the variation of the baseline activity of both muscle pairs across conditions would indicate mechanical (i.e. intrinsic) modulation of the system dynamics through co-contraction. The EMG of these four muscles (bilateral SCM and SEMI) was also used to calculate a “weighted neck EMG signal” ew(t) = w1e1(t) + w2e2(t) + w3e3(t) + w4e4(t) following methods similar to those of (Kiemel et al. , 2008) for human stance. The weights wj were optimized per subject for all conditions simultaneously to generate maximum coherence γ2
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weighted neck EMG signal (see “System identification”) and were subject to constraints wj ≥ 0 for SCM muscles, wj ≤ 0 for SEMI muscles, and
∑
4j=1wj =1.System identification
Frequency response functions (FRF) were estimated to describe the kinematic and EMG response to the perturbations. FRFs describe the dynamic behavior of a system as a function of frequency, where the FRF gain indicates the magnitude of the output relative to the input, and the FRF phase indicates the relative timing of the output relative to the input.
The power spectra (auto and cross) were calculated and averaged in the frequency domain over the four 20 s segments and four adjacent frequencies within each cluster to improve the estimate (Jenkins and Watts, 1968). Kinematic FRFs (kFRFs) were estimated (Eq. 1) between the input perturbation and head kinematics. The resulting estimates therefore include effects of the associated feedback pathways. To study the feedback pathways, EMG FRFs (eFRFs) were estimated (Eq. 2) between head kinematics and weighted neck EMG (ew). Referred to as the joint input–output approach (van der Kooij et al. , 2005), the equation provides an inferred open-loop estimate (Fitzpatrick et al. , 1996) of the reflexive activity caused by head motion:
( )
( )
ˆ ˆ ˆ = dy dy dd S f H S f (1)( )
( )
ˆ ˆ ˆ = de ye dy S f H S f (2)where d is the input velocity disturbance measured at the torso(X ), e is the weighted neck EMG (ew), and y ɺT1 is the output head kinematics ( ɺXGH, ɺXRH and θɺH). Variation of kFRFs will reflect condition-dependent nonlinearities in the system due to modulation of neural feedback and/or mechanical characteristics such as co-contraction. Variation of eFRFs will indicate modulation of neural (i.e. reflexive) feedback only.
In each condition, the coherence between the two signals was used to evaluate the quality of correlation between the input and output; ranging from zero to one, the coherence describes how much of the output power is related with the input power (Pintelon and Schoukens, 2001). Coherence for kinematics (γ2
k) and EMG (γ2 e) were estimated:
( )
( ) ( )
2 2 ˆ ˆ ˆ γ = dy k dd yy S f S f S f (3)Investigation of neuromuscular control in healthy subjects 32
( )
( ) ( )
2 2 ˆ ˆ ˆ γ = de e dd ee S f S f S f (4) Statistical analysisThe effects of amplitude (A1, A2, A3) and bandwidth (V1, V2, V4) on the root-mean-square (RMS) of
kinematic responses and the baseline EMG (eb) were evaluated using repeated-measures ANOVAs. Additional repeated-measures ANOVAs were used on the same responses to evaluate the effect of vision (EO, EC) across the four bandwidths (V1, V2, V4, V8) at amplitude A2. Similarly, an equivalent repeated-measures MANOVA was used to evaluate these effects on the kFRFs and eFRFs using gain and phase at the lowest three coincident frequency points (i.e. the common bandwidth, 0.3–1.2 Hz). A significance of 0.05 was used for all analyses.
RESULTS
Time domain
Figure 5 provides a 5-s segment of a subject’s kinematic response under four conditions (V1/V2/V4/V8), with amplitude A2. For V1 and V2 ɺXGH closely matched the input perturbation waveform (X ) indicating that ɺT1 subjects moved their head with their torso. For V4 and V8, the slow variations of the input signal were present in ɺXGH while the fast variations were attenuated. The magnitude of XGH and ɺXGH decreased with increasing stimulus bandwidth even though input perturbations had the same RMS velocity. On the other hand, ɺXRH and θɺH increased with increasing stimulus bandwidth while XRH remained relatively constant. The similarity in shape and timing of ɺXRH and θɺH indicate a coupled response typical of a single inverted pendulum.
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Figure 5 Kinematics of a typical subject during four bandwidth (V1/V2/V4/V8) conditions at amplitude A2 with eyes open (EO) aver-aged across repetitions (shaded region is ±SD). Input perturbation velocity (at T1) is shown in the frequency domain in the left column. The power spectra show the clusters of four frequency points used for averaging. The second column shows the input perturbation velocity (at T1) in the time domain, for a period of 5 s, followed by global head (XGH) displacement and velocity, head pitch (θH) velocity, and relative head (XRH) displacement and velocity. Head pitch velocity is considered both a global and relative measure and categorized under both.
Time segments of the same subject’s kinematic (XɺGH/XɺɺGH, XɺRH/XɺɺRH and θ θɺ /ɺɺ
H H) and corresponding EMG responses are shown in Figure 6 (left plots) during condition V4A3. Bursts of muscle activity often occurred at negative ɺɺXRH peaks for SCM and positive ɺɺXRH peaks for SEMI, indicating reciprocal activation patterns. The continuous muscle activity between EMG bursts indicates that muscle co-contraction (i.e. intrinsic stiffness) was present. This activity was quantified by the mode of EMG signals (eb) and indicated by a vertical line for each muscle pair in the accompanying histograms (Figure 6, right plots). The histograms are not normally distributed but resemble an inverse gamma distribution, weighing more heavily to higher amplitudes on account of reflex activity. The weighted EMG signal (Figure 6, bottom plot) was positive when flexors were primarily active and negative when extensors were primarily active.
Relative Head Global Head XT1 Perturbation 0 .4 m/ s 5 s Frequency [Hz] 0 .2 m 0 .4 m/ s 1 2 0 °/ s 0 .0 2 m 0 .4 m/ s V1 V2 V4 V8 XRH . XRH θH . XGH . X Velocity Power Spectrum GH . 100 101 10−5 100 10−5 100 10−5 10 0 10−5 100
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Figure 6 Time domain EMG results (1.5 s) from a typical subject in the V4A3 condition with the eyes open. Kinematic responses include global and relative head motion (top three plots). EMG signals were normalized by the mean EMG obtained from MVC tests and averaged bilaterally. Raw (green lines) and 20-Hz low-pass filtered (magenta lines) EMG signals are depicted for the mean bilateral SCM and SEMI muscles (third and fourth plot). The accompanying histograms (right plots) of the filtered signals depict the distribution of the signal amplitude along with the signal modes as vertical black lines. The weighted EMG signal (bottom plot) used the SCM (left/right) and SEMI (left/right) muscles with weights 0.34/0.28 and −0.28/−0.10, respectively.
Figure 7 plots the RMS head kinematics as a function of perturbation amplitude for all four (V1/V2/V4/V8) conditions with EO. Bandwidth had a significant effect on all kinematic RMS responses; decreasing XGH (P < 0.001, F2,22 = 357.8) and ɺXGH (P < 0.001, F2,22 = 77.5), increasing ɺXRH (P < 0.001, F2,22 = 281.1) and θɺ
H (P < 0.001, F2,22 = 369.0), and having no discernible pattern (i.e. neither increasing or decreasing with bandwidth) for XRH (P < 0.001, F2,22 = 33.9) and θH (P < 0.001, F2,22 = 19.6). Based on RMS measures, the head appeared to be more stationary relative to the torso at the lowest bandwidth and more stationary in space at the highest bandwidth. Baseline EMG activity had no discernible pattern with bandwidth, and no statistical significance could be detected for either muscle pair.
Amplitude had an increasing effect on all global and relative kinematic RMS responses (all P < 0.001, F2,22 > 91.4) in a proportional fashion. During EC tests, all RMS global and relative head motion responses increased significantly (not plotted, all P < 0.05, F1,11 > 7.7) compared with EO (increase averaged across V1, V2, V4, and V8: XGH = 23.3 ± 4.0 %, ɺXGH = 14.3 ± 2.7 %, θH = 13.1 ± 5.8 %, θɺH = 9.8 ± 7.0 %, XRH = 13.3 ± 3.6 %, ɺXRH = 7.0 ± 6.7 %). This suggests that subjects used visual feedback to reduce head motion in global and relative reference frames. Baseline EMG activity had no discernible pattern with amplitude or vision, and no statistical
0 0.3 −0.3 0.3 −0.3 0 −4 4 −4 4 0 0 0 0.05 0.10 SC M [ − ] 0 0.2 0.4 SEMI [ − ] 15 15.5 16 Time [sec] 16.5 −0.1 0 0.1 ew [ − ] XG H [ m/ s] . XR H [ m/ s] . XG H [ m/ s 2] XR H [ m/ s 2] .. .. −50 0 50 θH [ ° /s] . −1000 0 1000 θH [ ° /s 2] .. Velocity Acceleration
Mean raw EMG (left/right) Mean filtered EMG (left/right)
Weighted EMG 0 0.02 0.04 0.06 0 2 4 C o u n t (1 0 0 0 ’s) 0 1 2 C o u n t (1 0 0 0 ’s) 0 0.1 0.2 0.3 EMG [−]
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significance could be detected for either muscle pair, indicating a lack of co-contraction variation across both conditions.
Figure 7 Head kinematic RMS plotted relative to input (T1) velocity RMS averaged across subjects (error bars are ±SD); graphs present displacement and velocity RMS of global head motion (XGH), head pitch motion (θH), and relative head motion (XRH) for all eyes open (EO) conditions.
Frequency response functions
Stabilization dynamics
Figure 8 gives kFRFs with input X (m/s) and output ɺɺT1 XGH (m/s), θɺH (°/s), and ɺXRH (m/s), where
Figure 9 gives the eFRFs describing reflexive activity. Table 2 summarizes significance of both kFRF and eFRF comparisons across the coincident frequencies (0.3–1.2 Hz). Coherence was relatively high in all three kFRFs for all conditions indicating high-quality estimates of the correlations and justifying the system identification techniques.
In the ɺXGH kFRF, a gain of one and phase of zero occurs when the head perfectly follows the torso motion, which was observed up to ~1 Hz and resulted in relative head motion (i.e. ɺXRH kFRF gain) typically below 20% of the input. Up to approximately 5 Hz, the dynamics resembled a critically damped second-order low-pass system with a natural frequency of 1–2 Hz; however, thereafter higher order dynamics were apparent which generated a second gain peak at approximately 8 Hz. Similar behavior was found in a previous study during direct translational perturbations to the head (Viviani and Berthoz, 1975a), where the high-frequency dynamics were attributed to a shifting dominance of rotation from the base of the neck (i.e. a single inverted pendulum) to the occipital condyle joint (i.e. a double inverted pendulum). The θɺ
H and ɺXRH kFRFs were 0 0.04 0.08 0.12 0.16 0 0 0.04 0.08 0.12 0.16 0 0.05 0.10 0.15 0.20 V1 V2 V4 V8 0 0.04 0.08 0.12 0.16 0 2 4 6 0 0.04 0.08 0.12 0.16 0 20 40 60 0 0.04 0.08 0.12 0.16 0 0.005 0.010 0.015 0 0.04 0.08 0.12 0.16 0 0.05 0.10 0.15
Global Head Relative Head
D isp la ce me n t [m] Ve lo ci ty [m /s] D isp la ce me n t [° ] Ve lo ci ty [°/ s] D isp la ce me n t [m] Ve lo ci ty [m /s] 0.013 0.025 0.038 0.050
Torso Velocity (X ) [m/s].T1 Torso Velocity (X ) [m/s].T1 Torso Velocity (X ) [m/s].T1 θ θ X XGH H RH X XGH H RH . . . A2 A3 A1
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similar to one another at low frequencies but differed above 3–4 Hz; in the ɺXRH kFRF a gain of one and a phase of −180° indicated the head horizontal position being stationary in space, while the rotational θɺH kFRF gain on the other hand continued to increase.
The eFRFs show consistent EMG responses for all conditions with high coherence at higher frequencies (Figure 9). Examining the gain and phase of the eFRFs, we can attempt to attribute muscle activity to specific kinematic characteristics. For example, across the widest bandwidth (V8), the shape of the ɺXGH eFRF (Figure 9) demonstrated velocity feedback up to 2 Hz (flat gain and a phase of 0°), which shifted to acceleration feedback (slope of +1 in gain and a phase of +90°) up to approximately 4 Hz and back to velocity thereafter. In the θɺH and ɺXRH V8 eFRFs, more complex responses were observed. Both demonstrated position feedback with a gain slope of −1 up to 2 Hz; however, phases started at zero indicating velocity feedback. Additionally, while the gain behaviors of both eFRFs were comparable, their phase responses were opposing with θɺH
increasing to +90° indicating acceleration feedback (differential action) and ɺXRH decreasing to −90° indicating position feedback ɺXRH (integral action). These complex responses likely reflect multiple sensory contributions (vestibular and spindle) to muscle activity in relation to each kinematic response (see “Discussion”).
Figure 8 Bandwidth variation related kFRFs of head global (θGH), rotational (θH), and relative (XRH) velocity (left-to-right) averaged across all subjects (shaded region is ±SE) for four bandwidth conditions at amplitude A2 with eyes open (EO). Gains were log transformed before calculating the subject average and SE.
100 101 100 G a in [ m/ s/ m/ s] 100 101 10−1 100 G a in [ m/ s/ m/ s] 100 101 102 G a in [ °/ s/ m /s] Ph a se [ °] Ph a se [ °] Ph a se [ °] 100 101 Frequency [Hz] C o h e re n ce [ −] V2A2 V4A2 V8A2 V1A2 100 101 C o h e re n ce [ −] Frequency [Hz] 100 101 C o h e re n ce [ −] Frequency [Hz] 100 101 −270 −180 −90 0 100 101 −270 −180 −90 0 100 101 −270 −180 −90 0 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 103 10−1 X.T1 X . GH X . T1 X . RH X.T1 θ . H
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Figure 9 Bandwidth variation related eFRFs of head kinematics-to-weighted neck EMG for global (XGH), rotational (θH), and relative (XRH) velocity (left-to-right) averaged across all subjects (shaded region is ±SE) for four bandwidth conditions at amplitude A2 with eyes open (EO). Gains were log transformed before calculating the subject average and SE. Gains having a slope of −1, 0, and +1 and phases of −90, 0, and 90 degrees indicate sensitivity of EMG to position, velocity, and acceleration, respectively. Coherence is included for only one eFRF as all three are calculated using the same kinematic and EMG response measurements (see Eq. 4).
100 101 10−2 100 102 G a in [ −/ m/ s] 100 101 10−4 10−2 G a in [ −/ °/ s] 100 101 10−2 100 G a in [ −/ m/ s] 100 101 −180 −90 0 90 180 100 101 −180 −90 0 90 180 100 101 −180 −90 0 90 180 Ph a se [ °] Ph a se [ °] Ph a se [ °] 100 101 0 0.2 0.4 0.6 0.8 1 C o h e re n ce [ −] Frequency [Hz] Frequency [Hz] Frequency [Hz] V2A2 V4A2 V8A2 V1A2 10−3 10−1 10−1 101 101 10−1 X.GH ew θ ew X.RH ew . H
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Table 2 Significance of the effects of bandwidth, amplitude, and visual feedback on group mean kFRF and eFRF gain values at coincident frequency points (0.3–1.2 Hz).
FRF gain Frequency Bandwidth Amplitude Visual feedback
P F4,8 P F4,8 P F2,10 Kinematic 1 ɺT X XɺGH 0.3 <0.001 33.4 0.045 4.0 <0.001 23.5 0.7 <0.001 26.3 0.080 3.1 <0.001 28.9 1.2 <0.001 33.3 0.005 8.7 0.006 8.8 1 ɺ T X θɺH 0.3 <0.001 71.9 0.004 9.5 <0.001 38.4 0.7 <0.001 42.2 0.002 12.0 <0.001 72.1 1.2 <0.001 23.9 <0.001 20.4 <0.001 66.9 1 ɺT X XɺRH 0.3 <0.001 48.1 0.032 4.6 <0.001 39.1 0.7 <0.001 36.8 0.007 7.8 <0.001 20.5 1.2 <0.001 23.0 0.001 12.8 0.004 10.3 EMG ɺGH X EMG 0.3 0.010 6.9 0.423 1.1 0.310 1.3 0.7 0.371 1.2 0.273 1.6 0.207 1.9 1.2 0.304 1.4 0.275 1.6 0.074 3.4 θɺ H EMG 0.3 0.029 4.7 0.823 0.4 <0.001 39.1 0.7 0.005 6.7 0.319 1.2 0.058 3.8 1.2 0.037 4.4 0.068 3.4 0.221 1.8 ɺRH X EMG 0.3 0.013 6.3 0.262 1.6 0.012 7.1 0.7 0.049 3.8 0.191 2.0 0.105 2.8 1.2 0.014 6.2 0.190 2.0 0.114 2.7 Bandwidth effects
Changes in system dynamics due to bandwidth were observed in the gain and phase values at each frequency point for all kFRFs (see Figure 8). The effect of bandwidth was highly significant (see Table 2) at all
frequencies common in the perturbations (i.e. 0.3–1.2 Hz). Comparing first V1 and V2, ɺXGH kFRF gains decreased and the phase lag increased with bandwidth, while the θɺ
H and ɺXRH kFRF gains increased and the phase lag decreased. These responses indicate that the head was held more stationary in space during V2. Conversely, comparing V2, V4, and V8, the ɺXGH kFRF gains increased with bandwidth at low frequencies and decreased at high frequencies. Similarly, the kFRF gains of θɺH and ɺXRH decreased at the lowest frequency while they increased at all other frequencies, generating an inflexion point (i.e. where the gain remains the same) that increased with bandwidth. However, phase changes for all kFRFs were continuously graded with increasing bandwidth. For θɺH and ɺXRH kFRFs phase lag at the lowest frequency decreased with increasing bandwidth, approaching −180° in V1 and −90° in V8. The dropping gain and phase lags at lower frequencies therefore suggest a transition from head-in-space stabilization toward head-on-trunk stabilization as the perturbation progressively excited with increasing bandwidth.
Bandwidth-related changes were also observed in eFRFs (see Figure 9) which were significant (see Table 2) across the common frequencies (0.3–1.2 Hz) with the exception of the ɺXGH eFRF at 0.7 and 1.2 Hz. During V1, the slopes of θɺH and ɺXRH eFRF gains were positive at all frequencies, while during V2/V4/V8 conditions, the slopes were negative at low frequencies but shifted to positive at a point through-out the bandwidth,
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generating a u-shape across the band-width. The frequency of minimum gain (i.e. bottom of the u-shape) increased with bandwidth. In addition, the positive and negative slopes appeared similar in magnitude across bandwidths; however, a decrease in the positively sloped region is observed comparing V8 to V4. Across the common frequencies (0.3–1.2 Hz) gain at the lowest frequency (0.3 Hz) decreased with bandwidth while gain at the highest frequency (1.2 Hz) increased. This pattern was consistent in all comparisons of two different bandwidths and indicates a bandwidth-dependent modulation of reflex dynamics. Finally, within each bandwidth condition, eFRF gains were equal or higher at the highest frequency point relative to its lowest frequency point suggesting increasing reflexive participation with frequency in stabilization (Keshner, Cromwell, 1995b).
Amplitude effects
Figure 10 shows the θɺH kFRFs at the three amplitudes during the V4 perturbations with the eyes open. Amplitude effects were significant in all kFRFs (see Table 2) across almost all common frequencies. However, comparing Figure 8 and Figure 10, the effect of amplitude was markedly less dramatic than that of bandwidth. Nonetheless, increasing amplitude had a tendency to decrease gains for all responses but in particular those of
θɺ
H kFRFs above 0.7 Hz. Amplitude did not have a significant effect on the eFRFs (see Table 2). Although not shown, coherence for all kFRFs and eFRFs increased with amplitude, presumably due to the improved SNR.
Figure 10 Amplitude variation related kFRFs (gain and phase only) of head global (XGH), rotational (θH), and relative (XRH) velocity (left-to-right) averaged across all subjects (shaded region is ±SE) for amplitudes A1/A2/A3 during perturbation V4 (bandwidth = 4 Hz). Gains were log transformed before calculating the subject average and SE.
Visual feedback effects
For all kFRFs, visual feedback had a significant effect across the common frequencies (0.3–1.2 Hz, see Table 2). Eyes closed results primarily showed increasing gains in all perturbation bandwidths up to as high as 4 Hz. Figure 11 shows the kFRFs for the V4 perturbation in the EO and EC conditions. Similar to the RMS measures, with EC the kFRF results indicate increased head motion in global and relative space. During EO,
G a in [ m/ s/ m/ s] 1 4 1 4 1 4 10−1 100 1 4 1 4 1 4 10−1 100 102 103 Frequency [Hz] −270 −180 −90 0 Frequency [Hz] Frequency [Hz] G a in [ m/ s/ m/ s] Ph a se [ °] −270 −180 −90 0 Ph a se [ °] −270 −180 −90 0 Ph a se [ °] G a in [ °/ s/ m/ s] X.T1 X . GH X . T1 X . RH X.T1 θ . H V4A2 V4A3 V4A1
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X was lower than the perturbation amplitude (i.e. gain <1) for all bandwidth conditions and frequency points, while during EC ɺXGH exceeded the perturbation (i.e. gain >1) for frequencies ≤1.2 Hz. Most interestingly, at the lowest frequency point in the θɺH and ɺXRH, kFRFs gains were only marginally different between EO and EC conditions, while phase lags were substantially reduced during EC. Similar results were observed in all bandwidth conditions whereby phase shifted by approximately +90° during EC relative to EO (note V1/V2/V8 results are not shown). This indicates a tendency toward minimized head-in-space motion during EO (occurring at −180°) and maximized head-in-space motion during EC (occurring at 0°). These responses were only weakly associated with a modulation in reflex dynamics as the effect of vision on the eFRFs was not significant for the majority of points (see Table 2) across the common frequencies.
Figure 11 EO/EC variation related kFRFs (gain and phase only) of head global (XGH), rotational (θH), and relative (XRH) velocity (left-to-right) averaged across all subjects (shaded region is ±SE) for amplitude A2 during perturbation V4 (bandwidth = 4 Hz). Gains were log transformed before calculating the subject average and SE.
DISCUSSION
This study investigated variations in head-neck dynamics under varying perturbation properties (bandwidth and amplitude) and visual feedback conditions (EO and EC) which were attributed primarily to modulation of neural (reflexive) contributions. The experimental results supported two of three hypotheses: (1) reflex
contributions to head-neck stabilization are modulated with the frequency bandwidth of the
anterior-posterior torso perturbations, and (2) this modulation is maintained with increasing perturbation amplitude. Our third hypothesis that neck reflexes modulate with visual feedback was moderately supported, where eFRFs were significantly affected at only the lowest frequency point. Nonetheless, the same bandwidth-related reflex modulation observed during eyes open conditions was maintained during eyes closed conditions. In this study, we applied linear techniques to describe the neck system in each condition. The controlled head-neck system is clearly nonlinear which is reflected in the modulation of FRFs with varying conditions.
Reflex modulation to address frequency bandwidth of the perturbation
Invariance of the muscle baseline activation (i.e. eb) and variation of neural feedback (expressed in the eFRFs) across the bandwidth of the torso perturbations indicates that the changes in system dynamics (kFRFs) are due
G a in [ m/ s/ m/ s] 1 4 1 4 1 4 10−1 100 1 4 1 4 1 4 10−1 100 102 103 Frequency [Hz] −270 −180 −90 0 Frequency [Hz] Frequency [Hz] G a in [ m/ s/ m/ s] Ph a se [ °] −270 −180 −90 0 Ph a se [ °] −270 −180 −90 0 Ph a se [ °] G a in [ °/ s/ m/ s] X.T1 X . GH X . T1 X . RH X.T1 θ . H EO-V4 EC-V4 A2 A2
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to neural modulation of neck reflexes. In a similar experiment to our own, Keshner (2003) perturbed labyrinthine deficient (LD) subjects and healthy controls with nearly identical translation perturbations. Relative to healthy controls, LD subjects demonstrate reduced gains and phase close to zero at the lowest frequencies in θɺH kFRFs, which was argued to be due to a reduction in the VCR contribution. In our conditions, the same gain and phase changes occur when increasing perturbation bandwidth and indicate a shift from head-in-space to head-on-trunk stabilization (Keshner, 2003a). In our experiments, we suggest this to be due to a progressive reduction in VCR.
The u-shaped gain and increasing phase lead in our θɺH eFRFs have also been seen in prior studies during rotation experiments about the vertical plane (i.e. pitch) with humans (Keshner, Cromwell, 1995b) and open-loop VCR responses in animal preparations (Baker et al. , 1985, Goldberg and Peterson, 1986). The increasing gains (i.e. positive slope region) were suggested to facilitate neural control mechanisms at higher frequencies – in particular via VCR provided by both semicircular canals and otoliths – functioning to dampen the system dynamics (Keshner, Cromwell, 1995b, Peng, Hain, 1996). In perturbations which do not excite the natural frequency (V1/V2), the positive θɺH and ɺXRH eFRF gain slope began at lower frequencies than was observed in high-bandwidth perturbations (V4/V8). One could therefore argue for a more prominent contribution of VCR damping in these low-bandwidth conditions.
Similar neural modulation of CCR is expected to also contribute to the variations observed. Previous studies have modeled the head-neck system (Peng, 1996, Peng et al. , 1997) during rotation experiments in the vertical plane (i.e. pitch) and estimated an equal contribution of VCR and CCR across a perturbation bandwidth of 4 Hz. When plotted as a relative kFRF (i.e. similar to our ɺXRH or θɺ
H), variation of reflex contributions indicated that decreasing CCR induces a bandwidth wide increase in gain and that decreasing VCR decreases low-frequency gain and increases high-low-frequency gain (i.e. creating a gain inflexion point) (Peng, 1996, Peng, Hain, 1999). Therefore, the bandwidth wide increase in ɺXRH kFRF gain in V2 relative to V1 is suggested to be primarily due to a reduction in CCR contributions. However, as the bandwidth exceeded the natural
frequency (i.e. from V2-to-V4-to-V8), reduced gains at frequencies <0.6 Hz and increased gains at frequencies >0.6 Hz indicated a strong influence of VCR modulation.
In addition, low-frequency (<1.5 Hz) CCR contributions to head stabilization have been observed in squirrel monkeys during horizontal rotations (i.e. twist) which modulated with changes in head inertia (Reynolds, Blum, 2008, Reynolds and Gdowski, 2008). This seems appropriate considering that dominant frequencies of human head motion in the anterior-posterior direction during daily activities such as locomotion are between ~1–3 Hz (Grossman, Leigh, 1988, Hirasaki, Moore, 1999, Pozzo, Berthoz, 1990).
The above observations would align with bandwidth-related reflex modulation observed in the upper and lower limbs, where continued excitation of the system dynamics beyond the natural frequency reduces reflex contributions on account of the inherent time delays (Kearney, Stein, 1997, Mirbagheri, Barbeau, 2000,
