Neurological Sciences Institute, Oregon Health Sciences University, Portland, Oregon 97209
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ABSTRACT |
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Buchanan, John J. and Fay B. Horak. Emergence of postural patterns as a function of vision and translation frequency. We examined the frequency characteristics of human postural coordination and the role of visual information in this coordination. Eight healthy adults maintained balance in stance during sinusoidal support surface translations (12 cm peak to peak) in the anterior-posterior direction at six different frequencies. Changes in kinematic and dynamic measures revealed that both sensory and biomechanical constraints limit postural coordination patterns as a function of translation frequency. At slow frequencies (0.1 and 0.25 Hz), subjects ride the platform (with the eyes open or closed). For fast frequencies (1.0 and 1.25 Hz) with the eyes open, subjects fix their head and upper trunk in space. With the eyes closed, large-amplitude, slow-sway motion of the head and trunk occurred for fast frequencies above 0.5 Hz. Visual information stabilized posture by reducing the variability of the head's position in space and the position of the center of mass (CoM) within the support surface defined by the feet for all but the slowest translation frequencies. When subjects rode the platform, there was little oscillatory joint motion, with muscle activity limited mostly to the ankles. To support the head fixed in space and slow-sway postural patterns, subjects produced stable interjoint hip and ankle joint coordination patterns. This increase in joint motion of the lower body dissipated the energy input by fast translation frequencies and facilitated the control of upper body motion. CoM amplitude decreased with increasing translation frequency, whereas the center of pressure amplitude increased with increasing translation frequency. Our results suggest that visual information was important to maintaining a fixed position of the head and trunk in space, whereas proprioceptive information was sufficient to produce stable coordinative patterns between the support surface and legs. The CNS organizes postural patterns in this balance task as a function of available sensory information, biomechanical constraints, and translation frequency.
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INTRODUCTION |
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The CNS must coordinate many joints and muscles and regulate sensory information from the visual, somatosensory, and vestibular systems to maintain balance and postural orientation during standing, walking, and running. To better understand how the CNS adapts to different environmental conditions, it is important to study how postural coordination and the role of the senses change as a function of different task constraints. Tasks that translate the body at different frequencies require changes in the coordinative patterns of the head, trunk, and legs to accommodate the different forces acting on the body. Translating the body at different frequencies also moves the sensory systems within and outside of their optimal operating ranges. To maintain balance, it is important to control the position of the trunk for two reasons: 1) most of the body's center of mass (CoM) is located in the trunk and 2) the trunk serves as a platform for the visual and vestibular sensory organs. How does the CNS coordinate the legs and trunk to control postural stability, i.e., the position of the CoM over the base of support, as a function of translation frequency? What is the role of visual information in the control and coordination of postural orientation and equilibrium?
Although several studies have investigated postural responses to
sinusoidal surface perturbations, they have been limited either in the
range of frequencies studied or in their kinematic and dynamic
analysis. Over a small range of anterior-posterior sinusoidal
translations frequencies (0.25-0.5 Hz), Dietz et al. (1993) and Berger
et al. (1995)
observed a change in body inclination and some damping of
head and CoM motion as translation frequency increased. Neither study
observed any significant changes in interjoint coordination or affects
of visual information on body inclination and CoM. In contrast, others
have reported large increases in body sway with the eyes closed for
lateral and anterior-posterior oscillatory tilt (or pitch) rotations
of the surface (Gurfinkel et al. 1975
; Walsh
1973
). This suggests that visual information may be important
for controlling the position of the head and trunk in space. By
studying tilting oscillations at higher frequencies, Walsh (1973)
also
observed a change in postural strategy from moving in phase with the
platform at low frequencies to relative stabilization of the head in
space at high frequencies (0.66 Hz) (see also Gantchev et al.
1972
; Gurfinkel et al. 1975
).
A possible explanation for the reported differences in results is that
sinusoidal rotational tilting may perturb the head and CoM more than
sinusoidal anterior-posterior translation for the same frequency of
motion. During rotational tilting, when the body moves with the
platform, the CoM moves close to the support surface boundary at the
points of maximal tilt within each cycle. Without vision, this movement
of the CoM is accentuated as seen in an increase in upper body sway
(Walsh 1973). In contrast, during anterior-posterior
surface translations, when the body moves with the platform, the CoM
may not approach the support surface boundary at any time during a
cycle of translation, and posture remains quite stable. If the CoM does
not approach the support surface boundary, vision may be less critical
to maintaining stable posture. However, Dietz et al. (1993)
may not
have driven the system to fast-enough translation frequencies to tease
out this interaction between CoM motion and the role of visual
information in stabilizing posture as noted by Walsh (1973)
. Perhaps at
faster anterior-posterior translation frequencies, vision may play a
more important role at keeping the CoM away from the support surface boundary.
It is generally believed that vision plays its largest role in posture
at low frequencies of motion. Postural sway can be driven by linear
motion of a moving room (Lee and Lishman 1975) or visual
scene (Lestienne et al. 1977
). Frequency analysis of sinusoidal movement of the visual scene has shown the strongest effects
to be <0.4 Hz (Berthoz et al. 1979
; van Asten et
al. 1988
). However, the influence of moving visual fields on
postural stability depends not only on the characteristics of the
visual environment but also on the characteristics of the support
surface. For example, when the surface is compliant, the sway induced
by sinusoidal visual motion is four times as large as when the surface
is fixed (Peterka and Benolken 1995
). Thus vision may
play a more important role in postural control when the support surface
moves compared with when the surface is fixed, especially when both the
surface and visual scene oscillate at high frequencies that typically do not drive postural sway in studies of visual motion with subjects standing on a fixed surface. To further examine the role of vision in
controlling head and trunk position in space, we extend the work of
Dietz et al. (1993)
by translating subjects across a larger frequency
range (0.1-1.25 Hz) with their eyes open and closed.
Visual, like vestibular information, may be more important in
controlling the orientation of the trunk and head than coordinating lower leg responses to surface perturbations (Horak et al.
1994; Keshner et al. 1988
). Humans and cats with
profound loss of vestibular function show normally coordinated leg
postural responses to surface perturbations, but exaggerated and more
variable head kinematic and electromyographic (EMG) responses
(Horak et al. 1994
; Macpherson and Inglis
1993
). Recently, Mergner et al. (1997)
proposed a model of
postural control with vestibular information important for organizing
postural responses in a top-down frame of reference, whereas
proprioceptive information from the ankles organizes postural responses
in a bottom-up frame of reference. The role of vision in controlling
head and trunk position in space during sinusoidal surface translations
with the eyes open is addressed in this study.
Preliminary results from this experiment were reported at the 26th annual Society for Neuroscience meeting in Washington, DC.
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METHODS |
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Subjects
The experimental protocol presented here was approved by the Legacy Good Samaritan Hospital Institutional Review Board and performed in accordance with the 1964 Helsinki Declaration. Eight volunteers (3 females, 5 males) participated and gave informed consent before participation. Subject age ranged from 25 to 45 yr (mean = 31.5 yr), subject height ranged from 162.5 to 200.6 cm (mean = 178.3 cm), and subject weight ranged from 47.7 to 95.5 kg (mean = 70.2 kg). None of the subjects reported any history of otological, neurological, or orthopedic abnormality.
Protocol
Subjects stood on a support surface that was translated sinusoidally (hydraulically driven) in the anterior-posterior direction at six different frequencies: 0.1, 0.25, 0.5, 0.75, 1.0 and 1.25 Hz. Surface translation was fixed at 12 cm peak to peak, with 6 cycles of motion in a trial for the frequency of 0.1 Hz (60-s trial) and 15 cycles of motion for the other 5 frequencies (60-s trial at 0.25 Hz to 12-s trial at 1.25 Hz). Subjects performed 3 trials for each translation frequency, with their eyes open and closed, for a total of 36 trials. The 36 trials were presented in 3 blocks of 12 trials with 2 trials per frequency and 1 trial per visual condition in each block, with the 12 trials in a block randomized. Subjects were given a 5-min rest interval after the first and second block of trials. Setup and data collection lasted ~2 h, with 472 cycles of data collected per subject.
Initial body posture was monitored visually as was the subject's x, y center of pressure (CoP) on an oscilloscope to reduce anticipatory behavior, e.g., forward lean or knee bending, before platform motion. The feet were placed slightly less than shoulder width apart with the toes angled slightly outward. Foot position was marked on the platform to insure a consistent initial foot position within and across trial blocks. Fall prevention consisted of a shoulder and trunk harness worn by the subject and attached to a steel support track mounted in the ceiling. Subjects were read the following instructional set: 1) keep your arms folded across your chest at all times, 2) stand upright at the start of each trial, but once the trial starts you may flex and extend your hips, knees, and ankles in any manner to remain on the platform, but do not step except to prevent a fall, and 3) with your eyes open look straight ahead and not to the left, right, down, or up, and with your eyes closed face forward as if looking straight ahead.
Reflective markers attached to the subject and platform were recorded
(60 Hz) with a motion analysis three-dimensional optical system
consisting of three high-speed video cameras (742 × 360 pixel
accuracy). Eight markers were attached to the following landmarks:
1) cathi of right eye, 2) right lateral
mandibular joint, 3) seventh cervical vertebra,
4) right greater trocanter, 5) right lateral
femoral condyle, 6) right lateral malleolus, 7)
right heel, and 8) right fifth metatarsophalangeal. A ninth marker was positioned posterior to the subject's right heel on the
translated surface. Computation of the marker's x,
y, and z trajectories was performed off-line on a
SUN workstation. The x, y, and z
trajectories were then low-pass filtered (Butterworth) with a cutoff
frequency of 6 Hz. From the x and z trajectories of the markers, the ankle (markers 5, 6 vertex, 7), knee (markers 4, 5 vertex, 6), hip (markers 3, 4 vertex, 5), and head pitch (markers 1, 2 vertex, 3)1 angles in the
sagittal plane were calculated. At rest, the head pitch angle measured
from 200 to 230° across subjects, with an increase in pitch angle
representing head pitch up and a decrease representing head pitch down.
The xyz trajectories of the eight body markers and subject
morphological measures were input into a three-segment model (legs,
trunk, head) to estimate total body CoM position in the
anterior-posterior (x), lateral (y), and
vertical (z) directions. CoM positions (x,
y, z) were calculated as a weighted summation of
individual segment CoM positions (Vaughan et al. 1991).
The platform's two force plates allowed for standardized foot
placement. Four strain gauges (sampled at 120 Hz) embedded in each
plate (2 forward, F1 and
F2, and 2 backward, F3
and F4) measured the force exerted by the foot
against the surface of the plate. Summation of the four strain gauge
signals produced an estimate of vertical force,
Fz = F1 + F2 + F3 + F4, under each foot. With this measure of
vertical force, the anterior-posterior CoP under each foot was derived
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EMG activity was recorded from eight muscles with pairs of 2.5 cm silver-silver chloride surface electrodes mounted over the muscle belly: left and right medial gastrocnemius, left and right tibialis anterior, right biceps femoris, right rectus femoris, and left and right erector spinae. EMG signals were preamplified, band-pass filtered (70-2,000 Hz), full-wave rectified before sampling (480 Hz), low-pass-filtered (100 Hz), and stored for off-line analysis.
Data analysis
Each translation frequency was associated with a different
initial platform acceleration. The platform's initial movement (first
quarter-cycle of motion) was always from the 0 position to 6 cm in
the posterior direction. Any effect of the initial perturbation
disappeared within the first one-half cycle of motion for the slowest
translation frequency of 0.1 Hz. For the remainder of the translation
frequencies (0.25-1.25 Hz), the initial perturbation effect lasted
anywhere from one-half to three cycles of platform motion. We focus on
how the human postural control system coordinates head and body motion
to a continuously moving surface. Thus the perturbation associated with
the platform's initial backward motion was discarded as transient
data. For the slowest frequency, the first one-half cycle of data was
discarded, and for the other five frequencies the first two and
one-fourth cycles were discarded. All the kinematic, force, and EMG
data presented were computed from the following cycles: 0.1 Hz, from
the 1st posterior peak of platform motion to the 6th posterior peak (5 cycles of motion); all other frequencies, from the 3rd posterior peak
of platform motion to the 15th posterior peak (12 cycles of motion).
BODY KINEMATICS. To study the effects of translation frequency on anterior-posterior head (and trunk) and CoM motion, average measures of anterior-posterior head and CoM amplitude were derived. The head (marker 2, x-axis) and CoM anterior-posterior time series for each trial were mean centered and averaged across cycles within a trial. The posterior peaks of platform motion were used to define the cycles of head and CoM motion to average. From the averaged cycle trajectory for each trial, the maximum and minimum values were located, and the difference between them was taken as the average peak-to-valley amplitude for that trial. The head pitch angle was also divided into cycles and averaged within a trial. From the averaged cycle trajectory for each trial, the maximum and minimum values were found, and the difference between them was taken as the average head pitch amplitude in the sagittal plane. The head and CoM amplitude measures were used to study damping of head and trunk motion in the anterior-posterior direction as a function of translation frequency, and head pitch amplitude was used to study head motion in the sagittal pitch plane.
SD for each point in the head's average cycle trajectory was computed. From these SDs of the average cycle trajectory for each trial, a mean (HSURFACE FORCES.
To examine the relationship between surface forces and platform
translation frequency, spectral analysis was performed to measure the
amplitude and phase of anterior-posterior CoP motion. An analogue
trace from the platform (sampled at 120 Hz) was taken as the
translation signal. The CoP and platform time series were mean
centered, and Welch's average periodgram method was employed to
compute the spectra.2 The
anterior-posterior platform translation signal always had a single
dominant peak (f) in its spectra, and
the amplitude and phase of anterior-posterior CoP motion were examined
at the corresponding peak in the CoP spectra. CoP amplitude was defined
as CoP = CoP(f)/P(f),
with P(f) the magnitude of the
platform signal and CoP(f) the
magnitude of the CoP signal. The phase of anterior-posterior CoP
motion was defined as the phase of the coefficient in the CoP spectra
associated with the value f. Values of CoP phase in the
interval 0° < CoP < 180° are interpreted as
maximum toes under CoP leading the posterior turnaround point of the platform.
JOINT ANGLE KINEMATICS.
To study joint-platform coordination, a cycle-to-cycle point estimate
of relative phase between platform translation and ankle joint angular
motion was computed; to study interjoint coordination, a point estimate
of relative phase between ankle and hip joint angular motion was
computed. Balance had to be maintained across a wide range of
translation frequencies, and oscillatory motion about an individual
joint was not observed at all frequencies. The anterior-posterior
platform time series (marker #9, x-axis) and ankle,
knee, and hip joint angular time series for each trial were mean
centered, and the spectra were computed with Welch's average
periodgram method (see footnote 1). A single frequency peak,
f, was always found in the platform spectrum of each
trial, and when the largest peak in the spectrum of an angular time
series was at the value of f, i.e., joint motion was
frequency entrained to platform motion, that joint was considered
active in that trial. To compute relative phase measures, a
peak-picking algorithm was used to locate the cycle peaks of maximum
ankle flexion, hip extension and the platform's posterior turnaround
point within a trial. In the platform-ankle phase, platform motion was
taken as the reference event, r, with platform cycle
duration defined as the time between successive reference events,
ri and
ri+1. The platform-ankle phase
(PA) was computed as the ratio of the time of a target
event tj (maximum ankle flexion)
within the reference cycle of platform motion,
PA = (ri
tj )/(ri+1
ri) × 2
. The phase in radians was converted to degrees. If 0° <
PA < 180°, then
this positive value of the platform-ankle phase indicates that maximum
ankle flexion occurred before the platform's posterior turnaround
point within a reference cycle. The ankle was taken as the reference event and hip extension was taken as the target event in computing the
ankle-hip phase (
AH) between ankle flexion and hip
extension. Positive values of the ankle-hip phase indicate that hip
extension leads ankle flexion within a cycle of motion. In each trial
in which a phase was computed, ~12 cycle phase values were derived, and from these values a mean phase and phase SD for the platform-ankle (
PA) and ankle-hip (
AH) relative phases
were computed.
EMGS. A variety of muscle activity patterns was used to stabilize posture in this task. To study muscle activation patterns, individual EMG time series within a trial were averaged across cycles when a 1:1 relationship between muscle bursts and platform motion was observed.3 Each muscle EMG was normalized to the maximum value for that muscle within a trial. A peak picking routine located the posterior and anterior platform signal peaks, which were used to define 12 cycle intervals in the EMG and joint angle time series. The maximum value in the averaged EMG signal was located; searching backward from this point, the onset was defined as the first point that was 10% of the maximum value with the average signal remaining above this 10% value for the next 50 ms. To characterize the timing of burst onset in the average signal across different translation frequencies, the onset was expressed as a percentage of the one-half cycle duration of platform motion. For the gastrocnemius and tibialis anterior, the time difference (td) between burst onset (On) and the platform's anterior turnaround point (ATP) was computed, td = ATP-On. This temporal measure was then expressed as a percentage (HCp) of the one-half cycle duration (HC) of platform motion, HCp = td/HC. A negative value of td indicates onset before the ATP, and a positive value indicates onset after the ATP for gastroc and tibialis. For b. femoris, r. femoris, and e. spinae, the value of td was defined as the difference between On and time of maximum hip flexion, and HCp was defined with respect to the one-half cycle time from maximum hip flexion to maximum hip extension in the averaged hip signal (when the hip was classified as active). A positive value of this temporal measure indicates burst onset after maximum hip flexion, and a negative value indicates burst onset before maximum hip flexion for b. femoris, r. femoris, and e. spinae. Both the positive and negative burst onset values were binned in 50% intervals to create four one-fourth cycle intervals of muscle onset.
STATISTICAL ANALYSIS.
The trial values of the head amplitude measure (HA/P), the
head position variation measure (H), the CoM-to-heel
variation data, and the platform-CoP phase measure
(
CoP) were averaged across trials by subject as a
function of vision and translation frequency and analyzed in 2 × 6 ANOVAs with vision (eyes open or closed) and platform frequency (6 frequencies) as independent variables. Because the platform-ankle
(
PA) and ankle-hip (
AH) phase values
were not computed at all frequency plateaus and all trials, the
individual trial means and SDs were analyzed in 2 × 5 (5 frequencies, 0.25-1.25 Hz) and 2 × 4 (4 frequencies, 0.5-1.25 Hz) ANOVAs, respectively, with vision and translation frequency as
independent variables. Although we randomized across frequencies within
a block, plots of the trial values of CoM amplitude and CoP amplitude
for individual subjects revealed a strong trial × translation
frequency trend. The individual subject trial means of CoM amplitude
and CoP amplitude were analyzed in 2 × 6 × 3 repeated-measures ANOVAs, with vision, platform frequency, and trial as
independent variables.
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RESULTS |
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Postural patterns emerged as a function of platform frequency and available sensory information
Frequency-dependent postural patterns emerged with or without
vision in this task. For slow translation frequencies of 0.1 and 0.25 Hz, subjects rode the platform with little damping of head and trunk
anterior-posterior motion (Fig. 1,
A and B); for fast frequencies of 1.0 and 1.25 Hz, subjects damped head and trunk motion extensively at the
translation frequency (Fig. 1, C and D). Both
patterns were evident for intermediate platform frequencies of 0.5 and
0.75 Hz, with more observations of the ride pattern at 0.5 Hz. All
postural responses, however, do not fall into this dichotomy of balance
patterns. Three of eight subjects (in a total of 9 trials) responded
with an out-of-phase pattern between head and platform motion when
first encountering a fast translation frequency (0.75 Hz) with the
eyes open. The trial displayed in Fig. 1E was the first
exposure of one subject to the translation frequency of 1.0 Hz with the
eyes open. Even with this out-of-phase pattern available, the behavior
of these three subjects converged to the head fixed in space pattern
with repeated exposure to the same translation frequency.
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A clear decrease in head amplitude (increased damping of head motion)
emerged with increasing translation frequency, with little or no
damping of head motion at the slowest frequency of 0.1 Hz and extensive
damping for the fast translation frequencies of 0.75-1.25 Hz (Fig.
2A). Head anterior-posterior
motion was damped more with vision than without vision,
F(1,84) = 147.9, P < 0.01, and damping
increased with increasing translation frequency from 0.1 to 0.75 Hz
with no change from 0.75 to 1.25 Hz, F(5,84) = 32.8, P < 0.01 (Fig. 2A). A small but significant
difference was found between the visual conditions as a function of
translation frequency, F(5,84) = 2.66, P < 0.5. This interaction was limited to translation frequencies of 0.5
Hz (P < 0.05), with eyes open amplitude less than eyes
closed (Fig. 2A). The head amplitude results suggest a
frequency-dependent change in postural pattern with only a small
difference in vision across translation frequencies.
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Does such damping of head anterior-posterior motion for the fast
translation frequencies represent fixing the head in space (as shown in
Fig. 1, C and D) relative to the moving platform? In many trials with the eyes closed and translation frequency of 0.75
Hz, head position in spaced drifted, even with significant damping of
head motion at the translation frequency (Fig.
3A). In some trials,
higher-order frequency ratios between head and platform motion were
observed. The example in Fig. 3B clearly shows a 2:1 ratio
between platform and head motion. More importantly, the amplitude of
oscillation grows across cycles to a maximum of 13 cm. Table
1 portrays the number of specific
frequency ratios between the head and platform as a function of
translation frequency with the eyes closed. Frequencies ratios of 4:1,
3:1, and 2:1 were seen most often for the translation frequency of 0.5 Hz, whereas slow-sway motion as shown in Fig. 3A was found
only for frequencies of
0.75 Hz.
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Although vision had only a small effect on damping of head motion at
the translation frequency, it had a large effect on maintaining a fixed
position of the head in space. For example, compare the large amount of
drift (variability in the head's mean position, H = 4.5 cm) in Fig. 3A with the eyes closed with Fig. 1C with the eyes open (H
= 0.82 cm). The group means of the average cycle variability have been plotted in Fig. 2B as a
function of visual condition and translation frequency. The head's
variability in space differed as a function of vision,
F(1,84) = 9.7, P < 0.01, and varied
significantly with translation frequency, F(5,84) = 118.9, P < 0.01. Most importantly, the vision × frequency interaction was significant, F(5,84) = 6.79, P < 0.01, with the head's variability in space
significantly larger (P < 0.05) with the eyes closed than with the eyes open for translation frequencies of
0.75 Hz (Fig.
2B). Thus vision plays an important role in controlling the
head's position in space, especially for the fastest translation frequencies.
Head pitch angle remains constant across translation frequencies
Motion of the head in the pitch plane, whether discrete or oscillatory, was minimal in seven of eight subjects. In only a few instances was oscillatory motion of the head in the pitch plane observed, for example, when producing the antiphase pattern as in Fig. 1E or with the eyes closed as in Fig. 3B. The subject portrayed in Fig. 3B was the only subject to consistently produce oscillatory motion of the head in the pitch plane, with the eyes open or closed, that was frequency entrained to platform motion. For the other seven subjects, head motion in the pitch plane was consistent with the averaged cycles shown in Fig. 1, A-D, or the head angle trace shown in Fig. 3A. The group mean of head pitch amplitude was <2° across visual conditions [F(1,84) = 0.9, P > 0.3] and translation frequencies [F(5,84) = 0.9, P > 0.4]. Thus, except in the case of one subject, when the head was fixed in space with the eyes open in the A/P direction, it was also fixed in space with respect to pitch plane rotation. With the eyes closed, even if large drift in head and trunk motion was observed (Fig. 3A), there was little rotational motion of the head around c1 in seven of eight subjects.
CoM amplitude and position over the support surface vary with translation frequency and vision
Damping of head and trunk motion at the fast translation frequencies was associated with a reduction in the displacement of CoM in the anterior-posterior direction. Representative examples of CoM position within the support surface boundary have been plotted in Fig. 4, A and B. At slow translation frequencies, anterior-posterior CoM displacement was approximately equal to the platform displacement with the eyes open or closed (Fig. 4A), whereas at fast translation frequencies, CoM anterior-posterior motion at the translation frequency was damped extensively with the eyes open or closed (Fig. 4B). CoM amplitude decreased as translation frequency increased, F(5,35) = 152.3, P < 0.01, and varied as a function of trial, F(2,14) = 8.5, P < 0.01. From 0.1 (11.5 cm) to 1.25 Hz (2.4 cm), CoM peak-to-valley amplitude decreased with a significant difference (P < 0.05) between each translation frequency, except 1.0 and 1.25 Hz. CoM amplitude was ~10% larger on trial 1 than trials 2 and 3 (P < 0.05) and was larger with the eyes closed than with the eyes open, F(1,7) = 39.2, P < 0.01. The visual condition × frequency interaction was also significant, F(5,35) = 4.1, P < 0.01. CoM amplitude was ~10% larger without vision for slow translation frequencies: 0.1 Hz (closed = 12.1 cm, open = 10.9 cm), 0.25 Hz (closed = 10.3, open = 8.4), and 0.5 Hz (closed = 7.2 cm, open = 6.1 cm). Thus CoM amplitude was large (little damping) for the ride pattern at slow translation frequencies and was extensively damped for the head fixed in space and slow-sway patterns at high frequencies, with only a small effect of vision on CoM amplitude at slow frequencies.
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Although vision has only a small effect on CoM damping at slow translation frequencies, does that also mean that vision has little effect on postural stability, i.e., the movement of the CoM within the support surface? At slow frequencies, vision has little effect on CoM position that moved little within the support surface (Fig. 4A), whereas at fast frequencies vision limits the movement of CoM within the support surface (compare Fig. 4B, top and bottom). The variability of CoM-to-heel distance was constant across frequencies with the eyes open, although the actual distance of the CoM from the heel increased with faster frequencies (compare Fig. 4, A and B, top). Without vision, CoM-to-heel distance variability increased with each increase in translation frequency (Fig. 4C). CoM-to-heel distance variability was significantly different as a function of vision, F(1,180) = 184.4, P < 0.01, and platform frequency, F(5,180) = 17.9, P < 0.01. The visual condition × frequency interaction was also significant, F(5,180) = 10.6, P < 0.01. Contrast tests (P < 0.05) of the interaction revealed that the variability in CoM-to-heel distance was larger without vision for all platform frequencies of >0.1 Hz. These results converge with the analysis of head position in space (Fig. 2B) in showing the importance of visual information in stabilizing balance by reducing variability of both head (and trunk) and CoM motion in space. Thus fixing the position of the head and trunk in space corresponds to minimal motion of the CoM within the support surface defined by the feet, establishing a direct link of head and trunk control in space to stability at the support surface.
CoP amplitude increased with increasing translation frequency and was modulated across trials
As the CoM amplitude decreased, CoP amplitude increased with each
increase in translation frequency, but with only a small difference as
a function of vision. The time series in Fig.
5 show the frequency-dependent
differences in CoP amplitude that correspond with the ride postural
pattern at low frequencies (Fig. 5A), head fixed in space
pattern (Fig. 5B, top, eyes open), and slow-sway
pattern (Fig. 5B, bottom, eyes closed) at high
frequencies. The group mean CoP amplitudes have been plotted in Fig.
5C as a function of platform frequency and trial. CoP
amplitude increased with increasing translation frequency,
F(5,35) = 51.6, P < 0.01, and varied with
trial presentation, F(2,14) = 12.8, P < 0.01. The trial × frequency interaction was also significant,
F(10,70) = 10.92, P < 0.01 (Fig.
5C). The trial effect arose from differences at 0.75, 1.0, and 1.25 Hz, with CoP amplitude larger in trial 1 than trials 2 and 3 for all three frequencies and larger in trial 2 than trial 3 at 1.25 Hz
(P < 0.05). CoP amplitude was ~10% larger with the
eyes closed, F(1,7) = 10.5, P < 0.05, and from 10% (0.75 Hz) to 30% (1.25 Hz) larger with the eyes closed at
the fastest translation frequencies, F(5,35) = 5.6, P < 0.01. The increase in CoP amplitude corresponds
directly with the change in behavior from the ride pattern at slow
frequencies (0.25 Hz) to head fixed in space or slow-sway pattern at
fast frequencies (
0.75 Hz). The CNS increased CoP amplitude to
counter the increase in forces associated with increasing translation
frequency, with a clear adaptation with repeated exposure even when the
frequencies were randomly presented.
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The phase of anterior-posterior CoP was more consistent within a given subject than across subjects as a function of translation frequency and vision. Positive values of CoP phase indicate that the maximum forward position of CoP under the feet occurred before the platform's posterior turnaround point (Fig. 5B). The group mean CoP phase shifted significantly from 43° (between subject variability = 58°) at 0.1 Hz to 112° (variability = 32°) at 1.25 Hz, F(1,84) = 4.4, P < 0.05, with between-subject variability decreasing from low to high frequencies. The shift in CoP phase and decrease in between-subject phase variability correspond to the shift in postural pattern from the ride pattern at low frequencies to head fixed in space and slow-sway pattern at high frequencies. The phase of CoP-to-platform motion was the same with the eyes open (89°) or closed (78°), F(1,84) = 0.45, P > 0.5. Thus vision had only a small effect on anterior-posterior CoP phase. CoP phase was relatively constant across the fast translation frequencies, even with large modulations in CoP amplitude with repeated exposures to fast frequencies (Fig. 5C).
Joint motion was coordinated to support frequency-dependent balance patterns
To dissipate the larger forces associated with increasing translation frequency, fixed patterns of oscillatory joint motion emerged among the hip, knee, and ankle, with somewhat more variability in the joint coordinative patterns with the eyes closed. Figure 6 shows representative examples of the relationship between ankle-hip and ankle-knee for the ride pattern (column 1), head fixed in space pattern with the eyes open (columns 2 and 3), and slow-sway pattern with the eyes closed (column 4). When subjects rode the platform, there was little oscillatory joint motion, whereas at faster frequencies there were clear relationships between hip and ankle motion. Five of eight subjects consistently produced oscillatory knee motion (Fig. 6, columns 2 and 4), and three of eight subjects consistently fixed their knees at a specific angle (column 3). Table 2 shows that more oscillatory motion occurred at the ankle and hip compared with the knee, and the number of trials with oscillatory joint motion increased as translation frequency increased. Oscillatory joint motion was not found at a platform frequency of 0.1 Hz.
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The relative phase between ankle flexion and platform motion gradually
changed across frequencies, whereas ankle-hip relative phase remained
relatively constant with considerable intersubject variability. The
group mean values of the ankle-platform (PA) and
ankle-hip (
AH) phases have been plotted in Fig.
6B. At slow frequencies, ankle flexion occurred in advance
of the platform's posterior turnaround point, and at fast frequencies
maximum ankle flexion occurred simultaneously with the platform's
posterior turnaround point, F(4,201) = 21.0, P < 0.01. Post hoc tests (P < 0.05)
found that the mean relative phase at 0.25 Hz was larger than all other
frequencies, and the mean relative phase at 0.5 Hz was larger than the
phase at the two fastest frequencies (Fig. 6B). A small but
significant difference was found in the ankle-platform phase
relationship as a function of visual condition, F(1,201) = 4.4, P < 0.05 (eyes open,
PA = 7.9°;
eyes closed
PA = 13.2°). No significant effects were
found in the ankle-hip phase
AH as a function of vision
[F(1,154) = 1.7, P > 0.1] or platform
frequency [F(3,154) = 0.7, P > 0.5] (Fig.
6B).
The variability in ankle-platform and ankle-hip relative phase
measures decreased as translation frequency increased. The group mean
SDs for the ankle-platform and ankle-hip phases have been plotted in
Fig. 6C. Variability for both the ankle-platform [F(1,201) = 13.4, P < 0.01] and
ankle-hip [F(1,154) = 5.7, P < 0.01]
phase relationships decreased significantly with increasing platform
frequency. Post hoc tests (P < 0.05) revealed that the variability in the ankle-platform phase was largest at 0.25 and 0.5 Hz
and that the variability in the ankle-hip phase decreased from 0.5 to
1.25 Hz. Both the ankle-platform [F(1,201) = 43.1, P < 0.01] and ankle-hip [F(1,154) = 39.1, P < 0.01] phase relationships were less variable with
vision (PA = 16.8°,
AH = 24.3°) than without vision (
PA = 28.1°,
AH = 39.1°). Although the joint coordination patterns were less variable
with vision, the stability of interjoint coordination increased (i.e.,
became less variable) with increasing platform frequency with and
without vision.
Muscle activation was frequency dependent and functionally specific to the balance patterns
More muscles were active at fast than slow translation frequencies, and a variety of muscle activation patterns was used to stabilize the upper body in the head fixed in space pattern. Table 3 shows the number of trials in which some type (tonic, rhythmic, and intermittent) of muscle activity (act column) occurred and those trials in which rhythmic muscle activity (rhy column) was observed. The numbers in parenthesis correspond to trials with the eyes open. In general, some form of gastroc activity was observed more than any other muscle at slow platform frequencies, whereas activity in other muscles increased with increasing translation frequency. More rhythmic activity was associated with faster translation frequencies for all muscles monitored, with somewhat more rhythmic activity with the eyes open.
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Figure 7 portrays two different patterns of muscle activation associated with the head fixed in space posture at a fast translation frequency of 1.25 Hz. Gastroc and tibialis burst out of phase in both examples, with b. femoris bursting in-phase with gastroc in Fig. 7A, and r. femoris bursting in-phase with tibialis in Fig. 7B. Notice in Fig. 7A that the gastroc bursts before maximum ankle flexion; in Fig. 7B the gastroc bursts before maximum ankle extension and stays on as the ankle flexes. Similar differences arise in tibialis activity between the two trials. Gastroc was activated before maximum forward (toes) CoP, and tibialis activity was activated before maximum backward (heel) CoP in each example. In both trials, because the head and trunk were relatively fixed in space, hip extension and flexion resulted mainly from motion of the thigh as the platform moved from anterior to posterior. The onset of b. femoris in Fig. 7A occurred as the hip extended, as did the onset of r. femoris in Fig. 7B, which remained active as the hip flexed. Table 4 summarizes the frequency of muscle onset in four one-quarter cycle intervals as a percentage of the one-half cycle duration: gastroc and tibialis in relation to the platform cycle and b. femoris, r. femoris, and e. spinae in relation to the hip cycle. The four one-quarter cycles are 1) early joint extension, maximum flexion to mid extension; 2) late joint extension, midextension to maximum extension; 3) early joint flexion, maximum extension to mid flexion; and 4) late joint flexion, mid-flexion to maximum flexion. Gastroc activity was most often initiated during early flexion (Table 4, column 3) when it starts to lengthen as in Fig. 7A and next most likely during late extension similar to the example shown in Fig. 7B. Tibialis activity was most likely to be initiated in early extension as it started to lengthen as in Fig. 7A, with few burst onsets occurring during early or late flexion as the example in Fig. 7B. Biceps femoris onset was observed more often in the late extension phase when the muscle was shortened as shown in Fig. 7A, whereas r. femoris onset occurred more often during the late flexion or early extension phases when it was shortened just before or after maximum hip flexion. Erector spinae was activated most often during the late extension and early flexion phases when the muscle was shortened, very similar to b. femoris and gastroc.
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In some trials, rhythmic muscle activity disappeared without any obvious kinematic change, whereas in other trials muscle activity fluctuated in relation to slow-sway motion of the upper trunk. For example, Fig. 8A shows the sudden disappearance of frequency-entrained rhythmic muscle activity. Suppression of rhythmic muscle bursts was found with both the eyes open and closed and for all muscles. Figure 8B shows how muscles can be recruited and suppressed based on the slow drift in head and trunk anterior-posterior position. This trial corresponds to the trial in Fig. 3A. Just as the upper trunk and head reached their most posterior position (Fig. 8B, dotted line in 4th row), large bursts in tibialis occurred (while gastroc activity was suppressed considerably) that stopped the backward motion of the upper trunk and head. The tibialis bursts were followed by r. femoris and e. spinae bursts, which helped to control and maintain the upper trunk's current position as well as help to move the upper trunk forward. Once the upper trunk and head reached a certain forward position, tibialis activity was suppressed, and gastroc activity was recruited. Most distal muscle activity was associated with ankle and platform motion, and most proximal muscle activity was associated with hip and upper trunk motion, although ankle muscle activity, as shown in Fig. 8B, was activated to help control the position of the upper trunk and head in space when they drifted near the limits of stability.
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DISCUSSION |
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Our results suggest that both sensory and biomechanical constraints limit postural coordination patterns at different translation frequencies. Control of the head and trunk in space appears to be a critical strategy for balance at all translation frequencies because the trunk has so much of the body's mass and because it also serves as a platform for the visual and vestibular organs located in the head. Visual information is particularly important to control slow drift of the trunk and keep the CoM away from the limits of foot support at fast translation frequencies. Lower body joint motion is coordinated to dissipate the energy input of fast platform frequencies, in turn facilitating control of upper body motion. Muscle synergies are loosely assembled and functionally specific to the demands of limiting body CoM drift as well as modulating CoP amplitude.
Is postural coordination multistable?
We observed two distinct, stable postural patterns as a function
of platform frequency. Subjects typically maintained an erect stance
and rode the platform with or without vision for slow translation frequencies (0.1 and 0.25 Hz). These slow frequencies are within the
range for normal sway frequency during quite stance
(McCullum and Leen 1989) when most of the
control is located around the ankles (Nashner 1976
). For
fast platform frequencies (1.0 and 1.25) with the eyes open, subjects
fixed the head and trunk in space relative to the moving platform. Our
observation of two distinct postural patterns as a function of platform
frequency coincides with previous work on pitch and frontal plane
sinusoidal tilting (Gurfinkel et al. 1975
; Walsh
1973
) and confirms and extends the findings of Dietz et al.
(1993)
on anterior-posterior sinusoidal translation. Fixation of the
head and trunk in space over an oscillatory support surface is not due
only to the inertial constraints of damping trunk motion at higher
frequencies. Proximal muscle activity is added for the head fixed in
space pattern and controlling trunk position in space, whereas distal
muscle activity is related to CoP modulations under the feet for both
postural patterns, in turn representing active neural control of these
postural patterns.
Occasionally, an out-of-phase head-to-platform postural pattern was
observed with the eyes open at fast frequencies, but with repeated
exposure to a given frequency the out-of-phase pattern converged to the
head fixed in space pattern. Without vision, slow-sway head and trunk
motion emerged for fast frequencies (0.75 Hz) as did higher-order
frequency ratios between head and platform movement. Walsh (1973)
also
reported such subharmonic ratios between head and platform motion
during sinusoidal surface tilting at 0.66 Hz; however, it is not clear
in Walsh's study what ratios actually arose or how often. Our results
show that higher-order frequency entrainment of head-to-platform motion
occurs most often for the translation frequency of 0.5 Hz and consisted
of low-order integer ratios (2:1, 3:1, and 4:1). In multijointed arm
movements, such low-order frequency ratios (in this case 2:1) have been
shown to be stable, reproducible, coordinative patterns between
shoulder and elbow and shoulder and wrist motion during a spatial
tracing task (Buchanan et al. 1997
; deGuzman et
al. 1997
). A variety of bimanual coordination studies has also
shown that low-order frequency ratios (2:1, 3:1, 3:2) between the hands
are stable across several movement frequencies (e.g., Kelso and
deGuzman 1988
). With such short trials in this
experiment, we cannot say conclusively whether the higher-order
frequency ratios between head and platform and slow-sway motion are
stable, reproducible patterns (as in arm movements, which are also
multisegmented tasks) or just long transients as the system searches
for a stable postural pattern. To explore this possibility, it is
necessary to run trials with 24-36 cycles of motion without vision to
see if the subjects establish the head fixed in space pattern or
maintain a consistent 2:1-type pattern as seen in Fig. 3B.
The observation of out-of-phase patterns, higher-order frequency
patterns, head fixing, and slow sway are highly suggestive of
multistability in the postural control system for this task. However,
more experiments designed to study specific postural patterns as
initial patterns, pattern switching between postural patterns, and
fluctuation enhancement before a change in postural pattern are
required to solidify the idea of multistability in the postural control system.
Visual information stabilizes posture by helping to control the position of the head and trunk in space
For the slowest translation frequency of 0.1 Hz, inertial forces
acting on the head and trunk are minimal, and subjects ride the
platform with or without vision. Each increase in translation frequency
increases the forces acting on the body, and compensation arises in the
form of damping head, trunk, and CoM anterior-posterior motion at the
translation frequency. From 0.1 to 0.5 Hz, the increase in damping of
head and CoM motion is approximately linear, and above 0.5 Hz motion of
the head and CoM is damped. Such damping of head and CoM motion at the
platform's translation frequency is directly in line with the
observations of Berger et al. (1995) (see also Berger et al.
1992
; Dietz et al. 1993
) for sinusoidal anterior-posterior translations. Berger et al. (1995)
observed an
increase in head and CoM damping as the translation frequency increased
with or without vision, but they only tested postural responses to slow
frequencies of
0.5 Hz. Our slow translation results are consistent
with their results. Even for fast frequencies above >0.5 Hz, there is
little difference in the extent of head and CoM damping at the
translation frequency with or without vision.
Although vision plays a small role in damping head and CoM motion at
the translation frequency, it plays a large role in controlling the
position of the head and CoM in space, especially at high frequencies.
The effects of vision are first seen at the translation frequency of
0.5 Hz, the cutoff frequency in the work of Dietz et al. (1993) and
Berger et al. (1995)
, and the influence of vision increases as
frequency increases. At fast translation frequencies, the availability
of vision reduces the variation of the head's position, and this
reduced variability shows that the head is fixed in space with respect
to the moving platform. Analysis of the head pitch angle revealed only
a few examples of oscillatory head pitch motion, with most produced by
a single subject. This result further confirms the head amplitude
results in demonstrating the head fixed in space with the eyes open.
Also of interest is the lack of any head pitch angle difference with
the eyes open or closed, suggesting that the head is "strapped
down" (e.g., Nashner 1985
) to the trunk with the eyes
closed. Across all translation frequencies, the availability of vision
reduces the variation of the CoM's position within the support
surface, suggesting that fixing the head and trunk in space is
functionally associated with stabilizing posture. Removal of visual
input is associated with large-amplitude, slow-sway motion of the head
and trunk, reflected in large motions of the CoM over the support
surface. Thus head motion is not just damped at the translation
frequency in this task; it is actually maintained in a fixed position
in space when visual information is available. As a result, vision directly contributes to postural stability.
Our finding that vision is important at fast translation frequencies
may at first appear contradictory with previous studies suggesting that
vision plays a major role in posture at slow frequencies of body sway
(Berthoz et al. 1979; Dijkstra et al.
1994a
; Lee and Lishman 1975
; Lestienne et
al. 1977
; van Asten et al. 1988
). Studies of
postural sway have shown that sway can be driven by slow visual field
oscillations (both central and peripheral visual fields) between 0.1 to
0.3 Hz, whereas frequencies of
0.4 Hz may or may not induce coherent
postural sway (e.g., Dijkstra et al. 1994b
; van
Asten et al. 1988
). Dijkstra et al. (1994b)
reported that
visual scene oscillations of 0.5 Hz induced three types of postural
sway behavior: 1) sway phase locked or coordinated with visual scene oscillation; 2) sway exhibiting intermittent
behavior, i.e., phase locking interrupted by periods of no coordination between sway and visual scene oscillation; and 3) no
coordination between sway and visual scene oscillation. This variety in
postural sway coordination at 0.5 Hz observed by Dijkstra et al.
(1994b)
is directly in line with our postural coordination changes with respect to the positioning of the head in space. At 0.5 Hz, some subjects fix the head in space, whereas others continue to ride the
platform. Both the ride and head-fixed pattern may be interpreted as
postural behavior coordinated to surface translation. With the eyes
closed, it is at 0.5 Hz that other postural patterns such as 2:1
platform to head motion is first observed and at 0.75 Hz that slow-sway
emerges. This type of behavior is suggestive of either intermittent or
no coordination as observed in the work of Dijkstra et al. (1994b)
when
sway is decoupled from visual motion. However, our trials are not long
enough to clarify this issue, and more work consisting of trials
lasting 1-2 min at the fastest frequencies is needed.
In our experiment, visual field oscillation results from moving the subject's visual system through space on a moving platform, that is, we mechanically drive the body through a visual environment, and visual field oscillation studies drive body sway with sensory information. Movement of the body through space compared with movement of the visual field with the body standing on a fixed surface is different in three ways: 1) moving the body on a translating surface induces mechanical postural perturbations as well as visual field oscillations, 2) movement of the surface alters both proprioceptive as well as visual information, and 3) visual field perturbations can be ignored at fast frequencies, whereas the surface perturbations in our experiment cannot. Even with these differences in perturbation techniques to upright posture our findings are consistent with the work on visual field oscillations and point to the complex interaction among proprioceptive, visual, and vestibular information in the control and coordination of postural behavior. For slow surface translations with visual field oscillations below the critical frequency for decoupling visual information and postural sway (<0.5 Hz), subjects ride the platform and tolerate visual field oscillations induced by support surface translation. This demonstrates that the CNS actively suppresses visual information that has been shown to passively drive postural sway and selects proprioceptive and vestibular information to control and coordinate postural responses in this task. For fast surface translations above the critical frequency for coupling postural information to visual sway, subjects fix the head in space and remove the large perturbing effects of fast visual field oscillations induced by the support surface translation. The slow drift observed with the eyes closed at fast frequencies, when combined with the head fixed in space with the eyes open, demonstrates that the CNS actively uses visual information to control the positioning of the head in this task. Although the body may act as a low-pass filter because of its inertia and stiffness, the passive component to damping of CoM, and head excursions in this task, without the active control of visual information there is more drift in CoM and head position that destabilizes posture at fast translation frequencies. Perturbation of both head-trunk and leg motion with the eyes open and closed at fast translation frequencies is one way to test this assumption and may clarify how the CNS coordinates sensory information with passive biomechanical constraints in controlling not just posture but movement in general.
Assuming the body acts as an inverted pendulum, the visually driven
postural sway results suggest that visual information is filtered by a
second-order, low-pass filter (Lestienne et al. 1977;
van Asten et al. 1988
) such that postural sway is not
responsive to high-frequency visual information. As surface translation
frequency increases to >0.5 Hz, postural sway switches from an
inverted pendulum type of sway at slow frequencies to a multisegmented type of sway with significant motion about the hips and knees at fast
frequencies. This change in postural pattern is consistent with
biomechanical constraints on body sway. The human body swaying as an
inverted pendulum has a maximum sway of ~0.5 Hz. To sway at faster
frequencies, the body must break at the hips with the trunk and head
oscillating out of phase with the legs (McCollum and Leen
1989
). It is not clear from studies of visual field oscillation if subjects switch from an inverted pendulum to a multisegmented sway
at higher frequencies or whether they ignore vision at high frequencies
to avoid multisegmental sway on a fixed surface in which proprioceptive
information is unvarying. Movement of the support surface may be
interpreted as an unstable surface such that subjects rely on visual
(and vestibular) information more than when standing on a fixed surface
(Mergner et al. 1997
). Thus subjects in our study may be
using vision at higher frequencies compared with studies of visual
scene oscillation because of the influence of surface perturbations on
patterns of body sway and on sensory context.
This suggests that visual information about the velocity of optic flow may be important in establishing and maintaining a fixed position of the head in space when the CoM is perturbed by surface perturbations. When the eyes are open, fixing the head in space for translation frequencies of >0.5 Hz removes any visual field oscillation. The CNS, in this case, may use optical flow information in a feedforward manner to fix the head in space and in a feedback manner to help monitor head and upper trunk position once fixed. The neural strategy is one in which removal of sinusoidal optical flow immobilizes the head. Establishing a fixed position of the head and upper trunk in space achieves three things in this task: 1) stabilizes the visual field in terms of retinal image slip, 2) stabilizes CoM position within the support surface, and 3) minimizes external platform forces acting on the head and upper trunk. The loss of head and upper trunk fixation without vision supports the hypothesis of a top-down role for visual information in the control of head and upper trunk position in space, particularly on a moving surface.
Stable intersegmental coordination produces a stable support base for the trunk and head
Despite some small differences in phase relationship of hip-to-ankle motion among subjects, all subjects showed a decrease in the variability of the ankle-hip phase pattern with each increase in translation frequency of >0.5 Hz. Stable intersegmental coordination during fast surface oscillations (>0.5 Hz) suggests that oscillatory motion about the hips, ankles, and knees may be important for the efficient dissipation of external platform forces, in turn limiting the impact of these forces acting on the upper trunk and head. If stable joint coordination is representative of efficient dissipation of external forces, then any forces not dissipated by the lower body with the eyes open may be compensated for by small-amplitude motions of the pelvis and lower trunk. However, without vision, slow-sway upper trunk and head motion may reflect a resonance of any nondissipated forces from the legs to the upper body. Although these slow-sway movements in the upper trunk occur at fast translation frequencies, joint coordination patterns remain stable, strongly suggesting, although not conclusivley, that stable joint coordination may help to efficiently dissipate most of the forces created by the translating surface. Such stable intersegmental coordination demonstrates the importance of a stable base of support in the production of global postural patterns with or without vision (i.e., head fixed in space or slow-sway motion). The lack of any real differences in vision and nonvision conditions in joint coordination supports a bottom-up control of leg coordination to support surface motion with proprioceptive information.
Muscle synergies are flexibly assembled to stabilize CoM position and trunk orientation
Changes in muscle activation patterns accompanying the changes in kinematic postural patterns suggest these postural patterns are neurally controlled and are not simply a function of biomechanical constraints. Proximal muscle activity increased as translation frequency increased, suggesting that proximal muscle activity (r. femoris, e. spinae, or b. femoris) is associated with the control of trunk orientation. These muscles typically burst during their shortening phase when platform motion would move the trunk in the opposite direction. For example, e. spinae fire as the platform and legs move backward at fast translation frequencies, thus helping to keep the trunk erect and not allowing it to flex at the hips. In contrast, gastrocnemius and tibialis muscles are predominantly active when stretched and clearly associated with CoP changes and regulation of CoM position within the base of support. Distal muscle activity also loads the front and back of the feet (CoP) in anticipation of the platform's ATP and posterior turnaround point, respectively, helping to stabilize the system before the change in perturbation direction.
Oscillatory activity of the distal muscles can also be recruited and suppressed to help control the motion of the trunk in space when the CoM approaches the support surface boundaries when the eyes are closed. What sensory information triggers this recruitment-suppression effect of EMG burst activity? EMG activity associated with the slow drift in head and CoM motion (as seen in Fig. 8B) with the eyes closed suggests that either otolith or somatosensory information may trigger these muscle responses when the postural state reaches some critical position. In this case, the proximity of the CoM to the support surface boundary. The most interesting feature of this observation is that the CNS coordinates the responses of the same muscle to different parts of the body depending on the current state of the system. For example, in Fig. 8B, gastroc and tibialis activity are clearly related to ankle motion as well as motion of the head and trunk. Because there is little change in the nature of the ankle angle when the trunk and head slowly drift, the data are suggestive that the otolith organs may provide the necessary information enabling the CNS to switch the control of muscle activity from ankle to head and trunk motion. Amplitude of muscle bursts can also be modulated within a trial. For example, initial oscillatory proximal muscle activity disappeared in some trials without a change in kinematic posture, suggestive either of a passive balancing of the trunk or the recruitment of other muscles not recorded (Fig. 8B). While the recruitment-suppression of muscle activity seen when the head and trunk drift slowly are clearly sensory driven, the disappearance of rhythmic bursting with the eyes open and no kinematic modulations points to the active suppression of muscular activity by the CNS. Although a given muscle tended to be activated in a specific part of a translation cycle within a subject, quite a few differences in muscle activation patterns also occurred among subjects with very similar kinematic postural patterns.
Adaptation of postural control
Although frequency presentation was randomized, CoP
amplitude significantly decreased with repeated exposure to frequencies of >0.5 Hz. On first exposure to a fast translation frequency, the
nervous system responds with large CoP amplitudes to counter the
platform forces and helps control the motion of the head and trunk in
space. After repeated exposure to fast frequencies, the nervous system
adapts and reduces CoP amplitude to accomplish the same goal. For
translation frequencies of 0.75 Hz, CoP amplitude is always largest
on trial 1 and decreases through trial 3. Such a decrease in CoP
amplitude may represent a neural strategy of energy minimization. This
modulation in CoP amplitude occurs during the production of the head
fixed (eyes open) and large-amplitude, slow-sway patterns (eyes
closed). Because this adaptation of CoP is consistent with the eyes
open or closed, it suggests that this adaptation of CoP amplitude is
independent of vision.
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CONCLUSION |
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Results from this study suggest that the number of possible balance patterns is limited by biomechanical properties of the trunk and head, constraints on sensory information processes, and possible energy minimization requirements. However, the nervous system is not limited to a single solution and demonstrates flexibility of postural coordination in the form of a variety (head fixed in space, out-of-phase head to platform, slow-sway) of postural patterns at fast platform frequencies. Flexibility is also reflected in the motion and lack of motion in the knee as well as the recruitment and suppression of individual muscle activity that is task specific. The results also demonstrate the importance of vision in controlling the position of the head and trunk in space during high-frequency motion. To investigate the interaction of visual and vestibular information in this task, the same experiment is being repeated with bilateral vestibular loss subjects and aged-matched controls.
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ACKNOWLEDGMENTS |
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We thank C. Jones for help in experimental setup and data collection. We also thank Dr. Tjeerd Dijkstra for the use of peak-picking routines written for the analysis of oscillatory data.
This work was supported by National Institute of Deafness and Other Communications Disorders/National Aeronautics and Space Administration Research Grant P60DC-02072 to F. B. Horak, and by National Research Service Award Postdoctoral Grant F32 DC-00372-1 awarded to J. J. Buchanan.
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FOOTNOTES |
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Address for reprint requests: F. B. Horak, Neurological Sciences Institute, Oregon Health Sciences University, 1120 N. W. 20th Ave., Portland, OR 97202.
The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
1 Correlations of A/P motion (x-axis) of markers 2 and 3 were >0.96 across subjects and trials, showing that the head and upper trunk moved as a unit in this task. Although head pitch angle is relative to upper trunk motion, there was no indication of a confounding of trunk inclination on head pitch angle.
2 The Welch procedure decomposes a signal into overlapping segments of equal length and averages the spectral estimates of the segments to reduce the variance of the spectral estimate of the signal. Because our trials were of varying length, the segment lengths and number of overlapping segments were different for each frequency, ranging from 3.4 cycles/segment (2 overlapping segments) at 0.1 Hz to 5.2 cycles/segment (5 overlapping segments) at 1.25 Hz, with each segment length equal to an integer power of two. Each segment overlap was one-half the length of the entire segment.
3 This initial restriction limited the analysis of EMG bursting activity to translation frequencies of >0.1 Hz.
Received 16 June 1998; accepted in final form 2 February 1999.
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REFERENCES |
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