Persiani Michela tesi

Persiani Michela tesi
Alma Mater Studiorum – Università di Bologna
DOTTORATO DI RICERCA IN
Scienze Farmacologiche e Tossicologiche, dello Sviluppo
e del Movimento Umano
Ciclo XXVII
Settore Concorsuale di afferenza: 05/D1
Settore Scientifico disciplinare: BIO/09
Influence of optic flow on postural control
Presentata da: Michela Persiani
Coordinatore Dottorato
Prof.ssa Patrizia Hrelia
Relatore
Prof.ssa Milena Raffi
Esame finale anno 2015
Table of Contents
Abstract
4
1. Introduction
5
2
3
1.1 Neuronal integration of optic flow
6
1.2 Motor control
11
1.3 Human posture
17
1.3.1 Biomechanical measurments
20
1.3.2 Electromyography
24
Literature Review: Optic flow and postural stability
27
2.1 Optic flow and postural stability in male and female
29
2.2 Effect of dimension of optic flow visual field
30
2.3 Laterality of stance during optic flow stimulation in male and female young adults
34
2.4 Optic flow and postural stability in young and elderly people at high risk of falls
36
Aims of the studies
38
Study I: Importance of optic flow for postural stability of male and female young adults
3.1 Methods
42
3.1.1
Stimuli
43
3.1.2
Surface EMG and Stabilometry
44
3.1.3
Data analysis
45
3.2 Results
48
3.2.1
Effect of stimuli and gender on EMG signal
48
3.2.2
Stabilometry analysis: effect of stimuli and gender on prevalent direction
of oscillation
50
3.2.3
Stabilometry analysis: effect of stimuli and side on ankle torque
54
3.2.4
Limb loading
55
3.2.5
56
3.2.6
Stabilometry analysis: effect of stimuli, side and gender on postural
responses
Correlation between EMG and COP
3.2.7
Correlation between each limb and COP
59
3.2.8
Variation in the COP parameters
60
3.3 Discussion
58
62
3.3.1
Effect of optic flow on muscle activity
62
3.3.2
Effect of dimension of optic flow visual field
63
3.3.3
Limb load asymmetry
64
3.3.4
Contribution of individual leg on postural control
3.3.5
Optic flow and postural stability in male and female
3.4 Conclusions
65
66
68
Study II: Optic flow and postural stability in young and elderly people at high risk of falls
3.5 Methods
69
3.5.1
Sensorimotor function assessments (PPA)
70
3.5.2
Cognitive assessment and Health life stile questionnaire
72
3.5.3
Optic flow stimuli
73
3.5.4
Experimental protocol
74
3.5.5
Statistical analysis
78
3.6 Results
3.6.1
78
Effect of stimuli and age on postural responses: stabilometric measures
78
3.6.2
Effect of stimuli and age on postural responses: kinematic measures
3.6.3
4
86
Effect of stimuli and fall risk on postural responses: stabilometric and
kinematic measures
3.7 Discussion
88
3.8 Conclusions
95
Overall conclusions and discussions
96
Bibliography
91
99
Aknowledgements
I am grateful to Prof. Milena Raffi, my official advisor, for providing the intellectual and personal
support to this project. She constantly followed my progresses and always helped me with her
suggestions that improved significantly all the aspects of this thesis. I would like to thank Prof.
Salvatore Squatrito for the guidance and support provided. Gratitude is also expressed to Prof.
Stephen Lord, Prof. Simon Gandevia for and all people of Falls and Balance Group of the
Neuroscience Research Australia for the opportunity and the beautiful experience in their laboratory
in Sydney. Thank to my Italian colleague Dr. Alessandro Piras, and Australian friends. Thanks to
all subjects who participated in this research. Thank to my Italian friends. A grateful thank to my
family for the support. As usual, although he is the last one, he is the most important, Angelo,
thanks for your kindness, your support and your love.
To my family
Abstract
The study of optic flow on postural control may explain how self-motion perception contributes to
postural stability in young males and females and how such function changes in the old falls risk
population.
Study I: The aim was to examine the optic flow effect on postural control in young people (n=24),
using stabilometry and surface-electromyography. Subjects viewed expansion and contraction optic
flow stimuli which were presented full field, in the foveral or in the peripheral visual field. Results
showed that optic flow stimulation causes an asymmetry in postural balance and a different
lateralization of postural control in men and women. Gender differences evoked by optic flow were
found both in the muscle activity and in the prevalent direction of oscillation. The COP spatial
variability was reduced during the view of peripheral stimuli which evoked a clustered prevalent
direction of oscillation, while foveal and random stimuli induced non-distributed directions.
Study II was aimed at investigating the age-related mechanisms of postural stability during the view
of optic flow stimuli in young (n=17) and old (n=19) people, using stabilometry and kinematic.
Results showed that old people showed a greater effort to maintain posture during the view of optic
flow stimuli than the young. Elderly seems to use the head stabilization on trunk strategy.
Visual stimuli evoke an excitatory input on postural muscles, but the stimulus structure produces
different postural effects. Peripheral optic flow stabilizes postural sway, while random and foveal
stimuli provoke larger sway variability similar to those evoked in baseline. Postural control uses
different mechanisms within each leg to produce the appropriate postural response to interact with
extrapersonal environment. Ageing reduce the effortlessness to stabilize posture during optic flow,
suggesting a neuronal processing decline associated with difficulty integrating multi-sensory
information of self-motion perception and increasing risk of falls.
4
1. Introduction
The world perception, such as memories of it, is based on sight. In our daily life, it seems
easy to recognize a face, enjoy a landscape or a football match but those tasks require a complex
computational achievement. Visual perception has been compared to the operation of a camera
because as the lens of a camera, the eye’s lens focused an inverted image onto the retina (Figure
1.1). However, the visual system is more complex than a camera, firstly because it is able to create a
three-dimensional perception of the world starting from two-dimensional image projected onto the
retina. Secondly, the visual system is integrated with cognitive functions that permit to perceive an
object as the same under different visual conditions. When we move in the environment, the size,
shape, and brightness of an image projected onto the retina changes. Under this condition we do not
perceive a change in the object. For example, as a person walks toward you, you do not perceive
him or her as growing bigger but as coming closer, even though the image on the retina become
larger. A camera simply record images passively while the visual system transforms light on the
retina into a coherent and stable interpretation of a three-dimensional world. Hence, vision provides
rich information about the environment that can be analysed by the nervous system with continually
integrate information regarding the position and movements of body segments in relation to each
other in the guidance of locomotion and maintaining balance.
Figure 1.1. The lens of the eye projects an inverted image on the retina in the same way as a camera. Kandel E,
Schwartz J and Jessel T (2014). Principles of Neural Science. New York, Elsevier.
5
Postural control is a fundamental motor skill. The control of stance requires the integration
of different sensory modalities such as visual, vestibular and proprioceptive information [1-6]. The
integration of such signals generates the typical body oscillation defined as body sway. The body
sway is regulated by the neuromotor system and considered as a consequence of small postural
oscillations. This reflects the regulatory activity of the several control loops of stabilization of an
unstable structure, such as the human body, for maintenance of balance [7, 8]. Human bipedal
stance is inherently unstable, during standing we are constantly in a sway-like motion. This is
because we are long structures balanced on a small base of support (feet). Therefore, we easily lose
balance given a very little external disturbance in our daily life. Aging, but also clinical conditions,
can affect the balance system primary to reduced postural abilities and an increased risk of falling.
The human standing problems can results from a conflict or decreased of the sensory (i.e.
vestibular, proprioceptive and visual) inputs, interrupted or damage to brain structures or
neuromuscular and musculoskeletal systems associated with motor control. Certain amounts of
study have been pointed out that vision has a major role in maintenance of posture [3, 9-13] but
remains a critical research topic for several reasons. First, the postural control through vision
depends on the extent of the cortical representation of the visual stimulus but the pathways
underlying this phenomenon are still under discussion [14]. Second, the age-related changes in
those visuo-postural pathways, in order to better understand risk of falling associated with ageing
somatosensory declines [15].
1.1
Neuronal integration of optic flow
During our daily activity we can travel actively (e.g., walking) or passively (e.g., sitting on a
moving car or train) through the world and at the same time we are perfectly consciously aware of
our self-motion. This daily life experience involves perceptions of the speed and direction of selfmotion, as well as the time-to-contact with objects in the environment [16]. Multi-sensory network
6
provide online update to estimate our self-motion perception. In our daily life we use a constant
interactions of vision, vestibular and proprioceptive system related to self-motion, for obtaining a
dynamic map of extra personal space, suitable for self-motion guidance, and to maintain the correct
posture of the body [16]. Vision can resolve both accelerating and constant velocity self-motions
from the optic flow presented to the moving observer [17]. James J. Gibson, with his ground
breaking work, introduced the concept of optic flow during World War II. He defined optic flow as
information carried by light resulting from environmental structure and the human’s path through
the world [17]. When we move in the environment, the retina undergoes a whole field stimulation,
the optic flow [17], which depends on speed and direction of our movement and on the structure of
the visual scene. The optic flow seems to originate from a single central point, the focus of
expansion (FOE) that corresponds to the final destination of self-motion [17] (Figure 1.2).
Figure 1.2. The focus of expansion (FOE), the single central point in which seems to originate optic flow. FOE
corresponds to the final destination of self-motion.
For example, when the observer moves straight forward, all image motion is direct radially
away from the FOE, and when the observer moves to the left, all image motion is directed to the
right; this because the direction depends on the particular self-motion that the observer performs.
7
The speed of the optic flow motion depends on the distance of the FOE from the eye of the
observer, and objects near to him/her move faster in the retinal projection than the objects further
away. The integration of optic flow with proprioceptive and vestibular signals permits to the neural
network responsible for motion perception to create neural maps for driving self-motion and/or
maintain postural stability.
It is well known that optic flow can activate multiple brain regions (temporo-parietal cortex,
basal ganglia, brain stem, cerebellum) some of which are involved in a spatial encoding [18, 19].
The research field on optic flow have been started decades ago on animals in order to build up a
neuronal map of this visual process and to determine how animals, first, and human after, perform
visual navigation tasks [19-21]. Humans and animals appear to have brain cells dedicated to the
computation of optic flow and its analysis, particularly with respect to heading estimation, time-tocontact estimation, obstacle detection, and the structure of the environment. Authors reported that
the posterior parietal cortex is a fundamental link for the integration of visuo-motor signals [20-22].
Neurophysiological studies in primates have identified a visual motion hierarchy that begins in V1
and extends into posterior parietal cortex [23-25]. When the photoreceptors of the retina catch the
light of an object, they project the signal onto bipolar cells, which have synapses on retinal ganglion
cells. The axons of ganglion cells shape the optic nerve, which projects to the thalamus in the lateral
geniculate nucleus. This area projects to the primary visual cortex or striate cortex (Brodmann's area
17 or V1) and extrastriate areas (Brodmann’s area 18 and 19 or V2, V3, V4 and V5) in which there
is a specific order of projections that create a retinal neural map (Figure 1.3).
The preservation of the spatial arrangement of inputs from the retina is called retinotopy,
and the map of the visual field is called a retinotopic map or a retinotopic frame of reference. In
order to adjust the visual perception to the eye or head movements the brain has to construct three
successive frames of reference: retinotopic, head-centered, and body-centered frame of reference.
Each time the eye moves the retinotopic frame of reference, such as all information that is attached
8
to the frame reference, moves as well. In the parietal cortex selectively responses neurons to visual
information, have receptive fields that are modulated by the eye position. Indeed, the movement of
an object in the visual field with the head remains stable because parietal cortex neurons use the
information from the retina and the eye movements to maintain a stable head-centred representation
of the visual field [26]. Similarly, in the ventral premotor cortex and parietal cortex, specific
neurons combining information about posture, eye and head movements to establish the bodycentred frame of reference [26].
Figure 1.3. The ventral and dorsal pathways. Note the cross connections between the two pathways in several cortical
areas. Abbreviations: LGN = lateral geniculate nucleus; MT = middle temporal area. (Based on Merigan and Maunsell
1993). Kandel E, Schwartz J and Jessel T (2014). Principles of Neural Science. New York, Elsevier.
Each visual area is responsible for a particular aspect of vision, such as form, depth, motion
and colour and these features are carried out by two parallel and interacting pathways in the brain: a
ventral stream (P cells) is extending from V1 to the inferior temporal cortex, including area V4, it is
also known as the “what pathway” as it is associated with the cognitive processing of information
(assigning meaning to objects and events, guides the anticipation and planning of actions). The
9
dorsal stream (M cells) from V1 to the posterior parietal cortex, including the middle temporal area,
it is also known as the “where pathway” because it directs attention to location in space (involved
with processing the object's spatial location relative to the viewer) (Figure 1.3). In primate cerebral
cortex, there are neurons in multiple brain regions, in the dorsal visual stream pathways, that can
analyze different aspects of optic flow which are involved in the analysis of motion and spatial
encoding [27]. The medial superior temporal area (MST) which is located in the superior temporal
sulcus, process many neurons which respond selectively to one type of optic flow (i.e., rotation,
spiral and radial) [28, 29] and are also selective for the position of the FOE and tuned for different
speeds, suggesting that this neural population encodes heading during different types of self-motion
[30]. MST area is connected to subcortical centers of gaze stabilization and is involved also in the
control and guidance of eye movements [27]. Other cortical areas, such as the ventral intraparietal
area, the superior temporal polysensory area and area 7a form a network of information that
transforms retinal motion information in high level parameters that are used to direct and control
spatial behavior. Authors reported that area 7a neurons play a role in the speed representation of
multiple objects [19]. Although this neuronal population do not seem to be involved in the optic
flow direction analysis, it seems that they utilize the analysis of optic flow in order to make a spatial
representation of extra-personal space [19]. The superior temporal polysensory area (STP) is
divided in anterior and posterior portion (respectively STPa and STPp). The STPa receives
projections from area MST and 7a and its cells respond during object and self-motion [19, 23, 31,
32]. Previous studies suggested that this neuronal population process self-motion perception [32].
Anderson & Siegel 1999, reported that STPa neurons also respond to different optic flow stimuli
but they give stronger responses to radial expansion. This knowledge suggested that STPa analyze
the coding of specific signals that are used to control forward locomotion [33]. Physiological
findings reported that area PEc, in the superior parietal lobule, is a higher order association area.
This area showed a neuronal activity related to visual stimuli and in particular to optic flow (radial
10
expansion and contraction) [34] and hand-movements, suggesting an involvement in the visuomotor integration signals [35, 36]. Authors, also suggested that the responsiveness of the PEc cells
to optic flow and object motion might serve different mechanisms in the integration of visuo-motor
signals to prepare the body movements [19]. Interestingly, optic flow responsiveness has been also
found in the motor cortex (M1) has some neurons that respond to optic flow [37]. Although area M1
neurons are optic flow specific (especially for the radial expansion) they do not have a clear visual
receptive field. In this area the cells are tuned to detect a specific motion in order to guide an
appropriate movement. Motor cortex is involved in different aspects of movement control and
initiation, as well as motor command and processes interposed between a stimulus and the response
of it. Indeed, it is possible that the stimuli might have triggered neural events in the motor cortex in
preparation of a motor response to interact with the stimulus in a certain part of the visual field [37].
1.2
Motor control
Our brain is a powerful machine that can construct internal representations of the world by
integrating information from different sensory systems. The motor systems plan, coordinate, and
execute motor programs functional for our daily activity using sensory representations [26]. All
levels of control, from the spinal cord up to the cerebral cortex, are necessary and integrated to
provide the base of axial stability for more normal distal mobility and skilled or refined coordinated
limb movements [26]. Moreover, the environmental context and task influence on how the nervous
system organizes movement. An interesting aspect of motor function is the easiness with which we
perform the most complicated motor tasks. Although we are conscious about the intent to perform a
specific movement, such as walking along the street, the details of our task seem to occur
automatically. Indeed, conscious processes are not necessary for moment-to-moment control
movement [26]. The quality of movement carried out automatically depends on a continuous
integration of visual, somatosensory and postural information to the motor systems. The motor
11
systems can perform different motor tasks (reflex, rhythmic, and voluntary) with speed and
accuracy because of two features of their functional organization: peripheral and central. The
peripheral motor system includes muscles and both motor and sensory nerve fibres. The central
motor system has components throughout the central nervous system (CNS), including the cerebral
cortex, basal ganglia, cerebellum, brain stem, and spinal cord. A specific hierarchy of motor
representations depends on a parallel hierarchy of sensory input and commands to motor neurons
and muscles [26]. Hence, each level, from the spinal cord to the motor cortex, has circuits that can
organize or regulate more complex motor responses. The spinal cord is the lowest level of this
hierarchical organization; it contains the neuronal circuits that mediate a variety of reflexes and
rhythmic automatisms such as locomotion. The brain stem is the next level of the motor hierarchy;
it contains neuronal circuits that control eyes and head movements. The medial and lateral
descending systems of the brain stem, receive input from the cerebral cortex and subcortical nuclei
and project to the spinal cord. While the medial system contribute to the control of posture by
integrating visual, vestibular, and somatosensory information; the lateral system control more distal
limb muscles and are thus important for goal-directed movements, especially of the arm and hand.
Cerebellum and red nucleus are a higher level of motor control. Indeed the medial cerebellum is
involved in the postural control, whereas the lateral cerebellum participates more in voluntary
movements. Moreover, it is thought that signal motor error (or discoordination) is another input that
conveyed in the cerebellum from fibres originating in the inferior olivary complex. These signals
seem to play a role in motor learning. Cerebellum, receives mossy fibre input from red nucleus
principally via lateral reticular nucleus. Although the red nucleus appears to be related to cerebellar
function, in the human brain, seems to play an enigmatic role in motor control. The magnocellular
red nucleus send axons to the spinal cord (through rubrospinal tract), which may play an important
role in stabilizing the limb by co-activation of agonist-antagonist muscles. The highest level of
motor control is the cortex. The primary motor cortex, (M1) Broadmann’s area 4, lies in the anterior
12
bank of the central sulcus and contains a topographic representation of the body (homunculus,
Figure 1.4).
Figure 1.4. Body representation (Homunculus) on the primary motor cortex (M1). From Dynamic Brain. Brain Training
for Canadian web site (https://www.dynamicbrain.ca/brain-anatomy-images.html)
Premotor cortex and posterior parietal cortex (Broadmann’s areas 6 and 5 see Figure 1.5)
have key roles in generating the plan for a simple reaching movement; initial problem involves
kinematics and figuring out the current location of the target. Hence, the premotor areas are
important for coordinating and planning complex sequences of movement. They receive
information from the posterior parietal and prefrontal association cortices and project to the primary
motor cortex as well as to the spinal cord. Premotor and posterior parietal cortex cells respond to a
combination of signals relevant to voluntary reaching movement, including visuo-spatial and
proprioceptive input, as well as inputs reflecting gaze direction, the location of objects in the
environment, the orientation of spatial attention, and non-spatial visual information (such as color
and form).
The central nervous system needs to receive continuous feedbacks about movements. It
receives this information in the form of the status of muscles, (i.e. length, instantaneous tension)
and rate of change of length and tension [38]. Muscle spindles detect the rate and changes in the
length of a muscle, whereas Golgi tendon organs detect degree and rate of change of tension.
13
Signals from these sensory receptors operate at an almost subconscious level, transmitting
information into the spinal cord, cerebellum and cerebral cortex, where they assist in the control of
muscle contraction. The movement of each segment is restricted by the flexibility in muscles, joints
and tendons. Due to the high coordination between the body segments, the central nervous system
recruit postural muscles to achieve a global change posture in response to sensations of movement.
In part, human postural control is maintained by several kinds of reflex.
Figure 1.5. Representation of motor cortical areas.
The reticulo-spinal tracts form a direct pathway between the reticular formation in the brain
and the spinal motor neurons. The majority of the reflexes transmitted by the reticulo-spinal tracts
are important for the maintenance of postural control. Muscles spindles send information about the
length of the muscle to the CNS and in response the CNS initiate the contraction of the skeletal
muscle opposite to the muscle stretch (stretch reflex). The aim of this reflex is to maintain
constantly the length of the muscles and has an important role when an external balance
perturbation threatens posture. The spinal cord contains neural circuits to generate reflexes,
stereotypical and rapid movement produced in response to an external stimulus. Four types of spinal
14
reflex: the myotatic, the inverse myotatic, flexor withdrawal and the crossed extensor reflex
provides a strongest postural response. Myotatic or muscle stretch reflex is a result of monosynaptic
circuit in which an afferent sensory neuron synapses directly on the efferent motor neuron (Figure
1.6). For example, stretching a muscle causes it to contract within a short duration (i.e., when the
patellar tendon is tapped with a reflex hammer or when a quick change in posture is made). This
reflex produces rapid corrections of motor output in the moment-to-moment control of movement.
Moreover, it is important for maintaining antigravity muscles tone and upright posture. On the other
hand inverse myotatic reflex produces the opposite effect to that of the myotatic reflex (Figure 1.6).
Indeed, an active contraction of a muscle causes reflex inhibition of the contraction. The main
function of this reflex is adjusting the strength of contraction during sustained activity. These two
reflexes act together to maintain optimal responses in the antigravity muscles during adjustments
and to the smooth generation of tension in muscle by regulating muscle stiffness.
Figure 1.6. Schematic representations of the A. Myotatyc and B. Inverse Myotatyc reflexes. Example: patella reflex. ©
2009 Ebneshahidi.
15
The flexor withdrawal reflex consists of an ipsilateral contraction of the flexors to stabilize
the posture after a cutaneous stimulation (i.e., heat, cold tissue or damage) (Figure 1.7). The crossed
extensor reflex is evoked by the flexor withdrawal reflex and consists of a contraction of the
extensors on the controlateral side after cutaneous stimulation (Figure 1.7).
Figure 1.7. Schematic representations of the flexor withdrawal and crossed extensor reflex. Example: nocicettive
stimulus. © 2009 Ebneshahidi.
In the CNS, vestibular nuclei process information of motions and balance reflexes. Those
reflexes can be categorized in three groups. The first one, vestibulo-ocular reflex are an important
role in controlling eye movements, to compensate for the movement of the head. In particular, those
reflexes are involved in controlling eyes muscles to either contract or relax so that the eyes move in
the opposite direction to the head in order to keep the object of interest on the fovea and focused
while the head is in motion [39]. In order to keep head and body aligned and ensures that when the
head position is equilibrated, the rest of the body will follow. The vestibulo-collic reflex initially
cause a contraction or relaxation of the neck muscles to oppose gravitational forces and keep the
head steady level on the shoulders. Subsequently, there is a reflexive change of the body position
16
relative to the head [26, 39]. The third one, vestibulo-spinal reflex relax muscles groups on one side
of the body and contract the contra-lateral ones, in order to keep the upright position, and prevent
falls when unexpectedly the body is perturbed and there is a sudden head movement [26, 39]. All
together these reflexes provide for postural support and mobility, building up a foundation of
automatic responses on which more complicated voluntary movements are construct.
1.3
Human posture
Upright standing is one of humankind’s most important evolutionary achievements. Postural
control and balance involve controlling the body’s position in space for stability and orientation.
The nervous system participates in postural control by designing command signals and by providing
and integrating feedback through a number of receptors. We need to have a flexible control system
that can adapt in base on different demands. Postural control requires a combination of feed forward
and feedback mechanisms (i.e., production of movements or muscular contractions) that help in
keeping the body upright in space. In addition, the feedback mechanisms involve movements of the
head through the vestibular system in the inner ear, visual feedback, and feedback about pressure
changes through the support surfaces of the body [26] (Figure 1.8).
Figure 1.8. Schematic representation of the postural control.
17
The feed forward mechanisms include signals that are able to anticipate disturbances to the
postural control system that will arise as a consequence of movement [40]. However, the
organization of these feedback-control mechanisms is still unknown and whether these mechanisms
play a dominant or a minor role in postural control. Studies reported that feedback control alone is
insufficient to explain human postural control [5]. Some others works have suggested an important
role for predictive mechanisms [41] or have concluded that nonlinear mechanisms combining openand closed-loop control are used for stance control [7]. The human balance is regulated by the
neuromotor system that produces an active search process called body sway [8, 42]. This reflect the
regulatory activity of the several control loops of stabilization of an unstable structure, such as the
human body, for maintenance of balance [7]. Sway has been viewed as a consequence of noisy
processes within the human neuromotor system, as a reflection of an active search process [7, 8,
42], and as an output of a control process of stabilization of an unstable structure, the human body
[43]. Visual, proprioceptive, and vestibular systems clearly contribute to postural control [3, 9, 44,
45]. However, it is not completely clear how information from these senses is processed and
combined to generate an appropriate posture when there is conflicting or inaccurate orientation
information from different sensory systems. It is possible that sensory cues are linearly combined,
so that each sensory system detects an “error” indicating deviation of body orientation from some
reference position [46]. Hence, vestibular system detect deviations of head orientation from earthvertical (gravity), visual sensors perceive head orientation relative to the visual world, and
proprioceptors detect leg orientation relative to the support surface [46]. Some others studies shown
that a model based primarily on a feedback mechanism with a 150- to 200-ms time delay can
account for postural control during a broad variety of perturbations [13, 46, 47] and can yield a
spontaneous sway pattern that resembles normal or pathological spontaneous sway [48]. It has been
suggested is that the CNS contains an internal forward model that can predict the consequences of
motor commands [49, 50]. The internal model can captures the neuromuscular inputs and outputs in
18
order to simulate subsequent sensory consequences providing timely estimates of new sensory
information in the absence of actual sensory input due to temporal delays associated with feedback
control. For example, an internal model could predict a future state (e.g. body position and/or
velocity), given the current state and motor command [51]. Authors have investigated the simple
feedback model to simulate upright stance in humans [46, 47, 52, 53]. This model the standing
position looks like an inverted pendulum where the feet are fixed in position and head is free to
move (Figure 1.9).
Figure 1.9. Model of a two-link human inverted pendulum and the external forces acting on it in the sagittal plane
and the corresponding free-body diagrams. COG: body centre of gravity; COGv: COG vertical projection (horizontal
plane) in relation to the ankle joint; COP: centre of pressure in relation to the ankle joint; GRF: ground reaction force
(from a force platform); α: angle of the body in relation to the vertical direction; m: mass of the body minus feet; g:
gravity acceleration; Fa: resultant force at the ankle joint; Ta: torque at the ankle joint; h: height of the COG in
relation to the ankle joint; mf: mass of the feet and hf: height of the feet.
(http://nbviewer.ipython.org/github/demotu/BMC/blob/master/notebooks/IP_Model.ipynb).
However, the body is multi-segmented with a number of joints where rotation can occur,
and is incorrect simply describe the upright position in terms of one single link between ankle and
head (as in a pendulum) (Figure 1.8). Studying the postural control with the inverted pendulum
authors suggested that three distinct strategies achieve the upright stance: the ankle, the hip and the
stepping strategies [6, 54]. In order to maintain a stable standing position, the ankle strategy restores
the body by changing the angle of the ankle joint, while keeping the other joints rigid. A feedback
19
from various sensory organs can activate ankle muscles to correct the body alignment. This strategy
is mainly used, when external disturbances are small. On the other hand, the hip strategy is used for
maintain the postural stability under bigger disturbances. In this strategy the ankle and hip joints are
controlled cooperatively. When external disturbances are so large for the ankle or the hip strategies,
the balance of the body is restored by moving the feet to an appropriate position under the stepping
strategy.
Few studies have shown that moving visual fields can induce a power sense of self-motion, and
when visual input is ambiguous, we can observe an increase of body sway that correspond an active
search process by neuromotor regulation system [7, 8, 42]. The complex task that requires the
maintenance of postural stability has been studied by numerous investigators to elucidate the
relative contributions of each sensory system during standing. Typically, stabilometry,
electromyography and kinematic measurement are common approaches to investigating human
stance.
1.3.1 Biomechanical measurements
Stabilometry, is the measurement of forces exerted against the ground from a force platform
during quiet stance, is commonly used to quantify postural steadiness both in research and in the
clinic [55]. Since the 1970s, force platforms have been used to acquire indirectly assessment of
changes in postural sway in order to provide quantitative measures of postural sway. In general, the
force plate is a laboratory tools consists of a board in which some (usually four) force sensors of
load cell type or piezoelectric are distributed to measure the three force components, Fx, Fy and Fz
(x, y, and z are the anterior-posterior, medial-lateral, and vertical directions, respectively), and the
three components of the moment of force (or torque), Mx, My, and Mz, acting on the plate Figure
1.10).
20
Figure 1.10. A force platform (Kistler) with four load force sensors and relative forces: Fx, Fy and Fz.
Typically, stabilometry focuses on the properties of the COP time series, representing the
point location of the ground reaction force vector as it evolves on the horizontal plane (2D) or along
two orthogonal axes, fixed with the platform (antero-posterior and medio-lateral) [56] (Figure 1.11).
This single variable reflects both the balance controlling process and movements of the centre of
mass of the entire body and thus provides a single global measure of posture control. The COP in
both anterior-posterior and medial-lateral planes has proven to be a significant and reliable output
metric [57] such as path length, sway area sway ranges have also been shown to be effective
parameters for monitoring postural sway. The COP analysis could be used as an inexpensive
alternative to estimate the movement of the centre of gravity of the subject to give a more accurate
representation of the brain’s ability to correct balance [58].
Figure 1.11. Representaion of the COP displacement. From two different force platforms centres of pressure recorded
separately left and right feet during quiet standing in the side-by-side position. COPnet is calculated from the formula
and is a weighted average of COPl and COPr. Winter 2006
21
Static and dynamic are the two posturography paradigms. In the static paradigm the subjects
stand on a flat, horizontal, unperturbed surface with their eyes open or closed (Romberg’s test); the
spontaneous sway movements are typically recorded through the trajectory of the COP on the
support surface and the trajectory is parameterized according to different techniques. The dynamic
paradigm the spontaneous posture is measured under external perturbations by means of different
types of typically unpredictable stimuli in order to evaluate the relative contribution of the visual,
vestibular, and somatosensory channels in regaining the initial posture [59]. For example, a linearly
moving or tilting platform providing mechanical perturbations, such as moving mechanical
surround, video or using virtual reality methods have been used for visual stimulation in order to
understand how this feedback is processed to achieve postural stability. Posturographic analysis
with the force platform showed that subjects attempt to react, at the beginning of the visual
stimulus, with a postural adjustment, especially in the antero-posterior and vertical directions [60].
Until now, several researchers with a posturography investigation have been focused on the
maintenance of balance control and the spontaneous body sway movements studying the COP
oscillations.
Another technique to measure postural control is the human motion analysis that provides a
quantitative means of assessing whole body and segmental motion of subjects covering a wide
range of uses. The techniques behind data capture and processing can vary: some use active
markers, others passive markers. Some systems use magnetic fields and others infrared cameras to
determine the motion of the body. Motion capture analysis allows to investigate the upper body
movement patterns as well the lower limbs. Processing depends greatly on the programming and
algorithms used when determining landmarks (i.e. the hip joint centre), joint kinematics, and
kinetics. The method used for quantitative motion assessment defines a segmental model of the
skeletal region of interest with intersegmental joints. Optical cameras are used to record the position
of the external markers in space as the subject ambulates through a predetermined capture volume.
22
At least two cameras must simultaneously view each marker in order to determine its 3D
coordinates (Figure 1.12).
A
B
C
Figure 1.12. A motion capture system. A. representation of human stick figure from the cameras. B. Infrared cameras.
C. Markers organized in base of dimension.
Accurate measurements of joint angles, translations, and moments during gait and postural
analysis are important to understanding a variety of motor control phenomena. Human movement
coordination involves mechanical aspects, musculoskeletal intrinsic properties (i.e., muscle
viscosity and stiffness), and neural coordinating mechanisms [61]. The multi-segment coordination
in human balancing during a variety of behavioural scenarios considering postural responses in hip,
knee and ankle joints has already been studied [62, 63]. Other studies, in order to study intersegmental coordination, reduced the biomechanics of a standing human to a double inverted
pendulum with focus on hip and ankle joint responses in which it is know that the coordinated
responses of these joints depends on disturbance strength and context. Small disturbances mainly
23
evoke compensatory movements in the ankle joints (ankle strategy) [64, 65]. On the other hand,
when the ankle joint torque becomes insufficient for balancing (hip strategy), hip joint accelerations
produce shear forces under the feet to counteract body centre of mass excursions (that is the point
on a body that moves in the same way that a particle subject to the same external forces would
move) [64, 65]. During moderate disturbances, another aspect of hip–ankle coordination needs to be
considered. In such situations, while the ankle joints perform the primary task to maintain
equilibrium of the whole body [46, 48]; the hip joints tend to perform a secondary task, consisting
of the stabilization of the vertical orientation of trunk and head and thereby stabilizing the
workspaces of the hands and for the eyes [66]. This postural response, permit to reduce the head
movements during body oscillations (i.e., head stabilization in space strategy), and it is thought to
improve sensory feedback from the vestibular and visual cues during dynamic balancing [67-69].
Other kinematic studies reported that the destabilizing effect of standing on a foam surface is
expressed at key points of articulation between the major body segments [70-72]. Kinematic
analyses are helpful to understand the complicated mechanisms underlying human movement and
balance control.
1.3.2 Electromyography
Muscular contractions are the basis of coordinated movement. All skeletal muscle activity is
controlled via the motor nervous system. The Electromyography (EMG) is the study of the muscle
function through the observation of the electrical signal which comes from the muscle, being also
essentially the study of the activity of the motor unit [73]. Surface EMG is acquired by using
electrodes lightweight and small placed directly on the skin over the desired muscle of interest,
allowing subjects to move freely [74]. Surface electromyography uses bipolar electrode
configuration; usually consisting of two, parallel, metal sensing terminals [75] (Figure 1.13).
24
Figure 1.13. Example of EMG trace.
The use of surface electromyography (alone or combined with stabilometry or kinematic
measures) may offer important information about the muscles behaviour when submitted to the
many different types of overload, many angles and performance velocity, as well as the evaluation
of the myoelectrical behaviour in many circumstances. The capability to infer body and movement
from EMG signals detected by surface sensors attached to a subject’s body is useful for a large
variety of applications. In biomechanics, three applications dominate the use of the surface EMG
signal: its use as an indicator of the initiation of muscle activation, its relationship to the force
produced by a muscle, and its use as an index of fatigue processes occurring within a muscle. As an
indicator of the initiation of muscle activity, the signal can provide the timing sequence of one or
more muscles performing a task, such as during gait or in the maintenance of erect posture. Another
important application of the EMG signal is to provide information about the force contribution of
individual muscles as well as groups of muscles. Use in the individual muscle provides the greater
attraction. The resultant muscular moment acting on a joint during a specific task is only in
exceptionally rare cases due to one muscle. Dynamic postural control is the ability to maintain
balance in motion by either moving the centre of mass within the base of support. Nashner and
colleagues have used perturbations with supported surface and muscles responses were analysed
25
with the electromyography to study the postural strategies to maintain control of stance [6]. This
study reported that the common postural responses during stance to subtle support surface
perturbations involve an ankle strategy. They described this postural strategy as early activation of
dorsal ankle muscles followed by activation of dorsal thigh and trunk muscles in response to
backward translations. However, hip strategy is involved in postural control when the support
surface is narrow or the perturbations are large. The hip strategy was described as an early
activation of ventral trunk and thigh muscles associated with a relative increase of shear forces at
the base of support and small ankle phasic muscles activation. Postural muscles are located at
different sites in the human body, including lower body muscles such as calf muscles (tibialis
anterior, soleus and gastrocnemious) and thigh muscles (hamstring, quadriceps femoris, tensor of
fascia lata) playing fundamental roles in balance control as they oppose the destabilizing effects of
gravity [76]. In many postural studies, EMG activity of the lower legs is recorded from the muscles
responsible for the control of the ankle joints i.e., the tibialis anterior, soleus and gastrocnemius.
26
2. Literature review: Optic flow and postural stability
In everyday life, the optic flow field is the main cue to control self motion and upright
position producing adequate motor responses while a subject interacts with the extrapersonal
environment [77]. Lee et al (1977) demonstrated how the manipulation of optic flow can affect
postural stability. They developed the swinging room paradigm (Figure 2.1) in order that allow the
visual environment to be manipulated in a controlled manner, producing a convincing sense of
vection for the observer. In their study observers, (24 young, aged 18-30, and 7 toddlers, aged 13-16
months). The subjects were instructed to stand inside a moving room that was swinging in a
forward and backward direction. A sway meter was used to quantify the postural oscillations (body
sway) of each participant. They found that body sway was manipulated by the movement of the
room in which the subjects stood. The results showed that adults increased antero-posterior
oscillations that were in phase with the room when the misleading visual information was
presented. On the other hand, when the experimenter was moving the room backward, the children
fell and their balance was clearly disturbed in a predicted direction in the majority of the trials.
These findings indicate that for toddlers, who are learning to stand, the visual information are more
important than mechanical information [78].
Few studies have demonstrated that moving visual scenes can elicit postural responses [44,
78, 79]. Specific spatial and temporal properties of the optic flow, such as geometric structure [45,
80], amplitude [3, 46], velocity [4, 81-83], frequency [4, 81] and location in the visual field can
influence those postural responses [83-85].
In a moving visual environment, postural stability requires the dynamic coupling of vision
with the postural control system [3, 60]. In the nervous system, retinal stimulation related to selfmotion is integrated with proprioceptive and vestibular signals, in order to assess direction and
speed of self-movement, guide the locomotion, and/or maintain the correct posture [13]. The optic
27
flow structure apparently interacts with the stimulated retinal field in controlling stance [80, 83].
Indeed, in spite of the sway response produced by moving visual stimuli, the oscillation speed is
generally lower in the presence of a visual stimulus compared to the absence of it [44].
Figure 2.1. Schematic representation of the moving room. Steven M. Boker Thu Aug 17 10:10:02 EDT 1995
Few studies have shown that optic flow stimuli are crucial for the maintenance of quiet
stance in the upright position when integrated with other sensory signals like vestibular and
proprioceptive input [46]. Visual input changes, such as from forward to backward motion or from
dark to light environment, require an updating of the sensory integration to provide the premotor
and motor cortices with precise and reliable information about both the extrapersonal environment
and internal state. Such updating results in leg muscles activation to produce a compensating motor
response. It has been proposed that the postural reflex activity of the leg muscles evoked by postural
an vestibular disturbances, could be organized to minimize future disturbance rather than to correct
a past one [86]. Stabilometric analysis with the force platform showed that subjects attempt to react,
at the beginning of the visual stimulus, with a postural adjustment, especially in the anteroposterior
and vertical directions [60]. The apparent destabilizing effect of visual input on a steady subject is a
measure of the compensatory effect evident in case of real body movements like those simulated by
28
optic flow. In this particular situation, the body sway may be produced by the illusory selfmovement perception or by an automatic response integrated at a subcortical level. Studying the
role of optic flow in postural control, important aspects to keep into account is the potential role of
gender differences, the dimension of visual field, the contribution of each leg on postural response
and the age-related changes in the processing visual stimulation. These aspects all together can
point out the neuronal mechanisms that underling the postural control. The combined used of
stabilometry, kinematic and electromyographic analysis will provide a means to better understand:
the temporal relationship between postural response and visual stimulus, the neurophysiological
mechanisms responsible for balance and how these might change with increased age. This
information could be also valuable for understanding fall risk in elderly and guiding the treatment
and clinical rehabilitation in this group.
2.1
Optic flow and postural stability in male and female
It has long been reported that males and females navigate through the real world using
different strategies [87]. In previous researches was described that females seems to use landmarks
to navigate, while males tend to use the direction in which they are heading [88]. Previous studies
reported that females have been shown to rely more on visual information than males in a number
of spatial tasks related to perceived orientation [89, 90]. In females has been particularly noticeable
a greater dependence on visual information when retinal and non-retinal information is in conflict
during self-motion [91] or when executing visually guided movements [92]. During the past years a
number of explanations have been postulated as to why females may be more visually dependent
than males. Authors reported that those gender differences may lie in the integration of
multisensory information. Differences between male and female have been found in response to
circular vection [93], motion sickness [94], path integration [95] and recalibration of vestibular
perception following sensory adaptation to conflicting visual–vestibular stimuli [91]. Barnett29
Cowan (2010) supported the hypothesis of Berthoz & Viaud-Delmon (1999) who speculated that
gender differences may exist in central processing of visual–vestibular interactions [96, 97]. Other
consistent explanations attributed sex differences in perceived self-orientation to differences
between females and males in other measures of spatial ability [98, 99]. Previous studies also
reported that muscular activation during stance is different between males and females [100-103].
Bell and Jacobs 1986, demonstrated differences comparing the electromechanical delay (the time
interval between the change in electrical activity and movement) in men and women. These could
be explained by differences in neuromotor control which involves the conduction of the action
potential along the T tubule system, the release of Ca2+ by the sarcoplasmic reticulum, cross bridge
formation between actin and myosin filaments, the subsequent tension development in the
shortening elements [104, 105] and the series elastic component which in men is more resistant to
stretch [106]. Others potential contributing factors to the gender bias include differences in
mechanical properties of the ligaments, joint kinematics, and skeletal alignment [86, 107-109].
Authors reported that those differences can cause a slower hamstring muscle reaction time and
static postural faults in women [110, 111]. McNair and Marshall (1994) have suggested that muscle
activation patterns may develop to increase the joint stabilization or to compensate the joint laxity
[112].
2.2
Effect of dimension of optic flow visual field
In motor control, foveal and peripheral vision can be distinguished in base of their functional
and information processing characteristics. Before reaching the photoreceptor cell layer, the light
has to pass the ganglion cell, bipolar cells, amacrine cells and horizontal cells layers (Figure 2.2).
Photoreceptors, rods and cones, contain light sensitive pigments. Rods are longer with a
smaller diameter; they are sensible to light and can be activated by less light, for instance at night
(scotopic vision). Cones, indeed, are shorter with a bigger diameter; they work optimally under
30
daylight conditions (photopic vision) and are important for the high visual acuity and perception of
colours. In the retina there is a clear function distribution of those two photoreceptors: cones are
more concentrated in the fovea region, while in the periphery of the retina there are more rods
(Figure 2.3).
Figure 2.2. Schematic representation of the retina layer. Rods and Cones lie in the outer nuclear layer, interneurons
(bipolar, horizontal, and amacrine cells) in the middle nuclear layer, and ganglion cells in the inner cell layer.
Information flows vertically from photoreceptors to bipolar cells to ganglion cells, as well as laterally via horizontal
cells in the outer layer and amacrine cells in the inner layer. (Adapted from Dowling 1979.) © Pearson Education, Inc.
Figure 2.3. Schematic representation of the distribution of the cones and rods on the retina.
Foveal and peripheral vision present differences not only related to the photoreceptors
density but there are also some sensitivity differences due to differences in the convergence of
receptors onto bipolar cells, and of bipolar cells onto ganglion cells. Indeed, in the foveal region just
31
few cones project onto a single ganglion cell that means that the interconnection is exclusive,
resulting in high spatial resolution. The fovea is the region of highest visual acuity, because the
brain obtains information via the optic nerve from the ganglion cells and can distinguish which
photoreceptor in the fovea absorbed photons and in turn is able to compute the position of the light
source to build up the field of view. On the other hand, in the peripheral region, hundreds of rods
can project on a single ganglion cell that means that the convergence of photoreceptors is higher.
The brain cannot deduce which rod sent the initial signal and this turn results in low visual acuity.
Furthermore, the cortical representation is another reason for differences between foveal and
peripheral vision. Area V1, such as other parts of the visual stream, shows a retinotopic
organization, that means that adjacent locations on the retina are also located next each other in V1.
Foveal and para-foveal vision occupy about 50% of the whole primary visual cortex while the rest
of the visual field needs to share the residual half of the cortical representation capacity. This
overrepresentation again shows the amount of importance granted to foveal vision. Despite a less
cortical representation, the significance of peripheral vision should not be minimized. For instance,
the perception of movement is developed more highly in peripheral than in foveal vision. To sum it
up, foveal vision is mediated by visual information from the central retinal field and is assumed to
be responsible for detecting the physical characteristics of environmental objects. On the other
hand, peripheral vision is concerned with detecting the spatial characteristics of the surroundings
[113, 114]. Previous findings examined different features of foveal and peripheral vision in various
motor actions, such as postural control [115] or hand movements [116].
Several studies have investigated the contribution of central and peripheral vision to postural
control, distinguishing three different theories. The first theory, originally proposed by Brandt,
Dichgans and Koenig (1973) as peripheral dominance hypothesis, states that peripheral vision is
more important in the control of upright stance, whereas central vision has an accessory role and
flow-induced postural adjustments [84, 115, 117-120]. A second theory, the retinal invariance
32
hypothesis, suggested that heading perception is independent of the stimulated retinal region,
indicating that peripheral and central vision have the same functional role [121]. The last theory, the
functional sensitivity hypothesis, suggests that central vision was specialized for radial and lamellar
motion while peripheral vision for lamellar motion, indicating that peripheral and central vision
have functional differences and complementary roles [45, 80, 122, 123]. In the past years, several
papers were aimed at verifying whether peripheral or central vision may play a predominant role in
postural control, leading to different conclusions (cfr. [118, 119] and the reasonable hypothesis of
why such differences exist arises from the different experimental protocols and definitions of
central and peripheral vision [118, 119]. Some researchers considered neuro-anatomical definitions
of central vision in their study, others used definitions of central vision in based on functional
criteria. Although the central vision definitions may be formulated on the basis of behavioural
viewpoints, a neuroanatomical definition indicates that central vision should refer to either the
central 2° to 4° of the visual field defined on the basis of the retinal distribution of the corn and rod
photoreceptors [124] or the central 7° of the visual field projecting to the particular area of the
primary visual cortex responsible to process central vision [125]. The peripheral visual field is
generally considered the area surrounding the central visual field. Few papers investigated the role
of central vision with stimulus dimensions ranging from 7° to 60° [84, 124, 126]. Some studies used
a pattern of random dots where their motion only provided spatiotemporal changes in the visual
field [118, 127]. Some others used patterns of horizontal or vertical alternating black and white
stripes [115]. Certainly, the use of different visual stimuli and/or diverse dimensions of the
stimulated visual field changes the evoked postural response.
33
2.3
Laterality of stance during optic flow stimulation in male and female young adults
Standing on two feet is an important reach to initiation of several activities of our daily living.
A well-stabilized posture is necessary to provide support for voluntary limb, head, or trunk
movements. When we are in a standing position, few muscles of the back and lower limbs are
active. The distribution of body weight determined the line of gravity position, which is important
in modulating the muscular activity involved in maintaining posture. The line of gravity extends
superiorly through the junctions of the vertebral column, inferiorly in a line posterior to the hip
joints and anterior to the knee and ankle joints. When we are in a standing position, the hip and
knee joints are extended and are in their most stable positions, because the line of gravity passes
posterior to the hip joint and anterior to the knee joint, the weight of the body tends to hyperextend
these articulations. The postural control system stabilizes head, trunk orientation and limb axes in
order to provide an equal margin of stability in every direction. Theoretically, this implies a fully
symmetrical system that would provide an optimal stability [128, 129]. Previous research has
examined a postural asymmetry in people with different pathologies, assuming that this asymmetry
is detrimental to postural control [130]. However, it has been suggested that there is a functional
asymmetry in the weight distribution on the legs that allows humans to prepare a preferred leg to
make a step should it become necessary due to an unexpected perturbation [131]. It is well known
in the neuroscience literature, that the right-left brain asymmetries are functionally attributed to
many of the higher brain functions as visual-spatial abilities, language, motor or musical skills.
Delacato suggested an interesting theory in which proper sensorimotor control would only occur if
one side of the brain dominated over the over [132]. Several studies have reported that there are
differences in brain asymmetry between men and women, some that the male brain might be more
asymmetrical than the female brain [133]. Some studies also reported a greater lateralization of
auditory or visual processing skills in men than in women. Those sex differences in brain
organization, both within and between hemispheres, are thought to underlie sex differences in motor
34
and visuospatial skills and linguistic performance [134-136]. Pérennou et al. demonstrated a right
hemisphere dominance in the visual control of body balance but left-handers were not considered
[137]. The concept that asymmetries can be functional in the motor behaviour literature is
surprisingly recent. In human walking, healthy individuals typically adopt locomotor patterns that
are not symmetric. Authors suggested that the right limb’s primary function is to provide a
propulsive force during midstance, while the left limb function is to provide support [138, 139].
Functional asymmetries between limbs have been observed during bimanual tasks. When a person
performs bimanual tasks, the non-dominant hand control and stabilizes the action, while the
dominant hand generates the movement and performs the manipulative action [131, 140, 141].
Until now, few studies have examined postural asymmetry in human and some of those
focused only on the asymmetry in weight distribution. Other studies focusing on visuo-spatial tasks,
handedness was analysed, but the incidence of posture on these tasks was not taken into account.
For example, authors found that right-handers have a visual dominance different from the lefthanders one. Right-handers have a left visual field and left-handers a right visual field dominance
[142, 143] and this might affect the quiet stance. Furthermore, an asymmetry in each leg
contributions to postural sway has been revealed during short-and long-term free postures, when
subjects are required to load evenly in tandem and side-by-side stances [131, 144].
Abductors/adductors hip muscles are responsible for producing the loading and unloading of the
limbs, they are responsible for generating the vertical reaction forces under the feet [145]. Authors
suggested that the majority of normal adults stand with their body weight unequally distributed
across the two feet [146]. Few studies have addressed the laterality or asymmetry during quiet
stance; however these studies were performed with the eyes open or closed or under twodimensional visual stimulation [131, 144, 147].
35
2.4
Optic flow and postural stability in young and elderly people at high risk of falls
Recently, authors reported that falls are one of the major health problems especially in the
older population. More than 30% of people aged 65 years and 50 % in those above 80 years fall
once a year and about half of those who do fall, do so repeatedly [148]. The 18% of falls occur
while turning [149], and those falls occurring during a turn are almost eight times more likely to
lead to a hip fracture than falling during forward walking [150]. Redfern et al (2004) showed that
the age-related changes to the sensory and motor system appear to increase the requirement of
cognitive or attention resources for sensory-motor activity indicating that aging also affect postural
control in cognitive or attention-demanding tasks [151]. Declining visual capacity, specifically
decreased contrast sensitivity and impaired depth perception have been associated with an increased
falls risk [15]. It is important understand predisposing factors associated with falls in order to begin
more effective therapies. One way to achieve this, is to determine the mechanisms related to poor
balance in elderly population. Lord and Ward 1994, have developed a Physiological Profile
Assessment (PPA), which consists in a series of quantitative validated assessments to identify key
physiological risk factors [152]. The short form PPA is comprised of five tests involving vision
(edge contrast sensitivity), reaction time (a finger press as the response), peripheral sensation
(proprioception), lower extremity strength (knee extension strength) and balance (sway when
standing on the medium-density foam rubber mat). These five tests were chosen as they have been
shown to best discriminate between fallers and non-fallers [152, 153]. Lord identified cognitive
impairment as a predictor of multiple falls in a hostel population with a relative risk 2.37 [153].
The type of visual field motion together with aging could be a significant factor in the
increased occurrence of falls [154]. Vision can compensate a reduction of vestibular and
proprioceptive balance functions that physiologically decrease with age [155] but also in vestibular
and anxiety disorders [156, 157]. Several studies have shown that impaired vision negatively affects
postural stability and increase the risk of falling and fracture in older people [158]. Moving visual
36
environments can cause postural changes, disequilibrium, and motion sickness in healthy adults and
in patients with balance disorders [159]. In fact, changing the visual input, such as from forward to
backward motion or from dark to light environment, requires an updating of the sensory integration
that results in leg muscles activation to produce a compensating motor response. It has been
proposed that the postural reflex activity of the leg muscles evoked by postural and vestibular
disturbances could be organized to minimize future disturbance rather than to correct a past one [5].
Studying falls and other mobility issues in the elderly related to the use of visual motion and optic
flow is important for two reasons: 1) older adults have difficulties in integrating multi-sensory
information appropriately during a complex task, and may be excessively dependent on visual cues
[15, 160]; 2) older adults have poorer perceptions of moving visual stimuli. Until now, several
researchers with a posturography investigation have been focused on the maintenance of balance
control and the spontaneous body sway movements studying the centre of pressure (COP)
oscillations. Furthermore, in a fall clinic population the illusory self-motion perception is associated
to a stiffening strategy to maintain stance, dizziness symptoms and an increased risk of falls [161].
37
3
Aims of the studies
Most people do not find difficulties in performing daily activities such as maintain stance,
walking, running or driving along the street because visual motion information helps for navigation
in the environment as it carries proprioceptive information about the location, orientation and
movement of our body (self-motion). The theoretical approach to understand how visual perception
happens started at the beginning of World War II, with J.J Gibson. Gibson stated that we use optic
flow (the pattern of motion flow available at the eye as an observer moves through their
environment), rather than object position, to control our direction of locomotion [17]. He noted that
the visual motion in the optic array surrounding a moving observer radially expands out of a
singular point (focus of expansion FOE) along the direction of heading. When the observer moves
forward fixating his/her final destination, optic flow pattern seems to expand; while he/she moves
backward, optic flow pattern seems to contract.
During our daily activities we continuously use the visual feedback. The synergistic input
from visual system, vestibular-proprioceptive postural reflexes and nervous system make
corrections so that we can maintain a more stable upright posture [12]. Subjects with normal vision,
can reduce postural sway of about 50% during a visual feedback (open eyes) as compared to
absence of it (eyes close) [3, 15, 44, 45, 162]. Early work by Lee and Lishman, 1975, with the
moving room paradigm, measured the importance of visual information for postural control
suggesting that visual motion information can override information obtained from stretch receptors
in the limbs and gravity receptors in the inner ear [44]. It is important to take into account that
stimulation of visual system can lead to two perceptions: the self movement perception through the
environment, versus the environment movement perception around the self that is the classic
example of when we are sitting in a stationary train, and the train next to us is moving forward.
During this situation motion we may perceive that we are moving backward and that the train
beside is stationary; alternatively, we may correctly perceive that we are stationary and that the
38
other train is moving. A similar vection phenomenon can result if we stand in a forward-moving
visual environment. So that we may perceive that we are moving backward in stationary
surroundings, which will cause a notable compensatory postural adjustment in the forward
direction. This sensory stimulation technique is known to activate multiple brain regions (temporoparietal cortex, basal ganglia, brain stem, cerebellum) some of which are involved in a spatial
encoding and postural stability [163].
The complex task that requires the maintenance of postural stability has been studied by
numerous investigators to elucidate the relative contributions of each sensory system during
standing. Recently the research field is focused on the muscle activation during visual stimulation
(comparing eye open to eye closed conditions)[164]. Some studies analyzed the muscle activity of
postural muscles such as the tibialis anterior, soleus, vastud medialis, biceps femoris, erector spinae,
and abdominals. These studies point out how these muscles are activated during the body sway
induced by the visual stimulation. For example, during a backward translation subject tend to lean
forward with a compensatory reaction of the gastrocnemius, first, followed by biceps femoris and
erector spinae. On the contrary when subject is swaying forward, he/she tend to lean backward with
a compensatory activation of tibialis anterior first, followed by quadriceps femoris and abdominal
muscles. To the best of our knowledge, no previous studies have been investigated the bilateral
activation of postural muscles (i.e. tibialis anterior, ganstrocnemius medialis, vastus medialis and
biceps femoris) integrating the stabilometry analysis in order to correlate temporally the optic flow
stimulus onset and the buildup of postural response in order to understand the neural mechanism.
Vision can compensate a reduction of vestibular and proprioceptive balance functions that
physiologically decrease with age [155]. Several studies have shown that impaired vision negatively
affects postural stability and increase the risk of falling and fracture in older people [158].
Moreover, not only the quality of visual information is compromised in older age, but there are also
differences in the time period between acquiring visual information and executing a movement.
39
Studies reported older adults are excessively dependent on vision and have difficulty integrating
multi-sensory information appropriately during a complex task that results in a poorer perception of
moving visual field [15, 160].
Little is known about the inter-leg coordination dynamics, the modulation of postural
muscles, and the correspondence between body sway and muscle activation in young adults during
different dimension visual field stimulation. Moreover no study examined extensively the postural
responses in older people at increased risk of falls during radial optic flow.
Given the large contribution from the literature review, the overall aim of this research was
to explore the role of the optic flow on postural stability: i) when there is a different stimulation of
the visual field (i.e., foveal, peripheral and full visual field) in young adults; ii) exploring different
postural strategies adopted during visual stimulation between genders; iii) exploring the age-related
mechanisms to encode the optic flow information in order to understand the postural responses,
during visual stimulation, in older people at increased risk of falls.
The aim of the Study I was to evaluate the role of different optic flow stimuli directions and
dimensions on the postural muscles, directly linking the muscle activity to the evoked oscillation
measured by changes in the center of pressure (COP). We investigated the effect of full field, foveal
and peripheral radial expansion and contraction optic flow on the muscular activation of tibialis
anterior, gastrocnemius medialis, vastus medialis and biceps femoris, four couples of main
antagonist postural muscles. We also studied in deep the variations of specific COP parameters in
each leg during different oscillations caused by foveal, peripheral and full field optic flows. The
combined use of EMG and stabilometry would shed light significant information on the role of
optic flow dimensions on postural control in the male and female groups. Given that recent
experiment during balancing motor task revealed a differential modulation of extensor and flexor
muscles by an optic flow pattern, we hypothesize that a specific flow field dimension should
modulate the muscles activation.
40
The aim of the Study II was to investigate the role of different optic flow stimuli directions
on postural control in older people at increased risk of falls. We investigated with stabilometry and
kinematic analysis the optic flow effect on postural balance in two different age groups: young (20
to 40 years old) and old (60 o 85 years old) in order to extent the knowledge of the effects of ageing
on postural control. The combined use of stabilometry and kinematics would provide important
information on the temporal relationship between postural response and the visual stimulus is
important to understand the neurophysiological mechanisms responsible of the balance and how
these can change with age. Given that the human body declines during ageing and physical and
functional changes (i.e., reduction in muscle mass, inaccurate and delayed perception of visual,
proprioceptive and vestibular information and variable and inaccurate movement execution) are
related to increased risk of falling, we hypothesise that the body sway in the elderly, will be differ
remarkably in velocity and periodicity from that in young adults. We also hypothesize that faller
and non-faller population can use sensory different integration in the ankle, trunk and head joints
control and the coordination of those segments during optic flow stimulation especially in a fall
population in which the illusory self-motion perception would be associated to a stiffening strategy
to maintain stance, dizziness symptoms and an increased risk of falls.
41
STUDY I: IMPORTANCE
OF OPTIC FLOW ON
POSTURAL STABILITY OF YOUNG MALE AND
FEMALE, STABILOMETRY AND EMG ANALYSIS
4.1 Methods
Surface EMG and postural responses were recorded in 24 healthy volunteers, 12 male and
12 female, aging from 20 to 30 years (average 24.5). Average height and weight with standard
deviation (SD) for females were 167 ± 5 cm and 62 ± 5 kg, and for males 178 ± 6 cm and, 72 ± 5
kg. All subjects had normal or corrected to-normal vision. All subjects provided signed written
informed consent to participate in the study. Recordings have been performed in accordance with
the ethical standards laid down in the 1964 Declaration of Helsinki. The experimental protocol was
approved by the Institutional Ethic Committee of the University of Bologna (Italy). Each subject
was asked to fill in a laterality questionnaire before the beginning of the experiment to test a
potential effect of the laterality on the muscle and postural responses. Questions regarding all bady
segments hand, arm and leg preferences were scored as left, right or equal [165] to perform a deep
analysis of the subject’s characteristics. Then, a score on a scale from -1 to 1 was calculated
according to the following formula [166]:
[(right preference – left preference)/(right preference + left preference)] x 100
A positive laterality index was indicative of a right dominance, while a negative laterality index was
indicative of a left dominance.
42
4.1.1
Stimuli
All experiments were performed in a dark room. Optic flow stimuli, expansion and
contraction were presented full field, in the foveal or in the peripheral region of the visual field by a
retro video projector (Sony VPL EX3) positioned 415 cm away from a translucent screen. The
screen covered 135 × 107° of visual field and was placed 115 cm from the subjects’ eyes. Optic
flow stimuli were made by white dots of a luminous intensity of 1.3 cd/m2. The dots had a width of
0.4° and moved on the screen at a speed of 5°/s. All stimuli had the same dot density with respect to
the retinal stimulation area (Figure 4.1). In the full field expansion stimulus, the dots originated
from the FOE moving radially towards the periphery (Figure 4.1a). In the full-field contraction
stimulus, the dots originated in the periphery moving radially towards the FOE (Figure 4.1b). In the
methodological preparation of this study, was decided to follow anatomical criteria [125], so were
considered central visual field to be the 7° surrounding the fovea. This includes the foveal,
parafoveal and perifoveal regions. In the foveal expansion stimulus, the dots originated from the
FOE moving radially towards the periphery (Figure 4.1c), while in the foveal contraction stimulus
the dots originated in the periphery (Figure 4.1d). Periphery was considered to be the visual field
outside the inner 20° of the central visual field, so as to be sure to analyze the retinal area
containing almost exclusively rod photoreceptors [167]. Peripheral stimuli covered the entire screen
except a central occlusion circle of 20° in radius. In the peripheral expansion stimulus, the dots
originated from the edge of the central black portion of the visual field moving radially towards the
periphery (Figure 4.1e). In the peripheral contraction stimulus, the dots originated from the
periphery moving radially towards the central occluded region (Figure 4.1f). Random dots motion
was used as control stimulus (Figure 4.1g). Optic flow stimuli were made using Matlab
psychophysical toolbox (The Mathworks Inc.).
43
Figure 4.1. Optic flow stimuli. Sketch of radial and random optic flow. Arrows represent the velocity vectors of moving
dots. a Expansion. b Contraction. c Foveal expansion. d Foveal contraction. In foveal stimuli the stimulated area had a
radius of 7°. e Peripheral expansion. f Peripheral contraction. In peripheral stimuli the blank area in the center had a
radius of 20°. g Random. All stimuli were made by white dots of 0.4° in diameter, retro-projected on a black screen at a
perceived forward or backward speed of 5°/s. The fixation point consisted in a white dot of 0.6° always positioned in the
middle of the screen. The focus of expansion was in the center of the screen. Stimuli had the same dot density with
respect to the retinal stimulated area. Full-field stimulus: 1,155 dots. Foveal stimulus: 36 dots. Peripheral stimulus: 992
dots. The screen covered 135 × 107° of visual field. Subjects were 115 cm away from the screen.
4.1.2
Surface EMG and Stabilometry
Stabilometric data were recorded using two Kistler force platforms placed side by side.
Before the beginning of each trial, subjects were asked to place a foot on each platform looking at a
fixation point positioned in the middle of the screen. Subjects were instructed to stand with both
arms along the trunk and to keep the gaze on the fixation point (Figure 4.2). They did not receive
any instruction of resisting to the evoked motion perception. Trial onset determined the stimulus
onset. EMG data were acquired by a 16 channels Pocket EMG BTS (BTS Bioengineering Inc.)
using Ag/AgCl disposable electrodes 32x32 mm (RAM apparecchi medicali s.r.l.). Electrodes had
an active area of 0.8 cm2 with an inter-electrode distance of about 2 cm. The skin was shaved and
cleaned with ethanol before placing the electrodes to improve the contact with the skin. Electrodes
were positioned on the muscular belly of the following muscles: right tibialis anterior (RTA), left
tibialis anterior (LTA), right gastrocnemius medialis (RGNM), left gastrocnemius medialis
(LGNM), right vastus medialis (RVM), left vastus medialis (LVM), right biceps femoris (RBF), left
44
biceps femoris (LBF). For each stimulus we acquired 5 trials lasting about 35 sec each. Four
baseline trials were acquired two at the beginning and the other two at end of recordings for about
30 s. During the baselines each paticipant was instructed to stand upright in the dark without any
visual stimulation.
A
B
Figure 4.2. A.Representation of the experimental set-up with the two BTS Kirstler Force Platforms. B.BTS
MyoLabPocket EMG.
4.1.3
Data analysis
Both EMG and stabilometric signals were recorded at 1000 Hz, because such frequency was
fixed by the instruments for the force platforms data acquisition. For avoiding fatigue effect were
analyzed the first 25 sec of each trial.
In the initial step of the analysis was resample EMG signals at 250 Hz. EMG signals were
positively rectified and band pass filtered (Butterworth, 20–450 Hz) using SMART Analyzer (BTS
Bioengineering Inc.). Then stimulus signals were normalized to the baseline signals according to
the baseline normalization analysis [168, 169]. The next step was to determine if the muscle was
activated with respect to the baseline. So, a muscle activity was considered significantly different
45
from baseline when greater than the baseline mean + 3SD [170, 171]. Then, EMGs have been
analyzed using an univariate analysis of variance (ANOVA), a Bonferroni pairwise comparison and
a Student T-test. The normalized root mean square (RMS) values were calculated over the 25
seconds considered for the analysis in 100 ms bin from EMG signals using Matlab. An univariate
analysis of variance (ANOVA) for repeated measures was performed on the normalized EMGs
where the fixed factors were stimulus motion (expansion, contraction, random), stimulus dimension
(full field, foveal, peripheral) and gender (males, females). A Geisser-Greenhouse correction was
used as well as a Bonferroni pairwise comparison.
Stabilometric data have been low-pass filtered at 15 Hz and resampled at 250 Hz. Ground
reaction forces were and COP measures recorded from each foot by the two platforms.
Stabilometric data were collected to access the postural oscillations in response to the optic flow
stimuli. Both antero-posterior (AP) and medio-lateral (ML) COPs of each foot using either SMART
Analyzer (BTS Bioengineering Inc.) and Matlab (The MathWorks, Inc) were analyzed. Then the
global COP was obtained, computed from a weighted average of the two COPs, according to the
following formula [172]:
COPglobal = COPL ∗ RVL / (RVL + RVR) + COPR ∗ RVR / (RVL + RVR)
(eq. 1)
where: RVL and RVR are the vertical reaction forces from left and right feet respectively.
Once computed the COPglobal, were calculated the maximal variance direction, which corresponds to
the prevalent oscillation direction, according to the following formula [173]:
Max variance direction = atan(VML/VAP)
+  if VML/VAP < 0
(eq. 2)
where VML and VAP are the eigenvectors corresponding to the maximum eigenvalues of
C = covariance (ML[n], AP[n]) where n is the sample index.
46
To evaluate the temporal correlation between the muscle activation onset and the build up of
postural response a Pearson correlation model was applied to all COP components (AP, ML, left,
right global and torque) and each EMG signal [174]. This analysis was performed on the mean
values of all trials in each stimulus and baseline of each subject. Given that it is known that the time
shift between muscular activity and stabilometric signals ranges from 0.14 to 0.19 s [174], the
correlation analysis was performed into 25 intervals of 1 s each. A threshold of the R coefficient
±0.7 was considered for determining the correlation. The general response to optic flow over the
entire stimulation period was assessed measuring the RMS of the AP ankle torque of each foot. This
analysis was used to describe the spatial variability of the subjects’ postural control [175]. The RMS
values were calculated over the 25 s considered for the analysis in 100 ms bin from the AP values
of the ankle torque of each foot using Matlab. Then, a multivariate ANOVA was performed on the
RMS of the AP torques, in which stimulus motion (expansion, contraction, random), stimulus
dimension (full field, foveal, peripheral) and gender (males, females) were the fixed factors.
To analyse he contribution of each leg on postural conntrol, five measures referred to the
COP of each foot and COPglobal were computed: The antero-posterior range of oscillation (APO),
which is the difference between the maximum and minimum range of oscillation in antero-posterior
direction and the medio-lateral range of oscillation (MLO), which is the difference between the
maximum and minimum range of oscillation in the medio-lateral direction. The antero-posterior
COP velocity (Vel AP), the medio-lateral COP velocity (Vel ML), the two latter measurements
reflect the total distance travelled by the COP over time on each axis and the COP area (AREA),
quantified within the 95% confidence ellipse, which is the enclosed area covered by the COP as it
oscillates within the base of support [54, 58, 140, 176, 177]. First was computed the percentage of
loading in the right and left foot using Smart-Analyzer software (BTS Bioengineering Inc.) and
Matlab (The Mathworks Inc.). Then values of the percentage of loading were analyzed with a
multivariate ANOVA (having as within factors the stimuli and as between factors side and gender).
47
The COP parameters APO, MLO, Vel AP, Vel ML and AREA were analysed using Sway
and Smart-Analyzer software (BTS Bioengineering Inc.) and Matlab (The Mathworks Inc.). The
analysis was performed separately for measurements of each limb and global. To analyze the
influence of optic flow stimuli on postural control, a repeated-measure ANOVA was performed in
which optic flow stimuli and side (right, left, global) were the within-subject factors, while gender
was the between-subjects factor. After having assessed the effects of stimuli, side, and gender, were
then analyzed in depth the relationship between the left and right foot in response to visual stimuli
using a bivariate Pearson linear correlation analysis. Last of all, the degree of variation of the right
and left foot in the five COP parameters using the coefficient of variation (CV) was computed as
the ratio of the standard deviation to the mean. The CV was computed for each trial of each
stimulus in each subject. Then, values for all subjects in each condition and group were averaged.
4.2 Results
All subjects were right-handed. Answers to the laterality questionnaire resulted in values
ranging from 16.6 to 100. Twenty-three subjects showed values greater than 64, meaning a strong
right laterality in all three body segments. No subject turned out to be completely left oriented, and
only one subject indicated an equal use of both hands in some daily activity.
4.2.1
Effect of stimuli and gender on EMG signal
To analyse the EMG signals in both time and amplitude domain, were applied a repeated
measures ANOVA to the normalized RMS values of each muscle for each stimulus. The results
revealed a significant main effect for muscle (p<0.001) and gender (p<0.001) and an interaction
effect of gender by muscle (p<0.001). No significant stimulus effect was found on the muscle
48
activity. The Bonferroni pairwise comparison demonstrated that all muscles showed significant
differences between each other (p<0.01) except for the following comparisons: RTA versus LBF,
LTA versus RGNM and LTA versus LGNM. Figure 4.3 shows the strong interaction effect of
gender by muscle, illustrating the different activation of the thigh muscles (RVM, LVM, RBF and
LBF) in both groups. Figure 4.3 shows the mean normalized RMS values in each subject. Given the
non-significant stimulus effect, the muscular activity of all stimuli was averaged in each muscle of
each subject. A different muscular activity in signal amplitude and muscle emerged from the
analysis.
Figure 4.3. Time course of the mean normalized root mean square (RMS) values (100 ms bin) showing the different
muscular activity in males and females. RMS computed for all muscles are shown for the experimental condition of
foveal contraction (only one stimulus is shown given the non significant stimulus effect). a Female. b Male. RTA right
tibialis anterior, LTA left tibialis anterior, RGNM right gastrocnemius medialis, LGNM left gastrocnemius medialis,
RVM right vastus medialis, LVM left vastus medialis, RBF right biceps femoris, LBF left biceps femoris.
During the stimulus presentation, females activated primarily the RVM. The LVM and the
RBF showed less activity, while the remaining muscles were poorly activated (Figure 4.4 gray
lines). On the contrary, males activated primarily the LVM (Figure 4.4 black lines), meaning that
males and females have different postural arrangements during optic flow presentation. In both
49
groups the RVM had a tonic activity and the RBF a burst activity, but females activated those
muscles with higher amplitude (p<0.001).
Figure 4.4. Distributions of the mean normalized RMS values (100 ms bin) computed for all muscles in each subject.
Each curve represents the distribution of a single subject. Gray lines represent females, black lines males. Given the non
significant stimulus effect, the muscular activity of all stimuli have been averaged (+SE) in each muscle of each subject.
Conventions as in Fig. 4.3.
4.2.2
Stabilometry analysis: effect of stimuli and gender on prevalent direction of oscillation
To evaluate the COP amplitude in response to the visual stimuli and baseline, a univariate
factorial ANOVA were applied to the mean values of the AP and ML directions of the COP
components, having height and weight variables as covariances to verify their influences on the
evoked postural response. A significant effect of sex in the ML direction of COPleft and COPright
(p<0.001) was found, while, a stimulus effect was found in the AP direction of COPglobal, COPleft
and COPright (p<0.001). Moreover a significant effect of sex in the AP direction of the COPglobal
(p=0.02) and COPright (p=0.01) due to the height and weight differences in subjects was found. An
example of the different COP amplitude evoked by the different stimuli on the COP components is
shown in Figure 4.5 All COP components (left, right and global) of the baseline (Figure 4.5a) are
larger and more disorganized than those of the visual stimuli (Figure 4.5b–h). In agreement with the
50
literature, these data demonstrate that optic flow visual stimuli stabilize postural sway, while the
absence of visual stimulation provokes larger body sway. It is worth noting that optic flow
stimulation evokes a different COP response in the two feet. In this example, this is particularly
evident in foveal and random stimuli (Figure 4.5d–f) which have longer and more disorganized
COPright traces than COPleft. COPglobal data were used to calculate the maximum variance of
direction (Eq. 2). The resultant value is the angle that defines the prevalent direction of postural
sway that has been computed for each trial [173].
Figure 4.5. Example of center of pressure (COP) traces recorded for both right and left feet during optic flow stimulation
and baseline. Trace drawings are scaled on the force platforms. COPglobal is computed according to Eq.1. ML mediolateral, AP antero-posterior. Data set: subject n. 12, male, age 22.
51
Then, were analysed the mean vector of each stimulus and baseline in each subject
(ORIANA, Kovach Computing Services). Figure 4.6a–c shows the distributions of the prevalent
direction of oscillation for full field stimuli, while Table 3.1 illustrates the mean vector values ± SE
and significance value. Considering the whole population (n = 24), the mean vector distributions of
the three stimuli resulted uniform at the Rayleigh test (Table 4.1).
Figure 4.6. Distributions of preferred sway
directions for the whole sample and for each
gender. Rose diagrams show the frequency
distribution of the mean vectors of all trials
computed for each stimulus of each subject.
Solid line crossing each diagram indicates
the mean vector. Bars are 20° in width. a–c
Expansion, contraction and random for the
whole sample (n = 24). d–f Expansion,
contraction and random for the male group
(n = 12). Asterisks indicate non-uniform
distribution of significant values (Rayleigh
test of uniformity): male contraction: p =
0.03. g–i Expansion, contraction and random
for the female group (n = 12).
Figure 4.7a–d shows the distributions of the prevalent direction of oscillation for foveal and
peripheral stimuli. The Rayleigh test of uniformity showed that both peripheral stimuli had a nonuniform distribution (Table 4.1). To verify the optic flow selectivity upon the sway oscillation, a
Pearson correlation model was performed between the mean vectors of all optic flow stimuli.
Results showed no correlation between the stimuli. These results are consistent with the usually
perceived motion induced by optic flow; indeed expansion evokes a forward prevalent sway, while
contraction evokes a backward prevalent sway. Given the strong sex effect and the very different
muscular activation, we assessed the distribution of the prevalent sway directions within male and
52
female groups. Figure 4.6d–i shows the distributions of the prevalent direction of oscillation for full
field stimuli, while Figure 4.7e–l for foveal and peripheral stimuli. In males, the distributions in the
peripheral stimuli and in the full field contraction were non-uniform at the Rayleigh test (Table 4.1).
Unlike those in males, in females all distributions were uniform (Table 4.1). No correlation was
found between the stimuli and genders groups.
Figure 4.7. Distributions of preferred sway directions with different retinal stimuli for each gender. Rose diagrams show
the frequency distribution of the mean vectors of all trials computed for each stimulus of each subject. Solid line crossing
each diagram indicates the mean vector. Bars are 20° in width. a Foveal expansion, b Foveal contraction, c Peripheral
expansion, d Peripheral contraction for all subjects. a–d n = 24. Asterisks indicate non uniform distribution of significant
values (Rayleigh test of uniformity): Peripheral expansion: p = 0.048; Peripheral contraction: p = 0.004. e Male foveal
expansion, f Male foveal contraction, g Male peripheral expansion. h Male peripheral ontraction. i Female foveal
expansion, j Female foveal contraction, k Female peripheral expansion, l Female peripheral contraction. e–l n = 12.
Asterisks indicate non-uniform distribution of significant values (Rayleigh test of uniformity): Male peripheral
expansion: p = 0.004; male peripheral contraction: p = 0.007.
53
Table 4.1 Mean vector values of the prevalent direction of oscillation ±SE and significance value at the
Rayleigh test
All subjects
Rayleigh
Males
Rayleigh
Females
Rayleigh
(n = 24)
test (p)
(n = 12)
test (p)
(n = 12)
test (p)
Expansion
127 ± 25
0.79
179 ± 25
0.35
41 ± 28
0.53
Contraction
223 ± 19
0.27
232 ± 18
0.03*
99 ± 36
0.9
Random
211 ± 23
0.66
201 ± 25
0.34
334 ± 38
0.95
Exp Fovea
109 ± 56
0.6
129 ± 37
0.93
102 ± 114
0.56
Contr Fovea
322 ± 34
0.26
264 ± 33
0.82
337 ± 29
0.12
Exp Periphery
71 ± 22
0.048*
103 ± 16
0.004*
5 ± 33
0.17
Contr Periphery
228 ± 16
0.004*
211 ± 17
0.007*
255 ± 33
0.17
Values are shown for each stimulus in the whole population and in males and females groups
* Significant values
4.2.3
Stabilometry analysis: effect of stimuli and side on ankle torque
The general response to optic flow over the entire stimulation period was assessed using the
RMS values of the AP ankle torque of each foot. Such measurements are used to describe the
spatial variability of the subjects’ postural control. Figure 4.8 shows the RMS signal (100 ms bin)
of left and right ankle torque for each stimulus. In the baseline signal and during foveal and random
stimulation was observed increase values of the RMS, indicating an increasing of body sway
variability. During peripheral optic flow stimulation RMS values were generally reduced and less
variable. A multivariate ANOVA of the RMS values was performed across stimuli in each subject.
Regarding the right limb, 23 subjects showed significant differences across stimuli (p<0.03). A
Bonferroni pairwise comparison showed that in only one subject full-field expansion and random
had similar values (p=0.28), with no significant difference in foveal contraction (p=0.16) and
peripheral contraction (p=0.22). Regarding the left limb, the RMS values were different across all
stimuli in 18 subjects (p<0.01): one subject showed a significant difference only in peripheral
contraction (p<0.001), four subjects did not show a significant difference in peripheral contraction
(p>0.05) while one subject did not show a significant difference in foveal expansion (p=0.77) and
in full field expansion versus random (p=0.12).
54
Figure 4.8. Time course of the root mean square (RMS) values (100 ms bin) of the anteroposterior right and left
ankle torque computed for all stimuli and baseline signals. a, c Torque left. b, d Torque right. Exp expansion,
Contr contraction, ExpF foveal expansion, ContrF foveal contraction, ExpP peripheral expansion, ContrP
peripheral contraction.
4.2.4
Limb loading
To quantify the asymmetry, firstly the limb loading was computed. Mean values of the
percentage of loading are shown in Figure 4.9. Women had an almost equal load, while men
55
consistently loaded the left leg more than the right. The results of the multivariate ANOVA showed
an effect of side in all stimuli (F(8,35)=10,57, p<0.001) and an interaction effect of side x gender in
all stimuli (F(8,35)=7,74, p<0.001). No main effect of gender was found (F(8,35)=0.31, p=0.95).
Figure 4.9. Average values of left and right percentage of loading in the right and left foot of men and women.
Data are shown for all stimuli and baseline. ContrF: foveal contraction, Contr: full field contraction, ContrP:
peripheral contraction, ExpF: foveal expansion, Exp: full field expansion, ExpP: peripheral expansion.
.
4.2.5
Stabilometry analysis: effect of stimuli, side and gender on postural responses
All COP parameters showed significant main effects of stimuli, side, and gender as
summarised in Table 4.2. Vel ML showed significant interaction effects (stimulus x gender and
stimulus x gender x side), while no significant gender effect was found. AREA showed an
interaction effect between stimulus and side. The results of the between-subjects analysis
(ANOVA) showed that among the COP parameters, MLO showed more differences between men
56
and women. The gender effect was examined in each stimulus of the right and left leg allowing the
analysis in 16 conditions. A significant effect was found in almost all stimuli (14/16). The two nonsignificant effect were found in expansion (p=0.14) and foveal contraction (p=0.08) of the left foot.
No difference emerged in the stimuli of the MLOglobal. In APO however, significant gender effects
were found for foveal (p<0.024) and peripheral contraction stimuli in the left leg (p<0.028), while
no differences were found in the APOglobal. The Vel AP and Vel ML showed similar results: in Vel
AP, a significant gender effect was found in the left foot only for baseline, random, and foveal
stimuli (p<0.05), while in Vel ML significant gender differences were observed in the left foot for
baseline, random, and peripheral contraction stimuli (p<0.05). Similar to MLO, the AREA
parameter showed a significant gender effect in the right and left foot for in 13 out of 16 stimuli
(ANOVA, p<0.05). The three non significant effect were found in expansion (p=0.09) and
peripheral contraction (p=0.08) of the left foot and foveal contraction of the right foot (p=0.22). No
differences were found for the AREAglobal parameter. Figure 4.10 shows the mean values of the
COP parameters in both feet and the global data for men and women. All parameters yielded larger
values in men. The left foot had larger values of APO and AREA (Figure 4.10A,E), while the right
foot showed higher values in MLO, Vel AP and Vel ML (Figure 4.10B-D).
Table 4.2. COP parameters with significant main effects of stimuli, side, and gender
57
Figure 4.10. Average values of COP parameters in the left and right limb and global data. Values are shown for men
and women during optic flow stimuli and baseline. A. Antero-posterior range of oscillation. B. Medio-lateral range
of oscillation. C. Antero-posterior velocity. D. Medio-lateral velocity. E. Sway area. Each data point shows mean ±
standard error (SE). Conventions as in Figure 4.9.
4.2.6
Correlation between EMG and COP
Stabilometry was performed together with EMG to study the correlation between muscular
activation and postural sway. As in the results of the EMG and stabilometric data, significant
differences between males and females in the calf and thigh muscles were found. In both males and
females, the data from the tibialis anterior and gastrocnemius medialis were poorly correlated with
58
the data from the biceps femoris, vastus medialis and COP components. Only six subjects (6/24, 25
%) showed a correlation between the data from the calf and thigh muscles or the data from the eight
muscles and COP components. Only in females’ thighs, RVM activity resulted highly correlated
with LVM and RBF in 11 subjects (11/12, 92 %) and all these three muscles were correlated with
ML COPleft
4.2.7
Correlation between each limb and COP
A bivariate Pearson correlation was used to test whether the relationship between the right
and left foot in each COP parameter was linear. The analysis was performed separately for men and
women on left vs. right foot for all stimuli and baseline values of each COP parameter. In women
(Figure 4A), significant linear correlations between the two feet were found only in MLO (baseline:
R(9)=0.659, p=0.05; random: R(11)=0.737, p=0.01; foveal contraction: R(11)=0.67, p=0.02;
contraction: R(11)=0.634, p=0.036; peripheral contraction: R(11)=0.731, p=0.011; peripheral
expansion: R(12)=0.778, p=0.003). The values of the right and left foot in the other COP
parameters showed very low correlation coefficients, often negative (Figure 4.11A). Men, however,
showed few significant correlations between right and left foot COP values (Figure 4.11B) but the
two feet seem to have more similar movements than those of women (APO random: R(11)=0.603,
p=0.049; APO peripheral expansion: R(11)=0.644, p=0.032; Vel AP foveal contraction:
R(11)=0.733, p=0.01; Vel AP contraction: R(11)=0.641, p=0.033; Vel AP foveal expansion:
R(12)=0.877, p<0.001; MLO foveal contraction: R(10)=0.631, p=0.05; MLO contraction:
R(9)=0.72, p=0.029; AREA contraction: R(11)=0.688, p=0.019; AREA foveal expansion:
R(11)=0.736, p=0.01).
59
Figure 4.11. Correlation coefficients for the correlation analysis between the right and left foot. A. Women. B. Men.
Asterisks indicate significant values (bivariate Pearson correlation, p<0.05). Conventions as in Figure 4.9.
4.2.8
Variation in the COP parameters
To examine the variability of postural adjustments during optic flow stimulation, we
computed the CV for the five COP parameters in the right and left foot. MLO consistently showed
greater variability than APO. Baseline stimuli always had the highest CV, indicating that the
absence of visual stimulation caused a greater instability. In women, different variability was
observed in the left and right foot: MLOleft always showed higher CV than MLOright, while in almost
all stimuli, APOright showed higher CV than APOleft (Figure 4.12A). In men, MLO had still higher
variations than APO; however they were smaller when compared to those of women (Figure
4.12B). The greatest variations were observed in the COP velocity (Figure 4.12C,D). In both men
and women, Vel ML showed always greater variations than Vel AP suggesting that subjects
consistently experienced a loss of balance control on the medio-lateral axis. Both genders showed
greater variability for AREA of the left foot for the majority of stimuli (Figure 4.12E,F). Men
60
showed the greatest variability of the COP Area. As these observations on the CV were largely
descriptive, the CV values were further analyzed to quantify the variability related to gender and
foot. A one-way ANOVA, with side as between factor and stimuli as within factor, was performed
separately for men and women. Significant differences between the left and right foot were found
only in women in Vel ML for all visual stimuli (foveal contraction: F(1,23)=4.69, MS=630.29,
p=0.041; contraction: F(1,23)=20.73, MS=166.48, p<0.001; peripheral contraction: F(1,21)=15.23,
MS=144.67, p=0.001; foveal expansion: F(1,21)=24.05, MS=187.27, p<0.001; expansion:
F(1,23)=13.61, MS=125.18, p=0.001; peripheral expansion: F(1,23)=12.7, MS=125.26, p=0.002;
random: F(1,22)=5.04, MS=696.75, p=0.036; baseline: F(1,23)=3.84, MS=55.72, p=0.063). No
significant differences between the two feet were found in the other parameters in men.
Figure 4.12. Coefficients of variations (CV) of COP parameters across the right and left feet in men and women. A.
Women antero-posterior range of oscillation (APO) and medio-lateral range of oscillation (MLO). B. Male APO and
MLO. C. Women antero-posterior velocity (Vel AP) and medio-lateral velocity (Vel ML). D. Men Vel AP and Vel
ML. E. Female sway area. F. Male sway area.
61
4.3 Discussion
The optic flow is a key input for maintaining postural stability during self-motion [44]. The
first aim of this study was to assess how optic flow stimuli contributes to the control of stance. We
tested if full field optic flow stimuli modulate postural control more than a nonspecific retinal
stimulus unrelated to self motion perception (i.e. random stimulus) and quantified, using full field
foveal and peripheral stimuli, how the dimension of the stimulated visual field can play a role in
stance control. The human body is fundamentally asymmetrical, manifesting in the functional
antero-posterior and medio-lateral asymmetries observed in balance control [178]. Hence, the
second aim was to investigate whether the body sway during foveal, peripheral or full-field optic
flow stimulation is lateralized, and whether antero-posterior and medio-lateral components of
specific COP parameters of the right and left foot. Male and female have different biomechanical
properties [179-181]. Moreover previous study reported that female are more visual dependent than
male[91], so the third aim of this work was to investigate the postural strategy between genders.
4.3.1
Effect of optic flow on muscle activity
This work demonstrates an optic flow modulation of the activity of vastus medialis and
biceps femoris with a lower activity of tibialis anteriror and gastrocnemius medialis. However, no
stimuls effect was found on these muscles. A possible explanation for such lack of effect could be
related to the methodology used in this study. Were recorded bipolar EMG signals from the
muscular belly, leading a global signal of the muscle activity. It is possible that the use of an
electrode array may highlight a different muscular activation in response to the optic flow stimuli.
flow stimuli. The reduced spatial variability during expansion and contraction stimulation
underlines their role in postural stabilization. The absence of correlation between the mean vectors
of the optic flow stimuli indicates that each stimulus evokes a peculiar postural sway different from
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the others. This study agrees with previous papers about the importance of optic flow in postural
stability [44, 45, 79], demonstrating that postural sway responses are influenced by the spatial and
temporal properties of the visual input.
4.3.2
Effect of dimension of optic flow visual field
The importance of the stimulus structure in postural control has already been suggested on
the basis of stabilometry [182]. In addition, present results show that a visual stimulus always
evokes an increased excitatory input on muscles, but the stimulus structure produces different
postural responses. The analysis of the COP traces revealed that postural sway during foveal
stimulation is much larger and less organized than those of full field and peripheral stimulation.
This suggests that visual stimuli always evokes an increased excitatory input on muscles, but the
amplitude of the stimulated visual field produces different postural responses. Thus, it seems that
peripheral visual stimuli likely stabilize postural sway, while foveal visual stimuli provoke larger
body sway similar to those evoked in absence of visual stimulation (i.e. baseline). The analysis of
the prevalent direction of oscillation clearly demonstrated the prevalent role of peripheral visual
stimulation on postural stability. Only peripheral stimuli evoked non uniform prevalent direction of
oscillation and postural responses to peripheral stimuli significantly show behaviorally consistent
responses, in that expansion optic flow causes forward sway, while contraction backward sway.
Furthermore, the COP spatial variability was reduced during peripheral stimulation. Present results
extend those of previous papers about the importance of peripheral optic flow in postural stability
[115, 117-120]. As previously pointed out Berencsi and coll. [118] take into account the
discordances between previous studies are needed. In their work Berencsi and coll underlie that the
main reason of such discordances are the different experimental protocols used in those studies. For
example, few papers investigated the role of central vision but the stimuli dimensions ranged from
7° to 60° [84, 124, 126]. In the methodological preparation of this study was decided to follow the
63
anatomical criteria [125], so central visual field the 7° of visual field surrounding the fovea was
considered. This includes the foveal, parafoveal and perifoveal regions. On the other hand,
peripheral visual field the area surrounding the first 20° of the central visual field was considered,
so to be sure to analyze the retinal area containing rod photoreceptors. The dimensions of the
stimulated visual field in this study is comparable to those used by Berencsi and coll. [118], indeed
results of this work agree with those of Berencsi about the predominant role of peripheral visual
stimulation in controlling posture. Until now, a number of studies have pointed out that central and
peripheral vision play different roles in postural stability (cfr. [118, 119]. Such different functional
roles can explain the lack of directionality found with full field stimuli. The present work,
demonstrated that darkness and random stimuli evoke very large body sway. Foveal optic flows still
evoke large body sway, peripheral optic flows evoke smaller body sway than foveal stimuli and full
field optic flows evoke the smallest body sway. So, it seems that there is a continuum in the body
sway stabilization depending on structure and dimension of the stimuli. This results suggest that the
full field optic flow, although more effective than random stimuli in stabilizing body sway, might
not be the best stimulus to evaluate the effect of self motion perception on postural responses, given
that the stimulation of the peripheral visual field can reflect the directionality of the stimulus
evoking a non-uniform distribution of prevalent directions of oscillation. A potential mechanism
underlying such phenomenon arise from the different funtional roles of the central and peripheral
portions of the retina.
4.3.3
Limb load asymmetry
An important issue in studying postural asymmetry is limb loading. Some evidence seems to
support the idea that healthy subjects unequally distribute their weight across the two feet in
conditions of eyes open and closed [183, 184]. Our female subjects showed an almost even limb
loading while men loaded the left limb more than the right. This is a first indication of gender
64
differences in the postural control during optic flow stimulation. The left loading preference of men,
irrespective of handedness and footedness, can be explained by the different muscular activity as
pointed out the electromyography analysis showing that greatest muscles activation of the left thigh
in men.
4.3.4
Contribution of individual leg on postural control
Footedness entails postural asymmetry [185]. All subjects were right-footed. This, together
with our analysis model, allowed us to broaden the knowledge on the contribution of each leg to
postural control during optic flow stimulation. Some authors suggest differential effects on the
recurrent dynamics of the individual leg COPs and COPglobal trajectories [186, 187]. The detailed
analysis of left, right and global data shows that each leg contributes individually to side-by-side
postural control, which is not obvious when analyzing the global data. As pointed out by King and
co-workers [186, 187], the degree of asymmetry between left and right leg COP dynamics differed
across all postural stances and COPglobal dynamics. Analyzing each foot separately, revealed
variation of postural control in terms of different variability between the left and right foot
parameters. The present study emphasizes asymmetries between the two limb in the postural
maintenance showing different dynamics between the two feet in each parameter. The limb
asymmetry analysis point out important characteristics of the feet asymmetry; the fact that the two
feet exhibit different values in distinct parameters may indicate that each foot has its own role in
balance control. As suggested by Anker an co-workers [140], the muscles of the unloaded leg lose
their capacity to generate effective stabilizing ankle torques, while the velocity of COP under the
loaded leg increases, reflecting the generation of compensatory ankle moments. The participants of
this study did not show significant relationship between limb dominance and the side of load
preference meaning a continuous load/unload balance between the two feet. These findings seem to
suggest that foot asymmetry induces inter-leg coordination dynamics based on postural demands
65
during optic flow stimulation and during increasing difficulty to maintain correct body balance.
This might reveal the use of multiple timescale processes within each leg to produce a stable and
flexible postural strategy.
4.3.5
Optic flow and postural stability in male and female
The present results suggest that optic flow stimuli produced different COP oscillations,
velocities and area dimensions. Several stabilometric studies addressed the influence of gender on
postural control, but results have been very contradicting. Few studies reported gender differences
[188, 189], some others did not find significant differences in sway parameters such as COP
displacements and COP sway area [190, 191]. Although the majority of such studies have been
performed using different methodologies and equipments, leading to different comparisons, it is
noteworthy that gender sway comparisons were usually made in quiet standing without any sensory
perturbation. Present data show that male and female react to the optic flow with a different
muscular activation.
Gender differences in some aspects of neuromuscular control of the knee joint have already been
suggested [102]. Potential contributing factors to the gender bias include differences in mechanical
properties of the ligaments, joint kinematics, and skeletal alignment (cfr. [109]. Quiet standing
requires the integration of various body segments, joints and sensory system integrations for
balance control and for avoiding falls. It has been demonstrated that females incur in a postural
instability due to the joint knee laxity [179]. To compensate for that, women may adopt a muscle
activation pattern increasing lateral hamstring activity, stiffening leg muscles, knee and hip joints
[180]. Results of the present study are consistent with this view showing that women produce cocontractions of the upper leg muscles using the ankle joint to maintain postural stability. It is
possible that the activation of dorsoextensor and dorsoflexor during the trial caused a continuous
66
oscillation in antero-posterior direction requiring the generation of a stronger vertical force to keep
postural stability and to avoid backward fall. A recent paper show that during normal standing
females display a greater medio-lateral sway than males [192]. Authors relate such results to the Qangle, given that females have a greater Q-angle than males due to the femur length and the bigger
pelvis area [181]. Furthermore, the increase in the evertor activity causes COP shifts in a medial
direction [54]. The mechanical and skeletal differences allow hypothesizing a different neural
control on body sway resulting in a different postural response to optic flow. Biomechanical gender
differences can explain in part the different postural control in male and female. Indeed, it has been
reported that females are more visual dependent than males [97]. Hence, the limb asymmetry
analysis reveals that male and female use the limb differently. Gender differences in brain
asymmetry are well-documented and may explain the different postural strategies exhibited by men
and women. The brain of adult women is, from the functional point of view, less asymmetrical than
that of men [193, 194]. A recent study showed a larger left > right asymmetry in women in anterior
brain regions, and a larger right > left asymmetry in men orbitofrontal, inferior parietal and inferior
occipital cortices [195]. The brain asymmetry is also evident in motor function, as it is known that
the gray matter density in the corticospinal tract shows an hemispheric asymmetry related to hand
preference, and the maturation of the corticospinal tract during adolescence differs between men
and women due to the influence of testosterone [196]. It seems that the leftward asymmetry of the
corticospinal tract may reflect an early established asymmetry in the corticomotoneuronal fibres.
The present results, together with those of previous findings [197], suggest that the two gender
seems to use different postural alignment and they adapt differently to cortical and corticospinal
asymmetry leading to different behaviours of the right and left limb.
67
4.4 Conclusions
This study provides new evidence on the postural strategy used by men and women in the
control of stance under visual optic flow stimulation. Males react to the optic flow stimulus with
prevalent activity of the LVM and LBF, indicating that right and left legs sustain different postural
arrangements. Females however, primarily use the contraction of the RVM, LVM and RBF
indicating a similar action in both legs. Visual stimuli always evoke an excitatory input on postural
muscles, but the stimulus structure produces different postural effects. A possible explanation for
this effect might be that a different muscular activation could cause a different posture during the
stimulus presentation. These novel results contribute to broaden the knowledge on the spatial
features of visual stimuli, given that random stimulus activated the muscle as well as the optic flow
stimuli, but the COP displacement during random stimulation resembled much more that of
baseline signal rather than those of radial optic flow stimuli. The feet asymmetry observed during
optic flow stimulation causes specific inter-leg coordination dynamics necessary to maintain the
control of posture. This might suggest that the postural control system uses various mechanisms
within each leg to produce the most appropriate postural response to interact with the extrapersonal
environment. Results of the present study suggest that visual feedback differently influences the
neural control of body sway in males and females. The neural activity seems to provide with
different afferent inputs in response to disturbances of the body balance.
68
STUDY II: THE
POSTURAL
ROLE OF OPTIC FLOW STIMULI ON
CONTROL
POPULATION,
IN
KINEMATIC
YOUNG
AND
AND
OLDER
STABILOMETRIC
ANALYSIS
5.1 Methods
Participants were recruited via a volunteer database stored at Neuroscience Research
Australia and flyers. The volunteers received a telephone call inviting them to participate in this
study. Potential participants were initially screened for eligibility via telephone. 17 young and 19
older people participated in this study. Their mean (±SD) age, height, and body mass are reported in
Table 5.1. All subjects were healthy, had no history of any neurological disorder, and had normal
vision. The inclusion criteria were age 20-40 for younger adults and 60-85 for older people.
Exclusion criteria were unable to stand unassisted, have a significant visual, cognitive or
neurological impairment (including Dementia, Alzheimer’s, Parkinson’s disease or Multiple
Sclerosis) and insufficient English language skills to understand the assessment procedure. The
Participant Information Sheet and Consent Form, a copy of Ethics approval and the directions to
NeuRA were sent via mail to each participant before the assessment. Participants gave their written
informed consent for this study after receiving a detailed explanation of the purpose, potential
benefits and risks concerned with participating in the study. The experimental procedures used in
this study were approved by the Human Research Ethics Committee at the University of New South
Wales, Sydney, Australia.
69
Table 5.1. Anthropometric, fall risk, fall history, health and medical characteristics of young and older
participants.
Mean (SD)
Old (n=19)
Young (n=17)
Height (cm)
168.3 (6.4)
161.1 (11.17)
Weight (Kg)
72.4 (9.1)
59.3 (15.7)
Physiological Profile Assessment (PPA) falls risk score
0.70 (0.83)
-
Montreal Cognitive Assessment (MOCA) range 0 – 30
27 (1.2)
-
Female gender
10 (52.6)
8 (47.1)
One or more falls in previous year
6 (31.6)
-
Two or more medical conditions*
9 (47.4)
-
Fear of falling
11 (57.9)
-
Number (%)
*Medical conditions surveyed were; peripheral vascular disease, diabetes, stroke, trans-ischemic attack,
heart attack, angina, high blood pressure, heart/blood vessel problems, and arthritis.
5.1.1
Sensorimotor function assessments (PPA)
The Physiological Profile Assessment (PPA) is a falls risk assessment tool comprising five
complementary physiological measures; vision, reaction time, proprioception, lower extremity
strength and balance. Contrast vision was tested using the Melbourne Edge Test (MET) which has
been reported to be more important in predicting fallers when compared to visual acuity letter tests.
Using an iPad the MET contains 15 circular patches containing edges with reducing contrast.
Subjects wore their usual reading glasses when doing the test. A response card was used and a
response was forced (Figure 5.1A). The last correct (lowest contrast) response was recorded.
70
Reaction time was assessed using a simple reaction time paradigm, using a light stimulus
and depression switch (by the dominant hand) as the response. Subjects were given five practice
and 10 experimental trials. Reaction time was recorded in milliseconds (Figure 5.1B).
Quadriceps strength was measured using a spring gauge. A strap (with padding) was placed
around the subject’s dominant leg approximately 10cm above the lateral malleolus, with the hips in
90° flexion and the test knee 90°. The subject was asked to extend the knee at a moderate pace and
forcefully push against the strap as strongly as possible in three experimental trials. The best result
was entered (Figure 5.1C).
Joint position sense (a measure of proprioception) was tested using an apparatus based on a
design by De Domenico and McCloskey. With eyes closed, subjects attempted to extend both legs
and simultaneously place the big toe of the right foot on the right side of a Perspex sheet (60x 60 x
1cm) and the big toe of the left foot on the corresponding position on the left side of the sheet. The
Perspex was mounted vertically with the apex between the knees and errors in matching toes was
measured in degrees from the knee joints [198] (Figure 5.1D).
Balance was assessed on a firm surface and on 15 cm medium density foam mat with
subjects barefoot. Sway was measured using a swaymeter that measures displacement of the body at
the level of the waist. The device consisted of a rod attached to the subject at the waist level by a
firm belt. The rod was 40 cm in length and extended behind the subject. An iPad was positioned
behind the subject. The height was adjusted so that the rod was horizontal and the pen (attached at
the end of the rod) could record movement. Before beginning the test, the subjects standardised
their position by marching on the spot. They were instructed to look ahead and stand as still as
possible for 30 seconds. Mediolateral and anteroposterior displacement and total sway path was
recorded in millimetres (Figure 5.1E).
The five PPA components are weighted to compute a composite PPA fall risk score
expressed in standard (z-score) units, with high scores indicating poorer physical performance. In
71
multivariate models, weighted contributions from these five variables provide a fall risk score that
can predict community-dwelling older people at risk of multiple falling with 75% accuracy over a
12-month period [11].
Figure 5.1. PPA. A Melbourne Edge Test: contrast sensitivity; B Reaction time tests (hand); C Muscle strength tests
(knee extension); D Proprioception; E Postural sway tests standing on a foam rubber mat.
5.1.2
Cognitive assessment and Health life stile questionnaire
Each participant completed a questionnaire, to determine demographic and health details
including age, gender, number of falls in the last 12 months, number and type of medications, visual
acuity, physical activity levels, gait aids, medical conditions relevant to falls (e.g. arthritis, syncope,
diabetes and depression). Furthermore, old participants were assessed with the Montreal Cognitive
Assessment (MOCA) in order to verify the eligibility criteria. The Montreal Cognitive Assessment
(MoCA) is a rapid screening instrument for mild cognitive dysfunction. The total score has a range
from 0 to 30 points; a score of 26 or above is considered normal. Each participant was assessed
within different cognitive domains: attention and concentration, executive functions, memory,
language, visuo-constructional skills, conceptual thinking, calculations and orientation.
72
5.1.3
Optic flow stimuli
Participants stood barefoot in a dark room facing a projector screen. Stimuli were randomly
presented by a video projector (Benq MP720p) positioned 420 cm behind a translucent (retroprojection) video screen (3x2.5m), positioned 115 cm from the participant, covering 107x74° of the
visual field. The dots had a mean width of 0.9° and moved on the screen at a speed of 5°/s. Radial
Expansion and Radial Contraction stimuli were presented, simulating a movement in forward and
backward direction respectively. Full screen optic flow stimuli were made by white dots moving on
a black background, using in-house software programmed in Matlab (The Mathworks Inc.) (Figure
5.2). In addition to creating the optic flow stimulation, this software also synchronized data
acquisition from the Kistler force plate (Kirstler Inc. Intrumente AG Winterthur) and Vicon motion
capture system (Vicon Nexus 1.8.3).
.
Figure 5.2. Optic flow stimuli. Sketch of radial and random optic flow. Arrows represent the velocity vectors of moving
dots. a Static Dots b Contraction c static dots d Expansion. All stimuli were made by white dots of a mean of 0.9° in
diameter, retro-projected on a black screen at a perceived forward or backward speed of 5°/s. The focus of expansion was
in the centre of the screen. The screen covered 107x74° of visual field. Subjects were 115 cm away from the screen.
73
5.1.4
Experimental protocol
Before the beginning of each experiment, subjects were fitted with a singlet and running
shorts pants. Small, spherical reflective markers (diameter of 14 mm) were attached to the skin at
specific locations on the body using double-sided tape and a headband, according to Vicon’s
standard Full body Plug-in-gate marker set: lateral malleolus, second metatarsal head and
calcaneous (ankle), shank and lateral malleolus tibia (knee), thigh, posterior superior iliac spine and
anterior superior iliac spine (hip), sternum, T10, clavicle, C7 and right shoulder (thorax), left and
right front and left and right back (head) (see Table 5.2). Anthropometry measures of height (mm),
weight (Kg), distance between anterior superior iliac spine (mm), knee and ankle width (mm) and
leg length (mm) were taken for each participant in order to accurately reconstruct the body
alignment with the Vicon motion capture system.
After this preliminary set-up, subjects were asked to stand on the force plate placed in front of the
screen and the FOE was adjusted to their eye level. Calibration of the participant was made before
data collection. In this situation subjects were asked to stand still and look straight ahead with their
arms crossed on the chest for 5 seconds in order to caputre and label body markers. After this last
step each subject was instructed to maintain an upright posture on a Kistler force platform with the
arms crossed over their chests and look at the center of the screen. In order to prevent injuries,
especially with the older participants, a research assistant was positioned behind the participant to
assist them if they lost balance. Each trial lasted 60 seconds (Figure 5.3).
During the first 30s (baseline condition), a still image of dots was presented, while in the last 30s
the dots moved as per the optic flow stimulation. Three trials for each condition were acquired, and
in between each trial, participants rested on a chair for 2 minutes in order to prevent fatigue.
74
Figure 5.3. Representation of the experimental set-up.
Stabilometry (forces measured underfoot) and kinematics (body fixed marker positions) data
were acquired in each trial. Ground reaction forces were collected while participants stood on a
calibrated 400600mm Kistler force plate. Force plate data were acquired using a CODAmotion 64
channel analogue interface (Charnwood Dynamics, UK), sampling at 1000Hz and low-pass filtered
at 15 Hz offline. The total area range in antero-posterior oscillation was computed, which is the
enclosed area covered by the COP as it oscillates within the base of support [54, 58, 140, 176, 177]
using custom Matlab software (The Mathworks, Inc). The mean difference of the COP position in
the AP direction between the baseline and optic flow conditions (COPb-f) were calculated in order to
quantify the change in postural oscillation during the visual stimulation. The path length on the
antero-posterior direction (PL AP), which is the total distance travelled by the COP, was calculated
in order to quantify the postural control during the stimuli. The mean power frequency (MPF AP)
reflects a global shift in the power distribution and is operationalised as the ratio between the
weighted products of frequency, and the power in each frequency component and the total power.
The root mean square RMS of the COP velocity was computed in order to calculate the magnitude
75
of the body sway velocity. The RMS was calculated over the 30 s of baseline and 30s of optic flow
for each trial of each stimulus in each subject. Then, values for all subjects in each condition and
group were averaged. Based on the Winter inverted pendulum paradigm [54], human sway back and
forth while standing erect on a force plate, creates an ankle torque that reflects the magnitude of the
body sway in the antero-posterior plane by the ankle musculature. Hence measuring the horizontal
force components reflect the acceleration of the centre of mass movements and reflect the
performance of the postural control system. The standard deviation (STD) of the horizontal forces
was computed in order to calculate the magnitude of those forces during the baseline and the visual
stimulation [199].
For kinematic measures, marker positions in the sagittal plane were acquired using sixcamera Vicon Bonita (B10) 1 megapixel cameras, (Vicon). Maker positions were sampled at 100
Hz and processed using the Vicon motion capture system Plug-in-gait Full body model in Vicon
Nexus software. In order to provide insights into the single axis of rotation assumption associated
with the inverted pendulum model, the time-varying difference in the ankle, torso and head and
sway angles in the sagittal plane were exported following data processing in Vicon Nexus software.
The ankle angle was defined as the angle between the support surface and the shank (segment
defined by ankle and knee markers). The trunk angle was defined as the angle between a vertical
line and a line created by connecting points at the greater trochanter and the acromion (segment
defined by hip and C7 neck markers) [200]. The head angle was defined from the head segment
relative to the vertical defined by laboratory coordinate system. The mean, range and coefficient of
variation (CV) of each angle in the sagittal plane over the 30 s of baseline and 30s of optic flow for
each trial of each stimulus in each subject were computed. The range was expressed as the
difference between the maximum and minimum angles of movement in sagittal plane. The
coefficient of variation (CV) was expressed as the ratio of the standard deviation to the mean in
order to quantify the degree of variation of each angle.
76
Table 5.2. Full body markers position
LFHD
RFHD
LBHD
Left front head
Right front head
Left back head
RBHD
Right back head
C7
T10
CLAV
STRN
RBAK
7th Cervical Vertebrae
10thThoracic Vertebrae
Clavicle
Sternum
Right Back
LASI
RASI
LPSI
PSI
Left ASIS
Right ASIS
Left PSIS
Right PSIS
LKNE
RKNE
LTHI
Left knee
Right knee
Left thigh
RTHI
Right thigh
LANK
Left ankle
RANK
Right ankle
LTIB
Left tibial wand
RTIB
Right tibial wand
LTOE Left toe
RTOE Right toe
LHEE Left heel
RHEE Right heel
Head Markers
Located approximately over the left temple
Located approximately over the right temple
Placed on the back of the head, roughly in a horizontal plane of
the front head markers
Placed on the back of the head, roughly in a horizontal plane of
the front head markers
Torso Markers
Spinous process of the 7th cervical vertebrae
Spinous Process of the 10th thoracic vertebrae
Jugular Notch where the clavicles meet the sternum
Xiphoid process of the Sternum
Placed in the middle of the right scapula. This marker has no
symmetrical marker on the left side. This asymmetry helps the
auto-labelling routine determine right from left on the subject
Pelvis
Placed directly over the left anterior superior iliac spine
Placed directly over the right anterior superior iliac spine
Placed directly over the left posterior superior iliac spine
Placed directly over the right posterior superior iliac spine
Leg Markers
Placed on the lateral epicondyle of the left knee
Placed on the lateral epicondyle of the right knee
Place the marker over the lower lateral 1/3 surface of the thigh,
just below the swing of the hand, although the height is not
critical.
Place the marker over the higher lateral 1/3 surface of the thigh,
just below the swing of the hand, although the height is not
critical.
Placed on the lateral malleolus along an imaginary line that
passes through the transmalleolar axis
Placed on the lateral malleolus along an imaginary line that
passes through the transmalleolar axis
Similar to the thigh markers, these are placed over the lower 1/3
of the shank to determine the alignment of the ankle flexion axis
Similar to the thigh markers, these are placed over the higher 1/3
of the shank to determine the alignment of the ankle flexion axis
Foot Markers
Placed over the second metatarsal head, on the mid-foot side of
the equinus break between fore-foot and mid-foot
Placed over the second metatarsal head, on the mid-foot side of
the equinus break between fore-foot and mid-foot
Placed on the calcaneous at the same height above the plantar
surface of the foot as the toe marker
Placed on the calcaneous at the same height above the plantar
surface of the foot as the toe marker
77
5.1.5
Statistical analysis
For continuous variables with skewed distributions, data were log-transformed and
parametric analyses were conducted on the normalised data. To evaluate the influence of optic flow
stimuli on postural control, a repeated-measures ANOVA was performed on the COP and angle
parameters with optic flow stimuli (expansion and contraction) and age-group (young and old) as
fixed factors. An independent t-test was used to compare of the change in COP position between
the optic flow and baseline conditions between the young and old groups. A second repeatedmeasures ANOVA was then performed in the old group alone with fall risk (based on a PPA cutpoint of 0.6 [201] as the fixed factor.
5.2 Results
Descriptive statistics for each of the sensorimotor measures (PPA) are provided in Table 5.3.
The older participants showed a mean PPA fall risk score of 0.70 with most (57.9%) reporting a
moderate fear of falling.
5.2.1
Effect of stimuli and age on postural responses: stabilometric measures
For both expansion and contraction stimulus conditions, all COP parameters (excepting
MPF AP for the expansion condition, p=0.057) showed significant main effects of stimuli condition
(flow effect), as summarised in Table 5.4 and 5.5 indicating an increase of area range in the anteroposterior plane (AREA AP) and magnitude velocity with a consequent reduction of the mean power
frequency during the stimuli relative to baseline.
78
The results of the between-subject analysis (RM ANOVA) (flow x age) showed the AREA
AP (p≤0.001), but no age effect or age x flow interaction. With the independent t-test no difference
emerged in the young and old of the COPb-f.
Table 5.3. Descriptive statistics for the cognitive and sensorimotor measure (PPA)
n=19
Variable
Mean (±SD)
Age
71.79 (±5.05)
MOCAa
27.00 (±1.67)
METb
21.32 (±2.87)
Reaction Timec
233.55 (±45.36)
Sway Pathd
172.63 (±46.47)
Proprioceptione
2.12 (±1.17)
Knee Extentionf
25.50 (±8.10)
Falls Riskg
0.70 (±0.83)
a score 0 to 30. score < 26 indicate cognitive impairment
b Melbourne Edge test cotrast sensitivity, dB log contrast
c ms
d mm2 traversed by swaymeter pen in 30 s score
e degree difference in matching
f Nm
g expressed in standard (z-score) units, with high scores indicating poorer physical performance.
Regarding the path length (PL AP) results showed a main flow effect during both stimuli
(contraction: p≤0.001; expansion: p≤0.001) indicating a significant reduction of the path length
during the flow in both young and old. Moreover, the PL in old was significantly higher than in the
young in both stimuli and baseline as revealed by significant age effect between the two groups
(contraction: p≤0.001; expansion: p≤0.001). A flow effect was found in the mean power frequency
(MPF AP) during contraction stimulus (p=0.003) while during expansion results showed a trend of
(p=0.057) suggesting a possible modulation effect induced by direction of visual stimulation. An
79
age effect emerged in MPF AP (contraction: p≤0.001; expansion: p=0.002). Furthermore, a strong
interaction effect was found in MPF AP during both visual conditions (contraction: p≤0.017;
expansion: p=0.002) (Figure 5.4).
Table 5.4. COP parameters in young and old group during Contraction stimulus
Contraction
Young
N
Baseline
Optic Flow
N
AREA AP
17
1.29±0.08
1.41±0.19
18
PL AP
17
203.67±66.62
119.19±38.19
18
MPF AP
17
-0.54±0.11**
-0.53±0.15**
18
STD Force AP
17
0.21±0.15**
0.13±0.16**
19
RMS Vel AP
17
0.93±0.12**
1.02±0.16**
19
Baseline
Old
Optic Flow
p
Flow effect .000
1.32±0.11
1.46±0.12
Age effect .295
Int. effect .706
Flow effect .000
302.23±98.31
172.59±76.74
Age effect .000
Int. effect .635
Flow effect .003
-0.43±0.12**
-0.32±0.12**
Age effect .000
Int. effect .017
Flow effect .000
Age effect .000
0.06±0.15**
0.12±0.16**
Int. effect .001
Flow effect .000
1.09±0.12**
1.29±0.16**
Age effect .000
Int. effect .024
*planned comparison p<0.05 compared with baseline
Table 5.5. COP parameters in young and old group during Expansion stimulus
Expansion
Young
N
Baseline
Optic Flow
N
AREA AP
17
1.32±0.14
1.44±0.13
18
PL AP
17
203.01±67.58
120.15±33.53
18
MPF AP
17
-0.5±0.10***
-0.52±0.12***
18
STD Force AP
17
0.69±0.24***
0.86±0.34***
18
RMS Vel AP
17
0.94±0.13***
1.04±0.150***
19
Old
Baseline
Optic Flow
p
Flow effect .000
1.32±0.13
1.49±0.13
Age effect .571
Int. effect .194
Flow effect .000
293.03±95.55
174.54±75.28
Age effect .001
Int. effect .721
Flow effect .057
-0.43±0.11***
-0.34±0.13***
Age effect .002
Int. effect .002
Flow effect .000
0.82±0.21***
1.34±0.34***
Age effect .002
Int. effect .000
Flow effect .000
1.08±0.12***
1.32±0.16***
Age effect .000
Int. effect .000
*planned comparison p<0.05 compared with baseline
The magnitude of the force (STD Force AP) reveals a main age effect during both visual
stimulations (contraction: p≤0.001; expansion: p≤0.002) and an interaction effect (contraction:
p≤0.001; expansion: p≤0.001) indicating an increase of the effort during the flow in old than in
young see Figure 5.5. Lastly, the magnitude of the velocity (RMS VEL AP) showed a main effect
of flow (contraction: p≤0.001; expansion: p≤0.001) an age effect (contraction: p≤0.001; expansion:
p≤0.001) and an interaction and effects (contraction: p≤0.024; expansion: p≤0.001) (Figure 5.6). As
80
in STD Force AP, RMS VEL AP showed a greater magnitude in old population than in young. An
example of the COP magnitude velocity and force evoked by the different is shown in Figure 5.75.8.
***
Figure 5.4. Mean Power Frequencies (MPF) in young and old during baseline and the optic flow. A. Represent
the interaction effect during contraction visual stimuli. B. Represent the interaction effect during Expansion
visual stimuli. Asterisks indicate significant values (bivariate Pearson correlation, p<0.05).
81
***
***
Figure 5.5 Horizontal force magnitude in young and old during baseline and the optic flow. A. Represent the interaction
effect during contraction visual stimuli. B. Represent the interaction effect during Expansion visual stimuli. Conventions as in
Figure 5.4.
82
igure 5.6 Velocity magnitude in young and old during baseline and the optic flow. A. Represent the interaction
effect during contraction visual stimuli. B. Represent the interaction effect during Expansion visual stimuli.
Conventions as in Figure 5.4.
83
Figure 5.7 Example of antero-posterior horizontal force traces recorded for both young and old group during
A contraction and B expansion optic flow stimulation and baseline. Data set: young: subject n. 03, male, age
36 and old: subject n 31, male age 80.
84
Figure 5.8 Example of antero-posterior magnitude of velocity recorded for both young and old group during A
contraction and B expansion optic flow stimulation and baseline. Data set: young subject n. 03, male, age 36
and old subject n 31, male age 80.
85
5.2.2
Effect of stimuli and age on postural responses: kinematic measures
Results from the repeated measures ANOVAs of the kinematic parameters are summarised
in Tables 5.6 and 5.7. The mean angle of the head on the sagittal plane reveals a flow effect during
both stimulations (contraction: p≤0.002; expansion: p≤0.001) and an age effect during contraction
stimuli (p=0.040) while showing a trend during expansion (p=0.064), indicating a reduction of
head movement during the flow stimulation in both young and old. Regarding the mean ankle angle
on the sagittal plane results showed no significant flow effect during contraction and a trend
(p=0.052) during expansion. Although no age and interaction effect were found in the mean ankle
angle, data seems suggest an increase ankle mean during the flow in both young and old and in this
group the mean of ankle angle was bigger than in young. A trend in the flow effect on the mean
thorax angle was found in both stimuli conditions (contraction: p=0.050; expansion: p =0.062),
while no age and interaction effect were emerged. The results of the between-subject analysis (RM
ANOVA) showed that among the Kinematic parameters, the range of the ankle showed a flow
effect only during expansion (p≤0.003). As for the mean ankle angle, although any age and
interaction effect were found, data reveal an increase of the ankle range during the flow in young
and old group, and this group the ankle range was slightly bigger than young. No main flow, age or
interaction effects were found in the mean head and thorax range during both stimuli. Hence any
significant results, this data suggest that old and young reduce the head movement than those the
thorax. Lastly, the coefficient of variation COV in head, ankle and thorax angles showed no stimuli,
age or interactions effect during expansion or contraction conditions.
86
Table 5.6. Kinematic parameters in young and old group during Contraction stimulus
Contraction
Young
N
Baseline
Optic Flow
N
Mean Ankle angle
13
2.65±3.21
2.79±3.31
15
Mean Head angle
11
13.21±6.82
11.44±7.92
13
Mean Thorax angle
12
5.01±2.14
-3.66±4.02
13
COV Ankle angle
13
0.03±0.23
0.03±0.23
16
COV Head angle
12
0.09±0.08
0.04±0.37
14
COV Thorax angle
12
-0.1±0.05
-0.03±0.24
14
Range Ankle angle
12
1.01±0.41
1.25±0.92
14
Range Head angle
11
3.73±1.92
3.41±1.60
13
Range Thorax angle
11
1.93±0.71
1.89±0.46
14
Table 5.7. Kinematic parameters in young and old group during Expansion stimulus
Expansion
Young
N
Baseline
Optic Flow
N
Mean Ankle angle
13
2.63±3.28
2.76±3.37
16
Mean Head angle
11
12.85±6.79
12.12±6.68
15
Mean Thorax angle
12
5.39±2.24
-5.36±2.44
14
COV Ankle angle
13
0.06±0.17
0.01±0.19
17
COV Head angle
12
0.10±0.12
0.11±0.13
15
COV Thorax angle
12
0.10±0.06
-0.10±0.07
15
Range Ankle angle
10
0.95±0.32
1.12±0.38
16
Range Head angle
12
3.67±1.81
3.60±1.56
15
Range Thorax angle
12
0.28±0.18
0.33±0.19
15
Old
Baseline
Optic Flow
p
Flow effect .168
4.23±2.61
5.42±4.20
Age effect .091
Int. effect .275
Flow effect .002
20.82±9.27
18.93±9.56
Age effect .040
Int. effect .905
Flow effect .050
-4.83±3.39
4.09±3.45
Age effect .921
Int. effect .555
Flow effect .476
0.15±0.21
0.08±0.07
Age effect .136
Int. effect .544
Flow effect .776
0.01±0.25
0.09±0.17
Age effect .844
Int. effect .277
Flow effect .263
-0.04±0.18
-0.02±0.31
Age effect .704
Int. effect .513
Flow effect .130
1.05±0.43
1.27±0.57
Age effect .873
Int. effect .944
Flow effect .349
3.76±2.05
3.46±0.92
Age effect .949
Int. effect .974
Flow effect .269
1.88±0.59
2.35±0.73
Age effect .268
Int. effect .185
*planned comparison p<0.05 compared with baseline
Old
Baseline
Optic Flow
p
Flow effect .052
4.30±2.85
4.52±2.65
Age effect .138
Int. effect .623
Flow effect .001
19.46±8.62
17.88±9.12
Age effect .064
Int. Effect .165
Flow effect .062
-4.19±3.70
-3.42±3.82
Age effect .218
Int. Effect .085
Flow effect .451
0.07±0.05
0.088±0.15
Age effect .423
Int. effect .254
Flow effect .669
0.08±0.11
0.09±0.12
Age effect .672
Int. effect .982
Flow effect .909
-0.05±0.36
-0.03±0.25
Age effect .291
Int. effect .889
Flow effect .003
0.97±0.40
1.36±0.49
Age effect .382
Int. effect .196
Flow effect .460
4.70±2.73
4.24±2.11
Age effect .276
Int. effect .557
Flow effect .154
0.25±0.18
0.33±0.15
Age effect .802
Int. effect .707
*planned comparison p<0.05 compared with baseline
87
5.2.3
Effect of stimuli and fall risk on postural responses: stabilometric and kinematic measures
Repeated measures ANOVAs were used to test whether the older adults at high risk and low
risk of falls showed different postural responses to the visual stimulation of expansion and
contraction. All stabliometric and kinematic parameters for high and low risk older adults are
summarised in Table 5.8-5.11. The stabilometric parameter showed a flow effect (p≤0.001)
indicating an increase of area, force and velocity with a decrease of sway path and mean power
frequency during both stimuli compared with the baseline. No main falls risk or interaction effects
were found in those stabilometric parameters. Regarding the kinematic analysis, the data reveal a
flow effect in the mean of the ankle (p=0.049) only during expansion. No fall risk effect was found
during both visual conditions, while an interaction effect (p=0.046) emerged from the analysis,
indicating no changes between the baseline and the flow in people at high risk of falls than those
with low risk. Head and thorax angle reveal a main flow effect during contraction and expansion
(contraction: p≤0.012; expansion: p≤0.05), indicating that both groups seems to reduce the head
movement than those the thorax. No fall risk or interaction effects were found on the mean head and
thorax angles. The range reveal a flow effect (p=0.006) at the ankle angle only during expansion.
No fall risk and interaction effects were found in ankle range. On head range no significant flow,
fall risk or interaction effect emerged. A flow effect trend on the Thorax angle was found during
both stimulations (expansion: p=0.072; contraction: p=0.076) while no fall risk or interaction effect
were found. The variability (CV) showed no flow, age or interaction effect on the ankle, head and
thorax angles during each stimulation compared to the baseline.
88
Table 5.8. COP parameters in old at high and low risk of falls group during Contraction stimulus
Contraction
Low Fall Risk
N
Baseline
Optic Flow
N
Baseline
AREA AP
9
1.29±0.08
1.43±0.07
9
PL AP
9
300.50±60.90
154.27±56.83
9
MPF AP
9
-0.41±0.12
-0.32±0.10
9
STD Force AP
10
-0.05±0.10
0.10±0.09
9
RMS Vel AP
10
1.10±0.08
1.26±0.12
9
High Fall Risk
Optic Flow
p
Flow effect .000
Fall Risk effect
1.35±0.13
1.48±0.16
.301
Int. effect .976
Flow effect .000
Fall Risk effect
303.96±129.70
190.90±92.36
.665
Int. effect .215
Flow effect .003
Fall Risk effect
-0.44±0.12
-0.33±0.13
.729
Int. effect .805
Flow effect .000
Fall risk effect
-0.05±0.19
0.15±0.22
.783
Int. effect .387
Flow effect .000
Fall risk effect
1.09±0.16
1.32±0.20
.763
Int. effect .274
*planned comparison p<0.05 compared with baseline
Table 5.9. COP parameters in old at high and low risk of falls group during Expansion stimulus
Expansion
Low Fall Risk
N
Baseline
Optic Flow
N
Baseline
AREA AP
9
1.28±0.09
1.48±0.09
9
PL AP
9
284.27±58.02
161.34±62.17
9
MPF AP
9
-0.43±0.08
-0.35±0.16
9
STD Force
AP
10
-0.09±0.08
0.12±0.079
9
RMS Vel AP
10
1.07±0.07
1.29±0.122
9
High Fall Risk
Optic Flow
p
Flow effect .000
1.35±0.157
1.51±0.17
Fall Risk effect.395
Int. effect .339
Flow effect .000
301.79±125.95
187.74±88.24
Fall Risk effect.712
Int. effect .379
Flow effect .001
-0.44±0.13
-0.33±0.10
Fall Risk effect .965
Int. effect .572
Flow effect .000
-0.05±0.19
0.16±0.21
Fall risk effect .604
Int. effect .994
Flow effect .000
1.08±0.17
1.34±0.21
Fall risk effect .685
Int. effect .413
*planned comparison p<0.05 compared with baseline
89
Table 5.10. Kinematic parameters in old at high and low risk of falls group during Contraction stimulus
Contraction
Low Fall Risk
N
Baseline
Optic Flow
N
Baseline
Mean Ankle angle
7
3.62±2.28
5.72±5.74
8
Mean Head angle
6
23.66±10.43
21.03±11.27
7
Mean Thorax angle
7
-4.14±3.12
-3.42±2.50
6
COV Ankle angle
8
0.17±0.27
0.07±0.09
8
COV Head angle
7
0.09±0.14
0.12±0.23
7
COV Thorax angle
7
-0.02±0.25
0.04±0.44
7
Range Ankle angle
6
0.96±0.47
1.006±0.47
8
Range Head angle
6
0.5±0.23
0.49±0.18
7
Range Thorax angle
7
0.29±0.12
0.31±0.13
7
p
Flow effect .170
5.16±2.63
Fall Risk effect .864
4.76±2.92
Int. effect .337
Flow effect .009
18.38±8.12
17.12±8.29
Fall Risk effect .400
Int. effect .291
Flow effect .012
-5.64±3.79
4.87±4.45
Fall Risk effect.458
Int. effect .923
Flow effect .317
0.12±0.14
0.09±0.06
Fall Risk effect .738
Int. effect .601
Flow effect .275
-0.07±0.31
0.06±0.07
Fall risk effect .214
Int. effect .496
Flow effect .640
-0.06±0.07
-0.09±0.10
Fall risk effect .525
Int. effect .205
Flow effect .320
1.12±0.42
1.47±0.58
Fall risk effect .119
Int. effect .427
Flow effect .931
0.55±0.07
0.52±0.25
Fall risk effect .642
Int. effect .797
Flow effect .076
0.21±0.15
0.38±0.13
Fall risk effect .941
Int. effect .187
*planned comparison p<0.05 compared with baseline
Table 5.11. Kinematic parameters in old at high and low risk of falls group during Expansion stimulus
Expansion
Low Fall Risk
N
Baseline
Optic Flow
N
Baseline
Mean Ankle angle
7
3.27±2.19
3.78±2.30
9
Mean Head angle
7
22.41±9.40
20.49±10.18
8
Mean Thorax angle
7
-4.40±2.74
-3.45±2.89
7
COV Ankle angle
8
0.06±0.062
0.04±0.19
9
COV Head angle
7
0.07±0.12
0.09±0.14
8
COV Thorax angle
7
-0.11±0.48
0.036±0.30
8
Range Ankle angle
8
0.79±0.21
1.23±0.37
8
Range Head angle
7
0.55±0.16
0.54±0.18
8
Range Thorax angle
7
0.222±0.08
0.35±0.15
8
High Fall Risk
Optic Flow
High Fall Risk
Optic Flow
p
Flow effect .049
5.10±3.16
5.10±2.89
Fall Risk effect .270
Int. effect .046
Flow effect .003
16.87±7.51
15.60±8.06
Fall Risk effect .270
Int. effect .465
Flow effect .004
-3.99±4.70
-3.39±4.83
Fall Risk effect.913
Int. effect .431
Flow effect .745
0.09±0.05
0.12±0.10
Fall Risk effect .268
Int. effect .406
Flow effect .727
0.09±0.10
0.09±0.11
Fall risk effect .833
Int. effect .537
Flow effect .838
-0.001±0.22
-0.09±0.20
Fall risk effect .537
Int. effect .349
Flow effect .006
1.16±0.47
1.49±0.58
Fall risk effect .105
Int. effect .645
Flow effect .390
0.66±0.28
0.61±0.23
Fall risk effect .457
Int. effect .520
Flow effect .072
0.29±0.23
0.31±0.15
Fall risk effect .868
Int. effect .177
*planned comparison p<0.05 compared with baseline
90
5.3 Discussion
Vision is a key input for stabilizing posture, providing continually updated information
regarding the position and movements of body segments in relation to each other and the
environment. The contribution of visual inputs to balance, such as moving visual field or conflicting
visual inputs, has been previously investigated [11]. Such studies have reported that moving visual
fields can induce a strong perception of self-motion and that significant increases in body-sway are
observed when visual input is misleading. Ageing is associated with a reduction of visual
performance and older people display more postural sway during quiet stance indicating reduced
postural stability and increased risk of falling [160]. The ability to stand steadily when exposed to
moving visual information also declines with age and may be an additional risk factor for falls in
older age. The aim of this research was to assess how optic flow stimuli contribute to the control of
stance. Participants were tested full-field expansion and contraction to determine the extent to
which optic flow stimuli influence postural control compared with static (i.e. baseline) visual
stimulation in young and older people and in older people at high and low risk of falls.
This study revealed different effects on postural control in the young and old groups during
optic flow stimulation. As expected, the younger group displayed better postural control than the
older group during baseline and visual stimulation, consistent with previous study findings [202].
The main flow effect emerged by the results, suggest that young and old seems to increase the range
of the area, path length and velocity from a situation of static visual field (i.e. baseline) to a
movement of visual field (i.e. optic flow), indicating that both groups are able to make on-line
postural adjustment to changing static to dynamic visual scene. Moreover the force and velocity
results point out a strong interaction effect (age x stimulus) of the postural control. This is a first
indication of age differences in the postural control during visual stimulation, due to greater
dependence on vision in the older group may relate to detrimental aging effects on the
proprioceptive system. The data revealed that while the young spent less effort to maintain their
91
balance during baseline and flow, the old group attempt to react at the visual stimulation increasing
the force magnitude in order to maintain the centre of gravity (COG) within the base of support.
Another strong interaction was found in the mean power frequency (MPF) that is a reliable measure
for stiffness strategy [203, 204]. Given that physiological and mechanical limitations that have been
associated with aging, including insufficiency muscle response and stiffer body mechanisms, may
underlie the more rapid and bigger postural response to visual flow in elderly [204]. Young showed
a reduction of MPF during the flow while the old group increase their stiffness suggesting that
exposure to visual field motion conditions induces, has been cited as, a compensatory response to
balance perturbations in the elderly [205, 206]. Moreover no age or interaction effects were found
on the COP and on the PL. These results taken together revealed that although the old group showed
a modulation effect of the optic flow, they increase the ankle stiffness during quiet stance when
compared to young adults. Morasso and Sanguineti described this postural behaviour as an effective
stiffness strategy in which there is a combination of three contributing systems: the passive
elasticity at the ankle, the torque due to segmental reflexes and anticipatory or voluntary torque
generated by descending motor commands [207]. The different postural strategies exhibited by the
young and older groups may arise from age-related physiological changes affecting balance
resulting from a diminution in the capacity of brainstem centres controlling posture, integrate
multisensory cues and to select appropriate sensory information [208].
Furthermore, the young group showed a slight increase of MPF during the contraction
stimulus and a reduction of MPF during the expansion condition indicating a modulation in relation
to the direction of the optic flow. In contrast, the older group showed an increase of MPF during
both stimulations, suggesting a possible decline in the neural processing of self-motion perception.
These results are in line with findings from Berard et al. 2009 who reported that older adults were
less able to use optic flow cues to guide their locomotion [209]. Furthermore, using a test of relative
92
heading perception, Warren et al. 1989, found a small but statistically significant age-related decline
of about 1° in the ability to see where one is heading [210].
Regarding the kinematic analysis, the main postural differences between these young and
old group were found at the head and thorax joints. Head stability in space is a fundamental goal of
the postural control system [67, 211].Previously was reported that older adults rely more on visual
cues than young adults and are therefore more unstable with greater head movement [212-214].
Moreover, increased head movement during postural disturbances [215] and poor head stability has
been associated with falls in this population [216]. Other study have proposed that an head
stabilization on trunk strategy needed in orther to reduces the anticipatory correction of the head
position because head and trunk tend to move as a single segment [217, 218]. Our results showed a
reduction of head movement connected with a consequently increase of trunk mean range
movement during contraction stimuli in old people indicating an use of the head stabilization on
trunk strategy. While during expansion stimuli young and old showed the same head and trunk
postural control suggesting an age-related modulation of optic flow direction on postural control. In
fact, for forward movements (that should be evoked by the expansion stimulus) that are more
common in our daily life, old group seems to react similarly as the young, while for backward
moments (that should be evoked by the contraction stimulus), usually less common, young react
prevalently with the ankle strategy, while old try to compensate increasing also trunk movement in
order to adjust their balance. This further suggests an age-related difficulty in maintaining postural
control during different direction of visual optic flow stimulation. A previous paper has reported
that a more mobile trunk leads to reduced stability of the head in space because elderly individuals
lock their heads to their trunks [215]. Moreover, older participants seem to use this head locking
strategy to decrease the controlled degrees of freedom, resulting in greater torques transmitted to
the head [219]. These results suggest that a decrease in sensitivity in the optic flow processing
might increase fall risk by old people [215].
93
The analysis of risk of falls on optic flow suggest that people with low risk of falls are more
able to use an ankle strategy in order to adjust the balance when exposed to the flow stimuli than
those with high risk of falls. Although these results did not show significant differences between
high and low falls risk population, the data seems to suggest that the people at high risk of falls
spent more effort to try to stabilize their posture increasing the velocity magnitude suggesting that
the postural stabilization maybe due to a major stiffness related to ankle and head while the torso
seems to be constantly looking for the suitable compromise to ensure a lower loss of balance
One possible explanation should be that elderly may underestimate the disequilibrium that is
signalled by the visual, vestibular and proprioceptive systems. Moreover, for this is that frail people,
as people at high risk of fall, may be less able to produce sufficient ankle torques as a result of a
distal muscle weakness or decreased proprioceptive acuity at the ankle. It is acknowledged that this
study has certain limitations. First, the sample size was relatively small with consequent reduced
statistical powerful to reveal all postural characteristics that may differ between older people at low
and high risk of falls. Second, the older participants were recruited from a volunteer database and
had a mean PPA fall risk score of 0.70, indicating that the sample was relatively healthy, without
any older people at significantly high fall risk. Therefore, the findings, with respect to the fall risk,
need to be considered with caution and larger studies are required in older participants with a
greater range of functional performance to definitively determine the influence of optic flow on fall
risk in the elderly.
94
5.4 Conclusion
This study provides new evidence on the postural strategies used by young and old in the
control of stance under visual optic flow stimulation. The young had better postural control during
both the baseline and visual stimulation conditions than the old, and it was evident that during the
baseline and the optic flow conditions, the older group had increased MPF, horizontal forces and
velocity magnitudes indicating a stiffening postural strategy that is disadvantageous for maintaining
stance. Moreover, the older group seems to use more head stabilization on the trunk strategy during
visual stimulation for maintaining a balanced upright stance. It is possible speculate that the
detrimental age effect on proprioceptive and somatosensory systems in older may increases the
sensory thresholds to complex stimuli, inducing a greater reliance on visual inputs and making it
more difficult for them produce an appropriate postural response.
These findings indicate that the elderly are more visual dependent than their younger
counterparts, which may result from significant age-related declines in proprioceptive and
vestibular sense. Though no participants had clinically detectable peripheral sensory disorders,
these findings suggest age-related changes in central processing of the sensory input. Previous
studies have shown that increased sway velocity is a risk factor for decreased mobility [220], fear of
falling [221] and risk of falls [222]. In the current study, the sample size was insufficient to reveal
all postural characteristics resulting from optic flow stimulation that may differ between older
people at low and high risk of falls. Further studies should conduct similar work with larger samples
with a greater range of fall risk propensity. Such studies could also examine EMG activity in lower
limb muscle groups and complementary optic stimuli such as roll vection and optical illusions.
95
6
Overall discussions and conclusions
The aim of this work was to investigate the role of optic flow stimulation on postural
control. We first studied the different postural strategies adopted by male and female and the limb
asymmetries in those two populations during optic flow visual stimulation (Study I). Then, were
investigated the role of optic flow in young and old groups and in people at high and low risk of fall
(Study II).
In our daily life, the optic flow field is the main cue to control self motion and upright
position producing adequate motor responses while a subject interacts with the extrapersonal
environment [77]. Although the sway response is produced by moving visual stimuli, the oscillation
speed is generally lower in the presence of a visual stimulus compared to the absence of it [44]. The
apparent destabilizing effect of visual input on a steady subject is a measure of the compensatory
effect evident in case of real body movements like those simulated by optic flow. In this particular
situation, the body sway may be produced by the illusory self-movement perception or by an
automatic response integrated at a subcortical level.
Visual stimuli always evoke an excitatory input on postural muscles, but the stimulus
structure produces different postural effects. Peripheral optic flow stimuli stabilize postural sway,
while random and foveal optic flow provoke larger sway variability similar to those evoked in the
absence of visual stimulation. It is possible hypothesize that the dimension of the stimulated visual
field may differently activate the postural muscles. Subsequently, such muscle activity may involve
in a different way the contribution of each leg suggesting neural descending visuo-motor maps.
Such complex control mechanisms would lead to the most appropriate postural response to interact
with the extrapersonal environment.
The perception of each body segment relative to each other and to the environment is
important to maintain or correct posture. This is trained by the multisensory integrations (vision,
vestibular and proprioceptive) systems and kinaesthetic information [1, 81, 223]. Gurfinkel and
96
Levik (1978) reported that it is impossible estimate accurately the spatial orientation of each
segment, so that they proposed that the motor system may use an internal model of the body, the
body scheme, term previously introduced by Head (1920) [224, 225]. These Authors studied the
representation in the internal model of unconscious biomechanical characteristics (structural,
kinematic, dynamic) as well as the multi-sensorymotor components. It is well known that male and
female have differences in mechanical properties of the ligaments, joint kinematics, and skeletal
alignment. Authors reported that those gender differences may lie in the integration of multisensory
information. The visual feedback differently influences the neural control of body sway in males
and females suggesting that the two genders seems to use different postural alignment and they
adapt differently to cortical and corticospinal asymmetry leading to different behaviours of the right
and left limb. In particular, women produce co-contractions of the upper leg muscles using the
ankle joint to maintain postural stability. It is possible that the activation of dorsoextensor and
dorsoflexor during the trial caused a continuous oscillation in antero-posterior direction requiring
the generation of a stronger vertical force to keep postural stability and to avoid backward fall, as
reported in the Study I.
Balance control change with ageing [226]. Age-related changes in balance are attributed to
physiological and psychological factors such as fear of falling. Ageing is associated with a
reduction of visual performance and older people display more postural sway during quiet stance
indicating reduced postural stability and increased risk of falling [160]. Young people have a better
postural control than the older group during visual feedback. Old people spent a greater effort to
stabilize the posture during the optic flow, suggesting a neuronal processing decline, associated
with difficulty integrating multi-sensory information of the self-motion perception causing an
increased risk of falls. Although the results of this study are still preliminary, it is possible to
hypothesize that people with high and low risk of falls have peculiar postural strategies in order to
maintain the centre of mass within the base of support. Frail people are less able to produce
97
sufficient ankle torque as a result of a distal muscle weakness or for a decrease of sensitivity at the
ankle. So they try to stabilize the head with a greater thorax adjustments in order to find the right
comprise to prevent an eventually fall and stabilize their balance, as reported in the Study II.
98
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