GIUSEPPE RIVA (Ed.)
Virtual Reality in Neuro-Psycho-Physiology
1997, 1998 © Ios Press: Amsterdam, Netherlands.
Virtual Reality Therapy of Multiple Sclerosis
and Spinal Cord Injury:
Design Considerations for a Haptic-Visual
Interface
Morris Steffin
Swank Multiple Sclerosis Clinic and Foundation
13655 SW Jenkins Road
Beaverton, Oregon 97005
Tel: (503) 520-1050
E-mail: morste@delphi.com
Abstract. Multiple sclerosis and spinal cord injury patients can benefit by
interaction with a haptic-visual system to increase the accuracy of movements
in cases of spasticity, cerebellar tremor, and weakness. The device would
apply a counterforce to constrain the upper extremity to a force corridor, a
region of force/velocity space, designed to increase movement accuracy.
Execution of movements with counterforce assistance under certain conditions
improves accuracy and should enable patients to develop enhanced strategies
for dealing with the movement disorders resulting from their neurologic
deficits. Generation of appropriate force feedback requires dynamic
adjustment of feedback plant characteristics and integration of visuospatial
information in a virtual reality environment. Sensory augmentation, including
compensation for visual and proprioceptive loss, can theoretically also be
achieved with this approach. The underlying principles in the development of
such a system are presented.
1. Introduction
Multiple sclerosis (MS) is a multisystem disease whose manifestations result from
involvement of widely diverse functional and spatial components of the central nervous
system (CNS). The neurologic symptoms and deficits derive from partial disconnection
affecting all major motor and sensory central systems. Similarly, spinal cord injury (SCI)
patients suffer from the results of disconnection, but on a more localized level. However,
many of the problems from which they suffer arise from neurophysiologic derangements
(weakness and spasticity) that derive from disconnection effects like those found in MS.
Approaches to physical therapy of MS and SCI depend on two convergent approaches.
The first is neuroplasticity, the capability of the central nervous system to reprogram itself
at a neuropil level. The second is the burgeoning capability of virtual reality (VR) systems
that provide a multimodality (haptic, visual, auditory, tactile) stimulus-response
environment. This training and therapeutic milieu allows full utilization of the plastic
properties in an individual patient such that major increases in functionality should be
attainable in most cases. The basic principles of neuroplasticity and its use in VR
GIUSEPPE RIVA (Ed.)
Virtual Reality in Neuro-Psycho-Physiology
1997, 1998 © Ios Press: Amsterdam, Netherlands.
environments have been described [1]. This presentation expands the neurophysiologic and
quantitative description of the theory involved in construction of VR therapeutic and
training environments for MS and SCI.
Two benefits would accrue to patients from the development of these therapeutic
techniques. In certain patients, actual levels of functionality may improve. In nearly all
patients, greater exposure to useful and enjoyable activities should be possible so as to
improve quality of life and allow reemployment in many cases.
2. The physiologic basis of retraining techniques: neuroplasticity.
Training effects produce major physiologic and cytoarchitectonic changes in the central
nervous system, especially sensorimotor cortex [2]. Similar results have been observed in
the visual system, especially regarding the dynamics of neuronal response to given stimuli
[3]. Even regional cerebral blood flow measurements confirm the major adaptability of
neural networks in acquisition of new motor skills [4]. Spatial expansion of areas activated
in motor learning include primary sensorimotor and premotor cortex. Such expansion
occurs as the efficiency of task performance increases. Premotor cortex appears to be
bilaterally organized and involved in this process. Mesial frontal cortex becomes involved
in overall guidance and pacing of motor sequences. Anterior and posterior cingulate inputs
from other limbic structures (amygdala and subiculum) probably are recruited in the initial
attention and drive to carry out complex activities, and superior parietal areas are also
activated, probably providing sensorimotor feedback. The basal ganglia also play a pivotal
role in the initiation of motor behavior. Even motor ideation concerning a learned task can
be detected as increased activity in the supplementary motor area, and cerebellar and
thalamic loops are also probably involved, though less obviously [5]. These processes are
the basis of the fact that "practice makes perfect."
Motor organization demonstrates similar plasticity. With training in a motor task, motor
cortical areas producing activity in relevant areas expand quickly, as determined by evoked
potentials resulting from transcranial magnetic stimulation [6]. It is well known that
cortical motor maps are expanding and contracting greatly on voluntary muscle activation
and relaxation [7]. Such reconfiguration of cortical motor maps can expand quite rapidly
while skilled sensorimotor tasks are being performed and then contract within hours to days
after cessation of such tasks [8].
Recent cortical mapping experiments have shown that sensory and motor regions are in
fact interspersed and intermingled at the central sulcus; there is no clear functional
separation of motor and sensory areas, although there may still be functional concentration
gradients of motor and sensory elements [9].
3. Clinical correlation of neuroplasticity: cerebral palsy and stroke.
How does this functional neuroanatomical plasticity correlate with patient responses to
neurologic insult? There is evidence in several disease processes that plastic behavior plays
a significant role in amelioration of functional deficits.
GIUSEPPE RIVA (Ed.)
Virtual Reality in Neuro-Psycho-Physiology
1997, 1998 © Ios Press: Amsterdam, Netherlands.
Motor reorganization occurs in children with cerebral palsy such that spinal motoneurons
innervating the affected body side additionally receive input from the ipsilateral
(unaffected) cortex, along with the development of mirror movements. Normally unilateral
cutaneomuscular reflex patterns are altered, being expressed bilaterally with sensory
stimulation of the unaffected side and absent with sensory stimulation of the affected side,
and cortical magnetic stimulation also showed bilateral projection from the unaffected
cortex [10]. Some of this effect may result from branching of corticospinal axons to
innervate regions of spinal cord that have lost descending connections [11]. In fact, motor
reconfiguration can occur with peripheral deafferentation by limb ischemia within an hour
[12]. A similar enlargement of motor representation occurs with spinal cord injury [13].
After hemiplegic stroke, patients often develop excessive flexor dyssynergy that impedes
voluntary movement. To some extent, these abnormal patterns develop from "learned
disuse" of the involved limb. However, by temporarily immobilizing the uninvolved limb,
such patients can overcome some of these deleterious effects, and the modification of motor
performance persists after the training period. One explanation for such improvement is the
notion of "unmasking," that is increasing the utilization of existing, but inactive, neuronal
systems as a direct result of the training activity [14]. Different pathways appear to be
involved in functional reorganization in late (post-mature) injuries compared to early
(developmental) brain injuries. In both cases, developing ipsilateral corticospinal
connections are important. In the developmental injuries, the formed connections are more
direct, whereas in the post-mature injuries, connections appear to involve synaptic
reorganization at several levels, resulting in a more complex and slower response [15].
After striatocapsular infarction, increased function in the contralateral caudate and
premotor cortex can be demonstrated by PET, and there is increased activation of bilateral
parietal and insular cortex. These findings suggest the potential for plastic adaptation
within the cerebral cortex. This more localized response is in addition to activation of
bilateral systems, including cerebellum, that altogether constitute a widespread and complex
bilateral and multisystem response to a relatively localized, but functionally severe, injury
[16].
Holographic processing within neuropil, constituting a massively parallel processing
environment, is fundamental to physiologic mechanisms of rehabilitation in neurologic
injury [17], [18], [19]. It is in this realm of functional augmentation that input from VR
systems can be most useful.
4. Neuroplasticity in MS.
Clinically, there are very obvious indications that substantial plastic reserve exists in the
MS patient. First, from the strictly histopathologic point of view, axons are preserved,
though incompletely and in a quite likely damaged condition, in MS plaques (see below).
Second, remissions occur faster than remyelination can take place (see below). Third, it is
well known that many MRI lesions are silent clinically, implying that substantial
compensatory reserves probably exist [20].
There is every reason to believe that mechanisms like those described in cerebral palsy
GIUSEPPE RIVA (Ed.)
Virtual Reality in Neuro-Psycho-Physiology
1997, 1998 © Ios Press: Amsterdam, Netherlands.
and stroke may play a role to promote functional recovery in the MS patient. Beyond these
effects, there are additional modes of adaptation at the neuropil level that are likely to
enhance such effects and probably underlie the phenomenon of remission, as well as
retraining effects in SCI rehabilitation.
5. Electrophysiologic mechanisms of rapid remission and retraining in MS.
Some degree of remission theoretically could result from remyelination. However, it is
known that remyelination tends to be a fragile process in equilibrium with continuing
destruction of oligodendrocytes [21] in the presence of continuing loss of integrity of the
blood brain barrier [22]. In chronic lesions, demyelination predominates, with loss of many,
but not all, axons [23]. While there may be overall deterioration of function with increasing
burden of plaques, there is substantial substrate for remission through the effects of axonal
compensation, especially in more recent lesions. In particular, there is an optimizing
redistribution of Na+ gating channels along the length of the demyelinated axon.
This redistribution can compensate, at least partially, for the loss of dielectric properties
of normal myelin, and thus allow return of conduction after acute demyelination. The
improved conduction resulting from this mechanism probably accounts for some of the
rapid (days) remissions that are often observed clinically [24]. While this adaptive increase
in the internodal density of sodium channels can lead to recovery of conduction initially,
such improved performance often cannot be sustained in chronic lesions, especially when
glial loss is more severe [25]. Are neurons, compromised in their transmission by
demyelination of their axons, also affected secondarily, by some toxic substance or by
altered energetics consequent upon the loss of myelin? Presently this question is
unanswered, but bases for such direct axonal effects exist [26].
Effects like these leave open the possibility that retraining is possible to an as yet
unexplored degree in MS patients. One approach to the elucidation of electrophysiologic
mechanisms of remission would involve investigation of neural net action at the neuropil
level. Neuroplasticity is probably largely based on the holographic aspects of central
nervous system function. Quantitative assessment of such properties at the neuropil level
will require the development of new technology. Methods of such approaches are at present
largely in a theoretical stage, but eventually will likely improve understanding of
information transfer at this level [17].
The work reviewed here indicates that the neurologic basis for retraining exists to the
extent that efforts to apply sensory and motor training protocols appear, at least
conceptually, likely to provide the benefit of improved function in several spheres. The
question has been raised whether treatment strategies can be derived that will optimize the
plastic capabilities demonstrated experimentally. In some cases, recovery of only a small
percentage of neural input can lead to a functionally significant improvement [27].
It has been well demonstrated that motor compensation mechanisms are highly plastic in
a training environment. Major alteration of movement responses to environmental stimuli
produce easily measurable alterations in upper extremity motor control set [28], with
additional mechanisms for integrating proprioceptive input [29], and in response dynamics
of cerebellar control of posture [30]. In Parkinsonian patients, change of 'set' of the
GIUSEPPE RIVA (Ed.)
Virtual Reality in Neuro-Psycho-Physiology
1997, 1998 © Ios Press: Amsterdam, Netherlands.
kinesthetic system with resulting imbalance and inaccuracies of movement has been
demonstrated adversely to affect performance [31].
By analogy, several training paradigms applied to other types of neurologic injury would
prove useful in exploring this question.
1. Cervical root/brachial plexus injury; effects of biofeedback. In cases of upper
extremity paralysis as the result of cervical root avulsion and brachial plexus injury,
anastomosis of the musculocutaneous nerve to the intercostal nerves was performed [32].
Initially, after axon penetration, biceps brachii motor unit potentials (MUPs) were noted
with respiration, but patients could not control biceps MUPs independent of respiration.
Visual and auditory biofeedback (oscilloscope and loudspeaker presentation to the patient of
MUPs) was begun at this stage. With training, patients were able to produce MUPs and
observable contraction of biceps independent of respiration. During this transition, cortical
mapping showed an expansion of the regions from which biceps activity could be evoked.
These cortical areas were wider than the areas exciting intercostal muscle and were wider
than the biceps excitation area on cortex associated with the unaffected side. Ultimately, 12 years after anastomosis, patients could sustain biceps contraction with useful weight
bearing, and they could separate biceps contraction from respiration. Age played a role in
the neuroadaptation capability: patients under 40 years, and especially under 25 years, had
less difficulties, but usually the older patients who failed to improve were unable to have
biofeedback training. Interestingly, the localization of the excitable cortical region
specifically to the biceps occurred when the patients developed the ability to flex the elbow
and to control movement independent of respirations. The biofeedback training appeared to
play a pivotal role, although the exact contribution of this specific modality to the neural
reorganization occurring in these patients remains open for study [33].
2. Biofeedback in stroke. EMG feedback has been used to facilitate recovery after
hemiplegic stroke for some years. The efficacy has been debated [34]. Unfortunately,
variability of techniques and measurement criteria contribute to ambiguity in some studies,
but overall results with traditional methods have been favorable, if modest [35],[36]. A
method of enhancing EMG feedback is the use of combined monitoring and stimulation.
Low-intensity stimulation of wrist extensors can decrease flexor spasticity and may
facilitate voluntary movement. A multimodality approach has been found significantly to
increase voluntary motor performance [37]. Patients received transcutaneous muscle
stimulation to target muscles based on-low level contraction of those muscles detected
through EMG monitoring. Low-level constant stimulation was also delivered to wrist
extensors that allowed increased range of motion. Benefits of therapy persisted for at least
9 months. Another useful technique involves training patients to reproduce EMG patterns
from the unaffected extremity in the paretic extremity through biofeedback.
6. Neurorehabilitation of the MS and SCI patient: disabilities
retraining.
responsive to
The presence of neural mechanisms of training suggests that appropriate multimodality
GIUSEPPE RIVA (Ed.)
Virtual Reality in Neuro-Psycho-Physiology
1997, 1998 © Ios Press: Amsterdam, Netherlands.
stimulation will have a beneficial effect, as is the case for Parkinson's patients who display
kinesia paradoxica in whom presentation of an electronically generated cueing stimulus,
presented in the viewfinder of a video tape recorder, has greatly increased walking
capabilities for the duration of the stimulus [38]. In MS and SCI patients, dysmetria,
spasticity, weakness, and visual loss are potentially amenable to therapy and assistive
devices. Haptic (force) feedback plays a prominent role in approaching all but the pure
visual deficits
7. General considerations of VR Interventional Motor Feedback Systems.
The general treatment of tracking extremity movements with force feedback must
address the several aspects of neuromuscular control systems. We will consider an ideal
situation, the movement of a fingertip as a unit with the distal upper extremity. The
synergistic contraction of many muscle groups is required to hold the finger stationary and
to move it predictably.
However, the forces generated by all of these groups would not be accessible to a haptic
device. As long as the finger is in fact held stationary, with respect to haptic rest
coordinates, the haptic input device would sense 0 force. Similarly, a position monitor
would detect a 0 velocity vector for the finger. To achieve a dissection of the individual
muscular events maintaining upper extremity position, EMG sampling would also be
necessary (see below).
Figure 1. Quantitative aspects of spasticity.
For finger movement to occur, net unbalanced muscular force must operate; such a
differential force is required to accelerate the finger tip toward the target. This force is
incremental with respect to the overall tone needed to maintain the finger stationary in
space. (We will consider analysis of the problem in one dimension. Spatial 3-D analysis
GIUSEPPE RIVA (Ed.)
Virtual Reality in Neuro-Psycho-Physiology
1997, 1998 © Ios Press: Amsterdam, Netherlands.
follows with orthogonal vector operations for generalization, with the exception that there
may be some advantage interchannel comparisons as well, particularly with regard to
anticipation of motion by comparing EMG activity; see the section on EMG data
acquisition.) Consideration of spasticity, dysmetria, and weakness will illustrate use of the
general methods of approach.
8. Spasticity.
Spasticity may occur with tremor and dysmetria, or may occur separately. Most likely, it
will be associated also with weakness. However, spasticity presents unique control
problems apart from weakness. Training and assistance procedures are more likely to be
successful when spasticity is slight to moderate, but certain strategies may be successful
also in more severe situations.
Figure 2. Feedback force generator.
The fundamental problem is disinhibition of muscular contraction, primarily flexor in the
upper extremities, though also involving extensors in more severe cases. The abnormal
contraction tends to be enhanced by limb movement, so as to oppose passive or active
movement. Consider Figure 1. The force that develops in a spastic muscle increases as a
function of joint displacement, Q, angular velocity at the joint, Q', and angular acceleration,
Q''. There is also a persistent contraction after angular velocity drops, the "clasp knife,"
which is a function of the of the total force developed, and also decays with time.
If there is spasticity in an antagonistic muscle group (for example, biceps brachii during
extension at the elbow joint), there will be a decrease in the velocity before the target is
GIUSEPPE RIVA (Ed.)
Virtual Reality in Neuro-Psycho-Physiology
1997, 1998 © Ios Press: Amsterdam, Netherlands.
reached. A patient might then attempt to compensate by increasing contraction in the
agonist (triceps). This effort may again excite a spastic contraction in the biceps. A
quasioscillation will then occur, and if spasticity is severe, there may even be a spastic
response in the agonist. The result will be a highly irregular, "jerky" approach to the target.
No two approaches are likely to be the same, even with a constant task setup, because of
variability in the multiple system interactions described.
Formulation of a theoretical and experimental approach to this type of variability has
achieved a measure of accuracy calibrated to the difficulty of the task [39]. This approach
allows definition of a transfer characteristic of the feedback system to produce force
correction of inaccurate movements.
Consider the system shown in Figure 2. The subject's hand movement is to be
constrained to a force corridor. This is a region of displacement-velocity space within
which the subject's hand should operate without corrective force. Creation of the corridor
requires that the haptic device determine the hand position relative to the target, and also to
the force corridor. Force feedback would be generated if the subject's hand is outside the
spatial corridor, or if the hand velocity is such that it would leave the force corridor if
uncorrected. Under these conditions a corrective counterforce would be applied..
In configuring such a feedback plant, adjustment of the transfer characteristic of their
force feedback generator was accomplished in two ways. First, the 'walls' of the corridor
could be sharpened by increasing the spatial force gradient in the target area, that is, for a
given (take X) dimension, to maximize ¦ f x / ¦x 1, especially near the boundaries of the
corridor. Their approach was to create a force, such that the X-component would be:
f x (x)= kx n ,
(1)
where n was made as high as possible (typically 3). (These experiments involved one
degree of freedom, as is the analysis in this section. However, in the proposed application,
we must extend to 3 spatial and velocity dimensions.)
The next problem is the transfer characteristic of the force generator. The position error
(in one dimension for simplicity) e(t) arises from the difference between the patient's finger
or hand as sensed by the haptic device and the programmed position as reflected in the
visual display. The haptic output then must be in response to both the displacement error
(for a stationary target) and the force exerted by the patient on the haptic device. In the
work described, the initial transfer characteristic was such that an exponential decay of
force correction resulted from a step e(t) input. However, in certain cases, resonances
developed in the interaction between the actual human subject and the force generating
system, with degradation of tracking performance. This resonance effect necessitated
modifications in the frequency-phase response of the force generator to stabilize the output
and overall responses. When this was done, there was a substantial improvement in
performance in a high-error situation, in this case spasticity. However, once optimized, the
step response characteristics were maintained constant within the experimental design. That
is,
£{fout (t)}
T (S) =
(2)
£{e(t)}
GIUSEPPE RIVA (Ed.)
Virtual Reality in Neuro-Psycho-Physiology
1997, 1998 © Ios Press: Amsterdam, Netherlands.
where T(s) is the transfer characteristic of the entire system from the Laplace transforms of
e(t) and the output force of feedback generator, fo(t).
Generation of erroneous output in spasticity raises an additional question, one of overall
system linearity (including the patient). The transfer characteristic employed was a linear
one, i. e., constant within the limits of the range of the device. When spasticity-generated
noise, containing frequencies near the functional limit of the feedback system, is added,
resonance effects can arise which degrade system performance. In these situations, the
linear model may show deficiencies in control. Dynamic modulation of the transfer
characteristic is likely to become necessary in that event [40].
What are the origins of this type of noise in the spastic motor system? Normal control of
muscle contraction is well known to be dependent upon four major systems [41]:
1. Excitatory corticospinal projections. Output is from the large cortical pyramidal
cells (especially Betz cells), and projects directly through the corticospinal tracts, normally
primarily contralateral, to excite spinal motoneurons directly.
2. Inhibitory projections. Supplementary cortical areas probably project to
inhibitory spinal interneurons and also modify reticulospinal responses. Certain limited
cerebral lesions (limited to large pyramidal cells) are likely to cause flaccid paresis and
hyporeflexia. But more widespread inactivation as a consequence of cortical injury,
especially supplementary motor areas, produces profound alteration in muscle tone and
reflex responses. For example, inactivation of inhibition due to diffuse cortical or brain
stem injury results in decorticate and decerebrate rigidity when disconnection is at the upper
and mid- (collicular) mesencephalic levels respectively. In many instances, with more
limited hemispheric injury, flaccid paresis converts spontaneously to spastic paresis over
several days to weeks.
3. G-efferent system. Increasing input causes muscle spindle receptor contraction,
and sensitizes the muscle stretch sensing system (muscle spindles) to increase reflex
activity.
4. Basal ganglia (extrapyramidal) contribution. Extensive basal ganglion-cortical
and -thalamic inputs, as well as probable brainstem projections, modulate basic muscle tone
(rigidity component in contradistinction to classical "clasp-knife" spasticity), and can
produce dyskinesia and distinctive, primarily rest tremor.
The dynamics of each of these systems are complex, and difficult to model from first
principles. Modulation of direct corticospinal output is highly influenced by the
neurointegrative effects of the supplementary motor areas, probably bilaterally, as reviewed
in the discussion of neuroplasticity. The consequence of this complexity is that a constant
transfer characteristic in the force generator plant will probably not be optimizable in terms
of maximizing patient performance, especially across a wide range of patients and
conditions for a given patient. A dynamic transfer characteristic, encompassing intelligently
modulatable frequency-phase filtering and gain adjustment, will probably be needed. This
process would need to be under computer control and would require the pattern of noise
(spastic behavior) in a given patient to be incorporated into the control of the transfer
characteristic of the haptic device.
Why is this so? Consider the typical spastic contraction in response to a limb movement
GIUSEPPE RIVA (Ed.)
Virtual Reality in Neuro-Psycho-Physiology
1997, 1998 © Ios Press: Amsterdam, Netherlands.
introduced by voluntary contraction or externally applied force. A family of curves like
those in Figure 3 may be generated, for example by application of an external step force
producing biceps elongation in a moderately spastic patient. The following analysis gives
the basis for expecting curves like these, which, in a qualitative way, are generally observed
in spastic patients.
Response to a given force input depends not only upon that immediate input, but also on
the previous movement history and the confluence of all of these described motor
integration functions. The history of a movement at first might first be approximated by:
H=k1D2+k2D+k3
(3)
where H is an operator generating a family of equations describing spastic behavior in a
given patent over several trials, and D is the differential operator D=d/dt. (In the Figure,
the H-axis represents the effects of different parameters k of the H operator, leading to
different output functions from the same system.).
Figure 3. Q(t) families.
This history is important in generating a family of spastic displacement functions Q(t) in
response to a given external step force input, or in the more general case in response to the
difference between the applied force and the resulting spastic force. (For greater rigor,
torque would be a more accurate measure than force in the given example, but the effector
of rotation resulting in elbow flexion is in fact the force developed by the biceps
contraction. The force for this analysis will be assumed proportional to the output torque at
the wrist, where the haptic device would be applied, since the radius of rotation, that is the
length of the forearm for output measured at the hand, is constant.)
GIUSEPPE RIVA (Ed.)
Virtual Reality in Neuro-Psycho-Physiology
1997, 1998 © Ios Press: Amsterdam, Netherlands.
Let H then generate a set of possible differential equations describing spastic force and
displacement generation that takes this history into account. (For clarity in this example, the
generated equations will be limited to second order, as expressed by Eq. (3).) Spastic force
will be related to Q(t) as shown in Figure 1. Precise characterization of H, with more
generalized parametric functions, will depend on data acquired in the proposed system
development.
The traditional approach, as indicated above, can be used for calculating a suitable
transfer characteristic. By using an approach like this, a transfer characteristic, T(s), that
might be computed as:
T(S) =
£{f0 (t)}
£{k1 q (t) + k2 qÕ (t) + k3 qÓ (t)}
(4)
will likely lead to aberrant oscillatory loop behavior similar to that arising in the work
described, especially in the case of more severe spasticity with high amplitude (relative to
the correct limb force) and high frequency (relative to the cross-over frequency of the
feedback plant.
Clearly, Eq. (4) is a simplification which does not fully respond to the dynamics of the
spasticity system. Moreover, the patient's level of spastic response is likely to change with
a variety of factors, including fatigue, stress level, and sometimes just time of day, as can be
observed clinically. Pharmacologic intervention, with medications like diazepam and
baclofen, can profoundly alter spastic responses for a given patient, and such effects are
both dose- and time-dependent in typical patients. The more general alternative (as defined
in Eq. (6)), involving a time-varying and position-dependent H operator:
T(S) =
£{f0 (t)}
£{H[q (t)]}
(5)
can encompass time variance of H but is very difficult to implement in an analog-only
physical system or in a reasonable approximation of real time on a digital system because of
the time- and displacement-variant properties of H. For the same applied force repetitively
generated, H(Q) will not be invariant because of our inability to create an analytic definition
of H based on presently possible measurements in a patient.
Figure 4 shows the basic components of the three-dimensional signal flow in the spastic
processing network being designed in this laboratory. To circumvent the problems inherent
in the above treatment, it is intended to design a three-dimensional force comparator to
generate the required force feedback. The transfer characteristic for each dimension will be
digitally derived, with the necessary correlation among the visual, positional, and force
inputs to implement the necessary force output. The essential features will be directed
toward a practical solution to the following problem.
GIUSEPPE RIVA (Ed.)
Virtual Reality in Neuro-Psycho-Physiology
1997, 1998 © Ios Press: Amsterdam, Netherlands.
9. Time variant H(Q).
As a consequence of the foregoing observations, the formulation of the spastic force
Figure 4. Dynamic feedback system.
generation conditions in any spastic-controlling feedback loop must be generalized to
include the variant properties of the defining system characteristics, which will be expressed
in this example as H This is the rationale for characterizing the spastic force F as shown in
Figure 4.Assume as a simplified example that the spastic force generator operates as a
second-order system in joint displacement Q.To illustrate the principle, take
H=r(t)D2+s(t)D+w(t)
(6)
The functions r, s, and w are in general referable to the interactions of the four CNS
systems referenced above, but only in an empirical way. They may be viewed as system
parameters that lead to the variable behavior in spasticity (the family of limb displacements
for a given applied force function), dependent on preceding movement events and also on
more general CNS variability. Such events may include previous attempts at limb
movement which may augment the response, and voluntary or involuntary changes in
muscle tone originating in cortex. For example, it is well known that voluntary contraction
beginning just before stimulation has a strong facilitating effect on magnetically evoked
motor potentials.
Then, where F a(t) is a force applied to the spastic system, either externally or by a
muscle antagonistic to the spastic muscle and activated voluntarily by the patient,
H[Q(t)]=Fa(t)
(7)
GIUSEPPE RIVA (Ed.)
Virtual Reality in Neuro-Psycho-Physiology
1997, 1998 © Ios Press: Amsterdam, Netherlands.
Now consider, again for heuristic purposes, the special case where r(t), s(t), and w(t) are
constant over a time interval t1£t£t2 such that:
(8)
r(t)=R, s(t)=S, w(t)=W,
and Fa(t) is a force function applied to move the limb, derived either from a source external
to the spastic generator, such as the haptic input or an examiner moving the limb. The
forcing function may also result from the action of a muscle group antagonistic to the
spastic muscle group, such as the triceps if the patient is attempting to extend the elbow
against the spastic contraction of the biceps. This highly idealized case might be visualized
as the situation where the joint motion resembles a watch balance wheel, with linear
acceleration, damping, and displacement components.
We recognize that the actual system dynamics are much more complex. Even in the
second order system, r'(t), s'(t), and w'(t) will be nonzero, and series solutions would likely
need to be implemented in attempts to solve Eq. (7) explicitly, requiring much computation
time. The goal here is to reduce computations to the extent that a reasonable approach will
be possible with a desktop computer. However, the following analysis will assume the
constant-coefficient second-order system to demonstrate the principles and motivations
underlying the proposed feedback approach.
Under the constant-coefficient conditions, the solution for the homogeneous case, Qh(t),
will take the form
q h (t) = c1 ez 1t + c2 ez 2 t
(9)
where c1 and c2 are constants, and
z1=
- S + S 2 - 4RW
- S - S 2 - 4RW
,z 2 =
2R
2R
(10)
Now suppose the spastic system includes an effector muscle, like the biceps brachii.
Consider further that at t=t0 a step force, which might be generated by a haptic system, is
applied to extend the elbow. Then the nonhomogeneous generalization of Eq. (9) becomes,
q gen = c1 q 1 + c2 q 2 - q Step q 1
q2
q1
dt + q Step q 2
dt
W( q 1 ,q 2 )(t)
W( q 1 ,q 2 )(t)
(11)
where QStep is a constant dependent on initial conditions, W(Q1,Q2)(t) is the Wronskian,
W(q1,q2) (t) =
q1 (t) q2 (t)
qÕ1 (t) qÕ2 (t)
(12)
and Q1 and Q 2 are the homogeneous solutions of Eq. (7), whose linear combination (the
general homogeneous solution) shown in Eq. (9).
From this approach, it is evident that a large family of responses may be generated by a
GIUSEPPE RIVA (Ed.)
Virtual Reality in Neuro-Psycho-Physiology
1997, 1998 © Ios Press: Amsterdam, Netherlands.
spastic system under the constraints even of this second-order constant-coefficient
description, that is H(Q) as defined by Eqs. (7) and (8). This is the derivation of the
example curves of Figure 3, where u(t) is a unit step function at t=t0, FStep is a constant,
H(Q)=FStepu(t-t0)
(13)
This approach to the problem of controlling Q(t) by the application of external force
illustrates the following conclusions:
1. Even under the assumption of the simplest possible empirical description of the
spastic generator, managing feedback with static dynamics will be difficult, if even possible,
especially with attempts at controlling complex tasks. The foregoing description has been
limited to a single degree of freedom, and with a full three-dimensional spatial approach,
the analytic and computational problems also multiply.
2. Integration of haptic feedback in a clinical setting demands that the method of
force feedback computation vary dynamically during the process control with reasonable
utilization of computational resources. This means that an ongoing formal solution of
system differential equations may not be the optimum way to proceed.
10. A dynamic, digitally controlled force feedback generator.
Figure 4 illustrates a proposed solution to these problems which is being developed
in this laboratory. The displacement error vector, parameterized in t, e(t) will be passed
through a three-channel dynamic correction signal generator. Each channel will be
processed independently in real time in a procedurally independent manner, with provision
for interchannel comparisons. (For simplicity, only one channel dimension is shown in the
Figure). Again concentrating on a single dimension for processing, consider the Xcomponent of the displacement error, e(t). This signal will be divided into several (the
number may be expanded as necessary) frequency analysis channels, with a set of channels
for each spatial component. The channels will be processed individually with regard to
overall gain and frequency-phase characteristics. The Gn and Gm in the Figure represent
the overall channel amplitude gains, and the operators (Dr and Ds)represent first and second
order differentiation (r,s>0 and integration (r,s <0). These operations will be performed
digitally, so that the desired amplitude and phase characteristics can be modified rapidly,
that is over intervals small compared with the frequency spectrum of the spastic generator
noise. An advantage of the digital approach is that a more complex filter than the simple
high-pass and low-pass approaches illustrated in Figure 4 can be implemented as required;
the configuration shown is a first approach. The key to this approach is the capacity of
monitoring the individual filter gain channels on the basis of the form of the output
function. Suppose, for example, that the spastic generator, in response to an step applied
force, behaved as a linear oscillator and produced a movement of the following form:
q (t) = q F + q A e-(S / 2R)t cos( wt + f )
(14)
GIUSEPPE RIVA (Ed.)
Virtual Reality in Neuro-Psycho-Physiology
1997, 1998 © Ios Press: Amsterdam, Netherlands.
where QA, QF, and f depend on initial conditions, and
w=
4RW - S 2
2R
(15)
with H(Q) such that
S2-4RW<0
(16)
Especially note the great instability that occurs when S/2R<0. Of course, such a situation
cannot be sustained in a patient, but, sometimes vigorous spastic oscillations can occur,
approximating this condition for a short time and producing very erratic limb movement.
Design requirements include filters that will actively sense such oscillations and follow
an algorithm to dampen them by altering the transfer characteristic over time intervals
substantially less than: 2 P / w . 8
This approach has worked well in the control of other feedback loops where the
characteristics of the controlled process are unpredictable from one stimulus to the next
[42]. However, it is anticipated that substantial experimentation will be required to optimize
dynamic modulation of transfer characteristics in the present context.
As indicated earlier, the amplitude/frequency/phase modulating systems will also require
some orthogonal dependence. Particularly where several spastic groups are active,
anticipation of error generation in the X-dimension may be assisted by monitoring of earlier
movements in the Y- or Z-dimension. EMG sampling will also be helpful (see below).
The dynamic approach does not assume any a priori knowledge of spastic system
configuration or parameters. It is to be emphasized that the second order system is used
here as an example only to demonstrate the wide range of possible behaviors that would
need to be accommodated even in the simplest system model if traditional techniques were
used.
11. Dysmetria and tremor: statement of the problem.
A patient with dysmetria, and possibly proprioceptive loss, of the upper extremity will
have difficulty reaching for an object, such as a drinking glass, and grasping it. The
premorbid strategies employed by the patient are no longer effective because of the
following derangements in sensorimotor mechanisms:
1. Cerebellar modulation of cortically initiated motor activity is deficient. There is
frequent overshoot in the approach movements, with a secondary attempted compensation.
The result of the combined inaccuracy and attempted compensation is oscillation in the
form of a coarse tremor. The result is not just a single overshoot, but a continuing
oscillatory cycle of overshoot and attempted compensation, which itself overshoots in the
opposite direction (undershoots the target).
2. Proprioceptive input may be deficient. Thus, cortically mediated attempts to
compensate for the cerebellar deficits suffer further error. With steady posture, such deficit
is manifest as pseudoathetosis, but dynamic ranging activities will be impaired more
GIUSEPPE RIVA (Ed.)
Virtual Reality in Neuro-Psycho-Physiology
1997, 1998 © Ios Press: Amsterdam, Netherlands.
definitively.
3. Visuomotor integrative activity, primarily cortically initiated, may fail. This
function is dependent upon the integrity of occipital, parietal, frontomesencephalic and
brainstem connectivity, and may be further impaired in because of diplopia and loss of
acuity that will compromise stereoscopic perception.
As in the case of spasticity, to evaluate and manage this movement disorder, a
system of quantification must be established that is capable of determining the error
generated by the patient's movement. For the most part, amplitudes of aberrations have
been used as extensions of disability scales, that is used passively with regard to patient
interaction [43]. Interaction in a corrective or training mode has been limited because
inadequate bidirectional haptic devices and inadequate computational power to address the
highly nonlinear characteristics of the targeting behavior. Moreover, there has been no
means of addressing the effects of the multisystem input (proprioceptive, visuospatial,
tactile, and internal systems dynamics) in an operationally satisfactory manner.
An overview of the tremor problem is evident in Figure 5. As the patient attempts to
reach the target, there is movement inaccuracy in three dimensions. Consider the vector
trajectory components as parameterized functions,
P=x(t)i+y(t)j+z(t)k,
(17)
where P is the position vector of a point on the patient's hand directed to the target, and i,
j, and k are respectively unit vectors along the X, Y, and Z axes. (Of course, P is only one
of a large set of vectors that describes the position of points on the hand and forearm. These
points are accessible in real time using both video and haptic tracking systems, some of
which are currently being developed in this laboratory.
If representative parameterized functions are graphed against time, they would appear
much as shown in the Figure. While we can fairly easily derive a root mean square
amplitude for the tremor as a quantitative measure of the error amplitude, and we might use
this as a clinical measure of disability, such an approach would not provide the possibility
of any corrective interaction. Still, such an approach will allow us to view the complexity
of the situation.
We are concerned here with providing feedback to the patient through which she can
modify her behavior. We shall see that providing force feedback can significantly improve
accuracy in reaching a target. With the same general approach as outlined above for the
case of spasticity, a traditional method to analyze a system like this would be to attempt a
characterization of P(t) by means of a set of differential equations, where the terms each
might correspond to a neural mechanism, similar to the proposed neural integrator in the
maintenance of eye position. Thus, suppose the tremor curve, in one dimension, looked like
that shown in Figure 6. As in the case of spasticity, the coefficient functions would be timevarying. This variation would again depend on the operation of multiple systems and would
change the dynamics of the tremor generator, so that a fixed transfer characteristic in a
corrective feedback system will probably again lead to substantial errors in attempts to
reduce oscillatory behavior.
In the normal individual, settling is highly damped, shown by the curve in the Figure for
the normal case. Indeed, the oscillatory form is so inapparent in the normal case that the
GIUSEPPE RIVA (Ed.)
Virtual Reality in Neuro-Psycho-Physiology
1997, 1998 © Ios Press: Amsterdam, Netherlands.
oscillatory model would be difficult to extrapolate. But an even more severe problem is that
of the nonlinearity of the oscillatory system in the abnormal case where the oscillations at
least can clearly be seen.
Thus, to attack the tremor problem in a rigorously quantitative manner, we would be
faced with very serious problems that would make it difficult to attribute components of the
putative oscillatory loop to discrete neural mechanisms. And, of course, we are considering
only one spatial dimension; reaching the target depends of the simultaneous zeroing of all
components of the position difference vector, such that P=T, where:
T=Xi+Yj+Zk
(18)
and X is the x-coordinate of the target, Y is the y-coordinate of the target, and Z is the zcoordinate of the target. For stability, P'(t) and P''(t) would also have to be near 0, or the
patient's hand would soon be off target.
Figure 5. Quantitative aspects of tremor.
12. Dysmetria and tremor: a workable approach.
How, then, is the problem of training to be approached? As in the case of spasticity,
observation of individual position function curves will be very unlikely to provide
meaningful feedback to the patient. VR affords an opportunity to examine the hypothesis of
retrainability on a systematic basis.
With a fully configured VR system, we can provide corrective signals to the patient,
based on her overall performance irrespective of the individual neural mechanisms involved
in generating aberrant motor behavior. In the case where visual sensing is essentially intact,
that is no scotoma, diplopia, or cortical deficit to interfere with stereoscopic visual
perception, the major problem to be addressed is the lack of kinesthetic and motoric control.
GIUSEPPE RIVA (Ed.)
Virtual Reality in Neuro-Psycho-Physiology
1997, 1998 © Ios Press: Amsterdam, Netherlands.
Figure 6. Gradations of tremor; the normal case versus abnormal.
From the point of view of the proposed VR trainer, the differences among spasticity,
cerebellar tremor, and movement disorders deriving from basal ganglion impairment are
largely parametric.
Figure 7. Computer-patient feedback systems.
VR can be used to provide cueing in a haptic (force-feedback)/visual system. This
approach is shown in Figure 7. The patient initiates an attempt to reach the target. The VR
GIUSEPPE RIVA (Ed.)
Virtual Reality in Neuro-Psycho-Physiology
1997, 1998 © Ios Press: Amsterdam, Netherlands.
system locates the target through sensing the patient's eye position and facial EMG. A
camera can be used to notify the computer of the target position.
Alternatively, a head-mounted display can be used to track the target while
simultaneously showing the target to the patient, though this can be annoying in some cases.
The patient can look at the target and use additional facial musculature, or can initiate
upper extremity movement while looking at the target. The force corridor is then generated
to guide the patients wrist. If the patient's forearm moves outside that corridor, which can
also be displayed visually, there will be corrective counterforce.
The patient can then practice the movement, with much of the tremor and other
inaccuracy suppressed by direct counterforce from the haptic device. We know already that
application of appropriate counterforce suppresses even severe cerebellar tremor effectively,
with marked increase in the accuracy of movement [1].
EMG cues can also be used both to trigger the target location selected by the patient and
to indicate to the computer that inappropriate muscle responses are occurring [44]. Such
EMG input can trigger near threshold electrical stimulation to modulate muscular activity
directly, as described by Kraft, cited above, and tactile stimulation which can provide
additional cueing to the patient.
The proposed feedback system can utilize EMG anticipatory information by measuring
activity of both agonist and antagonist groups. Consider the extension of the biceps
spasticity example. With voluntary extension of the elbow by triceps contraction, the onset
of triceps activity will precede the development of spastic activity in the biceps. Taking
account of this anticipatory activity would aid in compensating the feedback system for the
spastic response to follow in the biceps. In fact, some of the oscillatory activity that can be
observed in biceps may in fact involve secondary modulation of triceps activity, and there
may be some spastic activity in triceps as well that would be unnoticed without EMG
monitoring.
Evaluation of these various possible contributions to the development of the family of
spastic Q(t), or the oscillations in P(t) in the tremor case, is more difficult when based only
on the final movement, which results from the sum of unbalanced forces. Thus, a
multichannel spatial EMG input would be useful in continuously adjusting the parameters
(gain, frequency response, phase shift) of the force feedback generator. Such parameter
manipulation can be placed under computer control, indeed approaching the goal of an
'intelligent' feedback system.
The basic method is to extract the EMG envelope for analysis. Surface electrodes will
usually suffice, although more spatial accuracy can be obtained with monopolar electrodes,
as utilized by Kraft et al., cited above.
For each muscle channel, the integral E(tf) of the rectified EMG signal, M(t), over a short
(approximately 50 ms) time interval Ti, preceding measurement at tf, for each muscle, will
be entered as an array element.
The individual channel signals of such an array are then given by:
tf
E0
E( t f ) =
| M(t)|dt
T i t f -T i
(19)
GIUSEPPE RIVA (Ed.)
Virtual Reality in Neuro-Psycho-Physiology
1997, 1998 © Ios Press: Amsterdam, Netherlands.
Sufficient channels will be monitored to provide a spatial map of muscle activity in
conjunction with the force feedback and visual error signals generated by the above
described components of the feedback system.
Patients may thus be able to retrain themselves by repetition of motoric activity with the
computer 'tutor', with a total system configured as in Figure 8. The potential for such
retraining is essentially unknown for MS patients, since to this point VR systems have not
been applied in this situation. However, VR systems have been used extensively for
motoric training in normal individuals. The technique, including both visual and tactile
feedback, has been particularly useful in training surgeons to perform "virtual procedures."
These include simulation of endoscopic surgery [45], [46]. Modifications of the technique
have been used therapeutically to decondition patients with acrophobia and flying phobia
[47], [48].
Advantages exist in this approach beyond the vastly increased flexibility of training
procedures that might be implemented. First, the training program can be integrated into a
larger planning session. Rather than having a patient execute a rote movement repetitively,
the elements of the training, in this case maintaining the forearm and hand within the force
corridor, may be made part of a more complex task, such as a sequence involved in feeding,
or even a game environment. The task may thus be linked to a useful, and even entertaining
activity that nevertheless provides the repetitive features that would recruit plastic
adaptation.
In those instances where the procedure does not produce improvement in motoric activity
per se, there remains the possibility of developing strategies to perform basic activities of
daily living (ADL), such as feeding, within the VR environment. In such cases, the
computer performs not so much as a tutor or therapist, but more as an actual assistant. If, by
using the force corridor approach, a patient can add sufficient upper extremity control to
allow self feeding, that in itself would be a major accomplishment. The computer may thus
be utilized continuously during an activity as an 'assistant', as in the example for
Parkinsonism. The focus of our approach is also to minimize and, if possible, ameliorate
the effects of loss of neurologic function in MS patients, and for spasiticy and weakness, in
SCI patients.
13. Weakness.
In some respects, this is the simplest case dynamically, but may require the most haptic
assistance just to complete the intended movement. Where the weakness is accompanied by
spasticity and the spasticity can be controlled by training, improvement in voluntary activity
may occur. Further complications will result when weakness is combined either with
uncontrolled spasticity or with tremor and dysmetria. In most patients, there will not be an
isolation, but rather a combination, of these disorders of movement. It will be essential to
configure a multimodality system that has the flexibility to deal with these complex cases
(see below).
GIUSEPPE RIVA (Ed.)
Virtual Reality in Neuro-Psycho-Physiology
1997, 1998 © Ios Press: Amsterdam, Netherlands.
14. Visual loss.
Visual loss can take several forms. A central scotoma is quite common, and it is this
deficit that lends itself to computer intervention. Training will not be likely to ameliorate
this deficit. The major approach would be to magnify critical portions of the field and
translate them outside of the region of the scotoma (see Figure 9).
Figure 8. Overview of VR system components.
GIUSEPPE RIVA (Ed.)
Virtual Reality in Neuro-Psycho-Physiology
1997, 1998 © Ios Press: Amsterdam, Netherlands.
For example, letters whose image falls peripheral to the fovea can be recognized if they
are sufficiently magnified. Text-reading programs may be helpful, but these cannot
improve pattern recognition for other than printed material. Note that objects within the
camera field are automatically translated by the computer to fall in regions of the display
that are unaffected by the central scotoma mapped into computer memory. Especially with
the eye tracking or head-mounted display (not shown in the Figure), the VR system can
alter the translation with the patient's head movements.
Such a system might have very productive application for anyone unemployed because
of this visual incapacity. Successful use of this system would allow such a patient to
resume visually oriented work, at least partially.
Figure 9. Compensating for visual loss (scotoma).
Again, considerable experimentation will likely be required to develop VR assistance
with this problem. But with the increasingly flexible capabilities of VR systems, the
computational aspects of the problem are coming within range on a practical basis for
individual patients, especially if the possibility of reemployment is factored in.
Another application of VR for the visually impaired involves tactile patterns and
stereophonic audio spatial approaches [49] that can be synthesized in real time from camera
input and could be used to generate braille-like or audio cueing patterns to supplement
visual translation and transformation. This approach would have special importance in
pattern recognition of the environment for patients with severe visual compromise.
15. Neurorehabilitation: the VR Assistant.
It is evident that there is a continuum between retraining and assistance. MS and certain
GIUSEPPE RIVA (Ed.)
Virtual Reality in Neuro-Psycho-Physiology
1997, 1998 © Ios Press: Amsterdam, Netherlands.
types of SCI are progressive diseases, while traumatic SCI may be more stable. Retraining
efforts would hopefully allow patients additional years of useful activity, hopefully
including employment. But as disabilities increase, retraining effects will be less
successful, and ultimately for many patients, activities will no longer be possible on an
independent basis. The issue then arises as to whether VR systems can provide patients
with a measure of independence at least in ADL. Such a resource would reduce the burden
on another important but scarce resource, the caregiver.
Consider the following example. Suppose the patient can reach a target and grasp it
while assisted by the VR system, but cannot do so independently. Still, if the patient could
feed herself with assistance, and write or answer the telephone electronically, these would
be major assets in her life. Performance of these acts with machine assistance is perfectly
analogous to navigating the house with a wheel chair. In fact, by use of virtual systems,
some of the necessity for movement would be eliminated. Intelligent video systems could
be placed at various points in the patient's environment so that the patient might not have to
be physically present or send a caregiver to attend to every event.
Such approaches would improve quality of life for disabled patients in purely
communicative and cognitive spheres as well. With VR, severely disabled patients could be
exposed to communication and experiences otherwise physically denied to them. A few
examples include electronic travelogues, playing virtual electronic musical instruments
reasonably well with limited physical capabilities, and communication through the Internet
despite handicaps that would otherwise preclude computer use.
Cognitive and esthetic stimulation become increasingly poignant issues as disabilities
increase. At this stage of technologic evolution, it is becoming no more reasonable to deny
such experiences to the severely disabled MS or SCI patient than to deny them wheelchairs,
autolifts, and hand controls. All of these are but mechanical devices, though with increasing
levels of sophistication and applicability.
It might be argued that innovations like VR are too costly an investment for the
individual patient. It should be remembered that, like the personal computer, VR systems in
the next few years will become as readily available as household appliances.
16. Conclusion.
Advances in computer technology, particularly VR, have opened new avenue of physical
therapy and experiential expansion for disabled MS and SCI patients in several spheres. At
the same time, new information has indicated the basis for a greater level of adaptability and
plasticity on a neural level than was previously suspected.
Together, these developments provide a point of departure to improve our capability in
increasing functionality among MS and SCI patients who are moderately to severely
disabled. This is a new area of research, but has been well validated among normal
individuals learning complex sensorimotor skills. Hopefully, the experience being gained in
these applications can be successfully translated into opportunities to enhance the abilities
and quality of life for these patients. The proposed methods are intended as an initial
serious attempt to bring that goal to fruition.
GIUSEPPE RIVA (Ed.)
Virtual Reality in Neuro-Psycho-Physiology
1997, 1998 © Ios Press: Amsterdam, Netherlands.
References
[1] M. Steffin, Computer Assisted Therapy for Multiple Sclerosis and Spinal Cord Injury Patients; Application
of Virtual Reality. In: K. S. Morgan, H M Hoffman, D Stredney, S J Weghorst et al. (eds.), Medicine Meets
Virtual Reality 5, IOS Press, Amsterdam, 1997, pp. 64-72.
[2] G. H. Recanzone, M. N. Merzenich, and C. E. Schreiner, Changes in the Distributed Temporal Response
Poperties of SI Cortical Neurons Reflect Improvements in Performance on a Temporally Based Tactile
Discrimination Task, J. Neurophys. 67(5) (1992) 1071-90.
[3] E. Zohary, S. Celebrini, K. H. Britten, and W. T. Newsome, Neuronal Plasticity that Underlies
Improvement in Perceptual Performance, Science 263 (1994) 1289-91.
[4] G. Schlauag., U. Knorr., and R. Seitz. Inter-subject Variability of Cerebral Activations in Acquiring a
Motor Skill; a Study with Positron Emission Tomography, Exp. Br. Res. 98 1994 523-34.
[5] S. Grafton, J. Mazziotta, S. Presty, K. Friston, R. S. J. Frackowiak, and M. Phelps, Functional Anatomy of
Human Procedural Learning Determined with Regional Cerebral Blood Glow and PET, J. Neurosci. 12(7)
(1992) 2542-2548.
[6] A. Pascual-Leone, J. Grafman, and M. Hallett, Modulation of Cortical Motor Output Maps During
Development of Implicit and Explicit Knowledge, Science 263 (1994) 1287-8.
[7] K. R. Mills, S. J. Boniface, and M. Schubert, Magnetic Brain Stimulation with a Double Coil: the
Importance of Coil Orientation, Electroenceph. Clin. Neurophys. 85 (1992) 217-21.
[8] A. Pascual-Leone, E. Wasserman, and N. Sadato, M. Hallett, The Role of Reading Activity on the
Modulation of Motor Cortical Outputs to the Reading Hand in Braille Readers. Ann. Neurol. ; 38(6) (1995)
910-915.
[9] Y. Nii, S. Uematsu, R. Lesser, and B. Gordon, Does the Central Sulcus Divide Motor and Sensory
Functions? Cortical Mapping of Human Hand Areas as Revealed by Electrical Stimulation through
Subdural Grid Electrodes? Neurol. 46(2) 1996 360-7.
[10] L. J. Carr, L. M. Harrison, A. L. Evans, and J. A. Stephens, Patterns of Central Motor Reorganization in
Hemiplegic Cerebral Palsy, Brain 116 (1993) 1223-47.
[11] S. F. Farmer, L. M. Harrison, D. A. Ingram, and J. A. Stephens, Plasticity of Central Motor Pathways in
Children with Hemiplegic Cerebral Palsy, Neurology 41(9) (1991) 1505-10.
[12] J. P. Brasil-Neto, J. Valls-Sole, A. Pascual-Leone, A. Cammarota, V. E. Amassian, R. Cracco, P.
Maccabee, J. Cracco, M. Hallet, and L. G. Cohen, Rapid Modulation of Human Cortical Motor Outputs
Following Ischaemic Nerve Block, Brain (1993); 116:511-25.
[13] H. Topka, L. G. Cohen, R. A. Cole, and M. Hallet, Reorganization of Corticospinal Pathways Following
Spinal Cord Injury. Neurology 41 (1991) 1276-83.
[14] S. Wolf, D. Lecraw, L. Barton, and B. Jann, Forced Use of Hemiplegic Upper Extremities to Reverse the
Effect of Learned Nonuse among Chronic Stroke and Head-Injured Patients, Exp. Neurol. 104 (1989) 12532.
[15] L. Benecke, B. U. Meyer, and H. J. Freund, Reorganization of Descending Motor Pathways in Patients
After Hemispherectomy and Severe Hemispheric Lesions Demonstrated by Magnetic Brain Stimulation,
Exp. Br. Res. ; 83 (1991) :419-26.
[16] C. Weiller C., F. Chollet, K. J. Friston, R. J. S. Wise, and J. Frackowiak, Functional Reorganization of the
Brain in Recovery from Striatocapsular Infarction in man. Ann. Neurol. 31(5) 919920 463-72.
[17] M. Steffin, Multineuronal Clamping/Recording with the Ultrafast Single-Electrode Neuronal Voltage
Clamp: Overview of Research Goals and Uses. In: M Witten and D J Vincent (eds.), Computational
Medicine, Public Health, and Biotechnology, , World Scientific Press, Singapore, 1995, pp. 861-886.
[18] 25. K. H. Pribram, Brain and Perception, L Earlbaum & Assoc., 1991.
[19] MacLennan, B, Field Computation in the Brain. In: K. Pribram (ed.), Rethinking Neural Networks:
Quantum Fields and Biological Data, L. Earlbaum & Assoc., 1993, pp. 199-232.
[20] D. H. Miller, P. S. Albert, F. Barkof, G. Francis, J. A. Frank, S. Hodgkinson, F.D. Lublin, D. W. Paty, S.
C. Reingold, and J. Simon, Guidelines for the Use of Magnetic Resonance Techniques in Monitoring the
Treatment of Multiple Sclerosis, Ann. Neurol. 39(1) (1996) 6-16.
[21] D. Grinspan, J. Stern, B. Ranceschini, T. Yasuda., and D. Pleasure, Protein Growth Factors as Potential
Therapies for Central Nervous System Demeylinative Disorders, Ann. Neurol. 36 (1994) S140-S142.
[22] D. Barnes D., P. M. G. Munro, B. D. Youl, J. W. Prineas, and W. I. McDonald, The Longstanding MS
Lesion. A Quantitative MRI and Electron Microscopic Study Brain 114 (1991) 1271-1280.
[23] S. Ludwin, Central Nervous System Remyelination: Studies in Chronically Damaged Tissue, Ann.
GIUSEPPE RIVA (Ed.)
Virtual Reality in Neuro-Psycho-Physiology
1997, 1998 © Ios Press: Amsterdam, Netherlands.
Neurol. 36 (1994) S143-S145.
[24] C. Moll, C. Mourre, M. Lazdunski, and J. Ulrich, Increase of Sodium Channels in Demyelinated Lesions
of Multiple Sclerosis, Br. Res. 556 (1991) 311-316.
[25] J. A. Black, P. Felts, K. J. Smith, J. D. Kocsis, and S. G. Waxman, Distribution of Sodium Channels in
Chronically Demyelinated Spinal Cord Axons: Immuno-Ultrastructural Localization and
Electrophysiological Observations, Br. Res. 544 (1991) 59-70.
[26] S. G. Waxman, Editorial: peripheral nerve abnormalities in multiple sclerosis. Muscle and Nerve 1993
(Jan): 1-5.
[27] B. Dobkin, Neuroplasticity: Key to Recovery after Central Nervous System Injury, West. J. Med. 159
(1993) 56-60.
[28] F. Gandolfo, F. A. and Mussa-Ivaldi, and E. Bizzi, Motor Learing by Field Approximation, Proc. Nat.
Acad. Sci. USA, 93 (1996) 3843-3846.
[29] P. Cordo, L. Bevan, V. Gurfinkel, L. Carlton, M. Carlton, and G. Kerr, Proprioceptive Coordination of
Discrete Movement Sequences: Mechanism and Generality, Can. J. Pharmacol. 73 (1995) 305-315
[30] F. B. Horak and H. C. Diener, Cerebellar Control of Postural Scaling and Central Set in Stance, J.
Neurophys. 72(2) (1994) 479-493.
[31] M. Demirci, S. Grill, L. McShane, and M. Hallett, A Mismatch Between Kinesthetic and Visual
Perception in Parkinson's Disease, Ann. Neurol. 41(6) (1997) 781-788.
[32] Y. Mano, T. Nakamuro, R. Tamura, T. Takayanagi, K. Kawanishi., S. Tamai, and R. Mayer, Central
Motor Reorganization after Anastomosis of the Musculocutaneous and Intercostal Nerves Following
Cervical Root Avulsion, Ann. Neurol. 38(1) 1995 15-20.
[33] M. Hallet, The Plastic Brain, Ann. Neurol. 38(1) (1995) 4-5.
[34] J. Moreland and M. A. Thompson, Efficacy of Electromyographic Biofeedback Compared with
Conventional Physical Therapy for Upper-Extremity Function in Patients Following Stroke: a Research
Overview and Meta-Analysis, Phys. Ther. 74(6) (1994) 534-543.
[35] S. Wolf, Commentary on Moreland and Thompson. Phys. Ther. 74(6) (1994) 544-545.
[36] R. E. Schleenbaker and A. G. Mainous, Electromyographic Biofeedback for Neuromuscular Reeducation
in the Hemiplegic Stroke Patient: a Meta-Analysis, Arch. Phys. Med. Rehab. 74 1301-1304.
[37] G. H. Kraft, Hemiplegia: Evaluation and Rehabilitation of Motor Control Disorders, Phys. Med. Rehab.
Clin. North Am. 4(4) (1993) 687-705.
[38] S. J. Weghorst, Therapeutic Augmented Reality, In: S. J. Weghorst, H. B. Sieberg, and K S Morgan
(eds.), Medicine Meets Virtual Reality: 4. Health Care in the Information Age. IOS Press, Washington DC
(1996) p. 730.
[39] D. W. Repperger, C. A. Phillips, and T. L. Chelette, A Study on Spatially Induced 'Virtual Force' with an
Information Theoretic Investigation of Human Performance. IEEE Transac. Systems, Man, and
Cybernetics 25(10) (1995) 1392-1404.
[40] D. Repperger, Biodynamic and spasticity reduction in joystick control via force reflection, Crew Systems
Directorate Biodynamics and Biocommunications Division Wright Patterson Air Force Base Final Report
for the Period March 1992 to September 1995.
[41] R. DeJong, The Neurologic Examination Fourth Edition . Harper & Row, Hagerstown, Md., 1979
[42] M. Steffin, High-Speed Single-Electrode Voltage Clamp, U.S. Patent 4,441,507 (1984).
[43] K. Syndulko, W. W. Tourtellote, R. Baumfefner, G. W. Ellison, L. M. Myers,, G. Belendiuk, apnd G.
Kondraske, Neuroperformance Evaluation of Nultiple Sclerosis Disease Progression in a Clinical Trial:
Implications for Neurological Outcomes, J. Neuro. Rehab. 7 (1993) 153-176.
[44] V. Gupta and N. Reddy, Surface Electromyogram for the Control of Anthropomorphic Teleoperator
Fingers. In: S J Weghorst, H B Sieberg, and K S Morgan (eds.), Medicine Meets Virtual Reality: 4.
Health Care in the Information Age, IOS Press, Washington DC, 1996, pp. 482-7.
[45] L. Rosenberg L. and D. Stredney, A Haptic Interface for Virtual Simulation of Endoscopic Surgery. In: S.
J. Weghorst, H. B. Sieberg, and K. S. Morgan (eds.), Medicine Meets Virtual Reality: 4. Health Care in the
Information Age, IOS Press, Washington DC, 1996, pp. 371-87.
[46] J. F. Jensen and J. W. Hill, Advanced Telepresence Surgery System Development, In: S. J. Weghorst, H.
B. Sieberg, and K. S. Morgan (eds.), Medicine Meets Virtual Reality: 4. Health Care in the Information
Age, IOS Press, Washington DC, 1996, pp. 107-117.
[47] R. J. Lamson and M. Meisner, Clinical Application of Virtual Reality to Psychiatric Disorders: Theory,
Research, Practice. In: S. J. Weghorst, H. B. Sieberg, and K. S. Morgan (eds.), Medicine Meets Virtual
Reality: 4. Health Care in the Information Age, IOS Press, Washington DC, 1996, pp. 723-74.
GIUSEPPE RIVA (Ed.)
Virtual Reality in Neuro-Psycho-Physiology
1997, 1998 © Ios Press: Amsterdam, Netherlands.
[48] N. North, S. North, and J. R. Coble, Center for the Use of Virtual Reality Technology in the Treatment of
Psychological Disorders. In: S. J. Weghorst, H. B. Sieberg, and K. S. Morgan (eds.), Medicine Meets
Virtual Reality: 4. Health Care in the Information Age, IOS Press, Washington DC, 1996, pp. 725-6.
[49] C. M. Wegner and D. B. Karron, Surgical Navigation Using Audio Feedback. In: K. S. Morgan, H M
Hoffman, D Stredney, S J Weghorst et al. (eds.), Medicine Meets Virtual Reality 5, IOS Press, Amsterdam,
1997, pp. 450-458.