Vahdat S, Darainy M, Ostry DJ (2014)
Structure of plasticity in human sensory and motor
networks due to perceptual learning. J Neurosci
34:2451-63.
Abstract
| Article in PDF format (2.25
MB)
As we begin to
acquire a new motor skill, we face the dual challenge of
determining and refining the somatosensory goals of our
movements and establishing the best motor commands to
achieve our ends. The two typically proceed in parallel,
and accordingly it is unclear how much of skill
acquisition is a reflection of changes in sensory
systems and how much reflects changes in the brain's
motor areas. Here we have intentionally separated
perceptual and motor learning in time so that we can
assess functional changes to human sensory and motor
networks as a result of perceptual learning. Our
subjects underwent fMRI scans of the resting brain
before and after a somatosensory discrimination task. We
identified changes in functional connectivity that were
due to the effects of perceptual learning on movement.
For this purpose, we used a neural model of the
transmission of sensory signals from perceptual decision
making through to motor action. We used this model in
combination with a partial correlation technique to
parcel out those changes in connectivity observed in
motor systems that could be attributed to activity in
sensory brain regions. We found that, after removing
effects that are linearly correlated with somatosensory
activity, perceptual learning results in changes to
frontal motor areas that are related to the effects of
this training on motor behavior and learning. This
suggests that perceptual learning produces changes to
frontal motor areas of the brain and may thus contribute
directly to motor learning.
Darainy M, Vahdat S, Ostry DJ (2013)
Perceptual learning in sensorimotor adaptation. J
Neurophysiol 110: 2152-2162.
Abstract
| Article in PDF format (935
KB)
Motor learning
often involves situations in which the somatosensory
targets of movement are initially, poorly defined, as
for example, in learning to speak or learning the feel
of a proper tennis serve. Under these conditions, motor
skill acquisition presumably requires perceptual as well
as motor learning. That is, it engages both the
progressive shaping of sensory targets and associated
changes in motor performance. In the present paper, we
test the idea that perceptual learning alters
somatosensory function and in so doing produces changes
to motor performance and sensorimotor adaptation.
Subjects in these experiments undergo perceptual
training in which a robotic device passively moves the
arm on one of a set of fan shaped trajectories. Subjects
are required to indicate whether the robot moved the
limb to the right or the left and feedback is provided.
Over the course of training both the perceptual boundary
and acuity are altered. The perceptual learning is
observed to improve both the rate and extent of learning
in a subsequent sensorimotor adaptation task and the
benefits persist for at least 24 hours. The improvement
in the present studies is obtained regardless of whether
the perceptual boundary shift serves to systematically
increase or decrease error on subsequent movements. The
beneficial effects of perceptual training are found to
be substantially dependent upon reinforced
decision-making in the sensory domain. Passive-movement
training on its own is less able to alter subsequent
learning in the motor system. Overall, this study
suggests perceptual learning plays an integral role in
motor learning.
Ito S, Darainy M, Sasaki M, Ostry DJ
(2013) Computational model of motor learning and
perceptual change. Biol Cybern 107:653-667.
Abstract
| Article in PDF format (1.28
MB)
Motor learning
in the context of arm reaching movements has been
frequently investigated using the paradigm of
force-field learning. It has been recently shown that
changes to somatosensory perception are likewise
associated with motor learning. Changes in perceptual
function may be the reason that when the perturbation is
removed following motor learning, the hand trajectory
does not return to a straight line path even after
several dozen trials. To explain the computational
mechanisms that produce these characteristics, we
propose a motor control and learning scheme using a
simplified two-link system in the horizontal plane:We
represent learning as the adjustment of desired
joint-angular trajectories so as to achieve the
reference trajectory of the hand. The convergence of the
actual hand movement to the reference trajectory is
proved by using a Lyapunov-like lemma, and the result is
confirmed using computer simulations. The model assumes
that changes in the desired hand trajectory influence
the perception of hand position and this in turn affects
movement control. Our computer simulations support the
idea that perceptual change may come as a result of
adjustments to movement planning with motor learning.
Bernardi NF, Darainy M, Bricolo E,
Ostry DJ (2013) Observing motor learning produces
somatosensory change. J Neurophysiol 110: 1804-1810.
Abstract
| Article in PDF format (264
KB)
Observing the
actions of others has been shown to affect motor
learning, but does it have effects on sensory systems as
well? It has been recently shown that motor learning
that involves actual physical practice is also
associated with plasticity in the somatosensory system.
Here, we assessed the idea that observational learning
likewise changes somatosensory function. We evaluated
changes in somatosensory function after human subjects
watched videos depicting motor learning. Subjects first
observed video recordings of reaching movements either
in a clockwise or counterclockwise force field. They
were then trained in an actual force-field task that
involved a counterclockwise load. Measures of
somatosensory function were obtained before and after
visual observation and also following force-field
learning. Consistent with previous reports, video
observation promoted motor learning. We also found that
somatosensory function was altered following
observational learning, both in direction and in
magnitude, in a manner similar to that which occurs when
motor learning is achieved through actual physical
practice. Observation of the same sequence of movements
in a randomized order did not result in somatosensory
perceptual change. Observational learning and real
physical practice appear to tap into the same capacity
for sensory change in that subjects that showed a
greater change following observational learning showed a
reliably smaller change following physical motor
learning. We conclude that effects of observing motor
learning extend beyond the boundaries of traditional
motor circuits, to include somatosensory
representations.
Nasir SM, Darainy M, Ostry DJ (2013)
Sensorimotor adaptation changes the neural coding of
somatosensory stimuli. J. Neurophysiol. 109:2077-85.
Abstract
| Article in PDF format (692 KB)
Motor learning
is reflected in changes to the brain’s functional
organization as a result of experience. We show here
that these changes are not limited to motor areas of the
brain and indeed that motor learning also changes
sensory systems. We test for plasticity in sensory
systems using somatosensory evoked potentials (SEPs). A
robotic device is used to elicit somatosensory inputs by
displacing the arm in the direction of applied force
during learning. We observe that following learning
there are short latency changes to the response in
somatosensory areas of the brain that are reliably
correlated with the magnitude of motor learning:
subjects who learn more show greater changes in SEP
magnitude. The effects we observe are tied to motor
learning. When the limb is displaced passively, such
that subjects experience similar movements but without
experiencing learning, no changes in the evoked response
are observed. Sensorimotor adaptation thus alters the
neural coding of somatosensory stimuli.
Mattar AAG, Darainy M, Ostry DJ (2013)
Motor learning and its sensory effects: The time course of
perceptual change, and its presence with gradual
introduction of load. J. Neurophysiol. 109:782-91.
Abstract
| Article in PDF format (413 KB)
A complex
interplay has been demonstrated between motor and
sensory systems. We showed recently that motor learning
leads to changes in the sensed position of the limb
(Ostry DJ, Darainy M, Mattar AA, Wong
J, Gribble PL. J Neurosci 30: 5384–5393, 2010). Here, we
document further the links between motor learning and
changes in somatosensory perception. To study motor
learning, we used a force field paradigm in which
subjects learn to compensate for forces applied to the
hand by a robotic device. We used a task in which
subjects judge lateral displacements of the hand to
study somatosensory perception. In a first experiment,
we divided the motor learning task into incremental
phases and tracked sensory perception throughout. We
found that changes in perception occurred at a slower
rate than changes in motor performance. A second
experiment tested whether awareness of the motor
learning process is necessary for perceptual change. In
this experiment, subjects were exposed to a force field
that grew gradually in strength. We found that the shift
in sensory perception occurred even when awareness of
motor learning was reduced. These experiments argue for
a link between motor learning and changes in
somatosensory perception, and they are consistent with
the idea that motor learning drives sensory change.
Mattar AAG, Nasir SM, Darainy M, Ostry
DJ (2011) Sensory change following motor
learning. in Green AM, Chapman CE, Kalaska JF
and Lepore F (Eds), Progress in Brain Research, Volume 191
(pp 29-42).
Abstract
| Article in PDF format (1.10
MB)
Here we
describe two studies linking perceptual change with
motor learning. In the first, we document persistent
changes in somatosensory perception that occur following
force field learning. Subjects learned to control a
robotic device that applied forces to the hand during
arm movements. This led to a change in the sensed
position of the limb that lasted at least 24 h. Control
experiments revealed that the sensory change depended on
motor learning. In the second study, we describe changes
in the perception of speech sounds that occur following
speech motor learning. Subjects adapted control of
speech movements to compensate for loads applied to the
jaw by a robot. Perception of speech sounds was measured
before and after motor learning. Adapted subjects showed
a consistent shift in perception. In contrast, no
consistent shift was seen in control subjects and
subjects that did not adapt to the load. These studies
suggest that motor learning changes both sensory and
motor function.
Vahdat S, Darainy M, Milner TE, Ostry
DJ (2011) Functionally specific changes in
resting-state sensorimotor networks after motor
learning. J Neurosci. 31:16907–16915.
Abstract
| Article in PDF format
(603 KB)
Motor learning
changes the activity of cortical motor and subcortical
areas of the brain, but does learning affect sensory
systems as well? We examined inhumansthe effects of
motor learning using fMRI measures of functional
connectivity under resting conditions and found
persistent changes in networks involving both motor and
somatosensory areas of the brain. We developed a
technique that allows us to distinguish changes in
functional connectivity that can be attributed to motor
learning from those that are related to perceptual
changes that occur in conjunction with learning. Using
this technique, we identified a new network in motor
learning involving second somatosensory cortex, ventral
premotor cortex, and supplementary motor cortex whose
activation is specifically related to perceptual changes
that occur in conjunction with motor learning. We also
found changes in a network comprising cerebellar cortex,
primary motor cortex, and dorsal premotor cortex that
were linked to the motor aspects of learning. In each
network, we observed highly reliable linear
relationships between neuroplastic changes and
behavioral measures of either motor learning or
perceptual function. Motor learning thus results in
functionally specific changes to distinct resting-state
networks in the brain.
Ostry DJ, Darainy M, Mattar AAG, Wong
J, Gribble PL (2010) Somatosensory plasticity and motor
learning. J Neurosci 30:5384-5393.
Abstract
| Article in PDF format (1016 KB)
- J Neurosci Journal Club
Commentary PDF format (210 KB) - J Neurophysiol Neuro Forum
Commentary PDF format (106 KB)
Motor learning
is dependent upon plasticity in motor areas of the
brain, but does it occur in isolation, or does it also
result in changes to sensory systems? We examined
changes to somatosensory function that occur in
conjunction with motor learning. We found that even
after periods of training as brief as 10 min, sensed
limb position was altered and the perceptual change
persisted for 24 h. The perceptual change was reflected
in subsequent movements; limb movements following
learning deviated from the prelearning trajectory by an
amount that was not different in magnitude and in the
same direction as the perceptual shift. Crucially, the
perceptual change was dependent upon motor learning.
When the limb was displaced passively such that subjects
experienced similar kinematics but without learning, no
sensory change was observed. The findings indicate that
motor learning affects not only motor areas of the brain
but changes sensory function as well.
Darainy M, Mattar AAG, Ostry DJ
(2009) Effects of human arm impedance on dynamics learning
and generalization. J Neurophysiol 101:3158–3168.
Abstract
| Article in PDF format (408 KB)
Previous
studies have demonstrated anisotropic patterns of hand
impedance under static conditions and during movement.
Here we show that the pattern of kinematic error
observed in studies of dynamics learning is associated
with this anisotropic impedance pattern. We also show
that the magnitude of kinematic error associated with
this anisotropy dictates the amount of motor learning
and, consequently, the extent to which dynamics learning
generalizes. Subjects were trained to reach to visual
targets while holding a robotic device that applied
forces during movement. On infrequent trials, the load
was removed and the resulting kinematic error was
measured. We found a strong correlation between the
pattern of kinematic error and the anisotropic pattern
of hand stiffness. In a second experiment subjects were
trained under force-field conditions to move in two
directions: one in which the dynamic perturbation was in
the direction of maximum arm impedance and the
associated kinematic error was low and another in which
the perturbation was in the direction of low impedance
where kinematic error was high. Generalization of
learning was assessed in a reference direction that lay
intermediate to the two training directions. We found
that transfer of learning was greater when training
occurred in the direction associated with the larger
kinematic error. This suggests that the anisotropic
patterns of impedance and kinematic error determine the
magnitude of dynamics learning and the extent to which
it generalizes.
Darainy M, Ostry DJ (2008) Muscle
cocontraction following dynamics learning. Exp Brain Res
190:153-163.
Abstract
| Article in PDF
format (1.0 MB)
Coactivation of
antagonist muscles is readily observed early in
motor learning, in interactions with unstable
mechanical environments and in motor system
pathologies. Here we present evidence that the
nervous system uses coactivation
control far more extensively and that patterns of cocontraction during
movement are closely tied to the specific
requirements of the task. We have examined the
changes in cocontraction
that follow dynamics learning in tasks that are
thought to involve finely sculpted feedforward adjustments to
motor commands. We find that, even following
substantial training, cocontraction
varies in a systematic way that depends on both
movement direction and the strength of the external
load. The proportion of total activity that is due
to cocontraction
nevertheless remains remarkably constant. Moreover,
long after indices of motor learning and electromyographic measures
have reached asymptotic levels, cocontraction still accounts
for a significant proportion of total muscle
activity in all phases of movement and in all load
conditions. These results show that even following
dynamics learning in predictable and stable
environments, cocontraction
forms a central part of the means by which the
nervous system regulates movement.
Darainy M, Towhidkhah F, Ostry DJ (2007)
Control of hand impedance under static conditions and
during reaching movement. J Neurophysiol 97:2676–2685.
Abstract
| Article in PDF format
(1890 KB)
It is known
that humans can modify the impedance of the
musculoskeletal periphery, but the extent of
this modification is uncertain. Previous
studies on impedance control under static conditions
indicate a limited ability to modify impedance,
whereas studies of impedance control during
reaching in unstable environments suggest a
greater range of impedance modification. As a first
step in accounting for this difference, we
quantified the extent to which stiffness
changes from posture to movement even when there
are no destabilizing forces. Hand stiffness was
estimated under static conditions and at the
same position during both longitudinal (near
to far) and lateral movements
using a position-servo technique. A new
method was developed to predict the hand "reference"
trajectory for purposes of estimating stiffness.
For movements in a longitudinal direction,
there was considerable counterclockwise rotation
of the hand stiffness ellipse relative to stiffness
under static conditions. In contrast, a small
counterclockwise rotation was observed
during lateral movement. In the modeling studies,
even when we used the same modeled cocontraction level
during posture and movement, we found that there
was a substantial difference in the
orientation of the stiffness ellipse, comparable with
that observed empirically. Indeed, the main determinant
of the orientation of the ellipse in our modeling
studies was the movement direction and the
muscle activation associated with movement.
Changes in the cocontraction level and the balance
of cocontraction had smaller effects. Thus even
when there is no environmental instability,
the orientation of stiffness ellipse changes
during movement in a manner that varies with movement
direction.
Darainy M, Malfait N, Towhidkhah F,
Ostry DJ (2006) Transfer and durability of acquired
patterns of human arm stiffness. Exp Brain Res
170:227-237.
Abstract
| Article in
PDF format (308 KB)
We used a
robotic device to test the idea that impedance control
involves a process of learning or adaptation that is
acquired over time and permits the voluntary control of
the pattern of stiffness at the hand. The tests were
conducted in statics. Subjects were trained over the
course of three successive days to resist the effects of
one of three different kinds of mechanical loads, single
axis loads acting in the lateral direction, single axis
loads acting in the forward/backward direction and
isotropic loads that perturbed the limb in eight
directions about a circle. We found that subjects in
contact with single axis loads voluntarily modified
their hand stiffness orientation such that changes to
the direction of maximum stiffness mirrored the
direction of applied load. In the case of isotropic
loads, a uniform increase in endpoint stiffness was
observed. Using a physiologically realistic model of
two-joint arm movement, the experimentally determined
pattern of impedance change could be replicated by
assuming that coactivation of elbow and double joint
muscles was independent of coactivation of muscles at
the shoulder. Moreover, using this pattern of
coactivation control we were able to replicate an
asymmetric pattern of rotation of the stiffness ellipse
that was observed empirically. The present findings are
consistent with the idea that arm stiffness is
controlled through the use of at least two independent
cocontraction commands.
Darainy M, Malfait N, Gribble PL,
Towhidkhah F, Ostry DJ (2004) Learning to control arm
stiffness under static conditions. J
Neurophysiol 92:3344-3350.
Abstract
| Article
in PDF format (267 KB)
We used a robotic device to test the idea that
impedance control involves a process of learning or
adaptation that is acquired over time and permits the
voluntary control of the pattern of stiffness at the
hand. The tests were conducted in statics. Subjects
were trained over the course of three successive days
to resist the effects of one of three different kinds
of mechanical loads, single axis loads acting in the
lateral direction, single axis loads acting in the
forward/backward direction and isotropic loads that
perturbed the limb in eight directions about a circle.
We found that subjects in contact with single axis
loads voluntarily modified their hand stiffness
orientation such that changes to the direction of
maximum stiffness mirrored the direction of applied
load. In the case of isotropic loads, a uniform
increase in endpoint stiffness was observed. Using a
physiologically realistic model of two-joint arm
movement, the experimentally determined pattern of
impedance change could be replicated by assuming that
coactivation of elbow and double joint muscles was
independent of coactivation of muscles at the
shoulder. Moreover, using this pattern of coactivation
control we were able to replicate an asymmetric
pattern of rotation of the stiffness ellipse that was
observed empirically. The present findings are
consistent with the idea that arm stiffness is
controlled through the use of at least two independent
cocontraction commands.
Conference presentations / published abstracts
Darainy M, Vahdat S,
Ostry DJ. Changes to motor learning following sensory
training. The 42st annual meeting of the society for
neuroscience, New Orleans, LA October 2012.
Vahdat S, Darainy M,
Milner TE, Ostry DJ. Motor learning alters sensorimotor
resting-state networks in the brain. The 41st annual
meeting of the society for neuroscience, Washington, DC
November 2011.
Darainy M, Mattar
AAG, Ostry DJ. Exploring the links between motor
learning and changes to somatosensory function. The 40th
annual meeting of the society for neuroscience, San
Diego, CA November 2010.
Mattar AAG, Darainy M,
Ostry DJ. Generalization of sensory change following
dynamics learning. The 40th annual meeting of the
society for neuroscience, San Diego, CA November 2010.
Darainy M, Mattar
AAG, Wong J, Gribble PL, Ostry DJ. The sensed position
of the limb changes following dynamics learning.
Presented at the 39th Annual Meeting of the Society for
Neuroscience, Chicago, IL, 2009.
Ostry DJ, Darainy M,
Mattar AAG, Nasir SM, Wong J, Gribble PL. Sensory
plasticity and motor learning. Presented at the 19th
Annual Neural Control of Movement Meeting, Waikoloa, HI,
2009.
Darainy M, Mattar
AAG, Ostry DJ. Anisotropic pattern of hand impedance can
affect dynamics learning and generalization. Presented
at the 38th annual meeting of the society for
neuroscience, Washington, DC, 2008.
Darainy M, Ostry DJ.
Hand interaction with the environment determines the
desired trajectory. Presented at the 18th annual neural
control of movement meeting, Naples, FL, 2008.
Darainy M, Ostry DJ.
Muscle cocontraction following motor learning. Presented
at the 37th annual meeting of the society for
neuroscience, San Diego, CA, 2007.
Darainy M. A model control
based on feed-forward and impedance
control. Presented at the 36th annual meeting of
the society for neuroscience, Atlanta, GA, 2006.
Darainy M, Towhidkhah F, Ostry
DJ. A matched comparison of hand impedance under
stationary conditions and during reaching movement.
Presented at the 35th annual meeting of the society for
neuroscience, Washington DC, 2005.
Darainy M, Malfait N, Gribble
PL, Towhidkhah F, Ostry DJ. Coordinate system of arm
stiffness control in statics. Presented at the 34th
annual meeting of the society for neuroscience, San
Diego, CA, 2004.
Darainy M, Malfait N, Gribble
PL, Towhidkhah F, Ostry DJ. Control of human arm
impedance in statics. Presented at the 2nd international
symposium on measurement, analysis and modeling of human
Functions, Genova, Italy, 2004.
Darainy M, Malfait N, Gribble
PL, Towhidkhah F, Ostry DJ. Learning impedance control
in statics. Presented at the 14th annual neural control
of movement meeting, Barcelona, Spain, 2004.
