|
David J Ostry, PhD |
 |
Journal Articles
Ebrahimi S, van der Voort B, Ostry DJ (2024) The consolidation of newly learned movements depends upon the somatosensory cortex in Humans. 44 (32) e0629242024
Abstract | PDF
Studies
using magnetic brain stimulation indicate the involvement
of somatosensory regions in the acquisition and retention
of newly learned movements. Recent work found an
impairment in motor memory when retention was tested
shortly after the appli- cation of continuous theta-burst
stimulation (cTBS) to the primary somatosensory cortex,
compared with stimulation of the primary motor cortex or a
control zone. This finding that the somatosensory cortex
is involved in motor memory retention whereas the motor
cortex is not, if confirmed, could alter our understanding
of human motor learning. It would indicate that plasticity
in sensory systems underlies newly learned movements,
which is different than the commonly held view that
adaptation learning involves updates to a motor
controller. Here we test this idea. Participants were
trained in a visuomotor adaptation task, with visual
feedback gradually shifted. Following adaptation, cTBS was
applied either to M1, S1, or an occipital cortex control
area. Participants were tested for retention 24 h later.
It was observed that S1 stimulation led to reduced
retention of prior learning, compared with stimulation of
M1 or the control area (with no significant difference
between M1 and control). In a further control, cTBS was
applied to S1 following training with unrotated feedback,
in which no learning occurred. This had no effect on
movement in the retention test indicating the effects of
S1 stimulation on movement are learning specific. The
findings are consistent with the S1 participation in the
encoding of learning-related changes to movements and in
the retention of human motor memory.
Ebrahimi S, Ostry DJ (2024) The human somatosensory cortex contributes to the encoding of newly learned movements. Proc Natl Sci USA 121: e2316294121.
Abstract | PDF
Recent
studies have indicated somatosensory cortex involvement in
motor learning and retention. However, the nature of its
contribution is unknown. One possibility is that the
somatosensory cortex is transiently engaged during
movement. Alternatively, there may be durable
learning-related changes which would indicate sensory
participation in the encoding of learned movements. These
possibilities are dissociated by disrupting the
somatosensory cortex following learning, thus targeting
learning-related changes which may have occurred. If
changes to the somatosensory cortex contribute to
retention, which, in effect, means aspects of newly
learned movements are encoded there, disruption of this
area once learning is complete should lead to an
impairment. Participants were trained to make movements
while receiving rotated visual feedback. The primary motor
cortex (M1) and the primary somatosensory cortex (S1) were
targeted for continuous theta-burst stimulation, while
stimulation over the occipital cortex served as a control.
Retention was assessed using active movement reproduction,
or recognition testing, which involved passive movements
produced by a robot. Disruption of the somatosensory
cortex resulted in impaired motor memory in both tests.
Suppression of the motor cortex had no impact on retention
as indicated by comparable retention levels in control and
motor cortex conditions. The effects were learning
specific. When stimulation was applied to S1 following
training with unrotated feedback, movement direction, the
main dependent variable, was unaltered. Thus, the
somatosensory cortex is part of a circuit that contributes
to retention, consistent with the idea that aspects of
newly learned movements, possibly learning-updated sensory
states (new sensory targets) which serve to guide
movement, may be encoded there.
Darainy M, Manning TF (2023) Disruption of somatosensory cortex impairs motor learning and retention. J Neurophysiol 130: 1521-1528.
Abstract | PDF
This
study tests for a function of the somatosensory cortex,
that, in addition to its role in processing somatic
afferent information, somatosensory cortex contributes
both to motor learning and the stabilization of motor
memory. Continuous theta-burst magnetic stimulation (cTBS)
was applied, before force-field training to disrupt
activity in either the primary somatosensory cortex,
primary motor cortex, or a control zone over the occipital
lobe. Tests for retention and relearning were conducted
after a 24 h delay. Analysis of movement kinematic
measures and force-channel trials found that cTBS to
somatosensory cortex disrupted both learning and
subsequent retention, whereas cTBS to motor cortex had
little effect on learning but possibly impaired retention.
Basic movement variables are unaffected by cTBS suggesting
that the stimulation does not interfere with movement but
instead disrupts changes in the cortex that are necessary
for learning. In all experimental conditions, relearning
in an abruptly introduced force field, which followed
retention testing, showed extensive savings, which is
consistent with previous work suggesting that more
cognitive aspects of learning and retention are not
dependent on either of the cortical zones under test.
Taken together, the findings are consistent with the idea
that motor learning is dependent on learning-related
activity in the somatosensory cortex. NEW & NOTEWORTHY
This study uses noninvasive transcranial magnetic
stimulation to test the contribution of somatosensory and
motor cortex to human motor learning and retention.
Continuous theta-burst stimulation is applied before
learning; participants return 24 h later to assess
retention. Disruption of the somatosensory cortex is found
to impair both learning and retention, whereas disruption
of the motor cortex has no effect on learning. The
findings are consistent with the idea that motor learning
is dependent upon learning-related plasticity in
somatosensory cortex.
Franken M, Liu B, Ostry DJ (2022) Towards a somatosensory theory of speech perception. J Neurophysiol 128: 1683-1695.
Abstract | PDF
Speech perception is known to be a multimodal process,
relying not only on auditory input but also on the visual
system and possibly on the motor system as well. To date
there has been little work on the potential involvement of
the somatosensory sys- tem in speech perception. In the
present review, we identify the somatosensory system as
another contributor to speech per- ception. First, we
argue that evidence in favor of a motor contribution to
speech perception can just as easily be interpreted as
showing somatosensory involvement. Second, physiological
and neuroanatomical evidence for auditory-somatosensory
interac- tions across the auditory hierarchy indicates the
availability of a neural infrastructure that supports
somatosensory involvement in auditory processing in
general. Third, there is accumulating evidence for
somatosensory involvement in the context of speech
specifically. In particular, tactile stimulation modifies
speech perception, and speech auditory input elicits
activity in somatosen- sory cortical areas. Moreover,
speech sounds can be decoded from activity in
somatosensory cortex; lesions to this region affect
perception, and vowels can be identified based on somatic
input alone. We suggest that the somatosensory involvement
in speech perception derives from the
somatosensory-auditory pairing that occurs during speech
production and learning. By bringing together findings
from a set of studies that have not been previously
linked, the present article identifies the somato- sensory
system as a presently unrecognized contributor to speech
perception.
Ebrahimi S, Ostry DJ (2022) Persistence of adaptation following visuomotor training. J Neurophysiol 128:1312-1323.
Abstract | PDF
Retention tests conducted after sensorimotor adaptation
frequently exhibit a rapid return to baseline performance
once the altered sensory feedback is removed. This
so-called washout of learning stands in contrast with
other demonstrations of retention, such as savings on
re-learning and anterograde interference effects of
initial learning on new learning. In the present study, we
tested the hypothesis that washout occurs when there is a
detectable discrepancy in retention tests between visual
information on the target position and somatosensory
information on the position of the limb. Participants were
tested following adaptation to gradually rotated visual
feedback (15 degree or 30 degree). Two different types of
targets were used for retention testing, a point target in
which a perceptual mismatch is possible, and an arc-target
that eliminated the mismatch. It was found that, except
when point targets were used, retention test movements
were stable throughout aftereffect trials, indicating
little loss of information. Substantial washout was only
observed in tests with a single point target, following
adaptation to a large amplitude 30 degree rotation. In
control studies designed to minimize the use of explicit
strategies during learning, we observed similar patterns
of decay when participants moved to point targets that
suggests that the effects observed here relate primarily
to implicit learning. The results suggest that washout in
aftereffect trials following visuomotor adaptation is due
to a detectable mismatch between vision and
somatosensation. When the mismatch is removed
experimentally, there is little evidence of loss of
information.NEW & NOTEWORTHY Aftereffects following
sensorimotor adaptation are important because they bear on
the understanding of the mechanisms that subserve
forgetting. We present evidence that information loss
previously reported during retention testing occurs only
when there is a detectable discrepancy between vision and
somatosensation and, if this mismatch is removed, the
persistence of adaptation is observed. This suggests that
washout during aftereffect trials is a consequence of the
experimental design rather than a property of the memory
system itself.
Kumar N, Sidarta A, Smith C, Ostry DJ (2022) Ventrolateral prefrontal cortex contributes to human motor learning. eNeuro
Abstract | PDF
This study assesses the involvement in human motor
learning, of the ventrolateral prefrontal cortex (BA
9/46v), a somatic region in the middle frontal gyrus. The
potential involvement of this cortical area in motor
learning is suggested by studies in nonhuman primates
which have found anatomic connections between this area
and sensorimotor regions in frontal and parietal cortex,
and also with basal ganglia output zones. It is likewise
sug- gested by electrophysiological studies which have
shown that activity in this region is implicated in
somatic sensory memory and is also influenced by reward.
We directly tested the hypothesis that area 9/46v is in-
volved in reinforcement-based motor learning in humans.
Participants performed reaching movements to a hidden
target and received positive feedback when successful.
Before the learning task, we applied continu- ous theta
burst stimulation (cTBS) to disrupt activity in 9/46v in
the left or right hemisphere. A control group received
sham cTBS. The data showed that cTBS to left 9/46v almost
entirely eliminated motor learning, whereas learning was
not different from sham stimulation when cTBS was applied
to the same zone in the right hemisphere. Additional
analyses showed that the basic reward-history-dependent
pattern of movements was preserved but more variable
following left hemisphere stimulation, which suggests an
overall deficit in so- matic memory for target location or
target directed movement rather than reward processing per
se. The re- sults indicate that area 9/46v is part of the
human motor learning circuit.
Sidarta A, Komar J, Ostry DJ (2022) Clustering analysis of movement kinematics in reinforcement learning. J Neurophysiol 127:341-353.
Abstract | PDF
Reinforcement learning has been used as an experimental
model of motor skill acquisition, where at times movements
are suc- cessful and thus reinforced. One fundamental
problem is to understand how humans select exploration
over exploitation during learning. The decision could be
influenced by factors such as task demands and reward
availability. In this study, we applied a clustering
algorithm to examine how a change in the accuracy
requirements of a task affected the choice of exploration
over ex- ploitation. Participants made reaching movements
to an unseen target using a planar robot arm and received
reward after each successful movement. For one group of
participants, the width of the hidden target decreased
after every other training block. For a second group, it
remained constant. The clustering algorithm was applied to
the kinematic data to characterize motor learning on a
trial-to-trial basis as a sequence of movements, each
belonging to one of the identified clusters. By the end of
learning, movement trajectories across all participants
converged primarily to a single cluster with the greatest
number of suc- cessful trials. Within this analysis
framework, we defined exploration and exploitation as
types of behavior in which two succes- sive trajectories
belong to different or similar clusters, respectively. The
frequency of each mode of behavior was evaluated over the
course of learning. It was found that by reducing the
target width, participants used a greater variety of
different clusters and displayed more exploration than
exploitation. Excessive exploration relative to
exploitation was found to be detrimental to subsequent
motor learning. NEW & NOTEWORTHY The choice of
exploration versus exploitation is a fundamental problem
in learning new motor skills through reinforcement. In
this study, we employed a data-driven approach to
characterize movements on a trial-by-trial basis with an
unsupervised clustering algorithm. Using this technique,
we found that changes in task demands and, in particular,
in the required accuracy of movements, influenced the
ratio of exploration to exploitation. This analysis
framework provides an attractive tool to investigate
mechanisms of explorative and exploitative behavior while
studying motor learning.
Sedda G, Ostry DJ (2021) Self-operated stimuli improve subsequent visual motion integration. J Vision 21:13,1-15.
Abstract | PDF
Evidences of perceptual changes that accompany motor
activity have been limited primarily to audition and
somatosensation. Here we asked whether motor learning
results in changes to visual motion perception. We
designed a reaching task in which participants were
trained to make movements along several directions, while
the visual feedback was provided by an intrinsically
ambiguous moving stimulus directly tied to hand motion. We
find that training improves coherent motion perception and
that changes in movement are correlated with perceptual
changes. No perceptual changes are observed in passive
training even when observers were provided with an
explicit strategy to facilitate single motion perception.
A Bayesian model suggests that movement training promotes
the fine-tuning of the internal representation of stimulus
geometry. These results emphasize the role of sensorimotor
interaction in determining the persistent properties in
space and time that define a percept.
Ohashi H, Ostry DJ (2021) Neural development of speech sensorimotor learning. J Neurosci 41:4023-4035.
Abstract | PDF
The development of the human brain continues through to
early adulthood. It has been suggested that cortical
plasticity during this protracted period of development
shapes circuits in associative transmodal regions of the
brain. Here we considered how cortical plasticity during
development might contribute to the coordinated brain
activity required for speech motor learning. Specifically,
we examined patterns of brain functional connectivity
whose strength covaried with the capacity for speech
audio-motor adaptation in children ages 5-12 and in young
adults of both sexes. Children and adults showed distinct
patterns of the encoding of learning in the brain. Adult
performance was associated with connectivity in transmodal
regions that integrate auditory and somatosensory
information, whereas children rely on basic somatosensory
and motor circuits. A progressive reliance on transmodal
regions is consistent with human cortical development and
suggests that human speech motor adaptation abilities are
built on cortical remodeling that is observable in late
childhood and is stabilized in adults.
Kumar N, van Vugt FT, Ostry DJ (2021) Recognition memory for human motor learning. Curr Biol 31:1678-1686.
Abstract | PDF
Motor skill retention is typically measured by asking
participants to reproduce previously learned movements
from memory. The analog of this retention test (recall
memory) in human verbal memory is known to under-estimate
how much learning is actually retained. Here we asked
whether information about previously learned movements,
which can no longer be reproduced, is also retained.
Following visuomotor adaptation,we used tests of recall
that involved reproduction of previously learned movements
and tests of recognition in which participants were asked
whether a candidate limb displacement, produced by a robot
arm held by the subject, corresponded to a movement
direction that was experienced during active training. The
main finding was that 24 h after training, estimates of
recognition memory were about twice as accurate as those
of recall memory. Thus, there is information about
previously learned movements that is not retrieved using
recall testing but can be accessed in tests of
recognition. We conducted additional tests to assess
whether,24 h after learning, recall for previously learned
movements could be improved by presenting passive
movements as retrieval cues. These tests were conducted
immediately prior to recall testing and involved the
passive playback of a small number of movements, which
were spread across the workspace and included both adapted
and baseline movements, without being marked as such. This
technique restored recall memory for movements to levels
close to those of recognition memory performance. Thus,
somatic information may enable retrieval of otherwise
inaccessible motor memories.
van Vugt FT, Near J, Hennessy T, Doyon J, Ostry DJ (2020) Early stages of sensorimotor map acquisition: neurochemical signature in primary motor cortex and its relation to functional connectivity. J Neurophysiol 122: 1708-1720.
Abstract | PDF
One of the puzzles of learning to talk or play a musical
instrument is how we learn which movement produces a
particular sound: an audiomotor map. The initial stages of
map acquisition can be studied by having participants
learn arm movements to auditory targets. The key question
is what mechanism drives this early learning. Three
learning processes from previous literature were tested:
map learning may rely on active motor outflow (target), on
error correction, and on the correspondence between
sensory and motor distances (i.e., that similar movements
map to similar sounds). Alternatively, we hypothesized
that map learning can proceed without these. Participants
made movements that were mapped to sounds in a number of
different conditions that each precluded one of the
potential learning processes.We tested whether map
learning relies on assumptions about topological
continuity by exposing participants to a permuted map that
did not preserve distances in auditory and motor space.
Further groups were tested who passively experienced the
targets, kinematic trajectories produced by a robot arm,
and auditory feedback as a yoked active participant (hence
without active motor outflow). Another group made
movements without receiving targets (thus without
experiencing errors). In each case we observed substantial
learning,therefore none of the three hypothesized
processes is required for learning. Instead early map
acquisition can occur with free exploration without target
error correction, is based on sensory-to-sensory
correspondences, and possible even for discontinuous maps.
The findings are consistent with the idea that early
sensorimotor map formation can involve instance-specific
learning. NEW & NOTEWORTHY: This study tested learning
of novel sensorimotor maps in a variety of unusual
circumstances, including learning a mapping that was
permuted in such as way that it fragmented the
sensorimotor workspace into discontinuous parts, thus no
preserving sensory and motor topology. Participants could
learn this mapping, and they could learn without motor
outflow or targets. These results point to a robust
learning mechanism building on individual instances,
inspired from machine learning literature.
Ito T, Bai J, Ostry DJ (2020) Contribution of sensory memory
to speech motor learning. J Neurophysiol 124:1103-1109. Abstract | PDF
Speech
learning requires precise motor control, but it likewise
requires transient storage of information to enable the
adjustment of upcoming movements based on the success or
failure of previous attempts.The contribution of somatic
sensory memory for limb position has been documented in
work on arm movement; however, in speech,the sensory
support for speech production comes from both
somatosensory and auditory inputs, and accordingly sensory
memory for either or both of sounds and somatic inputs
might contribute to learning. In the present study,
adaptation to altered auditory feed-back was used as an
experimental model of speech motor learning.Participants
also underwent tests of both auditory and somatic sensory
memory. We found that although auditory memory for speech
sounds is better than somatic memory for speech-like
facial skin deformations, somatic sensory memory predicts
adaptation, where as auditory sensory memory does not.
Thus even though speech relies substantially on auditory
inputs and in the present manipulation adaptation requires
the minimization of auditory error, it is somatic inputs
that provide the memory support for learning. NEW &
NOTEWORTHY: In speech production, almost everyone achieves
an exceptionally high level of proficiency. This is
remark-able because speech involves some of the smallest
and most care-fully timed movements of which we are
capable. The present paper demonstrates that sensory
memory contributes to speech motor learning. Moreover, we
report the surprising result that somatic sensory memory
predicts speech motor learning, whereas auditory memory
does not.
Patri JF, Ostry DJ, Diard J, Schwartz JL, Trudeau-Fisette P, Savariaux C, Perrier P (2020) Speakers are able to categorize vowels based on tongue somatosensation. Proc Natl Acad Sci U S A. 117:6255-6263.
Abstract | PDF
Auditory speech perception enables listeners to access
phonological categories from speech sounds. During speech
production and speech motor learning, speakers' experience
matched auditory and somatosensory input. Accordingly,
access to phonetic units might also be provided by
somatosensory information.The present study assessed
whether humans can identify vowels using somatosensory
feedback, without auditory feedback.A tongue-positioning
task was used in which participants were required to
achieve different tongue postures within the /e,?, a/
articulatory range, in a procedure that was totally
non-speech like, involving distorted visual feedback of
tongue shape.Tongue postures were measured using
electromagnetic articulography. At the end of each
tongue-positioning trial, subjects were required to
whisper the corresponding vocal tract configuration with
masked auditory feedback and to identify the vowel
associated with the reached tongue posture. Masked
auditory feedback ensured that vowel categorization was
based on somatosensory feedback rather than auditory
feedback. A separate group of subjects was required to
auditorily classify the whispered sounds.In addition, we
modeled the link between vowel categories and tongue
postures in normal speech production with a Bayesian
classifier based on the tongue postures recorded from the
same speakers for several repetitions of the /e,?, a/
vowels during a separate speech production task. Overall,
our results indicate that vowel categorization is possible
with somatosensory feed-back alone, with an accuracy that
is similar to the accuracy of the auditory perception of
whispered sounds, and in congruence with normal speech
articulation, as accounted for by the Bayesian classifier.
Darainy M, Vahdat S, Ostry DJ (2019) Neural basis of sensorimotor learning in speech motor adaptation. Cereb Cortex 29:2876-2889.
Abstract | PDF
Motor learning is associated with plasticity in both motor
and somatosensory cortex. It is known from animal studies
that tetanic stimulation to each of these areas
individually induces long-term potentiation in its
counterpart. In this context it is possible that changes
in motor cortex contribute to somatosensory change and
that changes in somatosensory cortex are involved in
changes in motor areas of the brain. It is also possible
that learning-related plasticity occurs in these areas
independently. Tobetter understand the relative
contribution to human motor learning ofmotor cortical and
somatosensory plasticity, we assessed the time course of
changes in primary somatosensory and motor cortex
excitability during motor skill learning. Learning was
assessed using aforce production task in which a target
force profile varied from one trial to the next. The
excitability of primary somatosensory cortex was measured
using somatosensory evoked potentials in response to
median nerve stimulation. The excitability of primary
motor cortex was measured using motor evoked potentials
elicited by single-pulse transcranial magnetic
stimulation. These two measures were inter-leaved with
blocks of motor learning trials. We found that the
earliest changes in cortical excitability during learning
occurred in somatosensory cortical responses, and these
changes preceded changes inmotor cortical excitability.
Changes in somatosensory evoked potentials were correlated
with behavioral measures of learning. Changes in motor
evoked potentials were not. These findings indicate that
plasticity in somatosensory cortex occurs as a part of the
earliest stages of motor learning, before changes in motor
cortex are observed.NEW & NOTEWORTHY: We tracked
somatosensory and motorcortical excitability during motor
skill acquisition. Changes in both motor cortical and
somatosensory excitability were observed during learning;
however, the earliest changes were in somatosensory
cortex,not motor cortex. Moreover, the earliest changes in
somatosensory cortical excitability predict the extent of
subsequent learning; those in motor cortex do not. This is
consistent with the idea that plasticity insomatosensory
cortex coincides with the earliest stages of human motor
learning.
Ohashi H, Valle-Mena R, Gribble P, Ostry DJ (2019) Movements
following force-field adaptation are aligned with altered
sense of limb position. Exp Brain Res 237:1303-1313. Abstract | PDF
Previous work has shown that motor learning is associated
with changes to both movements and to the somatosensory
perception of limb position. In an earlier study that
motivates the current work, it appeared that following
washout trials, movements did not return to baseline but
rather were aligned with associated changes to sensed limb
position. Here, we provide a systematic test of this
relationship, examining the idea that adaptation-related
changes to sensed limb position and to the path of the
limb are linked, not only after washout trials but at all
stages of the adaptation process. We used a force-field
adaptation paradigm followed by washout trials in which
subjects performed movements without visual feedback of
the limb. Tests of sensed limb position were conducted at
each phase of adaptation, specifically before and after
baseline movements in a null field, after force-field
adaptation, and following washout trials in a null field.
As in previous work, sensed limb position changed in
association with force-field adaptation. At each stage of
adaptation, we observed a correlation between the sensed
limb position and associated path of the limb. At a group
level, there were differences between the clockwise and
counter-clockwise conditions. However, whenever there were
changes in sensed limb position, movements following
washout did not return to baseline. This suggests that
adaptation in sensory and motor systems is not independent
processes but rather sensorimotor adaptation is linked to
sensory change. Sensory change and limb movement remain in
alignment throughout adaptation such that the path of the
limb is aligned with the altered sense of limb position.
van Vugt FT, Ostry DJ (2019) Early stages of sensorimotor map acquisition: learning with free exploration, without active movement or global structure. J Neurophysiol 122:1708-1720.
Abstract | PDF
Early stages of sensorimotor map acquisition: learning
with free exploration, without active movement or global
structure. J Neurophysiol 122: 1708-1720, 2019. First
published August 21, 2019; doi:10.1152/jn.00429.2019. One
of the puzzles of learning to talk or play a musical
instrument is how we learn which movement produces a
particular sound: an audiomotor map. The initial stages of
map acquisition can be studied by having participants
learn arm movements to auditory targets. The key question
is what mechanism drives this early learning. Three
learning processes from previous literature were tested:
map learning may rely on active motor outflow (target), on
error correction, and on the correspondence between
sensory and motor distances (i.e., that similar movements
map to similar sounds). Alternatively, we hypothesized
that map learning can proceed without these. Participants
made movements that were mapped to sounds in a number of
different conditions that each precluded one of the
potential learning processes. We tested whether map
learning relies on assumptions about topological
continuity by exposing participants to a permuted map that
did not preserve distances in auditory and motor space.
Further groups were tested who passively experienced the
targets, kinematic trajectories produced by a robot arm,
and auditory feedback as a yoked active participant (hence
without active motor outflow). Another group made
movements without receiving targets (thus without
experiencing errors). In each case we observed substantial
learning, therefore none of the three hypothesized
processes is required for learning. Instead early map
acquisition can occur with free exploration without target
error correction, is based on sensory-to-sensory
correspondences, and possible even for discontinuous maps.
The findings are consistent with the idea that early
sensorimotor map formation can involve instance-specific
learning. NEW & NOTEWORTHY This study tested learning
of novel sensorimotor maps in a variety of unusual
circumstances, including learning a mapping that was
permuted in such as way that it fragmented the
sensorimotor workspace into discontinuous parts, thus not
preserving sensory and motor topology. Participants could
learn this mapping, and they could learn without motor
outflow or targets. These results point to a robust
learning mechanism building on individual instances,
inspired from machine learning literature.
Kumar N, Manning TF, Ostry DJ (2019) Somatosensory cortex participates in the consolidation of human motor memory. PLoS Biol 17:e3000469.
Abstract | PDF
Newly learned motor skills are initially labile and then
consolidated to permit retention. The circuits that enable
the consolidation of motor memories remain uncertain. Most
work to date has focused on primary motor cortex, and
although there is ample evidence of learning-related
plasticity in motor cortex, direct evidence for its
involvement in memory consolidation is limited.
Learning-related plasticity is also observed in
somatosensory cortex, and accordingly, it may also be
involved in memory consolidation. Here, by using
transcranial magnetic stimulation (TMS) to block
consolidation, we report the first direct evidence that
plasticity in somatosensory cortex participates in the
consolidation of motor memory. Participants made movements
to targets while a robot applied forces to the hand to
alter somatosensory feedback. Immediately following
adaptation, continuous theta-burst transcranial magnetic
stimulation (cTBS) was delivered to block retention; then,
following a 24-hour delay, which would normally permit
consolidation, we assessed whether there was an
impairment. It was found that when mechanical loads were
introduced gradually to engage implicit learning
processes, suppression of somatosensory cortex following
training almost entirely eliminated retention. In
contrast, cTBS to motor cortex following learning had
little effect on retention at all; retention following
cTBS to motor cortex was not different than following sham
TMS stimulation. We confirmed that cTBS to somatosensory
cortex interfered with normal sensory function and that it
blocked motor memory consolidation and not the ability to
retrieve a consolidated motor memory. In conclusion, the
findings are consistent with the hypothesis that in
adaptation learning, somatosensory cortex rather than
motor cortex is involved in the consolidation of motor
memory.
Ohashi H, Gribble PL, Ostry DJ (2019) Somatosensory cortical excitability changes precede those in motor cortex during human motor learning. J Neurophysiol 122:1397-1405.
Abstract | PDF
Motor learning is associated with plasticity in both motor
and somatosensory cortex. It is known from animal studies
that tetanic stimulation to each of these areas
individually induces long-term potentiation in its
counterpart. In this context it is possible that changes
in motor cortex contribute to somatosensory change and
that changes in somatosensory cortex are involved in
changes in motor areas of the brain. It is also possible
that learning related plasticity occurs in these areas
independently. To better understand the relative
contribution to human motor learning of motor cortical and
somatosensory plasticity, we assessed the time course of
changes in primary somatosensory and motor cortex
excitability during motor skill learning. Learning was
assessed using a force production task in which a target
force profile varied from one trial to the next. The
excitability of primary somatosensory cortex was measured
using somatosensory evoked potentials in response to
median nerve stimulation. The excitability of primary
motor cortex was measured using motor evoked potentials
elicited by single-pulse transcranial magnetic
stimulation. These two measures were interleaved with
blocks of motor learning trials. We found that the
earliest changes in cortical excitability during learning
occurred in somatosensory cortical responses and these
changes preceded changes in motor cortical excitability.
Changes in somatosensory evoked potentials were correlated
with behavioral measures of learning. Changes in motor
evoked potentials were not. These findings indicate that
plasticity in somatosensory cortex occurs as a part of the
earliest stages of motor learning, before changes in motor
cortex are observed.
Vahdat S, Darainy M, Thiel A, Ostry DJ (2018) A single session of robot-controlled proprioceptive training modulates functional connectivity of sensory motor networks and improves reaching accuracy in chronic stroke .Neurorehabil Neural Repair 33:70-81.
Abstract | PDF
The relationship between neural activation during movement
training and the plastic changes that survive beyond
movement execution is not well understood. Here we ask
whether the changes in resting-state functional
connectivity observed following motor learning overlap
with the brain networks that track movement error during
training. Human participants learned to trace an arched
trajectory using a computer mouse in an MRI scanner. Motor
performance was quantified on each trial as the maximum
distance from the prescribed arc. During learning, two
brain networks were observed, one showing increased
activations for larger movement error, comprising the
cerebellum, parietal, visual, somatosensory, and cortical
motor areas, and the other being more activated for
movements with lower error, comprising the ventral putamen
and the OFC. After learning, changes in brain connectivity
at rest were found predominantly in areas that had shown
increased activation for larger error during task,
specifically the cerebellum and its connections with
motor, visual, and somatosensory cortex. The findings
indicate that, although both errors and accurate movements
are important during the active stage of motor learning,
the changes in brain activity observed at rest primarily
reflect networks that process errors. This suggests that
error-related networks are represented in the initial
stages of motor memory formation.
Sidarta A, van Vugt FT, Ostry DJ (2018) Somatosensory working memory in human reinforcement-based motor learning. J Neurophysiol 120:3275-3286.
Abstract | PDF
Recent studies using visuomotor adaptation and sequence
learning tasks have assessed the involvement of working
memory in the visuospatial domain. The capacity to
maintain previously performed movements in working memory
is perhaps even more important in reinforcement-based
learning to repeat accurate movements and avoid mistakes.
Using this kind of task in the present work, we tested the
relationship between somatosensory working memory and
motor learning. The first experiment involved separate
memory and motor learning tasks. In the memory task, the
participant's arm was displaced in different directions by
a robotic arm, and the participant was asked to judge
whether a subsequent test direction was one of the
previously presented directions. In the motor learning
task, participants made reaching movements to a hidden
visual target and were provided with positive feedback as
reinforcement when the movement ended in the target zone.
It was found that participants that had better
somatosensory working memory showed greater motor
learning. In a second experiment, we designed a new task
in which learning and working memory trials were
interleaved, allowing us to study participants memory for
movements they performed as part of learning. As in the
first experiment, we found that participants with better
somatosensory working memory also learned more. Moreover,
memory performance for successful movements was better
than for movements that failed to reach the target. These
results suggest that somatosensory working memory is
involved in reinforcement motor learning and that this
memory preferentially keeps track of reinforced movements.
NEW & NOTEWORTHY The present work examined
somatosensory working memory in reinforcement-based motor
learning. Working memory performance was reliably
correlated with the extent of learning. With the use of a
paradigm in which learning and memory trials were
interleaved, memory was assessed for movements performed
during learning. Movements that received positive feedback
were better remembered than movements that did not. Thus
working memory does not track all movements equally but is
biased to retain movements that were rewarded.
Bernardi NF, Van Vugt FT, Valle-Mena R, Vahdat S, Ostry DJ (2018) Error-related persistence of motor activity in resting-state networks. J Cogn Neurosci 20:1-19.
Abstract | PDF
Background. Passive robot-generated arm movements in
conjunction with proprioceptive decision making and
feedback modulate functional connectivity (FC) in sensory
motor networks and improve sensorimotor adaptation in
normal individuals. This proof-of-principle study
investigates whether these effects can be observed in
stroke patients. Methods. A total of 10 chronic stroke
patients with a range of stable motor and sensory deficits
(Fugl-Meyer Arm score [FMA] 0-65, Nottingham Sensory
Assessment [NSA] 10-40) underwent resting-state functional
magnetic resonance imaging before and after a single
session of robot-controlled proprioceptive training with
feedback. Changes in FC were identified in each patient
using independent component analysis as well as a seed
region-based approach. FC changes were related to
impairment and changes in task performance were assessed.
Results. A single training session improved average arm
reaching accuracy in 6 and proprioception in 8 patients.
Two networks showing training-associated FC change were
identified. Network C1 was present in all patients and
network C2 only in patients with FM scores >7.
Relatively larger C1 volume in the ipsilesional hemisphere
was associated with less impairment (r = 0.83 for NSA, r =
0.73 for FMA). This association was driven by specific
regions in the contralesional hemisphere and their
functional connections (supramarginal gyrus with FM scores
r = 0.82, S1 with NSA scores r = 0.70, and cerebellum with
NSA score r = ?0.82). Conclusion. A single session of
robot-controlled proprioceptive training with feedback
improved movement accuracy and induced FC changes in
sensory motor networks of chronic stroke patients. FC
changes are related to functional impairment and comprise
bilateral sensory and motor network nodes.
Milner TE, Firouzimehr Z, Babadi S, Ostry DJ (2018) Different adaptation rates to abrupt and gradual changes in environmental dynamics. Exp Brain Res 236:2923-2933.
Abstract | PDF
Adaptation to an abrupt change in the dynamics of the
interaction between the arm and the physical environment
has been reported as occurring more rapidly but with less
retention than adaptation to a gradual change in
interaction dynamics. Faster adaptation to an abrupt
change in interaction dynamics appears inconsistent with
kinematic error sensitivity which has been shown to be
greater for small errors than large errors. However, the
comparison of adaptation rates was based on incomplete
adaptation. Furthermore, the metric which was used as a
proxy of the changing internal state, namely the linear
regression between the force disturbance and the
compensatory force (the adaptation index), does not
distinguish between internal state inaccuracy resulting
from amplitude or temporal errors. To resolve the apparent
inconsistency, we compared the evolution of the internal
state during complete adaptation to an abrupt and gradual
change in interaction dynamics. We found no difference in
the rate at which the adaptation index increased during
adaptation to a gradual compared to an abrupt change in
interaction dynamics. In addition, we separately examined
amplitude and temporal errors using different metrics, and
found that amplitude error was reduced more rapidly under
the gradual than the abrupt condition, whereas temporal
error (quantified by smoothness) was reduced more rapidly
under the abrupt condition. We did not find any
significant change in phase lag during adaptation under
either condition. Our results also demonstrate that even
after adaptation is complete, online feedback correction
still plays a significant role in the control of reaching.
Van Vugt FT and Ostry DJ (2018) The structure and acquisition of sensorimotor maps. J Cogn Neurosci 30: 290-306.
Abstract | PDF
One of the puzzles of learning to talk or play a musical
instrument is how we learn which movement produces a
particular sound: an audiomotor map. Existing research has
used mappings that are already well learned such as
controlling a cursor using a computer mouse. By contrast,
the acquisition of novel sensorimotor maps was studied by
having participants learn arm movements to auditory
targets. These sounds did not come from different
directions but, like speech, were only distinguished by
their frequencies. It is shown that learning involves
forming not one but two maps: a point map connecting
sensory targets with motor commands and an error map
linking sensory errors to motor corrections. Learning a
point map is possible even when targets never repeat.
Thus, although participants make errors, there is no
opportunity to correct them because the target is
different on every trial, and therefore learning cannot be
driven by error correction. Furthermore, when the
opportunity for error correction is provided, it is seen
that acquiring error correction is itself a learning
process that changes over time and results in an error
map. In principle, the error map could be derived from the
point map, but instead, these two maps are independently
acquired and jointly enable sensorimotor control and
learning. A computational model shows that this dual
encoding is optimal and simulations based on this
architecture predict that learning the two maps results in
performance improvements comparable with those observed
empirically.
Sidarta A, Vahdat S, Bernardi NF, Ostry DJ (2016) Somatic and reinforcement-based plasticity in the initial stages of human motor learning. J Neurosci 36:11682-11692.
Abstract | PDF
As
one learns to dance or play tennis, the desired
somatosensory state is typically unknown. Trial and error
is important as motor behavior is shaped by successful and
unsuccessful movements. As an experimental model, we
designed a task in which human participants make reaching
movements to a hidden target and receive positive
reinforcement when successful. We identified somatic and
reinforcement-based sources of plasticity on the basis of
changes in functional connectivity using resting-state
fMRI before and after learning. The neuroimaging data
revealed reinforcement-related changes in both motor and
somatosensory brain areas in which a strengthening of
connectivity was related to the amount of positive
reinforcement during learning. Areas of prefrontal cortex
were similarly altered in relation to reinforcement, with
connectivity between sensorimotor areas of putamen and the
reward-related ventromedial prefrontal cortex strengthened
in relation to the amount of successful feedback received.
In other analyses, we assessed connectivity related to
changes in movement direction between trials, a type of
variability that presumably reflects exploratory
strategies during learning. We found that connectivity in
a network linking motor and somatosensory cortices
increased with trial-to-trial changes in direction.
Connectivity varied as well with the change in movement
direction following incorrect movements. Here the changes
were observed in a somatic memory and decision making
network involving ventrolateral prefrontal cortex and
second somatosensory cortex. Our results point to the idea
that the initial stages of motor learning are not wholly
motor but rather involve plasticity in somatic and
prefrontal networks related both to reward and
exploration.
Ito T, Coppola JH, Ostry DJ (2016) Speech motor learning changes the neural response to both auditory and somatosensory signals. Sci Rep 6:25926
Abstract | PDF
In
the present paper, we present evidence for the idea that
speech motor learning is accompanied by changes to the
neural coding of both auditory and somatosensory stimuli.
Participants in our experiments undergo adaptation to
altered auditory feedback, an experimental model of speech
motor learning which like visuo-motor adaptation in limb
movement, requires that participants change their speech
movements and associated somatosensory inputs to correct
for systematic real-time changes to auditory feedback. We
measure the sensory effects of adaptation by examining
changes to auditory and somatosensory event-related
responses. We find that adaptation results in progressive
changes to speech acoustical outputs that serve to correct
for the perturbation. We also observe changes in both
auditory and somatosensory event-related responses that
are correlated with the magnitude of adaptation. These
results indicate that sensory change occurs in conjunction
with the processes involved in speech motor adaptation.
Ostry DJ, Gribble PL (2016) Sensory plasticity in human motor learning. Trends Neurosci 39:114-123
Abstract | PDF
There is accumulating evidence from behavioral,
neurophysiological, and neuroimaging studies that the
acquisition of motor skills involves both perceptual and
motor learning. Perceptual learning alters movements,
motor learning, and motor networks of the brain. Motor
learning changes perceptual function and the sensory
circuits of the brain. Here, we review studies of both
human limb movement and speech that indicate that
plasticity in sensory and motor systems is reciprocally
linked. Taken together, this points to an approach to
motor learning in which perceptual learning and sensory
plasticity have a fundamental role. Trends Sensorimotor
adaptation results in changes to sensory systems and
sensory networks in the brain. Perceptual learning
modifies sensory systems and directly alters the motor
networks of the brain. Perceptual changes associated with
sensorimotor adaptation are durable and occur in parallel
with motor learning.
Ito T, Ostry DJ, Gracco VL (2015) Somatosensory event-related potentials from orofacial skin stretch stimulation. J Vis Exp e53621-e53621
Abstract | PDF
Cortical processing associated with orofacial
somatosensory function in speech has received limited
experimental attention due to the difficulty of providing
precise and controlled stimulation. This article
introduces a technique for recording somatosensory
event-related potentials (ERP) that uses a novel
mechanical stimulation method involving skin deformation
using a robotic device. Controlled deformation of the
facial skin is used to modulate kinesthetic inputs through
excitation of cutaneous mechanoreceptors. By combining
somatosensory stimulation with electroencephalographic
recording, somatosensory evoked responses can be
successfully measured at the level of the cortex.
Somatosensory stimulation can be combined with the
stimulation of other sensory modalities to assess
multisensory interactions. For speech, orofacial
stimulation is combined with speech sound stimulation to
assess the contribution of multi-sensory processing
including the effects of timing differences. The ability
to precisely control orofacial somatosensory stimulation
during speech perception and speech production with ERP
recording is an important tool that provides new insight
into the neural organization and neural representations
for speech.
Bernardi NF, Darainy M, Ostry DJ (2015) Somatosensory contribution to the early stages of motor skill learning. J Neurosci 35: 14316 -14326
Abstract | PDF
The early stages of motor skill acquisition are often
marked by uncertainty about the sensory and motor goals of
the task, as is the case in learning to speak or learning
the feel of a good tennis serve. Here we present an
experimental model of this early learning process, in
which targets are acquired by exploration and
reinforcement rather than sensory error. We use this model
to investigate the relative contribution of motor and
sensory factors to human motor learning. Participants make
active reaching movements or matched passive movements to
an unseen target using a robot arm. We find that learning
through passive movements paired ith reinforcement is
comparable with learning associated with active movement,
both in terms of magnitude and durability, with
improvements due to training still observable at a 1 week
retest. Motor learning is also accompanied by changes in
somatosensory perceptual acuity. No stable changes in
motor performance are observed for participants that
train, actively or passively, in the absence of
reinforcement, or for participants who are given explicit
information about target position in the absence of
somatosensory experience. These findings indicate that the
somatosensory system dominates learning in the early
stages of motor skill acquisition.
Lametti DR, Rochet-Capellan A, Neufeld E, Shiller DM, Ostry
DJ (2014) Plasticity in the human speech motor system drives
changes in speech perception. J Neurosci 34:10339-10346. Abstract | PDF
Recent studies of human speech motor learning suggest that
learning is accompanied by changes in auditory perception.
But what drives the perceptual change? Is it a consequence
of changes in the motor system? Or is it a result of
sensory inflow during learning? Here, subjects
participated in a speech motor-learning task involving
adaptation to altered auditory feedback and they were
subsequently tested for perceptual change. In two separate
experiments, involving two different auditory perceptual
continua, we show that changes in the speech motor system
that accompany learning drive changes in auditory speech
perception. Specifically, we obtained changes in speech
perception when adaptation to altered auditory feedback
led to speech production that fell into the phonetic range
of the speech perceptual tests. However, a similar change
in perception was not observed when the auditory feedback
that subjects' received during learning fell into the
phonetic range of the perceptual tests. This indicates
that the central motor outflow associated with vocal
sensorimotor adaptation drives changes to the perceptual
classification of speech sounds.
Lametti DR, Krol SA, Shiller DM, Ostry DJ (2014) Brief periods of auditory perceptual training can determine the sensory targets of speech motor learning. Psychol Sci. 25:1325-1336.
Abstract | PDF
The perception of speech is notably malleable in adults,
yet alterations in perception seem to have little impact
on speech production. However, we hypothesized that speech
perceptual training might immediately influence speech
motor learning. To test this, we paired a speech
perceptual-training task with a speech motor-learning
task. Subjects performed a series of perceptual tests
designed to measure and then manipulate the perceptual
distinction between the words head and had. Subjects then
produced head with the sound of the vowel altered in real
time so that they heard themselves through headphones
producing a word that sounded more like had. In support of
our hypothesis, the amount of motor learning in response
to the voice alterations depended on the perceptual
boundary acquired through perceptual training. The studies
show that plasticity in adults' speech perception can have
immediate consequences for speech production in the
context of speech learning.
Ito T, Johns AR, Ostry DJ (2014) Left lateralized enhancement of orofacial somatosensory processing due to speech sounds. J Speech Lang Hear Res. 56:1875-81.
Abstract | PDF
PURPOSE:
Somatosensory information associated with speech articulatory movements affects the perception of speech sounds and vice versa, suggesting an intimate linkage between speech production and perception systems. However, it is unclear which cortical processes are involved in the interaction between speech sounds and orofacial somatosensory inputs. The authors examined whether speech sounds modify orofacial somatosensory cortical potentials that were elicited using facial skin perturbations.
METHOD:
Somatosensory event-related potentials in EEG were recorded in 3 background sound conditions (pink noise, speech sounds, and nonspeech sounds) and also in a silent condition. Facial skin deformations that are similar in timing and duration to those experienced in speech production were used for somatosensory stimulation.
RESULTS:
The authors found that speech sounds reliably enhanced the first negative peak of the somatosensory event-related potential when compared with the other 3 sound conditions. The enhancement was evident at electrode locations above the left motor and premotor area of the orofacial system. The result indicates that speech sounds interact with somatosensory cortical processes that are produced by speech-production-like patterns of facial skin stretch.
CONCLUSION:
Neural circuits in the left hemisphere, presumably in left motor and premotor cortex, may play a prominent role in the interaction between auditory inputs and speech-relevant somatosensory processing.
Somatosensory information associated with speech articulatory movements affects the perception of speech sounds and vice versa, suggesting an intimate linkage between speech production and perception systems. However, it is unclear which cortical processes are involved in the interaction between speech sounds and orofacial somatosensory inputs. The authors examined whether speech sounds modify orofacial somatosensory cortical potentials that were elicited using facial skin perturbations.
METHOD:
Somatosensory event-related potentials in EEG were recorded in 3 background sound conditions (pink noise, speech sounds, and nonspeech sounds) and also in a silent condition. Facial skin deformations that are similar in timing and duration to those experienced in speech production were used for somatosensory stimulation.
RESULTS:
The authors found that speech sounds reliably enhanced the first negative peak of the somatosensory event-related potential when compared with the other 3 sound conditions. The enhancement was evident at electrode locations above the left motor and premotor area of the orofacial system. The result indicates that speech sounds interact with somatosensory cortical processes that are produced by speech-production-like patterns of facial skin stretch.
CONCLUSION:
Neural circuits in the left hemisphere, presumably in left motor and premotor cortex, may play a prominent role in the interaction between auditory inputs and speech-relevant somatosensory processing.
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 | PDF
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 | PDF
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.
Bernardi NF, Darainy M, Bricolo E, Ostry DJ (2013) Observing motor learning produces somatosensory change. J Neurophysiol 110: 1804-1810.
Abstract | PDF
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.
Ito S, Darainy M, Sasaki M, Ostry DJ (2013) Computational model of motor learning and perceptual change. Biol Cybern 107:653-667.
Abstract | PDF
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.
Nasir SM, Darainy M, Ostry DJ (2013) Sensorimotor adaptation changes the neural coding of somatosensory stimuli. J. Neurophysiol. 109:2077-85.
Abstract | PDF
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 | PDF
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.
Lametti DR, Nasir S, Ostry DJ (2012) Sensory preference in
speech production revealed by simultaneous alteration of
auditory and somatosensory feedback. J Neurosci
32:9351-9359. Abstract | PDF
The idea that humans learn and maintain accurate speech by
carefully monitoring auditory feedback is widely held. But
this view neglects the fact that auditory feedback is
highly correlated with somatosensory feedback during
speech production. Somatosensory feedback from speech
movements could be a primary means by which cortical
speech areas monitor the accuracy of produced speech. We
tested this idea by placing the somatosensory and auditory
systems in competition during speech motor learning. To do
this, we combined two speech-learning paradigms to
simultaneously alter somatosensory and auditory feedback
in real time as subjects spoke. Somatosensory feedback was
manipulated by using a robotic device that altered the
motion path of the jaw. Auditory feedback was manipulated
by changing the frequency of the first formant of the
vowel sound and playing back the modified utterance to the
subject through headphones. The amount of compensation for
each perturbation was used as a measure of sensory
reliance. All subjects were observed to correct for at
least one of the perturbations, but auditory feedback was
not dominant. Indeed, some subjects showed a stable
preference for either somatosensory or auditory feedback
during speech.
Rochet-Capellan A, Richer L, Ostry DJ (2012) Non-homogeneous
transfer reveals specificity in speech motor learning, J
Neurophysiol 107(6):1711-1717. Abstract | PDF
Does motor learning generalize to new situations that are
not experienced during training, or is motor learning
essentially specific to the training situation? In the
present experiments, we use speech production as a model
to investigate generalization in motor learning. We tested
for generalization from training to transfer utterances by
varying the acoustical similarity between these two sets
of utterances. During the training phase of the
experiment, subjects received auditory feedback that was
altered in real time as they repeated a single consonant
vowel-consonant utterance. Different groups of subjects
were trained with different consonant-vowel-consonant
utterances, which differed from a subsequent transfer
utterance in terms of the initial consonant or vowel.
During the adaptation phase of the experiment, we observed
that subjects in all groups progressively changed their
speech output to compensate for the perturbation (altered
auditory feedback). After learning, we tested for
generalization by having all subjects produce the same
single transfer utterance while receiving unaltered
auditory feedback. We observed limited transfer of
learning, which depended on the acoustical similarity
between the training and the transfer utterances. The
gradients of generalization observed here are comparable
to those observed in limb movement. The present findings
are consistent with the conclusion that speech learning
remains specific to individual instances of learning.
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 | PDF
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 | PDF
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.
Rochet-Capellan A, Ostry DJ (2011) Simultaneous acquisition
of multiple auditory-motor transformations in speech. J
Neurosci. 31:2648-2655. Abstract | PDF
The brain easily generates the movement that is needed in
a given situation. Yet surprisingly, the results of
experimental studies suggest that it is difficult to
acquire more than one skill at a time. To do so, it has
generally been necessary to link the required movement to
arbitrary cues. In the present study, we show that speech
motor learning provides an informative model for the
acquisition of multiple sensorimotor skills. During
training, subjects were required to repeat aloud
individual words in random order while auditory feedback
was altered in real-time in different ways for the
different words. We found that subjects can quite readily
and simultaneously modify their speech movements to
correct for these different auditory transformations. This
multiple learning occurs effortlessly without explicit
cues and without any apparent awareness of the
perturbation. The ability to simultaneously learn several
different auditory-motor transformations is consistent
with the idea that, in speech motor learning, the brain
acquires instance-specific memories. The results support
the hypothesis that speech motor learning is fundamentally
local.
Ito T, Ostry DJ (2010) Somatosensory contribution to motor
learning due to facial skin deformation. J Neurophysiol
104:1230-1230. Abstract | PDF
Motor learning is dependent on kinesthetic information
that is obtained both from cutaneous afferents and from
muscle receptors. In human arm movement, information from
these two kinds of afferents is largely correlated. The
facial skin offers a unique situation in which there are
plentiful cutaneous afferents and essentially no muscle
receptors and, accordingly, experimental manipulations
involving the facial skin may be used to assess the
possible role of cutaneous afferents in motor learning. We
focus here on the information for motor learning provided
by the deformation of the facial skin and the motion of
the lips in the context of speech. We used a robotic
device to slightly stretch the facial skin lateral to the
side of the mouth in the period immediately preceding
movement. We found that facial skin stretch increased lip
protrusion in a progressive manner over the course of a
series of training trials. The learning was manifest in a
changed pattern of lip movement, when measured after
learning in the absence of load. The newly acquired motor
plan generalized partially to another speech task that
involved a lip movement of different amplitude. Control
tests indicated that the primary source of the observed
adaptation was sensory input from cutaneous afferents. The
progressive increase in lip protrusion over the course of
training fits with the basic idea that change in sensory
input is attributed to motor performance error. Sensory
input, which in the present study precedes the target
movement, is credited to the target-related motion, even
though the skin stretch is released prior to movement
initiation. This supports the idea that the nervous system
generates motor commands on the assumption that sensory
input and kinematic error are in register.
Lametti DR, Ostry DJ (2010) Postural constraint on movement
variability. J Neurophysiol 104:1061-1067. Abstract | PDF
Movements are inherently variable. When we move to a
particular point in space, a cloud of final limb positions
is observed around the target. Previously we noted that
patterns of variability at the end of movement to a circular target were not circular, but instead reflected patterns of limb stiffness in directions where limb stiffness was high, variability in end position was low, and vice versa. Here we examine the determinants of variability at movement end in more detail. To do this, we have subjects move the handle of a robotic device from different starting positions into a circular target. We use position servocontrolled displacements of the robot's handle to measure limb stiffness at the end of movement and we also record patterns of end position variability. To examine the effect of change in posture on movement variability, we use a visual motor transformation in which we change the limb configuration and also the actual movement target, while holding constant the visual display. We find that, regardless of movement direction, patterns of variability at the end of movement vary systematically with limb configuration and are also related to patterns of limb stiffness, which are likewise configuration dependent. The result suggests that postural configuration determines the base level of movement variability, on top of which control mechanisms can act to further alter variability.
Mattar AAG, Ostry DJ (2010) Generalization of dynamics
learning across changes in movement amplitude. J
Neurophysiol 104:426-438. patterns of variability at the end of movement to a circular target were not circular, but instead reflected patterns of limb stiffness in directions where limb stiffness was high, variability in end position was low, and vice versa. Here we examine the determinants of variability at movement end in more detail. To do this, we have subjects move the handle of a robotic device from different starting positions into a circular target. We use position servocontrolled displacements of the robot's handle to measure limb stiffness at the end of movement and we also record patterns of end position variability. To examine the effect of change in posture on movement variability, we use a visual motor transformation in which we change the limb configuration and also the actual movement target, while holding constant the visual display. We find that, regardless of movement direction, patterns of variability at the end of movement vary systematically with limb configuration and are also related to patterns of limb stiffness, which are likewise configuration dependent. The result suggests that postural configuration determines the base level of movement variability, on top of which control mechanisms can act to further alter variability.
Abstract | PDF
Studies on generalization show the nature of how learning
is encoded in the brain. Previous studies have shown
rather limited generalization of dynamics learning across
changes in movement direction, a finding that is
consistent with the idea that learning is primarily local.
In contrast, studies show a broader pattern of
generalization across changes in movement amplitude,
suggesting a more general form of learning. To understand
this difference, we performed an experiment in which
subjects held a robotic manipulandum and made movements to
targets along the body midline. Subjects were trained in a
velocitydependent force field while moving to a 15 cm
target. After training, subjects were tested for
generalization using movements to a 30 cm target. We used
force channels in conjunction with movements to the 30 cm
target to assess the extent of generalization. Force
channels restricted lateral movements and allowed us to
measure force production during generalization. We
compared actual lateral forces to the forces expected if
dynamics learning generalized fully. We found that, during
the test for generalization, subjects produced reliably
less force than expected. Force production was appropriate
for the portion of the transfer movement in which
velocities corresponded to those experienced with the 15
cm target. Subjects failed to produce the expected forces
when velocities exceeded those experienced in the training
task. This suggests that dynamics learning generalizes
little beyond the range of one's experience. Consistent
with this result, subjects who trained on the 30 cm target
showed full generalization to the 15 cm target. We
performed two additional experiments that show that
interleaved trials to the 30 cm target during training on
the 15 cm target can resolve the difference between the
current results and those reported previously.
Ostry DJ, Darainy M, Mattar AAG, Wong J, Gribble PL (2010)
Somatosensory plasticity and motor learning. J Neurosci
30:5384-5393. Abstract | PDF
Complete List
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.