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Changing Brain Networks for Visuomotor Control With Incread Movement Automaticity
A.Floyer-Lea and P.M.Matthews
笛曲
Centre for Functional Magnetic Resonance Imaging of the Brain,University of Oxford,John Radcliffe Hospital,Headington,
Oxford OX39DU,United Kingdom
Submitted11November2003;accepted infinal form10February2004
Floyer-Lea,A.and P.M.Matthews.Changing brain networks for visuomotor control with incread movement automaticity.J Neuro-physiol92:2405–2412,2004;10.1152/jn.01092.2003.Learning a motor skill is associated with changes in patterns of brain activation with movement.Here we have further characterized the dynamics during fast(short-term)learning of a visuomotor skill using functional magnetic resonance imaging.Subjects(nϭ15)were studied as they learned to visually track a moving target by varying the isometric force applied to a pressure plate held in the right hand.Learning was confirmed by demonstration of improved performance and automa-ticity(the relative lack of need for conscious attention during task execution).We identified two distinct,time-dependent patterns of functional changes in the brain associated with the behavioral changes.An initial,more attentionally demanding stage of learning was associated with the greatest relative activity in widely distributed, predominantly cortical regions including prefrontal,bilateral nsori-motor,and parietal cortices.The caudate nucleus and ipsilateral cerebellar hemisphere also showed significant activity.Over time,as performance improved,activity in the regions progressively de-cread.There was an increa in activity in subcortical motor regions including that of the cerebellar
dentate and the thalamus and putamen. Short-term motor-skill learning thus is associated with a progressive reduction of widely distributed activations in cortical regions respon-sible for executive functions,processing somatonsory feedback and motor planning.The results suggest that early performance gains rely strongly on prefrontal-caudate interactions with later incread activ-ity in a subcortical circuit involving the cerebellum and basal ganglia as the task becomes more automatic.Characterization of the changes provides a potential tool for functional“disction”of pa-thologies of movement and motor learning.
I N T R O D U C T I O N
Neurological dias affecting movement frequently alter motor automaticity,the ability to perform a task accurately without exerting full attention.Automation with skill develop-ment allows general attentional resources to become available for other tasks(Doyon et al.1998).As automaticity increas, it therefore becomes easier to perform a cond,attention-demanding task simultaneously.In principle,impairment of automaticity could ari from dysfunction in any of veral brain regions within the network involved in motor control. Specific definition of the functional anatomical loci for changes in the control of more automatic learned motor behaviors could contribute to the development of strategies for enhancement of recovery after brain injury.
Several studies already have begun to characterize func-tional changes in a wide network of brain regions that are involved in learning to perform a novel quence of move-ments.During the early stages of learning a simple motor task, the dorsolateral prefrontal cortex(DLPFC)and premotor cor-tex are relatively active(Ghilardi et al.2000;Grafton et al. 1992,2002;Jueptner et al.1997).The ipsilateral lateral cere-bellar cortex also is active early in skill learning(Eliasn et al. 2001;Jenkins et al.1994;Penhune and Doyon2002).In-cread activation in the cerebellar dentate nucleus and basal ganglia characterizes later stages of motor-quence learning (Doyon et al.2002).The importance of subcortical structures is highlighted by the obrvation that lesions of either the cere-bellum or basal ganglia impair automatic(implicit)perfor-mance without affecting explicit knowledge of the motor quence(Doyon1997).However,technical limitations have prevented more detailed definition of the dynamics of relative activity changes across the brain during short-term motor skill acquisition and,specifically,their relation to the development of automaticity of movement.Also,although correlations of brain activity changes with performance have been made,the relevance of the changes to automaticity has not been ex-plored.
Here we have characterized brain activity changes with short-term visuomotor learning in greater detail than has been possible previously.We also have related the time-dependent changes to cha
nges in performance and movement automatic-ity.To do this,a tracking task was ud in which subjects tracked a continuously changing,visually prented target by varying the force exerted on a pressure nsor held in the right hand.This task is initially highly attentionally demanding but rapidly becomes highly automatic as performance improves. We explicitly measured the increa in automaticity of the task after learning by using a dual-task paradigm outside the scan-ner.
M E T H O D S
Volunteers
Fifteen healthy right-hand-dominant subjects participated in this study(mean age:25.4yr;range:20–31yr;8women,7men).All gave informed connt according to a protocol approved by the local rearch ethics committee.
Address for reprint requests and other correspondence:P.M.Matthews,
Centre for Functional Magnetic Resonance Imaging of the Brain,University of Oxford,John Radcliffe Hospital,Headley Way,Headington,Oxford OX3 9DU,UK(E-mail:ac.uk).
The costs of publication of this article were defrayed in part by the payment
of page charges.The article must therefore be hereby marked“advertiment”
in accordance with18U.S.C.Section1734solely to indicate this fact.
J Neurophysiol92:2405–2412,2004;
钠氢10.1152/jn.01092.2003.
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Image acquisition Data acquisition was performed on a 3Tesla Varian Inova MRI system,using a multislice gradient-echo EPI quence [repetition time (TR)]:3,000ms,echo time (TE):30ms,21ϫ6mm axial slices providing whole-brain coverage,field of view:256ϫ192mm 2,matrix:64ϫ64).Four “dummy ”scans were added at the beginning of the image quence to reach steady-state magnetization.A T1-weighted structural image also was acquired for each subject with a notional resolution of 1.5ϫ1.5ϫ3mm 3(IR 3D Turbo Flash,TR:30ms,TE:5ms,inversion time (TI):500ms,flip angle 15°,FOV:2
56ϫ256,matrix:256ϫ256)to allow functional image registration for preci localization of activations and to de fine individual regions of interest.Paradigm Subjects held a magnetic resonance (MR)compatible pressure nsor in their right hand between the thumb and fingers (Fig.1).The arm was held in a mi-pronated position,supported by an armrest,and cured to prevent arm or wrist movement.As the arm and wrist were held immobile,subjects ud the muscles of the thenar eminence and the finger flexor muscles to perform the task.The movement was isometric.Subjects viewed a large screen on which the movement quence was prented from a distance of 2m by means of prism glass.Two vertical bars were shown on the screen during the experiment.The target pressure,which the subject was required to match,was cued by the height of a red bar on the left-hand side of the screen.A cond blue bar on the right-hand side of the screen gave a continuous
measure of the subject ’s respon.Each subject was instructed to
maintain the two bars at equal heights on the screen at all times.For both the target and respon bars,incread pressure incread the level of the bar on screen.The force applied to the pressure nsor was sampled at 100Hz and projected to the screen at the maximum refresh rate of the projector (50Hz)so that there was no discernible lag between a respon by the subject and the on-s
creen feedback.The software ud to prent the experiment was written in C ϩϩby A.Floyer-Lea.The tracking error,calculated as the difference between the target and respon forces and measured as a percentage of each subject ’s maximum voluntary contraction,was recorded throughout the exper-iment with a sampling rate of 100Hz.The mean absolute tracking error was calculated for each repeat of the quence;but this infor-mation was not available to the subject.The pressure applied by subjects during rest also was recorded to con firm that subjects did not move their hand during this period.
The experiment was implemented as a block design with blocks of force tracking alternating with blocks of a visually matched rest condition.Instructions were shown on screen for 3s immediately before each tracking and rest block.Each tracking block consisted of a 1-s warm-up period in which the target force incread linearly from zero to the initial value of the quence followed by four repeats of the 8-s pattern.The warm-up period prevented an incread error on the first quence of each block.The tracking pattern was not shown during the rest periods;instead subjects were shown a sinusoidal moving pattern,attention to which was intended to prevent mental
rehearsal of the pattern to be learned during the rest block.The
matched learning and rest blocks lasted 70s and each experiment
includes 10of the blocks,giving a total experimental duration of 11
min 40s.The maximum,minimum,and median force exerted by the subject during each block were recorded to con firm that the param-eters remained constant throughout the experiment.
Prior to scanning,each subject was trained outside the scanner for a period of 10min on a randomly varying tracking quence.Tracking performance over this period was measured to ensure that subjects achieved and maintained a stable baline performance level before the start of the learning experiment.All subjects were able to perform the task easily.Immediately prior to each scanning ssion the force level required was calibrated to require a maximum force equal to 75%of each subject ’s maximum voluntary contraction to equalize task dif ficulty across subjects.
Measurement of automaticity
To explicitly asss improvements in automaticity which occurred during learning,a parate dual-task experiment was carried out outside the scanner.Ten of the subjects who were trained on the tracking task also trained in a rial subtraction task involving ver-bally counting backward from 99,100,or 101to 0,1,or 2,respec-tively,in steps of 3as quickly as possible.Training was performed until there was no further improvement in the total time necessary for completing the task over three
successive trials.In a quiet room,the subjects then engaged in a dual-task paradigm,performing the visuo-motor tracking task and the subtraction task (at 75%of their maxi-mum rate,paced by an auditory metronome cue)together.An ob-rver recorded the number of counting errors during each trial,while
the tracking error was recorded as described in the preceding text.
Subjects performed the tracking task with a novel quence for 1min,
followed by the dual-task paradigm for 1min (baline).They then
practiced the novel quence using the same protocol that was ud
for short-term visuomotor task learning during the scanning ssions.
After this learning period,they were tested again on both the tracking
task alone and on the dual-task paradigm as at baline.Increas in
automaticity of the tracking task were assd as a decrea in the
interference between the two tasks,measured from the change in error
rates when the tasks were performed together before and after short-
term learning of the tracking task.
Data analysis
The analysis was carried out using tools from the FMRIB Software
Library (ac.uk/fsl).The following prestatistics pro-
cessing was applied:motion correction (Jenkinson et al.2002),spatial
smoothing using a Gaussian kernel of full-width half-maximum 5
mm,and nonlinear high-pass temporal filtering (Gaussian-weighted LSF straight line fitting,with sigma ϭ50.0).Statistical analysis was carried out using the general linear model (GLM)with local autocor-relation correction (Woolrich et al.2001).Registration of EPI func-tional images to high resolution and into standard space
(Talairach
FIG .1.A :positioning of the force nsor and a subject ’s arm and hand.B :reprentation of the screen displayed to subjects in the scanner.The left bar showed the target and the right bar showed the subject ’s respon.Subjects had to try to keep the height of the right bar as clo as possible to that of the left bar at all times.C :respon target of the quences learned by the subjects.2406 A.F
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and Tournoux 1988)was carried out using an af fine transformation with 12df (Jenkinson and Smith 2001).Group random effects analysis was performed.Z (Gaussianized T/F)statistic images were corrected for multiple comparisons using cluster detection,with clusters determined by Z Ͼ2.3and a corrected cluster signi ficance threshold of P ϭ0.05(Forman et al.1995;Friston et al.1994;Worsley et al.1992).Activation maps were overlaid on the group mean high-resolution image,and the anatomical location of clusters was reported using macroscopic anatomical boundaries ap-proximating the cytoarchitecture (Crespo-Facorro et al.2000;Geyer et al.2000).The data from each subject were divided into two ctions,early and late,on the basis of each subject ’s behavioral results.The early ction was de fined as the period during which the subject showed a reduction in tracking error of Ͼ0.25%on each block and late as the period after they had attained stable performance.To identify regions
of interest for further analysis,learning was modeled within the GLM as a linear trend over the early p
eriod.A linear trend was chon for the model as it is nsitive to a wide variety of time-dependent activation patterns in the brain.Areas of learning-correlated change
were de fined as tho that showed changes in activation over the early period that correlated with the linear model and then showed no
signi ficant further changes in the late period.The instruction periods and the subject-movement parameters were included within the GLM to model out the signal attributable to the factors but were not included within the contrasts of interest.
The whole-brain group analysis was ud to de fine areas that were
functionally signi ficant during learning of this task,and a further
region of interest (ROI)analysis was conducted on the areas.ROIs
were created by anatomically de fining the signi ficant clusters within
the group learning-correlated random effects image and then register-
ing the clusters to the functional data in each subject.The mean
percentage signal change across all voxels within each ROI over every
experimental block was found for every subject individually.From
this,a mean group time cour across the whole experiment was
calculated.
A laterality index (LI)was also calculated to explore relative
hemispheric changes in cortical motor regions.An anatomical ROI
was created for each subject that included the primary motor cortex,
premotor cortex and the supplementary motor area (SMA)and pre-
SMA.This ROI was de fined to include the cortex from the anterior
bank of the central sulcus to a point midway between the central
sulcus and the anterior limit of the frontal lobes.The mean percentage
signal change within the ROI was found for each hemisphere,and the LI was calculated according to the following formula:LI ϭ(con-tralateral signal change –ipsilateral signal change)(contralateral sig-nal change ϩipsilateral signal change).Conquently,LI values ranged from ϩ1,indicating completely contralateral activation through to –1,indicating ipsilateral activation,with 0signifying an
even bilateral spread.
R E S U L T S
Behavioral results:evidence for learning and
incread automaticity
Subjects performed a novel tracking task alternating with a
matched perceptual task in rial blocks through the trial
period.The performance in each tracking block was measured
as the mean absolute tracking error.This short-term motor-skill
learning is associated with an increa in automaticity of
performing the task,de fined as a reduction of the error and incread speed of a simultaneous attentionally demanding task.Automaticity was assd speci fically using a dual visuomotor tracking and verbal subtraction task with subjects outside of the magnet (n ϭ10,Fig.2).After learning the visuomotor tracking quence,the mean number of errors in the subtraction task decread from 8.6Ϯ3.6to 4.8Ϯ3.0(SD)errors (paired t -test t ϭ2.85;P Ͻ0.01).
The increa in tracking error caud by the counting task (i.e.,the tracking error in the dual task paradigm minus the tracking error on the force tracking task alone)decread from 7.1Ϯ2.5(mean ϮSD)before learning to 2.8Ϯ1.4after practice equivalent to that ud for the fMRI task below (paired t -test t ϭ3.98;P Ͻ0.01).
All subjects showed performance improvement between the first and last blocks when the task was performed in the imaging experiment (t -test,P Ͻ0.03for every subject).After initial rapid increas in tracking accuracy,stable,improved performance was achieved after between four and ven trial blocks (median:5blocks).The maximum,median and mini-mum levels of force applied by the subjects did not change signi ficantly over the cour of the experiment (Fig.3).Tracking task-related activation
In a contrast with the perceptually matched rest periods,the tracking task was associated with activation in a widespread network of brain regions (Table 1).Activation was found in
乐高船
the FIG .2.Increa in automaticity over time.After learning,subjects were able to perform better on a condary verbal subtraction task.Performing the condary task also had less effect on their tracking performance,showing that the tracking task required less conscious attention after
learning.FIG .3.Mean tracking error on each block across all subjects together with the average maximum,median,and minimum force applied by the subjects.While the error decreas over the cour of the experiment,the normalized force parameters remain constant,suggesting that the activation changes detected were due to improvements in tracking performance and not a result of
changes in motor output.
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primary nsorimotor and premotor cortices bilaterally (with greater activation in the hemisphere contralateral to the hand moved),supplementary motor areas [Brodmann ’s area (BA)6and 8],prefrontal cortex (BA 46),frontal pole (BA 10),cingulate motor cortex (BA 24),thalamus,and basal ganglia.Activation also was found bilaterally in the cerebellar cortex and deep cerebellar nuclei with relatively greater activation in the cerebellar hemisphere ipsilateral to the hand moved.Changes in brain activity were associated with improved tracking performance Tracking performance and automaticity are related in this
short-term learning paradigm.To de fine brain regions in which activity changes with the development of greater automaticity in movement,we tested for tracking performance-related changes by de fining regions that showed time-dependent changes during the early pha of motor learning when changes were greatest.Decreasing activation over time was found in the frontal pole,left prefrontal cortex,bilateral primary nsori-motor cortex,left intra-parietal cortex,and the supplementary motor ar
ea,along with the caudate nucleus and Crus I and II of the right cerebellum.Decreasing activity also was found along the medial wall;but the preci anatomical localization of the changes was somewhat variable between subjects.Analysis of individual data ts indicated that this activity included contri-butions from centers of activation in the SMA/pre-SMA (12/15
subjects),the adjacent region of the superior frontal gyrus (11/15subjects),and the anterior cingulate cortex (11/15sub-
jects).Increasing activity was found in the right dentate nu-cleus and in the left ventral putamen and thalamus.Changes in the regions were then measured over the full time cour of the experiment using a ROI approach (Fig.4A ).Dynamic changes in primary motor cortex activation Decreasing activation was found bilaterally in the hand area of the primary motor cortex (Yousry et al.1997)as perfor-mance improved.Analysis of the mean (over all subjects)time
cour of signi ficantly activated voxels within the precentral
sulcus (Fig.4,A and B )con firmed that primary motor cortex
activation decread monotonically toward baline levels over
the cour of the experiment.The relative decrea was greater
in the right hemisphere (ipsilateral to the hand moved);sub-stantial activation (mean signal intensity Ͼ1.5%)was main-
tained in the left primary motor cortex (contralateral to the hand moved)throughout the experiment.The hemispheric lateralization of activation in the cortical motor areas thus also became progressively left-shifted:the mean laterality index
moved from a relatively bi-hemispheric (0.08ϩ0.11)to a predominantly contralateral pattern (0.22ϩ0.08)over the cour of the experiment.This pattern was consistent between individual subjects;an increa in the laterality index was measured in comparisons of the final with initial trial blocks for
13/15subjects (2,P Ͻ0.01).
Dynamic changes in prefrontal and parietal cortex activation
Early decreas in activation were en in the DLPFC (BA 46),frontal pole (BA 10)and in the IPS,(BA 40;Fig.4,C –F ).
The DLPFC showed a rapid decrea in activation,reaching a
创业型公司stable,minimum value by the fifth trial block,approximately when performance reached an asymptote with a relative in-crea in movement automaticity.Activation in the frontal pole had a similar time cour,showing an initial rapid decrea through the first five practice blocks to reach a relatively constant level maintained until the end of the experiment.The activation time cour in the midline SMA structures was signi ficantly different during this early period (ANOVA;P Ͻ
0.05).SMA activity did not begin to decrea until the DLPFC
activation reached its minimum activity.Activation in the IPS showed a gradual decrea through the experiment.
Dynamic changes in activation of subcortical gray matter There were two distinct patterns of activation change in subcortical gray matter over the cour of the experiment (Fig.5).The left caudate showed a progressive decrea in activa-tion in the first five blocks (cf.DLPFC and frontal pole changes in Fig.4).Activity in the right cerebellar cortex (Crus I)also decread during task learning.There was a strong correlation between decreas in right cerebellar cortical activation and decreas in tracking error (r 2
ϭ0.94,P Ͻ0.01).A different pattern of relative activation change was found in the right dentate region,the left thalamus and the left putamen.Activity in all three regions incread throughout the period that per-formance improved and then remained relatively stable.D I S C U S S I O N
We have characterized dynamic changes in brain activation associated with improved performance and greater automatic-ity for execution of a visually guided motor tracking task.Subjects were trained on the task prior to scanning until stable
performance was reached and thus were familiar with the
general demands of the task and the equipment they were using
before the start of the experiment.The speci fic tracking pattern
was novel,however.The high tracking error at task ont suggests that accurate performance initially was attentionally
TABLE 1.Brain regions activated by the tracking task Region of Interest Brodmann ’s Area Maximum Activated Voxel (Z )MNI Coordinates (x,y,z )Left primary motor cortex 47.37Ϫ30,Ϫ15,58Right primary motor cortex 4 4.4530,Ϫ22,54
Left primary nsory cortex 1 6.37Ϫ38,Ϫ26,58Supplementary motor area 6 5.31Ϫ6,Ϫ8,54Pre-supplementary motor area 8 4.46Ϫ2,12,56Left premotor cortex 6 6.36Ϫ38,6,52Right premotor cortex 6 4.49Ϫ28,Ϫ2,58
Prefrontal cortex 46 4.86Ϫ32,20,38Frontal pole 10 5.54Ϫ15,49,20Cingulate motor area 24 5.42Ϫ6,0,44Left intraparietal sulcus 7 5.45Ϫ28,Ϫ42,60Right intraparietal sulcus 7 3.4434,Ϫ42,56Left insula 27 4.49Ϫ42,8,2
Right insula 27 2.6546,12,0Left cerebellum N/A 3.52Ϫ26,Ϫ4,Ϫ24Right cerebellum N/A 5.4334,Ϫ20,Ϫ42
Left thalamus N/A 5.55Ϫ12,Ϫ26,4Right thalamus N/A 2.7410,Ϫ22,0
Left striatum N/A 3.64Ϫ26,2,2Right striatum N/A 3.4430,8,2Random Effects,Z Ͼ2.3,P Ͻ0.01corrected.2408 A.FLOYER-LEA AND P.M.MATTHEWS
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demanding.This was con firmed by the behavioral interference obrved in the dual-task experiment,a measure of task auto-
maticity.As performance on the tracking task improved,au-tomaticity incread.The mean force parameters did not change throughout the cour of the experiment,so the acti-vation changes described are not due simply to changes in the subjects ’motor output but must re flect changes in mechanisms of motor control.Activity in the primary motor cortex decread with learning The task involved fast learning (Karni et al.1995).As previously reported with other examples of the fast motor learning,activation decread in the primary motor cortex as performance improved (Toni et al.1998).This early decrea of activity in the motor cortex may be due to increasingly speci fic afferent input to the primary motor cortex as the movement pattern becomes better de fined.The decrea was greatest in the hemisphere ipsilateral to the hand moved.Previous studies also have suggested that novel or more dif fi-cult motor tasks involve activation of ipsilateral motor cortex as well as in the hemisphere contralateral to the hand moved
(Chen et al.1997;Rao et al.1993).Prefrontal circuit is engaged in the initial period of learning Our obrvation that the DLPFC and the frontal pole (BA 10)are active in early stages of learning the tracking task extends prior lesion and functional-imaging studies.Patients with prefrontal cortex lesio
ns are impaired in both verbal and visuomotor quence learning (de Gui et al.1999).The role of PFC appears speci fic for aspects of learning as subjects with prefrontal damage are unimpaired on a tracking task that does not involve learning a speci fic quence of movements (Gomez et al.2002).In functional-imaging studies of learning,the prefrontal cortex is activated primarily when subjects learn new motor quences (Jenkins et al.1994)and thus may be involved in the acquisition or encoding of explicit knowledge of the task (Hazeltine et al.1997).The prefrontal cortex also is connected to the cerebellar cortex;in monkeys,area 46neu-
rons project via the pons to Crus II of the cerebellum,which in
turn projects back to area 46via the dentate and thalamus
(Kelly and Strick 2003).
Our results also showed decreas in caudate activity over a similar time cour to the prefrontal cortex activity changes.This correlated activity and the strong anatomical connectivity between prefrontal cortex and caudate (Yeterian and Pandya 1991)is consistent with the hypothesis that development of the type of complex,integrated motor plan necessary for rapid and accurate performance of complex movements is mediated in part by activity in the striatum,which has strong a
fferent input from both the SMA and the premotor cortex (Alexander et al.1986).
Activity in a cerebello-thalamo-striate network increas
as learning progress
The cerebellum showed regionally distinct patterns of activ-ity change over the cour of motor learning.The ipsilateral
cerebellar cortex (Crus I and II)was most active initially,with a subquent progressive decrea.In contrast,activation in the ipsilateral dentate nucleus incread during the early period.A similar pattern of activation changes previously was en in studies of motor adaptation (Nezafat et al.2001)and with a quence learning paradigm (Doyon et al.2002),
although
FIG .4.Cortical areas of activation that corre-
late with the improvement in task performance
together with their time cours (group random
effects image,z Ͻ2.3,P Ͻ0.01,corrected).
李时炯Areas that showed an increa in activation are
shown in red,whereas tho areas that showed a
decrea in activation are shown in blue.At the
start of the experiment,the level of activation is
similar in the right (A )and left (B )motor cortex.
Although both sides show a decrea in activation
over the cour of the experiment,the right hemi-
sphere shows a more signi ficant decrea than the
left hemisphere,so that by the end of the exper-
iment the activation is almost completely left-
lateralid.The dorsal lateral prefrontal cortex
(DLPFC)and pre-SMA show contrasting time
cours.The DLPFC (E )shows initially high
activation that drops off sharply,reaching ba-
line levels before the early learning pha is
complete.The pre-SMA (D )shows an initial
increa in activation before decreasing,reaching
a peak at the time when activation in the DLPFC
has declined.The frontal pole (C )has a time
cour clor to that of the behavioral learning
itlf,whereas the intraparietal sulcus (IPS,F )
shows a more sustained decrea that continues
into the postlearning pha.
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