Discrete sequence production with and without a pause: the role of cortex, basal ganglia, and cerebellum

Abstract

Our sensorimotor experience unfolds in sequences over time. We hypothesize that the processing of movement sequences with and without a temporal pause will recruit distinct but cooperating neural processes, including cortico-striatal and cortico-cerebellar networks. We thus, compare neural activity during sequence learning in the presence and absence of this pause. Young volunteer participants learned sensorimotor sequences using the discrete sequence production (DSP) task, with Pause, No-Pause, and Control sequences of four elements in an event related fMRI protocol. The No-Pause and Pause sequences involved a more complex sequential structure than the Control sequence, while the Pause sequences involved insertion of a temporal pause, relative to the No-Pause sequence. The Pause vs. No-Pause contrast revealed extensive fronto-parietal, striatal, thalamic and cerebellar activations, preferentially for the Pause sequences. ROI analysis indicated that the cerebellum displays an early activation that was attenuated over successive runs, and a significant preference for Pause sequences when compared with caudate. These data support the hypothesis that a cortico-cerebellar circuit plays a specific role in the initial processing of temporal structure, while the basal ganglia play a more general role in acquiring the serial response order of the sequence.

Full text

ORIGINAL RESEARCH ARTICLE
published: 18 September 2013
doi: 10.3389/fnhum.2013.00492
Discrete sequence production with and without a pause:
the role of cortex, basal ganglia, and cerebellum
Anne-Lise Jouen1, Willem B. Verwey2, Jurjen van der Helden 2,3 , Christian Scheiber4, Remi Neveu 5,
Peter F. Dominey 1and Jocelyne Ventre-Dominey 1*
1INSERM U846, Stem Cell and Brain Research Institute, Bron, France
2Department Cognitive Psychology and Ergonomics, University of Twente, Enschede, Netherlands
3Radboud University Nijmegen, Donders Institute for Brain, Cognition and Behaviour, Netherlands
4Center for Nuclear Imagery, HCL, Lyon, France
5CNRS UMR5229, Center for Cognitive Neuroscience, Bron, France
Edited by:
Srikantan S. Nagarajan, University of
California, San Francisco, USA
Reviewed by:
Qingbao Yu, The Mind Research
Network, USA
Caroline A. Niziolek, University of
California, San Francisco, USA
*Correspondence:
Jocelyne Ventre-Dominey, INSERM
U846, 16 Avenue Doyen Lepine,
69675 Bron Cedex, France
e-mail: jocelyne.ventre-dominey@
inserm.fr
Our sensorimotor experience unfolds in sequences over time. We hypothesize that the
processing of movement sequences with and without a temporal pause will recruit
distinct but cooperating neural processes, including cortico-striatal and cortico-cerebellar
networks. We thus, compare neural activity during sequence learning in the presence
and absence of this pause. Young volunteer participants learned sensorimotor sequences
using the discrete sequence production (DSP) task, with Pause, No-Pause, and Control
sequences of four elements in an event related fMRI protocol. The No-Pause and Pause
sequences involved a more complex sequential structure than the Control sequence,
while the Pause sequences involved insertion of a temporal pause, relative to the
No-Pause sequence. The Pause vs. No-Pause contrast revealed extensive fronto-parietal,
striatal, thalamic and cerebellar activations, preferentially for the Pause sequences. ROI
analysis indicated that the cerebellum displays an early activation that was attenuated
over successive runs, and a significant preference for Pause sequences when compared
with caudate. These data support the hypothesis that a cortico-cerebellar circuit plays a
specific role in the initial processing of temporal structure, while the basal ganglia play a
more general role in acquiring the serial response order of the sequence.
Keywords: cerebellum, basal ganglia, discrete sequence production task, motor skills, sequence learning
INTRODUCTION
The discrete sequence production (DSP) task (Verwey, 1999)isa
paradigm that is well suited to investigate sensorimotor sequence
learning, and the effects of temporal pauses on sequence learn-
ing. In the DSP task, participants typically produce two novel,
discrete sequences by responding to successive stimuli mapped
to response keys in what is called a reaction mode of processing
(Verwey, 2003). These sequences typically involve 2–6 elements.
So, initially each stimulus must be processed to determine the
correct response. With practice, subjects gradually change to
producing these sequences in a sequencing mode,inwhichpar-
ticipants respond to the first stimulus by executing the entire
keying sequence while taking little notice of later stimuli (Ve r we y ,
2003; Verwey and Abrahamse, 2012). When temporal pauses are
introduced in DSP sequences, subjects learn to incorporate these
pauses into the sequence (Verwey, 1996; Verwey et al., 2009).
Likewise, changing the placement of pauses in motor sequences
can disrupt subjects’ ability to recognize and perform sequences
that they had previously learned (also see Stadler, 1993; Dominey,
1998a,b).
It is generally acknowledged that the basal ganglia are heav-
ily involved in the sequential organization of motor sequences
(Hikosaka et al., 1998). This is corroborated by studies with
Parkinson patients, who have a basal ganglia deficit related to
nigro-striatal dopaminergic dysfunction. These studies indicate
that these patients have special difficulty performing later parts
of movement sequences (e.g., Benecke et al., 1987a,b). Also, when
executing two successive and different three-key sequences they
were especially slow on the first element of the second sequence
(Hayes et al., 1998). According to the model of Doyon et al.
(2009), the striatum would be involved in motor sequence learn-
ing (i.e., the order of the movements) while the cerebellum would
be involved in the initial identification and learning of the specific
spatio-temporal structure of the sequence during motor adapta-
tion, with a subsequent reduction in activity (also see, Penhune
and Steele, 2012).
We have previously demonstrated that when a temporal pause
is introduced in discrete sensorimotor sequences, subjects learn
to incorporate these pauses into the sequence (Verwey, 1996;
Verwey et al., 2009). The current research tests the hypothesis
that processing sequences with a pause inserted at a fixed loca-
tion will recruit neural processes that are distinct from those
for processing related sequences with no pause (Doyon et al.,
2009), with the striatum playing a more global role in sequence
learning, and the cerebellum more involved in learning the spatio-
temporal structure in the initial phase of learning, particularly in
the presence of the pauses. To test this hypothesis we measured the
blood oxygen level-dependent (BOLD) response using functional
Frontiers in Human Neuroscience www.frontiersin.org September 2013 | Volume 7 | Article 492 |1
HUMAN NEUROSCIENCE

Jouen et al. Pause effects in sequence learning
MRI while subjects performed the DSP task, learning novel, 4-
element sequences, that were either Pause or No-Pause.ThePause
sequence included a pause following the second response. In the
No-Pause sequence such a pause did not occur.
METHODS
PARTICIPANTS
Eighteen right-handed healthy volunteers participated in the
study (mean age 22.5, SD =1.8; 8 men). Handedness was deter-
mined by individual specification of the writing hand, a principal
criteria in the Edinburgh inventory (Oldfield, 1971). The par-
ticipants were all students from Lyon University. Prior to the
scanning session, participants underwent an examination to val-
idate their medical state and MRI compatibility. No participant
had a history of neurological nor psychiatric disorders. All the
participants completed the entire fMRI test, but two of them were
discarded from the analysis because of the high number of motion
related artifacts in the cerebral images. The protocol was approved
by the Lyon Ethics Committee and the participants gave their
informed consent before the scanning session.
TAS K
The participants executed the DSP task (Verwey, 1999). They
restedfourfingersoftherighthandonfourkeysofakeypad.The
stimulus displayed on the screen involved filling one of four per-
manently displayed squares to which the participant responded
by pressing the spatially corresponding key (Figure 1). As soon
as the correct key had been pressed, the square was filled again
with the background color and immediately [the response stim-
ulus interval (RSI) was zero] another square was filled until four
keys had been pressed. Participants were instructed to press the
associated key as fast as possible while keeping errors to a mini-
mum. Faulty key presses were immediately followed by an error
message (by way of a change from a white to a colored visual
stimulus). Key presses that anticipated the target during the pause
were not counted as errors, and the sequence continued once the
pause was complete.
STIMULI
There were four (complex) experimental sequences: IRML,
MLIR, RIML, and LMRI (Index, Middle, Ring, Little finger), and
two (simple) control sequences: IMRL, LRMI. So, in the experi-
mental sequences key presses were never carried out by adjacent
fingers while the control sequences involved an order that is easy
to learn, namely a left-to-right or a right-to-left succession of key
presses.
These six sequences were split into a familiar and a novel set.
Familiar sequences had been practiced the prior to the scanning
(see Procedure below). Novel sequences were new to the subjects.
Each set consisted of one control sequence and two experimen-
tal sequences. One of the experimental sequences in each set
included a pause, a RSI of 800 ms between the second response
(R2) and the third stimulus (S3)(allotherRSIswerezero).No
such pause occurred in the No-Pause and Control sequences.
Across all participants, the experimental sequences were
balanced so that each of the four sequences occurred as fre-
quently in each of the four experimental conditions (familiar
vs. novel ×Pause vs. No-Pause). Likewise, the two Control
FIGURE 1 | Stimuli and experimental procedure. Visual stimuli indicate
the current key in the sequence. Each stimulus is made up of four unfilled
vertical bars that are individually illuminated on each trial in the appropriate
order. Each of the four bars corresponds to the spatially congruent key on the
response pad. After a correct touch the next stimulus in the sequence is
displayed. Faulty key presses were immediately followed by an error
message (as a change from a white to a colored visual stimulus). The figure
illustrates two stimuli and the corresponding responses.
sequences were evenly distributed across the familiar and novel
sequence sets.
PROCEDURE
Theexperimentincludedapracticesessionandthesubsequent
fMRI scanning session. Response times RT1–RT4were measured
in both sessions by the computer that presented the stimuli. In
the practice session, participants sat in front of the computer
display located on a table with the fingers of their right hand
on a key pad. Practice involved 14 blocks, each including the
three familiar sequences (Pause, No-Pause, and Control) in a
random order. The entire training session included 1500 tri-
als (∼500 trials with each sequence) and lasted ∼2h 30min.
A 10 min pause was inserted at the middle of the training
session.
In the scanning session, which was preceded by a 30 min
break after the practice session, the participant was comfortably
installed in the MRI scanner. Head movements were prevented
using a foam cushion and a frontal band that were attached to the
scanner bed. The visual stimuli (see Figure 1)weredisplayedby
video-projector on a translucent screen located behind the scan-
ning bay. The participant looked at the screen via a mirror fixed
inside the scanner at 20 cm over the participant’s head. The key
pad was located comfortably on the participant’s lap.
The scanning session involved 4 runs, two with the three famil-
iar and two with the three novel sequences. Half the participants
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Jouen et al. Pause effects in sequence learning
started with a familiar sequences run, the other half with a novel
sequences run. Then familiar and novel runs alternated. Each
run lasted ∼10 min and successive runs were separated by 2 min
breaks. Each run included 150 trials with the three sequences in
random order (i.e., 50 of each sequence in each run). The inter-
val between the first stimulus of two successive sequences was 4 s,
with occasional trials with an 8 s interval, to help ensure that acti-
vation signals for different conditions did not overlap. fMRI data
was acquired with a 1.5T system (Siemens CTI) at the Imaging
Center of Lyon (CERMEP “Imagerie du vivant”).
Brain scans involved assessment of the BOLD fMRI signal.
The anatomical images of the brain (3D MPR) were recorded
using a T1-weighted sequence. For each run, whole brain coverage
was obtained with Echo Planar Imaging (EPI) images (repetition
time TR =2500 ms, echo time TE =60 ms, and flip angle 90◦).
Twenty-six brain sections were acquired in an interlaced mode
parallel to the AC-PC plane. Slices had a thickness of 4.4 mm
[matrix 64 ×64;andfieldofview(FOV)=230 mm, voxel
size, 3.4×3.4×4 mm]. Following functional image acquisition,
a high-resolution T1-weighted anatomical image was acquired
(TR =1880 ms; TE =3.93; lip angle 15◦; matrix 256 ×256; and
slice thickness 1 mm).
DATA ANALYSIS
The focus of the current analysis is on the effects of the temporal
pause in DSP learning. Analyzes comparing data from the familiar
sequences with and without the pause will be presented later in a
separate paper. Thus, in order to examine learning in the presence
vs. absence of the temporal pause, we focus on results for the set
of novel sequences, which included the Pause sequence (with the
800 ms RSI separating R2and S3), and the No-Pause and Control
sequences. The response time (RT) analysis involved an analysis of
variance (ANOVA) using a three-way repeated measures design,
with factors RUN (2) ×PAUSE (3—No-Pause, Pause, Control) ×
KEY (4).
Preprocessing of fMRI data was conducted using SPM 2. For
the fMRI analysis, the first five scans of each run (i.e., the first
12.5 s and hence the first two or three sequences) were dis-
carded to eliminate non equilibrium effects of magnetization and
warming up effects in the participants. For preprocessing, the
functional images were realigned with respect to the first func-
tional image and were corrected for slice acquisition timing in
reference to the middle slice in each scan. The resulting volumes
werespatiallynormalizedtofittoanEPItemplateinMontreal
Neurological Institute (MNI) space, with 2 ×2×2mm voxels.
Thenormalizedimageswerethenspatiallysmoothedusingan
isotropic Gaussian filter kernel at an 8 Hz bandwidth. For each
participant the BOLD impulse response to different event types
were modeled in the context of the general linear model (GLM)
by using the hemodynamic response function (HRF) convolved
with a delta (event-related) function.
Processing of the fMRI data involved the Statistical Parametric
Mapping software (SPM 2, Wellcome Department of Imaging
Neuroscience, London UK; http://www.fil.ion.ucl.ac.uk/spm)
running under Matlab (The Mathworks, Inc., Natick, StateMA,
USA). On the basis of the GLM model (Friston et al., 1994), the
task related BOLD changes were estimated as linear combinations
of the individual regressors and stored as participant specific con-
trast images. Several contrasts were realized using the different
regressors in order to study the differences between the three
sequences (Pause =P, N o - Pa u s e =N, Control =C) i.e., P >C,
N>C, P >N, and N >P. These contrasts were selected to extract
the activated neural structures specifically involved in processing
sequences with and without the imposed pause.
For the statistical group analysis, the individual contrast
images were then processed in a second–level random effects
model by using a one sample t-test model to extract significant
neural activations. Significance level was established at a false
discovery rate (FDR) threshold of p<0.05 corrected for whole-
brain voxels with minimal spatial extent of 15 contiguous voxels
per cluster. To determine the neural structures activated in com-
mon during the Pause and No-Pause sequences, we performed a
conjunction analysis based on Nichols’ procedure (Nichols et al.,
2004) for the two Pause vs. Control and No-Pause vs. Control
contrasts (puncorrected <0.001 for multiple comparisons).
All MNI coordinates of the cerebral activation foci were trans-
formed into Talairach coordinates using the formula developed
by Matthew Brett (http://imaging.mrc-cbu.cam.ac.uk/imaging/
MniTalairach), and Brodman areas were determined using the
stereotaxic atlas (Talairach and Tournoux, 1988).
By using the Marsbar toolbox (http://marsbar.sourceforge.
net), regions-of-interest (ROIs) were built from the “peak” activa-
tion coordinates of each relevant cluster as a 10 mm radius cubic
3-D volume. For each ROI, we evaluated the percentage of signal
change from “beta” values (slope of the regression line for each
regressor) for that ROI. The estimated changes of activity were
analyzed by a repeated measures ANOVA, including the within-
subject factors Sequence (Pause, No-Pause, Control), Hemisphere
(right vs. left), and Run (first vs. second). Post-hoc comparisons
were performed with a Scheffé post-hoc analysis.
APPARATUS
The experimental protocol was implemented in Presentation
(Neurobehavioral Systems, Albany, USA) on a Windows XP based
PC. In the DSP task, a Luminar key pad (Cedrus, USA) that
is suited for used in MRI scanners was used for registering key
presses. The reaction time data were analyzed using Statistica
(Statsoft Inc.).
RESULTS
BEHAVIORAL RESULTS
Figure 2 illustrates the RTs obtained while the novel sequences
were carried out during scanning. We recall that each sequence
has a unique order that is signaled to the subject by the first key, so
while the first key is not predictable by learning, keys 2–4 are pre-
dictable and their RTs should display learning effects. We observe
that RTs are clearly reduced from Run 1 to Run 2, for keys 2–4, and
not key 1. The Run main effect, F(1,15)=15.4, p<0.01, con-
firmed that RTs are reduced across the two successive runs. The
Run ×Key interaction, F(3,45)=17.5, p<0.001, confirms that
this was caused exclusively by learning the predictable responses
(RTs 2–4 reduced 57 ms), F(1,15)=29.6, p<0.001, and not by
simple preparation speedup RT1, F(1,15)<1, p>0.05. Thus, the
execution rate of all three sequences improved to a similar degree
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Jouen et al. Pause effects in sequence learning
while preparation did not improve. The Run ×Sequence inter-
action [F(2,30)=1.5, p=0.2] indicates that the improvement
from Run 1 to Run 2 does not vary as a function of sequence type.
This confirms significant and equivalent learning of the novel
Pause and No-Pause sequences.
The results in Figure 2 indicate the pause before key 3 did not
produce an increased RT in the Pause sequences. Post-hoc com-
parisons revealed that RTs for individual keys, including key 3,
of the Pause and No-Pause sequences did not differ. The simi-
larity of the RTs for key 3 in the Pause and No-Pause sequences
suggest that in the Pause sequence participants quickly learned to
anticipate when S3would occur.
An ANOVA with the same design on arcsine transformed error
proportions showed that more errors were made in the Pause
(1.8% per key) than in the No-Pause and Control (1.1, 1.0% per
key, respectively) sequences, F(2,30)=5.1, p<0.05, and error
rate differed across the 4 keys, F(3,45)=10.3, p<0.001. Planned
comparison following the Sequence ×Key interaction, F(6,90)=
3.7, p<0.01, showed that more errors were made on R3in the
Pause sequence relative to the other sequences (3.4 vs. <2%),
F(1,15)=24.0, p<0.001. Thus, while anticipating onset of S3in
the Pause sequence was learned rapidly, the actual response at the
third position was somewhat more difficult to learn in the Pause
than in the No-Pause sequence.
fMRI RESULTS
Sequence formation activation by conjunction analysis
We determined the cerebral sites activated in common during
the execution and learning of the Pause and No-Pause sequences,
relative to the Control sequence, by using a conjunction anal-
ysis. To that end, we analyzed the conjunction of Pause minus
Control (P >C), and No-Pause minus Control (N >C) con-
trasts (at puncorrected <0.001), in order to identify the common
regions activated when learning a new sequence independently of
the temporal pause (Tabl e 1 ). The common neural substrates of
FIGURE 2 | Reaction times for the four key presses in the No-Pause,
Pause, and Control conditions for the two Novel sequence runs during
scanning.
developing a motor sequence representation—relative to the con-
trol sequence—are revealed as an antero-posterior network with
a major bilateral temporo-prefrontal activation. In the superior
temporal cortex, the activation formed a two-fold pattern with a
large activation including BA42/22 and a smaller and more pos-
terior activation in BA22. In the precentral gyrus, we identified
bilaterally two activation clusters at the surface and in the depth of
the precentral gyrus in BA6. A more extended pattern was iden-
tified bilaterally in the anterior cingulate cortex (24/32) and in
the prefrontal cortex including the inferior and middle gyri BA
(BA47/11) on the left. A small BOLD change was found in the left
inferior parietal lobe BA7.
The conjunction analysis in Ta b l e 1 revealed activity in two
principle subcortical structures: (1) the cerebellum, as substan-
tial clusters in the right culmen and dentate nucleus and in the
left declive of the cerebellum, and (2) the basal ganglia, forming a
significant activation in the body of caudate nucleus, lateral part.
In summary, sequence formation in the Pause and No-Pause
sequences together, relative to the Control sequence, implicated
a large bilateral cortico-subcortical network including temporo-
prefrontal cortex, the cerebellum and basal ganglia preferentially.
A summary visualization of this sequence formation network is
presented below.
Pause vs. No-Pause sequence contrast analysis
In order to examine the effect of processing the pause we exam-
ined the contrast Pause >No-Pause (P >N), revealing the effect
of the pause on BOLD changes (Tab l e 2 and Figures 3–6).
Table 1 | Common areas obtained by conjunction analysis of
No-pause vs. Control (N >C) and Pause vs. Control (P >C) contrasts
(p uncorrected <0.01).
Anatomical area BA x,y,zt-stat Ke
R Superior temporal gyrus 22/42 69, −25, 12 4.76 139
69, −23, 3 3.99
R Superior temporal gyrus 22 36, −44, 15 3.72 21
L Inferior parietal gyrus 7 −40, −64, 42 3.07 21
L Precentral 6 −50, −5, 22 3.65 16
R Precentral 6 40, −4, 30 3.80 45
28, 1, 26 3.00
L Middle frontal gyrus 11 −37, 36, −18 4.15 85
L Inferior frontal gyrus 47 −50, 40, −12 3.74
R Cingulate 23/24 26, −16, 32 4.19 141
32, −29, 29 3.61
L Cingulate 24/32 −30, 15, 25 4.09 73
−22, 17, 32 3.41
R Cerebellum—Dentate 15, −59, −19 4.28 87
22, −61, −20 3.23
26, −69, −20 3.22
R Cerebellum—Culmen 4, −55, −64.02 28
L Cerebellum—Declive −30, −59, −16 3.88 51
−24, −67, −17 2.80
R Caudate 14, 5, 15 3.67 87
Ke—cluster size in voxels.
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Jouen et al. Pause effects in sequence learning
Table 2 | Specification of regions with significant activation in the
Pause vs. No-pause contrast (P >N) at p<0.01 (FDR) and p<0.05
(FDR) and in the No-pause vs. Pause contrast (N >P) at p<0.001
(uncorrected).
Anatomical area BA x,y,zt-stat Ke
PAU S E >NO-PAUSE p<0.01 (FDR)
L Inferior parietal lobule*40 −38,−46,56 9.25 621
−32, −50, 49 7.87
7−26, −60, 44 6.88
R Postcentral gyrus 2 48, −32, 62 8.33 110
R Inferior parietal lobule*40 48,−36 48 6.25
R Inferior parietal lobule 40−7 24, −50, 45 6.34 28
26, −60, 44 5.23
L Middle prefrontal gyrus*6−28,10,49 7.9157
−34, 5, 55 6.72
−30, 2, 46 6.5
L Middle prefrontal gyrus*46−10 −34,49,77.12 117
−30, 45, 12 6.44
−38, 45, 16 5.88
L Middle prefrontal gyrus*9−44 −53,9,33 6.44 61
−46, 9, 35 5.96
−42, 8, 42 5.13
L Cerebellum—Declive*−10,−67,−13 7.43 76
L Cerebellum—Declive −26, −57, −11 7.02 45
−34, −55, −17 5.36
R Cerebellum—Declive*14,−74,−13 6.71 67
20, −79, −16 6.01
26, −73, −18 5.33
PAU S E >NO-PAUSE p<0.05 (FDR)
L and R Caudate*14,−3,15 4.26 175
LThalamus −4, −1, 11 4.68
NO PAUSE >PAU S E p<0.001 (uncorrected)
R Superior prefrontal gyrus 8 18, 49, 44 57 5.47
*ROIs used for statistical analysis. Ke—cluster size in voxels.
This revealed an extended network of cerebral activation
spreading from the parietal cortex to the prefrontal cortex. These
BOLD changes formed 4 principle cortical activation loci, pre-
dominantly in the left hemisphere at pcorrected (FDR) <0.01.
The parietal activation was bilateral with the largest cluster
located in the left hemisphere including BAs 7 and 40 in the infe-
rior and the superior parietal lobes (Figure 3). In the prefrontal
cortex, significant activation was found in 3 principle regions:
(1) a caudal locus focused in the middle prefrontal gyrus BA6
(Figure 3); (2) ventrally a smaller locus in BA 44 of the mid-
dle frontal gyrus; and (3) the most rostral activation extending
from the middle to the inferior frontal gyrus in BAs 46 and 10
(Figure 4). Significant subcortical activation was identified bilat-
erally in the cerebellum declive forming a set of postero-dorsal
clusters (Figure 5).
Significant BOLD change was observed in the midbrain in
the thalamus and in the body of caudate nucleus with a voxel
threshold at pcorrected (FDR) <0.05 (Tab l e 2 and Figure 6). At
this plevel, the clusters of the antero-posterior cortical network
activation remained in the same location but were enlarged.
Contrasting the No-Pause and Pause (N >P) conditions revealed
a small focus of activation identified at p<0.001 uncorrected in
the right superior frontal gyrus BA8.
In summary, the P >N contrast revealed significant activa-
tion in the parietal lobe (BA 7/40), in the frontal lobe (left middle
prefrontal cortex, BA6, 44/9, and BA 10/46), in the cerebellum
(declive), and in the midbrain (thalamus and caudate). In con-
trast, the N >P contrast was associated with significant activity
in the frontal cortex (superior frontal gyrus BA 8).
Specific cerebral activation by regions of interest analysis
On the basis of the voxel based analysis of the P >Nactivations
we defined six significant (p<0.01, FDR: corrected for multi-
ple comparisons) ROIs (Tab le 2). These were identified in the
cerebellum (CB), the parietal cortex (P-BA 40, 7), the premotor
cortex (PreM-BA 6), the dorsolateral prefrontal cortex (DLPFC-
BA 46, 10) and the middle prefrontal gyrus (BA 44). Activations
in the caudate nucleus (C) were also found, at a lower thresh-
old (FDR corr, p<0.05). As illustrated in Figures 3,4,direct
comparisons of rCBF changes (beta values) between the differ-
ent sequence conditions (main Sequence effect) demonstrated
significant pause related activation bilaterally in the inferior
parietal cortex [Left: F(2,30)=32 and Right: F(2,30)=16; p<
0.001] and only in the left hemisphere for the premotor cortex
[F(2,30)=10, p<0.001], the DLPFC [F(2,30)=12, p<0.001],
the inferior prefrontal cortex [F(2,30)=18, p<0.001]. In the
subcortical structures (Figures 5,6), a main Sequences effect
was found bilaterally in the cerebellar declive [Left: F(2,30)=
16 and Right: F(2,30)=12; p<0.001] and in the caudate
nucleus [Left and Right: F(2,30)=14, p<0.001]. As these ROIs
were defined based on the P vs. NP contrast, these results are
expected.
In contrast, the RUN effect on rCBF changes was restricted
to few regions: in the parietal cortex [Left: F(1,15)=7, p<0.02,
Right: F(1,15)=16, p<0.001], in the left inferior prefrontal cor-
tex [F(1,15)=7, p<0.02]andinthecerebellardeclive[Left:
F(1,15)=14, p<0.001, Right: F(1,15)=12, p<0.001]. Post-hoc
comparison (Scheffé) revealed a pattern of learning related activ-
ity (i.e., Run1 vs. Run2) that was differentiated between a parieto-
cerebellar network and prefrontal cortex. In both cerebellum and
parietal cortex ROIs, BOLD activity decreased between the 2 runs
for Pause (p<0.001) and No-Pause (p<0.01) sequences. In
contrast, in prefrontal ROI (BA9-44), we observed a Sequence
×Run interaction [F(1,15)=4.6, p<0.05] demonstrating a sig-
nificant Run1 vs. Run2 reduction for the Pause (p<0.001) but
not the No-Pause sequence. No effects of the run were found in
the other prefrontal ROIs and in the caudate nucleus. Figure 6
shows the effect of the runs on the rCBF changes of these
ROIs.
Overall, the contrast and ROI analyzes demonstrate that (1)
three regions (cerebellum, parietal cortex, and inferior prefrontal
cortex) display a significantly increased activation for the Pause
sequences. (2) In two of these regions (cerebellar and parietal
ROIs), there is a significant and equivalent reduction in activa-
tion for both Pause and No-Pause sequences in the transition
from Run 1 to Run 2 where sequence learning occurs. (3) In the
prefrontal cortex, this reduction from Run 1 to Run 2 is only
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Jouen et al. Pause effects in sequence learning
FIGURE 3 | Premotor and inferior parietal cortical activation obtained with
P>N contrast. Activated prefrontal regions shown on template axial sections
including the left premotor cortex, BA 6 (A) and bilaterally the inferior parietal
cortex, BA 40 and 7 (B). The graphs represent mean beta values extracted at
the maximal peak of activation during No-Pause (N) and Pause sequences (P) in
prefrontal clusters. Pcorrected <0.01. Bars =standard deviations.
FIGURE 4 | Dorso-lateral prefrontal and inferior prefrontal cortical
activation obtained with P >N contrast. Activated prefrontal regions
shown on template axial sections including the left hemisphere the
dorsolateral prefrontal cortex, BA 46-10 (A) and the inferior prefrontal
cortex, BA 44 (B). The graphs represent mean beta values extracted at
the maximal peak of activation during No-Pause (N) and Pause
sequences (P) in the prefrontal clusters. Pcorrected <0.01.
Bars =standard deviations.
observed for the Pause sequences, with equal activation for both
sequence types in Run 2.
Figure 7 illustrates on lateral 3D views of the brain the neu-
ral structures underlying (1) the sequence formation network
identified by conjunction analysis, (2) the temporal integration in
Pause sequence by ROIs analysis of Pause vs. No-Pause sequences
and (3) the activated prefrontal area BA8 identified by No-Pause
vs. Pause sequences.
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Jouen et al. Pause effects in sequence learning
FIGURE 5 | (A) Cerebellar activation obtained with P >N contrast. Bilateral
activation in cerebellum displayed on the template axial sections. The graph
represents mean beta values extracted at the maximal peak of activation
during No-Pause (N) and Pause sequences (P) in right and left cerebellum
clusters (indicated in the Figure). Pcorrected <0.01. Bars =standard
deviations. Caudate and Thalamic activation. (B) Bilateral activation in
caudate nucleus displayed on template axial sections. The graph represents
mean beta values extracted at the maximal peak of activation during
No-Pause (N) and Pause sequences (P) in right and left caudate clusters.
Pcorrected <0.05. Bars =standard deviations.
DISCUSSION
The organization of our sensorimotor experience is typically not
uniform in time (Allen and Ferguson, 1994; Dominey, 1998a,b;
Dominey and Ramus, 2000; Ventre-Dominey et al., 2002; Shin
and Ivry, 2003; Bengtsson et al., 2004). In the current study
we investigated brain mechanisms for the learning of sequential
behavior in the situation that a temporal pause is introduced into
the sequence.
GENERAL LEARNING EFFECTS IN THE DISCRETE SEQUENCE
PROCESSING TASK
Given that the present study is one of the first to examine the
development of fixed sequences of limited length, that are typ-
ical for the DSP task, we first consider the conjunction analysis
for the activation common to the Pause and No-Pause sequences
relative to a Control sequence requiring little serial control. This
analysis revealed an extended cortical and subcortical network for
FIGURE 6 | Bars graph indicating the rCBF changes (Beta) in the cortical
and subcortical ROIs defined in the left and right hemispheres (LH and
RH, respectively). Note that only the cerebellum and the inferior parietal
cortex BA40 (iP 40) and to a less extent the inferior prefrontal cortex BA 44,
are the most activated bilaterally during run 1 and significantly decrease their
activity during run 2. Bars: standard deviation. iP 40: inferior parietal cortex
BA40—iPF44: inferior prefrontal cortex BA44—Pc6: precentral cortex
BA6—DLPFC. ∗∗∗ p<0.001; ∗∗p<0.01; ∗p<0.05.
FIGURE 7 | Representation on rendered templates of the activated
networks involved in the sequence formation (pink: N AND P
conjunction), in the temporally Pause sequence (yellow: P >N), and in
the temporally No-Pause sequence (blue: N >P). Thecutouts (three lower
images) display the activated structures in the depth of cortical gyri and in the
sub-cortical structures, including basal ganglial. iP, Inferior parietal cortex; sT,
Superior temporal cortex; Pc, Precentral cortex; sPF, Superior prefrontal
cortex; DLPF, Dorso-lateral prefrontal cortex; iPF, Inferior prefrontal cortex;
mPF, Middle prefrontal cortex; BG, Basal ganglia; Cb, Cerebellum.
sequence learning that included precuneus (left BA7), anterior
cingulate and premotor cortices (bilateral BA6). These regions
are known to participate in finger sequence learning (Harrington
et al., 2000; Jenkins et al., 2000; Haaland et al., 2004; Halsband
Frontiers in Human Neuroscience www.frontiersin.org September 2013 | Volume 7 | Article 492 |7

Jouen et al. Pause effects in sequence learning
and Lange, 2006). Activation in the superior temporal gyrus
(BA22/42) as well as the inferior frontal gyrus (BA47) was also
identified in this common sequence formation process. These
areas have been implicated in implicit finger sequence learning
in a serial reaction time task too, in which successive items were
presented for 1 s during which the subjects were to respond with a
keypress (Daselaar et al., 2003). Interestingly, activation in BA22
in a finger tapping task has also been associated with finger-
specific activation (Aoki et al., 2005). Likewise, BA47 is activated
in rhythmic tapping tasks (Vuust et al., 2011). In addition to these
cortical sites, we observed a subcortical activation involving cere-
bellum and basal ganglia whose implications have been previously
described in sequence formation. In this context, the cerebel-
lum would be involved in optimizing sensory information and
monitoring the motor output while basal ganglia-cortical loops
would be involved in movement selection with sequence learn-
ing (Jueptner and Weiller, 1998). Accordingly, the co-activation
pattern of cerebellum and basal ganglia has been described in
explicit and implicit sequence learning with a cerebellar decrease
as sequence learning proceeds and a fronto-striatal activation
maintenance during the learning plateau (Grafton et al., 1995;
Rauch et al., 1995, 1997; Doyon et al., 1996, 2009; Doyon, 2008).
In short, the initial practice of new, short fixed sequences is asso-
ciated with enhanced activation—relatively to a simple Control
sequence—in a network involving frontal areas (bilateral premo-
tor cortex BA6 and anterior cingulate cortex- left orbitofrontal
cortex BA11/47), associative visuomotor areas (left precuneus
cortex BA7), temporal areas (right secondary auditory cortex
BA22/42), and in the cerebellum and right caudate.
PROCESSING OF PAUSE AND NO-PAUSE SEQUENCES
We now consider the specific effects Pause vs. No-Pause
sequences. Behavioral analyzes showed that for the Pause and No-
Pause sequences, learning was demonstrated as their execution
times improved over the two experimental runs. The similarity
between the RTs of the Pause and No-Pause sequences demon-
strates that participants learned the two sequences. It is well
documented that the insertion of such pauses produces a reliable
and specific effect on learning, which is to induce a segmenta-
tion of the sequence at that location (Ve r w e y a n d E i k el b o o m ,
2003; Verwey et al., 2009). Likewise, we have clear evidence that
repeated presentation of pauses at specific locations in sequences
leads to an integration of these pauses into the learned repre-
sentation, as the expression of learning is significantly impaired
when the pause structure is modified during testing (Dominey,
1998a,b).
It is worth noting that in contrast, related studies of sequence
learning have shown that when pauses are inserted randomly,
sequence learning is disrupted and even eliminated. Stadler
(1995) demonstrated that when the response-to-stimulus inter-
val is increased on a random selection of half of the trials from
400 to 2000 ms there is a significant disruption of sequence
learning, with learning effects reduced by more than 50% (from
227 to 97 ms for simple sequences, and 74 to 21 ms for more
complex sequences). Similarly, Deffains et al. (2011) demon-
strated impaired sequence learning in the non-human primate
when all elements of the sequence were separated by long pauses.
Our observation of equal learning for the Pause and No-Pause
sequences indicates that the pause was likely integrated within
the sequence structure and learned, and was not processed as a
temporal disruption of the sequence, thus, distinct from the per-
turbing effects of random pauses demonstrated by Stadler (1995)
and Deffains et al. (2011).
Learning the Pause and No-Pause sequences was associated
with a distributed network including bilateral cerebellar-parietal
circuits as well as left fronto-striatal circuits, including caudate.
In the case of our Pause sequences, the system must accommodate
both the linear order of the sequence elements and the integration
of the pause. These dimensions correspond to the Pause factor,
and the progression of learning over successive runs, i.e., the Run
factor. Interestingly, we observed interactions between sequence
types and temporal learning process when comparing the dif-
ferent sites of activation. As learning progresses, the cerebellar,
parietal and prefrontal activation displays a significant reduc-
tion of activation for the Pause sequences (see Figure 6). For the
prefrontal cortex, this reduction results in equivalent activation
for Pause and No-Pause sequences in Run 2. Combined with
the observation that significant learning took place for the Pause
sequences, this indicates that the pause was learned. This suggests
that the effects observed for Pause sequences are not simply due
to the non-specific effects of the presence of the pause, but rather,
it is linked to the integration of the pause within the sequence.
Future research could address this question by examining neural
and behavioral responses to randomly inserted pauses.
NEURAL SUBSTRATES OF SERIAL AND TEMPORAL PROCESSING
The comparison of caudate and cerebellum activity revealed the
potential existence of dissociated systems for different structural
dimensions of sequence learning. Both cerebellum and caudate
display increased activation for the Pause vs. No-Pause sequences.
In the progression from Run 1 to Run 2, the cerebellar activa-
tion is reduced, while the caudate displays no effect of Run. This
suggests that the temporal integration process may take place in
two domains. One requires the identification and calibration of
the temporal structure, and the second implies the integration
of this structure into the holistic representation of the sensori-
motor sequence. The first process may be reflected by cerebellar
activation, with a greater activation for Pause sequences, and a
reduction in activation from Run 1 to Run 2. The second process
likely corresponds to caudate activation, with a greater activa-
tion for Pause sequences that is maintained from Run 1 to Run 2.
Interestingly,atthelevelofthecerebralcortex,whiletheactivity
in the inferior parietal cortex bilaterally formed a similar pat-
tern as the cerebellum, the left inferior prefrontal cortex activity
changed as the learning progressed only for the Pause sequences.
Indeed, the prefrontal area BA44 displayed a Pause ×Run inter-
action, with a significant reduction in activation from Run 1 to
Run 2 for Pause but not No-Pause sequences. This is consis-
tent with findings that a cerebellar and prefrontal circuit may be
involved in the discrimination of temporal information (Mathiak
et al., 2004). These data support the hypothesis that a cortico-
cerebellar circuit plays a specific role in the initial processing of
temporal structure, while the basal ganglia play a more general
role in acquiring the serial response order of the sequence.
Our Pause sequences activated, relative to the No-Pause
sequences, predominantly the left sensori-motor cortical network
Frontiers in Human Neuroscience www.frontiersin.org September 2013 | Volume 7 | Article 492 |8

Jouen et al. Pause effects in sequence learning
including the premotor cortex and DLPFC. By comparing het-
erogeneous vs. simple, repetitive sequences, Haaland et al. (2004)
reported that complex finger sequences preferentially engaged
parietal cortex and cerebellum. Moreover, they described a rela-
tionship between the asymmetry of the sensorimotor cortical
activation and the sequence complexity. It is worth noting that
in our study the insertion of the pause in the Pause sequences
can be considered as a complexity increase compared to the
No-Pause sequences. In agreement with Haaland et al.’s findings
(2004), our Pause sequences activated, relative to the No-Pause
sequence, predominantly the left sensori-motor cortical network
including the premotor cortex and DLPFC which might be impli-
cated in advanced planning and abstract organization of complex
motor sequences. These results are consistent with a number
of related studies, while extending them based on the analysis
of Pause vs. No-Pause sequences. The cerebellum and parietal
cortex have been implicated in sensorimotor sequence learning
(Hikosaka et al., 2002), particularly in the early stages of learn-
ing (Rauch et al., 1997; Doyon and Ungerleider, 2002; Lehericy
et al., 2005; Halsband and Lange, 2006; Doyon et al., 2009). For
example, in an fMRI study of motor sequence learning, Doyon
and Ungerleider (2002) demonstrate that cerebellum as well as
inferior parietal and dorsal premotor cortices diminished their
activity at the stable learning stage while the striatum activity
coupled to anterior motor cortical areas is maintained, suggest-
ing a role of basal ganglia in storage and automatization of the
motor performance. Similarly, neurophysiological changes have
been subsequently observed by Lehericy et al. (2005) during
early and advanced motor learning. Compared to these stud-
ies, we provide new insights in the feedback control role of the
cerebellum in early sequence learning, as the bilateral cerebello-
parietal circuits might also contribute to the temporal integration
and sequence segmentation of initial learning. In the context
of temporal structure, Sakai et al. (1999, 2004) demonstrated
cerebellar posterior lobe activation in the processing of non-
metrical (vs. metrical) rhythm, which would correspond to the
insertion of the pause in our sequences. Indeed, these authors
point out that the posterior cerebellum has been implicated
in explicit temporal representation. In a related study, Sakai
et al. (2002) examine the learning of serial order vs. tempo-
ral structure and observe prefrontal, premotor, inferior pari-
etal cortical and cerebellar activation when both are learned.
Interestingly, when timing alone was learned, activation was
restricted to areas including parietal cortex and cerebellum, while
sequence learning in the absence of temporal structure eliminated
the cerebellar activation. This is consistent with our interpre-
tation that posterior cerebellar (declive) activation reflects that
subjects are integrating the temporal structure of the pause,
and that as the pause is integrated the effect is reduced as the
sequence learning progresses (Run 2). Learning the larger time
scale structure of the entire sequence is reflected by the stri-
atal activity that is less influenced by run. In the context of
such a distinction, it has been suggested that the cerebellum is
involved in processing of temporal structure at a timescale of tens
to hundreds of milliseconds, while the basal ganglia would be
involved more at a timescale of seconds (Mauk and Buonomano,
2004).
Further examining this distinction, Shin and Ivry (2003)
studied concurrent temporal and spatial sequence learning in
patients with Parkinson’s disease, and with cerebellar lesions.
They observed that while PD patients were capable of learning
both serial and temporal structure, they failed to integrate the
two into a unified representation, as would be required in our
task, where the temporal pause must be integrated into the spa-
tial sequence. The cerebellar lesion patients displayed a global
sequence learning deficit. In addition, we previously demon-
strated in Parkinson patients a dissociation between reaction
time processing implicating the striatum in impaired stimulus-
response timing adaptation vs. a preserved frequency timing
processing involving other structures than striatum, likely the
cerebellum (Ventre-Dominey et al., 2002).
CONCLUSION
In summary, we interpret these results in the context whereby
both the striatum and cerebellum preferentially intervene in pro-
cessing of the pause. Over the course of the learning, the cerebellar
activation is reduced. In parallel with this, the caudate continues
to participate in the integration of the pause within the sequence
over the duration of the two runs. These processes make up
part of the neural system that exploits natural spatio-temporal
organization of sensorimotor sequences.
ACKNOWLEDGMENTS
This research was supported in part by the CNRS under the
Eurocores action of the ESF, and by the FP7 ICT projects Organic
and EFAA.
REFERENCES
Allen, J., and Ferguson, G. (1994).
Actions and events in interval tem-
poral logic. J. Logic Comput. 4, 531.
doi: 10.1093/logcom/4.5.531
Aoki, T., Tsuda, H., Takasawa, M.,
Osaki, Y., Oku, N., Hatazawa, J.,
et al. (2005). The effect of tap-
ping finger and mode differences
on cortical and subcortical activi-
ties: a PET study. Exp. Brain Res.
160, 375–383. doi: 10.1007/s00221-
004-2008-9
Benecke, R., Rothwell, J. C., Dick,
J. P., Day, B. L., and Marsden,
C. D. (1987a). Disturbance of
sequential movements in patients
with Parkinson’s disease. Brain 110,
361–379. doi: 10.1093/brain/110.
2.361
Benecke, R., Rothwell, J. C., Dick, J.
P., Day, B. L., and Marsden, C.
D. (1987b). Simple and complex
movements off and on treatment
in patients with Parkinson’s dis-
ease. J. Neurol. Neurosurg. Psychiatry
50, 296–303. doi: 10.1136/jnnp.50.
3.296
Bengtsson, S. L., Ehrsson, H. H.,
Forssberg, H., and Ullen, F. (2004).
Dissociating brain regions con-
trolling the temporal and ordinal
structure of learned movement
sequences. Eur. J. Neurosci. 19,
2591–2602. doi: 10.1111/j.0953-
816X.2004.03269.x
Daselaar, S. M., Rombouts, S. A.,
Veltman, D. J., Raaijmakers, J. G.,
and Jonker, C. (2003). Similar net-
work activated by young and old
adults during the acquisition of a
motor sequence. Neurobiol. Aging
24, 1013–1019. doi: 10.1016/S0197-
4580(03)00030-7
Deffains, M., Legallet, E., and Apicella,
P. (2011). Importance of the
temporal structure of movement
sequences on the ability of monkeys
to use serial order information.
Exp. Brain Res. 214, 415–425. doi:
10.1007/s00221-011-2839-0
Dominey, P. F. (1998a). Influences of
temporal organization on sequence
learning and transfer: comments
on Stadler (1995) and Curran and
Keele (1993). J. Exp. Psychol. Learn.
Mem. Cogn. 24, 14. doi: 10.1037/
0278-7393.24.1.234
Dominey, P. F. (1998b). A shared
system for learning serial and tem-
poral structure of sensori-motor
sequences? Evidence from simula-
tion and human experiments. Brain
Res. Cogn. Brain Res. 6, 163–172.
Frontiers in Human Neuroscience www.frontiersin.org September 2013 | Volume 7 | Article 492 |9

Jouen et al. Pause effects in sequence learning
doi: 10.1016/S0926-6410(97)
00029-3
Dominey, P. F., and Ramus, F. (2000).
Neural network processing of natu-
ral language: I. Sensitivity to serial,
temporal and abstract structure
of language in the infant. Lang.
Cogn. Process. 15, 40. doi: 10.1080/
016909600386129
Doyon, J. (2008). Motor sequence
learning and movement disorders.
Curr. Opin. Neurol. 21, 478–483.
doi: 10.1097/WCO.0b013e32
8304b6a3
Doyon, J.,Bellec, P.,Amsel, R., Penhune,
V.,Monchi,O.,Carrier,J.,etal.
(2009). Contributions of the basal
ganglia and functionally related
brain structures to motor learning.
Behav. Brain Res. 199, 61–75. doi:
10.1016/j.bbr.2008.11.012
Doyon, J., Owen, A. M., Petrides, M.,
Sziklas, V., and Evans, A. C. (1996).
Functional anatomy of visuomo-
tor skill learning in human sub-
jects examined with positron emis-
sion tomography. Eur. J. Neurosci. 8,
637–648. doi: 10.1111/j.1460-9568.
1996.tb01249.x
Doyon, J.,and Ungerleider, L. G. (2002).
“Functional anatomy of motor skill
learning,” in Neuropsy cholog y of
Memory, eds L. R. Squire and D.
L. Schacter (New York, NY: The
Guilford Press), 225–238.
Friston, K. J., Holmes, A. P., Worsley,
K. J., Poline, J. P., Frith, C. D.,
and Frackowiak, R. S. J. (1994).
Statistical parametric maps in
functional imaging: a general lin-
ear approach. Hum. Brain Mapp.
2, 189–210. doi: 10.1002/hbm.
460020402
Grafton, S. T., Hazeltine, E., and
Ivry, R. B. (1995). Functional map-
ping of sequence learning in nor-
mal humans. J. Cogn. Neurosci.
7, 497–510. doi: 10.1162/jocn.1995.
7.4.497
Haaland, K. Y., Elsinger, C. L., Mayer,
A. R., Durgerian, S., and Rao, S. M.
(2004). Motor sequence complex-
ity and performing hand produce
differential patterns of hemispheric
lateralization. J. Cogn. Neurosci.
16, 621–636. doi: 10.1162/089892
904323057344
Halsband, U., and Lange, R. K. (2006).
Motor learning in man: a review
of functional and clinical studies.
J. Physiol. Paris 99, 414–424. doi:
10.1016/j.jphysparis.2006.03.007
Harrington, D. L., Rao, S. M., Haaland,
K.Y.,Bobholz,J.A.,Mayer,A.
R., Binderx, J. R., et al. (2000).
Specialized neural systems under-
lying representations of sequential
movements. J. Cogn. Neurosci. 12,
56–77. doi: 10.1162/089892900511
37602
Hayes, A. E., Davidson, M. C., Keele,
S. W., and Rafal, R. D. (1998).
Toward a functional analysis
of the basal ganglia. J. Cogn.
Neuro sci . 10, 178–198. doi: 10.1162/
089892998562645
Hikosaka, O., Miyashita, K., Miyachi,
S., Sakai, K., and Lu, X. (1998).
Differential roles of the frontal
cortex, basal ganglia, and cerebel-
lum in visuomotor sequence learn-
ing. Neurobiol. Learn. Mem. 70,
137–149. doi: 10.1006/nlme.1998.
3844
Hikosaka, O., Nakamura, K., Sakai, K.,
and Nakahara, H. (2002). Central
mechanisms of motor skill learning.
Curr. Opin. Neurobiol . 12, 217–222.
doi: 10.1016/S0959-4388(02)
00307-0
Jenkins, I. H., Jahanshahi, M., Jueptner,
M., Passingham, R. E., and Brooks,
D. J. (2000). Self-initiated versus
externally triggered movements. II.
The effect of movement predictabil-
ity on regional cerebral blood flow.
Brain 123, 1216–1228. doi: 10.1093/
brain/123.6.1216
Jueptner, M., and Weiller, C. (1998). A
review of differences between basal
ganglia and cerebellar control of
movements as revealed by func-
tional imaging studies. Brain 121,
1437–1449. doi: 10.1093/brain/121.
8.1437
Lehericy, S., Benali, H., Van de
Moortele, P. F., Pelegrini-Issac, M.,
Waechter, T., Ugurbil, K., et al.
(2005). Distinct basal ganglia ter-
ritories are engaged in early and
advanced motor sequence learning.
Proc. Natl. Acad. Sci. U.S.A. 102,
12566–12571. doi: 10.1073/pnas.
0502762102
Mathiak, K., Hertrich, I., Grodd,
W., and Ackermann, H. (2004).
Discrimination of temporal
information at the cerebellum:
functional magnetic resonance
imaging of nonverbal auditory
memory. Neuroi mage 21, 154–162.
doi: 10.1016/j.neuroimage.2003.
09.036
Mauk, M. D., and Buonomano,
D. V. (2004). The neural basis
of temporal processing. Annu.
Rev. Neurosci. 27, 307–340. doi:
10.1146/annurev.neuro.27. 070203.
144247
Nichols, T., Brett, M., Andersson, J.,
Wager, T., and Poline, J. B. (2004).
Validconjunction inference with the
minimum statistic. Neuro image 25,
653–660. doi: 10.1016/j.
neuroimage.2004.12.005
Oldfield, R. C. (1971). The assessment
and analysis of handedness:
the Edinburgh inventory.
Neuro psychologia 9, 97–113. doi:
10.1016/0028-3932(71)90067-4
Penhune, V. B., and Steele, C. J.
(2012). Parallel contributions of
cerebellar, striatal and M1 mecha-
nisms to motor sequence learning.
Behav. Brain Res. 226, 579–591. doi:
10.1016/j.bbr.2011.09.044
Rauch, S. L., Savage, C. R., Brown,
H. D., Curran, T., Alpert, N. M.,
Kendrick, A., et al. (1995). A PET
investigation of implicit and explicit
sequence learning. Hum. Brain
Mapp. 3, 271–286. doi: 10.1002/
hbm.460030403
Rauch, S. L., Whalen, P. J., Savage,
C. R., Curran, T., Kendrick, A.,
Brown, H. D., et al. (1997). Striatal
recruitment during an implicit
sequence learning task as measured
by functional magnetic resonance
imaging. Hum. Brain Mapp. 5,
124–132. doi: 10.1002/(SICI)1097-
0193(1997)5:2<124 :: AID-HBM6>
3.0.CO;2-5
Sakai, K., Hikosaka, O., Miyauchi, S.,
Takino, R., Tamada, T., Iwata, N. K.,
et al. (1999). Neural representation
of a rhythm depends on its interval
ratio. J. Neurosci. 19, 10074–10081.
Sakai, K., Hikosaka, O., and Nakamura,
K. (2004). Emergence of rhythm
during motor learning. Tre nds C o gn.
Sci. 8, 547–553. doi: 10.1016/j.tics.
2004.10.005
Sakai, K., Ramnani, N., and
Passingham, R. E. (2002). Learning
of sequences of finger movements
and timing: frontal lobe and
action-oriented representation.
J. Neurophysiol. 88, 2035–2046.
Shin, J. C., and Ivry, R. B. (2003).
Spatial and temporal sequence
learning in patients with Parkinson’s
disease or cerebellar lesions. J. Cogn.
Neuro sci . 15, 1232–1243. doi:
10.1162/089892903322598175
Stadler, M. A. (1993). Implicit serial
learning: questions inspired by
Hebb (1961). Mem. Cogn. 21,
819–827. doi: 10.3758/BF03202749
Stadler, M. A. (1995). Role of attention
in implicit learning. J. Exp. Psychol.
Learn. Mem. Cogn. 21, 674–685. doi:
10.1037/0278-7393.21.3.674
Talairach, J., and Tournoux, P. (1988).
Co-planar Stereotaxic Atlas of the
Human B rain.NewYork,NY:
Thieme.
Ventre-Dominey, J., Dominey, P.
F., and Broussolle, E. (2002).
Dissociable processing of tem-
poral structure in repetitive
eye-hand movements in Parkinson’s
disease. Neuro psychologia 40,
1407–1418. doi: 10.1016/S0028-
3932(01)00207-X
Verwey, W. B. (1996). Buffer load-
ing and chunking in sequential
keypressing. J. Exp. Psychol. Hum.
Perce pt. Pe rfo rm. 22, 544–562. doi:
10.1037/0096-1523.22.3.544
Verwey, W. B. (1999). Evidence for a
multi-stage model of practice in a
sequential movement task. J. Exp.
Psychol. Hu m. Perc ept. Perform.
25, 1693–1708. doi: 10.1037/0096-
1523.25.6.1693
Verwey, W. B. (2003). Processing modes
and parallel processors in producing
familiar keying sequences. Psychol.
Res. 67, 106–122.
Verwey, W. B., and Abrahamse, E.
L. (2012). Distinct modes of exe-
cuting movement sequences: react-
ing, associating, and chunking. Acta
Psychol. 140, 274–282. doi: 10.1016/
j.actpsy.2012.05.007
Verwey,W.B.,Abrahamse,E.L.,etal.
(2009). Segmentation of short key-
ing sequences does not sponta-
neously transfer to other sequences.
Hum. Mov. Sci. 28, 348–361. doi:
10.1016/j.humov.2008.10.004
Verwey,W.B.,andEikelboom,
T. (2003). Evidence for lasting
sequence segmentation in the
discrete sequence-production task.
J. Mot. Behav. 35, 171–181. doi:
10.1080/00222890309602131
Vuust, P., Wallentin, M., Mouridsen,
K., Ostergaard, L., and Roepstorff,
A. (2011). Tapping polyrhythms
in music activates language areas.
Neurosci. Lett. 494, 211–216. doi:
10.1016/j.neulet.2011.03.015
Conflict of Interest Statement: The
authors declare that the research
was conducted in the absence of any
commercial or financial relationships
that could be construed as a potential
conflict of interest.
Received: 26 February 2013; accepted:
02 August 2013; published online: 18
September 2013.
Citation: Jouen A-L, Verwe y WB, van
der Helden J, Scheiber C, Neveu R,
Dominey PF and Ventre-Dominey J
(2013) Discrete sequence production
with and without a pause: the role of cor-
tex, basal ganglia, and cerebellum. Front.
Hum. Neu rosci. 7:492. doi: 10.3389/
fnhum.2013.00492
This article was submitted to the journal
Frontiers in Human Neuroscience.
Copyright © 2013 Jouen, Verwey, van
der Helden, Scheiber, Neveu, Dominey
and Ventre-Dominey. This is an open-
access article distributed under the terms
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