© 2004 Nature Publishing Group http://www.nature.com/natureneuroscience

ARTICLES

Rhythmic arm movement is not discrete
Stefan Schaal1,3, Dagmar Sternad2, Rieko Osu3 & Mitsuo Kawato3
Rhythmic movements, such as walking, chewing or scratching, are phylogenetically old motor behaviors found in many organisms,
ranging from insects to primates. In contrast, discrete movements, such as reaching, grasping or kicking, are behaviors that havereached sophistication primarily in younger species, particularly primates. Neurophysiological and computational research on arm
motor control has focused almost exclusively on discrete movements, essentially assuming similar neural circuitry for rhythmic
tasks. In contrast, many behavioral studies have focused on rhythmic models, subsuming discrete movement as a special case.
Here, using a human functional neuroimaging experiment, we show that in addition to areas activated in rhythmic movement,
discrete movement involves several higher cortical planning areas, even when both movement conditions are confined to the same
single wrist joint. These results provide neuroscientific evidence that rhythmic arm movement cannot be part of a more generaldiscrete movement system and may require separate neurophysiological and theoretical treatment.

From the viewpoint of vertebrate neurobiology, rhythmic movement
generation is primarily associated with pattern-generator circuits in the
spinal cord and the brainstem. However, it has been difficult to locate
elements of such circuits in higher vertebrates because of the complexity of these nervous structures and their additional modulation byhigher brain centers in the mature system1. In humans, studies of thedevelopment of infant locomotion and microstimulation in patientswith spinal cord injury have provided some behavioral evidence forpattern generators, again assumed to be on the spinal or brainstemlevel2–4. Owing to the ubiquitous presence of rhythmic movements, alarge proportion of behavioral research in motor control uses therhythmic pattern generation metaphor as a guiding model5–7.
Conversely, neuroscience research into primate arm movements
has almost exclusively been conducted on discrete and visually guided
reaching8–11, seemingly under the assumption that rhythmic arm
movements are a special case in which the neural-reaching circuit isused repeatedly. Several prominent computational models of arm
movement are based on discrete strokes between a start and an end
point12,13 or, to generate more complex trajectories, a start and an end
point with intermediate via points14,15, using either extrinsic (forexample, Cartesian) or intrinsic (for example, joint or muscle space)coordinates to plan and execute the trajectory between these landmarks16. From this viewpoint, rhythmic arm movement is a sequenceof movements between recurrent via points11,17–19.
However, recent behavioral evidence has created some doubt as towhether this subsumption of rhythmic arm movement by discrete
movement is appropriate. For instance, a series of studies20–22 havedemonstrated that certain kinematic features of the hand trajectoryin rhythmic arm movement, previously interpreted as signs of segmented movement generation, can actually be accounted for by oscillator-based and/or optimally smooth movement generation, that is,
without the need for any segmentation. Experiments that tested thepossibility of superposition of rhythmic and discrete patterns in arm
movements also concluded that rhythmic and discrete movement
might be two different movement regimes23–25. Similar conclusions
were obtained in a Fitts movement paradigm: in rhythmic performance, significantly higher movement speed can be tolerated at thesame level of goal accuracy as in discrete movement26. Some initial
computational models have been suggested to make the difference
between rhythmic and discrete movement explicit and to explore thegeneral value of such theories in computational neuroscience andbehavioral experiments23,27–29.
To examine the difference between rhythmic and discrete movement at a neural level, we compared brain activity in rhythmic anddiscrete wrist movements in a 4-Tesla human functional neuroimaging (fMRI) experiment. We predicted that if rhythmic movements
were just a sequence of discrete movements, rhythmic activity (RA)should be the same as or more than discrete activity (DA), that is, RA⊇ DA, owing to the extra demands of sequencing. In contrast, if discrete movements were a special case of rhythmic movement—for
example, an aborted limit cycle—we would expect RA ⊆ DA, owing
to the extra demands of terminating an ongoing movement. If discrete activity and rhythmic activity were different units of action,
they should overlap only to some extent and each of them shouldhave distinct sites of activity.
The task for participants was to perform single-joint wrist flexionextension movements inside the scanner (Fig. 1a). In Experiment 1, avisual signal instructed participants to perform one of three movement conditions: ‘Rhythmic’, ‘Discrete’ or ‘Rest’, each lasting for 30 s
(typical wrist trajectories from these movement conditions are shownin Fig. 1b). The ‘Rhythmic’ condition required continuous wrist oscillations at a self-chosen comfortable frequency. The ‘Discrete’ movement condition involved a moderately fast flexion-extension
movement; if the start position was a flexed wrist posture, the wrist

1Computer Science and Neuroscience, University of Southern California, Los Angeles, California 90089-2520, USA. 2Department of Kinesiology, Pennsylvania State

University, 266 Rec. Hall, University Park, Pennsylvania 16802, USA. 3ATR Computational Neuroscience Laboratories, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, 6190288 Kyoto, Japan. Correspondence should be addressed to S.S. (sschaal@usc.edu).
Published online 26 September 2004; doi:10.1038/mm1322

NATURE NEUROSCIENCE VOLUME 7 | NUMBER 10 | OCTOBER 2004

1137

ARTICLESab 1

Rhythmic

0–1
1

ExtendDiscrete0–11Position (rad)© 2004 Nature Publishing Group http://www.nature.com/natureneuroscience

Flex

Arm cast

RhythmicRest

0–11DiscreteRest

0
–1
5

6

7

8

9

10 11
Time (s)

12

13

14

15

Figure 1 Experimental setup and conditions. (a) View of one participant outside of the scanner,
showing the arm cast and direction of the wrist movements. (b) Angular position traces of wrist
flexion-extension movement of one representative participant. For summary statistics of kinematic
movement data, see Table 1.

was extended; if the start posture was an extended wrist posture, thewrist was flexed. Importantly, the movement had to come to a clear
stop between two movements. The onset times for discrete movements were self-chosen at random intervals by each participant aftersome practice outside of the scanner. The ‘Rest’ condition required no
movement. In a 6-min session, each condition was presented fourtimes in a randomized fashion. Eleven participants performed twosuch sessions each. Experiment 2 was similar to Experiment 1, but
four movement conditions were required: ‘Rhythmic’,‘RhythmicRest’, ‘DiscreteRest’, and ‘Rest’. Whereas ‘Rhythmic’ and‘Rest’ were the same as in Experiment 1, ‘RhythmicRest’ had five brief
pauses during the 30-s time period, and ‘DiscreteRest’ consisted onlyof six discrete movements within one 30-s time period. Thus, these
two movement conditions balanced the number of start and stops
between ‘Discrete’ and ‘Rhythmic’ in the two conditions. Six participants contributed to this experiment. Experiment 3, a control experiment, was the same as Experiment 1, except that both rhythmic anddiscrete movement were triggered and maintained by an auditory
metronome signal, denoted as ‘RhythmicSound’, ‘DiscreteSound’ and‘RestSound’. Eleven subjects participated in Experiment 3, each performing one session at metronome base frequency of 1.43 Hz and one
at 2.14 Hz (see Methods).

In all experiments, we used 4-Tesla fMRI
to scan the entire brain in intervals of 5 s, andaveraged activities over the normalized brain
images of all subjects per experiment. In
addition, for six participants, behavioral
movement data of all movement conditions
was collected outside the scanner. The summary statistics for these data verified that the
behavioral data was in accordance with theexperimental instructions (Table 1). In particular, as it is important for our main results,
it should be noted that in Experiment 1 thepeak velocity of the discrete movement was
not significantly different from that of rhythmic movement, as confirmed with a t-test
that did not show significance (P = 0.17).
Thus, the main kinematic parameters of discrete and rhythmic movement in Experiment
1 were comparable.

RESULTS
Experiment 1
As defined according the anatomical notation of Picard and Strick30,
in Experiment 1 (Fig. 2 and Table 2), rhythmic movement activatedsolely cerebral areas contralateral to the moving wrist (that is, the left
hemisphere for the right hand in our experiments), in particular primary sensorimotor and premotor cortices (S1, M1 and PMdc), supplementary motor area (SMA and pre-SMA) and cingulate cortex(RCZp and CCZ). Ipsilateral activation was only found in the cerebellum. The contributions of these brain areas to rhythmic movement
are not surprising, as they are all motor areas with direct projections
to the spinal cord and/or to M1 and have been found to be involved insimple movements before30–33. In contrast, discrete movement
showed very different activations in various brain regions, although it
was confined to the same joint. Exclusive to discrete movement (Fig. 2
and Table 2), we observed activity in the contralateral hemisphere in
the rostral part of the dorsal premotor cortex (PMdr), Broca’s area
(BA44, the assumed location of mirror neurons in humans34, a wellstudied motor planning area), parietal cortex (BA7, BA40), the anterior part of the rostral cingulate zone (RCZa) and area BA47.
Moreover, widespread activation occurred in the ipsilateral cerebral
hemisphere and the bilateral cerebellum. These brain regions have

Table 1 Summary statistics of kinematic movement data collected from six subjects outside the scanner
Amplitude(rad)Period(s)Max. velocity(rad/s)No.
movements

Rhythmic (Exp. 1)

1.13 (0.27)

0.79 (0.12)

6.20 (1.75)

78.00 (12.75)

2.00 (0.00)

Discrete (Exp. 1)

1.08 (0.22)

7.18 (2.17)

29.88 (4.80)

59.75 (9.60)

Rhythmic (Exp. 2)

1.20 (0.28)

6.33 (1.47)

82.62 (15.49)

2.00 (0.00)

DiscreteRest (Exp. 2)

1.19 (0.27)

7.24 (2.61)

6.14 (1.29)

12.28 (2.58)

RhythmicRest (Exp. 2)

1.19 (0.30)

0.76 (0.12)

6.43 (1.82)

65.50 (8.81)

11.40 (2.30)

RhythmicSound (Exp. 3–Slow)

1.14 (0.35)

1.38 (0.15)

DiscreteSound (Exp. 3–Slow)

1.18 (0.34)

RhythmicSound (Exp. 3–Fast)

1.13 (0.33)

DiscreteSound (Exp. 3–Fast)

1.17 (0.31)

Condition

0.74 (0.13)

0.91 (0.03)

No. starts +stops

4.93 (2.22)

44.00 (5.32)

2.00 (0.00)

6.89 (2.43)

19.00 (1.59)

38.00 (3.18)

5.35 (2.20)

65.88 (2.25)

2.00 (0.00)

7.01 (2.28)

27.44 (1.86)

54.88 (3.72)

Intermovement
period (s)Intermovement
variability (s)Duration(s)

1.00 (0.21)

0.45 (0.36)

0.34 (0.02)

4.53 (0.43)

1.10 (1.29)

0.35 (0.03)

1.57 (0.13)

0.67 (0.21)

0.42 (0.04)

1.10 (0.07)

0.47 (0.23)

0.38 (0.02)

All data are averages across all subjects; 1 s.d. is indicated in parentheses. Amplitude, period and peak velocity were extracted for every flexion or extension movement
individually and then averaged across the trial (see Methods for further details). The number of movements is the sum of the number of flexions plus the number of extensions pertrial. Similarly, the number of starts and stops is the sum of the number of movement starts plus the number of movement stops per trial. Inter-movement period is defined as thetime between two consecutive discrete movements, and inter-movement variability is the standard deviation of this inter-movement period, which indicates how variably a discrete
movement was triggered.

1138

VOLUME 7 | NUMBER 10 | OCTOBER 2004 NATURE NEUROSCIENCE© 2004 Nature Publishing Group http://www.nature.com/natureneuroscience

ARTICLES
Figure 2 Summary of brain activity inExperiment 1, superimposed on a Montreal
Neurological Institute (MNI) coordinatesnormalized T1 brain template, included in
the SPM software distribution. Four different
statistical comparisons are displayed in different
color codes that denote the t-values of thestatistical tests (refer to key on the left).
‘Rhythmic-Rest’ and ‘Discrete-Rest’ in the
middle plots (a–c) demonstrate the main effectsof brain activity during ‘Rhythmic’ and ‘Discrete’
movement conditions. When there is overlap
between the two contrasts, the Overlay Color
Legend on the left of the panels is used tohighlight the degree of overlap. ‘RhythmicDiscrete’ shows brain areas where rhythmic
movement has stronger activity than discrete
movement. Only areas that were active in‘Rhythmic-Rest’ were permitted to contributeto this contrast by using the active voxelsof ‘Rhythmic-Rest’ as an inclusive mask.
Analogously, ‘Discrete-Rhythmic’ displays areasthat showed significantly more activation thanrhythmic movement, using ‘Discrete-Rest’ as aninclusive mask. The right plot of all three panels
shows the Rhythmic-Discrete and ‘DiscreteRhythmic’ contrasts in isolation for the sake ofclarity (no overlap is possible); Table 2 lists thecoordinates of these differential activations. The
left plot in all panels superimposes the activities
from the other plots in the panel to allow an easycomparison of activation locations. (d) The major brain areas of interest are shown on a rendering on the left hemisphere; the areas and color coding
correspond to the contrasts in the rightmost images of a–c. All results shown are statistically significant at a level of P < 0.05, corrected for multiple
comparisons within the entire brain volume. AC, anterior commissure; PC, posterior commissure; VAC, vertical line perpendicular to the AC-PC, passing
through the AC; PAC, vertical line perpendicular to the AC-PC, passing through the PC. All other abbreviations are as in Table 2.

been previously observed to participate in point-to-point reaching
movements9,31,35–37 and complex sequential movements32,38. All
these areas are more associated with planning aspects of movement30—that is, they are not primary motor areas. Notably, this difference between rhythmic and discrete movement was insensitive tothe threshold of Student’s t-value used in our SPM analyses: even at
the most liberal P < 0.05 cutoff (uncorrected for multiple comparisons, not shown in figures or tables), the ‘Rhythmic-Rest’ contrast
did not reveal activity in any of the contralateral motor-related areas
specific to discrete movement, that is, PMdr, BA44, BA47, BA7, BA40
and RCZa. Thus, the notable finding was that rhythmic and discrete
movement robustly elicited such large differences in activity.
Experiment 2
To test whether the increased and more widespread activity in discrete
movement was the result of differences in the functional anatomy ofdiscrete and rhythmic movement and not some other factors, we conducted several control experiments. We proposed that the unequal
number of starts and stops in discrete movement could potentially bea source of more brain activation. Moreover, even if peak velocity ofrhythmic and discrete movement is roughly the same (Table 1), discrete movement usually has stronger acceleration components thanrhythmic movement, which could have led to stronger blood oxygen
level–dependent (BOLD) signals39 (although the amount of movement was significantly larger in rhythmic movement, as measured, on
average, by the occurrence of 78 flexion and extension movements in
one trial compared with about 30 in discrete movement; Table 1). To
control for such issues, Experiment 2 exactly balanced the number of

NATURE NEUROSCIENCE VOLUME 7 | NUMBER 10 | OCTOBER 2004

movement starts and stops, which also entailed significantly lessmovement in the ‘DiscreteRest’ condition than in the ‘RhythmicRest’condition; the static holding of the wrist between the few discrete
movements is unlikely to have influenced our data as it has insignificant effects on BOLD signals39 (Tables 1 and 2; Fig. 3a). Note that we
focused only on contralateral cerebral and ipsilateral cerebellar activation in Experiment 2 and Experiment 3 (Table 2), as these are ofprime interest. The findings of Experiment 2 (Tables 1 and 2, Fig. 3a)demonstrated that although ‘DiscreteRest’ involved only six movements in 30 s, when contrasted with ‘RhythmicRest’, it resulted in
more activity in almost all brain areas as the ‘Discrete–Rhythmic’comparison in Experiment 1 (Fig. 3a, right panel, versus Fig. 2 andTable 2)—in particular contralateral BA7, BA40, BA47, PMdr and
RCZa. Only for contralateral BA44 and the ipsilateral cerebellum werethese differences not statistically significant.
By contrasting the ‘RhythmicRest’ and ‘Rhythmic’ conditions(Fig. 3b), we also tested which parts of the brain were most likely to be
involved in triggering the start and stop of movements. Within theareas of ‘Discrete-Rhythmic’ from Experiment 1, at the most tolerant
statistical threshold (P < 0.05, uncorrected, which is the most pessimistic for our results), only contralateral BA40, the ipsilateral cerebellum, a small number of voxels in contralateral pre-SMA and RCZa,
and several ipsilateral areas showed activity in this comparison. No
activity was observed in contralateral PMdr, BA44, BA47 and BA7.
To summarize Experiment 2, we have strong evidence that at least
PMdr, BA7 and BA47 are indeed specific to discrete movement. BA40,
BA44, RCZa and the ipsilateral cerebellum seem to be involved tosome extent in movement initiation and termination, or perhaps, in

1139

ARTICLES
Table 2 Anatomical localization of differential brain activity from all experiments
Exp. 2: DiscreteRest–
RhythmicRest© 2004 Nature Publishing Group http://www.nature.com/natureneuroscience

Exp. 1: Discrete-Rhythmic
Area

Hemisphere

x

y

z

#

t

BA 10

Right

28

46

18

239

***8.79

BA 47

Left

–44

17

–6

189

***9.64

BA 47

Right

40

17

–3

406

***8.75

BA 40

Left

–45

–44

47

514

***8.22

BA 40

Right

48
61

–36
–33

47
31

489
513

***11.59***11.10

BA 9 & BA 44

Left

–59

9

20

152

***9.03

BA 9 & BA 44

Right

50

15

25

187

***7.17

BA 7

Left

–14

–67

51

92

***7.43

BA 7

Right

32

–63

51

493

***10.9

BA 8

Right

53

14

40

248

***8.64

Cereb. uvula

Left

–24

–69

–25

63

***6.74

culmen/tuber

Left

–40

–52

–29

463

***11.33

Cereb.
culmen/tuber

Right

40

–54

–29

150

***8.31

PMdr

Left

–26

0

48

365

***11.35

PMdr

Right

26

3

51

743

***14.45

Pre-SMA

Left

–2

5

55

429

***11.88

Pre-SMA

Right

2

9

55

682

***11.95

RCZp

Left

–2

12

43

223

***11.54

RCZa

Right

4

23

32

187

***9.43

RCZp

Right

2

12

45

265

***12.09

Exp. 3: DiscreteSound–
RhythmicSound

x

y

z

#

t

x

y

z

#

t

–46

14

–1

##

***4.66

–44

17

–6

153

**6.52

–50

–46

48

64

**3.94

–44

–46

52

145

*3.19

–53

6

37

12

**4.18**3.54

–14

–66

49

10

*2.95

0
–12

–69

50

24

50

–54

–29

131 ***5.99

–32

8

51

54

*2.99

–36

1

55

414 ***7.36

–2

11

55

##

***4.62–2851

339 ***13.63

–2

25

34

##

***4.49

–4

14

45

369 ***15.56

Cereb.

0Exp. 2: RhythmicRest–
DiscreteRest

Exp. 1: Rhythmic-Discrete
Area

Hemisphere x

Exp. 3: RhythmicSound–
DiscreteSound

y

z

#

t

x

y

z

#

t

x

y

z

#

t

–11

54

134

***7.32

–2

–7

50

17

*2.84

–2

–19

45

64

*3.35

–10

–25

40

134 ***5.10***9.73

26

–51

–16

##

*3.29

26

–47

–16

633 ***9.39***15.00

–34

–16

62

##

*3.61

–38

–21

54

### ***15.52

CCZ/SMA

Left

–4
–2

–21

42

99

***6.39

Cereb. culmen

Right

22

–49

–16

633

M1/S1

Left

–38

–22

56

###

Localization was done using Cartesian x,y,z coordinates of the atlas of Talairach and Tournoux43. #, number of 2 × 2 × 2 mm voxels in a cluster as determined by SPM at P < 0.05
(corrected for multiple comparisons for the volume of interest; see Methods). t, t-value (Student t-test) for the most activated voxel in a cluster. ***P < 0.0001, **P < 0.005,*P < 0.05. Owing partially to large cluster sizes (Figs. 2 and 3), brain coordinates between experiments seem to have more variance than was actually present, as it is highly
random which voxel in such large clusters happens to be the most activated one. Rows in boldface type denote areas that are directly involved in the control of the right wrist:that is, the left cerebral and the right cerebellar hemisphere. In Experiments 2 and 3, we focused our analyses only on these regions. Abbreviations30: CCZ, caudal cingulate zone;
RCZ, rostral cingulate zone, consisting of anterior (RCZa) and posterior (RCZp) portions; SMA, caudal portion of the supplementary motor area, corresponding to SMA proper; preSMA, rostral portion of the supplementary motor area; M1, primary motor cortex; S1, primary sensory cortex; PMdr, rostral part of the dorsal premotor cortex; PMdc, caudal partof the dorsal premotor cortex; cereb., cerebellum; BA, Brodman area; BA 7, precuneus in parietal cortex; BA 8, middle frontal gyrus; BA 9, middle frontal gyrus; BA 10, anterior
frontal lobe; BA 47, inferior frontal gyrus; BA 40, inferior parietal cortex; BA 44, Broca’s area.

the case of BA44 and the ipsilateral cerebellum, were affected by apotentially larger effort involved in the discrete movement condition
in Experiment 1.
Given the distinct activity of PMdr, BA7 and BA47 in discrete
movement even in the stringent control Experiment 2, we believe thatthese results provide strong evidence to refute the hypothesis thatrhythmic movement is generated with the help of the discrete movement system: in other words, RA ⊇ DA is not supported.
Experiment 3
Experiment 3 examined whether the cognitive effort of self-pacing ofrandomized discrete movements could have contributed to the difference of brain activation between discrete and rhythmic movement.
Essentially, we repeated Experiment 1 with auditory pacing from a
metronome that provided the movement frequency in rhythmic movement, and the randomized movement onsets in discrete movement, bymeans of a change in pitch frequency (see Methods section). We also

1140

examined two different pacing frequencies, 1.43 Hz and 2.14 Hz. This
experiment obtained qualitatively the same results as Experiment 1(Tables 1 and 2). Because the difference in movement frequencies in thetwo sound conditions only affected the intensity of activation in someareas, anatomical locations averaged over all sound sessions and subjects are shown (Table 2). As can be recognized from the t-values, except
for pre-SMA and RCZp, contralateral cerebral brain activations inmetronome-paced movement were, in general, significantly lower thanin self-paced movement, a difference that seems to be common whencomparing self-pacing and external pacing38.
DISCUSSION
Approaches to understanding the neural and behavioral basis of arm
movements have been traditionally divided into two separate fields:
one focusing on rhythmic pattern generators1 and the other on visually guided trajectory formation of reach and grasp movements10,11,16.
Consequently, both fields have developed computational models of

VOLUME 7 | NUMBER 10 | OCTOBER 2004 NATURE NEUROSCIENCE© 2004 Nature Publishing Group http://www.nature.com/natureneuroscience

ARTICLES
BA7, BA47 and PMdr, as well as a large
amount of bilateral activation, as uniquefeatures in discrete movement. Because
rhythmic movement activates significantly
fewer brain areas than discrete movement,
there does not seem to be support for theconcept that rhythmic movement is generated on top of a discrete movement system:that is, rhythmic arm movement is not composed of discrete strokes.
Given that rhythmic movement is not a
special case of discrete movement, it might
be that discrete movement is a special case ofrhythmic movement, indicated by RA ⊆ DA,
as detailed earlier. Rhythmic movement isaccompanied by stronger activity in M1/S1,
SMA, CCZ and the ipsilateral cerebellum(Fig. 2 and Table 2). However, all these activations can also be found in discrete movement, just to a lesser degree (Fig. 2), andBOLD activity in these areas is affected by theamount of movement in an experimental
Figure 3 Summary of brain activations of Experiment 2. (a) The middle plot in both rows shows thecondition32,38; our own data, by comparing
main effects of ‘RhythmicRest’ and ‘DiscreteRest’ from Experiment 1 as a comparison, that is, thethe fast with the slow sound sessions inred, yellow and orange activations are the same as in Figure 2. ‘DiscreteRest–RhythmicRest’ (blue)
Experiment 3, showed similar movementshows brain areas where the ‘DiscreteRest’ condition has stronger activity than the ‘RhythmicRest’condition. Similarly, ‘RhythmicRest–DiscreteRest’ (green) denotes brain activity where ‘Rhythmicrate dependencies. Thus, within the range ofRest’ had more activity than ‘DiscreteRest’. Activations are statistically significant at P < 0.05
our current fMRI recordings, RA ⊆ DA iscorrected for multiple comparisons for the spatial hypotheses created from Experiment 1 (seesupported, and discrete movement could
Methods). Color bars on the left of the figure indicate the magnitude of t-values. The left plot
indeed be generated with the help of thesuperimposes the middle and right plot. The figure should be compared to brain areas in Figure 2.
rhythmic movement system.
(b) Brain areas where the ‘RhythmicRest’ condition has stronger activation than the ‘Rhythmic’Our results have an interesting parallel to acondition. Only areas that were active in ‘Discrete-Rest’ from Experiment 1 were permitted tocontribute to this contrast by using the active voxels of ‘Discrete-Rest’ as an inclusive mask.
recent review on brain activity seen in associActivations are statistically significant at P < 0.05 (uncorrected for multiple comparisons; see color
ation with timing40. Here, the authors argue
bars on the left of figure for t-values).
that there are distinct brain structures
involved in tasks depending on whether these
are characterized more by automatic or morearm movement generation that differ in philosophy: pattern generator by cognitive control. Automatic control primarily draws on primarytheories seek an explanation for behavioral phenomena more in the motor circuits, whereas cognitive control requires additional preself-organization of coupled nonlinear dynamic systems, whereas dis- frontal and parietal areas. For example, central pattern generators
crete reaching and grasping movements were explained more in the would be a suitable candidate for the generation of timing in autoformal framework of optimal control. For the sake of a coherent the- matic tasks, whereas memory and attention systems are also involved
ory, models of pattern generators were extended to account for dis- in cognitively controlled tasks, which require more elaborate timing,
crete movement as a special case, and models of discrete trajectory such as is necessary in complex movement planning. From this view,formation incorporated rhythmic movement as a particular imple- one would intuitively classify rhythmic movement as automatic conmentation. Because of these different philosophies, there is rather little trol and discrete movement as cognitive control, and our data provides perfectly matching empirical evidence to support this view eveninteraction between researchers from these competing fields.
To determine whether discrete and rhythmic arm movements in as simple a movement as wrist flexion-extension.
In summary, our data provides the first empirical evidence that dishave a common neural basis, in the experiments reported here we
investigated the difference in brain activation between rhythmic and crete and rhythmic movements, even when performed with the same
discrete single-joint wrist movement in a functional neuroimaging single joint, use very different brain circuitries. Single-joint discrete
experiment. Our principal finding is that whereas rhythmic move- movement activates the same brain areas as have been found to be
ment activated only a small number of unilateral primary motor activated in complex reaching and pointing experiments, indicatingareas (M1, S1, PMdc, SMA, pre-SMA, CCZ, RCZp and cerebellum), that, even in such a simple discrete movement, higher-level planning
discrete movement activated a variety of additional contralateral areas are recruited. In contrast, rhythmic movement shows much lessnonprimary motor areas (BA7, BA40, BA44, BA47, PMdr and cerebral brain activity: that is, only contralateral areas that are known
RCZa) and, moreover, showed very strong bilateral activity in both to be major motor areas are activated, but no known higher planningthe cerebrum and cerebellum. Control experiments examined areas. From a theoretical point of view, this reduced activity in rhythwhether in discrete movement the much more frequent movement mic movement is not surprising, as rhythmic pattern generation is
initiation and termination and the associated cognitive effort could computationally much easier to realize (a pair of mutually inhibitory
account for the observed differences. Only BA40, BA44, RCZa and leaky-integrator neurons suffices41) than the more complex accelerathe cerebellum were potentially involved in such issues, which left tion and deceleration profiles of discrete movement. The brain activa-

NATURE NEUROSCIENCE VOLUME 7 | NUMBER 10 | OCTOBER 20041141

© 2004 Nature Publishing Group http://www.nature.com/natureneuroscience

ARTICLES
tion in rhythmic movement may thus indicate areas that could be
involved in a cortical pattern generator, and SMA, pre-SMA, RCZp,
CCZ and the cerebellum might be of particular interest in this respect.
Further neuroimaging and electrophysiological studies are needed toelucidate the details of these circuits. Notably, the entire functional
rhythmic movement circuit is included in the discrete circuit, leaving
open the possibility that discrete movement is indeed based on modulating the original pattern generator loop, for example, by smoothly
aborting the rhythmic movement after half a period. The areas thatare specific to discrete movement only, that is, PMdr, BA7 and BA47,
could contribute to the timing and required memory of such operations40. However, our current data do not provide further details on
this issue. As an alternative to assuming that the rhythmic movement
circuit is part of the discrete circuit, some recent data provide evidenceof the coexistence of rhythmic and discrete movement in single-joint
elbow flexion-extension23,24 tasks, thus supporting the hypothesisthat rhythmic and discrete movement might be separate circuits.
What our data strongly support, however, is that rhythmic movement
is not based on the use of the discrete movement systems. Thus, inorder to fully understand the neurobiological mechanisms of discrete
movement, it may first be necessary to better understand those ofrhythmic movement, or to consider rhythmic movement as a separate
fundamental movement primitive that requires distinct neurophysiological realizations and theoretical modeling.
METHODS
Task. Participants performed flexion-extension wrist movements with theirright hand while lying supine in an MRI scanner. Head movements wererestrained by vacuum beanbag pillows (Olympic Medical) surrounding thehead, neck and shoulders. The right arm was strapped in a pronated orientation into a horizontal cast so that only the wrist could be moved freely.
Flexions and extensions were performed in a vertical up-and-down movementthat avoided any impact with the scanner bore. Visual feedback from the wrist
movement was blocked by a paper screen. Participants were given a roughlytable tennis ball–sized paper ball to hold such that the fingers of the hand wereconstrained in a well-defined posture. Through a tilted mirror, a rear-projection screen outside of the scanner was visible to the participants. During eachmovement condition, the name of the condition was displayed on the screen;
no other cues were given during the experiment. The experimenter continuously monitored the correctness of participants’ movements through a video
camera outside of the scanner. The experimental procedure was approved by
the local institutional review board.
Experiment 1. Eleven participants performed in Experiment 1, which followed
an epoch-based design with three movement conditions. Each condition was
performed for 30 s and pseudo-randomly repeated four times in one session.
Two sessions were collected for each participant. The movement condition‘Rhythmic’ involved a continuous, smooth alternation between flexion andextension of the wrist at the participant’s individual comfort frequency, usuallychosen in the order of 1.5 Hz within a 60–80° angular amplitude. The movement condition ‘Discrete’ required moderately fast and discrete point-to-point
movements, either from flexion to extension if the wrist was previously flexed,
or from extension to flexion if the wrist was previously extended. The participants were instructed to perform a movement amplitude of about 80% of thepossible movement range, and the movement speed was kept about the same as
during one half-cycle of the oscillatory movement. Participants were trained torandomly insert a pause between individual discrete movements (on the orderof up to 1 s long) and to avoid a periodic triggering of discrete movements.
Thus, about 30–40 discrete movements were performed in a 30-s block. The
third movement condition was ‘Rest’, that is, no movement at all. Figure 1 illustrates a typical realization of the movement conditions.
Experiment 2. This experiment similarly used an epoch-based design withfour movement conditions and had six participants. Each condition was performed for 30 s and was pseudo-randomly repeated four times in one session.

1142

Two sessions were collected for each participant. The movement conditions‘Rhythmic’ and ‘Rest’ were the same as in Experiment 1, whereas the movement condition ‘RhythmicRest’ was the same as ‘Rhythmic’, except that participants paused the rhythmic movement five times at random, but with
sufficiently long intervals (on the order of approximately 4–6 s) such that a 30s trial included six start and stops of the rhythmic movement. Movement condition ‘DiscreteRest’ consisted of only six discrete movements, using random
but sufficiently long (about 4–6 s) pauses between the individual discrete
movements. Thus, the number of start-and-stops was balanced between the‘RhythmicRest’ and ‘DiscreteRest’ conditions, and the actual amount of movement time in ‘DiscreteRest’ was very short compared to ‘RhythmicRest’.
Experiment 3. This experiment was identical to Experiment 1, except that soundpacing was used instead of self-initiated movement. Through their headsets, subjects could hear a metronome beep at a certain frequency. The beep was presented
with the same frequency in all conditions of a session, that is, ‘RhythmicSound’,‘DiscreteSound’ and ‘RestSound’. In ‘RhythmicSound’, the pitch of the beep of themetronome alternated between a high and a low value, such that one period ofrhythmic movement corresponded to two beeps, that is, one high- and one lowpitch beep. In ‘DiscreteSound’, a high-pitch beep signaled participants to initiate
a new flexion or extension movement, whereas a low-pitch beep signaled them tofinish this flexion or extension movement and/or not to move. During the interval between two discrete movements, the low-pitch beep continued to be presented in a rhythmic fashion. Thus, discrete movement could be triggered in arandomized fashion by inserting high-pitch beeps at random times in themetronome beep sequence. In ‘RestSound’, only low-pitch beeps were presented
rhythmically. Eleven subjects performed two sessions each, one using a 1.43-Hz
metronome frequency and one using 2.14 Hz. These frequencies corresponded toeither 0.71 Hz or 1.07 Hz of rhythmic movement and were determined in pilot
experiments outside of the scanner, such that movement was not perceived to be
too slow or too fast by the subjects. These frequencies also allowed a reasonably
high time resolution to trigger discrete movements.
Participants. Seventeen neurologically normal, fMRI-experienced subjects(25–43 years of age; seven females and ten males) participated in the two
experiments. Each participant gave informed written consent before theexperiment. Participants were trained outside of the scanner until they could
perform the experimental conditions properly. Pseudo-random pauses
between discrete movements were initially trained with the sound cue used inExperiment 3. On average, 10 min of training sufficed for participants tobecome comfortable with the tasks.
Behavioral data acquisition. For six subjects, we recorded wrist movement
kinematics outside of the scanner for all experimental conditions using a
Sarcos Sensuit (Sarcos Inc.), an exoskeleton that directly measures joint angular data with Hall effect sensors. Position data was sampled at 100 Hz.
Behavioral data analysis. Position data was first centered by subtracting themean of each trajectory, then numerically differentiated to obtain movement
velocities, and digitally low-pass filtered with a second-order Butterworth filter using a 5-Hz cutoff frequency. By analyzing amplitude and zero crossing
information of both position and velocity data, we extracted descriptive statistics of the movement, such as amplitude, period, peak velocity, the number ofmovements and the number of start and stops in rhythmic movement, as wellas amplitude, peak velocity, movement duration, number of movements,
number of start and stops, inter-movement period and inter-movement variability of discrete movement.
Amplitude, period and peak velocity were extracted for every flexion or
extension movement individually and then averaged across the trial. The
number of movements was the sum of the number of flexions plus the numberof extensions per trial. Similarly, the number of starts and stops was the sum of
the number of movement starts plus the number of movement stops per trial.
Inter-movement period was defined as the time between two consecutive discrete movements, and inter-movement variability was the standard deviationof the inter-movement period, which indicates how randomly a discrete
movement was triggered.
Each statistic was first extracted in a trial-specific manner, and then averaged across all subjects.

VOLUME 7 | NUMBER 10 | OCTOBER 2004 NATURE NEUROSCIENCE© 2004 Nature Publishing Group http://www.nature.com/natureneuroscience

ARTICLES
Acquisition. Experiments were performed on a 4.0-Tesla Varian Siemens
UNITY INOVA whole-body imaging system equipped with whole-bodyshielded gradients to obtain BOLD contrast functional images. Twenty-fourcontiguous axial slices were used to image the entire brain. Functional data werecollected using navigator echo-corrected T2*-weighted segmented gradientechoplanar imaging (24 slices; 64 × 64 resolution; 20 cm in plane field of view(FOV); time to echo (TE) of 15 ms; volume acquisition time of 5 s; and a voxel
size of 3.1 × 3.1 × 6 mm). Functional data were superimposed on prepared highresolution three-dimensional inversion T1-weighted anatomical images of thebrain (64 slices; 256 × 256; TE of 6.2 ms; time to relaxation of 11.4 ms) using aphase reference image that corrected for high-field geometric distortions.
MRI analysis. Data analysis was performed with SPM2 (Wellcome Departmentof Cognitive Neurology). First, motion artifacts in all functional images were
removed by realigning images to the first functional image of each session.
Second, anatomical images were co-registered with functional images. Third,
anatomical images were stereotactically transformed to a standard template in
SPM, and this normalization was applied to all co-registered functional images.
As we noted that SPM did not achieve very accurate normalization in somesubjects, we manually corrected the normalization parameters when neededwith an additional affine transformation that used major brain landmarks(commissures, overall brain dimensions and sulci) as normalization criteria. To
enable cross-subject analyses, standardized images were spatially smoothedwith a 6-mm (full width at half maximum) Gaussian filter (other filter sizes
were explored without an effect on the results of our experiments being
observed). Before statistical data analysis, voxel time series were temporally
high-pass filtered (120 s cutoff period) to remove slow trends in the data, andtemporally low-pass filtered with a hemodynamic response filter42. Global scaling of the functional data was applied to normalize activity across subjects anddifferent scanning days. Areas of significant change in the brain were determined using t-statistics (SPM{t}) in a fixed-effects analysis and a box-car designof the conditions, convolved with the hemodynamic response function. We
used the effective degree of freedom adjusted for analysis of fMRI data42. In
assessing the statistical significance of activations, we corrected for multiple
comparisons based on Gaussian random field theory in terms of spatial extent
at a threshold of P < 0.05 for all experiments. Either the entire brain(Experiment 1) or more focused spatial hypotheses (Experiments 2 and 3) wereused (see below). For reporting spatial locations of activation, we used the atlasof Talairach and Tournoux43, derived from the Montreal Neurology Institute(MNI) coordinates in SPM by a coordinate transformation44.
For the analysis of Experiment 1, when determining differences between discrete and rhythmic activity, we always applied an additional mask according to
the main effects of the movement condition of interest. For the contrast ‘DiscreteRhythmic’, we applied the mask ‘Discrete-Rest’ as an inclusive mask to ensure that
only voxels that were truly active in discrete movement would be eligible for contributing to findings in ‘Discrete-Rhythmic’; analogously, for ‘RhythmicDiscrete’, we used the mask ‘Rhythmic-Rest’. Masks were determined as the areasof activation at a threshold of P < 0.05 corrected for multiple comparisons in theentire brain volume; no spatial extent threshold was applied to eliminate verysmall activation clusters, as all activations were of significant cluster size (Table 2).
For the analyses of Experiment 2 and Experiment 3, we used SPM’s small
volume correction feature to determine statistical significance45. For this purpose, activated brain areas from Experiment 1 (obtained at P < 0.05 corrected
for multiple comparisons in the entire brain volume and masked as describedabove) were extracted with the MarsBaR toolbox of SPM46. The individual
regions were converted into spatial hypothesis masks. Contrasts forExperiment 2 and 3 were first determined at P < 0.05 uncorrected, and subsequently we applied small volume correction with the help of the MarsBaR
masks for every area that was active in Experiment 1. Thus, we determinedwhich of the areas of Experiment 1 were active/inactive in Experiments 2 and3. Significance was determined at P < 0.05 corrected for multiple comparisons
within the spatial hypothesis mask; no spatial extent threshold was applied.
ACKNOWLEDGMENTS
This research was supported in part by US National Science Foundation grants
ECS-0325383, IIS-0312802, IIS-0082995, ECS-0326095 and ANI-0224419, theERATO Kawato Dynamic Brain Project funded by the Japanese Science and

NATURE NEUROSCIENCE VOLUME 7 | NUMBER 10 | OCTOBER 2004

Technology Agency, and the National Institute of Information andCommunications Technology (NICT).
COMPETING INTERESTS STATEMENT
The authors declare that they have no competing financial interests.
Received 20 July 2004; accepted 13 September 2004
Published online at http://www.nature.com/natureneuroscience/
1. Marder, E. Motor pattern generation. Curr. Opin. Neurobiol. 10, 691–698. (2000).
2. Lamb, T. & Yang, J.F. Could different directions of infant stepping be controlled by
the same locomotor central pattern generator? J. Neurophysiol. 83, 2814–2824.
(2000).
3. Pinter, M.M. & Dimitrijevic, M.R. Gait after spinal cord injury and the central patterngenerator for locomotion. Spinal Cord 37, 531–537 (1999).
4. Dimitrijevic, M.R., Gerasimenko, Y. & Pinter, M.M. Evidence for a spinal central pattern generator in humans. Ann. N. Y. Acad. Sci. 860, 360–376 (1998).
5. Kelso, J.A.S. Dynamic Patterns: The Self-Organization of Brain and Behavior (MITPress, Cambridge, Massachusetts, USA, 1995).
6. Turvey, M.T. Coordination. Am. Psychol. 45, 938–953 (1990).
7. Kugler, P.N. & Turvey, M.T. Information, Natural Law, and the Self-Assembly of
Rhythmic Movement (Erlbaum, Hillsdale, New Jersey, USA, 1987).
8. Andersen, R.A., Snyder, L.H., Bradley, D.C. & Xing, J. Multimodal representation of
space in the posterior parietal cortex and its use in planning movements. Annu. Rev.
Neurosci. 20, 303–330 (1997).
9. Kalaska, J.F., Scott, S.H., Cisek, P. & Sergio, L.E. Cortical control of reaching movements. Curr. Opin. Neurobiol. 7, 849–859 (1997).
10. Sabes, P.N. The planning and control of reaching movements. Curr. Opin. Neurobiol.
10, 740–746 (2000).
11. Flash, T. & Sejnowski, T. Computational approaches to motor control. Curr. Opin.
Neurobiol. 11, 655–662 (2001).
12. Morasso, P. Spatial control of arm movements. Exp. Brain Res. 42, 223–227 (1981).
13. Abend, W., Bizzi, E. & Morasso, P. Human arm trajectory formation. Brain 105,
331–348 (1982).
14. Flash, T. & Hogan, N. The coordination of arm movements: An experimentally confirmed mathematical model. J. Neurosci. 5, 1688–1703 (1985).
15. Uno, Y., Kawato, M. & Suzuki, R. Formation and control of optimal trajectory inhuman multijoint arm movement — Minimum torque-change model. Biol. Cybern.
61, 89–101 (1989).
16. Kawato, M. Internal models for motor control and trajectory planning. Curr. Opin.
Neurobiol. 9, 718–727 (1999).
17. Feldman, A.G. Superposition of motor programs. I. Rhythmic forearm movements inman. Neuroscience 5, 81–90 (1980).
18. Latash, M.L. Control of Human Movement (Human Kinetics, Champaign, Illinois,
USA, 1993).
19. Soechting, J.F. & Terzuolo, C.A. Organization of arm movements in three dimensional space. Wrist motion is piecewise planar. Neuroscience 23, 53–61 (1987).
20. Sternad, D. & Schaal, D. Segmentation of endpoint trajectories does not imply segmented control. Exp. Brain Res. 124, 118–136 (1999).
21. Schaal, S. & Sternad, D. Origins and violations of the 2/3 power law in rhythmic 3D
movements. Exp. Brain Res. 136, 60–72 (2001).
22. Richardson, M.J. & Flash, T. Comparing smooth arm movements with the two-thirds
power law and the related segmented-control hypothesis. J. Neurosci. 22,
8201–8211 (2002).
23. Sternad, D., Dean, W.J. & Schaal, S. Interaction of rhythmic and discrete patterngenerators in single joint movements. Hum. Mov. Sci. 19, 627–665 (2000).
24. Sternad, D., De Rugy, A., Pataky, T. & Dean, W.J. Interaction of discrete and rhythmic
movements over a wide range of periods. Exp. Brain Res. 147, 162–174 (2002).
25. Wei, K., Wertman, G. & Sternad, D. Interactions between rhythmic and discrete
components in a bimanual task. Motor Control 7, 134–155 (2003).
26. Smits-Engelsman, B.C., Van Galen, G. P. & Duysens, J. The breakdown of Fitts’ law
in rapid, reciprocal aiming movements. Exp. Brain Res. 145, 222–230 (2002).
27. Ijspeert, A., Nakanishi, J. & Schaal, S. in Advances in Neural Information
Processing Systems 15 (eds. Becker, S., Thrun, S. & Obermayer, K.) 1547–1554(MIT Press, Cambridge, Massachusetts, USA, 2003).
28. de Rugy, A. & Sternad, D. Interaction between discrete and rhythmic movements:
reaction time and phase of discrete movement initiation during oscillatory movements. Brain Res. 994, 160–174 (2003).
29. Sternad, D. & Dean, W.J. Rhythmic and discrete elements in multi-joint coordination. Brain Res. 989, 152–171 (2003).
30. Picard, N. & Strick, P.L. Imaging the premotor areas. Curr. Opin. Neurobiol. 11,
663–672 (2001).
31. Seitz, R.J., Stephan, K.M. & Binkofski, F. Control of action as mediated by thehuman frontal lobe. Exp. Brain Res. 133, 71–80 (2000).
32. Gordon, A.M., Lee, J.H., Flament, D., Ugurbil, K. & Ebner, T.J. Functional magnetic
resonance imaging of motor, sensory, and posterior parietal cortical areas during performance of sequential typing movements. Exp. Brain Res. 121, 153–166 (1998).
33. Debaere, F. et al. Brain areas involved in interlimb coordination: a distributed network. Neuroimage 14, 947–958 (2001).
34. Rizzolatti, G. & Arbib, M.A. Language within our grasp. Trends Neurosci. 21,
188–194 (1998).
35. Kertzman, C., Schwarz, U., Zeffiro, T.A. & Hallett, M. The role of posterior parietal1143

© 2004 Nature Publishing Group http://www.nature.com/natureneuroscience

ARTICLES
cortex in visually guided reaching movements in humans. Exp. Brain Res. 114,
170–183 (1997).
36. Grafton, S.T., Fagg, A.H., Woods, R.P. & Arbib, M.A. Functional anatomy of pointing
and grasping in humans. Cereb. Cortex 6, 226–237 (1996).
37. Fink, G.R., Frackowiak, R.S., Pietrzyk, U. & Passingham, R. E. Multiple nonprimary
motor areas in the human cortex. J. Neurophysiol. 77, 2164–2174 (1997).
38. Deiber, M.P., Honda, M., Ibanez, V., Sadato, N. & Hallett, M. Mesial motor areas inself-initiated versus externally triggered movements examined with fMRI: effect of
movement type and rate. J. Neurophysiol. 81, 3065–3077 (1999).
39. Thickbroom, G. W. et al. Differences in functional magnetic resonance imaging of
sensorimotor cortex during static and dynamic finger flexion. Exp. Brain Res. 126,
431–438 (1999).
40. Lewis, P.A. & Miall, R.C. Distinct systems for automatic and cognitively controlled
time measurement: evidence from neuroimaging. Curr. Opin. Neurobiol. 13,
250–255 (2003).
41. Brown, T.G. On the nature of the fundamental activity of the nervous centres;

1144

together with an analysis of rhythmic activity in progression, and a theory of
the evolution of function in the nervous system. J. Physiol. (Lond.) 48, 18–46(1914).
42. Turner, R., Howseman, A., Rees, G.E., Josephs, O. & Friston, K. Functional magnetic resonance imaging of the human brain: data acquisition and analysis. Exp.
Brain Res. 123, 5–12 (1998).
43. Talairach, J. & Tournoux, P. Co-Planar Stereotaxic Atlas of the Human Brain: 3Dimensional Proportional System: An Approach to Cerebral Imaging (Thieme,
Stuttgard, Germany, 1988).
44. Brett, M., Christiff, K., Cusack, R. & Lancaster, J. Using the Talairach atlas with the
MNI template. Neuroimage 13, S85 (2001).
45. Friston, K.J., Frith, C.D., Liddle, P.F. & Frackowiak, R.S.J. Comparing functional(PET) images: the assessment of significant change. J. Cereb. Blood Flow Metab.
11, 690–699 (1991).
46. Brett, M., Anton, J.-L., Valabregue, R. & Poline, J.-B. in 8th International
Conference on Funcational Mapping of the Human Brain (Sendai, Japan, 2002).

VOLUME 7 | NUMBER 10 | OCTOBER 2004 NATURE NEUROSCIENCE