www.elsevier.com/locate/ynimg

NeuroImage 33 (2006) 227–238

Investigating the human mirror neuron system by means of corticalsynchronization during the imitation of biological movements

Klaus Kessler,a,⁎ Katja Biermann-Ruben,b Melanie Jonas,c,d,e Hartwig Roman Siebner,d,e

Tobias Bäumer,c Alexander Münchau,c and Alfons Schnitzlerb,⁎

aDepartment of Psychology, Centre for Cognitive Neuroimaging (CCNi), University of Glasgow, UKbDepartment of Neurology, MEG Laboratory, University of Duesseldorf, GermanycDepartment of Neurology, University Medical Center Hamburg Eppendorf, GermanydDepartment of Neurology, Neuroimage Nord, Christian-Albrechts-University, Kiel, GermanyeDepartment of Systems Neuroscience, University Medical Center Hamburg Eppendorf, Germany

Received 20 February 2006; revised 23 May 2006; accepted 2 June 2006Available online 28 July 2006

The human mirror neuron system (MNS) has recently been a majortopic of research in cognitive neuroscience. As a very basic reflection ofthe MNS, human observers are faster at imitating a biological ascompared with a non-biological movement. However, it is unclearwhich cortical areas and their interactions (synchronization) areresponsible for this behavioural advantage. We investigated the timecourse of long-range synchronization within cortical networks duringan imitation task in 10 healthy participants by means of whole-headmagnetoencephalography (MEG). Extending previous work, weconclude that left ventrolateral premotor, bilateral temporal andparietal areas mediate the observed behavioural advantage ofbiological movements in close interaction with the basal ganglia andother motor areas (cerebellum, sensorimotor cortex). Besides leftventrolateral premotor cortex, we identified the right temporal poleand the posterior parietal cortex as important junctions for theintegration of information from different sources in imitation tasks thatare controlled for movement (biological vs. non-biological) and thatinvolve a certain amount of spatial orienting of attention. Finally, wealso found the basal ganglia to participate at an early stage in theprocessing of biological movement, possibly by selecting suitable motorprograms that match the stimulus.© 2006 Elsevier Inc. All rights reserved.

Introduction

It has been suggested that the ability to learn from other groupmembers by imitation is one of the most important steps in theevolution of mankind. Not surprisingly, humans can achieve a very

⁎ Corresponding authors. K. Kessler is to be contracted at Departmentof Psychology, Centre for Cognitive Neuroimaging (CCNi), University ofGlasgow, UK. A. Schnitzler, Department of Neurology, MEG Laboratory,University of Duesseldorf, Germany.

E-mail addresses: k.kessler@psy.gla.ac.uk (K. Kessler),schnitza@uni-duesseldorf.de (A. Schnitzler).

Available online on ScienceDirect (www.sciencedirect.com).

1053-8119/$ - see front matter © 2006 Elsevier Inc. All rights reserved.doi:10.1016/j.neuroimage.2006.06.014

high imitation accuracy depending on the complexity of themovement and on experience. In experimental settings, it wasfound that reaction times to biological cues were faster than to non-biological, spatial cues. Brass et al. (2000) reported faster fingerreactions when a finger movement was to be imitated than when aspatial cue was the response trigger. Accordingly, it has beensuggested that during imitation, the observer transforms the visualinput of a motor act, provided by a peer model, into a correspondingmotor output (Wohlschlager et al., 2003), leading to the so-called“action observation execution matching” (AOEM) hypothesis.However, the cortical structures and processes of visuomotor trans-formation underlying imitation are yet incompletely understood.One substrate proposed specifically for the imitation of goal directedactions in humans is the so-called mirror neuron system (MNS).

The first evidence for the existence of “mirror neurons” resultedfrom single cell recordings of neurons in the macaque monkeys’ventral premotor area F5 (di Pellegrino et al., 1992; Gallese et al.,1996; Rizzolatti et al., 1996). Those mirror neurons were similarlyactive during specific actions that were performed by the monkeyas well as during the observation of this action performed byanother individual. Some of these neurons were even found to beactivated by the specific sound of an action only (Kohler et al.,2002). A number of functional imaging studies in humans focusedon the MNS in the past decade. The left ventrolateral premotorcortex was found to be consistently activated during both actionobservation and imitation. Peak activity was mostly observed inBrodmann’s area (BA) 44 (also referred to as pars opercularis ofthe inferior frontal gyrus), which is supposed to be the humanhomologue of the monkey’s area F5 (for a recent review, seeRizzolatti and Craighero, 2004).

Parietal cortex receives direct input from the visual motionprocessing area as part of the dorsal visual stream (Rizzolatti andMatelli, 2003) and is specialized in visuospatial as well as insensorimotor analysis. Accordingly, parietal areas seemed likely toshow “mirror neuron properties” and supporting evidence was

228 K. Kessler et al. / NeuroImage 33 (2006) 227–238

indeed provided (Grafton et al., 1996; Buccino et al., 2001; Decety etal., 2002; Nishitani and Hari, 2002; Koski et al., 2003; Iacoboni etal., 1999). Fogassi et al. (2005) showed, for example, that neurons inthe inferior parietal lobule did not only reveal mirror neuronproperties by firing during performance as well as duringobservation of a specific motor act, but their firing was modulatedby the context of the motor act (grasping for eating vs. placing) andstarted at a very early stage, strongly suggesting that these neuronsmediate the understanding of the agent’s intentions.

Superior temporal cortex (Rizzolatti et al., 1996; Nishitani andHari, 2002; Decety et al., 2002) was also frequently activated byaction observation and/or execution yet cannot be perceived asconsisting of mirror neurons per se, but it seems to play a crucial rolein imitation behaviour (Iacoboni et al., 2001) by possibly mediatingthe integration of visual input of the observed motor act andreafferent copies of its imitation. In concordance macaque single cellrecordings revealed that the superior temporal cortex is involved inthe processing of biological motion (Perrett et al., 1989), which wasconfirmed for humans in neuroimaging studies using biologicalstimuli consisting ofmoving body parts (Grafton et al., 1996; Puce etal., 1998; Pelphrey et al., 2003) or using animated point-light figures(Bonda et al., 1996; Grossmann et al., 2000).

Although it seems that the MNS in ventrolateral premotor andparietal cortex in interaction with superior temporal areas mayconstitute the functional network that matches action observation toaction execution, it remains to be shown which part of this networkconveys the observed behavioural advantage of responses tobiological over non-biological stimuli. Tai and colleagues (2004)contrasted human (biological) and robotic (non-biological) graspingactors using positron emission tomography (PET) during anobservation task. They found significantly stronger activation ofthe left premotor cortex when a human grasped the object comparedwith a robot grasping it. In a functional magnetic resonance imaging(fMRI) study, Schultz et al. (2005) found bilateral activation of thesuperior temporal region to be correlated with the percept ofanimacy inmoving visual stimuli, stressing the role of this area whenfocusing on biological versus non-biological movements.

But still, it remains unclear which of these areas and theirinteractions mediate reaction time advantages to biological move-ments in humans. Furthermore, also other cortical areas might beinvolved that have not been consistently reported so far. Miall(2003), for example, discusses the relation between the MNS and

Fig. 1. Five picture frames characterizing the resting hand position and the four stimindex vs. little finger. Each movement consisted of 12 picture frames; here, only t

internal models for motor control. An important theoretical claim inthis review article is that the cerebellum might play a crucial role inobservation and imitation by providing information about actualmotor programs. It could therefore be that other motor-related areasbesides ventrolateral premotor cortex might also play an importantrole during imitation.

Using whole-head magnetoencephalography (MEG), werecorded neuronal responses to single biological finger move-ments and non-biological dot movements while the subjects wererequired to perform an imitation task. We analyzed the timecourse of long-range cortical synchronization during the task inorder to investigate the coupling networks underlying imitation.On the basis of the studies reported above, we expected thatventrolateral premotor, posterior parietal and superior temporalcortex might reveal a specific pattern of synchronization to eachother as well as to other cortical areas (e.g., perceptual or motor-related) that would explain the behavioural advantage for theimitation of biological movements.

Materials and methods

Subjects and stimulus material

Ten healthy male subjects (aged between 19 and 36 years, meanage: 29 years) participated in our study. All participants wereconsistent right-handers according to the revised Annett Handed-ness Questionnaire (Annett, 1985) and had no history ofneuropsychiatric disorder. All subjects had normal vision, werenaive with respect to the purpose of the study and signed aninformed consent prior to the experiment. The study was inaccordance with the Declaration of Helsinki (1964).

The basic visual stimulus was a picture of a left male hand restingon a white horizontal plane with fingers slightly flexed (Fig. 1). Thehand was videotaped from a slightly lifted frontal view and wasvisible up to approximately 1 cm above the wrist. As subjects weresupposed to respond with their right hand during the experiment, thevisual stimulus was presented in mirrored orientation with respect tothe responding hand. The decision for this orientation was guided byevidence for stronger engagement of the MNS in “mirror”-mode(i.e., specular) imitation as compared with anatomically correctimitation (Koski et al., 2003). The fingernails of the index and littlefinger were labeled with red dots of approximately 1 cm diameter.

ulus conditions: biological (finger) vs. non-biological (dot) and movement ofhe frames at maximum finger/dot lift are shown.

229K. Kessler et al. / NeuroImage 33 (2006) 227–238

On the horizontal midline of the visual stimulus and with equaldistance to the tip of the index and little finger, a white cross waspresented, which the subjects were required to fixate during theexperiment.

There were two types of movement stimuli: biological fingermovements consisted of a single up-and-down movement of theindex or little finger including the corresponding dot (the dotremained attached to the fingernail). In the non-biological move-ment sequence, the fingers remained fixed and one of the red dotsmoved up and down with the same kinematical profile as during thebiological movement. A single (up-and-down) movement lasted400 ms comprising 12 picture frames of about 33 ms each (tworefresh rates of the computer monitor). Visual stimuli were backwardprojected on a screen with a diagonal extension of 45 cm. Subjectswere seated at a viewing-distance of 1 m in front of the screen;hence, the stimuli comprised a visual angle of approximately 25.4°diagonally.

Experimental procedure and design

Participants were seated in a magnetically shielded room underdim light conditions. They observed sequences of visual stimulicontaining a resting hand and short movements, which they had torespond to. A trial started with the presentation of the resting hand,which was shown for 2000 ms on average (range 1.5–2.5 s, 200-mssteps). Then, a biological or a dot movement of either the indexfinger/left dot or the little finger/right dot was shown, which wasfollowed by the hand at rest. Subjects were instructed to produce afinger lift with the right index or little finger as fast as possible inresponse to the movement stimulus according to a simple spatial-motor mapping rule (cf. Fig. 1). Prior to each trial, the word “start”was presented along with the resting hand for 1 s and subjects wereasked to make unavoidable eye-blinks during this period.

Two factors were systematically varied: finger position (indexvs. little) and type of movement stimulus (biological vs. dotmovement). Each condition was presented 120 times to ensure thatthe signal-to-noise ratio is sufficient for the analysis of evokedresponses. Ten blocks comprising 48 trials each (both factorsrandomized within blocks) were presented to the participants forimitation. In another 10 interleaved blocks, participants wererequired to solely observe the stimuli without responding to them.In the present report, only imitation trials were analyzed in order toinvestigate the interaction between perception and motor subnet-works. The entire experiment lasted approximately 90min includingshort pauses between the blocks.

MEG data acquisition and processing

A 122-channel whole-head neuromagnetometer device was usedfor this study (Neuromag-122™). Magnetic signals were digitized at512Hz, filtered between 0.03 and 100Hz and continuously recordedfor offline analysis. Eye movements and blinks were recorded withhorizontal and vertical electrooculography (EOG) for offline artefactrejection (individually adjusted thresholds, range 75–180 μV). Theposition of the head within the magnetometer was found byattaching four small coils on the subject’s head, measuring theirlocation in the head coordinate system with a 3-D digitizer (Isotrak3S1002, Polhemus Navigation Sciences) outside the MEG system,and energizing them briefly while participants were seated in re-cording position, under the MEG sensors, to obtain their locations inthe magnetometer coordinate system. MEG sources were combined

with the individual anatomy by marking the three anatomical pointsin the individual MR images (high-resolution T1 weighted images,acquired on a 1.5 Siemens Tesla Scanner). The subjects’ right handwas placed in a light barrier device that differentially responded toupward movements of the index and little finger. Reaction times(RTs) were calculated from the onset of a visually presentedmovement to the subjects’ response as measured by the light barrier.

The purpose of this study was to analyze the MEG data in thefrequency domain in order to understand which areas interact duringwhich periods of time while subjects imitate a biological movement.The first step was to identify the relevant frequency band that wasmodulated by stimulus type (finger vs. dot movement). For eachcondition, time–frequency representations were calculated andnormalized with respect to baseline (−100 to stimulus onset) inconcordance with a number of previous studies (Rodriguez et al.,1999; Trujillo et al., 2005). The normalization procedure (all valuesbetween −100 and 600 ms after stimulus onset were corrected bysubtracting the mean and dividing by the SD of the interval −100 to0 ms) allows identifying whether the power of a given frequency issignificantly enhanced/decreased with respect to baseline. Fig. 2,Panels A and B, shows that in both conditions frequencies below15 Hz reach significance. Most importantly, when subtracting thetwo conditions (finger−dot), differences in the 10±4 Hz rangebecome apparent. At 180–280 ms after stimulus onset, the dotcondition yields more power in this frequency range than thebiological condition (reflected in Fig. 2 by the predominant colorblue in this time–frequency window). This pattern is reversed for theinterval between 320 and 420 ms (predominant color is red). From500 ms throughout movement execution, the dot condition againreveals stronger power. Accordingly, the frequency range of10±4 Hz was chosen for the identification of the coupling corticalnetworks involved in the processing of the two movement types.Also, the time interval from 320 to 420 ms seemed most promisingas an initial step for detecting the source of the behaviouraladvantage.

The underlying cortical networks were identified bymeans of theDICS method (Gross et al., 2001) that employs spatial filters in thefrequency domain to calculate cortical power and coherence mapsfor a given frequency. In a nutshell, spatial filtering techniques(beam forming) locate the main sources of a signal measured bysensors (MEG) or electrodes (EEG) surrounding the head of theparticipant, using a data-adaptive spatial filter. Specifically, for thebrain of each participant (here modeled by means of a boundaryelement model; BEM) a forward solution analytically defines therelation between dipolar current sources (used as mathematicalabstractions for groups of aligned neurons) in each segment of theindividuals’ brains to the array of sensors. Note that the forwardsolution forMEG is simpler andmore exact than for EEG recordingsbecause the magnetic field (MEG) of an electric conductor withinthe brain is much less distorted by the intervening tissue than theelectric current itself (EEG). The data-adaptive calculation localizesthe cortical sources for a given set of measured data. It integrates theforward solution with specific aspects of oscillatory activitymeasured by the sensors.

One important aspect is the power (strength) of a specificfrequency range. In our case, the 10±4 Hz range showed thestrongestmodulation bymovement condition.Based on thematrix ofcross-spectral densities for each pair of sensors in combination withthe forward calculation (technical details are provided in Gross et al.,2001), the main cortical generators for this frequency in a specifictime window (here 320–420 ms) can be identified. In the next step,

Fig. 2. Time–frequency representations (TFR) for finger and dot movement. TFR was calculated for each condition separately (Panel A: finger; Panel B: dot) andnormalized with respect to baseline (−0.1 to 0 s; 0 corresponds to the onset of the moving stimulus on the screen). All values between −1.96 and +1.96 are set to0 (dark blue). That is, all values are set to 0 that did not reach significance (the boundaries are the Z scores that correspond to a two-tailed significance level of5%). Concluding, statistically significant increases with respect to baseline are confined to frequencies at and below 15 Hz in the interval between 0 and 0.5 s(Panels A and B). No statistically significant decreases could be observed. Subsequently, the non-biological condition (dot) was subtracted from the biologicalcondition (finger). This difference TFR is shown in Panel C. Blue codes for more power in the dot condition whereas red indicates more power in the fingercondition. Different modulations are mainly observed in the 10±5 Hz frequency range. All Panels show averages across all subjects and all 122 channels. On they axis frequencies are shown in Hz.

230 K. Kessler et al. / NeuroImage 33 (2006) 227–238

the strongest power source may be used as a reference signal fromwhich other brain sources that are most coherent to this reference areidentified (coherence is a correlation measure in the frequencydomain; Gross et al., 2001). In turn, the most coherent source canserve as a new reference to find the next source (again, most coherentto the reference signal) and so forth.

In general, the accuracy of source localization with MEGdecreases dramatically with source depth. This problem has beendemonstrated, for example, in an extensive analytic/simulationstudy by Hillebrand and Barnes (2002). However, the authors haveexplicitly pointed out that their results crucially depend onparameters such as the source signal strength, source size andespecially signal-to-noise ratio (SNR). We have therefore employedas many as 240 trials per condition (finger vs. dot) to increase theaccuracy of localization. Furthermore, we have reduced the sensorsignals to the frequency range that was modulated by the task (10±4 Hz). This increases the likelihood and accuracy of detection of adeep source if it shows strong oscillatory activity in this frequencyrange. This argument applies even more strongly to a deep sourcethat is coherent to a given reference signal as this narrows down thesearch space even more (coherence in a narrow frequency band),which increases spatial resolution (Gross et al., 2001). Finally, to

allow for the highest possible accuracy in this study we have onlyincluded the strongest source at each step of analysis. The strongestsource was determined in the following way.

In this study, brain sources were not selected at the individuallevel but at the group level. That is, individual power or coherencemaps were exported to SPM99 (SPM99: Wellcome Department ofCognitive Neurology, Institute of Neurology, London), normalizedtogether with the individual MR images and averaged to groupmeans (Singh et al., 2002). Accordingly, on each iteration we wereable to identify the strongest generator (for power/coherence) thatwas supposed to be representative for the entire group.We employedtwo criteria to ensure that this was indeed the case.

Firstly, significance of the group maximum (power/coherence)was calculated with the standard one-sample t test procedure inSPM99. Only those sources were retained that were significantlydifferent from 0 (p<0.001). Secondly, a statistically significantnumber of participants were supposed to show a source (localmaximum in the power/coherence map) within a certain rangesurrounding the group maximum as specified below. This was toensure that oscillatory activity was consistently originating from thearea(s) represented by the groupmean across individuals. In our case,8 out of 10 subjects were expected to show such a local maximum.

231K. Kessler et al. / NeuroImage 33 (2006) 227–238

The number was derived from the fact that a one-tailed chi-squaretest is statistically significant with 8 out of 10 subjects. The range forthe localization of individual local maxima was defined by theBrodmann areas covered by 95% of the group maximum value.Sources at the group level that violated one of these two criteria wereneither included in the final sourcemodel nor used as reference signalfor a further iteration. This method based on power and coherenceenables an iterative construction of the cortical network underlyingthe oscillatory activity/coupling in a certain time–frequency interval.

The iterative procedure was applied to each condition (dot andfinger stimulus) separately in order to identify differences in theunderlying cortical networks. The networks for the two conditionswere identical in terms of source locations at the group level (seeTable 1), yet with variations at the level of the individual maxima foreach condition. The second iterative source analysis, applied to the180- to 280-ms interval, did not yield statistically significantadditional sources than the ones identified for the 320- to 420-msperiod.We did not analyze sources during the actual execution of theimitation movement (beyond 500 ms after stimulus onset), as wewere primarily interested in the planning stages of the imitation andhow these relate to the behavioural advantage.

With the iterative procedure, only the strongest coherent sourceis identified at every step for the specific time window underinvestigation. In order to reveal significant dynamic interactionsthat may exist within the network, the time-resolved phase syn-chronization index (SI, range 0:1) (Rosenblum et al., 1996) wascalculated for the trial duration for each pair of sources to unravelstimulus-specific interactions within the network during stimulusprocessing and execution planning (i.e., differences between fingerand dot stimuli).

In order to ensure optimal comparability between conditions, asingle individual source model was employed for both conditions(finger and dot condition). As we hypothesized that strongersynchronization related to biological stimuli would explain theobserved RTadvantage, we chose the source model identified for thenon-biological (dot) condition as the common model in order toapply a conservative test to this hypothesis (these sources wereoptimally fitted for the dot condition; hence, stronger SI in thebiological condition should reflect a true processing advantage). The

Table 1Source coordinates, Brodmann areas and labels

No. Hemisphere/label Power/coherence Talairach coordinates:finger condition

Ar

1 Right PPC Pow1 34 −48 44 BA2 Right OTC Coh1 32 −36 −6 BA3 Right TP Coh2 30 10 −47 BA4 Left vPMC Coh3 −56 −10 18 BA5 Left PPC Coh4 −8 −44 70 BA6 Left STC Pow2 −50 −34 12 BA7 Right PCC Coh1 6 −26 40 BA8 Right Cb Pow3 34 −34 −32 rC9 Right BG Coh1 20 10 10 Pt10 Left Cb Coh2 −32 −36 −34 lC11 Right SM1 Coh3 16 −21 69 BA

In the row at the far right, the two areas are indicated where most of the individual losource model for further analysis. All areas are listed where frequencies exceeded n=Note that localization of areas BA44 and BA4 is in concordance with the available pCb=cerebellum; NC=nucleus caudatus; OTC=occipito-temporal cortex; PCC=pparietal cortex; Pt=putamen; SM1=primary sensorimotor cortex; STC=superior t

SI for each pair of sources was normalized with respect to the 300msprior to the onset of the stimulus (all values between 0 and 800 msafter stimulus onset were corrected by subtracting the mean anddividing by the SD of the interval −300 to 0ms) in concordance witha number of previous studies (Rodriguez et al., 1999; Trujillo et al.,2005). Only those connections were further considered that revealeda peak of synchronization above baseline level to ensure that theobserved SI peaks were not random noise.

Next, three steps of analysis were employed to identifydifferences between stimulus conditions in synchronization (SI).Firstly, the integral from 50 to 500 ms (0=movement onset) ofthe SI time course was compared between conditions for eachsource pair to obtain a measure for the amount of synchroniza-tion across the period of stimulus processing. Secondly, SI grouppeaks were identified in the interval 50–500 ms for thebiological condition and compared with the SI at peak time inthe dot condition. Statistics were based on local maximaidentified for each individual within a time window of±50 ms around the group peak. Two-tailed non-parametricWilcoxon tests were employed to test for stronger synchroniza-tion in the biological as compared with the dot condition.Finally, RTs were correlated to SI peak latencies and amplitudesby means of non-parametric Spearman correlations. In particular,individual RT differences (dot–biological) were correlated toindividual latency differences (dot–biological) and to amplitudedifferences (biological-dot) to identify the network connectionsthat show a statistically significant relation to the size of thebehavioural effect.

In addition to the SI, the time course of the power in the 10±4 Hzrange was calculated for each source and normalized with respect tothe baseline interval (−300 to 0 ms before stimulus onset).

For these analyses (SI and amplitude), it is important to keepin mind that the source model for the non-biological conditionwas also applied to the biological condition in order to achieve aconservative comparison between the two. This procedure madeit harder for the biological condition to reach significantlystronger values than the non-biological condition due to lessperfect fit. As this approach is quite conservative, we will alsoreport and interpret statistical tendencies (p<0.1).

ea Talairach coordinates:dot condition

Area Most frequent local maxima:area (n)

40 43 −56 47 BA40 BA40 (7)37 32 −38 −6 BA37 BA37 (4); BA19 (2)38 28 12 −47 BA38 BA38 (6); BA20 (4)44 −56 −10 18 BA44 BA6aα inf (5); BA44 (2)5 −8 −46 70 BA5 BA5 (5); BA7 (5)29 −52 −30 6 BA22 BA22 (7)31 6 −28 40 BA31 BA31 (6); BA23 (3)b 36 −44 −32 rCb rCb (9)

22 10 10 Pt Pt (4); NC (4)b −32 −36 −38 lCb lCb (10)4 18 −21 68 BA4 BA4 (7); BA6aα sup (3)

cal maxima were found for the dot condition that was employed as common1 (subjects). In some cases, this is solely achieved for the most frequent one.robability maps (Amunts et al., 1999; Geyer et al., 1996). BG=basal ganglia;osterior cingulate cortex; vPMC=ventral premotor cortex; PPC=posterioremporal cortex; TP=temporal pole.

232 K. Kessler et al. / NeuroImage 33 (2006) 227–238

Results

Behaviour

Mean error rate (wrong finger lifted) was 4.5% (finger=4.38±4.2%; dot=4.63±3.7%), suggesting that subjects did not experi-ence difficulties in performing the task (nor did the differencebetween the conditions reach significance: paired t test, p>0.74).Trials with false responses were excluded from further RTanalyses. Mean RTs to biological movement stimuli were 558 ms(SD 97 ms) for index finger movements and 559 ms (SD 115 ms)for little finger movements. RTs to moving dots were 580 and577 ms, respectively. Mean difference between the dot andbiological condition (across fingers) was 20 ms and reached ahigh level of significance (paired t test, p<0.0002). Thisbehavioural advantage was evident in each subject: the relativedecrease in RT ranged between 4 and 34 ms.

Cortical sources

Fig. 3 shows the cortical sources identified for the twoconditions (biological is shown as an example) in the 10±4 Hzrange in the 320- to 420-ms time window. The networks did notdiffer between the two conditions at the group level (see Table 1).Source description will therefore encompass both biological anddot movement trials.

The strongest power source was found in the right posteriorparietal cortex (PPC), a cortical area involved in visuospatial pro-cessing and attention (Behrmann et al., 2004). Recently, this area

Fig. 3. Cortical network identified iteratively for the biological condition.Panel A shows the “chain” of sources found with the strongest power source(right PPC) in the 320–420 ms/10±4 Hz time–frequency as referencesignal. Panels B and C show the corresponding chains for the second (leftSTC) and the third (right Cb) power source, respectively. For further detailsplease see text. BG=basal ganglia; Cb=cerebellum; OTC=occipito-temporal cortex; PCC=posterior cingulate cortex; vPMC=ventrolateralpremotor cortex; PPC=posterior parietal cortex; SM1=primary sensorimo-tor cortex; STC=superior temporal cortex; TP=temporal pole.

(BA40) was also linked to motion processing (Taylor et al., 2000).The first coherent source was identified in the right occipito-temporal cortex (OTC) related to object-based visual processing(Grill-Spector, 2003). The third source (second coherent) waslocated in the right temporal pole (TP) that was recently related to theprocessing of sociobiologically relevant stimuli (Ranote et al., 2004;Nelson et al., 2003) and to theory of mind processing (Calarge et al.,2003). From here, the strongest coherence is found in the leftventrolateral premotor cortex (vPMC). The strongest coherencefrom left vPMC in the time window under investigation wasobserved with left PPC. From left PPC, no further coherent areascould be observed according to our criteria of significance andconsistency (see Materials and methods).

The second strongest power source was located in the leftsuperior temporal area (STC). STC was also consistently found tobe involved in imitation (Rizzolatti et al., 1996; Decety et al., 2002;Nishitani and Hari, 2002; Iacoboni et al., 2001). The right posteriorcingulate cortex (PCC) was identified next as the strongest coherentsource to left STC. It was furthermore the only source identified inthis course of analysis. PCC has been repeatedly related tostimulus-driven shifts of attention (Pierrot-Deseilligny et al., 2004;Dean et al., 2004), whereas Vogeley et al. (2004) reported PCC tobe involved in the mental generation of a first-person perspective incontrast to a third-person perspective of a visual scene along withprefrontal cortex and, most importantly, with STC.

The third and last power source that conformed to our criteriawas found in the right cerebellum (Cb). From the right Cb, threecoherent sources known to be related to motor planning and control(Brown andMarsden, 1998; Glover, 2004) were identified: the rightbasal ganglia (BG), the left Cb and finally the right primarysensorimotor cortex (SM1). It is important to point out that deepsources like BG and Cb cannot be identified with the same spatialaccuracy like sources on the surface of the cortex (e.g., Hillebrandand Barnes, 2002). However, by maximizing the number of trials aswell as by reducing the sensor signals to a narrow frequency bandand to coherence in this frequency band, respectively, accuracy wasoptimized (see Materials and methods for details). Our findings arein accordance with a number of other studies that have detecteddiencephalic and cerebellar sources by means of cortico-corticalcoherence (Butz et al., 2006; Gross et al., 2002; Pollok et al., 2005a,2005b; Timmermann et al., 2003; for a review, see Schnitzler andGross, 2005). Nevertheless, it is important to keep in mind thatlocalization of deep sources is less accurate and BG for examplemay not be clearly dissociable from other diencephalic areas (e.g.,thalamus) and may not be strictly confined to the right hemisphere.

Time course of power (10±4 Hz)

Analysis of the power time courses for each source allowedspecifying the generators of the power modulations shown in theTFR in Fig. 2. We observed more power for the dot condition in anearly 180- to 280-ms time window, whereas there was more powerfor the biological condition in a later 320- to 420-ms time window.The early advantage in power for the dot condition was probablydue to a stronger stimulus evoked power decrease in the biologicalcondition. As shown in Fig. 4, Panel A, the biological conditionshowed such a decrease whereas the dot condition revealed anincrease in two sources, i.e., a numerical effect in right OTC and astatistically significant effect in left STC (Wilcoxon test: Z=−2.395; p<0.017). Stimulus-evoked power reductions have been

233K. Kessler et al. / NeuroImage 33 (2006) 227–238

reported previously and related to the intensity of processing(Pfurtscheller, 1992; Pfurtscheller and Lopes da Silva, 1999). Incontrast, left Cb, right SM1 and left PPC showed a statisticallysignificant (all Z<−1.988; p<0.047) and right PPC a tendency (Z=−1.886; p<0.059) for a stronger power increase in the biologicalcompared with the dot condition during the 300- to 500-ms timewindow (see Fig. 4, Panel B).

Time course of synchronization (10±4 Hz)

It is important to point out that whereas all cortical areas withinthe identified network might communicate with each other at somepoint to solve the task, the analysis of synchronization that will bereported here aimed at highlighting those connections that weremore strongly involved (stronger synchronization) during theimitation of biological movements. As a first measure, wecalculated the integral of the SI for the interval from 50 to 500 msafter stimulus onset in order to test for general differences duringstimulus processing (before actual movement execution). Although

Fig. 4. Time courses of power (10±4 Hz). 0 ms denotes stimulus onset. Timstimulus onset; hence, the y axis indicates deviation from baseline. The black liIn Panel A, two sources are shown (right OTC, left STC) that revealed a powaround 100–300 ms after stimulus onset (denoted by vertical lines). These sourcafter 500 ms. In contrast, the sources in Panel B (rPPC, lPPC, lCb, rSM1) reve420-ms time window (denoted by vertical bars). STC=superior temporalCb=cerebellum; SM1=primary sensorimotor area.

we employed the source model derived from the dot condition (seeMaterials and methods), we only observed SI advantages (i.e., largerintegral areas) for the biological condition with this rather coarsemeasure. Significantly stronger synchronization across the 50- to500-ms period was observed in four connections (Fig. 5, Panel A):right PPC↔ right BG, right OTC↔ left PPC, right TP↔ right SM1and right PCC↔ right SM1 (all Z<−1.988; p<0.047). Mostsources and connections are confined to the right hemisphere,possibly suggesting a stronger role in continuous synchronizationduring processing of biological stimuli.

As a second measure, peaks were determined for thebiological condition for each source pair and tested whetherthese peaks would differ significantly (non-parametric Wilcoxontests) from the SI time course in the dot condition (see Materialsand methods for details). Six connections revealed a statisticallysignificant peak (all Z<−1.988; p<0.037) in the biologicalcondition (see Fig. 5): again, right PPC↔ right BG, rightTP↔ right SM1 and right PCC↔ right SM1, as observed for theSI integral analysis, yet in addition left vPMC↔ left PPC, right

e courses were normalized with respect to the 300-ms baseline beforene indicates the biological and the grey line the non-biological condition.er decrease in the biological and a power increase in the dot conditiones also show stronger oscillatory energy for dot stimuli in a later intervalaled a stronger power increase in the biological condition for the 320- tocortex; OTC=occipito-temporal cortex; PPC=posterior parietal cortex;

Fig. 5. Time courses for the synchronization index (SI) in the 10±4 Hz range. 0 ms denotes stimulus onset. Time courses were normalized with respect to the300-ms baseline before stimulus onset; hence, the y axis indicates deviation from baseline. The black bold horizontal bar over the x axis together with thetwo horizontal lines indicates the time interval of interest (50–500 ms) for each connection. Panel A shows the network connections that were synchronizedmore strongly for the biological compared with the dot condition for the entire trial period—after movement stimulus onset and prior to response (50–500 msafter stimulus onset). The connections shown in Panel B revealed only a significantly larger peak in the finger compared with the dot condition. Peaks areindicated by black arrows. Note that three of the four connections in Panel A also revealed a statistically significant peak as indicated by the arrows.OTC=occipito-temporal cortex; PPC=posterior parietal cortex; BG=basal ganglia; TP= temporal pole; SM1=primary sensorimotor area; PCC=posteriorcingular cortex; vPMC=ventrolateral premotor cortex; Cb=cerebellum; STC=superior temporal cortex.

1 For the interested reader, we would like to mention the advantages wefound for the dot condition. The analyses of SI peak amplitudes revealedonly two connections with statistically significant peaks in the dot conditionas compared with the finger condition (both Z<−1.988; p<0.047): rightPPC↔ right OTC and left vPMC↔ left Cb, with the latter revealing a peakalso in the biological condition (late, cf. Figs. 5 and 6). It is, however,important to note that these differences should be interpreted with care, asadvantages of the non-biological condition could be solely due to theoptimal fit of the source model (see Materials and methods).

234 K. Kessler et al. / NeuroImage 33 (2006) 227–238

OTC↔ left STC and left STC↔ right Cb. In addition, SIs forleft vPMC↔ left Cb and for left PPC↔ right BG revealedstatistical tendencies (Z=−1.682; p<0.093). These eight con-nections may be classified in early and late peak connections (cf.Fig. 6): one group (lvPMC↔ lPPC; lPPC↔ rBG; rPPC↔ rBG;rTP↔ rSM1) revealed peaks earlier than 300 ms after visualmovement onset (108–240 ms), whereas another group(lvPMC↔ lCb; lSTC↔ rOTC, lSTC↔ rCb; rPCC↔ rSM1)yielded peaks later than 300 ms (402–496 ms). Three hundredmilliseconds also corresponds to the time point of reversal inthe power differences across time as shown in Fig. 2, suggestinga two-stage process in the MNS. Fig. 6 provides two schematicdrawings for the early and the late network and integrates alsothose connections that yielded a more continuous synchroniza-tion advantage for biological stimuli (as revealed by the integralanalysis). All early peak connections (biological) were signifi-cantly faster than the dot condition (all p<0.002), whereas alllate peak connections were significantly later than the dotcondition (all p<0.002). Thus, connections that revealed astronger synchronization peak for biological stimuli showed ahighly differentiated picture of early versus late peak SI, whereas

the dot condition generally revealed peaks – if any – in themiddle of the stimulus processing period.1

Finally, there was a positive correlation (non-parametricSpearman Rho) between RT differences (dot–biological) and SIpeak amplitude differences (biological–dot) in three connections(two statistically significant p<0.048, one close to statisticalsignificance p<0.06, all r>0.612): right PPC↔ left vPMC (meanbiological peak at 240 ms), right TP↔ left vPMC (mean biologicalpeak at 172 ms) and right TP↔ to left Cb (mean biological peak at402 ms). These three connections (two early, one late peaking) arealso integrated into the network schemes in Fig. 6. Finally, RT

2 It is important to point out that lateralization of vPMC to the lefthemisphere could be solely due to the stimulus–response setup of ourexperiment, i.e., participants were required to imitate a displayed left handwith their own right hand. A recent meta-analysis of seven fMRI data sets(Molnar-Szakacs et al., 2005) did not support a strong lateralization ofvPMC in imitation tasks either way (see also Aziz-Zadeh et al., 2006).

Fig. 6. The network that was differentially involved in the imitation of fingercompared with dot movements. Network connections that revealed astatistically significant synchronization peak in the biological condition aredepicted as black lines. Dashed lines indicate connections that revealed astatistically significant correlation between amplitude differences and thebehavioural advantage for biological stimuli. Grey lines denote connectionsthat yielded a significantly greater SI in the finger compared with the dotcondition across the 50- to 500-ms period after stimulus onset. These latterconnections appear in both Panels. Panel A shows the connections thatrevealed an early peak (<300 ms after stimulus onset), whereas the later peakconnections (>300 ms after stimulus onset) are shown in Panel B. Blackellipses denote sources that revealed at least three connections to threedifferent sources across the entire trial period (late and early taken together).Exact SI peak latencies on the right are ordered in ascending order. Also,whenever a source is reaching an SI peak for the first time (to any othersource), the label is bold and underlined. This allows for a timeline of sourceinvolvement.

235K. Kessler et al. / NeuroImage 33 (2006) 227–238

differences were not correlated with SI peak latency differences(dot–biological).

Discussion

Humans respond faster to biological as compared with non-biological movements, which can probably be attributed to themirror neuron system (MNS) in interaction with a waster networkrelated to imitation. Although evidence has accumulated pointing tothe premotor (vPMC), posterior parietal (PPC) and superiortemporal (STC) regions as substrates of this network, it remainedunclear which other areas might interact with these regions at whichtime intervals and which interactions might be responsible for thebehavioural advantage during imitation of biological movements.Using whole-head MEG, we recorded neural responses to singlebiological finger movements and non-biological dot movementswhile the subjects imitated these movements. Confirming results ofprevious behavioural studies (Brass et al., 2000), reaction times tobiological movements were significantly faster (560 ms) than thoseto dot movements (580 ms). We further aimed at investigating thecortical origin(s) for this behavioural effect, and in this work wedecided to analyze the data in the frequency domain in order to

obtain insights into the underlying coupling networks (evokedresponses will be reported elsewhere).

The analysis of the magnetic fields by means of DICS, i.e.,spatial filtering in the frequency domain (10±4 Hz), did not yieldany qualitative differences between biological and non-biologicalmovements in terms of the involved cortical areas. However,significant quantitative differences between the conditions revealedthat the left vPMC area2 is involved more strongly in the imitationof biological movements as early as 108 ms after stimulus onset(cf. Figs. 5 and 6) along with bilateral posterior parietal cortex(PPC), right basal ganglia (BG), right occipito-temporal cortex(OTC), right temporal pole (TP) and right primary sensorimotorcortex (SM1). Furthermore, the amount of synchronization of leftvPMC with right PPC and right temporal pole (TP) correlated withreaction time advantages for the imitation of biological movementsin an early time window (100–250 ms after stimulus onset).

It follows that left vPMC is strongly involved in a subnetworkof early synchronization related to biological stimuli, where PPCmight represent an important junction between visuospatial andmotor-related information flow: both PPCs show early modulationsby stimulus type in the synchronization with left vPMC, but alsowith BG, suggesting early integration of motor information duringvisuospatial processing of the biological movement. Indeed,Fogassi et al. (2005) reported mirror neurons in the inferiorparietal lobule, whereas BG were associated with motor programselection and suppression at early stages of motor planning as wellas with control of movement simulation, i.e., switching from covertto overt behaviour (Hikosaka et al., 1993). Although it might bequestionable whether the oscillatory activity we have observedstrictly originates from BG or may also involve other diencephalicsources (see Materials and methods), BG are known to exert theirinfluence for learned movements mainly via thalamus (for areview, see Arbib et al., 1998, pp. 321–328) supporting the notionof a cortico-diencephalic loop for early integration of perceptualand motor information.

It fits nicely into this picture that OTC and TP are synchronizedwith this subnetwork of motor-related and visuospatial areas (Fig.6, Panel A). OTC and TP might provide visual information andbiological significance of the movement on the screen for a matchwith sensorimotor information (SM1) in order to select thecorresponding motor program (BG in interaction with PMC andPPC). Results of this integration/matching process may in turn beused to initiate the imitation movement. Taken altogether, weinterpret these findings as support for the notion of direct actionobservation execution matching (AOEM).

Surprisingly, left superior temporal cortex (STC) reaches thepeak of its differential impact (biological>dot movement) in a latephase (Fig. 6, Panel B), enhancing its synchronization with visual(right OTC) and motor areas (right cerebellum) about 50–150 msprior to the actual movement (400–500 ms after stimulus onset).However, an event-related decrease in amplitude (Fig. 4, Panel A) isobserved already around 200 ms after stimulus onset suggesting thatSTCmight indeed be differentially involved for biological stimuli atan early stage. The lack of early significant differences in syn-

236 K. Kessler et al. / NeuroImage 33 (2006) 227–238

chronization could be due to the conservative choice of the sourcemodel (see Materials and methods). Another possible explanationcould be that as the moving dot stimulus was aimed to perfectlymatch the kinematics of the moving finger, it may have been in facttoo similar, eliciting a nearly identical early processing pattern ofSTC in relation to other cortical areas. This is supported by studiesthat show STS activation by point-light patterns that only mimic abiological movement (Bonda et al., 1996; Grossmann et al., 2000).Our findings in terms of synchronization suggest that later re-entrantprocessing from the early processing network described above(PMC, PPC, BG, SM1, TP, OTC; see Fig. 6, Panel A) via OTCmightinduce a stronger resonance for finger movements in STS. Ac-cording to our results, the right temporal pole (TP) might have beenmore strongly involved in processing the biological vs. non-biological aspects of the moving stimulus. TP was possibly notrevealed in previous fMRI studies as field inhomogeneities due to itsexposed location adjacent to different types of tissue may haveinterfered (e.g., Huettel et al., 2004, p. 122).

Both cerebellar lobes (Cb) become involved at a late processingstage (after 300 ms; cf. Fig. 6, Panel B) together with right primarysensorimotor cortex (SM1) and posterior cingulate cortex (PCC),marking the transition between stimulus processing and movementinitiation. Synchronization between left vPMC and left Cb reachesthe peak in this period prior to movement execution (about 100 msbefore response), suggesting that left vPMC might also play a keyrole in applying the “matching results” of the earlier processing stageto the initiation/control of the imitation movement. Accordingly,fronto-cerebellar interactions have been repeatedly reported to beinvolved in motor control (e.g., Gross et al., 2002) and in switchingbetween internal models for motor control (Imamizu et al., 2004). Inthe latter case, it seems that frontal areas implement a gatingmechanism for switching between internal models whereascerebellum (and parietal cortex) are confined to the implementationof the internal models per se. Furthermore, Tamada et al. (1999)showed functional (possibly subserved by anatomical) connectivitybetween vPMC and the lateral part of the cerebellum during learningof tool usage, which requires a complex coordination of visual- andmotor-related information. The authors point out that this specificcerebellar region might even contribute essentially to the cognitiverepresentation of tool usage. Accordingly, in the context of thisstudy, one could expect that with more complex biologicalmovements the connection between vPMC and Cb could becomemore important and possibly involved earlier.

Taken altogether, our findings support a two-stage process, withan early observation–execution matching stage and a later motorinitiation stage. This interpretation dovetails nicely with therelation between MNS and internal models for motor controlproposed by Miall (2003). Internal models are usually divided intoinverse and forward models, depending on whether the model isderived inversely from the (perceived) outcomes of a movement orwhether the model consists of a sequence of movement commandsthat determines the forward transition from a start- to an end-state.Extending on an account by Iacoboni (Carr et al., 2003; Iacoboni,2005), who proposed premotor, posterior parietal and superiortemporal cortex to implement the MNS as well as inverse andforward models, Miall (2003) points out that the cerebellum mightactually play a crucial role by providing the necessary motorparameters for the selection of an inverse model (during actionobservation) and of a forward model (during later imitationexecution). We strongly agree with the claim that motor informationis a necessary prerequisite for internal model selection, yet we found

a stronger involvement of BG at an early stage, which wouldencompass the selection of an appropriate inverse model, whereasCb only seems to play a role for the instantiation of the appropriateforward model. Recent evidence supports the notion of BG beinginvolved in the human mirror neuron system. Galpin et al. (2006)reported that patients suffering from Parkinson’s disease were notable to profit from biological cue stimuli duringmotor execution (thestimuli were highly similar to the ones employed here), but insteadwere handicapped compared to control participants. However, aspointed out earlier, the importance of the cerebellum might increasewith more complex movements.

Also, Miall (2003) claimed that PPC rather than vPMC wouldactually be the critical interface between inverse and forward modelsdue to its multimodality (i.e., visuospatial; sensori-motor). Accord-ing to our data, this is not entirely the case. PPC indeed plays a crucialrole during early integration of perceptual and motor-relatedinformation, yet vPMC seems to be more important for the transitionbetween perception and action. In sum, our data help specifyingtheoretical accounts of action observation and imitation and provideempirical clues regarding the relation between theMNS, inverse andforward models.

Conclusions

Replicating and extending previous work, we conclude thatventrolateral premotor, temporal and parietal areas mediate theobserved behavioural advantage of biological movements in closeinteraction with motor areas (cerebellum, basal ganglia andsensorimotor cortex) and therefore may be essential for imitationof movements. The ventrolateral premotor area seems to be involvedin an early synchronizing subnetwork specialized on observation–execution matching for biological stimuli, as well as in a latersynchronizing subnetwork most likely related to control of theimitation movement. Besides premotor cortex, we identified theright temporal pole and the posterior parietal cortex as importantjunctions for the integration of information from different sources inimitation tasks that are controlled for movement (biological vs. non-biological) and that involve a certain amount of spatial orienting ofattention. Finally, we also found diencephalic areas, most likely thebasal ganglia, to already participate at an early stage in theprocessing of biological movement, possibly by selecting suitablemotor programs that match the stimulus. Therefore, our data alsoshed light on the relation between the human mirror neuron systemand inverse and forward models of motor behaviour.

We do not, however, claim to have resolved all aspects of theunderlying processes—the analysis presented here constitutes apossible consistent way to identify the relevant frequency band andthe cortical generators. For instance, some important processingaspects may have occurred in another frequency range (e.g., 20–30 Hz) that was less obviously modulated by the task. Further workwill be needed to clarify all aspects of imitation movements.

Acknowledgments

This study was supported by the Volkswagenstiftung (projectgrant I/78553). We thank Dr. Joachim Gross, Dr. Bettina Pollokand Dr. Markus Butz for helpful discussion and Erika Raedischfor the acquirement of anatomical MR images. We would alsolike to thank two anonymous reviewers for their very helpfulcomments on an earlier version of the manuscript.

237K. Kessler et al. / NeuroImage 33 (2006) 227–238

References

Amunts, K., Schleicher, A., Burgel, U., Mohlberg, H., Uylings, H.B., Zilles,K., 1999. Broca’s region revisited: cytoarchitecture and intersubjectvariability. J. Comp. Neurol. 412, 319–341.

Annett, M., 1985. Left, Right, Hand and Brain: The Right Shift Theory.Lawrence Erlbaum Associates Ltd, London.

Arbib, M.A., Erdi, P., Szentagothai, J., 1998. Neural Organization:Structure, Function, and Dynamics. MIT Press, Cambridge, MA.

Aziz-Zadeh, L., Koski, L., Zaidel, E., Mazziotta, J., Iacoboni, M., 2006.Lateralization of the human mirror neuron system. J. Neurosci. 26,2964–2970.

Behrmann, M., Geng, J.J., Shomstein, S., 2004. Parietal cortex and attention.Curr. Opin. Neurobiol. 14, 212–217.

Bonda, E., Petrides, M., Ostry, D., Evans, A., 1996. Specific involvement ofhuman parietal systems and the amygdala in the perception of biologicalmotion. J. Neurosci. 16, 3737–3744.

Brass, M., Bekkering, H., Wohlschlager, A., Prinz, W., 2000. Compatibilitybetween observed and executed finger movements: comparing symbolic,spatial, and imitative cues. Brain Cogn. 44, 124–143.

Brown, P., Marsden, C.D., 1998. What do the basal ganglia do? Lancet 351,1801–1804.

Buccino, G., Binkofski, F., Fink, G.R., Fadiga, L., Fogassi, L., Gallese, V.,Seitz, R.J., Zilles, K., Rizzolatti, G., Freund, H.J., 2001. Actionobservation activates premotor and parietal areas in a somatotopicmanner: an fMRI study. Eur. J. Neurosci. 13, 400–404.

Butz, M., Timmermann, L., Gross, J., Pollok, B., Dirks, M., Hefter, H.,Schnitzler, A., 2006. Oscillatory coupling in writing and writer’s cramp.J. Physiol. (Paris) 99, 14–20.

Calarge, C., Andreasen, N.C., O’Leary, D.S., 2003. Visualizing how onebrain understands another: a PET study of theory of mind. Am. J.Psychiatry 160, 1954–1964.

Carr, L., Iacoboni, M., Dubeau, M.C., Mazziotta, J.C., Lenzi, G.L., 2003.Neural mechanisms of empathy in humans: a relay from neural systemsfor imitation to limbic areas. Proc. Natl. Acad. Sci. U. S. A. 100,5497–5502.

Dean, H.L., Crowley, J.C., Platt, M.L., 2004. Visual and saccade-relatedactivity in macaque posterior cingulate cortex. J. Neurophysiol. 92,3056–3068.

Decety, J., Chaminade, T., Grezes, J., Meltzoff, A.N., 2002. A PETexploration of the neural mechanisms involved in reciprocal imitation.NeuroImage 15, 265–272.

di Pellegrino, G., Fadiga, L., Fogassi, L., Gallese, V., Rizzolatti, G., 1992.Understanding motor events: a neurophysiological study. Exp. BrainRes. 91, 176–180.

Fogassi, L., Ferrari, P.F., Gesierich, B., Rozzi, S., Chersi, F., Rizzolatti, G.,2005. Parietal lobe: from action organization to intention understanding.Science 308, 662–667.

Gallese, V., Fadiga, L., Fogassi, L., Rizzolatti, G., 1996. Action recognitionin the premotor cortex. Brain 119 (Pt 2), 593–609.

Galpin, A., Poliakoff, E., Tipper, S.P., 2006. Is implicit action activationaffected by Parkinson’s disease? Proceedings of the BirminghamMeeting of the Experimental Psychology Society, 10-12 April 2006.Birmingham University, UK, p. 71.

Geyer, S., Ledberg, A., Schleicher, A., Kinomura, S., Schormann, T.,Burgel, U., Klingberg, T., Larsson, J., Zilles, K., Roland, P.E., 1996. Twodifferent areas within the primary motor cortex of man. Nature 382,805–807.

Glover, S., 2004. Separate visual representations in the planning and controlof action. Behav. Brain Sci. 27, 3–24 (discussion 24–78).

Grafton, S.T., Arbib, M.A., Fadiga, L., Rizzolatti, G., 1996. Localizationof grasp representations in humans by positron emission tomography:II. Observation compared with imagination. Exp. Brain Res. 112,103–111.

Grill-Spector, K., 2003. The neural basis of object perception. Curr. Opin.Neurobiol. 13, 159–166.

Gross, J., Kujala, J., Hamalainen, M., Timmermann, L., Schnitzler, A.,

Salmelin, R., 2001. Dynamic imaging of coherent sources: studyingneural interactions in the human brain. Proc. Natl. Acad. Sci. U. S. A. 98,694–699.

Gross, J., Timmermann, L., Kujala, J., Dirks, M., Schmitz, F., Salmelin, R.,Schnitzler, A., 2002. The neural basis of intermittent motor control inhumans. Proc. Natl. Acad. Sci. U. S. A. 99, 2299–2302.

Grossmann, S., Lohse, D., Reeh, A., 2000. Multiscale correlations andconditional averages in numerical turbulence. Phys. Rev., E Stat. Phys.Plasmas Fluids Relat. Interdiscip. Topics 61, 5195–5201.

Hikosaka, O., Matsumara, M., Kjima, J., Gardiner, T., 1993. Role ofbasal ganglia in initiation and suppression of saccadic eye move-ments. In: Mano, N., Hamada, I., DeLong, M.R. (Eds.), Role of theCerebellum and Basal Ganglia in Voluntary Movement. ExcerptaMedia, Amsterdam, pp. 213–219.

Hillebrand, A., Barnes, G.R., 2002. A quantitative assessment of thesensitivity of whole-head MEG to activity in the adult human cortex.NeuroImage 16, 638–650.

Huettel, S.A., Song, A.W., McCarthy, G., 2004. Functional MagneticResonance Imaging. Sinauer Associates, Sunderland, MA.

Iacoboni, M., 2005. Understanding others: imitation, language, empathy.In: Hurley, S., Chater, N. (Eds.), Perspectives on Imitation: FromCognitive Neuroscience to Social Science, Volume 1: Mechanismsof Imitation and Imitation in Animals. MIT Press, Cambridge, MA,pp. 77–100.

Iacoboni, M., Woods, R.P., Brass, M., Bekkering, H., Mazziotta, J.C.,Rizzolatti, G., 1999. Cortical mechanisms of human imitation. Science286, 2526–2528.

Iacoboni, M., Koski, L.M., Brass, M., Bekkering, H., Woods, R.P., Dubeau,M.C., Mazziotta, J.C., Rizzolatti, G., 2001. Reafferent copies of imitatedactions in the right superior temporal cortex. Proc. Natl. Acad. Sci.U. S. A. 98, 13995–13999.

Imamizu, H., Kuroda, T., Yoshioka, T., Kawato, M., 2004. Functionalmagnetic resonance imaging examination of two modular architec-tures for switching multiple internal models. J. Neurosci. 24,1173–1181.

Kohler, E., Keysers, C., Umilta, M.A., Fogassi, L., Gallese, V., Rizzolatti,G., 2002. Hearing sounds, understanding actions: action representationin mirror neurons. Science 297, 846–848.

Koski, L., Iacoboni, M., Dubeau, M.C., Woods, R.P., Mazziotta, J.C., 2003.Modulation of cortical activity during different imitative behaviors.J. Neurophysiol. 89, 460–471.

Miall, R.C., 2003. Connecting mirror neurons and forward models.NeuroReport 14, 2135–2137.

Molnar-Szakacs, I., Iacoboni, M., Koski, L., Mazziotta, J.C., 2005.Functional segregation within pars opercularis of the inferior frontalgyrus: evidence from fMRI studies of imitation and action observation.Cereb. Cortex 15, 986–994.

Nelson, E.E., McClure, E.B., Monk, C.S., Zarahn, E., Leibenluft, E., Pine,D.S., Ernst, M., 2003. Developmental differences in neuronal engage-ment during implicit encoding of emotional faces: an event-related fMRIstudy. J. Child Psychol. Psychiatry 44, 1015–1024.

Nishitani, N., Hari, R., 2002. Viewing lip forms: cortical dynamics. Neuron36, 1211–1220.

Pelphrey, K.A., Mitchell, T.V., McKeown, M.J., Goldstein, J., Allison, T.,McCarthy, G., 2003. Brain activity evoked by the perception of humanwalking: controlling for meaningful coherent motion. J. Neurosci. 23,6819–6825.

Perrett, D.I., Harries, M.H., Bevan, R., Thomas, S., Benson, P.J., Mistlin,A.J., Chitty, A.J., Hietanen, J.K., Ortega, J.E., 1989. Frameworks ofanalysis for the neural representation of animate objects and actions.J. Exp. Biol. 146, 87–113.

Pfurtscheller, G., 1992. Event-related synchronization (ERS): an electro-physiological correlate of cortical areas at rest. Electroencephalogr. Clin.Neurophysiol. 83, 62–69.

Pfurtscheller, G., Lopes da Silva, F.H., 1999. Event-related EEG/MEGsynchronization and desynchronization: basic principles. Clin. Neuro-physiol. 110, 1842–1857.

238 K. Kessler et al. / NeuroImage 33 (2006) 227–238

Pierrot-Deseilligny, C., Milea, D., Muri, R.M., 2004. Eye movement controlby the cerebral cortex. Curr. Opin. Neurol. 17, 17–25.

Pollok, B., Gross, J., Muller, K., Aschersleben, G., Schnitzler, A., 2005a.The cerebral oscillatory network associated with auditorily paced fingermovements. NeuroImage 24, 646–655.

Pollok, B., Sudmeyer, M., Gross, J., Schnitzler, A., 2005b. The oscillatorynetwork of simple repetitive bimanual movements. Brain Res. Cogn.Brain Res. 25, 300–311.

Puce, A., Allison, T., Bentin, S., Gore, J.C., McCarthy, G., 1998. Temporalcortex activation in humans viewing eye and mouth movements.J. Neurosci. 18, 2188–2199.

Ranote, S., Elliott, R., Abel, K.M., Mitchell, R., Deakin, J.F., Appleby, L.,2004. The neural basis of maternal responsiveness to infants: an fMRIstudy. NeuroReport 15, 1825–1829.

Rizzolatti, G., Craighero, L., 2004. The mirror-neuron system. Annu. Rev.Neurosci. 27, 169–192.

Rizzolatti, G., Matelli, M., 2003. Two different streams form the dorsalvisual system: anatomy and functions. Exp. Brain Res. 153, 146–157.

Rizzolatti, G., Fadiga, L., Gallese, V., Fogassi, L., 1996. Premotor cortexand the recognition of motor actions. Brain Res. Cogn. Brain Res. 3,131–141.

Rodriguez, E., George, N., Lachaux, J.P., Martinerie, J., Renault, B., Varela,F.J., 1999. Perception’s shadow: long-distance synchronization ofhuman brain activity. Nature 397, 430–433.

Rosenblum, M.G., Pikovsky, A.S., Kurths, J., 1996. Phase synchronizationof chaotic oscillators. Phys. Rev. Lett. 76, 1804–1807.

Schnitzler, A., Gross, J., 2005. Normal and pathological oscillatorycommunication in the brain. Nat. Rev., Neurosci. 6, 285–296.

Schultz, J., Friston, K.J., O’Doherty, J., Wolpert, D.M., Frith, C.D., 2005.Activation in posterior superior temporal sulcus parallels parameterinducing the percept of animacy. Neuron 45, 625–635.

Singh, K.D., Barnes, G.R., Hillebrand, A., Forde, E.M., Williams, A.L.,2002. Task-related changes in cortical synchronization are spatiallycoincident with the hemodynamic response. NeuroImage 16, 103–114.

Tai, Y.F., Scherfler, C., Brooks, D.J., Sawamoto, N., Castiello, U., 2004. Thehuman premotor cortex is ‘mirror’ only for biological actions. Curr. Biol.14, 117–120.

Tamada, T., Miyauchi, S., Imamizu, H., Yoshioka, T., Kawato, M., 1999.Cerebro-cerebellar functional connectivity revealed by the lateralityindex in tool-use learning. NeuroReport 10, 325–331.

Taylor, J.G., Schmitz, N., Ziemons, K., Grosse-Ruyken, M.L., Gruber, O.,Mueller-Gaertner, H.W., Shah, N.J., 2000. The network of brain areasinvolved in the motion aftereffect. NeuroImage 11, 257–270.

Timmermann, L., Gross, J., Dirks, M., Volkmann, J., Freund, H.J.,Schnitzler, A., 2003. The cerebral oscillatory network of parkinsonianresting tremor. Brain 126, 199–212.

Trujillo, L.T., Peterson, M.A., Kaszniak, A.W., Allen, J.J., 2005. EEGphase synchrony differences across visual perception conditions maydepend on recording and analysis methods. Clin. Neurophysiol. 116,172–189.

Vogeley, K., May, M., Ritzl, A., Falkai, P., Zilles, K., Fink, G.R., 2004.Neural correlates of first-person perspective as one constituent of humanself-consciousness. J. Cogn. Neurosci. 16, 817–827.

Wohlschlager, A., Gattis, M., Bekkering, H., 2003. Action generation andaction perception in imitation: an instance of the ideomotor principle.Philos. Trans. R Soc. Lond., B Biol. Sci. 358, 501–515.