ABBREVIATED: Brain activity varies with temporal complexity in paced tapping

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1 Brain activity correlates differentially with increasing temporal complexity of rhythms during initialisation, synchronisation, and continuation phases of paced finger tapping ABBREVIATED: Brain activity varies with temporal complexity in paced tapping P. A. Lewis(* #) A. M. Wing(#), P. Praamstra(#), P. Pope(#) & R. C. Miall(*) Abstract: 249 words Introduction: 553 words Discussion: 1497 words Figures: 4 Tables: 2 Pages: 40 Affiliations: *University Laboratory of Physiology Parks Road, Oxford, OX1 3PT, UK #Psychology Department University of Birmingham, Birmingham, UK B15 2TT Address for correspondence: P.A. Lewis University Laboratory of Physiology Parks Road, Oxford, OX1 3PT TEL FAX KEYWORDS: timing, time perception, movement complexity, fmri, paced finger tapping, movement selection, presma Acknowledgement: This work was funded by MRC grant G ; PAL, AW, and PP were supported by the same. RCM is supported by the Wellcome Trust. Additional funding was provided by the MRC fmrib unit in Oxford. We thank fmrib staff and for generous technical support and advice. We are also grateful to Nick Roach for further technical assistance. 1

2 Abstract Activity in parts of the human motor system has been shown to correlate with the complexity of performed motor sequences in terms of the number of limbs moved, number of movements, and number of trajectories. Here, we searched for activity correlating with temporal complexity, in terms of the number of different intervals produced in the sequence, using an overlearned tapping task. Our task was divided into three phases: movement selection and initiation (initialise), synchronisation of finger tapping with an external auditory cue (synchronise), and continued tapping in absence of the auditory pacer (continue). Comparisons between synchronisation and continuation showed a pattern in keeping with prior neuroimaging studies of paced finger tapping. Parametric analysis to isolate activity correlating with temporal complexity showed activity during initialise in bilateral supplementary and pre-supplementary motor cortex (SMA & presma), rostral dorsal premotor cortex (PMC), basal ganglia, and dorsolateral prefrontal cortex (DLPFC), among other areas. During synchronise, activity was observed in bilateral SMA, more caudal dorsal and ventral PMC, right DLPFC and right primary motor cortex. No activity was observed during continue at p<0.01 (corrected), though left angular gyrus was active at p<0.05. We suggest that the presma and rostral dorsal PMC activities during initialise may be associated with movement selection, while activation in centromedial prefrontal cortex during both initialise and synchronise may be associated with error monitoring or correction. The absence of activity strongly dependent on temporal complexity during continue suggests that once an overlearned movement sequence has been selected and initiated, its continued production in absence of external cues does not involve strongly temporal complexity dependent processing. 2

3 Introduction The human motor system has the potential to orchestrate an almost infinite number of different movement sequences. These may encompass a wide range of complexity including number of limbs used, number of trajectories, sequence length, and relative timing of movement. After repeated practice, even very complex movement sequences can be executed without overt attention and are therefore sometimes referred to as automatic (Passingham, 1996). Because the brain activity observed during over-learned movements differs from that observed during less fully learned movements (Jenkins et al., 1994; Penhune and Doyon, 2002), it is important to avoid learning-related confounds when investigating movement related brain activity. Several studies of movement complexity have achieved this by using over-learned movement sequences (Boecker et al., 1998; Catalan et al., 1998; Harrington et al., 2000; Haslinger et al., 2002; Sadato et al., 1996). These authors varied different aspects of complexity and observed slightly different activity patterns. Dorsal premotor cortex (dpmc) and cerebellum were the most commonly reported areas, with activity in the former correlating with most types of complexity (Catalan et al., 1998; Harrington et al., 2000; Haslinger et al., 2002; Sadato et al., 1996), and in the latter specifically with the number of digits used (Harrington et al., 2000; Haslinger et al., 2002). An important aspect of movement complexity, which has been little investigated in terms of the underlying brain activity, is the temporal structure of a movement sequence. Rhythmic finger tapping (Wing, 2002) provides a convenient task in which to study this by varying the number of intervals in a sequence while keeping all other parameters 3

4 (number of movements, mean movement frequency, number of external stimuli) the same. Previous imaging studies of externally paced rhythmic tapping have shown a fairly consistent pattern of activity in the motor system. Thus contralateral sensorimotor cortex and ipsilateral cerebellum are normally involved, with additional activation of areas such as the basal ganglia, thalamus, and sensory cortices, depending upon task specifics such as the nature of the pacing stimuli (Jancke et al., 2000a; Lutz et al., 2000; Rao et al., 1997a; Rubia et al., 2000). Our goal here was to determine how brain activity observed during an over-learned movement sequence varies with the sequence s temporal complexity. We used a variant (Vorberg and Hambuch, 1978) of the synchronise / continue task (Wing and Kristofferson, 1973) with auditory cues (Rao et al., 1997a) in which subjects first synchronised finger tapping responses with an auditory rhythm and then continued to tap the rhythm in the absence of cues. Because recent work has shown that movement selection and initiation elicits activity in areas not directly involved in movement performance (Picard and Strick, 2001), we added a third phase, initialise, in which the subjects selected the rhythm and initiated tapping, to differentiate these activities from the synchronise phase. The demand placed on movement selection mechanisms can be expected to vary with the complexity of movement sequences, and the demand on the error detection / correction process has been shown to vary with sequence complexity (Large et. al., 2002). Brain activity associated with these processes should therefore correlate with temporal 4

5 complexity. Once a sequence has been selected and initiated, however, it is unclear whether execution in the absence of pacing stimuli requires complexity dependent processing (Vorberg & Wing, 1996). We were therefore particularly interested to determine whether activity dependent on rhythm complexity would occur during continue. Methods Subjects: 10 right handed subjects, who gave informed consent, participated. The mean age was 27 and 5 subjects were female. The experiment was approved by the Central Oxfordshire Research Ethics Committee. Task: The task involved the production of temporal rhythms by tapping with the right index finger on a force sensor. Each 42s trial consisted of three phases: in a 6 s initialise phase a sequence of auditory cues (100 Hz tones of 50 ms in duration, at an intensity audible over the background scanner noise) was used to define a rhythm. Subjects were instructed to attempt to tap in time with the rhythm as soon as they felt they had identified it. This was immediately followed by the 18 s synchronise phase in which subjects were to maintain their tapping accurately in phase with the auditory rhythm. This was immediately followed by the 18 s continue phase in which auditory cues ceased and subjects continued to reproduce the rhythm on their own. There was a brief (~700 ms) 5

6 interval between trials, allowing subjects to cease tapping before the next trial. A pseudorandom order of trials was employed to ensure that there was no consistent relation between the rhythm sequences used in successive trials, allowing dissociation of brain activity between different sequences despite the short inter-trial interval.. The stimulus set comprised 7 temporally distinct rhythms, each composed of a measure or repeating set of intervals that lasted 3 s and involved 6 responses. The least complex rhythm was an isochronous repetition of 500 ms intervals; the more complex multiinterval rhythms contained 2, 4, and 6 intervals respectively. In order to maintain attention, each 2, 4 or 6-element rhythm was presented with integer or non-integer ratio durations (figure 1). The two 2-element rhythms had intervals of 321 and 858 ms (3:8 ratio) or 282 and 936 ms (1:3.31 ratio). The 4-element rhythms had intervals of 214, 428, 536 and 857 ms (2:4:5:8 ratio) or 174, 389, 576 and 896 ms (1.0:2.24:3.31:5.15 ratio). The 6-element rhythm intervals were 214, 321, 429, 643, 536 and 857 ms (2:3:4:5:6:8 ratio) or 174, 282, 389, 683, 576 and 896 ms (1.0:1.62:2.24:3.92:3.31:5.15 ratio). In each case small corrections of up to +/-0.25 ms were made as required to add up to a 3000 ms total duration). Two further conditions, intended as controls, were used. An 18 s rest condition occurred after every 8 trials, in which subjects were instructed merely to maintain fixation, and a 24 s random condition (with initialise and synchronise but no continuation phase) in which subjects pressed a button in response to tones heard at two unpredictable intervals (either 282 or 936 ms, pseudo-randomly presented with a 2:1 presentation frequency). Each condition was presented 4 times in a single scanning session. The results from the random condition are not presented, since subjects reported 6

7 that it was much more difficult than all other conditions, hence it will not be discussed further. Subjects were pre-trained at least one day prior to scanning (range 1-5 days), performing all conditions of the task at least 6 times and until it was over-learned and their performance accuracy ceased to improve over the last 4 sessions (mean slope -.8 %/session). Accuracy was measured as 1-(expected-produced)/expected)*100, and collapsed across synchronisation and continuation conditions. The minimum accuracy reached in this manner was 84%, however all subjects except one attained a level above 90% accuracy prior to scanning. Every subject was given a final 10-minute training session just before the scanning session, while in the scanner and in the presence of the scanner background noise. fmri Data acquisition: 448 whole brain EPI data volumes were acquired on a 3 tesla Siemens-Varian scanner, using a T2 weighted GE modulated BEST sequence (TE 30ms, flip angle 90 degrees), 256x256 mm FOV, 64x64x21 matrix size, and a TR of 3 s. 21 contiguous 7 mm thick slices were acquired in each volume. The experiment lasted 22.4 minutes. T1 weighted structural images were also acquired, 42 in contiguous slices, with in-plane resolution of 1 mm. 7

8 fmri Data analysis: Data were analysed using the Oxford Functional MRI of the Brain (fmrib) s in-house analysis tool FEAT, on a MEDx platform ( Pre-statistics processing included motion correction using MCFLIRT (Jenkinson and Smith, 2001) to realign images, spatial smoothing with a Gaussian kernal of FWHM = 5 mm, mean-based intensity normalisation of all volumes; non-linear band-pass temporal filtering (lowfrequency rejection by Gaussian-weighted LSF straight line fitting, with sigma=35 s; high-frequency filtering above 2.8 Hz). Statistics were computed using a general linear model convolved with a gaussian kernel to simulate haemodynamics. In the GLM model we first fitted the main conditions (separate explanatory variables for isochronous, 2, 4 and 6-element rhythms). We then made pair-wise comparisons between the various phases of the task (synchronise>continue, continue>synchronise, and initialise>synchronise). We also performed a separate parametric analysis, using as a first step separate linear models for the initialise, synchronise and continue phases. These models increased linearly with the number of interval durations in each rhythmic condition (using contrast weights of 1, 0 +1 for 2, 4 and 6-element rhythms). Rest was the un-modelled baseline in all cases. The random condition was modelled as a covariate of no interest. Statistical images were produced for each subject by contrasting the parameters associated with each condition. Statistical maps were fit to the MNI canonical brain using fmrib s linear image registration tool (FLIRT), and then combined across subjects using 8

9 a fixed effects model. Z (Gaussianised T/F) statistic images were thresholded using clusters determined by Z>2.3 and a (corrected) cluster significance threshold of p=0.01 [Worsley 1992, Friston 1994, Forman 1995]. The statistical images resulting from direct subtraction contrasts (Test versus Control, eg Synchronise>Continue) were masked to guard against significant differences in activation due to negative activity in the control condition. This was accomplished by multiplying each statistical map by binarised maps of significant activity resulting from contrast of the appropriate Test versus Rest baseline condition. Masked probability maps were rendered onto the MNI canonical brain. Cluster maxima were localised anatomical landmarks (Duvernoy, 1999). DLPFC and VLPFC were determined as defined in (Rushworth and Owen, 1998), SMA and pre-sma, were determined according to the description in (Picard and Strick, 2001). The dividing line between dpmc and vpmc was set at 48 mm above the anterior commisure as described in (Jancke et al., 2000b). The frontal eye fields were determined according to (Paus, 1996). Task presentation: Behavioural tasks were presented and controlled by a DOS program running on a PC laptop. During fmri sessions, visual stimuli specifying task phases were projected by an InFocus LP1000 LCD projector onto a back-projection screen (image subtending approx. 14 degrees at the eye, VGA resolution) viewed from inside the magnet bore using 90 degree prism glasses. A fixation point was always present at the centre of the display. Responses were recorded using a force sensor made from resistive plastic (Interlink 9

10 Electronics Europe). This was calibrated outside the scanner and sampled at 1000 Hz using a 12-bit A/D converter. The time of each press was determined by the time when applied force crossed a minimum threshold, set above baseline noise levels at approx. 10% of peak force. Auditory cues were presented monaurally using an electrostatic headphone system designed and built by the Medical Research Council Institute of Hearing Research in Nottingham, UK. Statistical tests on the behavioural data were performed in SPSS. Because of variation in force of subject responses during the scanning session, it was not possible to detect every button press. Trials in which 2 complete repetitions of the rhythm could not be reconstructed from the presses recorded were excluded from the behavioural analysis. In total, 38 of the 640 blocks were excluded in this manner, constituting 5.9% of all behavioural data. Despite these missing data, we have no evidence that subjects failed to perform the tasks; in fact the detected responses show good compliance (Figure 2). Hence, since subjects failed to tap hard enough during these blocks, but otherwise likely performed all other aspects of the task normally, we have not excluded the associated fmri data from our analysis. 10

11 Results Behaviour: All results are for group data averaged across integer and non-integer conditions. Figure 2 shows the mean intervals in each rhythm produced by each subject during the fmri session. The mean accuracy, averaged across intervals produced, was 89.8% correct during synchronise, and 89.2% during continue. A 2 (synchronise / continue) X 4 (isochronous, 2, 4 & 6-element rhythm) repeated measures ANOVA, performed on the mean accuracy measures showed a significant main effect of rhythm type, F(3,7) =70.759, p< Bonferroni corrected paired t-tests showed that mean performance about target intervals was better for isochronous sequences than for the three rhythmic conditions (figure 3). A second repeated measures ANOVA, following the same 2 X 4 format but examining the ratio of mean to standard deviation (coefficient of variation [CV]) operating characteristics, also showed a main effect of rhythm type, F(3,7) =10.057, p<0.01. Bonferroni corrected paired t-tests showed that the CV was lower for isochronous sequences than for the three rhythmic conditions, where CV s were similar. This result also demonstrates that variability about the mean is constant across the three rhythmic conditions (figure 4). 11

12 Functional Imaging: All imaging results are based on the group data for integer and non-integer conditions. Task phase: Areas of fmri activity which were significant at p<0.01 for comparisons between task phases are rendered onto the MNI canonical brain in Figure 5. The (initialise>synchronise) comparison showed no activity with significance at this threshold. The (synchronise>continue) (figure 5 (blue) & Table 1A) contrast showed bilateral activity in the temporal cortex (figure 5 A) with local peaks in the bilateral superior temporal gyrus, and right transverse temporal gyrus. A separate peak was observed in right inferior parietal; in the left superior temporal activation extended into the same region of inferior parietal, but no local max was observed there. The opposite contrast (continue>synchronise) (figure 5 (red) & Table 1B) showed local peaks of activity bilaterally in SMA (figure 5 B), and VLPFC. More caudally, peaks were also observed in postcentral gyrus, inferior parietal, and in the occipital lobe. The cerebellar hemispheres and putamen (figure 5 C) also showed bilateral peaks of activity. Right hemisphere specific peaks were observed in presma, DLPFC, and primary motor cortex, and in the superior parietal lobe, superior temporal gyrus, and cingulate cortex. Left hemisphere specific peaks were observed in the dpmc, vpmc, intraparietal sulcus, insula, and parahippocampal gyrus. 12

13 Parametric variation in rhythm complexity: The areas where fmri signal correlated parametrically with temporal complexity of rhythmic sequences during initialise (red) and synchronise (blue) phases have been rendered onto the MNI canonical brain in figure 6. The same region of right hemispheric SMA proper activated in both phases (area of green overlap, figure 6 A), however activity in the presma occurred only during initialise (figure 6 B) with a larger area of activity and stronger peak intensity in the left hemisphere. Large areas of PMC were active in both initialise and synchronise, with dpmc activity occurring more anteriorly during initialise, and only small regions of overlap (figure 6 C). Because from necessity each initialise condition was immediately followed by the corresponding synchronize condition, some activity in the former might spread into the latter. However, the synchronize condition was 3 times longer (18 s vs. 6 s); moreover, there are clear differences in the pattern of activity (figure 6). Hence we do not expect that the overlap in activation patterns is an artefact of our design. In both hemispheres, synchronise related activity extended into vpmc, with additional local maxima in that region. Although some initialise related activity also extended into vpmc, no local maxima were observed there. In the right hemisphere, initialise and synchronise associated activity overlapped at the junction of the inferior frontal sulcus and inferior branch of the inferior precentral sulcus, in a region which may correspond to either dpmc or DLPFC, as the boundary between these is not clearly defined (see area of green overlap, figure 6 D). Note that this region is near the FEF, but peaks do not fall within it. Initialise associated activity extended anteriorly from here and covered a large portion of DLPFC (figure 6 E). A smaller region 13

14 of initialise related DLPFC activity was also observed in the left hemisphere. Only very small areas of DLPFC activity were observed during synchronise, and these were limited to the right hemisphere (see table 2B for coordinates). Initialise was also associated with bilateral activity in the right hemispheric primary sensorimotor cortex, basal ganglia (left head and tail of caudate, right tail of caudate, and bilateral putamen), corpus callosum (just above the lateral ventricals), right hemispheric anterior cingulate, and left hemispheric thalamus. No parametrically varying activity was found during the continue phase when data was thresholded at cluster level probability threshold of p<0.01. When the threshold was lowered to p<0.05, however, an area of left hemispheric inferior parietal activity emerged (angular gyrus, peak coords (x:y:z): -42, -33, 36 ). This area was also active at the p<0.05 threshold during synchronise, peak coordinates at (x:y:z -50,-30, 48), but not initialise. Discussion The purpose of this study was to describe the brain activity involved in production of movement sequences of varied temporal complexity. Our design allowed examination of three task phases: initialise (listening to a presented sequence and attempting to tap in time with it when ready, presumably selecting a prelearned movement sequence when the presented rhythm is overlearned), synchronise (tapping in time with auditory cues), and continue (continuing to tap, in absence of auditory cues). We used rhythms varying in 14

15 temporal complexity from isochronous to six intervals per measure. Each rhythm containing more than one interval was repeated in two variants: one using integer ratios between intervals and one using non-integer ratios. Task phase: Two prior studies (Jancke et al., 2000a; Rao et al., 1997b) have described brain activity associated with auditory synchronization and continuation tapping; our observations are in keeping with their findings. Our synchronise > continue contrast showed activity in the bilateral auditory cortex, as expected when comparing an auditory cued task to an uncued task (Jancke et al., 2000b). Our continue > synchronise contrast showed activity in the bilateral SMA and basal ganglia, regions previously reported as more active during continuation (Jancke et al., 2000a). A number of areas not reported by prior examinations of synchronisation and continuation tapping were also observed, perhaps due in part to our novel requirement that subjects produce temporally complex as well as simple sequences, and in part to the large volume of data collected: 192 volumes in synchronise and in continue for each of ten subjects, allowing a very sensitive contrast. Movement complexity: The most interesting aspects of our result relates to the way brain activity varies with the temporal complexity of a movement sequence. Prior studies varying the complexity of over-learned movements have shown dpmc activation relates to number of limbs used, number of limb transitions and sequence length (Catalan et al., 1998; Harrington et al., 15

16 2000; Haslinger et al., 2002; Sadato et al., 1996). Our observation of dpmc activity in both initialise and synchronise phases is consistent with the idea that this region responds to increased movement complexity since it shows that involvement of this structure varies in parallel with the degree of temporal complexity of a motor sequence. KEEP OR DROP?: The lack of temporal complexity related cerebellar activity, even at the lenient threshold of p<.05, is in keeping with the apparent specificity of movement complexity related activity in that structure to variation in number of digits used (Harrington et al., 2000; Haslinger et al., 2002). To our knowledge, only one prior study (Boecker et al., 1998), of over-learned movement has reported complexity related activity in the SMA. Our observation of SMA activity in both initialise and continue may therefore be specifically associated with our manipulation of time rather than other aspects of the movement complexity, as it has recently been suggested that SMA plays a critical role in temporal processing (Macar et al., 2002). Initialisation of a rhythm: Studies of movement selection have shown that presma and rostral dpmc are involved in higher hierarchical roles in motor control (Luppino et al., 1990; Picard and Strick, 2001; Rizzolatti et al., 1990), while SMA proper and caudal dpmc are more involved in motor execution (Dum and Strick, 1991a; Dum and Strick, 1991b; Hummelsheim et al., 1988; Picard and Strick, 2001; Wiesendanger et al., 1985). Specifically, presma has been associated with selection between movements (Deiber et al., 1991), while SMA proper has been associated with execution of cued motor commands (Colebatch et al., 1991; Matelli et al., 1993; Sadato et al., 1995). It has recently been suggested that rostral 16

17 dpmc is involved in cognitive processing associated with movement, while caudal dpmc is involved more directly in movement preparation (Picard and Strick, 2001). We observed stronger correlation with temporal complexity in presma and rostral dpmc during initialise than during synchronise or continue, with the former active only during initialise, and the latter active more rostrally in that phase. Since selection and initialisation of more complex rhythms likely requires more processing, initialisation related activity can be expected to vary with sequence complexity. The pattern we observed suggests temporal complexity dependent involvement of presma and rostral dpmc in movement initialisation. However, the goal of our initialise condition was not to isolate activity associated with initialisation of a movement, but rather to ensure that selection and movement initiation related activity did not occur during synchronise. Subjects were therefore encouraged to initiate movement during initialise, and it is thus likely that the activity we observed in SMA proper and more caudal dpmc during that phase is associated with movement initiation and the synchronisation with pacing stimuli. In order to distinguish more fully between activity varying with temporal complexity during sequence initialisation, and production phases it will be necessary to perform a further experiment in which movement is prohibited during the initialise phase. Synchronisation with auditory cues: A recent imaging study of synchronised tapping (Stephan et al., 2002) showed activity in DLPFC, and PMC in association with motor adjustments to perturbations in the pacing sequence. Our observation of activity in these areas during synchronise could be 17

18 associated with the same type of error-related processing. We also observed activity in the centromedial frontal cortex in this phase. In order to synchronise responses, subjects had not only to produce the remembered rhythm sequence, but also to attend to pacing stimuli, recognise errors, and correct them to remain in phase with the stimuli. Processing associated with sensorimotor transformation, error recognition, and error correction can therefore be expected in this condition. The error related negativity (ERN) is an event related potential elicited by error commision and sensitive to temporal aspects of performance (Luu et. al., 2000), relevant to synchronised tapping. This suggests that an ERN, (i.e. medial frontal cortex activation) might be expected if subjects make synchronisation errors. Indeed, anterior cingulate and SMA activity can be observed when subjects become aware of (induced) asynchronies between their tapping and auditory pacing signals (Stephan et al., 2002). In our design, any such effect should be more obvious during synchronise, when external cues indicate error magnitude, than during self-paced continue since error awareness and therefore monitoring is greater in the former. Despite this expectation, no medial frontal activity was observed in the synchronise>continue comparison, but parametrically related activity peaking in SMA and spreading into anterior cingulate, was observed during initialise and synchronise phases. Thus, medial frontal activity was only seen when task complexity was taken into account, an observation still supporting a performance monitoring account since more careful monitoring was likely required for the more complex tasks. 18

19 Continuation tapping without external cues: Unlike synchronise, the continue phase of our task required only execution of preselected over-learned motor sequences which were already being performed when the phase started. The absence of temporal complexity related activity in the frontal cortex, even at the lenient p <.05 threshold, shows that, once they have been selected and initiated, the execution of over-learned movements in absence of external cues does not require temporal complexity dependent processing in the frontal cortex. Further, the absence of temporal complexity dependent activity in frontal and prefrontal areas during initialise and synchronise phases from the activation map for continue strengthens our arguments, presented above, that these regions are involved in complexity dependent aspects of movement selection and error monitoring / detection rather than in motor execution. The only temporal complexity dependent activity we observed during continue was in inferior parietal (angular gyrus), which activated only at the lenient (p<0.05, corrected) threshold. Comparison of phases: The lack of activity linearly related to complexity in continue compared to synchronise and initialise is interesting at two levels. First, it suggests that once an overlearned movement sequence has been selected and initiated, the processing involved in continuing to perform it in absence of external cues does not depend strongly upon the temporal complexity of the sequence itself. This is consistent with the proposal of (Vorberg and Wing, 1996) in which hierarchical relations in a rhythm, relevant to its 19

20 selection and preparation, are converted to a linear sequence of intervals in execution and this suppresses the hierarchical complexity of the original. For the purposes of this interpretation, it would be interesting to know whether variation in other types of motor complexity also fail to elicit strongly correlated activity during the continuation phase, which would provide one possible future direction for research. The absence of strongly complexity related activity during continue is also interesting because it suggests that the parametrically related activity observed in other phases is due to something other that motor execution, thus supporting our suggestion that some of this activity is due to selection, error monitoring, and corrective action. 20

21 Figure 1: Schematic diagram showing the intervals and interval ratios (integer and noninteger) used in the various rhythms subjects performed. 21

22 Isochronous Two-element Mean interval duration (ms) Four-element Six-element Interval number Figure 2: Interval durations for group data averaged across integer and non-integer conditions, and synchronise and continue phases, as a function of ordinal position in each condition. Error bars indicate the variation for producing each interval. 22

23 Accuracy measures for each condition across both synchronise and continue 100 Mean accuracy (%) Isochronous Two Four Six Figure 3: Measures for accuracy for group data averaged across integer and noninteger, and synchronise and continue phases for each condition. Error bars indicate the standard error of the means for each condition. CV's for each condition across both synchronise and continue Mean CV (%) Isochronous Two Four Six Figure 3: The coefficient of variation (CV) for group data averaged across integer and non-integer, and synchronise and continue phases for each condition. Error bars indicate the standard error of the means for each condition. 23

24 Figure 5 Shows functional activity in response to the synchronise>continue (blue), and continue>synchronise (red) contrasts at the threshold of p<.01. The slices shown were taken at sagittal: -1, -31, -41, -51 axial: -53, , 7 coronal: 1, 41, 51, 61 mm. The figure is in radiological convention such that the L side corresponds to R and visa versa. Letters refer to specific structures: A = primary auditory cortex, B = SMA, C = basal ganglia. Figure 6: Shows functional activity in response to parametric modelling of initialise (red) and synchronise (blue) phases with areas of overlap shown in green. Data was thresholded at p<.01. The slices shown were taken at sagittal: -5, 10, 26, 36, axial: 40, 50, 60, 70, coronal: -33, -10, 7, 37, mm. The figure is in radiological convention such that the L side corresponds to R and vice versa, the white dividing lines show the location of the anterior commisure in some views, letters refer to specific structures: A = SMA, B = presma, C = dpmc, D = DLPFC/dPMC, E=DLPFC. 24

25 A: Synchronise > Continue X Y Z Value Anatomical Locus Temporal lobe L STG L STG L STG R STG R STG R STG R TTG Other R inferior parietal gyrus 25

26 B: Continue > Synchronise X Y Z Value Laterality Anatomical Locus Prefrontal lobe L anterior precentral gyrus L medial wall SFG L anterior precentral gyrus L medial precentral gyrus L posterior MFG L anterior precentral gyrus R IFG R IFG anterior to VVPCS R medial SFG R medial SFG R/L SFG Insula L insula L insula Limbic lobe L parahippocampal gyrus R cingulate R cingulate gyrus Temporal lobe L STG L STG R STG R STG Parietal lobe L intraparietal sulcus L supramarginal gyrus L supramarginal gyrus R supramarginal gyrus R supramarginal / angular gyrus R supramarginal gyrus R superior parietal lobe R supramarginal gyrus Occipital lobe L cuneus L cuneus L MOG L SOG L IOG L cuneus / SOG R cuneus R MOG Basal Ganglia L putamen / capsule R putamen R putamen Cerebellum L cerebellar hemisphere R cerebellar hemisphere R cerebellar hemisphere 26

27 Table 1: Shows MNI coordinates for the highest local maxima of BOLD activity found in each functional area associated with the Synchronise>Continue (A), Continue>Sychronise (B), and Synchronise>Random (C) contrasts. Columns show the coordinates in millimeters from the anterior commisure, the z-score value of each local max, laterality, and an anatomical description of the point s location on the SPM canonnical brain. Abbreviations: SFG = superior frontal gyrus, SFS = superior frontal sulcus, MFG = middle frontal gyrus, MFS = middle frontal sulcus, IFG = inferior frontal gyrus, IFS = inferior frontal sulcus, CC = corpus collosum, STG = superior temporal gyrus, TTG = transverse temporal gyrus, sup SPG = superior portion of superior precentral gyrus, IIPCS = inferior portion of inferior precentral sulcus, SIPCS = superior portion of inferior precentral sulcus, ISPCS = superior portion of superior precentral sulcus, SSPCS = superior portion of superior precentral sulcus, ISPCS = inferior portion of superior precentral sulcus, VVPCS ventral portion of ventral precentral sulcus, SOG=Superior occipital gyrus, MOG =Middle Occipital Gyrus, IOG = Inferior Occipital Gyrus. 27

28 A: Parametric analysis, initialise phase X Y Z Value laterality anatomical locus Prefrontal cortex L SFG R anterior SFG R anterior SFS R anterior SFS R MFG R SFG R superior bank SFS R anterior SFG Premotor / Supplementary motor cortex L posterior SFG L SFG L SFG L anterior medial SFG L SFG L posterior SFG R Junction IFS / IIPCS R SFS just anterior to ISPCS R IFS just anterior to ISPCS R SSPCS R SFG R anterior precentral gyrus Primary Sensorimotor cortex R central sulcus, posterior bank R posterior precentral gyrus Anterior cingulate R anterior cingulate gyrus Basal Ganglia L head of caudate / internal capsule L Caudate / putamen L Caudate L tail of caudate / white matter L tail of caudate L internal capsule / insula R Putamen R Putamen R tail of caudate Thalamus L Anterior thalamus L Thalamus Corpus Callosum L CC L CC R CC R CC / white matter R CC 28

29 B: Parametric analysis, synchronise phase X Y Z Value anatomical locus Prefrontal Cortex L IFS / IIPCS R superior bank, IFS R anterior SFG R middle SFG R dorsal posterior MFG Premotor / Supplementary motor cortex L posterior SFS L precentral gyrus L SFG L SFG R precentral gyrus R MFS R SFG R SFG R SFG R anterior inferior bank SFS Table 2: Shows MNI coordinates for the highest local maxima of BOLD activity found in each anatomical region in response to parametric modelling of the initialise (A), and synchronise (B) phases when thresholded at p<.001. Columns and abbreviations as in Table 1. 29

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