Neural Substrates of Impaired Sensorimotor Timing inAdult Attention-Deficit/Hyperactivity DisorderEve M. Valera, Rebecca M.C. Spencer, Thomas A. Zeffiro, Nikos Makris, Thomas J. Spencer,Stephen V. Faraone, Joseph Biederman, and Larry J. Seidman

Background: Timing abilities are critical to the successful management of everyday activities and personal safety, and timing abnormalitieshave been argued to be fundamental to impulsiveness, a core symptom of attention-deficit/hyperactivity disorder (ADHD). Despitesubstantial evidence of timing deficits in ADHD youth, only two studies have explicitly examined timing in ADHD adults and only at thesuprasecond time scale. Also, the neural substrates of these deficits are largely unknown for both youth and adults with ADHD. The presentstudy examined subsecond sensorimotor timing and its neural substrates in ADHD adults.

Methods: Using functional magnetic resonance imaging, we examined paced and unpaced finger tapping in a sample of 20 unmedicatedadults with ADHD and 19 control subjects comparable on age, sex, and estimated IQ. The blood oxygenation level-dependent contrastresponse was used to estimate task-related neural activity.

Results: Behavioral data showed no between-group differences in mean tapping rates but greater within-subject variability in tap-to-tapintervals for ADHD adults relative to control subjects. Importantly, ADHD adults had greater clock rather than motor variability, consistentwith a central timing locus for the atypical movements. The imaging results demonstrated that, relative to control subjects, ADHD adultsshowed less activity in a number of regions associated with sensorimotor timing, including prefrontal and precentral gyri, basal ganglia,cerebellum, inferior parietal lobule, superior temporal gyri, and insula.

Conclusions: Our findings show that subsecond timing abnormalities in ADHD youth persist into adulthood and suggest that abnormal-ities in the temporal structure of behavior observed in ADHD adults result from atypical function of corticocerebellar and corticostriataltiming systems.

Key Words: ADHD, basal ganglia, cerebellum, fMRI, frontal cortex,timing

Attention-deficit/hyperactivity disorder (ADHD) is a prev-alent neurobehavioral disorder estimated to affect up to10% of children and 5% of adults worldwide (1) and is

associated with morbidity and disability across the life cycle (2).Its core features include symptoms of inattention, impulsivity,and hyperactivity.

One source of morbidity in ADHD has been attributed todeficits in the capacity to accurately and consistently process andact on temporal information, namely deficits in timing abilities.Such a capacity is critical to personal safety and to successfulmanagement of everyday activities such as social interactions,driving, and performing cognitive tasks. It has also been argued

that abnormalities in timing functions are fundamental to impul-siveness (3), a core feature of ADHD.

Observational and experimental data on ADHD support thehypothesis that individuals with ADHD have widespread timingdeficits. For example, some impulsive behavior in ADHD indi-viduals could indicate an inability to adjust to environmentaltiming demands. Also, difficulties arriving at appointments ontime, poor planning, and tendencies to forgo delayed largerrewards for smaller immediate rewards (4) could suggest analtered sense of time. Experimental data of ADHD youth haveshown either less accurate or more variable performance thanmatched control subjects on a number of timing tasks, includingpaced finger tapping (5), duration reproduction (6–8), durationdiscrimination (9), verbal time estimation (10), and temporalanticipation ([5], for review see [11]). In fact, one of the mostconsistent findings in ADHD youth is increased within-subjectvariability on a range of tasks (as reviewed in [12,13]), indicatingan inability to register responses at evenly timed intervals. Thisvariability has been shown to be related to attentional ratings andto decrease in response to methylphenidate (5,14). Thus, anincreased understanding of this variability, as well as timing moregenerally, has potential implications for treatment of and under-standing core features of ADHD.

Although most support for timing deficits have been shown inADHD youth, abnormalities in time estimation, discrimination,and reproduction have been shown in ADHD adults (7,15).However, these data are limited and restricted to the suprasec-ond level.

In healthy adults, brain regions most commonly associatedwith timing processes are the frontal cortex, basal ganglia (BG),and cerebellum (16–18), with support for differential functionallocalization of timing involving short versus long time intervals.Timing of suprasecond intervals, as used in the previous adult

From the Clinical and Research Programs in Pediatric Psychopharmacologyand Adult Attention Deficit Hyperactivity Disorder (EMV, TJS, JB, LJS),and Psychiatric Neuroimaging Research Program (EMV), Department ofPsychiatry; Massachusetts General Hospital and Harvard Medical School,Boston, Massachusetts; Department of Psychology (RMCS), University ofMassachusetts at Amherst, Amherst, Massachusetts; Neural SystemsGroup (TAZ), Massachusetts General Hospital, Charlestown, Massachu-setts; Center for Morphometric Analysis (NM), Departments of Neurol-ogy and Radiology, Massachusetts General Hospital, Boston, Massachu-setts; Departments of Psychiatry and Neuroscience and Physiology(SVF), State University of New York Upstate Medical University, Syracuse,New York; and Massachusetts Mental Health Center Public PsychiatryDivision (LJS), Beth Israel Deaconess Medical Center, Harvard MedicalSchool, Boston, Massachusetts.

Address correspondence to Eve M. Valera, Ph.D., Department of Psychiatry,Room 2660, Massachusetts General Hospital, 149 13th Street, Charles-town, MA 02129; E-mail: eve_valera@hms.harvard.edu.

Received Dec 22, 2009; revised May 7, 2010; accepted May 7, 2010.

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ADHD studies, is thought to occur in BG and frontal cortexregions with task-related neural activity often being attributed toattention and working memory demands (11,19–21). Timing ofsubsecond intervals is associated with the cerebellum, which hasbeen described as a millisecond-range timekeeper or the “inter-nal clock” (19,20). Despite these general distinctions, there is alsoevidence of BG and frontal involvement in subsecond timing, aswell as cerebellar involvement in suprasecond timing (for re-views, see [20,22,23]).

Interestingly, although numerous brain regions have shownstructural or functional neuroimaging abnormalities in ADHD,some of the most consistent findings have been in areas associ-ated with timing, including frontal, BG, and cerebellar regions(24–30). There have been no studies using functional magneticresonance imaging (fMRI) to examine timing functions in ADHDadults. However, Rubia et al. (31,32) showed that ADHD ado-lescents had less activation than control subjects in cingulateregions during 5-sec interval synchronized tapping, although nodifferences were observed with a 600-msec interval. Smith et al.(33) found less activation in several frontal regions for ADHDadolescents relative to control subjects during performance on aduration discrimination task. Also, ADHD youth showed lesscerebellar activation than control subjects while performing atemporal expectancy task (34).

The neural signature of ADHD has been conceptualized interms of abnormalities in networks of response inhibition (35),attention (36), and reward (37), among others. However, despitedemonstrating behavioral impairments across a range of timing-related functions, the idea that the neural underpinnings ofADHD result from abnormalities in timing networks is not acommon model. The goal of this study was to elucidate theneural substrates of sensorimotor timing in ADHD using a simpletiming task to test the hypothesis that abnormalities in timingnetworks contribute to the pathophysiology of ADHD.

Thus, we used fMRI and a 500-msec tapping task in ADHDadults. This tapping interval was chosen both to examine sub-second timing in ADHD adults and also to decrease potentiallyconfounding influences from other cognitive processes such asworking memory. Additionally, for stimulus rates less than 2 to 3seconds, participants tend to anticipate when the next tone willoccur and tap synchronously with or before the tone, rather thanwaiting to hear it (38). Thus, at this interval length, timing isexpected to rely more strongly on the internal clock despite thepresence of pacing tones. We also mitigated the potential effectsof delay aversion (39) by using a temporal production, ratherthan an anticipation or reproduction task. As variability was ourprimary measure of timing control from the internal clock (40),we included unpaced tapping and used the Wing and Kristoffer-son (41) decomposition analysis of response variability. In thismodel, elevated central clock variance suggests problems withthe internal timing processes, whereas elevated motor variancesuggests problems with execution of motor plans. We predictedthat, relative to control subjects, ADHD adults would showtiming abnormalities as reflected by greater within-subject vari-ability on the tapping task for both paced and unpaced trials andthat for the unpaced trials, greater variability would be due toelevations in clock rather than motor variance. We also predictedthat, relative to control subjects, ADHD adults would show lessactivity in the frontal, BG, and cerebellar regions, which arecommonly engaged in timing and have been shown to beabnormal in ADHD.

Methods and Materials

ParticipantsParticipants were 21 adults with ADHD and 19 control

subjects comparable on age, sex, and estimated IQ. Participantswere recruited from ongoing studies of ADHD conducted atMassachusetts General Hospital. Written informed consent wasobtained for all participants, and the study was approved by thePartners Human Research Institutional Review Board Committee.

Participants were excluded if they: 1) were younger than 18 orolder than 55 years; 2) had an estimated full scale IQ ! 80; 3) hada current psychotic, depressive, or generalized anxiety disorder;4) had current alcohol or substance abuse or dependence(Structured Clinical Interview for DSM-IV Axis I Disorders [42]);5) had an inadequate command of the English language; (6) hadsensorimotor handicaps or neurological disorders; (7) had con-traindications to magnetic resonance imaging; or 8) were cur-rently taking psychotropic medications (other than short-actingpsychostimulants). One ADHD subject was left-handed and allperformed the tasks with their right hand after training. Fourteenof the 21 ADHD participants (67%) had taken medications forADHD in their lifetime: 5 had taken them in the past (notcurrently) and nine were taking short-acting psychostimulantsclose to the time of scan but underwent a 24-hour washoutperiod before the fMRI visit (Supplement 1 for more details).

The ADHD and control adults received identical assessmentsas in previous reports (43). To assess psychopathology, weadministered the Structured Clinical Interview for DSM-IV Axis IDisorders (42). To assess ADHD, we used a module derived fromthe Schedule for Affective Disorders and Schizophrenia forSchool Age Children (44). Two ADHD participants had an age ofonset of 8 years and one had an age of onset of 12 years.However, given prior studies failing to support the validity ofusing the age cutoff of 7 years (45,46), these participants wereconsidered ADHD. A committee of board-certified child andadult psychiatrists and licensed psychologists blind to referralsource resolved diagnostic uncertainties.

Cognitive testing included subtests from the Wechsler AdultIntelligence Scale-III (47). Vocabulary and either block design ormatrix reasoning were used to calculate an IQ estimate (48,49).

Task and DesignFunctional imaging runs contained 15-sec blocks of fixation

(F; central crosshair), paced tapping to a 50-msec tone at a rate of500 msec (P), unpaced tapping (U), and listening to the pacingtone without tapping (L). The F-P-U-L order repeated three timesper run (Figure 1). Tones were presented through a pair ofpneumatic headphones. The L blocks were included as a controltask for which we did not anticipate between-group differences.

Behavioral AnalysisBehavioral measures were the mean and standard deviation

of the intertap interval. Intertap intervals outside 50% of the goalinterval were excluded from analyses (e.g., [50,51]). Using alinear regression analysis, data were detrended to remove vari-ability associated with linear drift in tapping rate over time andpossible effects of fatigue (52,53). Variance associated withtiming versus motor implementation was partitioned using theWing and Kristofferson (41) model (Supplement 1 for moredetails). We also conducted correlational analyses betweenADHD symptoms and within-subject variability of the unpaceddata.

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Magnetic Resonance Imaging ParametersData were acquired using a Siemens Trio 3T scanner (Siemens

Medical Systems, Erlangen, Germany), 12-channel head coil (42axial slices, 3 mm thick, .6 mm interslice gap, gradient-echoecho-planar imaging, repetition time " 2500 msec, echo time "30 msec, 3.1 mm # 3.1 mm in-plane resolution, 85 total imagesper run).

fMRI AnalysesImage Analysis. The fMRI data were analyzed using SPM2

(Wellcome Department of Cognitive Neurology, London, UnitedKingdom) for preprocessing and SPM5 (Wellcome Departmentof Cognitive Neurology) for statistical modelling. Preprocessingincluded correction for head motion, spatial normalization, andspatial smoothing with a Gaussian filter (8-mm full-width half-maximum). Individual runs exhibiting a spike of more than 3 mmof scan-to-scan head motion were dropped. As a result, threeruns were dropped from the control group and one from theADHD group.

First-Level Analyses. First-level analyses used a generallinear model, with task blocks modeled as epochs convolvedwith a canonical hemodynamic response function. Contrasts oftask against the implicit baseline were created for the paced,unpaced, and listen blocks. The resulting parameter estimatesderived for each subject were used in the second-level analysis.

Second-Level Analyses. A mixed-effects second-level anal-ysis was performed using a voxel-wise factorial analysis ofvariance, with subject, group, and task factors. For our plannedcontrasts, the critical threshold for voxel-wise estimates of task-related activity was p ! .05, family-wise error corrected, with anextent threshold of 20 contiguous voxels. We computed statisti-cal parametric maps of: 1) simple effects for the paced, unpaced,and listen activities within and between groups; and 2) a groupby task interaction for paced versus unpaced tapping. We also

conducted regression analyses to look at the effect of ADHDsymptoms on unpaced task-related neural activation (Supple-ment 1 for details and additional analyses).

Results

DemographicsThere were no differences between ADHD and control adults

on age [t (38) " $.40, p % .05] or estimated IQ [t (38) " .24, p %.05] (Table 1). The distribution of ADHD subtypes based oncurrent symptoms was: combined, n " 5; inattentive, n " 12;and not otherwise specified, n " 4. Participants were categorizedas not otherwise specified if, as is typical of many ADHD adults(54), current symptoms were below the DSM-IV symptom thresh-old (Supplement 1 for more details).

Behavioral ResultsThere were no differences in mean tapping rate for either

paced [t (38) " $.08, p % .05] or unpaced [t (38) " $1.36, p %.05] tapping between ADHD and control adults. However,consistent with previous reports (13), ADHD participants exhib-ited higher within-subject variability for both paced [t (38) "$2.11, p " .042] and unpaced [t (38) " $2.89, p " .008] tapping(Table 2). The distribution of the unpaced tapping variance(Figure 2) illustrates that over half of the ADHD adults had highervariability than all but one control subject.

We applied the variance decomposition analysis (41) to theunpaced blocks. There were significant differences between

Figure 1. Diagram for paced, unpaced, and listen tasks. During the paced phase, participants made key presses with the right index finger on a button box intime with a series of 50-msec computer-generated tones presented binaurally. Participants were instructed to tap (press the key on the button box) to the beatof the tones; the tones would stop but the participants were to continue tapping while trying to maintain the original tapping rate until they saw the word“Stop” on the screen. For the listen blocks, participants were instructed to simply listen to the tones without tapping when they saw the words “Listen Only”on the screen. The listen blocks were included as a control task for which we did not anticipate differences between groups. (Instruction screens for startingthe paced blocks, “Ready? Tap along . . .,” stopping the unpaced blocks, “Stop,” and beginning the listen blocks, “Listen Only” are not shown in the diagram.)

Table 1. Participant Demographics

ADHD(n " 21)

Control Subjects(n " 19)

Mean SD Mean SD

Age (years) 34.0 10.1 32.7 10.6IQ Estimate 114.5 15.6 115.5 9.9

n n

Sex (F/M) 6/15 7/12

There were no significant differences between attention-deficit/hyper-activity disorder and control subjects for age, IQ estimate, or sex ratio.

ADHD, attention-deficit/hyperactivity disorder; F, female; M, male.

Table 2. Kinematic Results for Paced and Unpaced Tapping Tasks

ADHD M (SD) Control Subjects M (SD)

Tapping PhasePaced 490 (14.6) 490 (12.3)Unpaced 504 (27.6) 493 (21.3)

Unpaced VarianceTotal 1021.3 (520.3)a 672.2 (179.5)Clock 791.8 (402.6)a 497.2 (190.8)Motor 114.7 (126.1) 87.5 (83.7)

Between-group differences were assessed using independent sample ttests. Values are reported as mean and standard deviation—M (SD). Tappingtimes and variability are reported in milliseconds. Variance for the unpacedphase of the tapping task was generated using the method of analysisintroduced by Wing and Kristofferson (41) to dissociate the contribution ofthe central (clock) variance from variance associated with the motor imple-mentation.

ADHD, attention-deficit/hyperactivity disorder.aIndicates a significant difference between attention-deficit/hyperactiv-

ity disorder and control adults, p ! .01.

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ADHD and control groups for clock [t (38) " $3.00, p " .005] butnot motor [t (38) " $.80, p % .05] variance of unpaced tapping(Figure 3, Table 2).

There were no significant relationships between number ofADHD symptoms and total within-subject variability (r=s " $.00and .19, p % .05 for inattention and hyperactivity correlations,respectively).

Functional Imaging ResultsSimple Effects of Paced Tapping. Areas of activation for

control participants included numerous regions consistent withprevious tapping and timing studies (18,55,56) (Figure 4; TableS1A in Supplement 1).

Relative to control subjects, ADHD adults showed signifi-cantly decreased activity in right cerebellum (declive), left infe-rior frontal gyrus (IFG) and middle frontal gyrus (MFG), bilateralprecentral gyri and inferior parietal lobule (IPL), left middle andsuperior temporal gyri (STG), and bilateral insula (Figure 4, Table3). There were no regions for which ADHD adults showedsignificantly greater activation than control subjects.

Simple Effects of Unpaced Tapping. Areas of activation forcontrol subjects included numerous regions consistent withunpaced tapping and timing observed in previous studies(18,55,56) (Figure 4; Table S1B in Supplement; 1).

Relative to control subjects, ADHD adults showed relativelydecreased activation in bilateral cerebellum (culmen and dentatenuclei), bilateral IFG, right MFG, as well as left precentral gyrus,caudate tail, putamen, insula, and amygdala (Figure 4; Table 3).There were no regions for which ADHD adults showed signifi-cantly greater activation than control subjects.

Simple Effects of Listening to the Tones. Control subjectsand ADHD adults showed activation patterns consistent withwhat would be expected from an auditory task with primaryactivation in bilateral auditory cortex (Brodmann area 41/42,

STG) (results available upon request). As predicted, there wereno differences between ADHD and control adults in any regionsfor the listen task.

Group by Task Interaction. The group by task interactionfailed to show statistically significant differences in neural activitybetween paced versus unpaced tapping for the two groups. Thiswas not surprising because it is typical for participants to usetheir own internal timing rather than the tones to performsubsecond tapping tasks (38).

Symptom Relationships to Neural Activity. There were nosignificant relationships between number of ADHD symptomsand blood oxygenation level-dependent response for the un-paced data.

Discussion

We used a tapping task with fMRI to examine motor timingabilities and their neural substrates in adults with ADHD. Behav-iorally, we found that control subjects and ADHD adults showedsimilar mean tapping rates but differences in within-subjectvariability, with ADHD adults showing greater variability thancontrol subjects. Importantly, the ADHD adults showed in-creased clock rather than motor variability, suggesting a deficit intiming rather than motor execution. These behavioral resultswere paralleled by atypical patterns of activity in a network ofregions that are frequently associated with timing, namely themotor and premotor areas in frontal cortex, cerebellum, and BG.

The behavioral findings are notable in several ways. First,these data show that motor timing abnormalities (e.g., increasedwithin-subject variability), previously observed in ADHD youth(57), can also be observed in ADHD adults. There have been noprevious studies examining either tapping or subsecond timeintervals in ADHD adults. Thus, this study extends what weknow about timing irregularities in this population. Futurestudies designed to examine whether ADHD adults with highversus low within-subject variability systematically differ in neu-ral activity would be informative.

Second, the behavioral data show that, on average, ADHDadults have greater tapping variability than control subjects. Mostinterestingly, using a temporal variance decomposition method

Figure 2. Distribution of unpaced tapping variance for control and atten-tion-deficit/hyperactivity disorder (ADHD) adults. Twelve (57%) of the 21ADHD adults have higher variance scores (shown in the ellipse for the ADHDgroup) than all but 1 of the control subjects (shown in the ellipse for thecontrol group). Among the high variability ADHD subjects, half were cur-rently medicated and half were not, suggesting that differences in variabilitywere not simply due to medication effects. ADHD, attention-deficit/hyper-activity disorder.

Figure 3. Total, clock, and motor variance and their standard error for theunpaced tapping generated using the method of analysis introduced byWing and Kristofferson (41) to dissociate the contribution of the central(clock) variance from variance associated with the motor implementation.ADHD, attention-deficit/hyperactivity disorder.

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(41), we show for the first time that this variability is related tovariability of the central clock rather than motor implementation.Given the ubiquitous nature of the higher performance variabilityfor ADHD individuals, knowing more about the mechanism ofthis atypical variability is an important issue. Our data providesupport for abnormalities in timing mechanisms rather thanabnormalities in movement execution.

This result is consistent with conclusions drawn by Rom-melse et al. (58) who had ADHD and control children conducttapping tasks with and without a pacing stimulus. They foundthat ADHD children showed abnormal speed and variabilityfor paced tapping but typical performance while tapping asquickly as they could at their own rate. Rommelse et al. (58)interpreted this result as reflecting timing rather than motorfunction differences.

Neuroimaging results show that ADHD adults have abnormalpatterns of activation while performing the tapping task. Aspredicted, ADHD adults showed relatively less activity thancontrol subjects in frontal, cerebellar, and BG regions. Morespecifically, regions of relative hypoactivity were observed inprimary motor and premotor (IFG and MFG) frontal areas andthe cerebellum (culmen and declive lobules IV–VI and dentatenuclei), as well as the caudate and putamen.

Failure of the ADHD adults to activate frontal and cerebellarregions to the same degree as control subjects could indicatedifficulties in engaging frontocerebellar circuits critical for opti-mal task performance. For example, lobules IV to VI in the rightcerebellar hemisphere and the left primary motor cortex (Brod-mann area 4) comprise nodes of a network associated with motortiming (16,59,60). Strick et al. (61) have shown that there aremultiple corticocerebellar loops, one of which includes reciprocal

projections between the motor cortex and cerebellar hemispheresIV to VI. These data suggest a frontocerebellar substrate for timingprocesses engaged for movement control (16,60–62). It is possiblethat difficulties in optimally engaging this network could contributeto the relatively greater within-subject variability of the ADHD adultsover control subjects while performing this task.

The BG and IFG have been noted to be key timing areas(63,64), shown to be active in tapping tasks similar to those usedhere (55) and also in paradigms for which motor confounds havebeen controlled (22,65). The BG, in particular the putamen, havebeen shown to be preferentially involved in internally generatedmovements (66). The IFG may involve the internal timing ofmovements perhaps via subvocalization of the tones (55). Failureto activate the BG and IFG as strongly as control subjects maymake it more difficult for the ADHD adults to internally generatemovements at the appropriate rate.

Our neuroimaging findings contrast with the results of Rubia etal. (5,31) who reported no differences between ADHD and controlyouth for subsecond timing synchronization and differences incingulate regions for a suprasecond timing. However, these exper-iments differ from ours in that they were conducted with adoles-cents, relied on smaller samples (seven ADHD, nine control sub-jects), and used a visual pacing stimulus. That we did not findbetween-group differences in cingulate regions could also resultfrom our use of a subsecond, rather than suprasecond, timingpacing task. There is an abundance of evidence for differentialfunctional neuroanatomy for subsecond versus suprasecond timing.Our results show more consistency with the findings of Durston etal. (34) and Smith et al. (33) who found less activation in ADHDyouth relative to control subjects in cerebellar and frontal regions(including the IFG) during time expectancy violations and duration

Figure 4. Top panel. Activity associated with perfor-mance of paced tapping. We show t values for signalchange for the paced task greater than fixation baselinecontrast in the attention-deficit/hyperactivity disorder(ADHD) and control groups, as well as for the controlgroup activation greater than the ADHD group activation.Coordinates are in Montreal Neurological Institute space.Height threshold: p ! .05, family-wise error corrected.Extent threshold: k " 20 voxels. Bottom panel. Activityassociated with performance of unpaced tapping. Weshow t values for signal change for the unpaced taskgreater than fixation baseline contrast in the ADHD andcontrol groups, as well as for the control group activationgreater than the ADHD group activation. Coordinates arein Montreal Neurological Institute space. Height thresh-old: p ! .05, family-wise error corrected. Extent threshold:k " 20 voxels. ADHD, attention-deficit/hyperactivity dis-order.

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discrimination tasks, respectively. Notably, these studies were con-ducted with youth in contrast to our study using adults. Additionalresearch is needed for all age groups to gain converging evidenceon the neural substrates of timing in ADHD.

Functional abnormalities for nontiming-related tasks in theIFG and BG in ADHD samples have been well established(26,28). Also, our finding of decreased activation in the motorcortex is consistent with data of Mostofsky et al. (67) who founda smaller extent of activation in the motor cortex for ADHD

children relative to control subjects during performance of aself-paced sequential finger tapping task.

In addition to predicted regions of abnormality during tap-ping task performance, ADHD adults showed less activationcompared with control subjects in several other regions com-monly associated with sensorimotor timing. The insula has beenshown to be involved in sensorimotor synchronization (56,68).In conjunction with the IFG, the insula has been proposed tofacilitate timing via subvocal rehearsal of the target interval and

Table 3. Group Differences for the Paced and Unpaced Tasks

Region Label BA

Left Right

x y z t x y z t

Paced TaskCerebellum

Declive 30 $69 $18 5.3018 $57 $15 6.69

FrontalInferior frontal gyrus 45 $45 30 0 6.35

45 $51 36 3 6.649 $51 18 30 5.58

Middle frontal gyrus 47 $48 45 $6 6.466 $42 $3 48 6.016 $48 6 54 5.69

Precentral gyrus 4 $54 $18 27 6.24 36 $24 69 5.374 21 $27 54 6.634 15 $30 66 5.816 $45 $15 33 5.81 51 $9 42 7.696 $48 $6 36 6.62

ParietalInferior parietal lobule 40 $63 $42 30 8.18 54 $36 24 6.33

40 $66 $30 33 5.89Temporal

Middle temporal gyrus 22 $54 $42 0 5.65Superior temporal gyrus 22 $57 $54 6 6.01

38 $51 9 $12 5.23Subcortical

Insula 13 $51 $39 24 9.76 39 $33 18 5.6213 $45 12 9 8.98

Unpaced TaskCerebellum

Culmen $6 $42 $24 5.16 6 $54 $27 5.896 $48 $21 6.00

Dentate $15 $48 $36 6.05Frontal

Inferior frontal gyrus 47 48 27 0 5.0347 39 27 $9 5.2845 $60 15 3 5.57

Middle frontal gyrus 47 42 33 $3 5.43Precentral gyrus 4 $54 $18 27 6.27

44 $48 12 6 7.25Limbic

Parahippocampal gyrus Amygdala 24 $3 $12 5.88Subcortical

Caudate Caudate tail $36 $27 0 5.64Insula 13 $51 $39 24 7.19Lentiform nucleus Putamen $24 $15 12 5.38

$27 $3 $6 6.40$30 $21 0 5.58$30 $15 $9 6.47

Contrast of control group greater than attention-deficit/hyperactivity disorder group for the paced and unpaced tasks. Local maxima of signal change arelisted. Coordinates are in Montreal Neurological Institute space. Height threshold: p ! .05, family-wise error corrected. Extent threshold: k " 20 voxels.

BA, Brodmann area.

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through multimodal integration (69). Less activation for theADHD adults relative to the control subjects in the IFG and insulacould reflect more difficulty for the ADHD adults in integratingtheir internal rehearsal of the tones with the motor response.

The inferior parietal lobe’s involvement in time perception iswell established (22,70,71). One hypothesized role for the IPLinvolves its making covert shifts of attention to temporal stimuli(72). Thus, abnormalities in this region could contribute to moredifficulties in shifting and maintaining attention to the tones.

The role of the STG in timing appears to vary depending onthe location within the STG. Coull and Nobre (63) proposed ananterior-posterior timing gradient in the STG such that areas at&y " $20 are linked with automatic motor timing, such as pacedtapping to an auditory tone (55,73), and posterior regions areimplicated in cognitive perceptual timing (74,75), showing acti-vation in the absence of auditory cues (22). In our data, thelocation where the ADHD adults showed less activation thancontrol subjects, &y " $50, is consistent with the area that hasbeen shown to be active during perceptual duration discrimina-tion tasks. Thus, the failure of the ADHD adults to engage thisregion could contribute to difficulties perceiving the precisetiming of the tones and subsequently maintaining the propertiming of the taps.

While performing the tapping task, ADHD adults consistentlydemonstrated less activation than the control subjects, suggestingthat the ADHD adults are not sufficiently recruiting regions associ-ated with sensorimotor timing. Notably, the lack of activationdifferences between the ADHD adults and control subjects on thelisten task and lack of differences in areas of the auditory cortexassociated with hearing tones (&y " $20 in the STG) for the pacedtask indicate that the hypoactivity relative to the control subjects wasnot a nonspecific global reduction but rather more specific to thedemands of the temporally demanding tapping task.

In all, abnormal neural activity was demonstrated in our pre-dicted brain regions commonly associated with both ADHD andtiming (frontal cortex, BG, cerebellum), as well as other regionsoften involved in timing (e.g., insula, IPL, STG). These data supportour hypothesis that abnormalities in timing networks contribute tothe pathophysiology of ADHD and may represent a core dysfunc-tion of the disorder. The kinds of temporal processing deficitsobserved here are in keeping with the consistently reported in-creased within-subject response time variability in ADHD (12,13).We hypothesize that suboptimal engagement of this timing network(which overlaps with a cerebellar-prefrontal-striatal network longhypothesized to be abnormal in ADHD [76]) represents a coreneurofunctional abnormality and contributes to basic timing impair-ments, as well as possibly other behavioral features of ADHD (e.g.,delay aversion, inability to wait one’s turn). As such, testing hypoth-eses that these and other relevant behavioral manifestations ofADHD reflect abnormalities in timing-related neural networksshould further our understanding of the disorder.

Finally, there were no relationships between ADHD symp-toms and behavioral or imaging data for the unpaced tappingtask. This suggests that this timing abnormality is not associatedwith number of ADHD symptoms and supports a hypothesis thattiming abnormalities are core features of ADHD independent ofinattentive and hyperactive symptoms. However, it could also bethat other symptom measures that we did not use or perhaps amore precise measure of symptom severity (rather than symptomnumber) would have shown a relationship. Future studies de-signed to address such questions using dimensional ADHDrating scales could be revealing.

Our study and its conclusions need to be considered in light

of methodological limitations. First, ADHD individuals have beenshown to have abnormalities in both fine and gross motorabilities (77). The atypical activity associated with our tappingtask could be associated with either timing or movement imple-mentation problems rather than timing per se or could be a resultof the combination of the two. Our paradigm does not allow usto definitively resolve this question. However, analyses with theWing and Kristofferson model (41), which show differences inclock, but not motor, variance, suggest a timing explanation forour results. Also, the lack of any between-group differences forthe paced versus listen group by task interaction (Supplement 1)suggests that ADHD and control adults do not differ in move-ment-related activation. Additionally, many of the regions show-ing atypical activation in the ADHD adults are consistent withresults of a recent meta-analysis of timing that included a rangeof timing tasks that accounted for motor processes (18). Still,future studies that employ other timing paradigms withoutmovement would be helpful in addressing this question. Second,some of our ADHD participants did not meet the symptomthreshold at the time of scan. However, it is common for adultsto fall below symptom threshold (54). If anything, we wouldexpect this to diminish the strength of ADHD versus controlsubject effects rather than create spurious effects. Third, althoughmatched, the mean IQ of our groups was higher than averageand it is unclear how well these results would generalize toADHD adults with average mean IQs. Additionally, althoughgroup-matched on sex, both male and female subjects wereincluded in our sample and it is possible that between-groupdifferences may not be the same for men and women. Futurestudies should employ larger groups balanced by sex to addresspotential sex differences. The ADHD subjects who were takingpsychostimulants underwent a 24-hour washout period. How-ever, it is still possible that long-term or short-term effects ofpsychostimulants could have affected our results. If this were thecase, we would expect potential effects of psychostimulants todiminish our between-group effects (78). Finally, employing aparametric characterization of the tapping task or suprasecondtiming would be useful in evaluating the effects of timing loadand/or time scale on neural abnormalities in ADHD adults andshould be used in future studies.

Overall, these findings significantly increase our understand-ing of the behavioral and neurofunctional aspects of sensorimo-tor timing abnormalities in ADHD adults. These data demonstratethat timing abnormalities at the subsecond level persist intoadulthood and also provide the first evidence of the underlyingneural substrates for the observed differences in movement ratecontrol in adult ADHD.

This work was supported in part by Grants from the NationalInstitutes of Health (MH 071535 to EMV, MH 57934 to SVF, MH62152 to LJS, MH 064019 to TJS, AG29710 to RMCS); theNational Alliance for Research on Schizophrenia and Depressionto JB; Research Grants from Janssen and McNeil Pharmaceuti-cals to JB; and the philanthropic support from the ResearchCouncil for Pediatric Psychopharmacology at the MassachusettsGeneral Hospital, The National Center for Research Resources(P41RR14075), and the Commonwealth Research Center, Mas-sachusetts Department of Mental Health to LJS. These fundershad no role in the design and conduct of the study; collection,management, analysis, and interpretation of the data; andpreparation, review, or approval of the manuscript.

We thank the individuals who served as research participants,as well as Denise Boriel, Stacey Bilicki, Alysa Doyle, Ronna Fried,

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Brittany LeBlanc, Michael C. Monuteaux, and Timothy Wilensfor their contributions to this work.

Dr. Eve M. Valera has received travel support and honorariafrom Shire Pharmaceuticals and the McNeil and Janssen divi-sions of Ortho-McNeil-Janssen Pharmaceuticals. Dr. Joseph Bied-erman is currently receiving research support from the followingsources: Alza, AstraZeneca, Bristol-Myers Squibb, Eli Lilly andCo., Janssen Pharmaceuticals Inc., McNeil, Merck, Organon,Otsuka, Shire, National Institute of Mental Health, and NationalInstitute of Child Health and Human Development. In 2009, Dr.Joseph Biederman received a speaker’s fee from the followingsources: Fundacion Areces, Medice Pharmaceuticals, and theSpanish Child Psychiatry Association. In previous years, Dr.Joseph Biederman received research support, consultation fees,or speaker’s fees for/from the following additional sources: Ab-bott, AstraZeneca, Celltech, Cephalon, Eli Lilly and Co., Esai,Forest, Glaxo, Gliatech, Janssen, McNeil, National Alliance forResearch on Schizophrenia and Depression, National Institute ofDrug Abuse, New River, Novartis, Noven, Neurosearch, Pfizer,Pharmacia, The Prechter Foundation, Shire, The Stanley Foun-dation, UCB Pharma Inc., and Wyeth. Dr. Thomas Spencer hasreceived research support from, has been a speaker on a speakerbureau, or has been on an Advisory Board of the followingsources: Shire Laboratories Inc., Eli Lilly and Company, Glaxo-SmithKline, Janssen Pharmaceutical, McNeil Pharmaceutical,Novartis Pharmaceuticals, Cephalon, Pfizer, and the NationalInstitute of Mental Health. In the past year, Dr. Stephen Faraonehas received consulting fees and has been on Advisory Boards forEli Lilly, Ortho-McNeil, and Shire Development and has receivedresearch support from Eli Lilly, Pfizer, Shire, and the NationalInstitutes of Health. In previous years, Dr. Faraone has receivedconsulting fees, has been on Advisory Boards, or has been aspeaker for the following sources: Shire, McNeil, Janssen, Novar-tis, Pfizer, and Eli Lilly. In previous years, he has receivedresearch support from Eli Lilly, Shire, Pfizer, and the NationalInstitutes of Health. Dr. Larry J. Seidman reports no financialdisclosures or conflicts of interest for the past 2 years. He has beena speaker for Shire Pharmaceuticals and received an unre-stricted educational grant from Janssen Pharmaceuticals in thepast 5 years. All other authors report no biomedical financialinterests or potential conflicts of interest.

Supplementary material cited in this article is availableonline.

1. Faraone SV, Sergeant J, Gillberg C, Biederman J (2003): The worldwideprevalence of ADHD: Is it an American condition? World Psychiatry2:104 –113.

2. Spencer TJ, Biederman J, Mick E (2007): Attention-deficit/hyperactivitydisorder: Diagnosis, lifespan, comorbidities, and neurobiology. J PediatrPsychol 32:631– 642.

3. Rubia K, Halari R, Christakou A, Taylor E (2009): Impulsiveness as a timingdisturbance: Neurocognitive abnormalities in attention-deficit hyper-activity disorder during temporal processes and normalization withmethylphenidate. Philos Trans R Soc Lond B Biol Sci 364:1919 –1931.

4. Sonuga-Barke EJ, Taylor E, Sembi S, Smith J (1992): Hyperactivity anddelay aversion–I. The effect of delay on choice. J Child Psychol Psychiatry33:387–398.

5. Rubia K, Noorloos J, Smith A, Gunning B, Sergeant J (2003): Motor timingdeficits in community and clinical boys with hyperactive behavior: Theeffect of methylphenidate on motor timing. J Abnorm Child Psychol31:301–313.

6. Barkley RA, Koplowitz S, Anderson T, McMurray MB (1997): Sense of timein children with ADHD: Effects of duration, distraction, and stimulantmedication. J Int Neuropsychol Soc 3:359 –369.

7. Barkley RA, Murphy KR, Bush T (2001): Time perception and reproduc-tion in young adults with attention deficit hyperactivity disorder. Neu-ropsychology 15:351–360.

8. Meaux JB, Chelonis JJ (2003): Time perception differences in childrenwith and without ADHD. J Pediatr Health Care 17:64 –71.

9. Toplak ME, Rucklidge JJ, Hetherington R, John SC, Tannock R (2003):Time perception deficits in attention-deficit/hyperactivity disorder andcomorbid reading difficulties in child and adolescent samples. J ChildPsychol Psychiatry 44:888 –903.

10. Smith A, Taylor E, Rogers JW, Newman S, Rubia K (2002): Evidence for apure time perception deficit in children with ADHD. J Child PsycholPsychiatry 43:529 –542.

11. Toplak ME, Dockstader C, Tannock R (2006): Temporal information pro-cessing in ADHD: Findings to date and new methods. J Neurosci Methods151:15–29.

12. Klein C, Wendling K, Huettner P, Ruder H, Peper M (2006): Intra-subjectvariability in attention-deficit hyperactivity disorder. Biol Psychiatry 60:1088 –1097.

13. Leth-Steensen C, Elbaz ZK, Douglas VI (2000): Mean response times,variability, and skew in the responding of ADHD children: A responsetime distributional approach. Acta Psychol (Amst) 104:167–190.

14. Baldwin RL, Chelonis JJ, Flake RA, Edwards MC, Feild CR, Meaux JB, PauleMG (2004): Effect of methylphenidate on time perception in childrenwith attention-deficit/hyperactivity disorder. Exp Clin Psychopharmacol12:57– 64.

15. Marx I, Hubner T, Herpertz SC, Berger C, Reuter E, Kircher T, et al. (2010):Cross-sectional evaluation of cognitive functioning in children, adoles-cents and young adults with ADHD. J Neural Transm 117:403– 419.

16. Ivry RB, Spencer RM (2004): The neural representation of time. Curr OpinNeurobiol 14:225–232.

17. Meck WH, Benson AM (2002): Dissecting the brain’s internal clock: Howfrontal-striatal circuitry keeps time and shifts attention. Brain Cogn 48:195–211.

18. Wiener M, Turkeltaub P, Coslett HB (2010): The image of time: A voxel-wise meta-analysis. Neuroimage 49:1728 –1740.

19. Ivry RB (1996): The representation of temporal information in percep-tion and motor control. Curr Opin Neurobiol 6:851– 857.

20. Mangels JA, Ivry RB, Shimizu N (1998): Dissociable contributions of theprefrontal and neocerebellar cortex to time perception. Brain Res CognBrain Res 7:15–39.

21. Nenadic I, Gaser C, Volz HP, Rammsayer T, Hager F, Sauer H (2003):Processing of temporal information and the basal ganglia: New evi-dence from fMRI. Exp Brain Res 148:238 –246.

22. Lewis PA, Miall RC (2003): Brain activation patterns during measure-ment of sub- and supra-second intervals. Neuropsychologia 41:1583–1592.

23. Nichelli P, Clark K, Hollnagel C, Grafman J (1995): Duration processingafter frontal lobe lesions. Ann N Y Acad Sci 769:183–190.

24. Amat JA, Bronen RA, Saluja S, Sato N, Zhu H, Gorman DA, et al. (2006):Increased number of subcortical hyperintensities on MRI in children andadolescents with Tourette syndrome, obsessive-compulsive disorder, andattention deficit hyperactivity disorder. Am J Psychiatry 163:1106–1108.

25. Biederman J, Makris N, Valera EM, Monuteaux MC, Goldstein JM, Buka S,et al. (2008): Towards further understanding of the co-morbidity be-tween attention deficit hyperactivity disorder and bipolar disorder: AMRI study of brain volumes. Psychol Med 38:1045–1056.

26. Dickstein SG, Bannon K, Castellanos FX, Milham MP (2006): The neuralcorrelates of attention deficit hyperactivity disorder: An ALE meta-anal-ysis. J Child Psychol Psychiatry 47:1051–1062.

27. Ellison-Wright I, Ellison-Wright Z, Bullmore E (2008): Structural brainchange in attention deficit hyperactivity disorder identified by meta-analysis. BMC Psychiatry 8:51.

28. Paloyelis Y, Mehta MA, Kuntsi J, Asherson P (2007): Functional MRI in ADHD:A systematic literature review. Expert Rev Neurother 7:1337–1356.

29. Seidman LJ, Valera EM, Makris N, Monuteaux MC, Boriel DL, Kelkar K, etal. (2006): Dorsolateral prefrontal and anterior cingulate cortex volu-metric abnormalities in adults with attention-deficit/hyperactivity dis-order identified by magnetic resonance imaging. Biol Psychiatry 60:1071–1080.

30. Valera EM, Faraone SV, Murray KE, Seidman LJ (2007): Meta-analysis ofstructural imaging findings in attention-deficit/hyperactivity disorder.Biol Psychiatry 61:1361–1369.

366 BIOL PSYCHIATRY 2010;68:359–367 E.M. Valera et al.

www.sobp.org/journal

31. Rubia K, Overmeyer S, Taylor E, Brammer M, Williams SC, Simmons A,Bullmore ET (1999): Hypofrontality in attention deficit hyperactivitydisorder during higher-order motor control: A study with functionalMRI. Am J Psychiatry 156:891– 896.

32. Rubia K, Taylor E, Smith AB, Oksanen H, Overmeyer S, Newman S (2001):Neuropsychological analyses of impulsiveness in childhood hyperactiv-ity. Br J Psychiatry 179:138 –143.

33. Smith AB, Taylor E, Brammer M, Halari R, Rubia K (2008): Reduced acti-vation in right lateral prefrontal cortex and anterior cingulate gyrus inmedication-naive adolescents with attention deficit hyperactivity disor-der during time discrimination. J Child Psychol Psychiatry 49:977–985.

34. Durston S, Davidson MC, Mulder MJ, Spicer JA, Galvan A, Tottenham N,et al. (2007): Neural and behavioral correlates of expectancy violations inattention-deficit hyperactivity disorder. J Child Psychol Psychiatry 48:881– 889.

35. Aron AR, Poldrack RA (2005): The cognitive neuroscience of responseinhibition: Relevance for genetic research in attention-deficit/hyperac-tivity disorder. Biol Psychiatry 57:1285–1292.

36. Bush G (2010): Attention-deficit/hyperactivity disorder and attentionnetworks. Neuropsychopharmacology 35:278 –300.

37. Volkow ND, Wang GJ, Kollins SH, Wigal TL, Newcorn JH, Telang F, et al.(2009): Evaluating dopamine reward pathway in ADHD: Clinical impli-cations. JAMA 302:1084 –1091.

38. Mates J, Radil T, Muller R, Poppel E (1994): Temporal integration insensorimotor synchronization. J Cogn Neurosci 6:332–340.

39. Sonuga-Barke EJ (2003): The dual pathway model of AD/HD: An elabo-ration of neuro-developmental characteristics. Neurosci Biobehav Rev27:593– 604.

40. Ivry RB, Spencer RM, Zelaznik HN, Diedrichsen J (2002): The cerebellumand event timing. Ann N Y Acad Sci 978:302–317.

41. Wing AM, Kristofferson AB (1973): Response delays and the timing ofdiscrete motor responses. Percept Psychophys 14:5–12.

42. First MB, Spitzer RL, Gibbon M, Williams JBW (1997): Structured ClinicalInterview for DSM-IV Axis I Disorders. Washington, DC: American Psychi-atric Publishing.

43. Valera EM, Faraone SV, Biederman J, Poldrack RA, Seidman LJ (2005):Functional neuroanatomy of working memory in adults with attention-deficit/hyperactivity disorder. Biol Psychiatry 57:439 – 447.

44. Orvaschel H (1994): Schedule for Affective Disorder and Schizophrenia forSchool-Age Children Epidemiologic Version, 5th ed. Ft. Lauderdale, FL:Nova Southeastern University, Center for Psychological Studies.

45. Barkley RA, Biederman J (1997): Toward a broader definition of theage-of-onset criterion for attention-deficit hyperactivity disorder. J AmAcad Child Adolesc Psychiatry 36:1204 –1210.

46. Barkley RA, Murphey KR, Fischer M (2008): ADHD in Adults: What theScience Says. New York: Guilford.

47. Wechsler D (1997): Wechsler Adult Intelligence Scale III [Manual], 3rd ed.San Antonio, TX: Psychological Corporation.

48. Wechsler D (1981): Manual for the Wechsler Adult Intelligence Scale—Revised. San Antonio, TX: Psychological Corporation.

49. Wechsler D (1999): Wechsler Abbreviated Scale of Intelligence. San Anto-nio, TX: Psychological Corporation.

50. Krampe RT, Mayr U, Kliegl R (2005): Timing, sequencing, and executivecontrol in repetitive movement production. J Exp Psychol Hum PerceptPerform 31:379 –397.

51. Terry P, Doumas M, Desai RI, Wing AM (2009): Dissociations between motortiming, motor coordination, and time perception after the administrationof alcohol or caffeine. Psychopharmacology (Berl) 202:719–729.

52. Keele SW, Pokorny RA, Corcos DM, Ivry R (1985): Do perception andmotor production share common timing mechanisms: A correctionalanalysis. Acta Psychol (Amst) 60:173–191.

53. Vorberg D, Wing A (1996): Modeling variability and dependence intiming. In: Prinz W, Heuer H, Bridgeman B, Keele S, editors. Handbook ofPerception and Action. San Diego: Academic Press.

54. Faraone SV, Biederman J, Spencer T, Mick E, Murray K, Petty C, et al.(2006): Diagnosing adult attention deficit hyperactivity disorder: Arelate onset and subthreshold diagnoses valid? Am J Psychiatry 163:1720 –1729; quiz 1859.

55. Rao SM, Harrington DL, Haaland KY, Bobholz JA, Cox RW, Binder JR(1997): Distributed neural systems underlying the timing of move-ments. J Neurosci 17:5528 –5535.

56. Witt ST, Laird AR, Meyerand ME (2008): Functional neuroimaging corre-lates of finger-tapping task variations: An ALE meta-analysis. Neuroim-age 42:343–356.

57. Toplak ME, Tannock R (2005): Tapping and anticipation performance inattention deficit hyperactivity disorder. Percept Mot Skills 100:659 – 675.

58. Rommelse NN, Altink ME, Oosterlaan J, Beem L, Buschgens CJ, BuitelaarJ, Sergeant JA (2008): Speed, variability, and timing of motor output inADHD: Which measures are useful for endophenotypic research? BehavGenet 38:121–132.

59. Harrington DL, Lee RR, Boyd LA, Rapcsak SZ, Knight RT (2004): Does therepresentation of time depend on the cerebellum? Effect of cerebellarstroke. Brain 127:561–574.

60. Ivry RB, Keele SW, Diener HC (1988): Dissociation of the lateral andmedial cerebellum in movement timing and movement execution. ExpBrain Res 73:167–180.

61. Strick PL, Dum RP, Fiez JA (2009): Cerebellum and nonmotor function.Annu Rev Neurosci 32:413– 434.

62. Harrington DL, Boyd LA, Mayer AR, Sheltraw DM, Lee RR, Huang M, RaoSM (2004): Neural representation of interval encoding and decisionmaking. Brain Res Cogn Brain Res 21:193–205.

63. Coull J, Nobre A (2008): Dissociating explicit timing from temporal ex-pectation with fMRI. Curr Opin Neurobiol 18:137–144.

64. Livesey AC, Wall MB, Smith AT (2007): Time perception: Manipulation oftask difficulty dissociates clock functions from other cognitive de-mands. Neuropsychologia 45:321–331.

65. Stevens MC, Kiehl KA, Pearlson G, Calhoun VD (2007): Functional neuralcircuits for mental timekeeping. Hum Brain Mapp 28:394 – 408.

66. Taniwaki T, Okayama A, Yoshiura T, Nakamura Y, Goto Y, Kira J, Tobi-matsu S (2003): Reappraisal of the motor role of basal ganglia: A func-tional magnetic resonance image study. J Neurosci 23:3432–3438.

67. Mostofsky SH, Rimrodt SL, Schafer JG, Boyce A, Goldberg MC, Pekar JJ,Denckla MB (2006): Atypical motor and sensory cortex activation in atten-tion-deficit/hyperactivity disorder: A functional magnetic resonance imag-ing study of simple sequential finger tapping. Biol Psychiatry 59:48–56.

68. Rubia K, Overmeyer S, Taylor E, Brammer M, Williams SC, Simmons A, etal. (2000): Functional frontalisation with age: Mapping neurodevelop-mental trajectories with fMRI. Neurosci Biobehav Rev 24:13–19.

69. Cerasa A, Hagberg GE, Peppe A, Bianciardi M, Gioia MC, Costa A, et al.(2006): Functional changes in the activity of cerebellum and frontostria-tal regions during externally and internally timed movement in Parkin-son’s disease. Brain Res Bull 71:259 –269.

70. Bueti D, Walsh V, Frith C, Rees G (2008): Different brain circuits underliemotor and perceptual representations of temporal intervals. J CognNeurosci 20:204 –214.

71. Harrington DL, Haaland KY, Knight RT (1998): Cortical networks under-lying mechanisms of time perception. J Neurosci 18:1085–1095.

72. Rubia K, Smith A (2004): The neural correlates of cognitive time manage-ment: A review. Acta Neurobiol Exp (Wars) 64:329 –340.

73. Jantzen KJ, Steinberg FL, Kelso JA (2005): Functional MRI reveals theexistence of modality and coordination-dependent timing networks.Neuroimage 25:1031–1042.

74. Coull JT, Vidal F, Nazarian B, Macar F (2004): Functional anatomy of theattentional modulation of time estimation. Science 303:1506 –1508.

75. Ferrandez AM, Hugueville L, Lehericy S, Poline JB, Marsault C, Pouthas V(2003): Basal ganglia and supplementary motor area subtend durationperception: An fMRI study. Neuroimage 19:1532–1544.

76. Giedd JN, Blumenthal J, Molloy E, Castellanos FX (2001): Brain imagingof attention deficit/hyperactivity disorder. In: Wasserstein J, Wolf L,editors. Adult Attention Deficit Disorder: Brain Mechanisms and Life Out-comes. Annals of the New York Academy Sciences, vol. 931. New York: NewYork Academy of Sciences.

77. Pitcher TM, Piek JP, Hay DA (2003): Fine and gross motor ability in maleswith ADHD. Dev Med Child Neurol 45:525–535.

78. Bush G, Spencer T, Holmes J, Shin LM, Surman C, Valera EM, et al. (2008):Functional magnetic resonance imaging of methylphenidate and placeboin adults with attention-deficit/hyperactivity disorder during the multi-source interference task. Arch Gen Psychiatry 65:102–114.

E.M. Valera et al. BIOL PSYCHIATRY 2010;68:359–367 367

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