European Journal of Neuroscience, Vol. 43, pp. 494–508, 2016

doi:10.1111/ejn.13130

COGNITIVE NEUROSCIENCE

A functional magnetic resonance imaging investigation of motor control in Gilles de la Tourette syndrome during imagined and executed movements Laura Zapparoli,1,2 Mauro Porta,2 Martina Gandola,3 Paola Invernizzi,1 Valeria Colajanni,1 Domenico Servello,2 Alberto Zerbi,2 Giuseppe Banfi2,4 and Eraldo Paulesu1,2 1

Psychology Department and NeuroMI-Milan Center for Neuroscience, Piazza dell’Ateneo Nuovo 1, Milan, Italy IRCCS Galeazzi, Milan, Italy 3 Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy 4 University Vita e Salute San Raffaele, Milan, Italy 2

Keywords: blood oxygen level-dependent, functional magnetic resonance imaging, Gilles de la Tourette syndrome, motor imagery, voluntary motor control Edited by Christoph M. Michel Received 4 March 2015, revised 4 November 2015, accepted 5 November 2015

Abstract The current study investigated the neural correlates of voluntary motor control in 24 adult Gilles de la Tourette (GTS) patients. We examined whether imagination and the execution of the same voluntary movement – finger oppositions with either hand – were associated with specific patterns of activation. We also explored whether these patterns correlated with the severity of the syndrome, as measured by the Yale Global Tic Severity Scale (YGTSS) for motor tics. The presence of brain morphometric abnormalities was also assessed using voxel-based morphometry. Crucial to our experiment was the manipulation of the presence of an explicit motor outflow in the tasks. We anticipated a reduction in the ticking manifestation during the explicit motor task and brain activation differences between GTS patients and 24 age/gender-matched normal controls. The anticipated differences were all evident in the form of hyperactivations in the GTS patients in the premotor and prefrontal areas for both motor tasks for both hands; however, the motor imagery hyperactivations also involved rostral pre-frontal and temporo-parietal regions of the right hemisphere. The blood oxygen level-dependent responses of the premotor cortices during the motor imagery task were significantly correlated with the YGTSS scores. In contrast, no significant brain morphometric differences were found. This study provides evidence of a different neurofunctional organisation of motor control between adult patients with GTS and healthy controls that is independent from the actual execution of motor acts. The presence of an explicit motor outflow in GTS mitigates the manifestation of tics and the need for compensatory brain activity in the brain regions showing task-dependent hyperactivations.

Introduction The functional anatomical characterisation of self-produced movements in Gilles de la Tourette syndrome (GTS) deserves another look. GTS is a neurological disorder characterised by the production of tics (i.e. stereotyped movements or vocalisations) that is typically preceded by a premonitory urge (Leckman et al., 1993). GTS affects approximately 1% of the global population (Robertson, 2008). Symptom onset typically occurs in childhood and symptoms can persist across a patient’s entire life-span (Leckman et al., 2001). In approximately 90% of cases, vocal and motor tics are accompanied by psychological comorbidities, such as obsessive-compulsive disor-

Correspondence: Dr L. Zapparoli, 1Psychology Department, as above. E-mail: [email protected]

der (OCD) and attention deficit hyperactivity disorder (for a discussion of the syndrome, see Robertson, 2008, 2012). Motoric manifestations in GTS represent a core aspect of the syndrome, but still lack a mechanistic explanation as the pathophysiology of GTS remains incompletely understood. GTS is most likely associated with aberrant activity in the basal ganglia and with functional changes in the cortico-striato-thalamo-cortical circuits (for systematic reviews see Mink, 2006; Leckman et al., 2010; Felling & Singer, 2011; Ganos et al., 2013). The fact that multiple classes of drugs show an effect on the motoric manifestations of GTS testifies to the complexity of the underlying neurochemical abnormalities (Singer & Minzer, 2003). However, the primary role of dopamine antagonists in GTS treatment, with comparatively stronger effects obtained with typical neuroleptics (Eddy et al., 2011), supports the hypothesis that there is

© 2015 Federation of European Neuroscience Societies and John Wiley & Sons Ltd

Motor control in Tourette syndrome 495 dysfunction within the basal ganglia and substantial involvement in their motor subdivision. Activation experiments using positron emission tomography or functional magnetic resonance imaging (fMRI) are providing a new perspective regarding this syndrome (for a systematic review see Zapparoli et al., 2015). These studies can be classified as (i) studies of the functional correlates of tics or their premonitory urges (Bohlhalter et al., 2006; Lerner et al., 2007; Hampson et al., 2009; Neuner et al., 2014); (ii) studies of tic suppression using block designs with rapidly alternating conditions for tic suppression and free-ticcing baselines (Peterson et al., 1998; Neuner et al., 2014) or more prolonged alternating scanning conditions that are similar to those used in functional connectivity studies (Ganos et al., 2014a); (iii) functional connectivity studies at rest (Werner et al., 2010; Worbe et al., 2012); (iv) studies of motor functions as mapped by go/no-go paradigms (Thomalla et al., 2014); and (v) studies of selfproduced movements (Werner et al., 2011; Roessner et al., 2012, 2013). One fairly consistent observation from previous literature is that the basal ganglia, and specifically the putamen, are more active during tics than during a variety of reference tasks – the reference tasks varied from tic suppression, as in Peterson et al. (1998); sleep, as in Lerner et al. (2007); tic imitation made by normal controls, as in Wang et al. (2011); to an implicit baseline, as in Stern et al. (2000) and in Neuner et al. (2014) (see also Table S1). The functional anatomy of tic suppression is not as well established (see the diverging results of Peterson et al., 1998 and Ganos et al., 2014a,b,c). The present paper contributes to the fifth classification of studies from the aforementioned list, as it focuses on motor control as assessed via self-produced movements similar to those involved during the conscious/attentive generation of sequential motor acts. The wording self-produced is used to identify voluntarily generated motor acts, even when generated within the constraints of a guided experiment, as opposed to tics or automatic movements. Of course, both classes of movements are ‘internally generated’ and self-produced in that these are not reflex muscle twitches compared with induced movements, for example, by an external stimulation such as transcranial magnetic stimulation (TMS). To date, this subject has received surprisingly little attention in functional imaging experiments based on fMRI. In fact, the most robust evidence in this area has come, perhaps, from electroencephalography (EEG) or magnetoencephalography (MEG) studies. EEG experiments have shown augmented functional interactions between M1 and S1, prefrontal and fronto-mesial areas in GTS (Serrien et al., 2005). Previous MEG studies provide evidence of increased M1 activation during preparation and execution of voluntary finger movements in GTS patients (Franzkowiak et al., 2010). This increase was caused by augmented interactions between supplementary motor area (SMA) and M1 (Franzkowiak et al., 2012). In sum, this neurophysiological evidence concurs to suggest an augmented cortical excitability in GTS during the execution of self-produced movements. In contrast, current fMRI evidence on selfproduced movements is either inconsistent or outright contradictory despite the superior spatial resolution and more established analytical strategies available for group studies (see, for example, Roessner et al., 2012, 2013). For example, several group-specific patterns were only described qualitatively even though a pattern can be considered truly group-specific only when assessed with an interaction effect in comparison with a reference group (see Nieuwenhuis et al., 2011). In addition, previous studies had rather small sample sizes [see also the Supporting Information (Data S1) for a detailed discussion of the limitations of this literature]. Moreover, it is unclear

whether self-produced movements in GTS patients require some compensatory brain activity and whether this maps into the systems that were previously described for tic suppression (Peterson et al., 1998; Ganos et al., 2014a). Surprisingly, none of the EEG, MEG or fMRI experiments examining self-produced movements has taken advantage of motor imagery as an experimental probe of the motor system in GTS. In principle, the combination of an explicit motor execution task and its equivalent in a motor imagery format in the same experiment should allow for testing several relevant questions. First, can GTS patients imagine (i.e. represent) their motor acts in a similar manner as healthy controls? This question alone deserves an answer. A clear deficit here would suggest the presence of a substantial cortical disorder, as motor imagery is normally associated with dominant cortical activity (Hanakawa et al., 2003; Zapparoli et al., 2014). Second, is the frequency of involuntary movements modified by motor imagery activity to the same extent as by voluntary explicit motor acts? In other words, is the presence of an explicit motor outflow an important modulatory factor with regard to the manifestation of tics? Given that explicit motor execution or imagination are functionally related but not equivalent in terms of underlying physiology (i.e. there is a stronger involvement of subcortical structures and area M1 in motor execution, see Hanakawa et al., 2003; Zapparoli et al., 2013), using these two tasks should shed light on the relationship between the different levels of self-produced movements and the underlying spontaneous motor dysfunction of GTS. To examine these questions, we studied 24 adult GTS patients. The motor task was a simple sequential thumb-to-finger opposition task presented in executed or imagined forms (Zapparoli et al., 2013). This is a sufficiently simple task so as not to have performance as a concerning confound. We treated our motor tasks (i.e. overt or covert) as modulatory factors with regard to the ticcing manifestations. We hypothesised that the task that showed the most mitigation of the symptoms should do so by direct competition, in time and space, over the same brain structures that are involved in the generation of tics. Obvious candidates for this competition are area M1 and the connected subcortical structures, as these have been best identified by previous research on free-ticcing (Stern et al., 2000; Bohlhalter et al., 2006; Lerner et al., 2007; Wang et al., 2011). We also hypothesised that there would be compensatory activity related to ongoing implicit tic suppression, despite subjects not being instructed to do so. Our experiment aimed to characterise such compensatory activity in that we expected this activity to be more pronounced during the task with lower direct competition with the tic-related network. Possible candidates for the observation of this compensatory activity are the networks involved in tic suppression, which were described by Peterson et al. (1998) and Ganos et al. (2014a,b,c) as primarily involving the prefrontal networks. However, these networks may represent top-down controlling systems given the explicit nature of the tic suppression task used in those experiments. In our study, subjects were not instructed to focus on tic suppression due to the structure of our tasks and the instructions provided. Therefore, alternative candidates may be the premotor cortices, which are critical to motor adjustments during sudden perturbations of initial motor plans (for recent evidence in humans, see Lee & van Donkelaar, 2006; Buiatti et al., 2013). The clinical face validity of the fMRI patterns observed in the current research was analysed using correlations with the Yale Global Tic Severity Scale (YGTSS), which measures the severity of ticcing manifestations in daily life.

© 2015 Federation of European Neuroscience Societies and John Wiley & Sons Ltd European Journal of Neuroscience, 43, 494–508

496 L. Zapparoli et al. The fMRI studies were preceded by a behavioural study that evaluated the extent to which GTS patients showed explicit motor behaviours and motor imagery patterns that were similar to those shown by normal controls. The behavioural component of the current study was relevant for interpreting the fMRI patterns because this component indicated whether any hyper- or hypo-activation in GTS was a sign of successful compensation or an unsuccessful compensatory attempt (for discussion and illustration of multiple behaviour fMRI scenarios, see Berlingeri et al., 2010). Finally, we assessed the impact of brain morphometric differences using voxel-based morphometry (VBM) in the same patients (Ashburner & Friston, 2000, 2005).

Materials and methods Participants and clinical assessment We recruited 28 patients with GTS (six female, 22 male; mean age – 28.7  12 years; mean educational level – 11 years). Criteria for GTS, as assessed during a neurological examination, were defined by the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) and International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10). Preliminary screening of the patients was performed at the Movement Disorders and Tourette Syndrome Centre at the IRCCS Galeazzi. Four patients were excluded because they produced significant head movements during their ticcing manifestations that were not compatible with the MRI setting. Patient data were compared with those of 24 healthy subjects who were matched for sex and age with no history of neurological or psychiatric illness. All participants were right handed, as assessed by the Edinburgh Handedness Questionnaire (Oldfield, 1971). The study protocol was approved by the Institutional Review Board (Comitato Etico ASL Citt
a di Milano), and informed written consent

was obtained from all of the patients in accordance with the Helsinki Declaration (1964). All participants underwent a neuropsychological examination to assess their cognitive functioning, with a specific focus on the functioning of their frontal lobes (Table 1). The following psychopathological variables, which are frequently associated with GTS, were also assessed in the patients – the Baratt Impulsivity Scale (BIS) for impulsivity (Fossati et al., 2001); the Yale–Brown Obsessive Compulsive Scale for OCD (Goodman et al., 1989); the STAI X (1 and 2) for anxiety disorders (Goodman et al., 1989); and the Beck Depression Inventory for depression assessment (Beck et al., 1961). Table 2 provides more detailed information regarding these variables. Tic severity was measured according to the YGTSS (Leckman et al., 1989) and premonitory urge was quantified using the Premonitory Urge Tics Scale (PUTS; Woods et al., 2005). Patients with comorbidities were not excluded given their high prevalence in GTS (see Robertson, 2012) and consistent with previous studies (Werner et al., 2011). Most patients (n = 17) were on medication, as follows: six patients were on neuroleptics; two were on antidepressants; nine were on both neuroleptics and antidepressants; and seven were unmedicated (see Table 2 for drugs and dosages). Analysis of clinical data To identify a compact set of variables that accounted for most of the variance related to the psychopathological profile and clinical signs, a principal component analysis was performed on the scores for the seven previously described scales. Following factor extraction, an oblique rotation method was applied to factors with Eigenvalues ≥ 1.0 (see, for example, Cavanna et al., 2011). This procedure minimises the number of variables with high loadings on each extracted factor.

Table 1. Demographic and neuropsychological data of participants

Patient no.

Age (years)

Education (years)

Sex

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

30 29 19 26 54 22 38 37 22 34 19 19 24 18 34 19 19 19 34 21 61 25 38 50

8 8 10 8 10 13 13 13 13 11 8 11 13 11 8 13 8 13 8 13 13 18 11 13

M M M M M M M M M M M M F M F M M M M F F M M F

Trail A 29 37 45 65 45.5 52 34.5 40.5 51 – 57 53 39 114* 26 41 48 – 52 44.42 19 53 29.5 79

Trail B

Stroop TI

Stroop EI

FAB

Digit Span

Fluency V

Fluency S

Raven

Short Story

127 104 106 147 134 115 58 82 140 – 138 125 110 242 68 105 97 – N.A. 98.09 40.5

15.75 23 26.12 28 23.12 20.12 24 22.5 21 28 33 33.1 22.6 33.6 31.5 29.25 26 – 24 15.6 10.25 24.5 20.93 43.75

0 0 0 3.75 0.25 0 0 0 0 0 2.5 2.75 0 5.75* 1.5 0 0 – 0 0 0 0 0 0

17.22 16.73 15.3 16.72 15.44 16.72 16.78 15.78 15 15.15 16.7 17.1 15.72 15.1 16.76 16.71 16.7 – – 16.72 15.88 15.94 17.17 16.83

5.75 4.75 5.25 7.75 5.87 3.5* 6 6.5 6 3.69* 5.75 3.62 6.5 3.62 5 5.5 5.75 – 6 8.5 7.75 7 7.75 4.75

34 31 8.5* 26 45 39 29.5 33.5 13* 34.25 12* 24.5 21 7.5* 39 24 33 – – 31 40 41 29.5 17.5

31.5 48.50 30 16* 51 36 33 36 31 50.5 40 25 38 29 42 35 32 – – 43 59 42 26 30.50

24.5 31.3 32.5 25.1 17.5* 28.4 31.6 30.6 24.4 28.45 26.8 29.5 30.4 26.5 31.9 30.2 31.8 – – 29.4 33.1 28.9 30.85 16.8

12 21.25 8.5 6* 9.5 15 11.25 14 13.5 15.5 10.5 17.25 20 15.25 16 14 15.5 – 9.5 9.5 22.5 17 10.5 9

48 N.E.

FAB, Frontal Assessment Battery; N.A., not assessed; N.E. not executed. *Pathological scores. © 2015 Federation of European Neuroscience Societies and John Wiley & Sons Ltd European Journal of Neuroscience, 43, 494–508

Motor control in Tourette syndrome 497 Table 2. Clinical and medical data of participants Patient no.

BIS

Y-BOCS

BDI

STAI-X1

STAI-X 2

YGTSS-m

YGTSS-v

PUTS

Neuroleptic

Antidepressant

Other

Class

1 2 3 4 5 6

73 59 81* 70 65 59

28* 13* 13* 21* 29* 9*

14* 4 16* 8 23* 10*

37* 47* 21 39* 35 35

45* 37 46* 32 74* 42*

12 16 20 20 20 18

20 17 20 22 7 18

36 34 34 23 34 35

Citalopram 20 – Fluvoxamina 50 – – Paroxetine 20

– – – – – –

1 0 1 0 0.5 1

7 8

75 79*

0 0

5 7

40* 30

37 44*

11 12

9 0

22 11

Tiapride 100 – Pimozide 4 – Pimozide 4 Pimozide 4; Tiapride 100 Perfenazina 4 Pimozide 4; Clomipramine 10; Aripiprazole 5

– –

– –

0.5 0.5

9 10

63 63

0 12*

9 11*

32 59*

33 50*

14 21

0 20

9 29

Fluvoxamina 50 –

– –

2 0.5

11 12 13 14 15 16 17 18 19 20 21 22 23 24

52 68 80* 62 71 70 63 58 92* ND 76* 71 51 65

6 17* 28* 17* 0 5 0 26* 13* 15* 9 12* 10* 14*

5 9 12* 1 9 11* 6 2 25* ND 14* 22* 11* 10*

30 50* 53* 31 45* 44* 40* 35 48* ND ND 68* 29 36

30 45* 55* 35 45* 49* 39* 50* 68* ND 38 66* 46* 54*

13 19 16 10 1 16 10 13 13 23 7 16 11 13

0 18 16 8 25 8 14 9 4 20 7 18 9 0

28 29 24 15 33 31 16 32 33 23 9 31 22 32

Clomipramine 10 Citalopram 20 Duloxetine 30

– – – – – – – – – – – Baclofene 10 – –

1 1 1 0.5 1 1 0 0 1 0 0 2 0.5 0

Pimozide 4; Sulpiride 200 Aloperidolo 10 Tiapride 100 Pimozide 4 Sulpiride 200 Tiapride 100 Pimozide 4 – – Fluvoxamina 50 – – Aloperidolo 10 –

Duloxetine 30 Citalopram 20 – – Citalopram 20 – – Citalopram 20 –

ND, not determined. *Pathological scores.

Behavioural experiment The behavioural experiment aimed to assess the presence of behavioural differences in motor execution (ME) and motor imagination (MI) between patients and controls during tasks of increasing complexity. The following tasks were performed 30 min prior to the fMRI scan. There were three classes of executed or imagined movements, as follows: the identical finger opposition task that was used during the fMRI scans; a prono-supination of the forearm while keeping the hand extended; and a fist-making movement. Each class of movement included eight trials for each limb. Each trial involved a variable number of cycles (2–5) to collect meaningful behavioural data while keeping the subjects sufficiently involved in the task. Each trial (e.g. four cycles of the prono-supination of the left forearm) was repeated twice for a total of 24 trials overall for each upper limb. The entire protocol was performed separately for each hand. The order of the tasks and the number of cycles in each trial were counterbalanced. Half of the subjects started with the right upper limb. Across all conditions, each subject sat comfortably in front of an examination desk. During the finger opposition task, the basic cycle involved four taps (i.e. thumb-to-index, thumb-to-middle, thumb-toring and thumb-to-little finger) with the subject’s forearm lying in a supinated position. For the prono-supination of the forearm task, the subject laid a forearm on the desk in a half-pronated position (i.e. the starting position). For the fist-making movements, the subject’s forearm was placed in a supinated position. The tasks were performed with the subject’s eyes closed. For the MI tasks, the subjects were invited to perform the tasks using a kinaesthetic imagery modality, consistent with the subse-

quent fMRI scans. We timed the duration of each movement (explicitly executed or imagined). Each trial began with the experimenter providing a ‘go’ signal to start the execution/imagination of the requested movement. The subjects were instructed to say ‘stop’ as soon as the executed or imagined movement was completed. The durations to complete the ME and MI tasks were measured using an electronic stopwatch. Specifically, the duration of each trial was measured from the ‘go’ signal to the ‘stop’ signal (as selfreported by the participant). The temporal correlation between the execution and the imagination of the same finger movements was examined to determine whether motor imagery could be utilised as a tool to investigate the higher aspects of motor control, such as motor planning and action representation (Jeannerod, 2001). Statistical analyses of the behavioural data The ME and MI durations for the behavioural tasks performed outside of the scanner were analysed with a 2 (Group: GTS patients/ healthy controls) 9 3 (Movement: finger-opposition/fist-making/ prono-supination) 9 2 (Task: ME/MI) 9 2 (Hand: left hand/right hand) anaysis of variance (ANOVA). A Greenhouse–Geisser correction for non-sphericity was used when necessary. Correlation analyses using the Pearson coefficient were also performed to assess the temporal congruence between the ME and MI tasks. fMRI experiment MRI scans were performed using a 1.5-T Siemens Avanto scanner, which was equipped with echoplanar hardware for imaging with a flip

© 2015 Federation of European Neuroscience Societies and John Wiley & Sons Ltd European Journal of Neuroscience, 43, 494–508

498 L. Zapparoli et al. angle of 90°, TE = 60 ms, TR = 3000 ms, FOV = 280 9 210 mm and matrix = 96 9 64 for both scanners. Slice thickness was 5 mm. All the subjects were also scanned with an MPRAGE high-resolution T1-weighted volumetric scan for further visualisation of the results and for the voxel-based morphometry analysis. Scanning parameters were a flip angle of 35°, TE = 5 ms, TR = 21 ms, FOV = 256 9 192 mm, matrices 256 9 256, TI = 768 for a total of 160 axial slices with 1 9 1 9 1-mm voxels. The fMRI experiment consisted of the following ME and MI tasks. Finger opposition movements The subjects were asked to alternately move their right or left hand. The movements, which were performed at a frequency of approximately 1 Hz, involved touching the thumb to the fingers in the following sequence: thumb to index, thumb to middle finger, etc. Following a few practice trials, which were performed before the scanning procedure, the subjects were able to perform the motor task accurately at the desired speed. The subjects were monitored throughout the experiment for movement precision and speed. The task was self-paced, but the subjects were loosely cued in that once every 6 s they were given verbal reminders to perform the task. Performance of the tasks was alternated with resting state scans according to a block design. During the resting baseline control condition, the subjects were instructed to relax and to think of nothing. Consistent with the previous task, the subjects were loosely cued and received a verbal reminder every 6 s to remain in a relaxed resting state. We provided a verbal ‘stop’ signal at the end of each block through MRI-compatible headphones. Each block lasted 30 s (with 10 scans performed in each period). There were three motor blocks and three resting blocks for each hand presented in alternating order. Motor imagery of the finger opposition movements The subjects were asked to imagine the same movements that they had performed during the previous explicit task. The subjects were instructed to avoid any overt motion. As in the previous task, the design consisted of 30-s alternating blocks of motor planning/imagery and rest, including the same verbal cues. We provided a verbal ‘stop’ signal at the end of each block through MRI-compatible headphones. The task was self-paced, but the subjects were loosely cued to perform the task, similar to the explicit motor task. The subjects were instructed to imagine the movements from a kinaesthetic first-person perspective, not from a third-person perspective, and were told not to count or assign numbers to each finger. All the subjects were carefully observed by one of the experimenters in the scanner room who monitored for the correct execution of the ME tasks and the presence of spurious motor acts during the MI tasks. The subjects were also debriefed following each experimental session (i.e. behavioural or fMRI) about their experiences. All the subjects confirmed that they had performed the motor imagery task as instructed.

Pre-processing Following image reconstruction, raw data visualisation and conversion from DICOM to the NIFTI format were performed using the MRIcron software (www.mricro.com). All subsequent data analyses were performed in MATLAB 7.0 (Math Works, Natick, MA, USA) using the Statistical Parametric Mapping software (SPM8; Wellcome Department of Imaging Neuroscience, London, UK). First, the fMRI scans were realigned to account for any movement during the experiment, and then the scans were stereotactically normalised into MNI–EPI fMRI template spaces to permit group analyses of the data (Friston et al., 1995; Ashburner & Friston, 1999). At this stage, the data matrix was interpolated to produce voxels with dimensions of 2 9 2 9 2 mm. The stereotactically normalised scans were smoothed using a Gaussian filter of 10 9 10 9 10 mm to improve the signal-to-noise ratio. Statistical analyses The blood oxygen level-dependent (BOLD) signal associated with each experimental condition was analysed using a convolution with a canonical haemodynamic response function (Worsley & Friston, 1995). Global differences in the fMRI signal were removed from all the voxels with proportional scaling. High-pass filtering (128 s) was used to remove artefactual contributions to the fMRI signal, such as noise from cardiac and respiratory cycles. First, a fixed-effect blockdesign analysis was performed for each subject to characterise the BOLD response associated with each task when compared with rest. We created a contrast image for the effect of ME and MI for each hand of each subject within each group. For example, we created the following contrast images for both groups for the motor execution task: ‘ME with right hand > rest’ and ‘ME with left hand > rest’. To permit generalisation to the population level using group-based statistical inferences, the individual contrast images generated by the fixed-effect analyses were entered into second-level ANOVAs that conformed to random effect analyses (Holmes & Friston, 1998; Penny & Holmes, 2004). We performed a full factorial analysis of variance with the following three factors: Group (healthy controls/GTS patients), Task (ME/MI) and Hand (right hand/left hand). F-contrasts were obtained for the main effects and interactions. Post-hoc analyses were performed to examine the directions of the effects using linear contrasts and t-statistics. Similarities across the groups for each task were assessed as full conjunctions that were family-wise error (FWE)-corrected at the voxel level. To examine group differences, given that we anticipated a distributed pattern of perturbed functional anatomical responses in GTS, we controlled for false positives by considering only the brain regions that belonged to a cluster that was significant at P < 0.05 with regard to its spatial extent (Worsley et al., 1996) for maps visualised with a P < 0.01 voxel-wise threshold.

fMRI data acquisition and analysis

Analysis of head motion parameters measured on the fMRI data

There were 30 fMRI volumes for each condition (rest for ME, rest for MI, ME and MI) for a total of 120 complete brain volumes during the active tasks and baselines. For the 12 GTS patients who rated their urges, there were three additional fMRI volumes (9 s of scanning) at the end of each block. These data are not reported in this paper.

To assess the effect of motion artefacts on our results, we compared the degree of motion between the healthy controls and the GTS patients during the fMRI scan. The six realignment parameters that were identified by SPM during the fMRI data realignment were used to conduct these comparisons with multiple Mann–Whitney U tests.

© 2015 Federation of European Neuroscience Societies and John Wiley & Sons Ltd European Journal of Neuroscience, 43, 494–508

Motor control in Tourette syndrome 499 Correlations between clinical indices and fMRI patterns To investigate the possible relationships among the clinical indices and the neurofunctional patterns associated with motor control, we performed linear regression analyses on the fMRI data that were recorded during the experimental tasks using the YGTSSs (motoric sub-scale) and the urge quantification data recorded during the experiment as covariates.

There were usable volumetric T1 data from 20 GTS patients, which were compared with the data from the 20 best-matched controls. The structural differences between the two groups were estimated using a two-sample t-test analysis on a voxel-by-voxel basis.

Results Clinical data

Measurements of the urge to tic and tic occurrence during fMRI scanning Data regarding the urge to tic and tic occurrence during the fMRI scanning were available for 12 patients. All the subjects were carefully monitored during the fMRI scanning to count tics: patients were visually monitored online by one experimenter who was very close to the patient with a clear view of the patient’s whole body. The experimenter used a response-box connected to a computer to tag the times of tic onset. The information about the onset of the (rare) tics was used in ad-hoc fMRI analyses, which revealed no significant regional effect in any of the conditions or baselines. Moreover, at the end of each block, the subjects were asked to judge their urge to tic during the preceding 30 s using a scale from 1 (= no urge) to 5 (= felt the urge the whole time). The possibility that tics or urges were associated with a given experimental condition was assessed with two separate repeatedmeasures ANOVAs, each consisting of 2 (Task: ME/MI) 9 2 (Condition: active task/rest) factors. Correlations with neuroleptic medication levels To assess the possible effects of neuroleptic medication on the fMRI data, given their potent effects on the motor system in GTS patients even at very low doses, we performed linear regression analyses on the ME and MI fMRI data using chlorpromazine equivalent scores as covariates (Tibaldi et al., 1997; see also Table 2). The congruency of the neuroleptic-specific effects with the group-specific effects was tested by inclusively masking the two sets of results. These analyses were implemented across the entire sample of patients, including the 14 patients who were receiving neuroleptic medication at the time of the experiment and all of the un-medicated patients. For the un-medicated patients, a nominal zero dosage was assigned. Moreover, we extracted the fMRI-predicted response from the general linear model analysis of the local maxima of each of the group effects and we performed a correlation analysis between the BOLD response and the neuroleptic doses. Similar analyses were not possible with regard to antidepressant use as all the treated patients (n = 11) were medicated within the same lowest range of the recommended dosage, as classified by Hansen et al. (2009). VBM methods Data of the high-resolution volumetric T1-weighted images were analysed using MATLAB 7.0 and SPM8. The MRI data were processed using a unified segmentation protocol, as described by Ashburner & Friston (2005). The MRI scans were normalised and segmented into grey matter, white matter and cerebrospinal fluid compartments. A Jacobian modulation was applied to preserve the total regional amount of grey/white matter from the distortion introduced by the stereotactic normalisation.

The principal component factor analysis identified two factors with eigenvalues > 1. These two factors accounted for 60.1% of the variance (Table 3). Factor 1 explained 40.1% of the variance, with loadings from symptoms associated with the behavioural/emotional variables (i.e. impulsivity, anxiety and depression). Factor 2 explained 20% of the variance, with loadings from symptoms associated with the ticcing manifestations (i.e. motoric and sound tics, premonitory urges and obsessive-compulsive symptoms). Cavanna et al. (2011) found similar results with larger samples. This consistency suggests that our subjects represent a fairly typical sample of adult GTS patients (Robertson, 2012) and that GTS is a heterogeneous condition with a high prevalence of co-morbidity (only three of 24 patients did not exhibit behavioural symptoms). Behavioural results Two different experimenters carefully monitored each patient during the behavioural part of the study and no tics occurred during the execution/imagination of the movements. (In principle, video recordings of the experimental session should be preferable for offline measurements of tics. Yet, the presence of two examiners makes it fairly unlikely that a substantial number of tics were missed during the short burst of motor performance entailed by the experimental paradigm.) Table 4 presents the results for the Greenhouse–Geisser corrected repeated-measures 2 (Group: patient group/healthy controls) 9 3 (Movement: finger-opposition/fist-making/prono-supination) 9 2 (Task: ME/MI) 9 2 (Hand: left/right) ANOVA on reaction time (RT) data (median, in seconds). In sum, no overall group effect was observed, but there were clear movement- (finger opposition, fist making, prono-supination) and task-specific (execution or imagery) effects. The finger opposition movement had the longest RTs and the motor imagery task was associated with longer RTs. The substantial trend for a movement by task interaction effect was due to the finger opposition movement, which was slower during the motor imagery task. Next, we calculated the Pearson r-coefficient between the executed and imagined movement for each task with each hand for each group. For each motor task, there were significant positive correlations for both the healthy subjects and GTS patients, as follows: Table 3. Oblique three factor solution for 7 GTS symptoms in 24 patients Symptom

Factor 1

Hyperactivity/impulsivity Obsessive-compulsive behaviours Depression Anxiety Complex motor tics Complex vocal tics Premonitory urge

0.721

© 2015 Federation of European Neuroscience Societies and John Wiley & Sons Ltd European Journal of Neuroscience, 43, 494–508

Factor 2

0.762 0.911 0.882 0.613 0.627 0.763

500 L. Zapparoli et al. Table 4. Summary of the behavioural results Effect Main effects Group Movement Hand Task Within-group interactions Movement 9 Hand Movement 9 Task Hand 9 Task Movement 9 Hand 9 Task By-group interactions Movement 9 Group Movement 9 Task 9 Group Hand 9 Task 9 Group Movement 9 Hand 9 Task 9 Group

Urge quantification F

P-value

F1,38 = 0.005 F1.2,45.9 = 141.3 F1,38 = 0.8 F1,38 = 9.7

0.95 < 0.001 0.8 0.003

F1.5,57 = 1.7 F1.4,51.6 = 3.5 F1,38 = 0.035 F1.3,50.7 = 2.7

0.2 0.06 0.64 0.1

F1.2,45.9 = 6.7 F1.4,51.6 = 1.8 F1,38 = 0.2 F1.3,50.8 = 1.9

0.04 0.2 0.6 0.2

Repeated-measures ANOVA: 2 (Group: patients/healthy controls) 9 3 (Movement: finger-opposition/fist-making/prono-supination) 9 2 (Task: ME/ MI) 9 2 (Hand: left/right). Bold values indicate significant results (P < 0.06).

The magnitude of the urge to tic during the different fMRI conditions showed a trend similar to that for the number of tics, with a reduction from the MI baseline (mean: 3; SD: 1.2) to the MI experimental blocks (mean: 2.6; SD: 1.2) and from the ME baseline (mean: 2.1; SD: 0.6) to the ME experimental blocks (mean: 1.7; SD: 0.8). These results are presented in Table 5b. The repeated-measures ANOVA showed a significant effect of Task (ME or MI) (F1,11 = 7.9, P = 0.02), with a significant reduction in premonitory urges during the ME task. There was also a significant effect of Condition (active task/baseline) (F1,11 = 13.6, P = 0.004), with a significant reduction in premonitory urges during the experimental blocks. However, the Task 9 Condition interaction was not significant (F1,11 = 0.1, P = 0.7). fMRI results: similarities and differences between the normal controls and GTS patients

Healthy subjects: r = 0.8 for the right hand, P < 0.001; r = 0.9 for the left hand, P < 0.001. GTS patients: r = 0.8 for the right hand, P < 0.001; r = 0.9 for the left hand, P < 0.001.

Given the 2 9 2 9 2 design with two within-group factors (ME and MI), two sides of the body (right and left) and one betweengroup factor (GTS patients and healthy subjects), there are numerous results to report. In this paper, we focus on the most relevant results. More general results are illustrated in Fig. S1 and Fig. S2. The effects not mentioned in the current paper were not significant at the thresholds imposed on the statistical maps.

Prono-supination task

Similarities between GTS patients and healthy subjects

Healthy subjects: r = 0.7 for the right hand, P < 0.001; r = 0.9 for the left hand, P < 0.001. GTS patients: r = 0.9 for the right hand, P < 0.001; r = 0.9 for the left hand, P < 0.001.

For the ME task, both the GTS patients and healthy controls bilaterally activated the precentral gyrus (areas 4 and 6), the postcentral gyrus (area 2), the supramarginal gyrus, the cerebellum and the thalamus. Additional shared activations were located in the right rolandic operculum, the left SMA, the left superior parietal lobule, the left middle temporal gyrus and the left putamen. The results are presented in Fig. 1a (areas in orange) and Table 6a. For the MI task, the areas of shared activation across the groups were the bilateral precentral gyrus (areas 6 and 44), the bilateral superior and inferior parietal lobules, and the bilateral supramarginal gyrus. Shared activation was also found in the left middle and inferior frontal gyri, the left SMA, the left insula and the right superior frontal gyrus (area 6). The results are presented in Fig. 1a (areas in blue) and Table 6b.

Finger-opposition task

Fist-making task Healthy subjects: r = 0.9 for the right hand, P < 0.001; r = 0.9 for the left hand, P < 0.001. GTS patients: r = 0.9 for the right hand, P < 0.001; r = 0.9 for the left hand, P < 0.001. Analysis of head motion parameters measured with the fMRI data There were no significant between-group differences for any of the realignment parameters, for both the ME and the MI tasks (Table S2). Tic registration and urge to tic quantification during fMRI Number of tics Ticcing activity was maximal during the baseline blocks of the MI task (mean: 6.3; SD: 3.6), with a small yet significant reduction in the ticcing manifestation during the experimental blocks of the same task (mean: 4.6; SD: 3.4). In contrast, there was a decrease in the number of tics during the ME task, both during the baseline (mean: 2.8; SD: 2.4) and during the experimental blocks (mean: 2.4; SD: 1.9). These results are presented in Table 5a). The repeated-measures ANOVA showed a significant effect of Task (ME or MI) (F1,11 = 9.2, P = 0.01), with a significant reduction in the number of tics during the ME task. There was also a main effect of Condition (active task/baseline) (F1,11 = 4.9, P = 0.05) and a significant Task 9 Condition interaction (F1,11 = 6.1, P = 0.03). Note that the effect sizes for these effects were fairly small.

Differences between the GTS patients and healthy subjects (ME task) For the ME task, GTS patients showed additional activation bilaterally in the middle frontal gyrus, the right superior frontal gyrus (area 6) and the left inferior frontal gyrus (triangular part). The results are presented in Fig. 1b (areas in orange) and Table 6c. Differences between the GTS patients and healthy subjects (MI task) For the MI task, the analysis revealed greater recruitment in the GTS group of the middle frontal gyri bilaterally and of a series of right hemispheric structures, as follows: the superior frontal gyrus, the middle cingulum, the insula, the angular gyrus, the superior temporal gyrus and the temporal pole. For the right temporo-parietal and the medial rostral prefrontal regions, there were higher level (group 9 task) interactions (x = 40; y = –32; z = 2.0; Z score: 3.4; x = 0; y = 56; z = 26; Z score: 2.3), showing that the greater activa-

© 2015 Federation of European Neuroscience Societies and John Wiley & Sons Ltd European Journal of Neuroscience, 43, 494–508

Motor control in Tourette syndrome 501 a

b

Fig. 1. Patterns of significant BOLD responses recorded during the experimental tasks. The upper left half of the figure shows shared patterns across groups. The upper right half of the figure shows areas of increased activations in the GTS patients. The lower half of the figure shows the main effect of the group, across the two tasks.

tion for the GTS patients was highly specific to the motor imagery task. The results are presented in Fig. 1b (areas in blue) and Table 6d.

Table 5. Number of tics and urge to tic quantification for each experiment task and each condition

Confounding effect of tics on the fMRI results

(a) Number of tics 1 2 2 4 3 12 4 10 5 5 6 11 7 3 8 1 9 3 10 3 11 6 12 7 Mean 5.6 SD 3.7 (b) Urge to tic quantification 1 2.7 2 3.7 3 1.5 4 4.3 5 2.0 6 2.2 7 1.7 8 2.8 9 2.0 10 4.3 11 4.7 12 4.7 Mean 3.0 SD 1.2

Given that there were a significantly larger number of tics during the motor imagery task than the motor execution task, we compared the effects of our original generalised linear model analysis during which the number of tics was maximal (baseline for MI > MI) with the analysis during which the number was minimal (ME > baseline ME). No single voxel was active in this comparison. In addition, for each of the 12 patients for whom we had the timing of the tics, we characterised the BOLD responses associated with tic activity for each condition (i.e. motor execution, motor imagery and their time-matched baselines) using an event-related analysis in which the onset of the event corresponded with the onset of the tics. No single voxel survived a canonical uncorrected 0.001 threshold for any simple effect (e.g. event-related analysis of tics during motor imagery). This was also evident for the direct comparisons of the effects. fMRI results: correlations between the fMRI data and YGTSS There were positive correlations evident for the activity within the premotor areas (i.e. bilateral precentral gyrus, area 6 and SMA) (local maxima < 0.001; cluster-level uncorrected significance: < 0.05). An identical analysis of the motor imagery data revealed clusters that survived an FWE 0.05 correction for spatial extent that spanned the dorsal premotor cortex bilaterally, the SMA and the right posterior parietal cortex. The right superior frontal cortex also showed greater activation in the GTS patients compared with the controls. However, a vast part of the premotor/parietal regions that correlated with the YGTSS was outside the areas of hyperactivation in the GTS patients. The results are presented in Fig. 2 and Table 7. Effects of neuroleptics The linear regression analysis on the ME task showed no significant effect, whereas the identical analysis on the MI task revealed a sig-

Patient no.

Rest MI

MI

Rest ME

ME

0 4 10 10 3 8 1 0 5 3 3 3 4.2 3.5

3 0 7 1 5 1 4 1 1 1 7 5 3.0 2.5

2 2 3 4 1 1 1 2 1 0 7 4 2.3 1.9

2.3 3.3 1.5 4.3 1.8 1.7 1.0 3.2 1.0 4.3 2.5 3.8 2.6 1.2

2.7 2.8 1.7 2.7 2.2 2.0 1.0 2.8 1.8 2.3 1.7 1.8 2.1 0.6

1.2 2.5 1.0 2.0 2.0 2.0 1.0 3.5 1.2 1.0 1.8 1.5 1.7 0.8

Symptom loadings with coefficient absolute values > 0.400.

nificant positive correlation in the postcentral gyrus bilaterally (x = –52, y = 22, z = –34; Z score: 4.3; x = 62, y = –10, z = 24; Z score: 3.9; see Fig. 6d), the left temporal pole (x = –52, y = 8, z = –2; Z score: 4.6) and the SMA (x = –4, y = 2, z = 66; Z score: 4.1). None of these effects was evident in regions with larger activations for the GTS patients compared with the normal controls. The results are presented in Fig. 3.

© 2015 Federation of European Neuroscience Societies and John Wiley & Sons Ltd European Journal of Neuroscience, 43, 494–508

502 L. Zapparoli et al.

Fig. 2. Linear regression analyses between the YGTSS scores and the BOLD response in the GTS patients for the ME and the MI tasks.

Moreover, none of the regression analyses performed on the predicted fMRI response extracted in the four brain regions hyperactivated in the GTS group, using the neuroleptics medication dose as covariate, was significant (x = –28, y = 42, z = 32; r = –0.02; P = 0.95; x = 26, y = –2, z = 66; r = 0.07; P = 0.81; x = 32, y = 32, z = 32; r = –0.11; P = 0.71; x = 46, y = 12, z = –16; r = –0.1; P = 0.72). See Fig. S3. Region of interest analyses on the basal ganglia and thalami Both the normal controls and the GTS patients showed activation in these structures for both the motor execution and the motor imagery tasks. However, an examination of these activation patterns (see Fig. S4) revealed stronger activation for the motor execution task in the normal controls and the reverse pattern was evident in the GTS patients. When analysed as a group by task interaction effect (GTS MI > ME) > (NC MI > ME), these differences were diffusely significant at lenient uncorrected thresholds (P < 0.05) and the maximum peak for this high-order interaction effect was in the left pallidum (x = –12; y = 0; z = –2; P < 0.001). VBM results There were no significant morphometric changes in any of the brain areas showing significant fMRI effects in the VBM results. One region in the dorso-posterior part of the cingulate survived a correction for multiple comparisons, showing augmented grey matter density for the GTS patients (x = 6; y = –24; z = 48; Z score: 4.06; cluster voxel number: 898; cluster-level significance: P = 0.023 FWE-corrected).

Discussion This study was designed to re-assess whether the controlled execution of a motor act by GTS patients is functionally equivalent to what observed in normal controls. Providing that the motor task in hand is sufficiently simple, do GTS patients perform self-generated acts with the same ease and with the same brain patterns as normal controls? In the case of functional anatomical differences, are these represented by augmented or reduced activations (see, for example, the contradictory data of Roessner et al., 2012, 2013)? Do these differences involve cortical structures, subcortical structures or both? Are cortical abnormalities segregated to motor/premotor cortices or do they involve cortices associated with higher level functions (e.g. executive control/attentional functions), which may be engaged in the suppression of tic manifestation (see for a review Stern et al., 2008)? How can any differences be interpreted in the broader context of the GTS disorder? These questions have not been fully addressed by the previous literature on self-produced movements. The following two features of our study are worth noting here: the sample size, which was the largest sample used to examine voluntary motor execution in adult GTS patients, and, unique to this experiment, the use of both explicit and imagined motor tasks. The addition of a motor imagery task allowed us to examine the impact of the presence of an explicit motor outflow, rather than the mental rehearsal of it, on neurofunctional differences that are specific to GTS and the reduction in the urges to tick or explicit ticcing manifestations during scanning. Motor execution and imagination of the same finger opposition task typically share premotor and parietal cortical resources with important differences in the primary motor/

© 2015 Federation of European Neuroscience Societies and John Wiley & Sons Ltd European Journal of Neuroscience, 43, 494–508

Motor control in Tourette syndrome 503 Table 6. fMRI data:

ANOVA

Table 6 (continued)

full factorial analysis

MNI coordinates

MNI coordinates x Brain region (BA area)

y

z

Z-score

Left hemisphere

(a) Motor execution: GST ∩ Controls Rolandic – – – operculum Precentral gyrus –38 –18 62 (6) –30 –12 62 –58 4 32 Precentral gyrus –36 –26 54 (4) Postcentral gyrus –44 –30 46 (2) –54 –22 30 SMA (6) –2 –2 54 Sup. parietal –26 –52 62 lobule (7) Supramarginal –54 –22 22 gyrus (40) – – – Mid. temporal –48 –64 6 gyrus (37) Cerebellum –18 –50 –26 –4 –56 –14 Putamen –22 0 10 Thalamus –14 –22 8 –12 –20 4 (b) Motor imagery: GST ∩ Controls Sup. frontal gyrus – – – (6) – – – Mid. frontal gyrus –30 –4 50 (6) Mid. frontal gyrus –34 40 26 (46) Inf. frontal gyrus –38 40 14 (triangular part) –42 38 14 Inf. frontal gyrus –50 8 6 (orbital part) – – – Precentral gyrus –52 8 38 (44) Precentral gyrus – – – (6) – – – – – – SMA (6) –10 2 58 –6 4 54 Sup. parietal –18 –66 56 lobule (7) Inf. parietal lobule –42 –38 40 (40) –38 –52 54 –34 –56 56 Supramarginal –56 –36 34 – – – gyrus (40) Insula –32 18 0 –40 12 –2 (c) ME GTS > ME healthy controls Sup. frontal gyrus – – – (6) – – – –32 28 40 Mid. frontal gyrus (46) –34 30 34 –32 34 36 Mid. frontal gyrus – – – (9) – – – Inf. frontal gyrus –38 34 16 (Triangular part) (d) MI GTS > MI healthy controls Sup. frontal gyrus – – – (9) – – – Sup. medial gyrus –10 42 44 (32) –6 46 22 – – – – – –

x

y

z

Z-score

Right hemisphere –

54

–22

20

Inf Inf Inf Inf

34 – – 40

–12 – – –20

60 – – 56

Inf – – Inf

Inf 7.8 Inf 7.4

34 30 – –

–36 –46 – –

48 62 – –

Inf Inf – –

7.7 – 5.3

60 60 –

–16 –28 –

24 22 –

7.2 7.1 –

Inf Inf 5.7 6.1 6.1

18 – – 14 –

–52 – – –18 –

–24 – – 4 –

Inf – – 7.0 –

– – 7.0

26 20 –

4 8 –

58 58 –

4.9 4.6 –

6.0

–

–

–

5.1 5.1 6.9 – 7.0

– – 56 52 50

– – 12 8 6

– – 8 18 40

– – 5.6 5.0 5.0

– – – 6.8 6.7 5.5

32 50 46 – – 18

–2 6 2 – – –68

50 30 44 – – 60

5.7 5.4 5.4 – – 4.9

7.7 6.6 6.6 6.7 – 5.8 5.3

36 – – 44 48 – –

–48 – – –36 –34 – –

48 – – 42 44 – –

5.1 – – 5.6 5.5 – –

– – 3.4 3.3 3.2 – – 2.5

28 22 34 34 38 32 32 –

–4 –12 32 42 32 8 10 –

64 58 30 28 38 54 46 –

3.9 2.6 3.3 3.1 2.9 3.0 2.5 –

– – 2.7 2.4 – –

18 20 10 2 14 12

42 26 54 50 50 34

46 32 30 32 28 52

3.2 2.8 3.2 3.2 3.1 2.4

7.1

–

(continued)

x Brain region (BA area) Mid. cingulum (32) Mid. frontal gyrus (9) Insula Angular gyrus (39) Sup. temporal gyrus (22) Temporal pole (38)

y

z

Z-score

Left hemisphere – – –26 –24 – – – – – – – –

– – 44 36 – – – – – – – –

– – 34 44 – – – – – – – –

x

y

z

Z-score

Right hemisphere – – 4.1 2.7 – – – – – – – –

16 14 30 24 40 52 58 58 60 46 46 44

26 16 30 50 16 –52 –54 –46 –50 –44 –38 16

30 38 36 30 10 30 30 14 20 12 10 –18

2.6 2.4 3.4 3.4 3.3 3.0 2.8 3.4 3.3 2.8 2.7 3.3

x, y, and z are the stereotactic coordinates of the activations in MNI space. Statistical threshold P < 0.01 uncorrected, P < 0.05 at cluster level.

somatosensory mantle, which is only active during explicit motor tasks, whereas some prefrontal cortices appear more active during the imagery task (see, for example, Hanakawa et al., 2003; Zapparoli et al., 2013). The availability of the motor imagery task provided us with an intermediate viewpoint between a resting condition, which is typically associated with the largest number of tics or urges (Heise et al., 2010), and the explicit execution of a motor task, which was associated with a smaller number of tics in our data. In principle, the concurrent explicit execution of a motor act may reduce tics (Heise et al., 2010) through modulation of the cortico/ subcortical malfunctioning structures because the cortical outflowing system is engaged and may interact successfully with subcortical structures or other cortical regions that operate as tic suppressors (Peterson et al., 1998) or as contention schedulers [if tics are conceptualised as acts that interfere with primary motor goals, any brain area involved in tic suppression may be conceptualised as part of Shallice’s (1988) contention scheduler]. Yet, if the mere simulation of motor acts is sufficient to interact with the underlying spontaneous ticcing activity, one should expect similar, if not identical, neurofunctional differences between the GTS patients and controls during both the motor execution and the motor imagery tasks. Of course, our experiment was based on a delicate balance between the need to prove that the experimental manipulations during the scans were successful in bringing about some modification of the symptoms while not being confounded by a severe unbalance of ticcing manifestations across conditions. We believe that our data satisfied these requirements. We will start by discussing the behavioural motor experiment performed outside of the scanner. This behavioural part of the study was crucial for interpreting the fMRI findings. We will then address the meaning of the neurofunctional differences associated with GTS. These differences were located in premotor regions, as well as in brain areas typically associated with higher order aspects of motor control, including the dorsolateral prefrontal cortex. Next, we will address the relationship between the severity of the individual clinical pattern and the functional anatomical patterns. Finally, although our experiment was restricted to only one class of motor tasks, we will discuss the relevance of our findings with regard to a more general understanding of GTS.

© 2015 Federation of European Neuroscience Societies and John Wiley & Sons Ltd European Journal of Neuroscience, 43, 494–508

504 L. Zapparoli et al. Table 7. Correlation between fMRI and clinical data: multiple regression analysis MNI coordinates x Brain region (BA area)

y

z

Z-score

Left hemisphere

(a) Motor execution: motor tics Sup. frontal gyrus (6) – – Precentral gyrus (6) –16 –18 SMA (6) –12 –10 – – (b) Motor execution: sound tics* (c) Motor imagery: motor tics Sup. frontal gyrus (6) –18 –6 – – Mid. frontal gyrus (9) – – Precentral gyrus (6) –20 –12 –26 –16 –32 –14 SMA (6) –12 –8 –6 –10 Sup. parietal – – lobule (7) – – (d) Motor imagery: sound tics*

x

y

z

Z-score

Right hemisphere

– 70 62 –

– 3.5 3.2 –

14 – 2 16

–8 – –8 –2

74 – 64 68

2.7 – 3.0 2.8

62 – – 58 62 66 64 64 – –

3.6 – – 3.0 2.9 2.4 3.6 3.3 – –

28 28 34 – – – 12 4 30 18

–2 12 2 – – – –8 –10 –62 –70

58 58 62 – – – 66 62 60 52

4.1 2.9 4.1 – – – 3.2 3.7 4.6 3.8

*No voxel survived the statistical threshold.

Functional anatomical patterns of explicit and imagined motor control in GTS Fig. 3. Linear regression analyses between the fMRI activations during the tasks and the doses of neuroleptic medication.

Behavioural findings Performance during the purely behavioural experiment Previous data on voluntary motor behaviour in GTS showed a decline in performance that was associated with the disease: Georgiou et al. (1995) reported that GTS patients were more reliant than controls on external visual cues during a visually cued button press motor task, as they required an external sensory cue to program a motor sequence efficiently. However, results from recent studies with simple motor paradigms (e.g. simple hand movements) suggest that GTS patients perform like normal controls when the motor act is simple (Werner et al., 2011). Our results confirm these recent findings for all of the movements included in our behavioural experiment. Similar to healthy controls, the GTS patients showed increasing movement durations with the complexity of the movement (from the simplest fist-making movement to the more complex finger opposition movement) and RTs were longer during the MI task. In addition, the MI RTs were correlated with the ME times in a similar manner for both the GTS patients and the normal controls. Accordingly, we assume that (i) differences in task performance do not account for the fMRI findings, (ii) GTS patients are capable of motor imagination and (iii) the motor imagery task serves as a tool for studying the physiology of motor planning in GTS patients. However, these behavioural findings do not necessarily imply a physiological equivalence between GTS patients and normal controls, as comparable motor performance may be achieved through compensatory, or perhaps additional, neural activity that may contribute to counterbalancing the urges to tic, or the tics themselves. This possibility is discussed next in light of the fMRI findings.

Comparisons between the brain activations of the two groups revealed significant fMRI differences in that there was greater recruitment of premotor and/or prefrontal regions in GTS patients for both hands, whereas no area with decreased activation was found. Consistent with our predictions, there were task-dependent hyperactivations with a rostro-caudal gradient in the frontal lobe from the motor imagery task to the motor execution task, with the former characterised by a slightly higher number of tics during scanning. Yet, these tics were only occasional events (on average, 4.2 for MI and 2.3 for ME over approximately 4 min; see Table 5a and b). The motor imagery task provided additional information about the interplay between explicit motor acts, ticcing manifestations and ensuing neurophysiological patterns. Premotor/prefrontal hyperactivations were present during both tasks, revealing that the functional anatomical abnormalities generalised to stages of motor preparation/ rehearsal when subjects were engaged in the motor imagery task. Specifically, there were regions showing similar levels of hyperactivation during both tasks, whereas other regions showed greater activation in the typical rostro-caudal task-specific gradient from the motor imagery to the motor execution task in pre-frontal cortices, with greater involvement of the right temporo-parietal cortices as well (Hanakawa et al., 2003; Zapparoli et al., 2013). Furthermore, there were substantial higher-order group by task interaction effects (e.g. larger differences between the GTS patients and normal controls during the motor imagery task compared with the motor execution task). Additional analyses of the fMRI data (e.g. the event-related analysis with the timings of the tics used as onsets of the events) confirmed that the observed fMRI differences were not a direct manifestation of the tics themselves; rather, these differences may represent a compensatory cortical activity possibly needed to keep tics and urges under control, something that overall the subjects were able to do during the scanning procedure, even in the absence of an explicit instruction to do so. Tics can be seen as competing

© 2015 Federation of European Neuroscience Societies and John Wiley & Sons Ltd European Journal of Neuroscience, 43, 494–508

Motor control in Tourette syndrome 505 and to-be-suppressed motor acts that render the execution of motor tasks during fMRI more demanding. During the motor imagery task, the hyper-activations were evident in the cortical areas involved in the early stages of motor preparation, with no differences in the ‘implementation system’ of the primary motor cortex. The broader involvement of prefrontal regions may depend by the fact that tics where somewhat less controlled, because no explicit competition was present in the cortical outflow system, requiring a greater cognitive control. How can this compensatory activity be interpreted and how does this compensatory activity compare with the fMRI patterns described during previous explicit tic suppression experiments? In the current study, the patterns of hyperactivation showed by subjects in both tasks also involved premotor cortices. The premotor compensatory activity evident during tics is reminiscent of a similar contribution given by premotor cortices during online, non-conscious, corrections of motor plans after a perturbing factor over motor performance is introduced; a classical example are reaching tasks when the target suddenly moves (see Lee & Van Donkelar, 2006 for TMS evidence and Buiatti et al., 2013 for neuropsychological evidence). In contrast, fMRI patterns of explicit tic suppression, which are typically studied by comparing free-ticcing with no concurrent motor task, have shown activity primarily (Peterson et al., 1998) or exclusively (Ganos et al., 2014a,b,c) in prefrontal regions. It is possible that multiple levels of brain activity may concur with a suppression of tics depending on the concurrent task and the level of explicit attention paid to the tics. When the primary task implies explicit tic suppression with no additional task, the experimental setting may become more cognitively demanding, which results in greater recruitment of prefrontal activity. In contrast, when the concurrent task is motoric in nature and no explicit instructions are provided to suppress tics, tic suppression may occur more subconsciously and result in a positive interaction between tic generators and the network involved in self-produced movements. Thus, the more explicit the motor performance, the more efficient the tic suppression given the direct competition with the same structures that are involved in tic generation. fMRI patterns of self-produced movements and the nature of GTS Do these findings shed light on our general understanding of GTS? There is a set of recent TMS results that is consistent with our own and, taken together, provide a more general picture of the syndrome, particularly with regard to motor control (Ziemann et al., 1997; Gilbert et al., 2004; Orth et al., 2008; Orth, 2009). These results showed decreased inhibition, or increased excitability, of the primary motor cortex in GTS patients that correlated with the severity of the patients’ disease. This finding complements our fMRI findings with the increased spatial resolution and the panoramic anatomical sampling that are typical of this imaging technique. However, our study found that the hyperactivations of GTS patients were evident in the pre-motor and prefrontal cortices. Note that we fMRIimaged blocks of motion or mental imagery, which does not allow for fine-grained and time-resolved distinctions between preparation and execution. In Heise et al.’s (2010) experiment, the correlations between clinical scores and excitability disappeared for TMS stimulations during voluntary action. As such, the hyper-excitability of the motor cortex in GTS was evident only at rest or during earlyphase movement preparation. This cortical excitability was comparable to that of healthy controls during the last phases of motor preparation and during proper movement execution (Heise et al., 2010).

The authors interpreted their findings of cortical hyper-excitability as the manifestation of dysfunctional striato-thalamic inputs on the primary motor cortex and hypothesised regarding the interaction of pathophysiological and compensatory mechanisms: during the resting condition the abnormal activity of the striato-thalamic loop, afferent to the motor cortical areas, would result in a disinhibition of the motor cortex, facilitating occurrence of the tics; during movement execution, the higher-level motor areas and prefrontal cortex, usually engaged in the movement preparation processes, would operate a ‘top-down control’ on the abnormal excitability of the motor cortex, suppressing aberrant subcortical inputs and consequently the ticcing manifestation. The result is an adequate motor performance with a minimal number of tics (Heise et al., 2010). Our fMRI data are broadly consistent with this model: the greater activation of the premotor and prefrontal areas in GTS patients during our tasks may be the manifestation of top-down control operated by these areas on motor cortical excitability; the behavioural consequences of this top-down control would be a reduction of ticcing manifestations and of associated premonitory urges during the experimental tasks. Tic suppression is more efficient when the areas of the cortical motor outflow are directly engaged by the experimental task. Perhaps not surprisingly given their complex microanatomical structure and neurochemistry, the role of the basal ganglia and thalami remains elusive in our experiment. The trends for regional GTS-specific effects were described only to provide a complete description of the data that may prove useful in future similar studies or for meta-analyses. Face validity of the fMRI results: correlations with clinical scales The correlations among the fMRI patterns and the severity of GTS, as measured by the clinical scales of ticcing manifestations, provided clinical face validity to the present findings. In particular, the severity of motor tics in daily life was positively correlated with the activity of premotor regions (e.g. the SMA) during our tasks. Interestingly, these correlations were statistically stronger for the fMRI data recorded during the motor imagery task than for that of the real motor execution task (as demonstrated by the group differences with the healthy subjects). The explicit motor outflow during the explicit motor task, with the subsequent recruitment of the primary motor cortices, may compete, at least in part, with the neural circuits that participate in tic generation to produce a tic-free adequate motor behaviour as required by the task. Motor cortex activity should plateau during explicit motor behaviour, irrespective of the severity of the disease, causing the correlation between the BOLD signal and the YGTSS to be comparatively less powerful. Correlation analyses of the MI maps, the YGTSS scores and the group-specific hyper-activations overlapped in the right dorsal prefrontal region. However, significant correlations with the YGTSS scores were also observed in the premotor–dorsal parietal cortices, which are closer to the cortical motor outflow regions. This finding can be interpreted as an indication of greater pre-activation or disinhibition of the motor cortices, with more severe ticcing manifestations at a clinical level. The suggestion that some of these cortices are relevant for ticcing manifestations is supported by previous evidence; for example, Hampson et al. (2009) found a significant temporal correlation between the activation of SMA and the specific part of the primary motor cortex associated with tics. This correlation significantly decreased during the imitation of tics. Moreover, Neuner et al.

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506 L. Zapparoli et al. (2014) demonstrated abnormal resting-state network activity during a free-ticcing condition with GTS patients, with a specific significant correlation between the activation of SMA and the severity of the disease. Finally, a recent fMRI study by Ganos et al. (2014c) found a significant positive correlation between the activation of SMA and motor tic frequency during a stop-signal reaction-time task. Outstanding issues There are several outstanding issues that should be acknowledged. Exploration of motor control in GTS should be examined further The present work focused on one aspect of motor control, namely the voluntary production of hand movements, whereas other aspects of motor control, for example motor execution/inhibition as described with go/no-go tasks, were not considered. Accordingly, we provided an admittedly partial examination of motor control in GTS. The use of different paradigms may support the discovery of varying results that require more complex modelling that accounts for timing and the context of the motor behaviour. Indeed, recent work on this aspect with TMS (Jackson et al., 2013) provided results that partially conflict with our own results and those of Heise et al. (2010) in that Jackson et al. (2013) found a reduced excitability of M1. Thomalla et al. (2014) also described reduced activity using a go/no-go paradigm. However, their effects were more caudal (i.e. closer to M1) than our own results showed, and their go/no-go task differed considerably from a continuous motor execution task. In addition, the particular sample of GTS patients examined by Thomalla et al. (2014) had longer RTs and made more errors than subjects in our sample (i.e. the behaviour of GTS patients and controls in our experiment was balanced). Finally, the BOLD signal reduction in the motor cortices was evident at the onset of the ‘go’ instruction in their study, suggesting that controls had faster activation of the primary motor cortex during the experimental task (or an as high activity at rest in the GTS patients) while not excluding the possible (inhibitory) role of the premotor/prefrontal cortices that may hyper-activate during a continuous task, as was evident in our study. An additional important outstanding issue is that the dysfunctional role of the basal ganglia–thalamic loop remains uncharacterised by the present results. Other approaches are proving more successful here: for example, functional connectivity in resting-state fMRI or structural Diffusion Tensor Imaging (DTI) connectivity (e.g. Worbe et al., 2012, 2014). It remains to be seen whether effective connectivity analysis of fMRI data during active tasks provides more explicit evidence regarding perturbed connectivity of the basal ganglia in action. Do functional abnormalities of GTS have morphometric correlates? The VBM literature on GTS is still relatively immature. In no study was there a combined VBM and fMRI experiment. While fMRI was successful in detecting GTS-specific abnormalities, our VBM analysis failed to find significant morphometric differences between controls and GTS patients. This finding is similar to that of Roessner et al. (2009). However, others found some morphometric differences (M€uller-Vahl et al., 2009; Draganski et al., 2010; Liu et al., 2013; Ganos et al., 2014b). Given the relatively low sensitivity of VBM it is possible that larger samples are needed to find significant abnormalities in a consistent manner; the combination of classical VBM and DTI (see Draganski et al., 2010) may also prove instrumental in demonstrating significant abnormalities.

Sample heterogeneity and the effects of medication There were a variety of treatments that our patients were taking chronically at the time of our experiment, as for several previous imaging studies (Neuner et al., 2010, 2014; Werner et al., 2010, 2011; Wang et al., 2011; Worbe et al., 2011; Ganos et al., 2014a; Tinaz et al., 2014). The co-occurrence of medication, of course, hampers any firm conclusion based on imaging findings about the true nature of GTS, but makes the observations relevant for a substantial proportion of adult GTS patients from the real world, namely medicated patients. According to some estimates, these can be more than the 20% of the adult GTS population (Burd et al., 2001). In our sample, the treatment for each patient had been tailored on the basis of clinical response and the variety of treatments made it difficult to fully control statistically for their effect. In our attempt to explore the impact of neuroleptic medication, we found significant correlations, particularly for the motor imagery task, in regions that were nevertheless no more strongly activated in GTS patients when compared with the normal controls. In addition, linear regression analyses performed on the fMRI signals from the local maxima of the regions that were hyperactivated in the GTS patients had significance levels that ranged from P = 0.7 to P = 0.95 (almost proof of the null hypothesis). Finally, a previous study in schizophrenic patients on neuroleptic medication showed a reduced activity in motor networks that correlated negatively with chlorpromazine-equivalent dosage (Wenz et al., 1994). This is the opposite of what we found in our patients, namely augmented motor/premotor cortical response, uncorrelated with medication levels. Of course, observations in un-medicated GTS patients, particularly with longitudinal designs, would allow one to describe GTS as is develops naturally in adulthood. However, such patients would perhaps be from a milder part of the GTS spectrum, making the observations in such samples non-generalisable. Symptomatic GTS adults unable to stand the side effect of medication represent a further interesting subgroup: it remains to be seen what is the exact proportion of such patients and whether these represent a special population as regards their clinical pattern (see Silva et al., 1996; Cavanna et al., 2012). We already faced this risk by selecting patients compatible with the fMRI set-up. However, this selection bias was not such to prevent the observation of meaningful fMRI differences between GTS patients and age/gender-matched controls. The motor physiology of more severe patients remains to be explored, possibly with techniques that have fewer practical constraints than fMRI.

Limitations of the study: on line video-recording of the experimental sessions While our evaluation of the impact of tics on the fMRI results showed no impact on the level of head motion or on regional activations in any condition or baseline, we are aware that video-recording of the patients during the experimental session should be preferable (e.g. Thomalla et al., 2014)

Supporting Information Additional supporting information can be found in the online version of this article: Data S1. Limitations of the previous literature on voluntary motor control in GTS. Fig. S1. Areas of intersection of motor execution and motor imagery evaluated in each group independently.

© 2015 Federation of European Neuroscience Societies and John Wiley & Sons Ltd European Journal of Neuroscience, 43, 494–508

Motor control in Tourette syndrome 507 Fig. S2. Within-group comparisons of motor execution vs. motor imagery and vice versa. Fig. S3. Regression analyses of fMRI data using neuroleptic doses as covariates. Fig. S4. Regions of interest orientated analysis in the basal ganglia and thalami. Table S1. Short review of literature results on basal ganglia and tic production/suppression. Table S2. Analysis of head motion parameters measured on the fMRI data.

Acknowledgments We are grateful to the staff of the Department of Diagnostic Radiology and Bioimages of IRCCS Galeazzi for their invaluable help. This paper was supported in part by a PRIN grant 2010 to E.P. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. No additional external funding was received for this study.

Abbreviations BIS, Baratt Impulsivity Scale; BOLD, blood oxygen level dependent; EEG, electroencephalography; fMRI, functional magnetic resonance imaging; FWE, family-wise error; GTS, Gilles de la Tourette syndrome; ME, motor execution; MEG, magnetoencephalography; MI, motor imagery; PUTS, Premonitory Urge Tics Scale; OCD, obsessive-compulsive disorder; RT, reaction time; SMA, supplementary motor area; TMS, transcranial magnetic stimulation; VBM, voxel-based morphometry; YGTSS, Yale Global Tic Severity Scale.

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© 2015 Federation of European Neuroscience Societies and John Wiley & Sons Ltd European Journal of Neuroscience, 43, 494–508