Tourette syndrome (TS) is a neurological disorder of childhood onset that is characterized by the presence of chronic vocal and motor tics (Cohen, Leckman, & Bloch, 2013). Tics are involuntary, repetitive, stereotyped behaviours that occur with a limited duration (Cohen et al., 2013). Motor tics can be simple or complex in appearance, ranging from repetitive movements to coordinated action sequences. Verbal tics can consist of repetitive sounds, words or utterances, the production of inappropriate or obscene utterances, or the repetition of another’s words. Tics occur in bouts, typically many times in a single day, and are the most common form of movement disorder in children (Delgado & Albright, 2003).

Individuals with TS perceive a relatively constant demand to suppress their tics, particularly in social situations, and while the voluntary suppression of tics is possible in many cases, they typically report that it can be uncomfortable and stressful to suppress tics, and that the urge to tic becomes uncontrollable after a period of suppression (Cohen, Leckman & Bloch, 2013). Importantly, the majority of individuals with TS report that their tics are often preceded by ‘premonitory sensory/urge phenomena’ (PU) that are described as uncomfortable cognitive or bodily sensations that occur prior to the execution of a tic and are experienced as a strong urge for motor discharge (Cohen et al., 2013. Individuals who experience PU often report that: these experiences are more bothersome than their tics; that expressing their tics give them relief from, and temporarily abolishes, their PU; and that they would not exhibit tics if they did not experience PU (Cavanna, Black, Hallett, & Voon, 2017). For this reason, it has been proposed that PU should be considered as the driving force behind the occurrence of tics, and that tics are a learnt response to the experience of PU (Cavanna et al., 2017). PU are of particular clinical importance as they form a core component of behavioural therapies that are currently used in the treatment of tic disorders (Cohen et al., 2013).

Our understanding of PU and their relationship to tics is currently limited, and there are grounds for thinking that the occurrence of tics and the occurrence of PU are independent processes or only loosely associated. First, not all individuals with TS report experiencing PU. In particular, children under 10 years of age, who present with simple tics, do not typically report being aware of PU (Cohen et al., 2013). Second, tics have been observed during sleep, including slow-wave sleep, indicating that at least some tics are involuntary (Cohrs et al., 2001). Third, the occurrence of tics—and an individual’s ability to suppress them—may occur independently of the awareness of PU (Ganos et al., 2012). Finally, the generation of tics and the genesis of PU in TS have been linked to different brain networks (Bronfeld, Israelashvili, & Bar-Gad, 2013; Conceicao, Dias, Farinha, & Maia, 2017; Jackson, Parkinson, Kim, Schuermann, & Eickhoff, 2011; McCairn, Iriki, & Isoda, 2013). Previous studies have indicated that the urge for action more generally may activate a common set of brain areas across a wide range of behavioural domains (e.g., the urge to blink, the urge to yawn, the urge to micturate, the urge to scratch an itch, etc.), that includes the urge to tic in TS (Jackson, Parkinson, Kim, et al., 2011). Jackson, Parkinson, Kim et al. (2011) conducted a quantitative meta-analysis of functional brain imaging studies that investigated the ‘urge for action’ associated with everyday behaviours such as yawning, swallowing, and micturition, and demonstrated that the right anterior insula and the mid-cingulate cortex (MCC) were the only regions consistently activated across brain imaging studies associated with the perception of the urge for action in different behavioural domains. Importantly, these authors proposed that the right insula and MCC play a central role in a neural circuit that represents bodily sensations, generates urges for action, selects an action based upon an estimation of the likely outcomes associated with that action, and determines whether the conditions giving rise to the urge for action have been resolved once an action has been initiated.

Consistent with this proposal, functional brain imaging studies indicate that brain activity within the MCC increases 1 s prior to tic execution in individuals with TS (Bohlhalter et al., 2006; Neuner et al., 2014) and structural brain imaging studies demonstrate that there are alterations in grey matter (GM) volume throughout MCC and anterior cingulate cortex (ACC) that are correlated with clinical measures of tic severity (for a recent review see O’Neill, Piacentini, & Peterson, 2019). This has led some authors to conclude that the MCC plays an important role in the generation/representation of PU (e.g., O’Neill et al., 2019) whereas others have speculated that the role of the cingulate motor area may be to select/generate a particular action in response to PU that may be primarily generated elsewhere, most likely within the anterior insula (Jackson, Parkinson, Kim, et al., 2011). This latter view is consistent with recent studies demonstrating that while electrical stimulation of medial wall regions of cortex, including the MCC, was sufficient to induce movements, including goal-directed actions, there was no evidence that electrical stimulation of this region induced a phenomenological experience of an ‘urge to move’ (Caruana et al., 2018; Trevisi et al., 2018; although see Fried et al., 1991, for an alternative report that electrical stimulation of the posterior supplementary motor area (SMA) can induce the experience of an ‘urge to move’). In the current study, we focus specifically on the relationship between the cingulate cortex, measured using structural magnetic resonance imaging together with the analysis of structural covariance networks, and clinical measures of tic severity and PU.

Human brain imaging studies have identified a number of functional brain networks, often referred to as ICNs (intrinsic cortical networks) that reflect correlated brain activity across anatomically separate brain areas. Recent evidence indicates that these networks are dominated by common organizational principles and stable features, and may largely reflect enduring individual characteristics, including the consequence of brain health conditions (Gratton et al., 2018). Similarly, neuroimaging studies have repeatedly demonstrated covariance of GM cortical thickness or volume over widespread, distributed, brain regions; and these structural covariance networks (SCNs) have also been shown to be highly heritable and to reflect differences in age and disease status (Alexander-Bloch, Giedd, & Bullmore, 2013).

It has been proposed that structural covariance between brain regions may likely reflect brain areas that are functionally co-active and exhibit common patterns of maturational change—including shared long-term trophic influences; shared patterns of gene co-expression (Romero-Garcia et al., 2018; Zielinski, Gennatas, Zhou, & Seeley, 2010), and are selectively vulnerable to specific brain health conditions (Seeley, Crawford, Zhou, Miller, & Greicius, 2009). Importantly, recent studies have demonstrated that SCNs closely mirror the functional ICNs revealed using resting-state functional magnetic resonance imaging [fMRI] (Kelly et al., 2012; Seeley et al., 2009) and co-degenerate in distinct human neurodegenerative conditions (Cauda et al., 2018; Seeley et al., 2009). This suggests that analysis of SCNs, while currently under-utilized to study brain networks in neurodevelopmental conditions, may be a particularly useful method for investigating alterations in brain network development in children and adolescents for whom the use of conventional fMRI approaches is especially challenging. In this study we chose to investigate specifically how SCNs associated with different functional regions of the bilateral cingulate cortex may be altered in children and adolescents with TS -relative to a group of typically developing individuals.

Materials and methods

This study was approved by an appropriate local ethical review committee. Written informed consent was acquired from all participants and where appropriate from their parents/caregivers. No part of the study procedures or analyses were pre-registered prior to the research being conducted. We report how we determined our sample size, all data exclusions (if any), all inclusion/exclusion criteria, whether inclusion/exclusion criteria were established prior to data analysis, all manipulations, and all measures in the methods below. Finally, the conditions of our ethics approval do not permit public archiving of individual MRI data or clinical biographical data.

Participants

In total, 76 volunteers took part in this study: 39 had a confirmed diagnosis of TS (TS group) and 37 formed our control group (CS group) of age- and sex-matched, typically developing, individuals with no history of neurological disorders. The TS group were recruited either from the Child and Adolescent Psychiatry Clinic at the Queens Medical Centre in Nottingham or by advertising through the Tourettes Action charity or regional TS support groups. The CS group were recruited from local schools, by local advertising, and recruitment at science fairs. All volunteers were provided with a small inconvenience allowance for their participation.

After magnetic resonance imaging (MRI) the scans from twelve participants were found to be un-usable and data from these individuals were excluded from further analyses. The participants who remained included 28 individuals in the TS group (three females; mean age 14.62 ± 3.4 years) and 36 controls (three females; mean age 14.38 ± 3.2 years). 10 individuals with TS had a confirmed or suspected clinical diagnosis of a co-occurring neuropsychiatric condition in addition to their TS (attention deficit/hyperactivity disorder [ADHD] = 2; obsessive-compulsive disorder [OCD] = 2; and autism spectrum disorder [ASD] = 6). 10 patients were medicated at the time of scanning. Details of the TS group are reported in Table 1.

Table 1. Details of TS participants ID Gender Age IQ TIV YGSS (100) Motor (25) Phonic (25) Impairment (50) PUTS (36) Comorbidity Medication TS013 M 19.29 135 1,672.99 32 18 9 5 17 – – TS030 M 17.03 103 1,534.19 23 12 11 0 10 – – TS031 M 20.12 111 1,393.68 67 21 16 30 22 – Citalopram TS034 M 16.56 118 1,572.93 0 0 0 0 34 – – TS043 M 14.09 123 1,521.32 63 23 20 20 21 – Sertraline, haloperidol TS048 M 16.82 118 1,560.25 19 9 0 10 16 – Clonidine TS058 M 10.04 96 1,488.58 10 6 4 0 16 ADHD Stratera TS061 M 11 99 1,604.86 26 12 9 5 23 – – TS066 M 16.16 102 1,579.58 42 16 16 10 19 OCD Melatonin TS069 M 13.47 133 1,678.11 19 8 6 5 9 – – TS071 M 15.61 118 1,545.65 31 10 11 10 21 – – TS075 M 16.25 112 1,602.50 30 12 8 10 17 – Aripiprazole TS084 F 22.78 126 1,495.58 25 8 7 10 33 ASD, GAD, MDD Citalopram TS087 M 12.79 119 1,509.92 30 15 10 5 23 – – TS088 M 13 109 1,527.10 71 20 21 30 25 – Clonidine TS094 F 18.75 116 1,292.99 32 12 15 5 21 ASD – TS095 M 14.49 115 1,452.84 23 13 0 10 30 – Clonidine, aripiprazole TS096 M 8.61 126 1,544.50 23 10 3 10 18 ADHD – TS097 M 15.28 112 1,494.73 33 10 13 10 30 OCD – TS101 M 10.48 129 1,699.19 40 16 14 10 20 ASD – TS102 M 15 111 1,552.28 13 8 0 5 17 – – TS103 M 18.96 113 1,510.52 64 22 22 22 17 – – TS105 M 11.35 115 1,596.10 42 13 15 20 19 – – TS110 M 13.22 85 1,631.39 9 9 0 0 10 ASD – TS113 M 13.1 92 1,580.11 67 19 18 30 20 ASD – TS119 M 9.67 107 1,470.05 29 13 11 5 14 – – TS127 F 13.01 75 1,355.28 27 10 7 10 12 ASD Melatonin TS139 M 12.36 100 1,710.96 60 15 15 30 32 – – Means 25 M:3F 14.62 111.36 1,542.08 33.93 12.86 10.04 11.32 20.21 SD – 3.43 13.93 96.94 19.25 5.25 6.75 9.56 6.81 Note Motor, phonic and impairment scores were measured using the YGTSS = Yale Global Tic Severity Scale; PUTS = Premonitory Urge for Tics Scale; TIV = Total intercranial volume); ADHD = attention deficit/hyperactivity disorder; ODC = obsessive-compulsive disorder; ASD = autism spectrum disorder; MDD = major depressive disorder; GAD = generalized anxiety disorder. YGTSS scores were measured on the day of testing. Maximum score for clinical ratings is given in brackets. Diagnosis, symptom severity and screening

Diagnosis of TS was confirmed by an experienced clinician. In addition, all participants underwent comprehensive screening for current symptoms of TS by a highly experienced and trained research nurse/researcher. Measures of the current severity of tics were obtained using the Yale Global Tic Severity Scale (YGTSS) (Leckman et al., 1989). The YGTSS is a semi-structured clinician-rated measure assessing the nature of motor and vocal tics present over the past week. The YGTSS is a commonly used clinical assessment scale and has been found to have good psychometric properties (Leckman et al., 1989). It consists of three subscales: impairment rating, motor tic rating and vocal tic rating. Motor and vocal tic ratings are made up of the composite answers from questions relating to number, frequency, intensity, complexity and interference of tics reported in the previous week and observed during the interview. The current frequency and severity of premonitory sensory/urge phenomena [PU] was measured using the Premonitory Urge for Tics Scale (PUTS) (Woods, Piacentini, Himle, & Chang, 2005). The PUTS is a self-report measurement where items assess the intensity and frequency of PSP (on a scale of 1–4). Nine of the 10 items on the PUTS scaled were scored based on recommendation, and thus scores could range from 9 to 36 (Woods et al., 2005). Participants were screened for any indication of symptoms of ADHD, OCD and Autism using the Connors-3 Parent Report (Conners, 2008), Children’s Yale-Brown Obsessive-Compulsive Scale (CY-BOCS) (Scahill et al., 1997) and Social Communication Questionnaire (SCQ) (Berument, Rutter, Lord, Pickles, & Bailey, 1999), respectively. Based on these measures, a further eight patients were categorized as being at high risk of having OCD and/or ADHD. All participants also completed the Wechsler’s Abbreviated Scale of Intelligence (WASI-II) (Wechsler, 1999) used to assess intellectual ability. Two subtests were used (the verbal and performance subtests). Participants would be excluded from the study if their WASI score was < 70 (none were).

Image acquisition

Whole-brain, high-resolution, T1-weighted structural MRI brain images were acquired for each participant. Scanning was conducted at the Sir Peter Mansfield Imaging Centre at the University of Nottingham using a 3T Philips Achieva MRI scanner with a 32-channel SENSE head-coil and running a MPRAGE sequence (180 contiguous axial slices, 8.6 ms repetition time [TR], 4.0 ms echo time [TE], 256 × 224 × 180 matrix size, 1 × 1 × 1 mm raw voxel size, and a scan duration of 225 s). Prior to acquisition, participants were asked to lie as still as possible with their eyes open. Foam padding was added for extra stability and to reduce head movements. All participants wore also noise-cancelling headphones.

MR data pre-processing

Pre-processing of all MRI images was accomplished using SPM12 and the Computational Anatomy Toolbox (CAT12; http://www.neuro.uni-jena.de/cat/). First, raw structural T1-w MRI scans were oriented to have the origin lying on the AC-PC line using automated registration. Intensity normalization, bias and noise-correction was conducted using the Spatially Adaptive Non-Local Means (SANLM) tool in CAT12 and the images were spatially normalized using DARTEL (affine and non-linear registration, [Ashburner, 2007]) to standard space and segmented into different tissue classes: grey matter, white matter (WM) and cerebrospinal fluid (CSF). The images were then modulated—which involves scaling by the amount of contraction done during the normalization step—to ensure that GM in the normalized images remains the same as in the raw images. Finally, all de-noised, normalized, segmented and modulated GM maps were smoothed using an 8-mm full-width at half maximum (FWHM) Gaussian kernel. CAT12 implements a retrospective quality assurance framework for easy quantification of brain image quality. CAT12 labels each structural MR image with a nominal letter ranging from A+ (excellent quality) to F (unacceptable quality). All images rated below D- were excluded while acceptable quality images were inspected further visually and images with any visible artefact were excluded. Total intracranial volume (TIV) was estimated from all subjects. The GM maps were co-registered to the AAL2 atlas (Tzourio-Mazoyer, et al. 2002) to enable an anatomically defined region of interest (ROI) for the bilateral mid and anterior cingulate cortex to be generated.

Structural covariance Three separate functionally defined bilateral ROIs were created based upon the control data published by Balsters and colleagues (Balsters, Mantini, Apps, Eickhoff, & Wenderoth, 2016: connectivity based probability maps of the cingulate cortex for typically developing young adults are available for download at http://www.ncm.hest.ethz.ch/downloads/data.html). These functionally defined ROIs consisted of: a bilateral posterior mid-cingulate region (pMCC); a bilateral anterior mid-cingulate region (aMCC); and a bilateral posterior anterior cingulate region (pACC). Then, using the segmented whole-brain GM maps and a ‘seed-to-voxel' approach, we computed the structural covariance between the mean GM values for voxels within each of our empirically defined cingulate (seed) ROIs and the GM values obtained for all voxels in the GM maps. This analysis yielded a covariance map for each group in which the value at each voxel reflected the cross-subject Pearson correlation coefficient between the mean GM value for the seed region (the respective cingulate cortex ROI) and the GM value at that particular voxel. The correlation coefficients were converted to Z scores using Fisher’s r-to-Z transformation and the whole-brain Z(r) maps for each group were then statistically compared at group-level using the following equation (1)where, for each voxel: Z1 is the Z(r) value for that voxel for the TS group; Z2 is the Z(r) value for that voxel for the CS group; N1 the number of participants in the TS group; and, N2 the number of participants in the CS group. The computed Z-maps were corrected for multiple comparisons using FDR [p-FDR 1995) and a cluster threshold of KE ≥ 50 voxels was applied. Labelling of statistically significant clusters was accomplished using the Brain Anatomy Toolbox (Eickhoff et al., 2005). By definition, two regions ‘covary’ positively when increased GM values in one region is associated with increased GM values in another region. We defined negative covariance as an increase in GM values in one region that is associated with a reduction in GM values in a separate region. Results Preliminary analyses: group differences

The preliminary analyses of these data have been reported previously (Jackson et al., 2020) but for completeness they are reported here. An independent samples t-test confirmed that there was no significant difference in age between the TS (mean = 14.62 ± 3.43) and CS (mean = 14.38 ± 3.23) groups, t(62) = −0.28, p = .78. However, independent samples t-tests revealed that there was a significant between-group difference in TIV (TS mean = 1,544.97 ± 97.15; CS mean = 1,640.88 ± 158.71; t(62) = 2.81, p = .007), with controls having a higher TIV than individuals with TS, and a significant between-group difference in IQ (TS mean = 111.36 ± 13.93; CS mean = 118.58 ± 12.28; t(62) = 2.20, p = .03) with controls exhibiting a slightly higher average IQ. It should be noted however that both groups exhibited above-average IQ scores. For the whole-brain VBM and structural covariance analyses reported below the adjusted grey matter volumes were used after co-varying for age, sex, IQ and TIV.

Exploring differences in total intracranial volume

Preliminary analysis of the anatomical MRI images revealed a significant between-group difference in TIV, with the CS group exhibiting a significantly larger mean TIV value that the TS group. However, interpretation of this finding is challenging without further analyses due to differences in Age, Sex, and IQ between the groups, and because the TIV measure includes different tissue types (i.e., GM, WM, and CSF). To further explore this finding, and as this paper is concerned with GM morphometry, we calculated two additional measures: first, the total number of GM voxels within each GM map for each participant; and second, the average GM value across all of the GM voxels within each GM map for each participant.

For each of these measurements we conducted a separate stepwise multiple regression analysis, with the following variables as predictors: Chronological age; Sex; IQ; Group (CS vs. TS); and the Group x Age interaction. Note that in each case, the initial order of entry was fixed with Chronological age entered first into the model first.

Analysis of total number of GM voxels

A scatterplot illustrating how the number of GM voxels in each GM map decreases as a function of Chronological age for each group is illustrated in Figure 1A. Inspection of this figure illustrates that while there is a small and only marginally significant decrease in number of GM voxels with age for the CS group (R2 = .11, p = .054), the decrease with age for the TS group is much steeper and statistically significant (R2 = .48, p = .0001). The results of the stepwise multiple regression analysis demonstrated that Chronological age (t = −3.79, p < .0003) and the Age × Group interaction (t = 2.64, p < .01) were each independent and statistically significant and predictors of total number of GM voxels. The final regression model accounted for more than 24% of the variance in total number of GM voxels (F = 10.87, adjusted-R2 = .24, p < .0001). The results of this analysis confirm that after differences in chronological age have been taken into account, there is a large and statistically significant effect of group in the form of an Age x Group interaction, which indicates that the number of GM voxels decreases more steeply with age in the TS group.