Neurological soft signs in recent-onset schizophrenia: Focus on the cerebellum

Progress in Neuro-Psychopharmacology & Biological Psychiatry 60 (2015) 18–25

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Neurological soft signs in recent-onset schizophrenia: Focus on the cerebellum Dusan Hirjak a,⁎, Robert C. Wolf a,d, Katharina M. Kubera a, Bram Stieltjes b, Klaus H. Maier-Hein c, Philipp A. Thomann a a

Center for Psychosocial Medicine, Department of General Psychiatry, University of Heidelberg, Germany Department of Radiology, Section quantitative imaging based disease characterization, German Cancer Research Center (DKFZ), Heidelberg, Germany Medical Image Computing Group, Division of Medical and Biological Informatics, German Cancer Research Center (DKFZ), Germany d Department of Psychiatry, Psychotherapy and Psychosomatics, Saarland University, Homburg, Germany b c

a r t i c l e

i n f o

Article history: Received 5 December 2014 Received in revised form 13 January 2015 Accepted 21 January 2015 Available online 29 January 2015 Keywords: Cerebellum NSS Schizophrenia SUIT

a b s t r a c t Background: Previous structural neuroimaging studies linked cerebellar deficits to neurological soft signs (NSS) in schizophrenia. However, no studies employed a methodology specifically designed to assess cerebellar morphology. In this study, we evaluated the relationship between NSS levels and abnormalities of the human cerebellum in patients with recent-onset schizophrenia and healthy individuals using an exclusive cerebellar atlas. Methods: A group of 26 patients with recent-onset schizophrenia and 26 healthy controls were included. All participants underwent a high-resolution T1-weighted MRI scan on a 3 Tesla scanner. We used a voxel-based morphometry (VBM) approach utilizing the Spatially Unbiased Infratentorial (SUIT) toolbox to provide an optimized and fine-grained exploration of cerebellar structural alterations associated with NSS. Results: Compared with healthy controls, patients had significantly smaller cerebellar volumes for both hemispheres. In the patients' group, we identified a significant negative correlation between NSS levels and gray matter volume in the left lobule VI and the right lobule VIIa, corrected for multiple comparisons. Further, NSS performance was significantly associated with white matter volume in the left midbrain and corpus medullare and the right lobule VIIa. In contrast, no significant associations between NSS scores and cerebellar subregions in healthy subjects arose. Conclusion: Our results demonstrate the benefits of SUIT when investigating cerebellar correlates of NSS. These results support the view that distinct parts of sensorimotor and cognitive cerebellum play an important role in the pathogenesis of NSS in schizophrenia. © 2015 Elsevier Inc. All rights reserved.

1. Introduction Minor motor and sensory deficits or neurological soft signs (NSS) are frequently found in patients with schizophrenia at any stage of their illness (Schroder et al., 1991; Thomann et al., 2009a; Hirjak et al., 2012; Thomann et al., 2014). According to recent evidence, NSS might represent a trait feature of schizophrenia (Bombin et al., 2005). Over the past two decades, neuroimaging studies of NSS in schizophrenia have revealed structural and functional alterations in cortical and subcortical brain areas (see meta-analysis for detail Zhao et al., 2013). These findings strongly support the hypothesis that NSS may be related to a disrupted cortico-cerebellar-thalamo-cortical circuit as conceptualized in the model of “cognitive dysmetria” (Andreasen et al., 1998).

⁎ Corresponding author at: Center for Psychosocial Medicine, Department of General Psychiatry, University of Heidelberg, Voßstraße 4, Heidelberg D-69115, Germany. Tel.: +49 6221 5637539; fax: +49 6221 565327. E-mail address: [email protected] (D. Hirjak).

http://dx.doi.org/10.1016/j.pnpbp.2015.01.011 0278-5846/© 2015 Elsevier Inc. All rights reserved.

The cerebellum is crucial for motor function, control of muscle tone and balance. Furthermore, the cerebellum is also interconnected with cerebral networks involved in cognition (Ramnani, 2006; Buckner, 2013; Koziol et al., 2014). When compared to healthy controls, patients with schizophrenia have shown reduced cerebellar volumes (Andreasen and Pierson, 2008). Yet to date, only few structural MRI (sMRI) studies have examined the relationship between NSS and cerebellar morphology (Bersani et al., 2007; Bottmer et al., 2005; Keshavan et al., 2003; Mouchet-Mages et al., 2007; Thomann et al., 2009a), yielding inconsistent findings. This inconsistency might at least in part be attributable to methodological aspects like image analysis procedures. In particular, the complex and convoluted morphology of the cerebellum including thinner striations of gray and white matter is a methodological challenge to ordinary region-of-interest (ROI)based or whole-brain approaches (Kuhn et al., 2012a). Although there is some evidence suggesting an involvement of the cerebellum in the generation of NSS in schizophrenia, at present the question of if and how the cerebellar cortex and white matter contribute to the pathophysiology of NSS has not yet been addressed in a comprehensive way.

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Fig. 1. Statistical maps displaying corrected clusters (FWE; red = grey matter, blue = white matter) of significant associations between NSS scores and cerebellar morphology.

In the current study, we intended to better understand the relationship between cerebellar morphology and NSS in schizophrenia. In contrast to previous NSS studies on cerebellum, the present study has been extended by means of a novel method specifically designed to assess cerebellar morphology. We used the Spatially Unbiased Infratentorial (SUIT) toolbox (http://www.icn.ucl.ac.uk/ motorcontrol/imaging/suit.htm) with a new template (Diedrichsen, 2006), which has less spatial variance across individuals and preserves anatomical detail of cerebellar subregions (three lobes and lobules I-X) using automated nonlinear normalization methods, thus achieving a more accurate intersubject-alignment compared to whole-brain methods. Previous sMRI studies in patients with neuropsychiatric diseases successfully used SUIT to identify morphological changes in cerebellar subregions (Diedrichsen 2006; Kuhn et al., 2011; Wolf et al., 2015; D'Agata et al., 2011). Moreover, these studies showed that SUIT is more sensitive to cerebellar changes compared to voxel-based morphometry (VBM) approaches. This study had two goals: first, to assess structural cerebellar differences between patients with recent-onset schizophrenia and healthy controls and second, to examine the cerebellar phenotype underlying NSS total scores and NSS scores on the five subscales in both groups. We believe this to be important since some of the previous MRI studies on NSS in schizophrenia were only related to distinct motor domains (Baudendistel et al., 1995; Schroder et al., 1995, 1999). We predicted that schizophrenia patients would show higher NSS scores when compared to healthy controls. Drawing on scientific evidence linking cerebellar structure to NSS in schizophrenia, we further predicted that schizophrenia patients would exhibit reduced cerebellar volumes and

that higher NSS levels would be tied to structural abnormalities in both anterior and posterior subregions of the cerebellum in this group. 2. Methods 2.1. Subjects Twenty-six subjects with first-episode schizophrenia and twentysix healthy controls matched for age, gender, ethnicity, and handedness were enrolled. Patients were consecutively admitted to the inpatient unit of the Department of Psychiatry, University of Heidelberg, and diagnosed as suffering from recent-onset schizophrenia according to ICD-10 and DSM-IV criteria. The patient group consisted of 7 women and 19 men, all Caucasians with a mean age of 23.38 years (SD = 3.87) and a mean of 12.07 (SD = 1.32) years of education. All patients had an initial onset of psychosis within two years prior to study entry. The mean duration of illness was 7.1 ± 2.77 months (range 2 to 15 months). Patients were treated with an atypical antipsychotic according to their psychiatrists' choice (mean dose of 435.11 ± 240.41 mg chlorpromazine equivalents (CPZ) (Woods, 2003)). The mean duration of total neuroleptic treatment was 2.33 ± 2.94 months (range 0.25 to 15 months). None of the participants had a lifetime history of neurological or medical illness, head injury, severe substance abuse or lifetime substance dependence according to ICD-10 or DSM-IV criteria. Twentysix healthy controls were recruited through advertisements and screened for major psychiatric disorders before being included. Clinical evaluation included ascertainment of personal and family history and detailed physical and neurological examination. None of the participants

Left corpus medullare

Left lobule VI

Left midbrain

Right lobule VIIa

Fig. 2. Scatter plot of voxel-wise regression analysis of NSS total scores and local white matter volume (WMV) parameter estimates in schizophrenia.

Fig. 3. Scatter plot of voxel-wise regression analysis of NSS total scores and local gray matter volume (GMV) parameter estimates in schizophrenia.

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D. Hirjak et al. / Progress in Neuro-Psychopharmacology & Biological Psychiatry 60 (2015) 18–25

had a lifetime history of neurological or medical illness, head injury, or substance abuse. All subjects were dominantly right-handed (Oldfield, 1971). The investigations were approved by the ethics committee of the Medical Faculty, Heidelberg University. Written informed consent was obtained from all participants after the procedures of the study had been fully explained. 2.2. Clinical assessments Participants in our study were recruited and examined as soon as possible after acute symptom remission; at least partial remission is necessary for the assessment of NSS since florid psychotic symptoms, agitation and severe formal thought disorders considerably influence the patients' cooperation (both with respect to clinical investigations and to MRI scanning) as well as their ability to understand the instructions. NSS were examined with the Heidelberger Scale (Schroder et al., 1991), which consists of five items assessing motor coordination (MOCO) [Ozeretski's test, diadochokinesia, pronation/supination, finger-to-thumb opposition, speech articulation], three items assessing integrative functions (IF) [station and gait, tandem walking, two-point discrimination], two items assessing complex motor tasks (COMT) [finger-to-nose test, fist-edge-palm test], four items assessing right/left and spatial orientation (RLSPO) [right/left orientation, graphesthesia, face-hand test, stereognosis], and two items assessing hard signs (HS) [arm holding test, mirror movements]. Ratings are given on a 0 (no prevalence) to 3 (marked prevalence) point scale. A sufficient internal reliability and test–retest reliability have been established previously (Bachmann et al., 2005; Schroder et al., 1991). Patients were diagnosed as suffering from schizophrenia using the German version of the Structured Clinical Interview for DSM-IV (SCID-IV) (Wittchen et al., 1997). The Brief Psychiatric Rating Scale (BPRS) (Overall and Gorham, 1962), the Scale for the Assessment of Positive Symptoms (SAPS) (Andreasen, 1984), and the Scale for the Assessment of Negative Symptoms (SANS) (Andreasen, 1983) were used to assess the severity of clinical symptoms. BPRS, SAPS, and SANS measurements were conducted at the same time as the NSS. Predictors of outcome were rated on the Strauss-Carpenter Scale (SCS) (Strauss and Carpenter, 1974). Potential extrapyramidal side effects, parkinsonian signs, and abnormal involuntary movement according to Abnormal involuntary movement scale (AIMS) (Guy, 1976) were excluded before study entry by an experienced psychiatrist who was not directly involved in the study. In the control group, the Structured Clinical Interview for Axis I and Axis II DSM-IV Disorders (SCID-VI) (Wittchen et al., 1997) was administered to rule out Axis I and Axis II disorders. In addition, we used the PRIME early psychosis screening test [Prevention through Risk Identification, Management, and Education—(PRIME)] to screen for the presence of early psychotic symptoms, including information on any contact or treatment for any mental or psychological disorder (Miller et al., 2003). 2.3. Imaging data acquisition Patients in our study were investigated at the German Cancer Research Center, Heidelberg, Germany, with a 3 Tesla Magnetom TIM Trio MR scanner (Siemens Medical Solutions, Erlangen, Germany) using a T1-weighted 3D magnetization prepared rapid gradient echo sequence (MP-RAGE, 160 sagittal slices, image matrix = 256 × 256, voxel size = 1 × 1 × 1 mm3, TR = 2300 msec, TE = 2.98 msec, TI = 900 msec, flip angle = 9°). 2.4. Infratentorial VBM After checking for scanner artifacts and gross anatomical abnormalities, and setting the image origin at the anterior commissure in each subject, we isolated the infratentorial structures (i.e. cerebellum

and brainstem) from the surrounding tissue by using the Isolate function within the Spatially Unbiased Infratentorial Template (SUIT) toolbox (http://www.icn.ucl.ac.uk/motorcontrol/imaging/suit.htm). The isolation procedure includes the segmentation of the brain into tissue types using the unified segmentation approach (Ashburner and Friston, 2005) as implemented in SPM8 (http://www.fil.ion.ucl.ac. uk/spm/). Where necessary, the isolated maps were hand corrected using MRIcron (http://www.mccauslandcenter.sc.edu/mricro/mricron/ index.html), excluding any tissue included outside the cerebellum or brainstem. Subsequently, each individual's cerebellar gray and white matter segments were normalized onto the SUIT atlas template, allowing for an improved alignment of individual fissures and cerebellar subregions when compared to conventional wholebrain VBM (Diedrichsen, 2006). A modulation of the segmented gray and white matter probability maps was applied in order to compensate for volume changes during spatial normalization by multiplying each voxel's intensity value with the Jacobian determinants. Before statistical analysis, all probability images were smoothed with a 4 mm full-width at half-maximum (FWHM) smoothing kernel in SPM8. This relatively small smoothing kernel was chosen to preserve precision in the definition of cerebellar substructures. The use of a 4 mm kernel is in line with previous studies that focused on the cerebellum by means of the SUIT toolbox (D'Agata et al., 2011; Kuhn et al., 2012a). Anatomical localizations were determined by the probabilistic MRI atlas of the human cerebellum developed by Diedrichsen et al. (2009). 2.5. Statistical analysis We used independent two-tailed two-sample t-test (equal or not equal variance assumed on the basis of a Levene's test) and chi-square test (for categorical variables) to compare means of demographic and symptoms scores of schizophrenia patients and healthy individuals. Normality of the NSS scores distribution was evaluated with the Kolmogorov–Smirnov test. Differences in those NSS subscales that did not fit to a normal distribution according to Kolmogorov–Smirnov testing (p b 0.05) were analyzed using the nonparametric Mann–Whitney U test. NSS subscales that fitted to a normality distribution were analyzed using analyses of covariance (ANCOVA). The association between NSS scores and symptoms measures (SAPS, SANS, and BPRS) was explored using partial correlations by treating age, education, gender, and chlorpromazine equivalents as potential confounders. In SPM8, two-sample t-tests were calculated in order to assess regional gray and white matter differences between patients and controls. The relationship between NSS subscales and local gray/white matter volume was assessed using a voxel-wise regression analysis as implemented in SPM8 containing each individual's NSS subscore as explanatory variables. Age, gender, education, intracranial volume (ICV), and CPZ (in patients) were treated as nuisance variables. SPM maps were initially thresholded at p b 0.001, uncorrected. In order to minimize the possibility of false-positive results, only those differences or associations surviving a cluster level FWE correction (p b 0.05) were considered for reporting. Our hierarchical approach with an initial p b 0.001 thresholding followed by cluster correction is in line with that of prior publications that focused on cerebellum VBM by means of the SUIT toolbox (D'Agata et al., 2011; Kuhn et al., 2012a) [even less conservative initial thresholding] (Kuhn et al., 2012b). In order to evaluate the validity of our voxel-wise brain-behavior regression analysis as implemented in SMP8 and to report on the strength of the relationship between NSS and cerebellar subregions morphology, (Vul et al., 2009) correlations between NSS subscales and extracted local gray/white matter volume parameter estimates were assessed using either Pearson's or Spearman's correlation coefficient, depending on the distribution of the respective subscale values. Further, in order to account for false-positive findings within the identified associations, correlations coefficients were corrected for the

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number of tested NSS subscales in our main analysis using the Bonferroni method. To this end, α was set to p = 0.05/N, where N (= 12) equaled number of correlations (classical Bonferroni correction). For this reason, the corrected threshold was set to p = 0.0041 (α = 0.05/12 tests [total NSS + five subscale scores × two groups]).

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RLSPO (r = 0.116; p = 0.607), IF (r = 0.358; p = 0.102), and HS (r = 0.278; p = 0.210). 3.2. Regional volumetric differences between patients and controls When compared with healthy controls, patients with recent-onset schizophrenia were characterized by a reduced cerebellar gray matter (GM) volume within the left lobule V and the right lobule VIIa. Further, they showed a reduced white matter (WM) volume within the left corpus medullare. Anatomical and statistical details of these differences are summarized in Table 2.

3. Results 3.1. Demographic and clinical data Socio-demographic and clinical characteristics of the study sample are summarized in Table 1. DSM-IV assessment revealed diagnosis of schizophrenia in all patients. Comparison of the two groups revealed a significant difference in years of education [t = 2.53; df = 50; p = 0.014]. There were no significant differences in age [t = 0.47; df = 50; p = 0.64] and gender [chi-square test: χ2 = 0; df = 1; p = 1.0] among the two groups. In both study groups, NSS subscales IF, COMT, and RLSPO were normally distributed according to Kolmogorov– Smirnov testing (p b 0.05). NSS total scores and NSS scores on the subscales MOCO and HS were not normally distributed according to Kolmogorov–Smirnov testing (p N 0.05). Patients' total NSS scores and NSS scores on the subscale MOCO and HS were significantly increased when compared to controls (Mann–Whitney U nonparametric test) (Table 1). Further, patients' NSS scores on the subscale COMT were significantly increased when compared to controls (ANCOVA) (Table 1). There was no statistically significant difference between NSS scores on the subscale IF and RLSPO between patients and controls (ANCOVA) (Table 1). There was no statistically significant correlation of SAPS levels with NSS total scores (r = 0.091; p = 0.688) and NSS scores on the subscale MOCO (r = 0.075; p = 0.742), COMT (r = 0.085; p = 0.706), RLSPO (r = 0.073; p = 0.746), IF (r = 0.252; p = 0.257), and HS (r = 0.091; p = 0.688). Further, we found no statistically significant association between SANS and NSS total scores (r = 0.161; p = 0.474) and NSS scores on the subscale MOCO (r = 0.333; p = 0.131), COMT (r = 0.284; p = 0.201), RLSPO (r = 0.116; p = 0.607), IF (r = 0.358; p = 0.102), and HS (r = 0.278; p = 0.21). Last but not least, there was no statistically significant relationship between BPRS and NSS total scores (r = 0.107; p = 0.637) and NSS scores on the subscale MOCO (r = 0.333; p = 0.131), COMT (r = 0.284; p = 0.201),

3.3. Correlation of NSS with regional cerebellar morphology Significant associations between NSS and regional cerebellar morphology in patients with recent-onset schizophrenia are summarized in Table 3, Figs. 1–3 and supplementary data. In schizophrenia patients, NSS total scores and NSS scores on the subscales MOCO were normally distributed according to Kolmogorov–Smirnov testing (p b 0.05). NSS scores on the subscales IF, COMT, RLSPO, and HS were not normally distributed according to Kolmogorov–Smirnov testing (p N 0.05). According to statistical analysis (Pearson's correlation coefficient), higher total NSS scores were negatively associated with reduced GM volume within the left lobule VI (r = 0.542; p = 0.004) and the right lobule VIIa (r = 0.49; p = 0.011) (Fig. 3). Further, total NSS scores were also inversely correlated with WM volume of the left midbrain (r = 0.414; p = 0.035) and WM volume reduction in the left corpus medullare (r = 0.349; p b 0.046) (Fig. 2). Increased NSS scores on the subscale COMT (Spearman's rho) were associated with GM volume in the left lobule VI (r = 0.637; p b 0.0001) and WM volume in the right corpus medullare (r = 0.426; p b 0.0001). Scores on the item MOCO (Pearson's correlation coefficient) were inversely correlated with GM volume of the right lobule VIIa (r = 0.578; p = 0.002) and WM volume of the left lobule VIIb (r = 0.508; p = 0.008), right lobule VIIa (r = 0.586; p = 0.002) and left upper pons/midbrain (r = 0.488; p = 0.011). Finally, higher scores on the subscale RLSPO (Spearman's rho) were associated with a GM volume reduction in two peaks of the right lobule VIIa (r = 0.356, p = 0.074; r = 0.422, p = 0.032), the left lobule VIIa (r = 0.6; p = 0.001) and with WM volume the left midbrain (r = 0.229; p = 0.261) and corpus medullare (r = 0.424; p = 0.031). Six regions hold Bonferroni correction for multiple testing (Table 3).

Table 1 Demographic and clinical characteristics (n = 52). Characteristics

Patients

Controls

t

df

P

Age (years) Male/female sex Right-handedness (%) Education (years) Chlorpromazine equivalents (mg) Duration of illness (months) Duration of treatment (months) Neurological soft signs Total score Motor coordination Integrative functions Complex motor tasks Right/left and spatial orientation Hard signs SAPSa SANSb BPRSc SCSd

23.85 ± 3.87 17/9 100 12.07 ± 1.32 435.11 ± 240.41 7.1 ± 2.77 2.33 ± 2.94

23.88 ± 3.78 17/9 100 12.8 ± 0.63 – – –

0.47 – – 2.53 – –

50 1 – 50 – – –

0.64 1.0 – 0.014 – – –

14.92 ± 7.54 6.76 ± 3.91 1.92 ± 1.38 1.61 ± 1.67 1.65 ± 1.89 1.73 ± 1.34 20.11 ± 13.98 30.69 ± 18.81 23.0 ± 10.94 39.0 ± 15.72

5.57 ± 3.08 2.11 ± 1.55 1.42 ± 1.06 0.61 ± 0.89 0.8 ± 1.05 0.61 ± 0.89 – – – –

−5.84 −5.63 −1.46 −2.68 −1.98 −3.52 – – – –

50 50 50 50 50 50 – – – –

b0.001 b0.001 0.15 0.01 0.052 0.001 – – – –

Mean and SD are given except when noted. a Scale for the Assessment of Positive Symptoms. b Scale for the Assessment of Negative Symptoms. c Brief Psychiatric Rating Scale. d Strauss-Carpenter-Scale.

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D. Hirjak et al. / Progress in Neuro-Psychopharmacology & Biological Psychiatry 60 (2015) 18–25

Table 2 Cerebellar measures in patients and healthy controls. Cerebellar tissue

Comparison

Anatomical region

Cluster size (voxels)

Peak coordinates (mm), x y z

Z score

p (cluster level, FWE corrected)

Gray matter

HC N SZ

White matter

HC N SZ

Left lobule V Right lobule VIIa Left corpus medullare

391 333 670

9 −45 −7 31 −62 −40 −7 −60 −28

4.23 4.22 4.78

0.018 0.033 0.017

In healthy controls, no significant associations between NSS total or subscale scores and gray or white matter volume emerged. 4. Discussion In the present MRI study, we aimed at investigating structural cerebellar differences between patients with recent-onset schizophrenia and healthy controls, and at characterizing the relationship between the severity of NSS and regional cerebellar morphology in the respective groups. For the analysis of imaging data, a sophisticated voxel-based approach designated to allow for a detailed characterization of anatomical abnormalities in the cerebellum, was applied. Three main findings emerged: (i) recent-onset schizophrenia patients have significantly reduced cerebellar volumes with atrophy pronounced in the left lobule V, the right lobule VIIa and the left corpus medullare, (ii) increased NSS scores are related to decreased volumes in anterior and posterior cerebellar regions, and (iii) this association applies to schizophrenia patients but not to healthy controls. With regard to structural neuroimaging studies, both decreased as well as enlarged volumes of the cerebellum have been reported, while other studies found no significant cerebellar differences between schizophrenia patients and control subjects (for a review, see Bottmer et al. (Bottmer et al., 2005; Andreasen and Pierson, 2008). In particular, more recent sMRI studies have revealed relatively consistent findings of cerebellar atrophy in schizophrenia patients when compared with controls (Bottmer et al., 2005; Heuser et al., 2011; Thomann et al., 2009a, 2009b). Discrepant results might be explained by aspects of sample selection (e.g. age, stage and course of illness) and analysis methods used to characterize cerebellar morphology. In the present sMRI study, schizophrenia patients have significantly reduced cerebellar volumes with GM atrophy pronounced in the left lobule V and the right lobule VIIa and WM atrophy located mainly in the left corpus medullare, thus lending further support for cerebellar atrophy in schizophrenia. In this study, the application of the SUIT toolbox enabled us to automatically isolate the cerebellum and the brainstem from the cerebral cortex in order to identify the regional localization of tissue alterations underlying NSS more precisely. We found total NSS scores to be negatively associated with reduced volume within the cerebellar

cortex in the left lobule VI and the right lobule VII. These associations are important for two reasons: First, lobule VIIa that includes Crus I and Crus II and parts of the lobule VI constitute the anatomical substrate of the cognitive cerebellum (Buckner, 2013). According to previous fMRI studies, lobule VI and VIIa + b in the posterior lobe are activated during language, spatial, and executive functions (Stoodley and Schmahmann, 2009; Stoodley et al., 2010). Second, fine motor and visuomotor adaptation skills are subserved by the lobule VI (Bernard and Seidler, 2013a; Stoodley and Schmahmann, 2009; Stoodley et al., 2010) located in the anterior cerebellum (Bernard and Seidler, 2013a). Correspondingly, we also found cerebellar cortex reductions in the left lobule VI to be negatively associated with higher scores on the subscale COMT. These results indicate that sensorimotor functions of the lobule VI essentially apply to specific features assessed by the COMT subscale, which comprises the finger-to-nose and fistedge-palm test (Bombin et al., 2005). In accordance with a recent study conducted by Bernard and Seidler (2013b) that showed a significant relationship between the left anterior cerebellar volume and tapping variability, our results provide further evidence for anterior parts of the cerebellum being involved in the modulation of fine movements of the hand (Chan et al., 2009). Our results of a significant relationship between NSS scores on the subscale MOCO and the cerebellar cortex volume of the right lobule VIIa and WM volume of the left lobule VIIb and the right lobule VIIb are remarkable because they may represent a distinct internal model in the cerebellum (Bernard and Seidler, 2013a) underlying different sensorimotor and cognitive skills involved in Ozeretski's test, diadochokinesia, pronation/supination, finger-to-thumb opposition, and speech articulation. The crucial role of the cerebellar cortex for motor NSS in schizophrenia has also been emphasized by three previous sMRI studies, where increased NSS scores on the subscale MOCO were associated with morphological changes of the cerebellar posterior superior lobe and the right cerebellar hemisphere (Bottmer et al., 2005; Heuser et al., 2011; Thomann et al., 2009a). Further, our results are in line with sMRI studies of other independent research groups: For example, Keshavan et al. (2003) identified a significant relationship between higher scores for cognitive/perceptual abnormalities and repetitive motor tasks and cerebellar volume loss. The study conducted

Table 3 Correlations between NSS and cerebellar subregions in recent-onset schizophrenia. Brain tissue

NSS scale

Anatomical region

Cluster size (voxels)

Peak coordinates (mm) x y z

Z score

p (cluster level, FWE corrected)

Gray matter

NSS total

Left lobule VI* Right lobule VIIa Left lobule VI* Right lobule VIIa* Right lobule VIIa Left lobule VIIa* Right lobule VIIa Left midbrain Left corpus medullare Right corpus medullare* Left lobule VIIb Right lobule VIIa* Left upper pons/midbrain Left midbrain Left corpus medullare

298 278 369 508 266 307 253 430 245 252 277 262 594 407 563

−40 −52 −25 49 −57 −28 −35 −52 −26 48 −49 −28 53 −66 −33 −51 −61 −36 48 −57 −27 −10 −23 −15 −26 −51 −36 34 −57 −39 −35 −46 −44 42 −54 −39 −9 −21 −24 −10 −21 −14 −28 −49 −37

4.24 4.05 4.32 5.92 4.84 4.22 3.92 4.21 3.71 4.23 4.76 4.11 3.84 4.26 4.11

0.034 0.044 0.011 0.002 0.039 0.023 0.047 0.007 0.048 0.046 0.033 0.039 0.002 0.009 0.002

COMT MOCO RLSPO

White matter

NSS total COMT MOCO

RLSPO

MOCO = motor coordination, IF = integrative functions, COMT = complex motoric tasks, RLSPO = right/left and spatial orientation. Significant correlations (according to Pearson's or Spearman's correlation coefficient) that survived the Bonferroni correction (p b 0.0041) are marked with an asterisk (*).

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by Mouchet-Mages et al. (2007) supported the role of cerebellar cortex in the pathogenesis of motor NSS. Furthermore, our results are in line with previous findings presented by Thomann et al. (2009b) who found NSS total scores and MOCO levels to be associated with WM reduction in the right cerebellum. Nevertheless, our findings deviate from those presented by Bersani et al. (2007), who found no significant relationship between NSS levels and atrophy in the above mentioned cerebellar areas, but in the cerebellar vermis. In summary, our findings of cerebellar cortex atrophy in lobules VIIa and VIIb are of particular interest because the three respective subscales COMT, MOCO, and RLSPO include motor tasks which necessitate a tight link between cerebellar areas involved in one's own bodily movement, the spatial– temporal control and cognitive functions. Due to the fact that the subscale MOCO also includes the item “speech articulation,” our results also support the association between abnormalities in language processing and alterations of cerebellar morphology in schizophrenia (Kircher et al., 2001; Levitt et al., 1999). Further regions of pronounced association between NSS scores on the subscale MOCO and RLSPO and changes of cerebellar morphology are the left upper pons and the midbrain. Both structures are part of the brainstem and play an important role in the coordination of motor functions (Grinberg et al., 2011; Sasaki et al., 2008). The midbrain comprises circuits connecting the pons and mesolimbic regions with the cerebellum as well. Further, the dopaminergic neurons in the midbrain are connected to the frontal cortex, ventral striatum, thalamus, limbic system, and hence, they form the mesocorticolimbic dopaminergic system (Nishio et al., 2007; Tzschentke, 2001). Disorders of these ascending dopaminergic projections to the thalamo-cortico-stratial circuit have been previously implicated in psychotic disorders (Bogerts et al., 1983; Feinberg and Rapcsak, 1989; Murray et al., 2008), particularly with reference to the dopamine theory of schizophrenia (Shibata et al., 2008). Correspondingly, Mittal et al. (2014) showed that NSS in ultrahigh risk (UHR) youth are associated with fractional anisotropy in the superior cerebellar peduncles, which are theoretically relevant cerebellar-thalamic tracts in patients with schizophrenia. According to the authors, NSS were predictive of both abnormal white matter tract development and negative symptoms in the UHR group (Mittal et al., 2014). In line with the findings of Mittal et al., the results of our study provide further evidence for the fact that NSS may not be “soft” but reflect abnormal structural morphology, particularly in the cerebellum (Mittal et al., 2014; Zhao et al., 2013). Moreover, our findings are in line with previous studies on NSS and the brainstem in schizophrenia. In particular, a previous whole-brain VBM study of our own group in an independent sample found subjects high for the subscale HS to correlate with white matter reductions within the brainstem (Heuser et al., 2011). More recently, our group identified higher scores on the subscales MOCO, COMT, and HS to be associated with volumetric alterations of the brainstem (Hirjak et al., 2013). Using shape analyses these associations referred to regionally specific morphometric alterations predominantly in the midbrain and pons. Following this line, our findings also correlate well with the study presented by Nopoulos et al. (2001), which identified smaller volumes of the midbrain in schizophrenic men and supported the hypothesis that abnormal dopaminergic firing in the midbrain might result in dysregulation of dopamine release in the limbic system and thus worsens the psychotic symptomatology (Hirjak et al., 2013; Nopoulos et al., 2001). Finally, the question remains how results presented in this study contribute to the understanding of the pathophysiology of NSS in schizophrenia. We carefully suggest that our findings of a significant association between NSS and morphological alterations in lobule VII and midbrain reflect two important interconnections within the cerebral network: First, our findings may provide further evidence for a close relationship between lobule VIIa, i.e., Crus I and Crus II, and motor as well as prefrontal cortical areas (Bernard and Seidler, 2013b). Interconnections between these regions have been established

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previously and may reflect the involvement of lobule VII in cognitive and executive functions. According to Buckner (2013), cognitive objects such as constructed thoughts are operated by feedback mechanisms and internal models controlled by cerebellum (Schmahmann, 1991). Along these lines, our data suggest complex cerebellar mechanisms underlying distinct “cognitive” NSS domains (MOCO and RLSPO). Second, there is a strong involvement of the midbrain in the pathogenesis of NSS in schizophrenia. Correspondingly, Nopoulos et al. (2001) suggest that a morphological alteration of the cerebellum might lead to a “hyperdopaminergic state” in the midbrain and thus overstimulation in mesolimbic regions. Taken together, the association between morphological alterations in lobule VIIa and the midbrain and higher scores on the subscales MOCO and RLSPO, which include items assessing impairments in semantic and executive functioning, is in accordance with assumption of the “cognitive dysmetria” concept (Andreasen et al., 1998; Kuhn et al., 2012c). In summary, our data suggest complex cerebellar mechanisms underlying NSS, involving anterior mediated motor control together with cognitive processing located in the posterior cerebellum. 5. Limitations The present study has a number of limitations. First, drug-naïve patients would be preferred to exclude the potential influence of antipsychotic medication. However, structural brain changes related to antipsychotic medication have mainly been reported for the basal ganglia and frontal regions with the cerebellum being rather spared (Fusar-Poli et al., 2013; Scherk and Falkai, 2006). The cross-sectional design may be seen as further limitations of our study. Third, statistical difference in years of education might differentially affect NSS-brain structural relations. Fourth, while we used a novel approach to cerebellar morphology, other neuroimaging techniques are necessary for a more detailed investigation on how the cerebellum and the brainstem are contributing to NSS. Fifth, we found no statistically significant difference on the NSS subscales IF and RLSPO in schizophrenia patients when compared with healthy controls. While the NSS rates of healthy controls in this study are higher than in one of our previous study (Thomann et al., 2009a), we might speculate that in a proportion of healthy controls more severe NSS may have occurred. Another possible explanation for this limitation may be the fact that the mean score (SD) for the subscale IF and RLSPO in healthy individuals were 1.42 ± 1.06 and 0.8 ± 1.05. We are aware that correlations based on such restricted ranges can be misleading. However, in a very recent MRI study on NSS in healthy participants (Hirjak et al., 2014), we found statistically significant relationships between cortical structures and NSS subscales with a rather restricted range of NSS scores comparable to the range within the controls of the present sample. In line with this, even when considering the NSS total performance with a broader range of NSS scores (and even exceeding the range of distinct, morphology related NSS subscale scores within patients), we found no statistically significant relationship between NSS levels and cerebellar structure in the healthy sample. Last but not least, our results are to a great extent consistent with our previous studies in independent samples that showed a significant difference between schizophrenia patients and healthy controls (Thomann et al., 2009a, 2009b). 6. Conclusion To the best of our knowledge, the present study is the first using a cerebellum-optimized VBM (SUIT) procedure that investigated cerebellar correlates of NSS performance in schizophrenia. Our data suggest that future research on morphological correlates of NSS might benefit from considering both the sensorimotor as well as the cognitive cerebellum. In this respect, we strongly advocate neuroimaging studies combining both structural and functional MRI methods to elucidate the

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