Behavioural Brain Research 278 (2015) 514–519

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Research report

Neural network activity and neurological soft signs in healthy adults Philipp A. Thomann a,1 , Dusan Hirjak a,∗,1 , Katharina M. Kubera a , Bram Stieltjes b , Robert C. Wolf a,c a b c

Center for Psychosocial Medicine, Department of General Psychiatry, University of Heidelberg, Heidelberg, Germany Department of Radiology, Section Quantitative Imaging Based Disease Characterization, German Cancer Research Center (DKFZ), Heidelberg, Germany Department of Psychiatry, Psychotherapy and Psychosomatics, Saarland University, Homburg, Germany

h i g h l i g h t s • NSS expression is associated with activity of cortical sensorimotor regions. • Thalamic and striatal activity is not associated with the extent of NSS. • NSS are associated with cortically mediated motor planning, execution and control.

a r t i c l e

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Article history: Received 29 August 2014 Received in revised form 21 October 2014 Accepted 29 October 2014 Available online 4 November 2014 Keywords: Neurological soft signs Resting-state fMRI Cortex Basal ganglia Schizophrenia

a b s t r a c t Previous neuroimaging studies in schizophrenia have shown that neurological soft signs (NSS) are associated with abnormal brain structure and function, but it remains unclear whether these findings truly reflect pathological processes or if they may be confounded by antipsychotics. To address these issues, structural neuroimaging studies conducted in healthy populations have shown an association between NSS and cortical regions but to date, studies of brain function in healthy participants are scarce. In this study, using functional magnetic resonance imaging we investigated 37 healthy adults under “restingstate” conditions. Functional connectivity of motor cortical and subcortical neural networks was assessed using a group spatial independent component analysis (ICA). NSS were measured using the “Heidelberg Scale”. The relationship between functional connectivity at rest and NSS was analyzed using a regression model where age, gender and movement parameters were included as nuisance variables. We identified 35 stable components, from which five networks of interest were chosen for further analyses. Within three motor cortical networks, negative correlations were found between NSS levels and functional connectivity of the right precuneus, right superior frontal areas, supplementary motor area, and left paracentral gyrus. There were no significant associations between NSS scores and striatal or thalamic connectivity. In conclusion, the data indicate that in healthy young adults NSS are associated with regionally confined levels of cortical activity and not with striatal or thalamic function. The neural mechanisms underlying NSS in healthy individuals appear to rely on cortically mediated motor control and planning to a higher degree than on functions subserved by subcortical structures. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Neurological soft signs (NSS) comprise a broad range of subtle neurological deficits such as discrete impairments in sensory integration, motor coordination, balance, sequencing of complex motor acts, and occasionally clumsiness and occurrence of primitive reflexes [1]. NSS are frequently found in patients with

∗ Corresponding author. Tel.: +49 6221 5637539; fax: +49 6221 565327. E-mail address: [email protected] (D. Hirjak). 1 Both authors contributed equally to this work. http://dx.doi.org/10.1016/j.bbr.2014.10.044 0166-4328/© 2014 Elsevier B.V. All rights reserved.

schizophrenia at any stage of their illness [2,3]. NSS levels, both quantitatively and qualitatively, may discriminate between schizophrenia and other mental disorders [2,4]. Yet a higher prevalence of NSS has also been demonstrated in psychiatric disorders of significant neurodevelopmental origin such as borderline personality disorder [5] and autism [6]. Some studies have also demonstrated NSS in healthy individuals [7,8], suggesting a neurodevelopmental signature of motor function, probably as a continuum between health and disease. Previous MRI studies in patients with schizophrenia have shown that NSS are associated with abnormal cortical, thalamic and cerebellar structure and function [9], yet it still remains unclear whether

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these findings are associated with neuropathological processes underlying the disease or if they are confounded by antipsychotic treatment [8,10]. Some of these issues could be addressed in individuals at high-risk for psychosis or in first-episode schizophrenia patients [11,12]. But even in such cases it could remain unclear if the relationship between NSS and neural substrates would truly reflect correlates of disease onset, signs of an ongoing pathological process, neurodevelopmental signs or none of that. Moreover, since ultrahigh risk status for psychosis yields substantial symptom burden and psychiatric comorbidity [13,14], investigating neural correlates of NSS in such individuals is not entirely without bias. Given that NSS are also present in healthy individuals, investigating neural correlates of NSS in healthy participants could reveal associations between brain function or structure and NSS which are not biased by disease-specific processes or drug treatment [7]. So far, however, only a few neuroimaging studies investigated brain correlates of NSS in healthy controls using task-based protocols for fMRI [15–17], yielding divergent results. However, these paradigm-driven studies essentially need to be interpreted within their experimental framework since task type and task performance in conjunction with details of image acquisition, scanner field strength and group demographics might at least partly explain divergences among these studies. The purpose of the present fMRI study was to investigate the functional neuroanatomy of NSS in healthy adults. In contrast to previous studies, we assessed “baseline” neural activity, since task-driven protocols inherently constrain activation patterns to a specific experimental setting (e.g. finger-tapping or other paradigms of motor function). We used a “resting-state” fMRI (rs-fMRI) functional connectivity approach [18] to investigate neural networks characterized by ongoing spontaneous modulations of blood oxygen level—dependent (BOLD) signal in the absence of specific task-demand. These neural systems reflect specific spatiotemporal patterns of neural network activity and show a substantial spatial overlap with specific cognitive, affective, perceptual and sensorimotor processes [19–22]. Cortical and subcortical networks related to motor function have been consistently detected across multiple, large and independent data sets, suggesting an interindividually robust and stable neural signature of brain activity at rest [23]. Based on results of previous MRI studies in healthy adults [7,24], we predicted significant associations between NSS and functional connectivity of multiple cortical and subcortical neural systems related to motor function. Specifically, we expected significant associations between the total extent of NSS and functional connectivity of regions involved in sensorimotor functions (e.g. movement planning, execution and control), such as primary motor cortex, supplementary motor area, regions of the cingulate cortex, thalamus and the basal ganglia.

2. Methods 2.1. Participants A sample of 37 healthy participants was recruited from the general population through advertisement in Heidelberg, Germany between 2012 and 2013. The sample consisted of 23 women and 14 men, all right-handed individuals with a mean age of 23.35 ± 4.97 and a mean of 15.44 ± 4.79 years of education. Participants were not paid for participating in this study. All participants were recruited exclusively for this rs-fMRI study, i.e. no other experimental tasks were involved. Individuals were excluded if: (i) they were aged 40 years, (ii) they were currently suffering from a neurological or mental disorder, (iii) they had a history of mental disorders according to ICD-10, (iv) or a current or past history of other medical diseases potentially influencing brain function. None of the

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healthy subjects had first-degree relatives with a mental disorder according to ICD-10. Also, PRIME screening assessment [25] and the clinical interview revealed no “prodromal” symptoms of psychosis [26] or manifest psychotic symptoms among the study participants. The study was approved by the local ethics committee (University of Heidelberg, Germany), and all subjects gave written informed consent following a complete description of the study. 2.2. NSS assessment NSS were assessed using the Heidelberg Scale (HS) [1] that consists of sixteen items assessing motor coordination (Ozeretski’s test, diadochokinesia, pronation/supination, finger-to-thumb opposition, speech articulation), integrative functions (station and gait, tandem walking, two-point discrimination), complex motor tasks (finger-to-nose test, fist-edge-palm test), right/left and spatial orientation (right/left orientation, graphesthesia, face-hand test, stereognosis), and hard signs (arm holding test, mirror movements). Items were rated on a 0 (no prevalence) to 3 (marked prevalence) point scale. Sufficient internal reliability and test-retest reliability have been established previously [1,27]. 2.3. MR imaging data acquisition All participants underwent functional scanning at the German Cancer Research Center (DKFZ), Heidelberg, Germany, on a 3 T Magnetom TIM Trio MR scanner (Siemens Medical Solutions, Erlangen, Germany). Scans were performed in darkness, and participants were explicitly instructed to relax without falling asleep, keep their eyes closed, not think about anything in particular and move as little as possible. Adherence to these instructions was verified by verbal contact immediately after the functional scan. We obtained T2*weighted images using echo-planar imaging in an axial orientation (repetition time 2000 ms, echo time 30 ms, field of view 200 mm, matrix = 64 × 64, flip angle 90◦ , voxel size 3 × 3 × 3 mm, 33 slices, slice thickness 4 mm, gap 1 mm). Within a session, 180 whole-brain volumes were acquired. 2.4. MR data processing and analysis Data preprocessing was performed with SPM8 (http://www.fil.ion.ucl.ac.uk/spm) and MATLAB 7.3 (MathWorks, Natick, MA). Prior to data processing, the first eight volumes of the time-series were discarded to allow for scanner equilibration effects. The remaining functional images were corrected for motion artifacts and then spatially normalized to the Montreal Neurological Institute (MNI) template. All images were spatially smoothed with a 9 mm FWHM isotropic Gaussian kernel. For each participant the mean relative Euclidean distance between movement parameters derived from the individual realignment files. A spatial ICA was then computed using the “Group ICA for fMRI Toolbox” [GIFT; http://mialab.mrn.org/software/gift] [28]. To increase the stability of the components, we used the Icasso algorithm [[29]; http://www.cis.hut.fi/jhimberg/icasso] after repeating the ICA estimation 50 times with bootstrapping and permutation. The dimensionality of the functional data was reduced using principal component analysis (PCA) alternated with data concatenation across individuals, resulting in one aggregate mixing matrix. An ICA decomposition using the Infomax algorithm was used to extract Independent Components (ICs) corresponding to distinct spatiotemporal patterns. The “minimum description length” (MDL) criteria [30] were used to estimate the model order. The estimated ICs were used for a back reconstruction into individual ICs using the aggregate mixing matrix created during the dimensionality data reduction steps. The individual ICs consisting of individual spatial independent maps and time-courses were

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Fig. 1. (left) Motor cortical patterns of brain activity at rest in healthy individuals (n = 37): three “cortical” sensorimotor networks (blue: CSMN1, red: CSMN2, green: CSMN3). (right) Subcortical patterns of brain activity at rest in healthy individuals (n = 37): “striatal” (red) and “thalamic” systems (blue). Both figures display independent components (ICs) and their corresponding time courses, as identified by the group ICA. The color bars indicate Z-values, IC’s are thresholded at Z = 3. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

spatially sorted using a-priori masks, as defined by the automatic anatomical labeling (AAL) atlas [31]. To identify cortical and subcortical networks of interest for further correlational analyses, three masks were used for spatial sorting: First, we computed a “cortical sensorimotor” mask comprising the precentral gyrus, the cingulate cortex and bilateral medial and superior frontal regions [32]. Second, a “striatal” mask was computed comprising the bilateral caudate nucleus and the putamen. Third, we computed a “thalamic” mask, comprising the bilateral thalamus. Note that these masks were used as rough spatial templates only to subsequently identify networks of interest within the entire IC set. Masks were neither used as “seeds” nor used to constrain 2nd level within- and between-group analyses on a certain set of brain regions. ICs that showed a significant spatial correlation (p < 0.001) with these masks were chosen for 2nd level regression analyses (see below). Complementary voxelwise one-sample t-tests were used to calculate positive maps for each IC. Relationships between brain activity and total NSS levels was analyzed using SPM8 and a 2nd level regression model containing NSS total scores as regressor of interest and age, gender and movement parameters (mean relative Euclidean distance between movement parameters derived from the individual realignment files) as nuisance covariates. A total of five regression models was calculated, i.e. one analyses per network of interest (see Section 3). A threshold of p < 0.005, uncorrected at the voxel level, p < 0.05 corrected for spatial extent [33], was chosen. Stereotaxic coordinates are reported as coordinates of cluster-maxima in MNI space. Anatomical regions emerging from the regression analyses were labeled according Talairach Daemon and AAL atlas denominations (http://fmri.wfubmc.edu/software/PickAtlas).

Fig. 2. Negative associations between total NSS scores (included as regressor of interest in a 2nd level analysis model) and CSMN1 (left) and CSMN2 (right) activity. Results of 2nd level regression analyses, p < 0.005 uncorrected for height, p < 0.05 corrected for spatial extent. Maps were rendered onto the anatomical template implemented in SPM8; the color-bar indicates T-values. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

predominantly comprising superior frontal and cingulate cortical areas (CSMN3); (see also Fig. 1 left); (2) a “striatal” network, predominantly comprising the bilateral putamen and (3) a “thalamic” network, predominantly comprising the bilateral thalamus (see also Fig. 1 right); detailed stereotaxic coordinates, anatomical denominations and Z-scores available on request.

3. Results 3.1. Component estimation and network selection

3.2. Relation between functional connectivity indices and NSS performance

In total, 37 ICs were estimated, and 35 proved to be stable according to ICASSO stability criteria. From the 35 ICs, five networks of interest were chosen for further analyses (Fig. 1): (1) three “cortical” sensorimotor networks (CSMN), including a network predominantly comprising paracentral and supplementary motor regions (CSMN1), a network predominantly comprising bilateral pre- and postcentral cortices (CSMN2), and a network

Negative correlations were found between NSS levels (NSS total score = 6.8 ± 3.2) and rs-activity of the right posterior cingulate (CSMN3; x = 12, z = −42, y = 42, Z = 3.87, k = 79), right superior frontal cortex (CSMN3; x = 24, z = −12, y = 64, Z = 3.82, k = 304), supplementary motor area (SMA) (CSMN1; x = −16, z = −14, y = 64, Z = 3.9, k = 304), and left paracentral gyrus (x = 2, z = −18, y = 64, Z = 3.89, k = 304) (Fig. 2). Positive correlations were found between

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Fig. 3. Positive associations between total NSS scores (included as regressor of interest in a 2nd level analysis model) and CSMN2 (left) and CSMN3 (right) activity. Results of 2nd level regression analyses, p < 0.005 uncorrected for height, p < 0.05 corrected for spatial extent. Maps were rendered onto the anatomical template implemented in SPM8; the color-bar indicates T-values. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

NSS levels and functional connectivity of the right precuneus (CSMN3; x = 4, z = −72, y = 34, Z = 3.55, k = 110), right middle cingulate (CSMN3; x = 62, z = −4, y = 40, Z = 3.58, k = 113) and right precentral gyrus (CSMN2; x = 62, z = −4, y = 28, Z = 4.12, k = 152) (Fig. 3). Importantly, there were no significant associations between NSS scores and basal ganglia or thalamic activity. 4. Discussion This study investigated the association between NSS levels and resting-state brain activity in healthy adults. Three main findings emerged: First, the right superior frontal cortex, posterior cingulate gyrus, SMA and the left paracentral gyrus were regions exhibiting a significant negative relationship with NSS levels. Second, positive correlations were found between activity of the right precuneus, right middle cingulate and right precentral gyrus. Third, there were no significant associations between NSS scores and basal ganglia or thalamic activity. Associations between the superior frontal cortex and NSS levels are not surprising given the wealth of neuroimaging evidence on the involvement of the superior frontal gyrus (SFG) in complex motor tasks [34]. We also observed a significant negative relationship between NSS scores and paracentral gyrus activity. These findings are remarkable given that the SFG and paracentral gyrus are anatomically and functionally connected with the SMA, a region that is essential for preparation, initiation and monitoring of movement. The involvement of the SMA and sensorimotor cortices in NSS-subtests primarily tapping into motor function, such as finger-to-thumb opposition and pronation-supination, has been described previously [16], and the present data adds to this by showing that these relationships can be established for functional data acquired at rest. We expand previous findings by revealing significant relationships between cingulate cortical activity and NSS levels. Cingulate cortices are of special interest given converging neuroimaging evidence suggesting a crucial role of these regions in action monitoring, response inhibition and overt movement execution [35–37]. Specifically, our findings indicate a significant involvement of posterior cingulate cortical (PCC) activity in the expression of NSS levels, which is in good agreement with functions subserved by the PCC, such as visuospatial orientation

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and processing of self-relevant information [38]. These functions essentially apply to specific features assessed by the HS, such as graphesthesia, stereognosis, face-hand test or right/left and spatial orientation [2]. Additionally, this relationship is strengthened by fMRI studies investigating sensorimotor and inhibitory functions using Go/Nogo tasks [8,39]. The relevance of cingulate regions for the development of NSS is further emphasized by structural MRI (sMRI) studies in healthy adults [7]. Taken together, the extant taskdriven functional and sMRI data, together with the results from our study, strengthen the notion that activity levels in neural systems normally responsible for the fluid initiation and execution of movement, comprising the paracentral and posterior cingulate cortices and the SMA, are essentially linked to NSS expression. We also found positive associations between neural activity and NSS. Specifically, the right precuneus, right middle cingulate and right precentral gyrus were positively correlated with NSS. These findings are noteworthy for two reasons: First, the precuneus plays a crucial role in visuospatial tasks [40], integration of stimuli, language comprehension and experience of agency [41,42]. It has its major connections to the prefrontal and cingulate cortex. Second, the middle cingulate cortex subserves both cognitive and complex motor tasks [43]. In line with a previous meta-analysis [44] the positive association between middle cingulate activity and NSS could reflect regional coupling with sensorimotor areas such as precentral gyrus, SMA and precuneus [43]. Since both the precuneus and the middle cingulate gyrus are involved in limb position imagery, language and motor tasks requiring somatosensory control, our findings might represent a pattern of sensorimotor connectivity that is involved in finger-to-thumb opposition and speech. In particular, our findings support the notion that right precuneus and middle cingulate cortical regions contribute to movement execution predominantly at the level of sensorimotor and visuospatial control. The negative and positive associations between brain function and NSS levels indicate that the relationship between neural activity and NSS expression in healthy persons is not exclusively reciprocal, as suggested by structural data [8]. The different association directionalities could reflect neural mechanisms related to distinct functional domains relevant to NSS expression, i.e. initiation/execution of sensorimotor processes and their control. This said, we acknowledge that rs-fMRI is conducted within a poorly controlled experimental environment compared to taskbased functional neuroimaging. This means that it is more difficult to assign circumscribed behavioral phenomena to specific brain regions and to the extent of activity in these regions. To resolve this issues, future studies of NSS should complement rs-fMRI with selected activation paradigms that tap into specific NSS domains, e.g. preparation, execution, complexity and control of sensorimotor processes. In contrast to meta-analytical findings [8] we did not observe associations between NSS levels inferior frontal gyrus (IFG) activity. IFG activation is a consistent finding in task-based fMRI studies investigating relationships between neural activity and NSS [8,45]. Two variables may account for this divergence. First, previous paradigm-driven fMRI studies investigated neural correlates of response inhibition (RI), where robust activity of the IFG is not a surprising finding [46]. Thus, it is possible that relationships between IFG activity and NSS are more closely linked to active experimental conditions, rather than being associated with brain activity at rest. Second, task-driven brain activity in previous fMRI studies was related to distinct motor domains (palm tapping, supination/pronation and fist-edge-palm). In contrast, and unlike in our study, predominantly spatial and sensorimotor NSS (such as graphesthesia, stereognosis, face-hand test or right/left and spatial orientation) were not considered [16,17,24]. The main motivation of our study was to investigate the relationship between NSS levels and brain activity unbiased from

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disease processes. Given the lack of a clinical cohort explicit conclusions on NSS-related brain activity in mental disorders cannot be drawn. Yet the findings of this study provide a starting point for testing specific hypotheses in patients with schizophrenia, where NSS have been discussed as a potential endophenotype of psychomotor pathology [3]. For instance, previous fMRI studies in schizophrenia have shown that NSS are related to both cortical and subcortical regions, such as thalamus and basal ganglia [8,15,45]. Yet contrary to our expectation and in contrast to the extant literature on NSS in first-episode schizophrenia patients of comparable age [27,47,4], we did not confirm a significant relationship between NSS levels and subcortical activity in a healthy population. Critically reflecting our sample characteristics, i.e. healthy young adults, our results are in line with several structural MRI studies which found no relationship between NSS and subcortical structures in healthy adults [7,47–50]. The absent relationship between NSS levels and basal ganglia or thalamic activity may indicate that NSS in healthy individuals and patients with schizophrenia could be related to different brain phenotypes [8]. Also, one might speculate at this stage whether functional changes of subcortical structures such as basal ganglia or thalamus in schizophrenia are truly related to NSS only or whether they may mirror a variety of non-motor symptoms of the disease, unspecific disease-related processes, or drug-treatment effects. Taking into account the findings of this study these questions could be specifically addressed by future research of NSS in schizophrenia. 5. Limitations We acknowledge potential limitations of our work. First, it is difficult to compare our findings with those presented by recent neuroimaging studies on NSS because of the use of different imaging modalities (i.e. structural vs. functional), different experimental protocols (task-based vs. resting-state) and different data analysis techniques (uni- vs. multivariate statistical methods for fMRI data analysis). Second, the sample size and the cross-sectional design may be seen as further limitations of our study. With respect to sample size, however, it is noteworthy that previous fMRI studies investigating NSS in healthy controls were conducted in cohorts rarely exceeding 20 participants [8,51]. Third, as an inherent limitation of cross-sectional studies investigating NSS in both health and disease, we do not know if this is a stable neural signature of NSS in healthy individuals. Good within-subject reliability over time has been shown for several neural networks at rest, including motor systems [52]. Still, although we assume stability across individuals, both at the neurodevelopmental and the level of data acquisition and analysis we cannot fully rule out the possibility that over time, both the extent of NSS and the pattern of neural activity associated with these motor phenomena might change. In this respect, we strongly advocate longitudinal testing, both in healthy individuals and patient populations. 6. Conclusion We investigated multiple cortical and subcortial neural networks in healthy individuals to establish relationships between resting-state brain function and NSS levels. The findings of this study indicate that in healthy adults NSS are related to cortical activity and not to basal ganglia or thalamic function. The data suggest complex cortical mechanisms underlying NSS, involving cortically mediated motor planning and control together with visuospatial processing to a higher degree than functions subserved by subcortical regions. Future neuroimaging studies combining both resting-state and task-driven methods are warranted to elucidate the neural underpinnings of NSS in both health and disease.

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Neural network activity and neurological soft signs in healthy adults.

Previous neuroimaging studies in schizophrenia have shown that neurological soft signs (NSS) are associated with abnormal brain structure and function...
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