Abstract

Both animal and human data suggest that stimulation of the noradrenergic system may influence neuronal excitability in regions engaged in sensory processing and visuospatial attention. We tested the hypothesis that the neural mechanisms subserving motor performance in tasks relying on the visuomotor control of goal-directed hand movements might be modulated by noradrenergic influences. Healthy subjects were stimulated using the selective noradrenaline reuptake inhibitor reboxetine (RBX) in a placebo-controlled crossover design. Functional magnetic resonance imaging and dynamic causal modeling (DCM) were used to assess drug-related changes in blood oxygen level–dependent activity and interregional connectivity while subjects performed a joystick task requiring goal-directed movements. Improved task performance under RBX was associated with increased activity in right visual, intraparietal and superior frontal cortex (premotor/frontal eye field). DCM revealed that the neuronal coupling among these regions was significantly enhanced when subjects were stimulated with RBX. Concurrently, right intraparietal cortex and right superior frontal cortex exerted a stronger driving influence on visuomotor areas of the left hemisphere, including SMA and M1. These effects were independent from task difficulty. The data suggest that stimulating noradrenergic mechanisms may rearrange the functional network architecture within and across the hemispheres, for example, by synaptic gating, thereby optimizing motor behavior.

Introduction

The noradrenergic transmitter system may influence the discharge properties of neurons engaged in arousal, visuospatial attention and motor behavior (Posner and Petersen 1990; Berridge and Waterhouse 2003; Plewnia et al. 2004; Aston-Jones and Cohen 2005a). Stimulating noradrenergic brainstem nuclei such as the locus ceruleus in the dorsorostral pons was demonstrated to change both spontaneous and task-related neuronal discharge frequencies in cortical regions in response to novel or unexpected stimuli (Gibbs and Summers 2002; Berridge and Waterhouse 2003). These neuromodulatory effects of noradrenaline (NA) on cortical processing have been shown to be associated with improved task performance (Aston-Jones and Cohen 2005a). However, the neural mechanisms underpinning these noradrenergic effects in widely distributed visuomotor networks (Culham and Kanwisher 2001; Goodale et al. 2004; Grefkes and Fink 2005; Vogt et al. 2007) remain to be elucidated.

For example, NA mediated improvements in visuomotor performance might result from a general increase in cortical excitability as a consequence of a global activation of noradrenergic receptors following systemic pharmacological stimulation. This view is supported by data derived from transcranial magnetic stimulation (TMS) studies demonstrating that neuronal excitability of the primary motor cortex is enhanced under NA stimulation (Ziemann et al. 2002; Plewnia et al. 2004). However, more recent studies showed that enhanced motor excitability per se is insufficient to underlie the observed improvements in motor performance (Plewnia et al. 2006; Lange et al. 2007), and especially tasks which draw upon the visuomotor control of hand movements seem to be susceptible to the stimulation of the NA system (Wang et al. 2009). Hence, the neural mechanisms subserving the NA effects in visuomotor coordination paradigms might draw upon task associated processes other than solely motor execution, e.g., visual target detection or visuomotor transformation (Rizzolatti et al. 1997; Rushworth et al. 2001; Grefkes and Fink 2005). The underlying neural correlates may be found in parietofrontal circuits encompassing intraparietal areas and the dorsal premotor cortex (Aston-Jones 1985; Gibbs and Summers 2002; Grefkes et al. 2004).

To further investigate the neural mechanisms underlying NA mediated improvements of visuomotor performance, we designed a functional magnetic resonance imaging (fMRI) study in which healthy subjects performed a joystick task that relied on visuomotor processing and online control of precision movements (Eskandar and Assad 1999; Grefkes et al. 2004). Pharmacological challenge of the NA system was achieved using the selective NA reuptake inhibitor reboxetine (RBX) (Wong et al. 2000). We hypothesized that if motor performance is improved under RBX due to a facilitation of visuomotor information processing, neuronal activity might be enhanced in cortical areas involved in attention and motor control of hand movements, that is, within the aforementioned parietofrontal circuits. Enhancing the influences of modulatory neurotransmitters like NA on cortical information processing, however, may also impact on interregional coupling within the visuomotor circuits subserving the visuomotor control of goal-directed joystick movements. Changes in effective connectivity can be assessed by computational approaches such as dynamic causal modeling (DCM) estimating the intrinsic and task-dependent influences that a particular area exerts over the activity of another area (Friston et al. 2003). We, therefore, used DCM to assess drug-related changes in the interaction among visuomotor key regions in both hemispheres. Given the preferential role of the right hemisphere for visuospatial processing (Corbetta and Shulman 2002), we hypothesized that noradrenergic stimulation under RBX might enhance connectivity especially among visuomotor areas in the right hemisphere.

Material and Methods

Subjects

The study was approved by the local ethics committee (EK 001/06) and the German Federal Institute for Drugs and Medical Devices (BfArM, Bonn, Germany; EudraCT number 2006-000048-23), and conducted in accordance with the Declaration of Helsinki from 1964. Fifteen subjects (9 males) with no history of neurological or psychiatric disease gave informed consent. All participants had a right hand preference as determined by a handedness questionnaire (Oldfield 1971). The mean age of the subjects was 26.4 ± 4.2 years (age range from 20 to 33). Parts of the data, that is, the behavioral effects of RBX on the kinematics of simple finger tapping, joystick guidance and complex hand-object interaction movements, have already been published in another paper (Wang et al. 2009). We report here the effects of RBX on cortical activity and interregional connectivity during goal-directed joystick movements assessed with fMRI.

Study Design

A placebo (PBO)–controlled, double-blind, within-subject design was employed to investigate the behavioral and neural effects of a single dose administration of RBX, a selective NA reuptake inhibitor (SNRI) (Wong et al. 2000). Solvex tablets (4 mg Reboxetinemesylate, Merz Pharmaceuticals, Frankfurt, Germany) were used to produce a set of study capsules (NextPharma, Göttingen, Germany) containing either 8 mg RBX (clinical dose recommended by the manufacturer) or a PBO formulation with identical visual appearance and weight. Studies on the pharmacokinetics of RBX in humans showed rapid absorption (tmax ≈ 2 h), an elimination half-time of about 13 h, and a modest clearance and volume of distribution (ratio to bioavailability: CL/F ≈ 29 mL/min; Vz/F ≈ 32 L) (Edwards et al. 1995).

Seven subjects received a single dose of 8 mg RBX and, 7 days later, the PBO, thereby ensuring a sufficiently long wash-out phase (t1/2 of RBX ≈ 13 h). The other 8 subjects received PBO first and RBX 7 days later. Before drug administration, pulse and blood pressure were assessed in order to monitor cardiovascular effects of RBX. The fMRI sessions were started approximately 2 h after administration of the study capsules to ensure peak plasma levels of the drug (tmax(RBx) ≈ 2 h; Edwards et al. 1995). Upon completion of the experiments, venous blood samples (10 mL) were acquired for the analysis of the individual RBX levels.

Stimulation Devices

We used a joystick task to probe the visuomotor abilities of the subjects (Grefkes et al. 2004). The MR compatible joystick was a metal-free, glass fiber optic based device (Coldswitch Technologies, Inc., Burnaby, British Columbia, Canada) which was placed on the right side of the subjects near to the hip. The software “Presentation” (Version 9.9, Neurobehavioral Systems, Inc., CA, www.neurobehavioralsystems.com) was used for visual stimulus presentation, joystick control and movement recordings. Subjects viewed an MR-modified computer monitor (Apple 30” Cinema HD LCD display, resolution = 2560 × 1600 pixels) via a mirror mounted on the head coil from a total distance of approx. 245 cm (top end of the scanner bore).

Visuomotor Task

Subjects were asked to guide a cursor from a circle in the center of the screen to a target circle in the periphery of the screen (“center-out task,” Georgopoulos et al. 1982). This task probes the ability of the subjects to transform the visuospatial coordinates of the target circle into a corresponding movement vector, a process known as “visuomotor coordinate transformation” (Andersen et al. 1985; Ghahramani et al. 1996). The underlying neural processes also encompass online feedback mechanisms enabling a permanent control, adjustment and redirection of the actual movement with the intended movement, and hence resemble those processes required for visually guided reaching movements. The peripheral circle appeared in 1 of 8 possible directions relative to the central circle (0°, 45°, 90°, 135°, 180°, 225°, 270°, 315°; Fig. 1). The distance between the center and the peripheral circle was 12.5 cm (3° visual angle). We used 3 different circle sizes (small “S”: 1.1 cm/0.26°; medium “M”: 1.9 cm/0.45°; large “L”: 3.4 cm/0.79°) in order to vary task difficulty according to Fitt's law on the relationship of movement time, distance and target size (Fitt 1954).

Figure 1.

Visuomotor joystick task. Subjects were asked to use a joystick to move a cursor from the central circle to one of the randomly appearing peripheral circles. Difficulty levels were manipulated by using 3 different circles sizes with one size per block.

Figure 1.

Visuomotor joystick task. Subjects were asked to use a joystick to move a cursor from the central circle to one of the randomly appearing peripheral circles. Difficulty levels were manipulated by using 3 different circles sizes with one size per block.

We designed a 2-factorial blocked design experiment with the factor “drug” (comprising the levels “PBO” and “RBX”) and the factor “difficulty level” (comprising the levels “small circles” [S], “medium circles” [M], “large circles” [L]). Circles of the same size (S, M, or L) were presented in blocks of 5 trials (trial length 4 sec; block length 20 s). Each trial started from the central circle. Subjects were instructed to move the cursor as fast and as accurately as possible into the peripheral circle (randomly appearing in one of the 8 possible positions) and to hold the cursor within the target until the circle disappeared (2 s following its onset). Subjects then moved the cursor back to the central circle and waited until the next peripheral target circle appeared. Blocks were separated by resting baselines of 20 s during which subjects watched a black screen. Prior to scanning, subjects were trained inside the scanner for about 5 min until reaching stable performance. Subjects were then scanned in a single fMRI run. The scanning session comprised 21 activation blocks (7 blocks for each circle size, i.e., small [S], medium [M], and large [L]) and 22 baseline conditions, and lasted about 14.3 min. The order of conditions was preudorandomized and counterbalanced across the sequence to account for ordering effects.

Control fMRI Experiment

We performed a control fMRI experiment to assess the specificity of any differences in BOLD activity between RBX and PBO obtained in the joystick task, that is, whether the cortical regions specifically responding to RBX stimulation were indeed related to the requirements of the visuomotor task or rather reflected unspecific changes in regional excitability or neurovascular coupling. Subjects were asked to perform rhythmic fist closures with their right or left hand with a frequency indicated by a visual cue (1.5 Hz). This task did hence not draw upon neural mechanisms enabling visuomotor control of goal-directed movements as required in the joystick task. The paradigm and technical details have been described elsewhere in more detail (Grefkes, Eickoff, et al.2008; Grefkes, Nowak, et al. 2008). In short, we employed a block design in which fist closures were alternated with resting baselines, each of them lasting 15 s. Subjects were informed via the video screen which hand (left or right) to move in the upcoming activation block. Unlike in earlier versions of the experiment, however, subjects did not perform bimanual movements. The session comprised 2 (left, right) × 8 activation blocks (randomized in sequence) and lasted about 10.2 min. The order of conditions was again preudorandomized and counterbalanced across the sequence to account for ordering effects.

Functional Magnetic Resonance Imaging

Functional MR images were acquired using a Siemens Trio 3.0 T whole-body scanner. We employed a gradient echo planar imaging (EPI) sequence with the following blood oxygenation level–dependent (BOLD) imaging parameters (joystick task): repetition time [TR] = 2250 ms, echo time [TE] = 30 ms, field of view [FOV] = 200 mm, 37 axial slices, slice thickness = 3.0 mm, in-plane resolution = 3.1 × 3.1 mm, flip angle = 90°, distance factor = 10%. The slices covered the brain from the vertex to lower parts of the cerebellum. Each fMRI time series consisted of 383 images preceded by 4 dummy images allowing the MR scanner to reach a steady state in T2* contrast. For the control experiment (visually paced fist closures), the following EPI parameters were used: TR = 1600 ms, TE = 30 ms, FOV = 200 mm, 26 axial slices, slice thickness = 3.0 mm, in-plane resolution = 3.1 × 3.1 mm, flip angle = 90°, distance factor = 10%. The fMRI time series consisted of 382 images preceded by 4 dummy images.

Additional high-resolution anatomical images were acquired using a 3D MP–RAGE (magnetization–prepared, rapid acquisition gradient echo) sequence with the following parameters: TR = 2250 ms, TE = 3.93 ms, FOV = 256 mm, 176 sagittal slices, slice thickness = 1.0 mm, in-plane resolution = 1.0 × 1.0 mm, flip angle = 9°, distance factor = 50%.

Behavioral Data Analysis

The movement time (in ms), the length of the pathway (in mm) covered by the joystick cursor and reaction time (in ms) were calculated after 5 Hz filtering of the data with a dual low-pass Butterworth digital filter (Winter 1990). The onset of the joystick movement was calculated by applying the algorithm of Teasdale et al. (1993) to the velocity profile of each movement. The movement time was defined as the interval between the movement onset and the time when the cursor entered into the peripheral circle. The length of the cursor pathway was the distance that the cursor traversed during the movement time, which was computed by the displacement of 2 continuous time points during the movement time. The reaction time was the interval between stimuli onsets and movement onsets.

Trials were counted as errors when subjects did not reach the target circle before it disappeared. For each parameter (movement times, reaction time, pathway length) we calculated repeated-measures analyses of variance (RM-ANOVA) with the factors “drug” (2 levels: PBO, RBX) and the factor “difficulty level” (3 levels: S, M, L).

Image Processing

For MR image preprocessing and statistical analysis, we used the SPM software package (SPM5; Wellcome Department of Imaging Neuroscience, London, UK, www.fil.ion.ucl.ac.uk). The EPI time series of the 2 experiments (joystick task, control task) were processed separately. After removing the first 4 volumes of a session (dummy images), all EPI volumes were realigned to the now first EPI of the time series to correct for head movements. All subjects did not move more than 2 mm in x, y, z direction, and also head rotation was within acceptable limits (<1°). After coregistration with the anatomical 3D image, all volumes were spatially normalized to the standard template of the Montreal Neurological Institute (MNI, Canada) using the unified segmentation approach (Ashburner and Friston 2005). An isotropic smoothing kernel of 8-mm full width half maximum was applied to the EPI images to suppress noise and effects due to residual differences in functional and gyral anatomy.

Both experiments (joystick task, control task) were analyzed in separate design matrices. Box-car vectors for each condition were convolved with a canonical hemodynamic response function and its first temporal derivative in the framework of the general linear model (GLM) (Kiebel and Holmes 2004). The time series in each voxel were high-pass filtered at 1/128 Hz. The 6 head motion parameters as assessed by the realignment algorithm were treated as covariates (regressors of no interest) to exclude movement related variance from the image time series. Simple main effects for each of the experimental conditions were calculated for each subject by applying appropriate baseline contrasts.

For the group analysis of the joystick task data, the parameter estimates of all conditions were compared between subjects (n = 15) in a full-factorial ANOVA with the factors “drug” (factor levels “RBX” and “PBO”), and “circle size” (3 levels: “small”, “medium”, “large”), thereby effecting a random effects model. For the visuomotor control experiment, we used a separate ANOVA with the factors “drug” (RBX, PBO), and “hand” (left, right). In order to assess interactions between the 2 experimental tasks and the pharmacological challenge, we constructed an additional ANOVA with the factor “task” (levels: joystick movements with the right hand; fist closure movements with the right hand) and “drug” (RBX, PBO). A statistical threshold of P < 0.05 (family wise error corrected on the cluster level) was employed to identify significantly activated regions in both fMRI experiments.

Dynamic Causal Modeling

A key focus of the present study was to investigate whether and—if so—how RBX stimulation of the noradrenergic system modulates the interregional coupling of areas involved in visuomotor control. Such changes in interregional coupling may occur independently from the actual task or might be linked to a specific condition (e.g., joystick movements at different levels of task difficulty). DCM (Friston et al. 2003) allows assessing both the task-independent and task-dependent coupling of areas activated by the joystick task under RBX and PBO. DCM treats the brain as a nonlinear deterministic system in which external inputs cause changes in neural activity that in turn lead to changes in the fMRI signal (Friston et al. 2003; Penny et al. 2004; Kiebel et al. 2007; Stephan et al. 2007). As this approach explicitly models neuronal activity, which is then linked via a biophysically validated hemodynamic model (Friston et al. 2003) to the measured functional response (i.e., a change in the BOLD response), DCM is closely related to changes in neural dynamics in both time and space (Friston et al. 2003). The changes in neuronal states over time are modeled as 

graphic
where x is the neuronal state vector, A represents the intrinsic connectivity, B(j) represent the task-dependent modulations of the modeled region driven by the input function u (which in the present study is either 0 or 1 due to the box-car function of the employed block design), and C represents the influence of sensory inputs to the system. As becomes evident from this formulation, the intrinsic connectivity (A matrix) represents those interactions among areas which were independent from the specific influences a condition may have during any given fMRI session (i.e., RBX or PBO). By contrast, task-dependent modulations represented in the B matrix only contribute to the changes in neuronal states when the respective condition (here: joystick movements at different levels of task difficulty) is performed.

Regions of Interest

DCM is a hypotheses-driven approach to model effective connectivity between distinct regions, and has to rely on an a priori neurobiological model reflecting the hypothesis on relevant regions and connections involved in the task. As DCMs are fitted to subject-specific BOLD time series (Friston et al. 2003), we extracted the BOLD time series from 8 ROIs at subject specific coordinates (8-mm spheres around individual activation maxima) in the individual SPMs. All regions of interest were defined by functional and anatomical criteria based on the individual activation maps superimposed on the corresponding structural T1-volume. For each ROI, we used the group maximum MNI coordinate as origin to search for the closest local maximum in the individual SPM maps meeting the a priori defined anatomical constraints (see below). The coordinates of all individual ROIs are given in Supplementary Table 2.

We constructed a bihemispheric visuomotor network with ROIs based on the following assumptions: Subjects used their right hand for guiding the joystick to the visual targets, hence the underlying neural processes should engage motor areas predominantly in the left hemisphere (Fig. 2). Primary motor (M1), dorsal premotor (dPMC), and the supplementary motor area (SMA) feature key regions in of the motor system, and were hence included in the connectivity model. Anatomical landmarks for M1 were the “hand knob” in the central sulcus. dPMC was identified at the junction of the superior branch of the precentral sulcus and superior frontal sulcus. SMA was located on the mesial cortical surface anterior to the paracentral lobule, superior to the cingulate sulcus and posterior to the coronal plane running through the anterior commissure (y coordinate < 0). Furthermore, intraparietal cortex (IPS) as part of the dorsal visual stream (Ungerleider and Mishkin 1982) constitutes an important region for integrating sensory information into motor plans, and was also included in the connectivity matrix. In macaques, visuomotor integration-related areas are the lateral intraparietal area (LIP) for eye movements (Andersen et al. 1990), and the medial intraparietal area (MIP) for arm movements (Colby and Duhamel 1991). The putative human homologues of LIP and MIP are both most likely situated on the medial bank of the intraparietal sulcus (IPS) (Grefkes and Fink 2005). Figure 2A demonstrated that the joystick task strongly activated (medial) intraparietal cortex and the adjacent superior parietal lobe. We hence used the medial wall of the horizontal IPS branch as anatomical landmark for the IPS ROI. As subjects used visual information for guiding the joystick to the targets, we also incorporated V1 in the model. It is important to note that assuming a connection between V1 and IPS does not imply a direct anatomical connection, but rather a functional interaction mediated by other areas of the visual system linking occipital to parietal cortex such as area V6 (Galletti et al. 2001).

Figure 2.

Visuomotor network engaged in the joystick task. Compared with the resting baseline, guiding the joystick cursor into the peripheral targets activated a widespread network of visuomotor areas in both hemispheres (group analysis; random effects model; P < 0.05, FWE corrected). (A) BOLD activity under PBO (left) and after RBX (right). Color scale represents T-values. Major sulci are labeled: cs, central sulcus; ips, intraparietal sulcus; sfs, superior frontal sulcus. (B) The effect of circle size (small vs. large) was especially pronounced in right thalamus. The parameter estimates extracted from the local maximum demonstrated a decrease in BOLD activity for easier (i.e., larger) targets for both PBO and RBX stimulation.

Figure 2.

Visuomotor network engaged in the joystick task. Compared with the resting baseline, guiding the joystick cursor into the peripheral targets activated a widespread network of visuomotor areas in both hemispheres (group analysis; random effects model; P < 0.05, FWE corrected). (A) BOLD activity under PBO (left) and after RBX (right). Color scale represents T-values. Major sulci are labeled: cs, central sulcus; ips, intraparietal sulcus; sfs, superior frontal sulcus. (B) The effect of circle size (small vs. large) was especially pronounced in right thalamus. The parameter estimates extracted from the local maximum demonstrated a decrease in BOLD activity for easier (i.e., larger) targets for both PBO and RBX stimulation.

For the construction of the connectivity network, we then assumed that the 3 regions showing enhanced BOLD activity under RBX in the right hemisphere (Fig. 3A, Table 1) interact with the visuomotor network in the left hemisphere. Therefore, the connectivity model also included V1 in the right calcarine sulcus, right intraparietal cortex (IPS, horizontal branch), and the cortex at the junction of the right precentral sulcus and superior frontal sulcus. The latter region may correspond to the frontal eye field (FEF) (Bruce and Goldberg 1985) or to the “rostral subdivision of dorsal premotor cortex” which in macaques is also influenced by both eye and hand movements (Boussaoud 2001). We from now on refer to this region as FEF/dPMC as we cannot reliably distinguish between both areas based on the fMRI data.

Figure 3.

Drug specific effects in the joystick task. Compared with PBO, stimulating healthy subjects with RBX significantly increased cortical activity in right visual (V1), intraparietal (IPS) and superior frontal cortex (frontal eye field, FEF, and adjacent dorsal premotor cortex, dPMC), and decreased activity in left M1 (group analysis; P < 0.05, FWE corrected on the cluster level). The plots next to the figures demonstrate the neural responses in the local maxima of the 3, respectively, 4 activation clusters, separated for the different difficulty levels (i.e., circle sizes: s, small; m; medium; l, large) and drug sessions (PBO; RBX). Error bars: SEM. ro, rostral; oc, occipital; cs, central sulcus. Other abbreviations as in Figure 2.

Figure 3.

Drug specific effects in the joystick task. Compared with PBO, stimulating healthy subjects with RBX significantly increased cortical activity in right visual (V1), intraparietal (IPS) and superior frontal cortex (frontal eye field, FEF, and adjacent dorsal premotor cortex, dPMC), and decreased activity in left M1 (group analysis; P < 0.05, FWE corrected on the cluster level). The plots next to the figures demonstrate the neural responses in the local maxima of the 3, respectively, 4 activation clusters, separated for the different difficulty levels (i.e., circle sizes: s, small; m; medium; l, large) and drug sessions (PBO; RBX). Error bars: SEM. ro, rostral; oc, occipital; cs, central sulcus. Other abbreviations as in Figure 2.

The putative connections among the 8 ROIs in the left and right hemisphere were derived from invasive connectivity studies in nonhuman primates. We, accordingly, constructed an intrinsic connectivity matrix assuming connections between SMA and ipsilateral and contralateral M1 (Rouiller et al. 1994), between SMA and ipsilateral (Luppino et al. 1993) as well as contralateral dPMC/FEF (Boussaoud et al. 2005), SMA and ipsilateral IPS (Cavada and Goldman-Rakic 1989), between dPMC and ipsilateral M1 (Rouiller et al. 1994), as well as transcallosal connections between V1–V1 (Kennedy et al. 1986), IPS–IPS (Neal 1990; Padberg et al. 2005), and dPMC–dPMC/FEF (Marconi et al. 2003; Boussaoud et al. 2005). The IPS-dPMC/FEF connection used in the present study is thought to represent the parietofrontal circuits between area LIP and FEF, or MIP and dPMC, which cannot be clearly distinguished in the present task as the areas of both circuits in IPS (MIP, LIP) and in superior frontal cortex (dPMC/FEF) are adjacent regions sharing similar neuronal properties (Simon et al. 2002).

Note that the obtained connectivity parameters may not be assumed to necessarily reflect monosynaptic anatomical connections but rather the net effect a region exerts on the activity of another region, for example, transmitted via direct connections, a single relay area or more extensive (subcortical) loops.

Connectivity Models

DCMs were estimated separately for each of the 2 sessions (RBX, PBO) in each subject, thereby allowing an identification of changes in inter-regional coupling induced by the pharmacological challenge. As all connections outlined above have reliably been established in nonhuman primates, they can be assumed to exist in humans, and hence are likely to represent the anatomical (i.e., intrinsic) scaffold of our connectivity model.

In addition to the intrinsic coupling as outlined above, we also analyzed how guiding the joystick to circles of different diameter (S, M, L) modulated effective connectivity within this network. These task-dependent modulations, however, do not necessarily affect all of the intrinsic connections. We, therefore, constructed 19 different connectivity models (Supplementary Fig. 1) reflecting biologically plausible hypotheses about the context-specific modulations of interregional coupling. These models differed in the number of connections (i.e., complexity) and the routes of information transfer (e.g., bottom-up, top-down). For all models, we assumed that neural activity was driven by the visual cortex (V1) as all joystick movements depended on the visual analysis of the target circles.

Bayesian Model Selection

We used Bayesian model selection (Penny et al. 2004) as implemented in SPM5 to test which of the 19 connectivity models showed the highest evidence in the applied Bayesian framework in our data. Bayes factors can be interpreted in a similar way like P values in classical statistics. The Bayes factor is a summary of the evidence provided by the data in favor of one statistical model as opposed to another. The model evidence is approximated using both the Bayesian Information Criterion (BIC) and the Akaike Information Criterion (AIC), and a decision is only made if BIC and AIC concur (Penny et al. 2004; Stephan et al. 2007). The “winning” model should then represent the best balance between the relative fit and complexity of the model (Stephan et al. 2007) for both sessions (PBO, RBX). Following the estimation of all models and the computation of subject specific Bayes factors for pairwise model comparison (Penny et al. 2004), average Bayes factors (ABF) were assessed by multiplying the individual Bayes factors of the same model comparison across subjects and computing the geometric mean (Stephan et al. 2007). We additionally calculated the positive evidence ratio (PER) for each model comparison: The PER represents the number of subjects in whom the Bayes factor gave a positive evidence for model A in relation to the number of subjects showing a stronger evidence for the alternative model B (Stephan et al. 2007). We considered the model with the highest ABF and best PER in both sessions (RBX, PBO) as the “winning model”, that is, the model which received the maximum evidence from our fMRI data (Penny et al. 2004; Stephan et al. 2007). The connectivity parameters (intrinsic connections and modulatory influences) of the winning model were then entered in a second level analysis by means of a one-sample t-test for corresponding (RBX, PBO) coupling parameter from the subject specific DCMs. Connections were considered statistically significant if they passed a threshold of P < 0.05 (Bonferroni–Holm corrected for multiple comparisons). Paired t-tests were used to identify statistically significant differences in the intrinsic or contextual coupling between RBX and PBO (P < 0.05).

Results

Physiological Data

All subjects had significant drug plasma levels approximately 2 h after oral administration of 8 mg RBX (186 ± 86 ng/mL; one-sample 2-sided t-test; t = 8.7; P < 0.001). The analysis of cardiovascular parameters (pulse frequency, blood pressure) did not reveal significant differences before and after RBX administration (repeated measures ANOVA; F = 1.06, P = 0.32).

Behavioral Data

Repeated measures ANOVA revealed a significant main effect of “drug” (PBO, RBX; F1,14 = 8.31, P = 0.012) and “circle size” (small/medium/large; F2,28 = 300.09, P < 0.001) on movement times. Post hoc t-tests showed that movements were significantly faster for larger circles compared with smaller circles (P < 0.001), reflecting the impact of task difficulty on movement times. Furthermore, for all circles sizes (i.e., difficulty levels) subjects were significantly faster under RBX stimulation compared with PBO (P < 0.05; Table 2). The interaction of the factors “drug” and “circle size” was not significant (F2,28 = 0.30, P = 0.74) indicating no differential effect of RBX for different task difficulties. Correlating RBX plasma levels with the improvements in movement time (RBX–PBO) yielded a significant result for the most difficult condition (i.e., targeting at small circles) (r = 0.78, P < 0.01), whereas correlations between RBX concentrations and improvements for medium (r = 0.27) and large (r = 0.23) circles were not significant (P > 0.05). The average improvement in movement speed (collapsed over all 3 difficulty levels) was only weakly correlated with RBX blood concentrations and just failed the pre-set statistical threshold (r = 0.56; P = 0.057).

There was no significant difference for the movement times in early blocks compared with late blocks, neither for RBX nor for PBO (P > 0.05), indicating no relevant learning effects during the scanning sessions. Reaction times (stimulus onset—start of joystick movement) were not affected by the factor “drug” (F1,14 = 0.01, P = 0.94) or by the factor “difficulty level” (F2,28 = 0.35, P = 0.71). Furthermore, we found no statistically significant difference between RBX and PBO for pathway lengths (F1,14 = 1.81; P = 0.20) or error rates (F1,14 = 1.65; P = 0.22). In other words, the improvements in movement speed were not at the cost of movement accuracy, as would be reflected by longer pathways or more errors.

Functional Imaging Data

Figure 2A demonstrates the neural network activated by the visuomotor joystick task across all difficulty levels (small, medium, large circles) for both the PBO session (left) and the RBX session (right). The network revealed by this analysis comprised sensorimotor areas (left M1, SI, SII, bilateral ventral PMC, SMA, preSMA, and bilateral dPMC extending into FEF), parietal cortex (bilateral IPS, superior parietal lobule), visual areas (bilateral V1–V5), bilateral superior and inferior cerebellum, and subcortical regions (thalamus, bilateral putamen) (Table 1). Activation clusters were more extended on the right hemisphere when subjects were stimulated with RBX compared with PBO (Fig. 2A). This impression was statistically confirmed by testing for a differential effect between BOLD activity under RBX versus PBO. This analysis ([RBX_S + RBX_M + RBX_L] > [PBO_S + PBO_M + PBO_L]) identified 3 cortical right hemispheric areas which showed a significant increase in BOLD activity under RBX (P < 0.05; FWE corrected on the cluster level; Fig. 3 and Table 1): 1) right calcarine sulcus (V1), 2) fundus of the IPS extending from the medial bank to the rostral end, and 3) the frontal cortex at the intersection of the precentral sulcus and superior frontal sulcus (FEF/dPMC) (P < 0.05, corrected at the cluster level). Enhanced activity under RBX in subcortical regions was found in the thalamus and putamen in both hemispheres (Table 1), and at uncorrected thresholds (P < 0.001) also in left V1. The reverse contrast (PBO > RBX) identified left M1 to have stronger activity during PBO stimulation compared with RBX (Fig. 3 and Table 1). The main effect of circle size was not significant (P < 0.05, FWE corrected on the cluster level). Only directly comparing small circles against large circles [PBO_S + RBX_S] > [PBO_L + RBX_L]) yielded a significant difference in right thalamus (P = 0.05, corrected on the cluster level; Fig. 2B). At a more liberal statistical threshold (P < 0.001, uncorrected), the data showed that guiding the joystick cursor into small circles evoked stronger BOLD signal changes in cortical regions located in left inferior parietal lobule (−56, −18, 39), right ventral premotor cortex (60, 10, 27) and right dorsal premotor cortex (42, −12, 53). Trends for higher activity at more difficult conditions were also evident from the BOLD response estimates in Figure 3 extracted from the local maxima showing differential activity for RBX and PBO. Hence, the parametrical modulation of the visuomotor difficulty level indicated a stronger engagement of especially right hemispheric cortical and thalamus for more difficult conditions.

Table 1

Local maxima of significantly activated regions (FWE-corrected P < 0.05)

Coordinates Side Region T-value 
Effect of task (all conditions vs. baseline) 
    −38, −20, 51 Precentral gyrus, “hand knob” 26.8 
    −56, −18, 41 Postcentral sulcus 25.37 
    30, −96, −5 Occipital pole 21.01 
    −5, −12, 55 Paracentral lobule, SMA 20.03 
    −32, −10, 53 Precentral sulcus, premotor 19.66 
    36, −88, −7 Lateral occipital cortex 19.56 
    36, −42, 51 IPS 17.3 
    4, −2, 55 Paracentral lobule, SMA 17.29 
    40, −6, 51 Precentral sulcus 16.58 
    54, 4, 37 Precentral sulcus, ventral premotor 15.87 
    20, −64, 59 IPS 15.73 
    −20, −60, 63 IPS 15.63 
    −6, −20, 49 Cingulate sulcus 14.24 
    −52, 2, 39 Precentral sulcus, ventral premotor 14.11 
    −44, −26, 19 Parietal operculum, SII 13.15 
    −24, −76, 29 Parieto-occipital junction 13.06 
    46, −70, 1 Middle occipital/inferior temporal (V5) 12.32 
    −42, −66, 5 Middle occipital/inferior temporal (V5) 12.03 
    −08, −86, 3 Calcarine sulcus, V1 9.08 
    50, 8, 5 Frontal operculum 8.96 
    −42, 4, 5 Frontal operculum 8.12 
    16, −84, 3 Calcarine sulcus, V1 6.84 
    42, −26, 19 Parietal operculum, SII 5.53 
    32, −48, −31 Superior cerebellum 19.35 
    −14, −15, 5 Thalamus 16.44 
    18, −60, −49 Inferior cerebellum 14.33 
    −22, 6, −1 Basal ganglia, putamen 13.99 
    −28, −56, −23 Superior cerebellum 13.66 
    24, 4, 1 Basal ganglia, putamen 12.02 
    12, −14, 7 Thalamus 10.37 
    −16, −56, −45 Inferior cerebellum 5.55 
RBX versus PBO 
    26, 4, 57 Superior frontal sulcus/superior precentral sulcus 6.64 
    38, −54, 41 IPS, horizontal branch 5.62 
    18, −86, 1 Calcarine sulcus 5.57 
    21, −8, −3 Basal ganglia (putamen) 5.68 
    20, −26, 9 Thalamus 4.56 
    −16, −28, 5 Thalamus 4.39 
    −28, −8, −5 Basal ganglia (putamen) 4.11 
PBO versus RBX 
    −36, −26, 55 Precentral gyrus, “hand knob” 5.48 
Coordinates Side Region T-value 
Effect of task (all conditions vs. baseline) 
    −38, −20, 51 Precentral gyrus, “hand knob” 26.8 
    −56, −18, 41 Postcentral sulcus 25.37 
    30, −96, −5 Occipital pole 21.01 
    −5, −12, 55 Paracentral lobule, SMA 20.03 
    −32, −10, 53 Precentral sulcus, premotor 19.66 
    36, −88, −7 Lateral occipital cortex 19.56 
    36, −42, 51 IPS 17.3 
    4, −2, 55 Paracentral lobule, SMA 17.29 
    40, −6, 51 Precentral sulcus 16.58 
    54, 4, 37 Precentral sulcus, ventral premotor 15.87 
    20, −64, 59 IPS 15.73 
    −20, −60, 63 IPS 15.63 
    −6, −20, 49 Cingulate sulcus 14.24 
    −52, 2, 39 Precentral sulcus, ventral premotor 14.11 
    −44, −26, 19 Parietal operculum, SII 13.15 
    −24, −76, 29 Parieto-occipital junction 13.06 
    46, −70, 1 Middle occipital/inferior temporal (V5) 12.32 
    −42, −66, 5 Middle occipital/inferior temporal (V5) 12.03 
    −08, −86, 3 Calcarine sulcus, V1 9.08 
    50, 8, 5 Frontal operculum 8.96 
    −42, 4, 5 Frontal operculum 8.12 
    16, −84, 3 Calcarine sulcus, V1 6.84 
    42, −26, 19 Parietal operculum, SII 5.53 
    32, −48, −31 Superior cerebellum 19.35 
    −14, −15, 5 Thalamus 16.44 
    18, −60, −49 Inferior cerebellum 14.33 
    −22, 6, −1 Basal ganglia, putamen 13.99 
    −28, −56, −23 Superior cerebellum 13.66 
    24, 4, 1 Basal ganglia, putamen 12.02 
    12, −14, 7 Thalamus 10.37 
    −16, −56, −45 Inferior cerebellum 5.55 
RBX versus PBO 
    26, 4, 57 Superior frontal sulcus/superior precentral sulcus 6.64 
    38, −54, 41 IPS, horizontal branch 5.62 
    18, −86, 1 Calcarine sulcus 5.57 
    21, −8, −3 Basal ganglia (putamen) 5.68 
    20, −26, 9 Thalamus 4.56 
    −16, −28, 5 Thalamus 4.39 
    −28, −8, −5 Basal ganglia (putamen) 4.11 
PBO versus RBX 
    −36, −26, 55 Precentral gyrus, “hand knob” 5.48 
Table 2

Group data (n = 15) of the movement times (ms, mean ± SEM) from both drug sessions

Circle size PBO (ms) RBX (ms) Difference (ms) P value (t-test) 
Small 1007 ± 31 951 ± 25 55 ± 22 0.026 
Medium 806 ± 28 761 ± 17 44 ± 19 0.038 
Large 621 ± 22 581 ± 19 40 ± 17 0.035 
Mean 811 ± 27 765 ± 20 47 ± 20 0.012 
Circle size PBO (ms) RBX (ms) Difference (ms) P value (t-test) 
Small 1007 ± 31 951 ± 25 55 ± 22 0.026 
Medium 806 ± 28 761 ± 17 44 ± 19 0.038 
Large 621 ± 22 581 ± 19 40 ± 17 0.035 
Mean 811 ± 27 765 ± 20 47 ± 20 0.012 

Note: Subjects under RBX stimulation were significantly faster compared with PBO for all difficulty levels (2-tailed paired t-tests).

Control Experiment

The control experiment was performed to evaluate whether the RBX mediated increases in BOLD activity in the main experiment were specific to the visuomotor joystick task, or rather reflected unspecific effects, for example, due to changes in the neurovascular response by enhanced stimulation of noradrenergic receptors in blood vessels. As illustrated by Figure 4, the activation pattern for right hand movements, that is, the same hand as used for guiding the joystick, were almost identical between both sessions (P < 0.05, FWE corrected) which was confirmed by the differential contrast showing no statistically significant difference for either hand (P > 0.05). In other words, no differential effect of RBX on the activity pattern in this simple motor task was observed. In order to exclude possible confounds in sensitivity, we extracted the BOLD responses (parameter estimates) from the 3 RBX responsive regions in the joystick experiment (i.e., right V1, IPS, and FEF; see white circles in Fig. 4) The data showed that there was not even a trend for increased BOLD activity in the 3 regions during RBX stimulation (compared with PBO) in contrast to the first experiment. This was statistically confirmed by an interaction contrast between the factors “task” (levels: joystick movements with right hand, fist closures with right hand) and “drug” (PBO, RBX): Only when subjects were stimulated with RBX in the joystick condition, a differential increase in the BOLD response was observed in right V1, IPS, and FEF/dPMC (Suppl. Fig. 2). The reverse interaction contrast produced no significant voxels, even at uncorrected P values (P < 0.001). The data hence imply that the BOLD signal increase under RBX was specific to the visuomotor demands probed by the joystick task.

Figure 4.

Activity in the control task. The activity pattern for visually paced fist closures did not differ between RBX and PBO sessions (group analysis; random effects model; P < 0.05, corrected). The neural responses extracted from the peak voxels (see white circles in the right hemisphere) identified by the RBX versus PBO contrast in the main experiment (cf. Fig. 3) were not significantly different between both drug sessions (P > 0.05). RH, right hand; LH, left hand. Other abbreviations as in Figures 2 and 3.

Figure 4.

Activity in the control task. The activity pattern for visually paced fist closures did not differ between RBX and PBO sessions (group analysis; random effects model; P < 0.05, corrected). The neural responses extracted from the peak voxels (see white circles in the right hemisphere) identified by the RBX versus PBO contrast in the main experiment (cf. Fig. 3) were not significantly different between both drug sessions (P > 0.05). RH, right hand; LH, left hand. Other abbreviations as in Figures 2 and 3.

Connectivity Analysis

Following the GLM analysis, we estimated the effects of RBX on the effective connectivity among visuomotor key regions activated by the joystick task. Bayesian model selection identified one connectivity model (model 17, cf. Supplemental Fig. 1) to receive the highest statistical model evidence compared with all other models tested. Importantly, this model was indicated for the RBX and PBO sessions by both ABF and PER (see Suppl. Table 1).

Intrinsic Connectivity under PBO

The intrinsic coupling of areas can be regarded as baseline connectivity established by the entire experimental context (Friston et al. 2003) onto which context- (i.e., condition-) dependent modulations are added. The model we chose was based upon the assumption that the visual information (e.g., the position of the circles necessary for starting a movement in a given trial) entered the cortical system via V1 in both hemispheres (“driving input”, Fig. 5A). This information was then propagated to the IPS, which itself was reciprocally connected with the 2 premotor areas (dPMC, SMA) and with its contralateral counterpart via transcallosal connections. The model further featured that both frontal regions (dPMC, FEF) regions were reciprocally connected with each other, with left SMA and left M1. The SMA was also reciprocally connected to left M1 and to the IPS.

Figure 5.

Intrinsic connectivity (group analysis) between visuomotor key regions under PBO and RBX. Coupling parameters indicate connection strength, which is also coded in the size of the arrows representing effective connectivity. The greater the absolute value (reflecting the rate constant of the observed influence in 1/s), the stronger the effect one area exerts upon another. Colored arrows indicate significant increases (red) and decreases (blue) in the RBX session compared with PBO (P < 0.05, corrected). (A) Coupling parameters in the PBO session demonstrate strong influences between left V1 and left IPS, left IPS and left PMC/SMA, and both premotor regions to left M1. (B) Coupling parameters under RBX stimulation show a significant enhancement of effective connectivity 1) within the right hemisphere, and 2) between areas of the right and the left hemisphere (red arrows). By contrast, neuronal coupling was significantly reduced (compared with PBO) between some areas of the left hemisphere, especially those originating from left dPMC (blue arrows).

Figure 5.

Intrinsic connectivity (group analysis) between visuomotor key regions under PBO and RBX. Coupling parameters indicate connection strength, which is also coded in the size of the arrows representing effective connectivity. The greater the absolute value (reflecting the rate constant of the observed influence in 1/s), the stronger the effect one area exerts upon another. Colored arrows indicate significant increases (red) and decreases (blue) in the RBX session compared with PBO (P < 0.05, corrected). (A) Coupling parameters in the PBO session demonstrate strong influences between left V1 and left IPS, left IPS and left PMC/SMA, and both premotor regions to left M1. (B) Coupling parameters under RBX stimulation show a significant enhancement of effective connectivity 1) within the right hemisphere, and 2) between areas of the right and the left hemisphere (red arrows). By contrast, neuronal coupling was significantly reduced (compared with PBO) between some areas of the left hemisphere, especially those originating from left dPMC (blue arrows).

The parameter estimates for these intrinsic connections showed a strong positive coupling among, in particular, left hemispheric areas (Fig. 5A, Table 3). The reciprocal connections between V1-IPS, IPS-PMC, IPS-SMA, SMA-M1, and PMC-M1 were strongly asymmetric (P < 0.001 for left hemispheric connections, P < 0.05 for right hemispheric connections) suggesting a preferred flow of neural information from visual cortex via parietal to premotor areas and finally M1. Left IPS had a significantly stronger influence on right IPS than vice versa (P = 0.035). The strongest influence on intrinsic M1 activity was exerted by left dPMC, followed by left SMA and—significantly less (P < 0.001)—by right FEF/dPMC. Connectivity within the right hemisphere was significantly weaker for all connections except for the (retrograde) connection between IPS and V1 (P = 0.084).

Table 3

Significant coupling parameters for RBX and PBO in Hz (P < 0.05, Bonferroni–Holm corrected) for the intrinsic connections (A) and their condition-related modulations reflecting the influence of task difficulty on interregional coupling (B)

(A) Intrinsic connectivity
 
(A) Intrinsic connectivity (continued)
 
Connections
 
PBO
 
RBX
 
t-Test
 
Connections
 
PBO
 
RBX
 
t-Test
 
Origin–target Mean SEM Mean SEM P Origin–target Mean SEM Mean SEM P 
L_IPS–L_PMC 0.430 0.012 0.404 0.018 n.s. L_SMA–L_PMC 0.199 0.018 0.177 0.021 n.s. 
L_IPS–L_SMA 0.354 0.024 0.343 0.025 n.s. L_SMA–R_FEF 0.066 0.012 0.093 0.012 n.s. 
L_IPS–L_V1 0.067 0.019 0.046 0.014 n.s. L_V1–L_IPS 0.401 0.024 0.363 0.034 n.s. 
L_IPS–R_IPS 0.182 0.027 0.184 0.029 n.s. L_V1–R_V1 0.074 0.021 0.067 0.015 n.s. 
L_M1–L_PMC 0.132 0.012 0.106 0.015 0.046 R_IPS–L_IPS 0.099 0.031 0.147 0.040 0.025 
L_M1–L_SMA 0.116 0.016 0.095 0.016 0.060 R_IPS–R_FEF 0.047 0.011 0.122 0.023 0.001 
L_M1–R_FEF 0.046 0.007 0.053 0.009 n.s. R_IPS–R_V1 0.032 0.007 0.063 0.011 0.013 
L_PMC–L_IPS 0.202 0.025 0.148 0.024 0.016 R_FEF–L_M1 0.050 0.010 0.094 0.019 0.007 
L_PMC–L_M1 0.288 0.011 0.255 0.018 0.033 R_FEF–L_PMC 0.046 0.011 0.085 0.019 0.012 
L_PMC–L_SMA 0.214 0.019 0.186 0.021 0.037 R_FEF–L_SMA 0.042 0.010 0.073 0.016 0.017 
L_PMC–R_FEF 0.080 0.016 0.114 0.014 n.s. R_FEF–R_IPS 0.028 0.006 0.043 0.009 n.s. 
L_SMA–L_IPS 0.164 0.024 0.129 0.025 n.s. R_V1–L_V1 0.021 0.008 0.034 0.012 n.s. 
L_SMA–L_M1 0.224 0.020 0.203 0.020 n.s. R_V1–R_IPS 0.081 0.023 0.153 0.027 0.000 
(B) Small circle size
 
Medium circle size
 
Large circle size
 
Connections PBO
 
RBX
 
PBO
 
RBX
 
PBO
 
RBX
 
Origin–Target Mean SEM Mean SEM P Mean SEM Mean SEM P Mean SEM Mean SEM P 
L_IPS–L_PMC 0.113 0.013 0.089 0.018 n.s 0.092 0.011 0.075 0.013 n.s. 0.095 0.017 0.048 0.014 n.s. 
L_PMC–L_M1 0.078 0.011 0.049 0.014 0.037 0.071 0.013 0.041 0.012 n.s. 0.065 0.011 0.049 0.014 n.s. 
L_SMA–L_M1 0.053 0.007 0.033 0.011 n.s 0.049 0.010 n.s n.s. n.s. 0.048 0.008 0.036 0.011 n.s. 
L_V1–L_IPS 0.130 0.017 0.106 0.022 n.s 0.101 0.012 0.066 0.014 n.s. 0.073 0.015 0.059 0.017 n.s. 
R_IPS–R_FEF 0.028 0.010 0.053 0.016 n.s n.s n.s n.s n.s n.s. n.s n.s n.s n.s n.s. 
R_V1–R_IPS 0.037 0.011 0.058 0.019 n.s. n.s n.s n.s n.s n.s. n.s n.s n.s n.s n.s. 
(A) Intrinsic connectivity
 
(A) Intrinsic connectivity (continued)
 
Connections
 
PBO
 
RBX
 
t-Test
 
Connections
 
PBO
 
RBX
 
t-Test
 
Origin–target Mean SEM Mean SEM P Origin–target Mean SEM Mean SEM P 
L_IPS–L_PMC 0.430 0.012 0.404 0.018 n.s. L_SMA–L_PMC 0.199 0.018 0.177 0.021 n.s. 
L_IPS–L_SMA 0.354 0.024 0.343 0.025 n.s. L_SMA–R_FEF 0.066 0.012 0.093 0.012 n.s. 
L_IPS–L_V1 0.067 0.019 0.046 0.014 n.s. L_V1–L_IPS 0.401 0.024 0.363 0.034 n.s. 
L_IPS–R_IPS 0.182 0.027 0.184 0.029 n.s. L_V1–R_V1 0.074 0.021 0.067 0.015 n.s. 
L_M1–L_PMC 0.132 0.012 0.106 0.015 0.046 R_IPS–L_IPS 0.099 0.031 0.147 0.040 0.025 
L_M1–L_SMA 0.116 0.016 0.095 0.016 0.060 R_IPS–R_FEF 0.047 0.011 0.122 0.023 0.001 
L_M1–R_FEF 0.046 0.007 0.053 0.009 n.s. R_IPS–R_V1 0.032 0.007 0.063 0.011 0.013 
L_PMC–L_IPS 0.202 0.025 0.148 0.024 0.016 R_FEF–L_M1 0.050 0.010 0.094 0.019 0.007 
L_PMC–L_M1 0.288 0.011 0.255 0.018 0.033 R_FEF–L_PMC 0.046 0.011 0.085 0.019 0.012 
L_PMC–L_SMA 0.214 0.019 0.186 0.021 0.037 R_FEF–L_SMA 0.042 0.010 0.073 0.016 0.017 
L_PMC–R_FEF 0.080 0.016 0.114 0.014 n.s. R_FEF–R_IPS 0.028 0.006 0.043 0.009 n.s. 
L_SMA–L_IPS 0.164 0.024 0.129 0.025 n.s. R_V1–L_V1 0.021 0.008 0.034 0.012 n.s. 
L_SMA–L_M1 0.224 0.020 0.203 0.020 n.s. R_V1–R_IPS 0.081 0.023 0.153 0.027 0.000 
(B) Small circle size
 
Medium circle size
 
Large circle size
 
Connections PBO
 
RBX
 
PBO
 
RBX
 
PBO
 
RBX
 
Origin–Target Mean SEM Mean SEM P Mean SEM Mean SEM P Mean SEM Mean SEM P 
L_IPS–L_PMC 0.113 0.013 0.089 0.018 n.s 0.092 0.011 0.075 0.013 n.s. 0.095 0.017 0.048 0.014 n.s. 
L_PMC–L_M1 0.078 0.011 0.049 0.014 0.037 0.071 0.013 0.041 0.012 n.s. 0.065 0.011 0.049 0.014 n.s. 
L_SMA–L_M1 0.053 0.007 0.033 0.011 n.s 0.049 0.010 n.s n.s. n.s. 0.048 0.008 0.036 0.011 n.s. 
L_V1–L_IPS 0.130 0.017 0.106 0.022 n.s 0.101 0.012 0.066 0.014 n.s. 0.073 0.015 0.059 0.017 n.s. 
R_IPS–R_FEF 0.028 0.010 0.053 0.016 n.s n.s n.s n.s n.s n.s. n.s n.s n.s n.s n.s. 
R_V1–R_IPS 0.037 0.011 0.058 0.019 n.s. n.s n.s n.s n.s n.s. n.s n.s n.s n.s n.s. 

Note: n.s.: not significant.

Effect of RBX on Intrinsic Connectivity

RBX challenge evoked several changes in the intrinsic coupling within and across hemispheres (Fig. 5B). Significant increases were found for the coupling among right V1 and right IPS as well as between right IPS and right FEF/dPMC (red arrows in Fig. 5B) when subjects had received RBX. Likewise, transcallosal influences exerted from right IPS and right FEF/dPMC on left hemispheric areas were significantly enhanced under RBX. These enhancements were, however, not significantly correlated with the individual improvements in movement speed (P > 0.05 for all comparisons). Furthermore, none of the connections in the left hemisphere showed a stronger coupling under RBX. Rather, the connections originating from left PMC to ipsilateral IPS, SMA and M1 showed small, but significant (P < 0.05) reductions in coupling strengths.

Hence, the increased activity in right V1, right IPS and right FEF/dPMC under RBX (as demonstrated in the GLM analysis, Fig. 3) could be explained by a significantly enhanced driving influence of right V1 on right IPS, and right IPS on right FEF/dPMC. The data furthermore suggest that RBX mediated a stronger control of activity in left hemispheric areas by frontoparietal areas of the right hemisphere which was independent from task difficulty.

Task-Dependent Modulation under PBO

The strongest modulation of connectivity (depending on task difficulty) was observed for the connection between left V1 to left IPS which was most pronounced for the “small circle” condition (highest difficulty level; Fig. 6A). Connectivity from SMA and PMC onto M1 activity was also enhanced, albeit to a lesser degree than that along the V1-IPS-PMC axis. In the right hemisphere, there was a strong effect of circle size on effective connectivity: Although medium and large circles did not specifically modulate connectivity in the right hemisphere, guiding the cursor into small circles significantly enhanced neural coupling between right V1 and right IPS and between right IPS and right FEF/dPMC. In other words, higher difficulty levels in guiding the joystick caused a stronger coupling within the visuomotor system, also among right hemispheric areas.

Figure 6.

Specific modulatory effects of different difficulty levels (i.e., circles sizes) on effective connectivity. Arrows indicate significantly modulated pathways (P < 0.05, corrected) for small (s), medium (m), and large (l) circles. Smaller circle sizes evoked stronger coupling among the regions of interest, especially in the right hemisphere. Although there was a clear trend for smaller coupling parameters under RBX stimulation, the only connection that reached statistical significance was between left PMC and left M1 for small circles (coupling estimate in blue). The analysis suggests that task difficulty did not additionally modulate interhemispheric influences (P > 0.05, corrected). n.s., not significant. Other abbreviations as in Figure 4.

Figure 6.

Specific modulatory effects of different difficulty levels (i.e., circles sizes) on effective connectivity. Arrows indicate significantly modulated pathways (P < 0.05, corrected) for small (s), medium (m), and large (l) circles. Smaller circle sizes evoked stronger coupling among the regions of interest, especially in the right hemisphere. Although there was a clear trend for smaller coupling parameters under RBX stimulation, the only connection that reached statistical significance was between left PMC and left M1 for small circles (coupling estimate in blue). The analysis suggests that task difficulty did not additionally modulate interhemispheric influences (P > 0.05, corrected). n.s., not significant. Other abbreviations as in Figure 4.

Task-Dependent Modulations under RBX

The task-specific modulations of the interregional coupling under RBX were very similar as compared with PBO (Fig. 6B). The only statistically significant reduction in interregional coupling was observed for the connection between left PMC and left M1 for the highest difficulty level (i.e., the “small circle” condition). Right hemispheric connections were not differentially modulated by RBX for any different difficulty level (P > 0.05). Likewise, there were no significant correlations between changes in coupling rates and task improvements (P > 0.05). The connectivity data, therefore, suggest that there were only weak effects of RBX on interregional coupling for a specific difficulty level (matching the behavioral data).

Task-Specificity of the DCM Changes

In order to assess whether the RBX induced differences in interregional coupling were indeed specific to the joystick task, we performed a separate DCM analysis on the visuomotor control task using the ROI coordinates and connectivity matrices derived from the joystick task. We then computed a repeated measures ANOVA with the factors “task” (levels: “joystick task”, “hand clenching task”), “drug” (levels: “RBX”, “PBO”), and “connection” (26 intrinsic coupling parameters). Although the main effect of drug was not significant (P = 0.718), there were significant interactions between task and connection (P < 0.001), between drug and connection (P = 0.001) and for task × drug × connection (P < 0.001). Pairwise t-tests revealed that in contrast to the significant differences reported above for the joystick task none of the connections was significantly different between the PBO condition and the RBX condition for the hand clenching task (P > 0.05 for each comparison). Especially the significant 3-way interaction and the pairwise t-tests suggest that the differences in coupling rates observed under RBX were specific to the joystick task.

Discussion

We used the NA reuptake inhibitor RBX to modulate the neural mechanisms underlying visuomotor processing during goal-directed hand movements as probed by a joystick task. The behavioral data showed that stimulating healthy subjects with RBX significantly increased movement speed for target-directed joystick movements. The improvements in visuomotor performance were associated with enhanced activity in right hemispheric areas known to be involved in visuospatial attention and motor control (Culham and Kanwisher 2001; Grefkes and Fink 2005). The connectivity analysis showed that these differential activations can be explained by increased coupling of right V1, IPS, and FEF/dPMC with left hemispheric areas, which was independent from task difficulty. Hence, stimulating the NA system with RBX mediated a bihemispheric rearrangement of the functional network architecture that might have enabled a more efficient implementation of the visuospatial capacities of the right hemisphere (Seidler et al. 2004), thereby improving behavioral performance in the joystick task.

Dynamic Causal Modeling

DCM is an approach to assess neurobiological hypotheses about effective connectivity based on a neuronal-system-model of network interactions. Importantly, DCM is designed for modeling interactions in a priori assumed networks (though different alternative hypotheses are compared via Bayesian model selection). It is, however, not intended as an exploratory tool to test which areas in the brain interact with a particular area of interest, as would be possible using, for example, Granger causality models (Roebroeck et al. 2005) or psychophysical interaction (PPI) analyses (Friston et al. 1997; Stephan et al. 2003). DCM treats the brain as a deterministic system in which external inputs cause changes in neural activity that in turn lead to changes in the fMRI signal (Friston et al. 2003; Penny et al. 2004). The approach employed by DCM is to explicitly model neuronal activity, which is then linked via a biophysically validated hemodynamic model (Friston et al. 2003) to the measured functional response (i.e., a change in the BOLD response). DCM therefore is much closer related to changes in neural dynamics in both time and space than previous approaches used to estimate connectivity. One important consideration in the assessment of DCMs is that the modeled effects represent effective as opposed to axonal connectivity. That is, although one usually strives to constrain to anatomically plausible connections, DCM does not rely on a direct axonal connection between 2 regions. Rather, the observed functional effects may also be mediated by (implicitly captured) relays (Friston et al. 2003; Grefkes, Eickoff, et al. 2008).

Pharmacological Modulation of Performance

Interactions in cortical networks underlying behavioral performance are ultimately driven by the interplay of neurotransmitters with their specific receptors (Loubinoux, Pariente, Rascol, et al. 2002; Plewnia et al. 2004; Floel et al. 2005). However, the effects exerted upon the neural architecture by pharmacological stimulation most likely differ between the different receptor systems and the task under investigation. For example, there is growing evidence that stimulating the human NA system with RBX does not affect performance in simple motor tasks (resembling the hand clenching task of the present study) (Plewnia et al. 2006; Zittel et al. 2007, Wang et al. 2009), but rather improves those motor tasks relying on visuomotor integration and 3D-coordination (Plewnia et al. 2004; Wang et al. 2009). Loubinoux et al. (2002b) showed that stimulation of serotonergic receptors by means of paroxetine (a selective serotonin reuptake inhibitor) may significantly enhance visuomotor performance in tasks relying on practice. These use-dependent effects are associated with an increase of BOLD response in contralateral sensorimotor areas (Loubinoux, Pariente, Boulanouar, et al. 2002), that is, regions which have previously been associated with motor learning (Sakai et al. 1998; Muller et al. 2002). In contrast, in the present study, RBX stimulation did not significantly influence visuomotor learning (as the repetition by drug interactions for movement times or errors were not significant), and the fMRI results did not reveal enhanced recruitment of a sensorimotor “learning” network. Rather, increased activity was observed in the thalamus and particularly in right hemispheric regions in visual, parietal and frontal cortex resembling patterns of activations previously referred to as the “attention network” (Nobre et al. 1997; Corbetta et al. 2008).

Noradrenergic Mechanisms Influencing Visuomotor Processing

The neurotransmitter system that has been frequently associated with influencing alertness and attention is the noradrenergic system (Posner and Petersen 1990). The most important source for cortical NA is the locus ceruleus (LC) in the pontine brainstem which widely projects to the spinal cord, thalamus, and cortex (Berridge and Waterhouse 2003; Logan et al. 2007; Takano et al. 2008). Studies in rats demonstrated that RBX administration may inhibit the firing of LC neurons (Wong et al. 2000). Such effects may well result from increased extracellular NA acting at alpha-2 autoreceptors on LC neurons (Berridge and Abercrombie 1999; Wong et al. 2000) which might also help to explain opposite effects of RBX on the discharge of LC neurons and target neurons of the LC. However, the question remains why enhancing noradrenergic transmission selectively improves visuomotor network functions given the broad distribution of norepinephrine fibers and the presumably system wide action of RBX on NA release. Studies in rats showed that low-dose administration of the NA reuptake inhibitor methylphenidate may differentially enhance NA transmission in prefrontal cortex compared with other cortical regions (Berridge et al. 2006). This gradient of efficacy of RBX to raise NA levels may explain differences in connectivity changes between more frontal cortical regions and those more posterior. The cellular mechanisms underlying such regionally specific effects are likely to comprise differences in the distribution of adrenergic receptor subtypes (alpha-1, alpha-2, beta-receptors), in the respective receptor sensitivity for NA binding, and in the local modulation of NA release (Bonanno et al. 1989; Berridge et al. 2006; Devilbiss et al. 2006). Similar mechanisms may also underlie the regionally specific changes in BOLD signal and connectivity observed as a result of RBX stimulation. However, we found no correlation of the BOLD signal changes or DCM connectivity changes with RBX plasma levels. The findings that 1) neural changes were not correlated with RBX plasma concentrations and 2) RBX effects were absent in the visuomotor control task suggest that rather other, more indirect mechanisms (e.g., modulation of LC activity or synaptic gating mechanisms) than direct interference of RBX with cortical synapses might be responsible for the effects observed in the present study. For example, data derived from studies in macaques suggest that changes in the phasic discharge of LC neurons during target detection may enhance the gain of neural responses in sensorimotor regions, thereby speeding up behavioral responses (Aston-Jones and Cohen 2005b). Electrophysiological studies imply that an appropriate state of arousal might facilitate the task-related phasic discharge of the LC (Aston-Jones et al. 1999; Clayton et al. 2004; Rajkowski et al. 2004), thereby improving behavioral responses. Studies in rats showed that both frequency and pattern of LC discharge may determine NA release in cortical regions such as the prefrontal or parietal cortex (Florin-Lechner et al. 1996; Berridge and Abercrombie 1999; Devoto et al. 2005). Also within the same region, stimulation of the LC or application of NA can differentially modulate the responsiveness of neighboring neurons responding to the same peripheral stimulus (Devilbiss et al. 2006). The effects of RBX on extracellular levels of norepinephrine might also depend upon the impulse activity of the LC. This activity may change according to the behavioral state as might occur under conditions of higher LC output, for example, during the challenges of the joystick task opposed to the less demanding fist closure task.

However, the behavioral data did not show significant differences in reaction times when subjects were stimulated with RBX. Hence, RBX might not have facilitated stimulus detection but rather the neural mechanisms subserving the control of guiding the cursor into the target circle as suggested by the faster movements under RBX. Electrophysiological studies in rodents showed that enhancing NA transmission has divergent effects on neurons found in different cortical layers, thereby modulating stimulus feature coding and signal-to-noise ratio (Hurley et al. 2004). For example, in visual cortex, enhancing NA may increase the signal-to-noise ratio (measured as spike train activity) by decreasing spontaneous activity (Hasselmo et al. 1997). The current fMRI data also showed enhanced BOLD responses in visual cortex under RBX stimulation. Furthermore, global enhancement of NA transmission by systemic administration of a NA reuptake blocker might have facilitated the gating of neural information, that is, the increase in the responsiveness of neurons to otherwise subthreshold stimuli (Devilbiss and Waterhouse 2000). Accordingly, the better use of sensory (visual, proprioceptive) feedback information during movement execution (due to RBX-induced neural gating) might have speeded up the joystick movements in the present study. Although RBX may have induced a system-wide increase in NA levels, neuronal processing might have been especially facilitated in those regions contributing to the actual task. Consistent with this view, the imaging data point to a modulation of areas known to be involved in visuospatial attention and online control of hand movements. Also the connectivity analysis implies that neural gating mechanisms might play a crucial role for the RBX effects observed in the present study, for example, by enhancing the functional interactions among areas in the right hemisphere. Such a hypothesis is in line with a recent study in rats demonstrating that overall functional connectivity among ensembles of neurons may be enhanced with increasing LC output and NA efflux (Devilbiss et al. 2006).

Networks Engaged in Visuospatial Attention and Motor Control

The connectivity analysis suggested a preferred flow of neural information from V1 over IPS to premotor and motor areas (Figs 5 and 6), which is in good accordance with data derived from studies in nonhuman primates (Rizzolatti et al. 1997). The data imply enhanced influences of right IPS on right V1 under RBX stimulation. Such a top-down mechanism could explain the higher BOLD activity observed in right V1 compared with PBO (Fig. 3A; left V1 activity was much less enhanced under RBX and only significant at uncorrected thresholds). The IPS is, however, not only engaged in visual attention (Nobre et al. 1997; Thiel et al. 2004; Corbetta et al. 2008), but also in visuomotor intention (Rushworth et al. 2003) and online control of movements (Eskandar and Assad 1999; Grefkes et al. 2004). Our results suggest that the stronger implementation of right frontoparietal areas into the visuomotor network subserving the joystick movements may have reduced the computational load posed onto the left hemisphere. In macaques, medial intraparietal cortex (area MIP) is engaged in planning and execution of reaching movements (Colby 1998), and is strongly connected to the dorsal premotor cortex (Rizzolatti et al. 1998). Medial IPS is supposed to transform sensory (e.g., visual, auditory) target information into a common eye-centered reference frame which can be “read out” by the motor system independent of the type of action planned (Cohen and Andersen 2000, 2002). Therefore, stronger activation of intraparietal cortex mediated by RBX stimulation might reflect enhanced engagement of transformation processes facilitating the integration of visual information into planned motor programs (i.e., goal-directed joystick movements) (Rushworth et al. 2001; Astafiev et al. 2003; Grefkes and Fink 2005).

Although it is tempting to conclude that the critical neural correlate for improved task performance is the human homologue of area MIP, we cannot make such a clear anatomical statement as also the human homologue of the LIP area is found on medial IPS in humans (Koyama et al. 2004; Grefkes and Fink 2005). This area is involved in the transformation of (visuo-) spatial coordinates in saccadic eye movements (Andersen 1995; Snyder et al. 2000), and also projects to superior frontal cortex, that is, the frontal eye fields (FEF) which are located slightly rostral to the reaching related neurons in dPMC (Boussaoud et al. 1998; Schall and Thompson 1999). In a more conceptual framework, the FEF is thought to transform visual signals into motor commands (i.e., saccades), hence subserving similar properties as hand/arm related neurons in dorsal premotor cortex (Schall and Thompson 1999; Schubotz and von Cramon 2003). However, cell recordings in macaques demonstrated that more than half of FEF neurons can be modulated by hand position signals (Boussaoud et al. 1998; Thura et al. 2008), which may indicate that these regions have an important role beyond saccade programming, for example, in mediating visual salience and spatial attention for the control of hand and eye movements (Thompson and Bichot 2005). Although we cannot exclude that changes in eye movements might have played a role for the BOLD signal increases observed under RBX stimulation, the strong lateralization of activity to the right hemisphere and the asymmetric changes in interhemispheric connectivity speak against a purely saccade related effect: Both saccades and also the suppression of eye movements typically show strong bilateral activations of the frontal eye fields (Paus 1996; Corbetta et al. 1998; Connolly et al. 2002). Furthermore, the faster joystick movements under RBX were not associated with faster reaction times in starting the joystick movements after target onset. Hence, although we cannot rule out differences in eye movements between the RBX and PBO session, the missing effects on reaction times imply that target selection and control of saccades might not have played a dominant role for the neural effects observed. We rather suggest that the enhanced influence of right IPS onto the right FEF/dPMC region mediated by RBX may reflect the promotion of an interaction between eye- and hand-related neurons for the planning and execution of visually guided joystick movements (Boussaoud et al. 1998; Thura et al. 2008). Such a view is consistent with data showing that also human oculomotor areas in IPS and FEF are involved in pointing preparation and execution (Simon et al. 2002; Astafiev et al. 2003). It remains, therefore, difficult to separate the specific contributions of different attentional mechanisms to the joystick task. Andersen and Buneo (2003) also emphasize the extremely similar coding strategies of the reaching and saccade related areas in IPS suggesting that both regions (MIP, LIP) are parts of a single network for the purpose of coordinating hand and eye movements, and which might both have been activated by the current experiment.

Conclusions

The present study shows that improvements in visuomotor performance following noradrenergic stimulation with RBX underlie not only changes in regional activity but also complex network effects affecting both neural processing within and across the hemispheres. We, therefore, conclude that motor improvements under noradrenergic stimulation (Evans et al. 1976; Bütefisch et al. 2002; Plewnia et al. 2004) might not simply reflect general arousal effects impacting on motor cortex excitability, but rather a (task-) specific modulation of the neural networks subserving the visuomotor control of target-directed movements. Such a view is compatible with data from animal experiments showing that stimulation of the LC–NA system may change functional connectivity among selective ensembles of neurons, thereby maximizing information processing capabilities of cerebral networks (Devilbiss et al. 2006). Modulation of connectivity among cellular assemblies might also account for the beneficial effects of NA enhancing drugs in pathological conditions, for example, after a stroke (Zittel et al. 2007). As stroke patients may show profound changes in the effective connectivity among motor areas across both hemispheres (Grefkes et al. 2008b), drugs such as RBX might effectively interfere with disturbances in interregional coupling. Analyses of effective connectivity (e.g., by using DCM) may, therefore, contribute to further our understanding of the neural effects induced by novel treatment approaches (pharmacological or electrophysiological interventions) aiming at improving neurological functions.

Funding

Marie Curie Early Stage Training Programme “NovoBrain” funded by the European Union to L.E.W.; Initiative and Networking Fund of the Helmholtz Association within the Helmholtz Alliance on Systems Biology to S.B.E.

We thank Dr Manuel Dafotakis for his medical support. We are grateful to the M.R. staff for their technical support. Conflict of Interest: None declared.

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