Noradrenergic Modulation of Cortical Networks - Cerebral Cortex

Cerebral Cortex April 2010;20:783--797

doi:10.1093/cercor/bhp144

Advance Access publication August 17, 2009

Noradrenergic Modulation of CorticalNetworks Engaged in VisuomotorProcessing

Christian Grefkes1,2, Ling E. Wang3,4, Simon B. Eickhoff3,5 and

Gereon R. Fink2,3

1Neuromodulation & Neurorehabilitation, Max Planck Institute

for Neurological Research, 50931 Cologne, Germany,2Department of Neurology, University Hospital Cologne, 50924

Cologne, Germany, 3Institute of Neurosciences and

Medicine—Cognitive Neurology (INM3), Research Centre

Julich, 52425 Julich, Germany, 4International Graduate School

of Neuroscience, Ruhr-Universitat Bochum, 44801 Bochum,

Germany and 5Department of Psychiatry and Psychotherapy,

RWTH Aachen University, 52074 Aachen, Germany

Both animal and human data suggest that stimulation of thenoradrenergic system may influence neuronal excitability in regionsengaged in sensory processing and visuospatial attention. Wetested the hypothesis that the neural mechanisms subserving motorperformance in tasks relying on the visuomotor control of goal-directed hand movements might be modulated by noradrenergicinfluences. Healthy subjects were stimulated using the selectivenoradrenaline reuptake inhibitor reboxetine (RBX) in a placebo-controlled crossover design. Functional magnetic resonanceimaging and dynamic causal modeling (DCM) were used to assessdrug-related changes in blood oxygen level--dependent activity andinterregional connectivity while subjects performed a joystick taskrequiring goal-directed movements. Improved task performanceunder 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 wassignificantly enhanced when subjects were stimulated with RBX.Concurrently, right intraparietal cortex and right superior frontalcortex exerted a stronger driving influence on visuomotor areas ofthe left hemisphere, including SMA and M1. These effects wereindependent from task difficulty. The data suggest that stimulatingnoradrenergic mechanisms may rearrange the functional networkarchitecture within and across the hemispheres, for example, bysynaptic gating, thereby optimizing motor behavior.

Keywords: effective connectivity, noradrenaline, parietofrontal circuits,pharmacological fMRI, visuomotor control

Introduction

The noradrenergic transmitter system may influence the

discharge properties of neurons engaged in arousal, visuospa-

tial 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 mecha-

nisms 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 noradren-

ergic 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 mech-

anisms 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 visuomo-

tor 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 neurotransmit-

ters 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.

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Material and Methods

SubjectsThe 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 DesignA 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, Gottingen, 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 DevicesWe 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 3 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 TaskSubjects 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 ‘‘visuomo-

tor 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).

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 ExperimentWe 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 require-

ments 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) 3 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.

Figure 1. Visuomotor joystick task. Subjects were asked to use a joystick to movea 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 perblock.

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Functional Magnetic Resonance ImagingFunctional 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 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 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 3 1.0 mm, flip angle = 9�, distance factor = 50%.

Behavioral Data AnalysisThe 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 ProcessingFor MR image preprocessing and statistical analysis, we used the SPM

software package (SPM5; Wellcome Department of Imaging Neurosci-

ence, 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 ModelingA 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

dx

dt=

A + +

m

j=1

ujBðjÞ

!x +Cu

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 InterestDCM 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

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Supplementary Table 2

Supplementary Table 2

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).

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.

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 connec-

tivity 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

Figure 2. Visuomotor network engaged in the joystick task. Compared with the resting baseline, guiding the joystick cursor into the peripheral targets activated a widespreadnetwork 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). Colorscale 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) wasespecially pronounced in right thalamus. The parameter estimates extracted from the local maximum demonstrated a decrease in BOLD activity for easier (i.e., larger) targets forboth PBO and RBX stimulation.


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example, transmitted via direct connections, a single relay area or more

extensive (subcortical) loops.

Connectivity ModelsDCMs 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 non-

human 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 connec-

tivity 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 SelectionWe 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

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, FWEcorrected 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 thedifferent 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. Otherabbreviations as in Figure 2.


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Supplementary Fig. 1

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 signifi-

cantly 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 cerebel-

lum, 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 hemi-

spheric 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

Table 1Local 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 L Precentral gyrus, ‘‘hand knob’’ 26.8�56, �18, 41 L Postcentral sulcus 25.3730, �96, �5 R Occipital pole 21.01�5, �12, 55 L Paracentral lobule, SMA 20.03�32, �10, 53 L Precentral sulcus, premotor 19.6636, �88, �7 R Lateral occipital cortex 19.5636, �42, 51 R IPS 17.34, �2, 55 R Paracentral lobule, SMA 17.2940, �6, 51 R Precentral sulcus 16.5854, 4, 37 R Precentral sulcus, ventral premotor 15.8720, �64, 59 R IPS 15.73

�20, �60, 63 L IPS 15.63�6, �20, 49 L Cingulate sulcus 14.24�52, 2, 39 L Precentral sulcus, ventral premotor 14.11�44, �26, 19 L Parietal operculum, SII 13.15�24, �76, 29 L Parieto-occipital junction 13.0646, �70, 1 R Middle occipital/inferior temporal (V5) 12.32

�42, �66, 5 L Middle occipital/inferior temporal (V5) 12.03�08, �86, 3 L Calcarine sulcus, V1 9.0850, 8, 5 R Frontal operculum 8.96

�42, 4, 5 L Frontal operculum 8.1216, �84, 3 R Calcarine sulcus, V1 6.8442, �26, 19 R Parietal operculum, SII 5.5332, �48, �31 R Superior cerebellum 19.35

�14, �15, 5 L Thalamus 16.4418, �60, �49 R Inferior cerebellum 14.33

�22, 6, �1 L Basal ganglia, putamen 13.99�28, �56, �23 L Superior cerebellum 13.6624, 4, 1 R Basal ganglia, putamen 12.0212, �14, 7 R Thalamus 10.37

�16, �56, �45 L Inferior cerebellum 5.55RBX versus PBO

26, 4, 57 R Superior frontal sulcus/superiorprecentral sulcus

6.64

38, �54, 41 R IPS, horizontal branch 5.6218, �86, 1 R Calcarine sulcus 5.5721, �8, �3 R Basal ganglia (putamen) 5.6820, �26, 9 R Thalamus 4.56

�16, �28, 5 L Thalamus 4.39�28, �8, �5 L Basal ganglia (putamen) 4.11

PBO versus RBX�36, �26, 55 L Precentral gyrus, ‘‘hand knob’’ 5.48


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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.

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

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 mainexperiment (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.


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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 con-

firmed 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.

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.

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 <0.05for righthemisphericconnections) suggestingapreferred

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,

Figure 5. Intrinsic connectivity (group analysis) between visuomotor key regions under PBO and RBX. Coupling parameters indicate connection strength, which is also coded inthe 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 onearea exerts upon another. Colored arrows indicate significant increases (red) and decreases (blue) in the RBX session compared with PBO (P\ 0.05, corrected). (A) Couplingparameters 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) Couplingparameters under RBX stimulation show a significant enhancement of effective connectivity 1) within the right hemisphere, and 2) between areas of the right and the lefthemisphere (red arrows). By contrast, neuronal coupling was significantly reduced (compared with PBO) between some areas of the left hemisphere, especially those originatingfrom left dPMC (blue arrows).

Table 2Group data (n 5 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 0.026Medium 806 ± 28 761 ± 17 44 ± 19 0.038Large 621 ± 22 581 ± 19 40 ± 17 0.035Mean 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).


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Suppl. Fig. 2

Supplemental Fig. 1

Supplemental Fig. 1

Suppl. Table 1

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 (retro-

grade) connection between IPS and V1 (P = 0.084).

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, trans-

callosal 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.

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 differen-

tially 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

Table 3Significant 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 0.025L_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 0.001L_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 0.013L_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 0.007L_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 0.012L_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 0.017L_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.


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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 3 drug 3 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 model-

ing 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).

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. Althoughthere 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 forsmall circles (coupling estimate in blue). The analysis suggests that task difficulty did not additionally modulate interhemispheric influences (P [ 0.05, corrected). n.s., notsignificant. Other abbreviations as in Figure 4.


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Pharmacological Modulation of Performance

Interactions in cortical networks underlying behavioral perfor-

mance are ultimately driven by the interplay of neurotransmit-

ters 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 pharmacolog-

ical 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 visuo-

motor 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 VisuomotorProcessing

The neurotransmitter system that has been frequently associ-

ated with influencing alertness and attention is the noradren-

ergic 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 respon-

siveness 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 trans-

mission 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). Ac-

cordingly, 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


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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 MotorControl

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 sub-

serving 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 pro-

cesses facilitating the integration of visual information into

planned motor programs (i.e., goal-directed joystick move-

ments) (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 homo-

logue 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 bemodulated 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 move-

ments 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; Butefisch 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 informa-

tion 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


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approaches (pharmacological or electrophysiological interven-

tions) 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.

Notes

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.

Address correspondence to Christian Grefkes, MD, Neuromodulation

& Neurorehabilitation, Max-Planck-Institute for Neurological Research,

Gleueler Str. 50, 50931 Koln, Germany. Email: christian.grefkes@

uk-koeln.de.

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Noradrenergic Modulation of Cortical Networks - Cerebral Cortex

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