ORIGINAL ARTICLE

Shifted neuronal balance during stimulus–response integrationin schizophrenia: an fMRI study

Edna C. Cieslik • Veronika I. Muller •

Tanja S. Kellermann • Christian Grefkes •

Sarah Halfter • Simon B. Eickhoff

Received: 3 June 2013 / Accepted: 4 October 2013

� Springer-Verlag Berlin Heidelberg 2013

Abstract Schizophrenia is characterized by marked def-

icits in executive and psychomotor functions, as demon-

strated for goal-directed actions in the antisaccade task.

Recent studies, however, suggest that this deficit represents

only one manifestation of a general deficit in stimulus–

response integration and volitional initiation of motor

responses. We here used functional magnetic resonance

imaging to investigate brain activation patterns during a

manual stimulus–response compatibility task in 18

schizophrenic patients and 18 controls. We found that

across groups incongruent vs. congruent responses recrui-

ted a bilateral network consisting of dorsal fronto-parietal

circuits as well as bilateral anterior insula, dorsolateral

prefrontal cortex (DLPFC) and the presupplementary

motor area (preSMA). When testing for the main-effect

across all conditions, patients showed significantly lower

activation of the right DLPFC and, in turn, increased

activation in a left hemispheric network including parietal

and premotor areas as well as the preSMA. For incongruent

responses patients showed significantly increased activa-

tion in a similar left hemispheric network, as well as

additional activation in parietal and premotor regions in the

right hemisphere. The present study reveals that hypoac-

tivity in the right DLPFC in schizophrenic patients is

accompanied by hyperactivity in several fronto-parietal

regions associated with task execution. Impaired top-down

control due to a dysfunctional DLPFC might thus be partly

compensated by an up-regulation of task-relevant regions

in schizophrenic patients.

Keywords fMRI � Hypo-/hyperactivation �Prefrontal � Parietal � Manual � Executive control

Introduction

Schizophrenia is a mental disorder characterized by positive

productive-psychotic symptoms, negative symptoms, like

blunted affect, and cognitive impairments (Andreasen 1990;

Barch and Ceaser 2012). The latter are usually progressive

independently of psychotic episodes (Addington et al. 1997;

Heaton et al. 2001) and often severely affect long-term

quality of life and functional outcome (cf. Lesh et al. 2011;

Nuechterlein et al. 2011). Moreover, it has been shown that

cognitive dysfunctions are not simply the result of other

symptoms or antipsychotic treatment as even medication-

naıve patients feature neuropsychological profiles similar to

those with an antipsychotic medication history (Bilder et al.

E. C. Cieslik (&) � V. I. Muller � T. S. Kellermann �S. B. Eickhoff

Institute of Clinical Neuroscience and Medical Psychology,

Heinrich Heine University Dusseldorf, Dusseldorf, Germany

e-mail: e.cieslik@fz-juelich.de

E. C. Cieslik � V. I. Muller � S. B. Eickhoff

Institute of Neuroscience and Medicine (INM-1), Research

Center Julich, 52425 Julich, Germany

E. C. Cieslik � V. I. Muller � T. S. Kellermann � S. Halfter

Department of Psychiatry, Psychotherapy, and Psychosomatics,

RWTH Aachen University, Aachen, Germany

C. Grefkes

Neuromodulation and Neurorehabilitation, Max-Planck Institute

for Neurological Research, Cologne, Germany

C. Grefkes

Department of Neurology, University of Cologne, Cologne,

Germany

S. B. Eickhoff

JARA-Brain, Translational Brain Medicine, Julich/Aachen,

Germany

123

Brain Struct Funct

DOI 10.1007/s00429-013-0652-1

2000; Bowie and Harvey 2005; Saykin et al. 1994). It has

been hypothesized that many cognitive deficits found in

schizophrenia can be regarded as a failure in exerting

control over thoughts and actions with a central deficit in the

ability to maintain and update internal representations of

task-relevant context information (Braver et al. 1999;

Cohen and Servan-Schreiber 1992). A pivotal role for such

cognitive control and monitoring processes has been

attributed to the dorsolateral prefrontal cortex (DLPFC)

(Cieslik et al. 2013; Hoshi 2006; Miller and Cohen 2001),

which has likewise been hypothesized to be one of the key

structures in pathophysiology of schizophrenia (Bogerts

2005; Goldman-Rakic 1994; Goldman-Rakic and Selemon

1997). In line with that, neuroimaging studies consistently

reported a problem in schizophrenic patients to activate the

DLPFC in the same manner as healthy controls. This

‘‘hypofrontality’’ has been shown for a diversity of execu-

tive tasks such as the Tower of London, n-back task, or tasks

that require overriding prepotent response tendencies, even

in first-episode, medication-naıve patients (e.g. Andreasen

et al. 1992; Carter et al. 1998; Perlstein et al. 2003; Barch

et al. 2001; Weiss et al. 2007).

One of the most frequently used paradigms for investi-

gating cognitive impairments in schizophrenic patients and

their clinically unaffected relatives is the antisaccade task

(cf. Broerse et al. 2001; Calkins et al. 2004; Reuter et al.

2005, 2006, 2007). Here, the participant is asked to per-

form an antisaccade (saccade to the mirror-symmetrical

position) to a lateralized visual stimulus (Everling and

Fischer 1998; Hallett 1978; Munoz and Everling 2004).

Importantly, the latency of pro-saccades (saccade towards

the stimulus) seems to be unimpaired in schizophrenic

patients, suggesting preserved integrity of low-level motor

control (Broerse et al. 2001; Reuter et al. 2007). By con-

trast, marked impairments have been observed for per-

forming antisaccades with respect to error rates and

response latencies (Broerse et al. 2001; Brownstein et al.

2003; Calkins et al. 2004; Reuter et al. 2005, 2007). Poor

performance in the antisaccade task has been attributed to a

dysfunction of the prefrontal cortex (Pierrot-Deseilligny

et al. 2003). Consistent with this hypothesis, McDowell

et al. (2002) found increased activation of the dorsolateral

prefrontal cortex during antisaccades compared to pro-

saccades in healthy participants but failed to show the same

effect in schizophrenic patients. Moreover, a recent elec-

trophysiological study showed reduced evoked potentials

over lateral prefrontal cortex in schizophrenic patients

compared to healthy controls concomitant to poorer anti-

saccade performance (Kang et al. 2011). Even though most

studies have emphasized the role of impaired response

inhibition when patients fail to perform accurately in the

antisaccade task, a study by Reuter et al. (2007) indicated

more complex differences between generating pro- vs.

antisaccades. For example, whereas the prosaccade is

triggered by a visual stimulus, correct antisaccades require

the volitional initiation of a motor response. To test for

impaired volitional response initiation in schizophrenic

patients, the authors therefore used a delayed pro-/anti-

saccade task and furthermore included trials where the

direction of the saccade was indicated by centrally dis-

played arrows. Analysis of behavioral data showed less

errors in the delayed antisaccade task compared to the

standard version and also less pronounced deficits in

schizophrenic patients in this delayed compared to the

standard antisaccade task. Most importantly, however,

response latencies were normal in the standard prosaccade

task, but increased in all other tasks where patients had to

initiate the saccade endogenously, pointing towards a more

general cognitive control deficit of motor behavior and

going beyond an inhibition specific deficit in schizophre-

nia. We recently found that schizophrenic patients also

feature problems in a manual stimulus–response compati-

bility task (Behrwind et al. 2011). These results point

towards impaired stimulus–response integration in schizo-

phrenic patients, with a more severe deficit when the

cognitive control demands for stimulus–response selection

increase during incongruent responding.

To investigate the neural correlates of such potentially

disturbed stimulus–response integration in schizophrenic

patients, we conducted an fMRI experiment in which

patients and matched controls performed a manual stimu-

lus–response compatibility task. As cognitive dysfunctions

in schizophrenia have been especially associated with

negative symptoms and disorganization (for a review see

Lesh et al. 2011), we expected to see a relationship

between task performance and negative as well as general

PANSS symptomatology in the patient group. At the neural

level, differences in the recruitment of the right DLPFC in

the patient compared to the control group were expected.

Methods

Participants

18 patients with schizophrenia (F20 according to ICD-10)

and 18 matched healthy controls participated in this study.

All patients (2 inpatients, 1 day-treatment, 15 outpatients)

were recruited from the Department of Psychiatry, Psy-

chotherapy and Psychosomatics, RWTH Aachen Univer-

sity Hospital (Aachen/Germany). Diagnosis was

established by review of the clinical records and after

consulting the attending psychiatrist. 15 patients met ICD-

10 criteria for paranoid subtype (F 20.0) and 3 patients

were diagnosed with residual subtype (F 20.5). Patients

were free of any other psychiatric or neurological

Brain Struct Funct

123

comorbidity, organic mental illness or developmental

impairments and at least 6 months abstinent from illegal

drug use. Psychopathology was assessed by the Positive

and Negative Syndrome Scale (PANSS; Table 1). One

patient was unmedicated. All other patients were treated

with atypical anti-psychotics, none of the patients received

typical anti-psychotics. The healthy control group was free

of any neurological or psychiatric illness and family history

of psychosis (to 2nd degree relatives). Controls were

matched for age, gender and education (Table 1). All

participants had normal or corrected-to-normal vision and

were right-handed as confirmed by the Edinburgh Inven-

tory (Oldfield 1971). Furthermore, all subjects gave

informed written consent to the study protocol, which had

been approved by the ethics committee of the Medical

Faculty of the RWTH Aachen University.

Experimental protocol

Participants were placed comfortably in the magnetic res-

onance imaging (MRI) scanner, with both hands positioned

on MR-compatible response pads (LUMItouchTM, Light-

wave Technologies, Richmond, Canada). Visual stimula-

tion was provided by MR-compatible video goggles. The

same stimulus–response compatibility task that has

recently been used to investigate bottom-up and top-down

processes of motor behavior in healthy participants was

used in the present study (for details see Cieslik et al.

2010). Here, participants are instructed to respond as fast

and correctly as possible to a briefly presented (200 ms)

lateralized target stimulus (red dot) by pressing a button

according to the task condition: in the congruent condition

participants were instructed to respond with the ipsilateral

hand, i.e. pressing with their left index finger to a left-sided

stimulus (CL) and with their right index finger to a right-

sided stimulus (CR). In contrast, in the incongruent con-

dition participants were instructed to respond with the

contralateral hand, i.e. pressing with their left index finger

to a right-sided stimulus (ICL) and with their right index

finger to a left-sided stimulus (ICR; note that L and

R always indicate the respective response hand and not the

stimulus side).

Visual stimuli were presented using the presentation

software package (version 14.2, http://www.neurobs.com/).

During the experiment, task blocks were periodically

alternated with rest periods (‘‘baseline’’) lasting 15–19 s

(uniformly jittered). Each task block started with an

instruction presented for 2,000 ms, informing the subject

which of the two experimental conditions (congruent vs.

incongruent response) had to be performed in the upcom-

ing block of trials. Between 13 and 16 events per block

(randomized 50 % left-sided stimulus/50 % right-sided

stimulus, number of events randomized to avoid anticipa-

tion effects) were presented. The inter-stimulus interval

was uniformly jittered between 2 and 4.5 s. That is, a

mixed design with blocked task instructions (congruent/

incongruent), but event-related stimulus presentation (left/

right) was used. Over the course of the entire experiment,

each of the two conditions (congruent, incongruent) was

presented in 12 individual blocks. The order of the ensuing

24 blocks was pseudo randomized and counterbalanced

across subjects. Reaction times \150 ms or [1,600 ms

were discarded as anticipation errors and missing

responses.

Functional MRI data acquisition

Images were acquired on a Siemens Trio 3T whole-body

scanner (Erlangen, Germany) using blood oxygenation

level dependent (BOLD) contrast [gradient-echo planar

imaging (EPI) pulse sequence, repetition time = 2,200 ms,

echo time = 30 ms, flip angle = 90�; in-plane resolu-

tion = 3.1 9 3.1 mm, 36 axial slices, 3.1 mm thickness]

covering the whole brain from the vertex to lower parts of

the cerebellum. Image acquisition was preceded by four

dummy images allowing for magnetic field saturation.

These were discarded prior to further processing. Images

were analyzed using SPM8 (http://www.fil.ion.ucl.ac.uk/

spm). First, the EPI images were corrected for head

movement by affine registration using a two-pass proce-

dure, by which images were initially realigned to the first

image and subsequently to the mean of the realigned ima-

ges. After realignment, the mean EPI image for each subject

was spatially normalized to the Montreal Neurological

Institute (MNI) single-subject template using the ‘‘unified

segmentation’’ approach (Ashburner and Friston 2005). The

resulting parameters of a discrete cosine transform, which

define the deformation field necessary to move subject data

into the space of the MNI tissue probability maps, were then

combined with the deformation field transforming between

the latter and the MNI single-subject template. The ensuing

Table 1 Socio-demographic data of included patients and healthy

control participants

Characteristic Patients

(n = 18)

Healthy controls

(n = 18)

Age (years) 37.1 ± 9.2 36.6 ± 10.3

Education (years) 13.3 ± 3.2 12.9 ± 3.1

Female 8 8

BDI 8.56 ± 6.6 1.1 ± 1.4

Symptom severity (scale scores)

PANSS? 11.5 ± 4.2 –

PANSS- 13.9 ± 6.1 –

PANSS general 25.1 ± 6.9 –

Duration of illness 10.6 ± 7.7 –

Brain Struct Funct

123

deformation was subsequently applied to the individual EPI

volumes that were hereby transformed into the MNI single-

subject space and resampled at 2 9 2 9 2 mm3 voxel size.

The normalized images were spatially smoothed using an

8-mm full-width-at-half-maximum Gaussian kernel to meet

the statistical requirements of Gaussian random field theory

and to compensate for residual macroanatomical variations

across participants.

Statistical analysis

The behavioral measurements acquired during the fMRI

experiment were analyzed off-line using SPSS 15.0. To test

for significant differences in reaction times and performance

between the congruent and incongruent condition, Wilcoxon

ranks test was performed in the patient and the control group,

respectively. Behavioral differences between both groups

were tested using a Mann–Whitney U test because of vio-

lation of normality and non-homogeneity of variances as

tested by Levene-test. Correlations between reaction times

and PANSS scores as well as accuracy and PANSS scores

were tested by Spearman correlations and corrected for

multiple comparisons using Bonferroni’s method.

The fMRI data were analyzed in the framework of the

General Linear Model. Each experimental condition (CL,

CR, ICL, ICR—including only correct behavioral answers)

as well as errors for each experimental condition (CL-error,

CR-error, ICL-error, ICR-error) was modeled using a series

of stick functions denoting the individual events. In addi-

tion, individual trial-wise reaction times were incorporated

as a parametric modulator into the four task regressors of

the model to parcel out any variance that could be

explained by reaction time. The individual events were

then convolved with a canonical hemodynamic response

function and its first-order temporal derivative. Low-fre-

quency signal drifts were filtered using a cut-off period of

128 s. Parameter estimates were subsequently calculated

for each voxel using weighted least squares to provide

maximum likelihood estimators based on the temporal

autocorrelation of the data (Kiebel et al. 2003). No global

scaling was applied. For each subject, simple main effects

for each experimental condition were computed by apply-

ing appropriate baseline contrasts. The individual first-level

contrasts for CL, CR, ICL and ICR were then fed to a

second-level group ANOVA analysis (factor: condition,

separately per group; blocking factor: subject) using a

random-effects model. In the modeling of variance com-

ponents, we allowed for violations of sphericity by mod-

eling non-independence across images from the same

subject and allowing unequal variances between conditions

and subjects using the standard implementation in SPM8.

Error-related activity (CL-error, CR-error, ICL-error, ICR-

error) could not be modeled on the second level due to the

overall low number of errors made by the participants.

Simple main effects of each event type (vs. the resting

baseline) as well as comparisons between experimental

factors were tested by applying appropriate linear contrast

to the ANOVA parameter estimates. The resulting statis-

tical parametric T-maps [SPM(T)] were then thresholded at

p \ 0.05 (cluster-level FDR-corrected).

As behavioral data showed significant correlation

between negative as well as general PANSS (summary)

score and performance in the congruent and incongruent

condition two further second-level models were estimated

for the patient group only. That is, two second-level models

were estimated for the patient group including the negative

and general PANSS score, respectively, as a covariate.

All activations were anatomically localized using ver-

sion 1.7 of the SPM Anatomy toolbox (Eickhoff et al.

2005, 2007); (http://www.fz-juelich.de/ime/spm_anatomy_

toolbox).

Results

Behavioral data

Behavioral data revealed a significant increase in reaction

times for the incongruent compared to the congruent con-

dition in the patient (Z = -3.72, p \ 0.05) as well as in the

healthy control group (Z = -3.72, p \ 0.05). Moreover,

error rate significantly increased in the incongruent com-

pared to the congruent condition in the patient (Z = -2.4,

p \ 0.05) and the healthy control group (Z = -2.68,

p \ 0.05). The patient group displayed greater between-

subject heterogeneity in RTs and accuracy than the control

group. When testing for differences between the two groups

with a Mann–Whitney U test, there were no significant dif-

ferences neither for RTs (congruent condition: U = 109, ns;

incongruent condition: U = 121, ns) or accuracy (congruent

condition: U = 120, ns; incongruent condition: U = 135,

ns), (Table 2). Correlations between positive PANSS scores

and RTs in the congruent (r = -0.36, ns, Bonferroni cor-

rected) and incongruent condition (r = -0.37, ns, Bonfer-

roni corrected) as well as between positive PANSS score and

accuracy (percent of correct responses) in the congruent

(r = -0.05, ns, Bonferroni corrected) and incongruent

condition (r = -0.39, ns Bonferroni corrected) did not

reveal any significant association. Correlation analysis with

negative PANSS score did not reveal a significant relation

with RTs in congruent (r = -0.27, ns, Bonferroni corrected)

and incongruent condition (r = -0.31, ns, Bonferroni cor-

rected). In contrast, a negative correlation between negative

PANSS score and accuracy in the congruent (r = -0.58,

p \ 0.05 Bonferroni corrected) and incongruent condition

(r = -0.73, p \ 0.05 Bonferroni corrected) could be found.

Brain Struct Funct

123

For correlation analysis with general PANSS score a similar

effect was found. There was no significant correlation with

RTs in congruent (r = -0.22, ns, Bonferroni corrected) and

incongruent condition (r = -0.25, ns, Bonferroni cor-

rected) while a negative correlation between general PANSS

score and accuracy in the congruent (r = -0.62, p \ 0.05

Bonferroni corrected) and incongruent condition (r =

-0.72, p \ 0.05 Bonferroni corrected) could be found. That

is, the stronger the negative as well as general symptoms in

patients the more errors they made in the congruent as well as

the incongruent condition.

Imaging data

Task networks in both groups

To identify brain regions associated with the task main

effect, i.e. detection of target stimuli and planning as

well as execution of motor responses, the main effect of

all experimental conditions (CL ? CR ? ICL ? ICR)

across groups was contrasted against baseline (Fig. 1a).

To elucidate the ‘‘incongruency network’’, that is

regions associated with the increased executive demands in

the incongruent condition, we contrasted incongruent

responses versus congruent responses. This analysis

revealed activation across groups in a bilateral dorsal

fronto-parietal network involving the dorsal premotor

cortex (dPMC), superior parietal lobe (SPL, area 7P, 7A,

Scheperjans et al. 2008a, b) and intraparietal sulcus (IPS,

hIP3, Scheperjans et al. 2008a, b). Additional activations

were found in the left anterior insula extending into inferior

frontal gyrus, right anterior insula, bilateral DLPFC and the

supplementary motor area (SMA) extending into preSMA

(area 6; Geyer 2004) (Fig. 1b).

Differences between patients and healthy controls

In the main-effect comparing activity between groups

across all conditions the only region showing significant

lower activation in patients compared to healthy subjects

was the right DLPFC (MNI: 39/45/18; cf. Fig. 2). In con-

trast, patients showed significant increased activations in a

left hemispheric network including the dorsal premotor

cortex (-30/-14/50; area 6), inferior parietal cortex [-62/-27/

36; Pft (Caspers et al. 2006, 2008)], presupplementary

motor area (preSMA, -8/2/48; area 6) as well as the

superior parietal lobe [-33/-38/64, area 7A, 7PC (Sche-

perjans et al. 2008a, b)] (Fig. 3; Table 3). To further

examine whether increased activity in these regions might

reflect compensatory processes or altered mechanisms in

the patient group, a conjunction analysis was performed to

Table 2 Accuracy (in % correct responses) and reaction times (in

ms) in the congruent and incongruent condition

Patients Healthy controls

Median Interquartile

range

Median Interquartile

range

Accuracy-

congruent

98.0 2.0 99.0 1.0

Accuracy-

incongruent

95.5 5.5 97.5 3.25

RTs-congruent 411.7 122.0 386.5 67.8

RTs-incongruent 499.4 158.0 453.0 61.7

For each cell median and interquartile range across the diagnostic

group are provided. No significant differences were found between

groups

Fig. 1 a To identify regions

associated with the task main

effect, i.e. detection of target

stimuli and planning as well as

execution of motor response, the

main effect across all

experimental conditions was

contrasted against baseline.

b To delineate regions

specifically associated with the

incongruency effect, i.e. the

increased executive control in

the incongruent condition,

incongruent responses were

contrasted with congruent ones

Brain Struct Funct

123

restrict hyperactivity to regions showing significant activity

in the task main effect in both groups, i.e. task main effect

patients vs. task main effect controls \ task main effect

patients \ task main effect controls. This conjunction

analysis then revealed that the left parietal cortex, dPMC

and preSMA were significantly activated in both groups

and moreover showed significant increased activation in

the patient compared to the control group.

Moreover, when testing for group differences in par-

ticular in the incongruent condition, patients showed

significant increased activation for incongruent responding

in the network that showed increased activation in

patients for the group main effect. Here again, increased

activations for patients compared to healthy participants

were found in the parietal (IPC, IPS and SPL) and dorsal

premotor cortex as well as the preSMA. Moreover,

additional activations were found in parietal and premotor

regions in the right hemisphere (Fig. 4; Table 4). Here

again, a conjunction analysis was performed across the

group comparison and the incongruent condition in both

groups, i.e. incongruent responding patients vs. incon-

gruent responding controls \ incongruent responding

patients \ incongruent responding controls. This con-

junction analysis again revealed that the left parietal

cortex, left dPMC and left preSMA were significantly

activated during incongruent responding in both groups

and moreover showed increased activation during incon-

gruent responding in the patient compared to the control

group.

There were no regions showing significant decreased

activations in particular during incongruent responding for

patients relative to healthy control subjects. Furthermore,

no increased or decreased activations were found for

patients compared to controls in particular during congru-

ent responding. Testing for a group 9 task condition

interaction did not reveal significant activations. Hence, it

Fig. 2 Patients compared to healthy controls showed significant

(p \ 0.05, corrected) decreased activation of the right DLPFC in the

main effect across all conditions. Contrast estimates revealed

increased activation in the incongruent compared to the congruent

condition in the control group, whereas patients showed reduced to

absent recruitment throughout all experimental conditions (abbrevi-

ation: CL congruent left, CR congruent right, ICL incongruent left,

ICR incongruent right response)

Fig. 3 Patients compared to

healthy controls showed

significant (p \ 0.05, corrected)

increased activation in the left

dPMC, IPC and SPL as well as

the preSMA in the main effect

across all conditions

Table 3 Regions showing significant (p \ 0.05, cluster-level FDR-

corrected) increased activation for patients vs. controls

Region Cytoarchitectonicarea

x y z Z-score No. ofvoxels

Left dorsalpremotorcortex

Area 6 -30 -14 50 5.01 311

preSMA Area 6 -8 2 38 4.53 524

Left inferiorparietallobe

PFt, extendinginto OP1

-62 -27 36 4.31 883

Left superiorparietallobe

Area 7PC, (7A) -33 -48 64 3.91 300

Brain Struct Funct

123

seems that the main effect between patients and controls

was strongly driven by the incongruent task condition,

however, there was no brain region showing a significant

interaction effect.

Correlation with negative and general PANSS score

As patients’ negative and general PANSS score correlated

with accuracy in the congruent and incongruent condition,

we tested for brain regions showing a correlation of acti-

vation with negative and general PANSS score, respec-

tively. This analysis identified a region in the left inferior

parietal cortex (PFt) extending into anterior IPS (-33/-35/

39) in which activity during incongruent responses corre-

lated positively with more severe schizophrenic negative

symptoms. In contrast, there was no brain region showing

significant correlation with negative PANSS score during

congruent responses.

Testing for correlation with general PANSS score

revealed that activity in the right middle occipital gyrus

(36/-84/8) correlated negatively with PANSS general

score during congruent responding, while activity in the

posterior SMA (area 6; 0/-11/59) showed negative cor-

relation with PANSS general score in the incongruent

condition.

Discussion

The present study investigated putative disturbances of the

neural network underlying stimulus–response integration in

schizophrenia. We observed that, across congruent and

incongruent conditions, patients featured decreased acti-

vation in the right DLPFC as well as increased activity in

left lateralized parietal areas, dorsal premotor cortex and

preSMA relative to healthy controls. Moreover, when

testing for differences in particular in the incongruent

condition, patients showed significant increased activation

for incongruent responding in the former reported left

hemispheric network and additional activation of parietal

and premotor regions in the right hemisphere. The results

are well in line with the hypothesized reduced recruitment

of the DLPFC as a critical node in the cognitive control

network disturbed in schizophrenia, but likewise reveal

abnormal activity in regions associated with task perfor-

mance in the parietal and premotor cortex as well as the

preSMA.

Behavioral data

Behavioral data revealed a significant incongruency effect in

both groups, that is, an increase in error rate and RTs for

Fig. 4 Testing for incongruent

condition specific group

differences revealed significant

(p \ 0.05, corrected) increased

activation in patients compared

to healthy controls in the same

left hemispheric network as for

the main effect across all

conditions, with additional

activations in the right

hemisphere

Table 4 Regions showing significant (p \ 0.05 cluster-level FDR-corrected) increased activation for patients vs. controls in the incongruent

condition

Region Cytoarchitectonic area x y z Z-score No. of voxels

Left inferior parietal lobe PFt, extending into hIP3 -62 -24 32 4.85 2,811

preSMA Area 6 -4 -2 50 4.78 706

Right supramarginal gyrus OP1, PFop 64 -24 18 4.76 387

Left dorsal premotor cortex -30 -14 50 4.73 284

Left precentral gyrus Area 6 -56 -2 34 4.43 165

Right inferior frontal gyrus 42 3 22 4.32 154

Left superior parietal cortex 5 Ci -15 -28 46 4.09 209

Right inferior parietal cortex PFt, PF 60 -26 42 4.03 210

Right visual cortex 51 -60 -8 3.86 186

Right premotor cortex Area 6 28 -10 69 3.61 160

Brain Struct Funct

123

incongruent compared to congruent stimulus–response map-

pings. This incongruency effect has been consistently found in

manual stimulus response compatibility tasks, and interpreted

to reflect the extra computational load necessary to yield a

correct response in the incongruent condition (Cieslik et al.

2010; Iacoboni et al. 1996; Proctor and Reeve 1990). The

patient group showed a much higher variance in their per-

formance and RTs than the control group. This finding is well

known in the literature and possible parameters discussed in

the literature to contribute to increased variability in schizo-

phrenic patients are symptom severity as well as potential

medication effects (cf. Frecska et al. 2004; Roalf et al. 2013).

Somewhat surprising, we did not find any significant group

differences in task performance, neither for RTs nor for

accuracy. This result is in contrast to a previous behavioral

study of our group in which the same experiment was used to

investigate executive motor control in 28 schizophrenic

patients (Behrwind et al. 2011). There are some factors pos-

sibly contributing to this result. First, finding no significant

differences between patients and healthy controls in this study

might be due to a considerably smaller sample size in the

present study given the logistical challenges and exclusion-

criteria associated with fMRI measurements. Second, all but

three of the patients that participated in the fMRI experiment

were outpatients and hence not acutely ill. Due to increased

safety precautions, testing acute schizophrenic patients in an

fMRI scanner is difficult and therefore only patients that were

in a stable phase were included in the present study. In con-

trast, in the behavioral study by Behrwind et al. (2011) 20 out

of 28 patients were inpatients. Therefore, we assume that the

patient group that participated in the fMRI experiment might

have performed better because they were already in remission

and in a more stable phase of their illness.

Correlation analysis revealed significant negative corre-

lation between task performance and negative as well as

general PANSS score in the patients group. However, no

relation was found with positive PANSS score. This finding

is in accordance with previous studies showing that cognitive

control deficits are especially associated with negative

symptoms and disorganization in schizophrenia, whereas

they are relatively unrelated to positive symptomatology (for

a review see Lesh et al. 2011; Nieuwenstein et al. 2001).

Imaging data

Incongruency network

The functional imaging data revealed activation in a par-

ietal-premotor-prefrontal circuitry (Fig. 1b) for the incon-

gruency effect which replicated the network found for

incongruent responding in a previous study in healthy

subjects (Cieslik et al. 2010). In particular, increased

activation during incongruent responding was found in a

bilateral dorsal fronto-parietal attention network (Corbetta

et al. 2008), bilateral anterior insula and DLPFC as well as

the preSMA. This neural network is known to be involved

in the (re-)orientation to visual stimuli and generation of

motor sets in visuo-spatial tasks, especially for incongruent

manual responding during stimulus–response integration

(e.g. Cieslik et al. 2010; Schumacher et al. 2003; Sylvester

et al. 2003). In this context, the bilateral dorsal fronto-

parietal attention network (Corbetta et al. 2008; Corbetta

and Shulman 2002) has been associated with directing

attention to spatial locations as a prerequisite to look or act

towards these. In particular, the posterior parietal cortex

(IPS and adjacent SPL) has been hypothesized to code

visuo-spatial information (Andersen 1997; Wise et al.

1997; Grefkes and Fink, 2005) whereas the dorsal premotor

cortex may use this information to program a context-

dependent motor response (Cisek and Kalaska 2005;

Johnson et al. 1996). The DLPFC in turn has been asso-

ciated with monitoring processes of motor behavior

(Cieslik et al. 2013; Shallice 2004), particularly in the

context of response selection and suppression (de Zubica-

ray et al. 2000; Nee et al. 2007). Furthermore, the DLPFC

is regarded to feature a superordinate role in cognitive

control of behavior by modulating other brain regions

according to the context to ensure accurate and flexible

performance (Badre and D’Esposito 2009; Miller and

Cohen 2001; Munoz and Everling 2004) and individual

DLPFC activation has been shown to correlate with supe-

rior task performance in healthy subjects (MacDonald et al.

2000; Snitz et al. 2005). Due to the anatomical connectivity

of this regions with the parietal lobes (Andersen et al.

1990; Petrides and Pandya 1984) as well as motor areas in

the medial frontal lobe such as the SMA and preSMA

(Bates and Goldman-Rakic 1993) and the premotor corti-

ces (Lu et al. 1994) the DLPFC lays in an optimal position

to exert modulating top-down influences in the context of

increased stimulus–response selection. The anterior insula,

in contrast, has been proposed to play a key role in the

neural processes underlying the selection, implementation

and maintenance of task sets (cf. Dosenbach et al. 2006).

Here, the anterior insula seems to play a pivotal role in

modulating relevant task-related brain regions according to

the respective context. Hence, significant activation in

bilateral insula for the incongruency effect in the present

study should most possibly be driven by the demand to

maintain a more complex task set in the incongruent con-

dition and hence an increased need to modulate down-

stream processes of stimulus–response mapping (cf. Cies-

lik et al. 2010).

Finally, the preSMA operates as an important agent in

higher level motor control including motor selection or

inhibition (Nachev et al. 2008; Picard and Strick 1996;

Haggard 2008) and exerts context-specific influences on

Brain Struct Funct

123

down-stream regions that are more closely related to the

actual motor output (Cieslik et al. 2011). In the context of

the present task, activity in the preSMA itself would most

possibly be modulated by higher cognitive areas such as

the DLPFC and anterior insula and specifically be related

to increased response selection as participants had to gen-

erate an endogenous incongruent motor response on the

contralateral side and concurrently overcome the automatic

tendency to respond on the side where the visual stimulus

was presented.

Shifted balance in the neuronal network underlying task

performance

When comparing both groups we found significant

decreased activation of the right DLPFC for patients vs.

controls in the main effect across all conditions. Contrast

estimates of activity in the right DLPFC not only revealed

that this region showed increased activation for the

incongruent compared to the congruent condition, but

furthermore also confirmed that schizophrenic patients

indeed showed reduced to absent recruitment of this region

in all experimental conditions (Fig. 2). Thus, the DLPFC

was associated with increased cognitive control during

incongruent responding in healthy controls and patients did

not recruit this region in the same manner throughout

congruent and incongruent conditions. This effect is in

accordance with previous findings of abnormal and in

particular reduced prefrontal activity in schizophrenic

patients (Arce et al. 2006; Glahn et al. 2005; MacDonald

and Carter 2003; Minzenberg et al. 2009; Perlstein et al.

2003) which has been proposed to reflect impaired func-

tioning of the DLPFC and has likewise been found in first

episode, medication-naıve patients (Barch et al. 2001; Snitz

et al. 2005) pointing towards a pathology that is already

present at illness onset and prior to any medication treat-

ments. In particular, deficits in the antisaccade task have

consistently been associated with prefrontal dysfunctions

(Pierrot-Deseilligny et al. 2003, 2004) and neuroimaging

studies have already shown reduced activity in the right

DLPFC in schizophrenic patients during antisaccades per-

formance (McDowell et al. 2002). The present study hence

provides further evidence that the DLPFC—as a critical

node in the cognitive control network—is disturbed in

schizophrenia as patients fail to activate this region in the

same manner as healthy controls do.

Importantly, however, our results also point to abnor-

mally increased activity in other regions associated with

task performance. In particular, patients showed distinct

hyperactivity in the parietal cortex, the dorsal premotor

cortex and the preSMA. This effect was seen in the main

effect over all conditions, that is whenever a visual stimulus

had to be mapped to a motor response (Fig. 3). Moreover,

this effect was even stronger in the context of the need of

more controlled responding such as when participants had

to respond incongruently to a visual stimulus (Fig. 4).

These ‘‘hyperactivations’’ were particularly strong in the

rostral parts of the left parietal lobe. Anterior parts of the

parietal lobe, especially the rostral IPC and IPS are known

to activate in the planning and execution phase of a motor

response as well as when the intention of movement exe-

cution occurs (Grafton and Hamilton 2007; Tunik et al.

2007; for review see Fogassi and Luppino 2005). It has also

been hypothesized by Rushworth et al. (2003) that prepa-

ration and re-direction of movements are linked to regions

in parietal and premotor cortex in the left hemisphere. More

precisely, re-orienting of attention related to limb move-

ments, i.e. motor attention, is left lateralized while visuo-

spatial re-orienting is more strongly associated with the

right hemisphere. In this context, the parietal lobe is asso-

ciated with motor attention, i.e. processes related to prep-

aration and re-direction of movement intentions, while the

dorsal premotor cortex has an important role in the selection

of movements for execution (cf. Rushworth et al. 2003).

Conjunction analysis of group comparisons with the indi-

vidual effects in patients and controls revealed left-sided

activity in parietal regions as well as the dorsal premotor

cortex and the preSMA to be significantly activated in both

groups per se and moreover showing increased activity for

patients compared to healthy controls. We would hence

propose that up-regulation of activation in these task-rele-

vant regions represents a compensation mechanism in the

patients group to maintain task performance. Importantly,

schizophrenic patients did not show significant increased

error rate or reaction times compared to healthy controls in

the present study. It has been argued that differences in

behavioral task performance may bias brain activation, with

worse performance in patients related to reduced activity in

this group (e.g. Price and Friston 1999; Perlstein et al. 2007;

Ramsey et al. 2002). We would hence conclude that dif-

ferences in brain activations in the present paradigm should

not be confounded by differences in perceived task com-

plexity or a generalized performance deficit. Thus, up-reg-

ulation of attention and motor preparation processes in the

patient group might compensate for latent dysfunctions in

the mapping between stimulus and accurate motor response

due to reduced efficiency of top-down control through the

DLPFC.

As the DLPFC, in the context of the anti-saccade task,

has mainly been interpreted to exert inhibiting influences to

suppress the prepotent motor response in the direction of

the stimulus, one might argue that the up-regulation of

parietal and (pre)motor regions in the patient group might

reflect a disinhibition effect due to the dysfunctional

DLPFC. However, results from a previous DCM-study of

our group argue against a specific inhibitory function of the

Brain Struct Funct

123

DLPFC in the context of this task as no effective inhibiting

effects of the right DLPFC on the premotor cortices were

found (Cieslik et al. 2011).

When testing for correlations between symptom severity

and brain activation, a significant positive correlation

between activity in the left IPC during incongruent

responding and negative PANSS score was found. Hence,

patients with stronger negative symptoms especially recruit

the anterior IPC during (incongruent) stimulus response

integration. The inferior parietal cortex has been related to

higher-order aspects of motor control such as coding the

goal of actions and switch intended movements (Fogassi

and Luppino 2005; Rushworth et al. 2003). Moreover, it has

been discussed as being a crucial region for the generation

of movement intentions by linking action to perception and

the selection of motor responses not yet constructed (cf.

Desmurget and Sirigu 2012). Hence, we would like to

propose that increased activation in this region in patients

with more negative symptoms might reflect a stronger

compensation attempt the more negative symptoms patients

show. Together with evidence from the behavioral data that

showed negative symptom severity to be associated with

increased error rate in the congruent and incongruent task

condition it seems that negative symptoms in schizophrenia

are associated with increasing difficulties in the cognitive

control of stimulus response integration and that possible

compensation mechanisms (especially in the inferior pari-

etal cortex) might be more strongly upregulated the stronger

patients are affected by negative symptoms.

Moreover, a negative correlation between activity in the

SMA in the incongruent condition and general PANSS

score was found in the patient group. This activation was

lying posterior to the activation in the preSMA that showed

hyperactivity in the patient compared to the control group.

While the preSMA has been associated with higher cog-

nitive motor control, the SMA is more strongly related to

low-level processes of motor control (cf. Nachev et al.

2008). Reduced SMA activity in schizophrenic patients has

been shown in fMRI as well as EEG studies (Dreher et al.

1999; Schroeder et al. 1999) and has been related to

reduced volitional motor activity and psychomotor slowing

in schizophrenia (Morrens et al. 2007). This study now

shows that reduced SMA activity is especially associated

with general PANSS symptoms.

Schizophrenia as a network syndrome

While the present study provides further evidence for the

DLPFC as one of the key nodes in the pathophysiology of

schizophrenia, it moreover highlights the importance of

other nodes in the network underlying stimulus–response

integration. In particular, as detailed above, the hypoac-

tivity of the DLPFC was accompanied by hyperactivation

in regions that are associated with the re-orienting of motor

attention (parietal lobe) and the planning and selection of

motor responses (dorsal premotor cortex and preSMA).

Hence, an increase in brain activity was found in regions

that are associated with more low-level functions, while

decreased activation was specifically found in the right

DLPFC that is known to be involved in higher cognitive

control of attention and motor planning (Badre and D’Es-

posito 2009; Cieslik et al. 2013).

Such a dysbalance in specific task networks is well in

line with previous reports on abnormal activity patterns and

disturbed fronto-temporal interactions during working

memory processes in schizophrenia (Meyer-Lindenberg

et al. 2001). Moreover, a meta-analysis of abnormal

working memory-related activity in schizophrenic patients

showed not only consistently decreased activation in the

DLPFC, but also consistent up-regulation of regions such

as the anterior cingulate cortex and left frontal pole (Glahn

et al. 2005). The failure to up-regulate the DLPFC there-

fore seems to be a key component of schizophrenia path-

ophysiology, which, however, goes along with extensive

changes in the organization of functional networks. Con-

sistent with this hypothesis recent studies found dysfunc-

tional connectivity between DLPFC and distributed brain

areas such as the temporal lobe, parietal lobe or the thal-

amus and cerebellum during task execution (Kim et al.

2003; Schlosser et al. 2003; Spence et al. 2000). Moreover,

disturbances in intrinsic DLPFC connectivity have been

shown in the absence of a structured task, i.e. in resting-

state experiments (Zhou et al. 2007a, b). The present study

furthermore highlights that, while schizophrenic patients

may be less efficient in recruiting their executive control

system, they may up-regulate more low-level attention and

motor preparation-related processes which may permit

them—at least to a certain degree—to compensate for

latent dysfunctions in stimulus–response integration. In

line with that we would propose a mechanism where

impaired top-down control can—at least partly—be com-

pensated by an up-regulation of more low-level bottom-up

processes.

Conclusion

The present study demonstrates that performance in stim-

ulus–response integration is associated with symptom

severity in schizophrenia. In particular, performance in the

congruent and incongruent condition showed negative

correlation with negative as well as general PANSS score.

At the neural level, altered dynamics of neural activation

during stimulus–response integration were found in

patients compared to healthy controls. In particular,

hypoactivity in the right DLPFC as a potential locus of

Brain Struct Funct

123

top-down control was accompanied by hyperactivity in

regions associated with task performance. This included

parietal regions associated with (motor) attention as well as

regions involved in the preparation and low-level control of

motor responses (dPMC, preSMA). As no significant

behavioral differences between patients and healthy par-

ticipants were found, the observed shift of activation in the

task network possibly represents a mechanism by which

impaired top-down control due to a dysfunctional DLPFC

is compensated by an up-regulation of more low-level

attention and motor-related regions. We therefore propose

a model by which schizophrenia is characterized by deficits

in top-down control processes while more low-level bot-

tom-up functions seem to be preserved and up-regulation

of these processes may even permit patients to compensate

for reduced efficiency in cognitive control due to a dys-

functional DLPFC.

Acknowledgments This study was supported by the Human Brain

Project (R01-MH074457; S.B.E.) and the Initiative and Networking

Fund of the Helmholtz Association within the Helmholtz Alliance on

Systems Biology (Human Brain Model; S.B.E.).

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