NeuroImage 59 (2012) 3652–3661

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The link between visual exploration and neuronal activity: A multi-modal studycombining eye tracking, functional magnetic resonance imaging and transcranialmagnetic stimulation

Silvia Chaves a,b, Patrizia Vannini c, Kay Jann c, Pascal Wurtz a,b, Andrea Federspiel c, Thomas Nyffeler a,b,Mathias Luethi a, Daniela Hubl c, Roland Wiest d, Thomas Dierks c, René M. Müri a,b,⁎a Perception and Eye Movement Laboratory, Department of Neurology, University of Bern, Inselspital, 3010 Bern, Switzerlandb Perception and Eye Movement Laboratory, Department of Clinical Research, University of Bern, Inselspital, 3010 Bern, Switzerlandc Department of Psychiatric Neurophysiology, University Hospital of Psychiatry, University of Bern, 3000 Bern, Switzerlandd University Hospital, Inselspital, Department of Neuroradiology, 3010 Bern, Switzerland

⁎ Corresponding author at: Perception & Eye MovemNeurology and Clinical Research, University Hospital, 30131 632 97 70.

E-mail address: rene.mueri@insel.ch (R.M. Müri).

1053-8119/$ – see front matter © 2011 Elsevier Inc. Alldoi:10.1016/j.neuroimage.2011.10.094

a b s t r a c t

a r t i c l e i n f o

Article history:Received 12 November 2010Revised 15 August 2011Accepted 31 October 2011Available online 4 November 2011

Keywords:Eye trackingfMRITheta burstTranscranial magnetic stimulationVisual exploration strategy

In the present multi-modal study we aimed to investigate the role of visual exploration in relation to the neu-ronal activity and performance during visuospatial processing. To this end, event related functional magneticresonance imaging er-fMRI was combined with simultaneous eye tracking recording and transcranial mag-netic stimulation (TMS). Two groups of twenty healthy subjects each performed an angle discriminationtask with different levels of difficulty during er-fMRI. The number of fixations as a measure of visual explora-tion effort was chosen to predict blood oxygen level-dependent (BOLD) signal changes using the general lin-ear model (GLM). Without TMS, a positive linear relationship between the visual exploration effort and theBOLD signal was found in a bilateral fronto-parietal cortical network, indicating that these regions reflectthe increased number of fixations and the higher brain activity due to higher task demands. Furthermore,the relationship found between the number of fixations and the performance demonstrates the relevanceof visual exploration for visuospatial task solving. In the TMS group, offline theta bursts TMS (TBS) was ap-plied over the right posterior parietal cortex (PPC) before the fMRI experiment started. Compared to controls,TBS led to a reduced correlation between visual exploration and BOLD signal change in regions of the fronto-parietal network of the right hemisphere, indicating a disruption of the network. In contrast, an increasedcorrelation was found in regions of the left hemisphere, suggesting an intent to compensate functionalityof the disturbed areas. TBS led to fewer fixations and faster response time while keeping accuracy at thesame level, indicating that subjects explored more than actually needed.

© 2011 Elsevier Inc. All rights reserved.

Introduction

In the present studywe aimed to examine the role of visual explora-tion during visuospatial processing in an angle discrimination task. Tothis end, visual exploration was investigated in relation to the underly-ing neuronal activity and the resulting performance. We combinedevent related functional magnetic resonance imaging (er-fMRI), eyetracking and performance measuring. Furthermore, continuous thetaburst transcranial magnetic stimulation (TBS) was used to interferewith the functioning of the right posterior parietal cortex (PPC), a re-gion that plays a key role in visuospatial processing and eye movementcontrol.

ent Laboratory, Departments of0 Bern, Switzerland. Fax: +41

rights reserved.

Under natural conditions, human beings produce on average threesaccadic eye movements per second to explore their visual environ-ment. The sequence of saccades and fixations, during which visual in-formation is collected, is closely related to the visual perceptionprocess (Findlay and Gilchrist, 2003). The analysis of visual explora-tion strategies allows thus insight in how subjects acquire visual in-formation during solving a task.

The importance of optimal visual exploration strategies for suc-cessful task performance has been shown in activities of daily lifesuch as playing table tennis, driving or music reading (Land andFurneaux, 1997), or during reading (O'Regan, 1990; Rayner, 1998;Hyönä and Nurminen, 2006). The fixation pattern also reflects taskdifficulty. For instance, increasing difficulty during visual searchtasks results in longer fixation durations (Hooge and Erkelens,1996).

Brain areas responsible for the cortical control of eyemovements arealso involved in the control of visual perception and attention. A keyrole plays the PPC (e.g. parietal eye fields, PEF, Pierrot-Deseilligny

3653S. Chaves et al. / NeuroImage 59 (2012) 3652–3661

et al., 2004;Müri, 2006), but also regions in the frontal lobes (e.g. frontaleye fields (FEF) and supplementary eye fields (SEF)) are important.FMRI studies have shown that in the parietal lobe, spatial perception,eye movements and visual attention processes are closely linked to-gether (Corbetta et al., 1998; Perry and Zeki, 2000; Simon et al.,2002). PPC lesions impair visual and visuo-motor functions includingvisually guided eyemovements (Pierrot-Deseilligny et al., 1986). For in-stance, this region also showed a BOLD signal increase during spatialtransformation tasks or during angle discrimination in experiments inwhich subjects moved their eyes (Dierks et al., 1999; Vannini et al.,2004). The PPCwas also found to be involved in visuospatial processingwithout eye movements (see Sack et al., 2002a, b, 2007). The parietalcortex is thus involved in spatial cognition such as mental rotation, spa-tial transformation, spatial location or spatial memory, as shown in le-sion studies (Marshall and Fink, 2001) or in fMRI and TMS studies(Cohen et al., 1996; Goebel et al., 1998; Haxby et al., 1991; Müri et al.,2002; Nobre et al., 1997; Sack et al., 2002a, 2002b; Ungerleider andHaxby, 1994; Trojano et al., 2000; Vannini et al., 2004).

The advantage of an er-fMRI design is that it permits a randomizedtrial presentation, which reduces predictability and habituation. Usinger-fMRI, a number of studies have shown that parietal brain regions,specifically in the right hemisphere, are activated during successful vi-suospatial processing of an angle discrimination task (Dierks et al.,1999; Sack et al., 2002a, 2002b; Vannini et al., 2004). Some of thesestudies investigated the connection between network activity and per-formance asmeasured by reaction time (RT) (Vannini et al., 2004, 2007,2008). There is however no study that has investigated the connectionto the visual exploration behavior. Since the fronto-parietal network isinvolved in eyemovement control and the processing of visuospatial in-formation, any change in the activity of this network is expected to af-fect the visual exploration pattern, and thus performance.

Repetitive transcranial magnetic stimulation (rTMS) studies havealso shown the relevance of parietal regions essentially of the righthemisphere during visuospatial processing (Rushworth and Taylor,2006; Sack et al., 2002a, 2002b, 2007). TMS is a non-invasivemethod in-terfering with the neural activity in the stimulated brain area (Pascual-Leone et al., 2000) and has the capacity to alter behavior. By applyingrTMS over the right parietal cortex, Sack et al. (2002a, 2002b) wereable to demonstrate that this region plays an essential role in an anglediscrimination task, since the response time was increased in the TMSgroupwhen compared to a control group. Nyffeler et al. (2008) demon-strated an alteration in the visual exploration behavior after the applica-tion of TBS over the right PPC. They found reduced visual exploration inthe left screen-half and an increase in the right screen-half, reflecting aneglect-like exploration behavior. On a behavioral level, Nyffeler et al.(2006b) found significant stimulation effects for up to 30 min. In a fur-ther study, the application of two trains of TBS with an interval of15 min resulted in an increase of the stimulation effect for up to170 min (Nyffeler et al., 2006a). It has also been shown that TBSchanges neural activity far beyond the stimulation time (Hubl et al.,2008). Thus, the long lasting effect of TBS on brain activity can be mea-sured with an fMRI offline approach (Hubl et al., 2008; Siebner et al.,

Fig. 1. Examples of clock stimuli. The upper row shows the three different lengths of clock hexamples of the five different angles.

2009), preventing the technical problem of simultaneous combinationof both the TMS and the MRI techniques.

In the present study we investigated the role of visual explorationduring angle discrimination by combining er-fMRI, eye tracking andoffline TBS. First, we expected that natural visual exploration (as mea-sured by the number of fixations) correlates with the neural activityand task performance: Increasing task demand increases the numberof fixations, because subjects have to search for more information.Such an increase in visual exploration costs time and results in higherRT. Additionally, the more fixations subjects make for the judgment ofan angle, the stronger the BOLD signal increases, because more neuralresources were needed.

Second, we expected that TBS-induced inhibition of the right PPCchanges the activity of the fronto-parietal network in both hemi-spheres, which alters the exploration pattern and the task performance.

We expect an increase in the number offixation after TBS (reflecting ahigher need to search for information) as consequence of the impaired vi-suospatial processing. Since a high correlation between performance andeye movement is assumed, an increase in reaction time is also expected.

Materials and methods

Subjects

Forty healthy subjects were recruited and gave written informedconsent before participation. Half of them were measured withoutTMS (control group: mean age 28±5, 10 females) and the other halfhad TMS administered over the right parietal cortex (TMS group:mean age 25±4, 10 females). All subjects were right-handed asassessed by the Edinburgh Handedness Inventory and had normal orcorrected-to-normal vision. They were asked to abstain from caffeineand nicotine for at least 4 h before the fMRI experiment begun. Thestudy was performed in accordance with the Declaration of Helsinkiand approved by the local ethical committee.

Visuospatial paradigm

The visuospatial fMRI paradigm consisted of an angle discrimina-tion task. The angles were presented in clock-like images with threedifferent lengths of hands (short, middle and long) and five differentangular differences: 30°, 45°, 60°, 75° and 90° (see examples in Fig. 1).Twelve images, corresponding to the 12 possible positions of theclock hands, were created for each of the 15 combinations. In sum,180 different clock images were used. Control conditions consistedof 12 clock images without hands (empty clocks) and 38 blankscreens. In total, 230 stimuli were presented to each subject. Thetotal duration of the experiment was 23 min. Stimuli were presentedin a pseudo-randomized order. At the start and the end was a restperiod. The clocks were presented at the center of the screen andhad a diameter of 18.4° visual angle.

Clock-hands were blue colored, the clock face was displayed inlight gray.

ands, the empty clock and the rest condition (blank screen). The lower row illustrates

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Procedure and design

Prior to the fMRI experiment, all subjects received detailed oral in-structions and a short training session outside the MR scanner. Subjectswere asked to decide whether a given angle was of 60° (target angle)or not and to press the corresponding button, using a MR-compatiblemouse (right index finger for 60°, rightmiddle finger for the other anglesand the empty clock), which was interfaced with the external computerrunning the e-prime (Psychology Software Tools Inc) experiment outsidethe scanner. Subjectswere encouraged to respond as quickly and as accu-rately as possible. A failure to respond was considered a false answer.Subjects were instructed to not press any button during blank screens.After the training session, control subjects (without TMS) were posi-tioned inside the scanner, where the MR-compatible LCD video goggles(VisuaStim XGA, Resonance Technology, Los Angeles, CA, USA) and theMR-compatible infrared CCD camera (Viewpoint Eye Tracker, ArringtonResearch, Inc.) were fixed. The system was calibrated before the experi-ment. Subjects in the TMS group were stimulated between the trainingsession and before positioning in the scanner. After TMS stimulation theprocedure was the same as for the control group. On average this proce-dure took approximately 10–15 min. The goggles had a 800×600 pixelresolution, 32 bit color depth and a refreshing rate of 60 Hz. Subjectiveviewing distance was 50 cm. The angle of vision corresponded to30×22.5°.

The fMRI design followed an event-related paradigm. Stimulusonset asynchrony (SOA) was 6 s. Subjects had a response time limitof maximal 5 s. After pressing the response button the stimulus disap-peared and a fixation cross was presented for the remaining time ofthe SOA (see Fig. 2 for the experimental setup).

TMS procedure

A TMS stimulator (MagPro, Medtronic Functional Diagnostics, Skov-lunde, Denmark) was used to generate magnetic pulses. Pulses weredelivered with a non-focal circular coil (Magnetic Coil Transducer MC-B70, Medtronic) with an outer radius of 60 mm. TBS was applied over

Fig. 2. Experimental setup of the angle discrimination task. A) The SOA lasted 6 s (in which thave pressed the response button a fixation point appeared for the remaining time of the SOsignal predictors (pEmptyClock, pAngleClock and pFixations) is illustrated.

the right PPC. The right PPCwas localized according to the international10–20 system of electrode placement (P4), with the coil placed tangen-tially 3 cmposterior and 3 cm lateral to the vertex (Müri et al., 1996). In-dividualmotor thresholdwas identified by stimulating themotor cortexwith single TMS pulses until a movement of the contra-lateral thumbwas detected visually. TBS was applied at an average stimulation levelof 100% of subject's motor threshold. The coil was heldwith the positionof maximal magnetic field tangentially on the subject's skull over P4.

TBS consisted of bursts of stimuli applied repeatedly for 44 s (Nyffeleret al., 2008). Each burst contained three stimuli repeating at 30 Hz;bursts were repeated at 6 Hz. A total number of 267 bursts (801 pulses)were delivered twice with an interval of 10 min over the right PPC. Onetrain of TBS has an inhibitory behavioral effect that lasts up to 30 min(Nyffeler et al., 2006b). With the repeated application of TBS trains itis possible to prolong the inhibitory behavioral effects (Nyffeler et al.,2006a). With two TBS sessions we assured that TBS was effective forthe whole experiment, approximately 30 min including experimentalsetup. The stimulation site (e.g. P4) was marked with a vitamin E cap-sule, which provides a good contrast in the T1-weighted images. Theposition (x, y and z coordinates) of the individual Vitamin E capsuleswere averaged and used to create a visual illustration of the stimulationsite by calculating the projection vector from the averaged position ontothe pial surface (Fig. 3, left image). While the vitamin E capsule and theprojection represent the group average, the pial surface was recon-structed from a single subject. In Fig. 3 (right image) the statisticalmap of the TMS group (see below) is overlaid to visualize the spatial re-lation between BOLD activations and stimulation site.

Data acquisition

Behavioral performance dataThe stimulus presentation and recording of button presses were

performed with E-Prime software (Psychology Software Tools Inc)

hree MR volumes were acquired) and the response windowmaximal 5 s. After subjectsA, then the next stimuli was presented. B) Design matrix consisting of the three BOLD

Fig. 3. Left: projection of average vitamin E capsule positions of all subjects onto 3Drendered pial surface of one representative subject. Right: Same projection cut atTalairach z position 52 with overlaid statistical activation map of the TMS group(pb .01, correcting for false positives at αb .05; cf. Fig. 7). (Note: right is right in theseimages; mirrored with respect to the plots in the manuscript).

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(Schneider et al., 2002). Accuracy and RT were recorded with a MR-compatible optical mouse in order to get a measure of performanceefficiency.

Eye movement dataEye movements were assessed with an MR compatible infrared

video-based CCD camera (Arrington Research, Inc., Scottsdale, USA), ata sampling rate of 60 Hz and a spatial resolution of 0.15°. The infraredcamera was mounted on the goggles such that it recorded the eyemovements (based on pupil tracking) of the subjects' right eyes. Theraw data were parsed into fixations using Viewpoint Eye Tracker soft-ware (Arrington Research, Inc., Scottsdale, USA). Fixationswere definedto have a minimal duration of 80 ms and a maximal dispersion of 0.25°between video frames. Before the experiment, a 9-point calibration pro-cedure was performed. To avoid camera displacement the goggleswerefixed with surgical tape on the subjects' faces.

Image acquisitionA 3T whole body MRI system (Siemens Trio TIM Magnetom, Sie-

mens Medical Systems, Erlangen, Germany) was used for this study.Functional imaging was acquired using a gradient echo planar imag-ing sequence (repetition time TR=2000 ms, echo time TE=30 ms,flip angle FA=90°, FOV=230, 1 volume=33 slices, matrix size64×64, voxel size=3.59×3.59×4 mm, slice thickness=4 mm,interslice distance=0 mm). A total number of 690 volumes were col-lected during 23 min. Pillows on both sides of the head minimizedmovements. A 3D T1-weighted magnetisation prepared rapid acquisi-tion gradient echo (MP RAGE) high resolution structural scan(TR=1950 ms, TE=2.2 ms, sagittal slices=176, matrixsize=256×256, voxel size 1×1×1 mm3) covering the whole brainand lasting 4 min was recorded after the measurement.

Data analysis

Behavioral performance and eye movement analysisTo investigate differences in performance and visual exploration be-

tween the groups and task conditions, repeated measures ANOVAswere calculated for mean RT, mean accuracy, mean fixation durationand themean number offixations per trial using group as between factorand length of clock hands and angle difference as within factors. F valueswere corrected for sphericity violations by adjusting degrees of freedomusing the Huynh–Feldt procedure when necessary. SPSS softwareversion 16 (SPSS inc., Chicago, USA) was used to perform the calcula-tions. In order to analyze the relationship between performance and vi-sual exploration a Spearman correlation was calculated between RTand number of fixations as well as between accuracy and number offixations.

Image analysisAll image processing and analysis were performed in Brain Voyager

QX 1.9 software (Brain Innovation, Maastricht, Netherlands, www.

BrainVoyager.com). Preprocessing of the functional imagesincluded: slice scan time correction using cubic-spline interpolation,3D head motion correction using trilinear/sinc interpolation as well aslow frequency drift and linear trend removal with a GLM-Fourier filter(3 cycles). Functional images were realigned with their correspondinganatomical T1 weighted volumes and normalized to standard space(Talairach and Tournoux, 1988). Finally, for the multi-subject analysisthe co-registered and normalized functional volumes of each subjectunderwent spatial smoothingwith a Gaussian kernel (FWHM=8mm).

To account for the changes in visual exploration between all trials,we adapted a model proposed by Lehmann et al. (2006). This modeltakes into account the variability of the BOLD signal response due toperformance variations within and between subjects. For each subjecta design matrix composed of three predictors was created (see Fig. 2).First, a standard fixed boxcar function was created coding for each con-trol stimulus (pEmptyClock). Second, a standard fixed boxcar functionwas created as predictor coding for the angle stimuli (pAngleClock).With this function we modeled the BOLD signal change occurring dur-ing visuospatial processing. The third predictor goes beyond this infor-mation by taking into account the number of fixations made during theprocessing of each angle stimulus (pFixations). This predictor allowedus to model the amplitude increases of the BOLD signal, expected tohappen based on changes in task-solving behavior, i.e. as a result of in-creased visual exploration effort (percent BOLD signal change per fixa-tion). The duration of the boxcars was fixed to 1TR for each event of agiven condition, whereas the amplitude of the boxcar was either fixedto 1 in the case of pAngleClock and pEmptyClock or modulated by thenumber of fixations in the case of pFixation. To obtain independent pa-rameter estimateswe orthogonalized the pFixationswith respect to thepAngleClock as described in Lehmann et al. (2006).We then investigateddifferences in number of fixations dependent modulations of theBOLD signal between the two groups using statistical parametric mapsof t-statistics. Before we started themodel fitting analysis, the three pre-dictors were convolved with a double gamma hemodynamic responsefunction (HRF) (Glover, 1999). As the aim of the study was to indentifythe brain areas that are required for task execution and in addition de-pend on the number of fixations, we computed the conjunction betweenthe statistical maps of pAngleClock and pFixations for each subject.While the map resulting from pAngleClock revealed the areas neededfor solving this visuospatial decision task, the map generated frompFixation displays the areas dependent on fixations. Since the pFixationregressor was orthogonalized to the pAngleClock predictor, we ensuredindependent parameter estimates for the two regressors. The resultingstatistical map displays the intersection of the statistical maps of bothpredictors at a specific alpha rate (Nichols et al., 2005; Vannini et al.,2008), i.e. regions significantly activated when an angle was shownandwhere the increase in number of fixations predicted the BOLD signalchange. Then a second level multi-subject random effect GLM analysiswas calculated based on the conjunction of both predictors on the sub-ject level. The statistical maps were superimposed on the averagedbrains of the subjects in each corresponding group. The anatomical re-gions were specified with their peak activation and cluster size for eachgroup separately, as listed in Table 1. Only regions with a pb .01 andwith a minimal cluster size to correct for false positives are reported(alphab .05). This cluster size was calculated with the Cluster-levelStatistical Threshold Estimator Plugin from the BrainVoyager software.This Plugin implements a randomization technique (Monte-CarloSimulations) to estimate a cluster-level confidence on the current statis-tical map given the current voxel-level confidence level. Depending onthe underlying p-value distributions, the cluster size thresholds to cor-rect for false positives can differ between different contrasts: in acontrast with a greater amount of significant voxels (for a p-threshold),the clusters are likely to be bigger than in a contrast with less significantvoxels. This is accounted for in the calculation of the cluster thresholdand will result in a larger cluster size to correct for the same alpha. Theprinciple is described in detail in the paper by Forman et al. (1995).

Table 1Detailed description of the anatomical regions activated in the control and the TMSgroup during visuospatial processing.

Anatomical region BA Talairachcoordinates(activation peaks)

Threshold(t)

Cluster size(voxel)

x y z

Control groupPrecuneus R 7 19 −61 47 6.9 523Precuneus L 31 −23 −72 29 6.7 212Precuneus L 7 −16 −68 41 6.7 164Superior parietal lobe L 7 −21 −59 53 6.7 436Inferior parietal lobe R 40 41 −30 43 6.4 241Inferior parietal lobe R 40 32 −43 44 6.4 262Inferior parietal lobe L 40 −36 −40 43 5.5 429Superior frontal gyrus (SEF) 6 −2 8 49 7.4 475Inferior frontal gyrus R 9 46 8 31 5.8 598Middle frontal gyrus R(FEF sup)

6 24 −5 51 5.7 602

Middle frontal gyrus L(FEF sup)

6 −24 −6 51 5.5 405

Precentral gyrus (FEF sup) 6 −34 −9 58 5.5 287Cuneus R 19 28 −80 25 7.0 182Fusiform gyrus R 37 39 −55 −6 5.8 381Lingual gyrus R 17 13 −84 4 6.9 492Lingual gyrus R 18 30 −74 −3 6.7 388Lingual gyrus L 17 −5 −88 4 7.8 251Lingual gyrus L 18 −9 −79 −8 7.5 234Middle occipital gyrus R 18 24 −85 18 7.0 544Middle occipital gyrus L 18 −18 −88 14 7.8 226Middle occipital gyrus L 19 −32 −80 25 7.0 182Insula R 13 31 22 7 6.9 601Insula L 13 −30 20 5 6.4 624Thalamus R 8 −14 10 5.9 591Thalamus R 19 −25 5 5.4 290Thalamus L −10 −13 11 5.5 542Cerebellum L −13 −75 −12 6.9 303

TMS groupPrecuneus R 7 19 −66 38 6.3 392Precuneus R 31 26 −70 28 6.2 104Inferior parietal lobule R 40 36 −47 43 5.9 548Cuneus R 18 22 −76 19 6.2 72Cuneus L 17 −5 −86 7 5.9 466Cuneus L 18 −21 −77 16 5.5 282Cuneus L 30 −1 −69 6 5.3 244Lingual gyrus R 18 13 −72 −10 5.4 491Supramarginal gyrus L 40 −38 −42 36 5.2 365Medial frontal gyrus R 6 3 12 44 5.5 467Inferior frontal gyrus R 9 42 6 30 5.2 297Middle frontal gyrus R 6 26 −5 47 4.9 345Cingulate gyrus R 24 1 3 32 5.0 343Cingulate gyrus L 32 −2 24 36 5.5 406

Data derived from the conjunction analysis of the pFixations and pAngleClockpredictors for the control subjects and the TMS group separately. In all listed brainareas peak activations were significant (pb .01, correcting for false positives atαb .05). L = left, R = right, BA = Brodmann area, Talairach coordinates = locationin standard stereotaxic system, t = t-value of cluster peak.

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In a second step, we were interested in activation differences be-tween both groups. Therefore, the conjunction maps of the singlesubject analysis were compared using a t-test. The resulting mapwas superimposed on an anatomical map created by averaging thestructural scans of all subjects.

Results

Behavioral performance data

A repeated measurement ANOVA for RT revealed no statisticallysignificant main effect for group (with/without TMS). Significantmain effects and interactions were found for the different clock con-ditions (length (F[2,76]=118.8, pb .001, angle (F[4,152]=94.5,pb .001) and the interaction between angle and length (F[8,304]=

34.4, pb .001) suggesting that there are differences in difficulty be-tween clock conditions. RT increased with shorter hands and for an-gles close to the target angle (45°, 75°). A significant interactionbetween group×length×angle was found (F[8,304]=2.2, pb .05), in-dicating that the group that received TMS was faster than the controlgroup with increasing task difficulty.

A repeated measures ANOVA for accuracy demonstrated signifi-cant main and interaction effects for the different clock conditions(length (F[2,76]=16.0, pb .001), angle (F[4,152]=13.1, pb .001),angle×length (F[8, 304]=11.7, pb .001). Similar to RT, accuracy de-creased for clocks with angles close to target angle and with shorterclock hands. No statistically significant group effect (with/withoutTMS) as well as no interaction of group with task condition (lengthand angle) was found, demonstrating that both groups were perform-ing the task with similar accuracy. Results for the performance mea-sures RT and accuracy are depicted in Fig. 4.

Visual exploration data

For the mean fixation duration measure significant main effectswere found for the length (F[2,76]=26.4, pb .001) and the angle(F[4,152]=18.9, pb .001) condition, but no significant group ef-fects were found.

A repeated measures ANOVA for the mean number of fixationsrevealed significant main and interaction effects for the different clockconditions (length (F[2,76]=45.1, pb .001), angle (F[4,152]=55.2,pb .001), angle×length (F[8, 304]=18.3, pb .001)). Subjects mademore fixations if the presented angles were close to the target angleand if clock hands were short. Additionally, there was a significantmain effect for group (with/without TMS) (F[1,38]=6.48, pb .05), indi-cating that the TMS group made less fixations than the control group.There was a tendency suggesting that the more difficult the angles,the larger the group differences (angle×group: F[4,152]=2.4,p=.055). A planned comparison test was performed for clocks withshort hands only, and revealed a significant main effect between thegroups for the target angle as well as for angles close to the targetangle (45°, 60° and 75°) (45°: (F[1,38]=4.5, pb .05, 60°: (F[1,38]=5.3, pb .05, 75°: (F[1,38]=5.8, pb .05). The results for the eye move-ment measures are illustrated in Fig. 5.

Examples of the fixation distribution of five subjects (illustratedwith five different colors; three examples with long, middle andshort hand angles) are shown in Fig. 6. The figure shows that subjectsexplore the informative regions of the clocks, i.e. the clock hands andthe cues as well. Also, more fixations were used in the short clockhand condition, reflecting the need of subjects to collect moreinformation.

Relationship between behavioral performance and visual exploration

A Spearman's correlation was calculated between mean RT andmean number of fixations per condition and trial. Results showed asignificant positive correlation (r=0.646, pb .01). Accuracy was neg-atively related to the number of fixations, showing that the less fixa-tions subjects did, the more accurate they were (r=−0.4, pb .01).

Brain regions activated during the angle discrimination task

The conjunction analysis (mapped conjunction of pFixations andpAngleClock predictors) performed in both groups yielded severalbrain areas significantly activated during visuospatial processingand modulated by the visual exploration effort (t=3.1, pb .01, cor-recting for false positives at αb .05; Cluster volume larger than675 mm3 (25 adjacent voxels) in the condition without TMS and621 mm3 (23 adjacent voxels) in the condition with TMS). Fig. 7 illus-trates the results for the TMS group and the control group separately.Both groups demonstrated significant activation of a typical

Fig. 4. Behavioral performance. Mean values and SEM per group of all clock categories for reaction time (RT) in the upper row and for accuracy in the lower row.

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visuospatial processing network (Vannini et al., 2004). This networkincludes parieto-occipital regions (inferior parietal lobe (BA40) andsuperior parietal lobe (BA7), precuneus (BA7 and BA31), cuneus,middle occipital gyrus and lingual gyrus (BA17, 18 and 19)), as wellas frontal regions (SEF BA6 and FEF BA6). Additional significant clus-ters were found in the insula (BA13) and the thalamus. See alsoTable 1 which lists the coordinates of the peak activation foci and

Fig. 5. Visual exploration. Mean values and SEM per group for all clock categories for num

their cluster sizes. Note that some areas visible in Fig. 7 are not listedin Table 1. This apparent discrepancy occurs due to the peak-voxelidentification procedure used to list activated regions. While onlypeak-voxels are listed in Table 1, Fig. 7 shows activity for a determi-nate p value. For some areas significant voxels only appeared whenalready a spatial connection to another peak-voxel area was present.These areas were not counted as separate regions in Table 1.

ber of fixations in the upper row and for mean duration of fixation in the lower row.

Fig. 6. Examples of individual fixation location distribution in five subjects (coded indifferent colors).

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Visual exploration dependent differences in BOLD signal change betweenthe groups

A t-test was calculated between the control group and the TMSgroup in order to see whether there were visual exploration depen-dent BOLD signal differences during the angle discrimination task.Fig. 8 depicts the contrast (t-test; pb .01, correcting for false positivesat αb .05) between the TMS vs. control group. In red are the regionsshowing a higher correlation in the TMS group as compared to thecontrol group, whereas regions in blue show the inverted effect (seealso Table 2).

The control subjects had larger increases in BOLD signal in theright superior parietal lobe (BA 7), the right precuneus (BA 7), theright lingual gyrus (BA 18), the SEF located in the superior frontalgyrus (bilaterally) (BA 6), and the superior FEF located in the rightprecentral gyrus (BA 3) and the left precentral gyrus (BA 1) with in-creasing number of fixations. In these regions the relationship be-tween brain activation during angle discrimination and visualexploration was stronger for the control group than for the TMSgroup.

Areas in which the TMS group had larger increases in BOLD signalwith increasing number of fixations compared to the control group,were mainly located in regions of the left hemisphere (left postcen-tral gyrus (BA 40), left cingulate gyrus (BA 31), left superior temporalgyrus (BA 38), left amygdala and left superior frontal gyrus (BA 9).Fig. 9 depicts the mean beta values for each group and region.

Fig. 7. Areas activated during the angle discrimination task. Both pictures show the statisticathe pFixations and the pAngleClock (intersection of the statistical maps for each predictor)(correcting for false positives at αb .05). The numbers below each image depict the Talaira

As a control, we analyzed the group differences for the emptyclocks. There were no significant BOLD signal differences in then pari-etal cortex. A significant group difference was found in the primaryvisual areas (see Supplementary Fig. 1).

Discussion

The aim of the present study was to examine the relationship be-tween visual exploration, task performance, and neuronal activity inorder to determine the role of visual exploration during visuospatialtask solving.

We found that regions in the frontal and parietal cortex, corre-sponding to the FEF, SEF, and the PPC showed the highest correlationbetween the exploration behavior (number of fixations) and BOLDsignal change. These regions mainly correspond to the previously de-scribed visuospatial network activated during angle discrimination inan er-fMRI design (Vannini et al., 2004; Lehmann et al., 2006) and in ablock design (Sack et al., 2007). Importantly, in the study of Sack andcolleagues, the subjects were not allowed to perform eye movementsduring task performance suggesting that the observed activity in thefronto-parietal network does not simply reflect eye movements.

As expected, with increasing task difficulty we found increased RT,more errors, and a higher number of fixations. We assume, that theincreased number of fixations may reflect the greater requirementof visual information to solve the task. This was observed for anglesclose to the target angle and angles with shorter clock hands. One ex-planation for such a behavior might be that in the long hand condi-tion the ends of hands were close to the cues. Thus, one fixation inthe proximity may simultaneously give information about the clockhand position and cue. In the short hand condition, however, handleand cue are spatially separated, forcing subjects to perform more ex-ploratory fixations for the same visual information.

Whereas RT reflects the time span of all processes involved in tasksolving (Zelinsky and Sheinberg, 1997), the number of fixations re-flects the way subjects solve the task. Moreover, the correlationfound between number of fixations and RT might be an explanationwhy regions linked to visuospatial processing and the number of

l parametric maps calculated from 20 subjects per group (df 19) for the conjunction of. Threshold of the statistical maps for both groups is set at a significance level of pb .01ch horizontal (z) plane.

Fig. 8. Resulting statistical parametric map from the t-test between the TMS vs. the control group (df 38) for the conjunction of the pFixations and the pAngleClock (intersection ofthe statistical maps for each predictor). Threshold of the statistical map is set at a significance level of pb .01 (correcting for false positives at αb .05). Numbers below each imagedepict Talairach horizontal (z) plane.

3659S. Chaves et al. / NeuroImage 59 (2012) 3652–3661

fixations in our study match those found in previous studies linked toRT (Carpenter et al., 1999; Ng et al., 2001; Vannini et al., 2007, 2008).

TBS seems to have hemispheric specific effects on BOLD signalchange: correlations between BOLD signal change and the number offixations in regions of the right, stimulated hemispheric visuospatialnetwork were weaker after TBS. As a consequence, fewer brain areasshowed a significant positive correlation between number of fixationsand BOLD signal change. In the regions of the left hemispheric networkthe correlations were higher after TBS. Further, the findings show thatTBS not only affects directly stimulated areas, but also has remote ef-fects on other regions sub-serving visuospatial processing. This is inline with findings of a previous TBS study by Hubl et al., 2008.

On a behavioral level, TBS over the right PPC reduced the RT and thenumber of fixations without influencing accuracy and fixation duration.Since previous studies have shown that continuous TBS applied over thePPC induces an inhibitory effects on visual exploration (Nyffeler et al.,2006a, 2006b; Cazzoli et al., 2009) one could raise the questionwhetherexploration behavior occurring under natural conditions is not that

Table 2Detailed description of the anatomical regions from the between-group comparison.

Anatomical region BA Talairachcoordinates(activationpeaks)

Threshold(t)

Cluster size(voxel)

x y z

Control group>TMS groupSuperior parietal lobule R 7 13 −60 59 3.35 59Precuneus R 7 14 −56 55 3.3 183Lingual gyrus R 18 25 −78 1 3.1 165Superior frontal gyrus L (SEF) 6 −2 6 53 2.9 143Precentral gyrus R (FEF sup R) 6 32 −11 58 2.8 128Precentral gyrus L (FEF sup L) 6 −41 −11 60 2.8 102

TMS group>control groupPostcentral gyrus L 40 −64 −20 21 3.35 332Cingulate gyrus L 31 −10 −27 38 3.2 180Superior temporal gyrus L 38 −31 2 −9 3.2 130Amygdala L −18 −7 −8 2.9 172Superior frontal gyrus L 9 −16 47 20 2.8 201

Data derived from the conjunction analysis of the pFixations and pAngleClockpredictors for the control subjects and the TMS group separately. Regions listedabove show significant peak activations (pb .01, correcting for false positives atαb .05). L = left, R = right, BA = Brodmann area, Talairach coordinates = locationin standard stereotaxic system, t = t-value of cluster peak.

important for successful performance of this task: such a view is sup-ported by an additional experiment. We could demonstrate that sub-jects instructed to fixate the center of image during anglediscrimination were faster than, but as accurate as those who freely ex-plored the image (Chaves et al., unpublished results). One explanationmay be the fact that the more saccades we make, the less time wehave to process visuospatial information, since visual information isprocessed only during fixations. During saccades the magnocellularpathway responsible for visual localization is suppressed (Burr et al.,1994). Taken together with the presaccadic compression of space, thismay lead to a deformation of the internal representation of space(Ross et al., 1997), which in turn may influence angle discriminationjudgment. This may be especially relevant when only few fixations areneeded to solve the task. Indeed, on average the subjects made onlyabout 3–4 fixations to solve the task.

Accordingly, under natural conditions without restriction of eyemovements subjects actually seem to waste cognitive resources thatare not necessary to properly discriminate the presented angles. Similarobservations were reported by Land et al. (2002) in a patient unable toperform saccades, but whomade headmovements instead. Interesting-ly, this patient made fewer head movements than normal subjects dideye saccades, but her performance was comparable to the performanceof healthy controls.

On a first impression, our results seem to contradict those foundby Sack et al. (2007), who showed that TMS over the right PPC

Fig. 9. Relationship between the number of fixations and the BOLD signal (pb .01, correct-ing for false positives atαb .05).Mean change in theBOLD signal for 1 additionalfixation isshown for each group in each region detected with the t-test. Regions in the right hemi-sphere showed a larger increase in BOLD signal with increasing visual exploration effortin the control group compared with the TMS group. In contrast, almost all significant re-gions in the left hemisphere show a larger increase in BOLD signal with increasing visualexploration effort in the TMS group compared to the control group.

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leads to an increase in RT during performance of a similar angle dis-crimination task. However, both studies substantially differ in theirmethodology: Sack and colleagues used an on-line TMS protocol,while the current study used an off-line inhibitory TBS protocol(Robertson et al., 2003). Furthermore, Sack and colleagues asked sub-jects to fixate at the center of the screen during the presentation ofthe angles experiment. In our study, subjects were not instructed tofixate the center during the experiment and could freely look around.These differences make it difficult to compare the two studies.

Conclusion

In conclusion, the findings of the current study indicate that acti-vation changes in the fronto-parietal network are directly related tothe visual exploration effort. TBS over the PPC has the capacity toalter this relation. Interfering with the fronto-parietal network ledto faster performance and fewer fixations while leaving accuracyunchanged. We propose two explanations for this finding: 1) com-pensatory mechanisms on the neurophysiological level and/or 2)TBS inhibited the visual exploration network, reducing the abilityfor free and extensive eye movements. The reduction of fixationscould actually be beneficial for the performance as measured inshorter RT and unchanged accuracy. Therefore, it seems that addi-tional fixations may not be needed for higher accuracy but cost onlytime.

Supplementary materials related to this article can be found on-line at doi:10.1016/j.neuroimage.2011.10.094.

Acknowledgments

This work was supported by the Swiss National Foundation (grantnumber 320000-108146).

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