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. Author manuscript; available in PMC: 2021 Mar 27.
Published in final edited form as: Neuroimage. 2011 Jun 25;58(3):860–869. doi: 10.1016/j.neuroimage.2011.05.089

Dynamic interactions in the fronto-parietal network during a manual stimulus–response compatibility task

Edna C Cieslik a,*, Karl Zilles a,b,c, Christian Grefkes d, Simon B Eickhoff a,c,e
PMCID: PMC7998039  NIHMSID: NIHMS1681789  PMID: 21708271

Abstract

Attentional orienting can be modulated by stimulus-driven bottom-up as well as task-dependent top-down processes. In a recent study we investigated the interaction of both processes in a manual stimulus–response compatibility task. Whereas the intraparietal sulcus (IPS) and the dorsal premotor cortex (dPMC) were involved in orienting towards the stimulus side facilitating congruent motor responses, the right temporoparietal junction (TPJ), right dorsolateral prefrontal cortex (DLPFC) as well as the preSMA sustained top-down control processes involved in voluntary reorienting. Here we used dynamic causal modelling to investigate the contributions and task-dependent interactions between these regions.

Thirty-six models were tested, all of which included bilateral IPS, dPMC and primary motor cortex (M1) as a network transforming visual input into motor output as well as the right TPJ, right DLPFC and the preSMA as task-dependent top-down regions influencing the coupling within the dorsal network. Our data showed the right temporoparietal junction to play a mediating role during attentional reorienting processes by modulating the inter-hemispheric balance between both IPS. Analysis of connection strength supported the proposed role of the preSMA in controlling motor responses promoting or suppressing activity in primary motor cortex. As the results did not show a clear tendency towards a role of the right DLPFC, we propose this region, against the usual interpretation of an inhibitory influence in stimulus–response compatibility tasks, to subserve generic monitoring processes. Our DCM study hence provides evidence for context-dependent top-down control of right TPJ and DLPFC as well as the preSMA in stimulus–response compatibility.

Introduction

Attentional orienting in space for subsequent motor responses can be modulated by stimulus-driven bottom-up as well as task-dependent top-down processes. Various behavioural experiments have demonstrated that presentation of a lateralized stimulus facilitates a (congruent) response with the ipsilateral hand, e.g. (Eimer, 1995; Kornblum et al., 1990; Simon, 1969). In contrast, (incongruent) responses with the contralateral hand necessitate an inhibition of this prepotent response and an endogenous reorientation of motor attention to the opposite side resulting in significantly longer reaction times (Munoz and Everling, 2004; Nee et al., 2007; Proctor and Reeve, 1990). This effect has been shown for oculomotor responses in the context of the anti-saccade task (for review see (Munoz and Everling, 2004)) as well as manual stimulus–response compatibility (SRC) tasks (Proctor and Reeve, 1990).

The neurobiological basis for attentional (re-) orienting is assumed to rely on the interaction of a dorsal and a ventral fronto-parietal attention network (Corbetta et al., 2008; Corbetta and Shulman, 2002). The dorsal attention network contains the neural machinery for directing attention and motor acts for different effector organs (with eyes and hands being the best-studied) towards sensory stimuli and locations predominantly in contralateral space (Bremmer et al., 2001; Colby and Goldberg, 1999; Corbetta and Shulman, 2002; Halligan et al., 2003; Nobre, 2001). Core regions of the dorsal network are the intraparietal sulcus (IPS) and adjacent dorsal parietal cortex as well as the dorsal frontal cortex, in particular the dorsal premotor cortex (dPMC) and the frontal eye field (FEF). The ventral attention network, in contrast, is associated with target detection and reorienting towards salient events in either hemifield. Nodes of the ventral attention network encompass the (particularly right) temporoparietal junction (TPJ) and the ventral frontal cortex including parts of the middle and inferior frontal gyrus (Astafiev et al., 2003; Corbetta et al., 2000; Corbetta and Shulman, 2002; Corbetta et al., 2005b; Corbetta et al., 2008; Kincade et al., 2005).

In a recent study we investigated the interaction of bottom-up and top-down modulated attentional orienting in a manual SRC task where subjects responded to a visual stimulus in a congruent or incongruent manner (Cieslik et al., 2010). Our GLM analysis showed the IPS and dPMC to form a bottom-up driven neural network possibly facilitating congruent motor responses by automated spatial orientation and movement preparation. In contrast, the right TPJ, right DLPFC and the preSMA were specifically driven through the incongruent condition. These regions were hence proposed to exert a modulating influence on the dorsal attention network (IPS and dPMC) thereby allowing the subject to inhibit the automatically facilitated motor response, reorient to the contralateral side of space and endogenously generate the incongruent motor response.

While activation of a fronto-parietal attention network similar to the one summarised above has been shown by a variety of functional imaging studies manipulating attentional control, including task set, inference resolution, attention shifting and response inhibition (Fan et al., 2003; Fan et al., 2008; Kincade et al., 2005; Wager et al., 2005) only few studies have yet tried to investigate its effective connectivity. Brazdil et al. (2007) investigated the connectivity architecture of the neural network involved in oddball detection. The authors found significant target induced changes in effective connectivity among the right IPS, the anterior cingulate cortex and the right prefrontal cortex (PFC) as well as evidence for the ACC exerting top-down influence over right PFC. Another study investigated dynamic interactions between right IPS, right DLPFC and the ACC under the modulation of bottom-up (surprise) and top-down (conflict) processes (Wang et al., 2010). They found the IPS to be involved in processing of surprising targets, while the ACC and DLPFC interact with each other to resolve response conflicts. Due to the design of that study, modelling was restricted to the right hemisphere. However, spatial reorienting typically engages both hemispheres, enabling increased interhemispheric information transfer while shifting of attention from one hemifield to the other.

We here used dynamic causal modelling of fMRI responses to provide a mechanistic model of the neuronal interactions underlying bottom-up driven and top-down modulated (re-) orientation of motor attention. Thirty-six models were tested, all of which included bilateral IPS, dPMC and primary motor cortex (M1) as a network transforming visual input into motor output as well as right TPJ, DLPFC and the preSMA as task-driven top-down regions influencing the coupling within this network. The models differed in the way how and at which level the top-down influences on the visuo-motor transformation were modelled. In this neural network we suggested the TPJ to exert a task-dependent modulating role on the IPS and / or their transcallosal coupling thereby facilitating the reorientation process and shifting attention from one side to the other. The DLPFC was proposed to have a key role in the inhibition of the reflexive congruent motor response by modulating activity in the premotor cortex. Finally, the preSMA should control the motor response by modulating either the premotor or motor level.

Materials and methods

Subjects

The present effective connectivity analysis was based on the fMRI data previously reported in (Cieslik et al., 2010). Of the original 24 participants, five did not show reliable activation maxima in all nine functionally and anatomically defined regions included in the model (cf. below) and were thus excluded from the current analysis. The remaining 19 subjects (age range 20 to 59 years, mean age 30 years, 7 female) were free of any history of neurological or psychiatric disorders and had normal or corrected-to-normal vision. All subjects gave informed written consent to the study protocol, which had been approved by the local ethics committee of the University of Aachen. Right-hand dominance of participants was established by means of the Edinburgh handedness inventory (Oldfield, 1971).

Experimental protocol

Participants were instructed to respond as fast and correctly as possible to briefly presented (200 ms) lateralized (~11° eccentricity) target stimuli (red dot) by pressing a button on an MRI-compatible response pad (LumiTouch, Burnaby, Canada) according to the task condition. In the congruent condition subjects 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 the incongruent condition subjects 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 11.3) to customer-built, shielded TFT screen visible via a mirror mounted on the MR head coil. Task blocks started with a visual instruction consisting of written commands informing the subject of the experimental condition (ipsilateral / contralateral response) for the upcoming block. Task blocks were periodically alternated with rest periods (“baseline”) lasting 17–23 s. In each condition, 13 to 16 events per block (randomised 50% left / right sided stimuli, number of events randomised to avoid end-of-block anticipation) were presented with a jittered interstimulus interval of 2–4.5 s. Each condition (congruent, incongruent) was presented in 18 individual blocks in a pseudo-randomized manner.

Functional magnetic resonance imaging

Images were acquired on a Siemens Trio 3T whole-body scanner (Erlangen, Germany) using blood-oxygen-level-dependent (BOLD) contrast (Gradient-echo EPI pulse sequence, TR=2200 ms, resolution = 3.1 × 3.1 × 3.1 mm, whole-brain coverage). Image acquisition was preceded by 4 subsequently discarded dummy images. Images were analysed using SPM5 (www.fil.ion.ucl.ac.uk/spm). Preprocessing consisted of linear head movement correction, spatial normalisation into the MNI single-subject space, re-sampling at 2 × 2 × 2 mm3 voxel size and smoothing using an 8 mm FWHM Gaussian kernel (Cieslik et al., 2010).

Statistical analysis (GLM)

For the general linear model analysis, each experimental condition (CL, CR, ICL and ICR) was modelled using a series of stick-functions denoting the individual events convolved with a canonical hemodynamic response function and its first-order temporal derivative. Following first level single-subject parameter estimation (Kiebel et al., 2003), simple main effects for each experimental condition were computed per subject and fed to a second-level group analysis using an ANOVA (factor: condition, blocking factor: subject) employing a random-effects model. Simple main effects, conjunctions and comparisons between experimental factors were tested by applying appropriate linear contrast to the ANOVA parameter estimates. The resulting SPM(T) maps were then thresholded at p<0.05 (cluster-level corrected; cluster forming threshold: p<0.001, uncorrected) and anatomically localised using version 1.7 of the SPM Anatomy Toolbox; (Eickhoff et al., 2005; Eickhoff et al., 2007), http://www.fz-juelich.de/ime/spm_anatomy_toolbox.

Regions of interest for DCM (ROIs)

The aim of the present study was to assess the effective connectivity within the network sustaining bottom-up and top-down modulated attention and motor control identified in the GLM analysis (Cieslik et al., 2010). Regions of interest (ROIs) for the respective areas were selected according to the results of the group analysis. We defined location of the individual (single-subject) ROIs by the local maximum closest to the group coordinates in the respective cortical regions. Coordinates for the left and right primary motor cortex were obtained by contrasting conditions when subjects responded with their left vs. right hand and vice versa. The coordinates for the IPS, dPMC, TPJ, DLPFC and preSMA for each subject were determined by means of a conjunction analysis searching for increased activation for incongruent compared to congruent responses and each simple main effect for incongruent responses (i.e. ICL and ICR). Coordinates of the preSMA were located close to the midline with the individual local maxima falling on either side of the MNI-space origin (X=0) though never occurring bilaterally. This fits well with the distinction between SMA proper which often shows lateralised responses and holds more basic, executive functions (Grefkes et al., 2008; Kazennikov et al., 1999) while the preSMA is not lateralised as it subserves higher motor control such as selection or inhibition (Picard and Strick, 1996; Vogt et al., 2007) across both hands. Given that single-subject activations are associated with a spatial uncertainty of about 10 mm (Eickhoff et al., 2009b), it is thus not surprising that x-coordinates for an area in the inter-hemispheric fissure which does not display strong functional lateralisation were of either sign.

For each subject and area, the individual local maximum (p < 0.05 uncorrected; (cf. Eickhoff et al., 2008; 2009a; Grefkes et al., 2008; Mechelli et al., 2005)) for the respective contrast was identified as described above. Time series for these regions were then extracted as the first principal component of the 15 most significant voxels in the target contrast centered around the individual peak coordinates in a 4 mm radius and adjusted for the effects of interest. The standard procedure consists in defining a spherical volume of fixed radius around the peak voxel and to extract the principal component from that volume. However, when using this procedure, the default settings in spm do not take the whole sphere into account, but only those voxels that are significant on the chosen contrast at the current threshold. Hence, the number of significant voxels around the peak voxel that are taken into account may vary from subject to subject and region to region. Evidently, this will lead to variable degrees of averaging across voxel-specific time series. To avoid this problem, we here used the 15 most significant voxels around the peak voxel in a radius of 4 mm to keep the number of voxels constant for each subject and VOI in our analysis.

Dynamic causal modelling

Tested models

We used dynamic causal modelling to assess effective connectivity within the neural network underlying (in-) congruent stimulus–response mapping. We here used the DCM version as originally implemented in SPM8 and not the later revision termed “DCM10”.

As input functions, we used the time courses of the four conditions (CL, CR, ICL, and ICR) from the GLM analysis. Furthermore, we included three new regressors, namely StimL and StimR summarising left and right sided stimuli respectively, as well as Inc indicating the incongruent task context. We included these three extra regressors to be able to also modulate the main effects of stimulus side and incongruent response condition in our DCM analysis. Like this, we could test for main effects as well as for interactions between the four conditions.

We here modeled our inputs as stick function (events of duration 300 ms). Modulatory effects on connection strength are often regarded as something being extended in time as one of the best known mechanisms for this is the action of neuromodulatory transmitters such as dopamine. The modulatory effect of this transmitter has a very quick onset, which then often persists in time. However, there are various other mechanisms resulting in very brief transient changes in coupling such as changes in postsynaptic dendritic excitability due to previous synaptic input or due to back-propagating action potentials. These mechanisms can alter the opening probability of voltage-sensitive ion channels and thereby postsynaptic responses to presynaptic inputs and are also relevant for synaptic gating (Stephan et al., 2008). Thereby, even single events, when grouped appropriately, can lead to significant changes in effective connectivity.

All modelled networks included bilateral IPS, dPMC and primary motor cortex (M1), all of which are engaged in transforming visual input into motor output, as well as right TPJ and DLPFC and the preSMA as (top-down) modulatory regions influencing the coupling within this network. As the IPSs are known to respond mainly to stimuli in contralateral space and we did find stimulus-driven bottom-up effects in the IPSs in our GLM analysis, the left and right IPS were considered as input regions for the visual stimulation (StimL on right IPS and StimR on left IPS). In contrast, the right TPJ, right DLPFC and preSMA showed context-specific activations in the incongruent condition in our GLM analysis and were hence modeled as task-dependent regions driven through the incongruent condition (Inc).

As subjects had to respond with their left hand in the CL and ICL condition, in all models IPS → dPMC and dPMC → M1 connections on right hemisphere were allowed to be modulated by the CL and ICL condition. The modulatory influence of conditions CR und ICR was modelled for equivalent areas of the left hemisphere in all models.

We tested 36 models in each individual subject. The models differed in the way how and at which level the top-down influences on the visuo-motor transformation were modelled. The model space was systematically varied along four factors (see Figs. 1A–D).

Fig. 1.

Fig. 1.

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The model space was systemically varied at four factors (A–D). The different possible variations on each factor of the model space are shown in blue. By permutation of all possibilities, we received a full-factorial model space with 36 models. All models were estimated by means of DCM and then evaluated by Bayesian model selection.

  1. Variation at the transcallosal interaction between both IPSs was set up to test for the interhemispheric interaction between the intraparietal sulci during the incongruent condition when subjects were required to reorient attention to the contralateral side. According to the attention model of (Corbetta et al., 2008) attentional reorienting processes should depend on modulatory influences by the ventral attention network, particularly the right TPJ. The GLM results of the present data supported this notion by showing increased rTPJ activation when subjects had to break their current attentional focus to reorient to the behaviourally relevant other side of space. The mechanisms of this modulation, however, are unknown. We therefore modelled three different alternatives in order to investigate how the right TPJ might instantiate its effects on the IPS:
    1. In line with the view that the TPJ might exert direct top-down modulations in context-dependent reorienting, the first model family assumes a direct influence of right TPJ on both IPSs reflecting that the TPJ might alternatively stimulate or inhibit the IPS depending on the side of attentional focus.
    2. In accordance with a less direct influence of the TPJ on the IPS, we allowed the task-dependent right TPJ to have a modulatory influence on the transcallosal connections between both IPSs.
    3. Reflecting a view that the TPJ might not be directly involved in the transcallosal interaction of the IPS during shifting of attention, we also included the alternative that the transcallosal interaction between both IPS was modulated only through the task context, i.e. the incongruent condition (ICL and ICR). This family of models thus also covers influences on the IPS by regions not included in the model (cf. Eickhoff et al., 2009a).
  2. The IPS and dPMC are part of the dorsal attention network. Whereas the IPS is supposedly involved in coding space and in directing spatial attention (Bremmer et al., 2001; Corbetta and Shulman, 2002; Halligan et al., 2003; Nobre, 2001), the dPMC is associated with linking the spatial encoding of targets with movement plans (Cisek and Kalaska, 2005; Jackson and Husain, 1996; Rosen et al., 1999). We found side specific bottom-up effects in V5, IPS and dPMC contralateral to the stimulus side in our GLM analysis. We therefore proposed that stimulus information is forwarded in a bottom-up driven fashion from V5 over IPS to dPMC, facilitating the automatic preparation of congruent motor responses. However, it is known from non-human primate literature that the IPS and dPMC entertain bidirectional anatomical connections (Rizzolatti et al., 1998) giving evidence that the dPMC is not only driven by the IPS in a feed-forward manner but exerts some feedback influence on the IPS as well. To test if the effective connectivity between IPS and dPMC in our task was purely feed-forward driven or included feedback from the dPMC to the ipsilateral IPS, we tested for
    1. An unidirectional coupling from IPS to dPMC versus
    2. A bidirectionally coupling between IPS and dPMC in our model space.
  3. The dorsolateral prefrontal cortex was repeatedly shown to be activated in tasks that require inhibition of a preponent motor response, e.g., anti-saccades, stop-signal or go/no-go tasks (DeSouza et al., 2003; Ettinger et al., 2005; Ettinger et al., 2008; McDowell et al., 2002; Zheng et al., 2008). In our GLM analysis, we found the right DLPFC to show significant increased activation in the incongruent condition. The DLPFC has direct anatomical connections with the dPMC prompting the argument that inhibitory input to premotor areas originate from this region (Leung and Cai, 2007). However, up to now, the effective influence of the DLPFC on the dPMC during stimulus–response mapping is unknown. To elucidate the nature of modulatory influences of the DLPFC on the dPMC and the inter-hemispheric balance among them in the context of our experiment, three alternative levels of this factor were modelled:
    1. In line with data that the DLPFC is anatomically directly connected with the dPMCs, we tested for a task-dependent input of the DLPFC on both dPMCs hypothesizing that the DLPFC might alternatively promote or suppress the dPMC depending on the side the subject had to respond.
    2. To test if the DLPFC might have a more indirect influence, another family of models reflected a modulation of the transcallosal coupling between both dPMCs through the task-dependent DLPFC.
    3. Reflecting a view that the DLPFC does not directly drive the dPMC nor modulate transcallosal dPMC–dPMC coupling, we assumed that the interaction between the dPMCs was modulated through the task context, i.e. the incongruent condition (ICL and ICR) without contribution of the DLPFC. Again, this alternative also accommodates net influences from regions not included in the model.
  4. The preSMA is also thought to play an important role in the cognitive control of motor behavior, in particular in the context of response inhibition, switching between task rules or linking stimuli to changing responses (e.g. Cunnigton et al., 2003; Nachev et al., 2007; Nachev et al., 2008; Simmonds et al., 2008). In line with this view, we found the preSMA to show task-dependent activity when contrasting incongruent with congruent responses in our GLM analysis. TMS studies that tried to further investigate the nature of cognitive control of action selection found the preSMA to facilitate the M1 associated with the correct action selectively during switch trials (Mars et al., 2009; Neubert et al., 2010). However, as the dorsal premotor cortex is involved in sensorimotor integration and a central region for selection of voluntary actions (e.g. Hoshi and Tanji, 2004; Cisek and Kalaska, 2005; Rushworth et al., 2003) an alternative possibility would be that the preSMA exerts its top-down influences on an upstream level of the motor network, i.e. the dPMCs. To better define the specific involvement of the preSMA during motor control, we tested two different alternatives for the preSMA in this last model space factor:
    1. Firstly, to reflect a modulatory control of the preSMA during the preparation phase, i.e. at a higher cognitive stage of motor control, we modelled a task-dependent modulating influence of the preSMA onto the dPMCs. That is, we allowed the incongruent task conditions (ICL and ICR) to have a modulating influence on the connection strength between preSMA and the two dPMCs.
    2. Alternatively, we allowed the preSMA to have a task-dependent modulatory influence on the actual output level, i.e. the primary motor cortices. That is, again we allowed the incongruent task conditions (ICL and ICR) to modulate the connection strength between preSMA and the two M1s. It should be noted that mechanically these influences are most likely relayed by the SMA proper (Haggard, 2008) which would be activated in a lateralized fashion by the preSMA and then initiates motor output. Given theoretical and algorithmical limitations on model size (cf. Friston et al., 2003) we did not include the SMA explicitly in our model. DCM, however, captures the effective influences between regions including those mediated by relays (Friston et al., 2003; Stephan et al., 2007).

Again, it has to be mentioned, that modulations of connections through the task context are always a simplified model of the underlying neural network and represent influences mediated by other regions not included in our model (cf. Eickhoff et al., 2009a).

By permutating all possibilities, we received a full-factorial model space with 36 models.

Model selection and parameter inference

To identify the most likely generative model among the 36 different DCMs, we used a random-effects Bayesian model selection (BMS) procedure (Stephan et al., 2009) where all 36 models were tested against each other. This new variational Bayes method has the advantage that it treats the model as a random variable and estimates the parameter of a Dirichlet distribution which describes the probabilities for all models being considered and allows to compute the exceedance probability of one model being more likely than any other model tested.

For confirmation and comparability with previous DCM analysis, fixed-effects model comparison was also performed. To test if the coupling parameters were consistently expressed across subjects, the connectivity parameters (intrinsic connections, context-dependent modulations and non-linear influences) of the model with the highest exceedance probability were then entered in a second-level analysis by means of a Wilcoxon sign-test. Connections were considered statistically significant if they passed a threshold of p < 0.05 (Bonferroni corrected for multiple comparisons across all tested connections).

Results

Coordinates of the VOIs in each individual subject as well as the peak coordinates from the group analysis are shown in the supplementary material (Supplementary Table 1).

Bayesian model selection

Both Bayesian selection procedures (i.e., fixed-effects; random-effects) yielded congruent evidence in favour of a model family differing only in the specific impact of the DLPFC on both dPMCs (Fig. 2).

Fig. 2.

Fig. 2.

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All 36 models were compared by random-effects Bayesian model selection. Evidence emerged in favor of a family of three models (models 20, 22 and 24) that differed only in the specific impact of the DLPFC on the dPMCs.

All three models of this most likely model family thus supported:

  • the TPJ to have a modulating influence on the transcallosal coupling between both IPSs

  • a bidirectional coupling between the IPS and dPMC

  • the preSMA to exert a context-dependent modulation on the motor output at the level of the primary motor cortex

Intrinsic connections

Significant intrinsic connections for the model with the highest exceedance probability (model 20) are summarized in Fig. 3. In this context, “intrinsic” refers to interactions among brain regions which are not task-dependent and thereby represent the constant part of the inter-regional connectivity. In summary, all intrinsic connections (except for the connection between left dPMC and M1, for which only a trend was evident) differed significantly (p<0.05 corrected) from zero and indicated a positive coupling. Such positive intrinsic modulations imply a propagation of activation to the connected regions increasing activation-levels in the target area. In the present model this holds for the connections from IPS via the dPMC to M1, for the feedback between dPMC and IPS as well as for the effects of the TPJ on the IPSs, the DLPFC on the dPMCs and the preSMA on the dPMCs and M1s.

Fig. 3.

Fig. 3.

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Intrinsic connectivity in the fronto-parietal attention network as estimated by means of dynamic causal modelling in model 20. Coupling parameters (rate constants in 1/s [Hz]) indicate connection strengths. Positive (green → p<0.05 Bonferroni corrected) values refer to facilitation of neural activity.

Driving inputs

The values for the driving inputs were all significantly greater than zero (Fig. 4). For the stimulus-driven input into the two IPSs, driving inputs were symmetric at 0.2609 (left IPS) and 0.2686 (right IPS) and not statistically different from each other. For the task-dependent input, the context of the necessity to respond incongruently induced significant activation in the TPJ (rate constant (0.4093), DLPFC (0.7494) and the preSMA (0.5826). This indicates that activation in the IPSs was driven during presentation of a stimulus contralateral to the respective IPS and that activation in the “top-down” regions TPJ, DLPFC and preSMA increased whenever subjects were instructed to respond incongruently to the respective stimuli in the particular block.

Fig. 4.

Fig. 4.

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Context-dependent modulatory effects for congruent (CL and CR) and incongruent (ICL and ICR) response conditions in the model with the best evidence (model 20). Positive (greens → p<0.05 Bonferroni corrected) and negative (red → p<0.05 Bonferroni corrected) coupling values refer to facilitation and inhibition of neural activity respectively. Arrows in yellow signify driving inputs.

Task-dependent modulations

Responding to a lateralized stimulus in a congruent or an incongruent manner significantly increased connectivity strength between IPS and dPMC as well as between dPMC and M1 in the hemisphere contralateral the respective response hand (Fig. 4).

For the top-down influences of the task-driven regions TPJ, DLPFC, and preSMA we found the following mechanisms in our winner model:

  • Activation in right TPJ exerted a significant positive modulation of the transcallosal connection strength between the two IPSs. That is, increased activation in right TPJ mediated an increased transcallosal coupling between the IPS on either hemisphere. It has to be mentioned that we here have modeled connections where one area, such as the right TPJ, modulated another region (the IPSs) as well as its follow up connections (the transcallosal connections between the IPSs). Theoretically, these driving and modulatory parameters could be dependent from each other. However, we did not find any significant correlation when tested with a Pearson test between these driving and modulatory parameters across subjects in our winner model.

  • The preSMA, in contrast, had selective positive or negative influence on the primary motor cortices depending on the task condition, all of which were statistically significant. In particular, in the ICR condition, when subjects were required to respond incongruently with their right hand, the preSMA positively modulated left M1 and had a concurrent negative modulating influence on the right M1. Vice versa, in the ICL condition, when subjects were required to respond incongruently with their left response hand, the preSMA had a positive influence on right M1 and concurrently a negative modulating influence on left M1.

  • On the level of the DLPFC and its task-dependent influence, our results did not show clear evidence for one of the three tested possibilities (Fig. 2). However, the particular model of the winner family that received the highest overall exceedance probability showed evidence for a significant task-dependent positive modulation of the dPMCs through the right DLPFC in the incongruent conditions, ICL and ICR (Fig. 4).

Coupling parameters are shown for the model with the highest overall exceedance probability (model 20), given that it represents the most likely generative model for the observed data. We also tested if the coupling parameters estimated for the other two models (model 22 and 24) that showed high exceedance probabilities revealed the same effects. As can be seen in Supplementary Figs. 1 to 4, the parameters for the connections implemented in all three models were remarkably constant within the entire model family and did not differ significantly from each other when testes with an ANOVA.

Discussion

We here compared 36 different network models representing alternative hypotheses about the functional architecture of top-down modulations by the TPJ, DLPFC and preSMA on the dorsal fronto-parietal attention network during a manual stimulus–response compatibility task. When all thirty-six models were compared by random and fixed-effects analyses, congruent evidence emerged in favour of a family of three models differing only in the specific impact of the DLPFC on the dPMC. All models of the most likely model family, however, supported a modulatory effect of the TPJ in connectivity between the intraparietal cortices, feedback from the dPMC to the IPS and a task-specific influence of the preSMA on the motor output (M1) rather than motor preparation (dPMC). Our data, therefore helps to improve the understanding of the dynamic interactions and context-dependent modulations in the fronto-parietal network during attentional reorienting processes.

Role of dorsal and ventral attention networks and its interaction

The dorsal orienting network consists of the IPS and dPMC and is associated with the allocation of spatial attention and the selection of stimuli and responses. Parieto-frontal circuits provide an anatomical basis for the transformation of sensory information into actions (Matelli and Luppino, 2000; Rizzolatti et al., 1998) as the parietal lobes receive somatosensory and visual input and are reciprocally connected with motor areas in the frontal lobe. This view was well supported by our model showing significant feed-forward connectivity from the IPS to the dPMC and finally M1 transforming the visual input into motor output. However, it is known from invasive studies in non-human primates that the IPS and dPMC exhibit bidirectional anatomic connections. We thus tested in our DCM analysis, if the functional interactions between IPS and dPMC in our task would be purely feed-forward driven, or if the dPMC also exerts feedback influence on the IPS (Fig. 1B). Our winner model family showed preference for the IPS and dPMC to be connected in a bidirectional manner (Fig. 2) supporting the view that the dPMC indeed had a feedback influence on the IPS thereby possibly promoting the mapping between stimulus and response.

Even though it has long been assumed that reorienting relies on the interaction of a dorsal and ventral attention network (Corbetta et al., 2008; Corbetta and Shulman, 2002), the specific interaction between these two networks during the reorienting process were largely unknown. In our previous GLM analysis we found side specific bottom-up effects as well as task-dependent top-down effects independent of response hand in the IPS and dPMC. In contrast, the right TPJ was selectively recruited during incongruent responding independent of response hand. The TPJ was hence modeled as a top-down region driven through the incongruent task context in our DCM analysis. We tested three hypothesis how the TPJ might influence the two IPSs during the reorienting process (Fig. 1A).

Our model comparison showed strong preference for the alternative where the task-dependent TPJ had a modulating influence on the transcallosal connectivity between the two IPSs. In particular, our data suggest that the right TPJ exerts a permanent influence on activity in bilateral IPSs as shown by positive intrinsic connections (Fig. 3). Importantly, the right TPJ was specifically driven through the incongruent task condition (Inc) and had a positive modulatory effect on the transcallosal coupling between the two intraparietal sulci. That is, transcallosal coupling between the two IPSs was promoted through the task-dependent activation of the TPJ. Non-linear (indirect) influences in DCM characterize neurobiological mechanisms of how the connection between two neuronal units can be enabled or gated by activity in other units, representing key mechanisms for various neurobiological processes such as top-down modulation or learning (Stephan et al., 2008). Biophysically, these control processes can arise through various mechanism mediating interactions among synaptic inputs occurring close in time but not necessarily in the same dendritic compartment.

Our DCM results provide a mechanistic explanation how the dorsal and ventral parietal attention network might interact during reorienting processes. We propose the dorsal network to be the actual effector shifting the focus of attention towards a particular side. The TPJ, in contrast, represents a higher order structure which has a promoting influence on the transcallosal coupling between the two IPS during the reorienting process. Hereby it should facilitate the shifting of attention from one side to the other. This interpretation is well in line with studies investigating patients suffering from spatial neglect after stroke. Spatial neglect is a syndrome typically caused by white matter lesions in the ventral network, particularly the right temporoparietal junction (Husain and Kennard, 1996; Karnath et al., 2004; Mort et al., 2003). It is characterized by attentional deficits in perceiving and responding to stimuli in the contralesional side. Recent studies showed that affection of the TPJ may lead to functional imbalance between the nonaffected left and right dorsal parietal cortices which then correlated with the severity of contralesional inattention (Corbetta et al., 2005a; He et al., 2007). The current DCM results now provide a possible mechanism for these behavioural deficits found in neglect patients on the network level. Strokes affecting the ventral attention network might lead to an impaired or missing control of TPJ on the intrahemispheric coupling and hence imbalance between the two IPSs. This may then lead to impairment in shifting attention from one side to the other and spatial lateralized deficits as found in neglect patients.

Controlling motor output: Top-down influences of the DLPFC

The right DLPFC has repeatedly been shown to be activated during anti-saccade generation where it is believed to be specifically associated with the inhibition of the reflexive pro-saccade (DeSouza et al., 2003; Ettinger et al., 2005; Ettinger et al., 2008;Ford et al., 2005; McDowell et al., 2002, cf. Behrwind, 2011). Equivalently, a study investigating a stop-signal and a go/no-go task within the same subjects, demonstrated the right DLPFC to be the only region commonly activated in both tasks and, furthermore, showing correlation with behavioral performance (Zheng et al., 2008). The authors therefore interpreted this region to be the key structure for response inhibition. In our GLM analysis we found the right DLPFC to increase activity when subjects were required to respond incongruently, independent of response hand. DLPFC activation may hence reflect an inhibition of the stimulus-driven tendency to react with the ipsilateral hand in favor of the required, voluntarily executed, contralateral response (Cieslik et al., 2010). Following this interpretation, we would have expected the DLPFC to exert an inhibiting influence on the premotor stage, i.e., negative effective coupling with the dPMC on the side contralateral to the stimulus and related to the congruent response.

At the level of the DLPFC and its modulating influence, our DCM results did not show clear evidence for one of the three tested possibilities as the three models receiving the highest evidence differed only in the task-dependent influence of the right DLPFC on the dPMC (Fig. 2). In the model with the highest exceedance probability (model 20) the DLPFC featured task-dependent positive influences on both premotor cortices, whereas in the model with the second highest exceedance probabilities (model 22) the DLPFC was modelled to exert its influence on the transcallosal connectivity between the dPMCs.

Importantly, and contrasting the prevalent interpretation of the DLPFC as a source of inhibitory signals our DCM results speak against an inhibiting role of the DLPFC in the context of our experiment, since no negative influences were found. Rather, they are more in line with the theory of Shallice (2004) pointing the DLPFC to exert monitoring processes during motor behaviour such as controlling whether motor preparation match the required behavioural response. How these control processes are actually realized cannot be fully explained by our modelling, as the alternatives where the DLPFC had a direct task-dependent influence on the dPMCs and the one where the DLPFC modulated the interhemispheric coupling did differ only marginally in their exceedance probability. One possibility might be that in the more complex (incongruent) task condition the DLPFC positively modulates activity in dPMC because of the need of an increased demand in mapping of stimulus and accurate motor response. However, according to our model comparison, our observed data could as well be explained by an increase in interhemispheric activity driven through the task-dependent DLPFC. According to that alternative, the DLPFC might then positively modulate the interhemispheric coupling between the dPMCs, when premotoric activation had to be suppressed in one hemisphere and shifted to the contralateral one in trials where subjects were required to respond in an incongruent manner.

Controlling motor output: top-down influences of the preSMA

The preSMA is part of a frontal cognitive-motor network including the (pre)-motor and prefrontal cortices (Haggard, 2008). The role of the preSMA in volitional motor behavior has been verified by studies showing prolonged and increased negativity localized around the preSMA 1 s or more before the onset of voluntary action (Kornhuber and Deecke, 1965; Shibasaki and Hallett, 2006). This “readiness potential” is believed to be the source of a cascade of neural activity which is forwarded in the motor network until reaching the primary motor cortices and thus causing movement. We here investigated if the task-dependent modulating influences of the preSMA would affect the more preparatory (premotor) or output (primary motor) level of visuo-motor transformation.

Our DCM analysis showed that the preSMA exerts a permanent influence on the premotor (dPMC) and motor (M1) stage (Fig. 3) as suggested by positive intrinsic connectivity with these two areas. Intrinsic connectivity reflects the constant part of inter-regional connectivity which is condition unspecific. As the subjects performed a motor task which constantly required responding to an appearing stimulus, we would propose that positive intrinsic connections from the preSMA to the bilateral premotor and motor cortices reflect this general response readiness which then was transformed into a movement depending on the required response.

The current DCM model furthermore suggests that the preSMA exerted its context-specific influences not on the preparatory (premotor) but rather directly on the output level. In particular, the preSMA had an inhibiting influence on the M1 contralateral to stimulus side and a positive influence on M1 ipsilateral to the stimulus in the incongruent task conditions. That is, the preSMA suppressed activity in contralateral M1, on the same side where the dorsal premotor cortex showed automatic bottom-up driven activity in our GLM analysis. The preSMA therefore seemed to counter the automatic preparation of the congruent motor response by inhibiting contralateral M1. Concurrently, the preSMA activated ipsilateral M1 thereby promoting the endogenously triggered incongruent response. While the preSMA does not have direct connections towards the primary motor cortex, the strongly lateralized SMA proper exerts direct connections to the primary motor cortices. We therefore would suggest that the promoting and inhibiting influences of the preSMA are probably executed in a lateralized manner through the SMA to the M1. In line with other studies drawing the preSMA to exert higher processes of motor control including motor selection or inhibition (Nachev et al., 2007; Picard and Strick, 1996; Vogt et al., 2007) we would propose the preSMA to be the key region resolving competition between motor plans by exerting inhibitory and excitatory executive control on the motor network when subjects were required to respond to a stimulus in an incongruent manner.

Conclusion

We showed that cortical control of motor attention and reorienting involves differential modulation of neural activity in the dorsal attention network mediated through the top-down control of context-dependent areas (rDLPFC, rTPJ and preSMA). In particular, DCM provides evidence for the right temporoparietal junction to facilitate attentional reorienting processes by promoting interhemispheric connectivity between the intraparietal sulci. The right DLPFC, against its usual association with response inhibition, seemed to subserve generic monitoring processes in the context of our experiment. Furthermore, analysis of connection strength supported the proposed role of the preSMA in exerting inhibitory executive control on the executive rather than preparatory part of the motor network by promoting or suppressing activity in primary motor cortex, hereby controlling the accurate motor output.

DCM relies on the assumption of a simplified model of the mechanisms underlying neuronal dynamics. While DCM only allows for testing models with a specified number of regions, there might be other regions not included in our model that did participate in the response selection process. Therefore, our results can only be interpreted on the level of the chosen model space but might differ completely if other regions are included.

Nevertheless, DCM gives us the opportunity to examine competing mechanistic models of neural connectivity and context-dependent modulations. Investigating the functional integration of neuronal populations during stimulus–response selection is essential for a better understanding of cognitive and motor flexibility in healthy subjects as well as patients groups that show deficits in control of attentional reorienting such as neglect patients or patients suffering from psychiatric diseases such as schizophrenia.

Supplementary Material

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Acknowledgments

This work was partly funded by the Human Brain Project (R01-MH074457-01A1; S.B.E.), the Initiative and Networking Fund of the Helmholtz Association within the Helmholtz Alliance on Systems Biology (Human Brain Model; K.Z., S.B.E.), and the DFG (IRTG 1328, S.B.E.).

Footnotes

Supplementary materials related to this article can be found online at doi:10.1016/j.neuroimage.2011.05.089.

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