The Prefrontal Cortex Achieves Inhibitory Control by Facilitating Subcortical Motor Pathway Connectivity

Stop-signal task.

The study was approved by the local research ethics committee and subjects gave informed written consent before participation. Sixteen healthy right-handed adults (age 20–38, mean 28; 12 males) performed a variant of the stop-signal task while being scanned with fMRI. In the stop-signal task (Logan et al., 1984), actions were either specified as single-finger presses of the right hand, or single-finger presses chosen by the subject (Rowe et al., 2010a; Rae et al., 2014). These two trial types were collapsed together for network analysis (see Dynamic causal modeling) given the lack of a significant difference in behavior or regional activations in this task (Rae et al., 2014). Subjects viewed a picture of a right hand, with a circle above each finger. Responses were cued by a color change of the circles to green. On 25% of trials, after a short variable delay, the green “go” cue changed to a red “stop” cue in conjunction with an auditory tone (1000 Hz, 100 ms), indicating that subjects should withhold their response. Staircase tracking algorithms modified the delay between go and stop cues in increments or decrements of 50 ms, on a trial-by-trial basis, to maintain overall stopping accuracy close to 50% (including separate tracking algorithms for specified and chosen finger presses, but these did not significantly differ in the mean stop accuracy; Rae et al., 2014). Green “go” cues were presented for 1 s. An intertrial interval comprised continuous presentation of the hand picture, with no change in the gray background color of the circles. The stimulus onset asynchrony varied between 3 and 9 s (mean 4.5 s) to increase design efficiency. Subjects completed 432 go trials and 144 stop trials during continuous imaging, but divided into six blocks of equal length with a rest break of 20 s between blocks. Trial order was fully randomized. The stop-signal reaction time (SSRT) was used as the behavioral measure of stopping efficiency: a lower SSRT indicates less time is required to inhibit a response. SSRTs were calculated according to the integration method (Logan et al., 1984).

fMRI acquisition and preprocessing.

1450 BOLD-sensitive T2*-weighted echoplanar images were acquired on a 3T Siemens Trio, covering the whole brain (32 descending axial oblique slices 3 mm thick with 0.75 mm gap, in-plane resolution 3 × 3 mm, TR = 2000 ms, TE = 30 ms). The first five volumes were discarded to allow for steady-state magnetization. A high-resolution MPRAGE structural scan was acquired for registration and normalization (1 × 1 × 1 mm³ resolution, TR = 2250 ms, TE = 2.99 ms). fMRI preprocessing and statistical modeling used SPM8, patch version r4667 (http://www.fil.ion.ucl.ac.uk/spm/) in MATLAB 7.8. Images were converted from dicom to nifti format, realigned to the mean image, and sinc interpolated in time to correct for slice timing differences during acquisition. The MPRAGE was coregistered to the mean echoplanar image using mutual information, and iteratively segmented and normalized to the SPM MNI152 template. The resulting normalization parameters were applied to the realigned and slice-time corrected echoplanar images, before smoothing with a Gaussian kernel of full-width half-maximum 8 mm.

A first level event-related analysis for each subject used a general linear model. Events were modeled with a duration of 1 s from trial onset, and convolved with the canonical hemodynamic response function. The design matrix was organized to separate driving inputs (e.g., for all trials) from the modulatory inputs (e.g., successful stopping) for DCM (Friston et al., 2003; Stephan et al., 2008), while accounting for all principal experimental variables across the six trial types (go-specified, go-select, stop-specified-correct, stop-specified-incorrect, stop-select-correct, and stop-select-incorrect) and nuisance terms (e.g., motion parameters). The first regressor represented the driving input of all trial types. The second, third, and fourth regressors were parametric modulators of trials. Parametric Modulator 1, “selection,” comprised select > specified trials (go-select, stop-select-correct, stop-select-incorrect > go-specified, stop-specified-correct, stop-specified-incorrect). Parametric Modulator 2, “stopping,” comprised correct stop > go trials (stop-specified-correct, stop-select-correct > go-specified, go-select). Parametric Modulator 3, “correct stopping > incorrect stopping,” comprised correct stop > incorrect stop trials (stop-specified-correct, stop-select-correct > stop-specified-incorrect, stop-select-incorrect). Parametric Modulators 1 and 3 were not used as driving or modulatory inputs for DCM, but were included in the design matrix to account for experimental variance. Go trials on which a subject made an error (for example, pressing with the wrong finger on a specified trial) were modeled in separate regressors, according to the error type (omission: RT > 1000 ms, commission: RT < 100 ms; error: wrong finger pressed). Finally, six nuisance regressors modeled subject movement as three translations and three rotations. Activations for correct response inhibition (stop-correct > go) were examined using the stopping parametric modulator. A second-level SPM analysis used contrasts from the first level, with a one-sample t test (see Fig. 2). Cluster-based FDR (FDRc) at p < 0.05 was applied to correct for multiple comparisons (Chumbley and Friston, 2009). To extract regional time series for DCM, we specified an F test across the “all trials” regressor and three parametric modulators that were the first four columns of the design matrix (F contrast: p < 0.05, uncorrected).

We extracted the first eigenvariate of the BOLD time series from four regions-of-interest (ROIs): left primary motor cortex (M1), STN, preSMA, and right inferior frontal gyrus (Fig. 1). For the M1, preSMA, and inferior frontal gyrus ROIs, we defined group peak coordinates from a second level group analysis, using the all trials column of the design matrix for M1 (x = −34, y = −22, z = 56), and the conjunction contrast of parametric modulators (1, selection; 2, stopping) for the preSMA and inferior frontal gyrus (preSMA: x = −8, y = 20, z = 44; inferior frontal gyrus: x = 48, y = 14, z = 28). The inferior frontal gyrus peak corresponded to right superior pars opercularis (Aron and Poldrack, 2006; Hampshire et al., 2010; Verbruggen et al., 2010). For the M1, preSMA, and inferior frontal gyrus ROIs, we used each subject's F test to identify local maxima closest to the group peak, conforming to appropriate anatomy (cf. Rowe et al., 2010b), and extracted the first eigenvariate from a 5 mm sphere at the subject-specific peak. Four subjects showed a relatively posterior activation at the border of SMA/preSMA (y = 0; Nachev et al., 2008).

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Figure 1.

Lateral (a) and medial (b) views showing the regions included in the network models: right inferior frontal gyrus (pink), preSMA (green), STN (blue), left primary motor cortex (red).

For the STN ROI, we used a bilateral STN mask from the probabilistic maps described by Forstmann et al. (2012). Because the probabilistic maps do not correspond to masks of contiguous voxels, we smoothed the unthresholded left and right STN maps with a Gaussian kernel of 2 mm (original STN map voxel size: 0.5 mm), using the function “fslmaths” in FSL (www.fmrib.ox.ac.uk/fsl). Then, using “fslmaths,” the smoothed images were thresholded at 0.1 to produce left and right STN masks similar in spatial extent to the original STN maps, preserving the oblique 3D geometry of the nucleus, but composed of contiguous voxels. The left and right STN masks were combined. We applied this STN mask (total volume: 448 voxels at a resolution of 0.5 mm³) in each subject's F test image to find the subject-specific peak within the STN region. We then extracted a 5 mm sphere eigenvariate at this location.

Dynamic causal modeling.

We used DCM (Friston et al., 2003) to determine the most likely network with interactions between inferior frontal gyrus, preSMA, and the STN during successful response inhibition on the stop-signal task. DCM estimates the effective connectivity between brain regions according to (1) the average connections between the regions (DCM.A matrix), (2) modulatory influences on connections arising through experimental manipulations (namely successfully stopping an action; DCM.B matrix), and (3) condition-specific inputs that drive network activity (namely engaging in a response inhibition task; DCM.C matrix). If a model includes activity-dependent connections (that are modulated by regional activity, as opposed to condition-specific inputs), the model is equipped with a further set of D parameters, coupling regions (nodes) to connections. These models with a further set of parameters (the DCM.D matrix) are referred to as nonlinear DCMs, because the connectivity depends upon the square of activity.

In DCM, the neural dynamics of a model are mapped to the fMRI time series with a comprehensive forward model of the hemodynamic BOLD response. The DCM.A, DCM.B, and DCM.C (and DCM.D if specified) parameters of a model are estimated using Bayesian approximations to maximize the free-energy bound (F) on the Bayesian model evidence. F expresses the accuracy of a model given the data, adjusted for model complexity. F is then compared across different models to determine the most likely generative model of the fMRI data.

We estimated 20 models that represented alternative hypotheses of interactions among four regions of interest: the inferior frontal gyrus, preSMA, STN, and primary motor cortex (Fig. 1). The choice of four regions was determined by the minimum set required to test our hypotheses of interactions between prefrontal cortex and the STN, extending previous neuroimaging and electrophysiological models (cf. Duann et al., 2009; Neubert et al., 2010; Sharp et al., 2010; Swann et al., 2012; Zandbelt et al., 2013). One could consider the inclusion of additional nodes, especially in the putamen, pallidum, and thalamus (cf. Frank, 2006; Redgrave et al., 2010; Wiecki and Frank, 2013). However, this is not necessary to determine the interactions between cortex and STN, because DCM paths implicitly represent both monosynaptic and polysynaptic connections between regions (Stephan et al., 2010).

We limited our models to the four regions necessary and sufficient to test our hypotheses regarding prefrontal–STN interactions and motor output. The structural architecture of these models was informed by primate anatomical connectivity. Specifically, tracer studies in macaques show that the preSMA connects to area 44, the STN, and M1, either directly or indirectly through the SMA (Picard and Strick, 1996; Inase et al., 1999; Nambu et al., 2002; Schmahmann and Pandya, 2006), whereas the STN connects indirectly to M1 via the globus pallidus and thalamus (Nambu et al., 2002). Note that DCM is agnostic as to whether connections between two specified regions are monosynaptic or polysynaptic.

We systematically varied the combinations of permitted (average) connections (DCM.A), modulatory effects of trial type (DCM.B), and driving inputs (DCM.C) across our set of 20 models to test alternative hypotheses of causal prefrontal–subcortical interactions during response inhibition (see Fig. 3). The set of 20 models were divided into “families” (Rowe et al., 2010b; Stephan et al., 2010) that shared average connectivity structure, namely whether connectivity between inferior frontal gyrus and preSMA was absent, unidirectional, or bidirectional (see Fig. 3; families A–D). In each model, average self-connections were applied to all four network nodes (cf. Rowe et al., 2010b). Across all 20 models, the inferior frontal gyrus and preSMA influenced the STN, either directly or indirectly, by modulation of cortical projections from each other. The STN did not feedback directly to the inferior frontal gyrus or preSMA, but fed-forward through the network via thalamus to motor cortex (Nambu et al., 2002).

The models within each family differed in site of modulatory input (see Fig. 3, dotted arrows). The modulatory input represented successful stopping (parametric modulator comprising correct stop > go trials). We estimated 12 “linear” models (Friston et al., 2003), which varied in the prefrontal–STN connection site of modulatory input (DCM.B); and 8 “nonlinear” models (Stephan et al., 2008), in which the prefrontal region (inferior frontal gyrus or preSMA) serving as the site of driving inputs (in the DCM.C matrix) in turn gated the connection to the STN from the other prefrontal region (DCM.D matrix).

The driving input of a DCM, arising through condition-specific inputs, drives the network dynamics. Across all 20 models, driving inputs (DCM.C) represented engaging in the response inhibition task (“all trials” regressor of the design matrix including go, stop-correct, and stop-incorrect trials). We applied driving inputs to both frontal regions (the inferior frontal gyrus and preSMA), in all 20 models. Driving inputs are represented by solid bold arrows in Figure 3.

We used DCM10 in SPM8 (patch r4667) to estimate these 20 models for 16 subjects, estimating the free-energy bound on the model evidence (F). We compared the model evidences for the 20 models across the 16 subjects using Bayesian model selection (BMS; Stephan et al., 2009a) with fixed effects (see Fig. 4). This compares the group difference in F for each model (which can also be expressed in terms of the group Bayes factor). BMS with fixed effects also provides the posterior model probability, which represents the probability that a given model generated the observed group data (range: 0–1). We also performed BMS with a random-effects analysis, which accommodates differences in the generative models across subjects (Stephan et al., 2010). This provides the exceedance probability (0–1), namely, the probability that a given model is more likely than any other model tested, in a group of subjects who may have generated data from different networks.

Using Bayesian parameter averaging (Stephan et al., 2009a), we estimated the connectivity values of the most likely model at the group level, across subjects (see Fig. 3). In each subject, we extracted the connectivity values of the most likely group model to test for correlations between individual differences in the strength of the prefrontal–STN interactions and SSRT, using Pearson correlations in SPSS 19.0 (IBM).

Diffusion-weighted MRI acquisition.

Diffusion-weighted MRI data were acquired from the same subjects who participated in the fMRI stop-signal task, during the same scan session. Twice-refocused spin-echo diffusion-weighted images were acquired with a voxel size of 1.8 × 1.8 × 2 mm (70 interleaved 2 mm axial slices; matrix size 106 × 106, field-of-view 192 × 192, TR = 9300 ms, TE = 92 ms). Diffusion weighting was applied along 64 gradient directions, at a b value of 1000 s/mm². Two repetitions were acquired, with a volume with no diffusion weighting (b = 0 s/mm²) acquired at the beginning of each repetition.

Tractography between prefrontal cortex and STN.

Preprocessing and probabilistic tractography analyses used FSL 4.1.8. Diffusion images were corrected for eddy currents and subject motion using “eddy_correct.” Diffusion tensors were fitted using “dtifit.” For tractography, “bedpostx” was used to model the diffusion distribution at each voxel, with two fibers modeled per voxel (default options; Behrens et al., 2007).

Masks of the right inferior frontal gyrus, right preSMA, and right STN were created in each subject's native T1 space. To aid in mask construction, each subject's structural MPRAGE image was segmented into gray matter, white matter, and CSF using FSL “fast,” with default options.

For the inferior frontal gyrus masks, the BA44 and BA45 maps from the FSL Juelich histological atlas were transformed to subject structural space using “flirt.” The masks were inspected in subject structural space and any voxels not meeting anatomical criteria for the inferior frontal gyrus were removed: this included voxels posterior to the precentral sulcus, superior to the fundus of the inferior frontal sulcus, inferior to the vertical ramus of the lateral fissure (at pars opercularis) or inferior to the horizontal ramus of the lateral fissue (at pars triangularis), and anterior to the point at which the horizontal ramus of the lateral fissure ended. Any voxels in gray matter or CSF were removed by subtracting the segmented gray matter and CSF voxels using “fslmaths,” leaving only voxels corresponding to white matter underneath the inferior frontal gyrus.

For the preSMA masks, we used the combined preSMA+SMA map from the automatic anatomical labeling (Tzourio-Mazoyer et al., 2002) atlas. In the combined preSMA+SMA map, all voxels posterior to y = 0 were removed, defined as the posterior border of the preSMA (Nachev et al., 2008). The preSMA map was transformed to subject native T1 space and any voxels in gray matter or CSF were removed by subtracting the segmented gray matter and CSF voxels using “fslmaths,” leaving only voxels corresponding to white matter underneath the preSMA.

For the STN masks, we used the right STN mask created for STN fMRI time series extraction in the effective connectivity analysis, based on the STN maps described by Forstmann et al. (2012). The right STN mask was transformed to each subject's native T1 space using “flirt.”

Using “probtrackx,” we reconstructed tracts between (1) the right preSMA and right STN, and (2) the right inferior frontal gyrus and right STN (see Fig. 5). We chose to run tracts only in the right hemisphere due to the inclusion of the right inferior frontal gyrus region in the effective connectivity analysis (cf. Aron et al., 2003; Duann et al., 2009; Sharp et al., 2010). Diffusion MRI does not distinguish directionality of tracts, but results may differ slightly when different seeds are used. We therefore ran “probtrackx” twice: once with the cortical region applied as a seed and the STN as a target, and once with the STN applied as a seed and the cortical region as a target (default probtrackx options). An exclusion mask was applied at the midline, and the target mask was also set as a waypoint and as a termination mask.

The resulting output images for each tract (“fdt_paths”) represent the most likely pathways that the sample streamlines take from the seed region. These were thresholded at 98% probability (i.e., only voxels showing at least 100/5000 streamlines were retained). The two separate probtrackx analyses for each tract were combined by adding the thresholded fdt_paths together with “fslmaths.” Group-level tracts were created to remove spurious streamlines as follows: the thresholded tracts from each subject were binarized to remove the probabilistic connectivity values, and transformed to MNI space using FSL “applywarp.” In MNI space, the tracts from each of the 16 subjects were summed and the group-level tracts thresholded such that only tract voxels present in at least 50% (8/16) subjects were retained. This procedure removes spurious streamlines (e.g., descending posteriorly from the STN to the cerebellum through the cerebellar peduncle; cf. King et al., 2012). Group-level tracts were binarized, and transformed to subject diffusion space using “flirt.” In subject diffusion space, any tract voxels in gray matter were removed by thresholding the tracts at a fractional anisotropy threshold of >0.2.

Subjects' mean diffusivity images were then masked by the tracts and the average tract mean diffusivity extracted using “fslstats.” We tested for correlations between SSRT and the mean diffusivity of (1) the right preSMA and right STN tract, and (2) the right inferior frontal gyrus and right STN tract, using Pearson correlations in SPSS 19.0 (IBM). Finally, we investigated the relationship between structural and effective connectivity directly. Specifically, we tested for Pearson correlations (SPSS) between (1) mean diffusivity in the tract between preSMA and STN, and the DCM.A parameter representing the average connectivity from the preSMA to the STN in the most likely model; and (2) mean diffusivity in the tract between inferior frontal gyrus and STN, and the DCM.D parameter representing nonlinear modulation in the most likely model (the aspect of the model most anatomically relevant for this tract).