Response inhibition, generally defined as the ability to suppress a planned or already-initiated response (Logan, 1985), is an essential part of everyday motor control, and underpinned by a series of cortical and subcortical pathways. Defining the neural mechanisms underlying response inhibition in the neurotypical population has important consequences in the clinical neurosciences, where impairment in these pathways has been associated with a number of neurological and psychiatric diseases including Parkinson’s disease, addiction, and schizophrenia (Chowdhury et al., 2018; Claassen et al., 2015; Congdon et al., 2014; Noël et al., 2016; Rømer Thomsen et al., 2018; Seeley et al., 2009).

Response inhibition has been behaviourally examined using the stop-signal task (SST) for more than four decades. In the SST, participants make a motor response as quickly as possible in response to a go signal. In a minority of trials (usually around 25% of all trials), a stop signal appears shortly after the onset of the go signal, indicating that the participant should not respond to the go signal in that trial. The stop signal’s onset is normally adjusted after each stop-signal trial based on stopping success, such that each participant will be able to stop successfully on approximately 50% of trials (Verbruggen et al., 2019). Behavioural dynamics during the SST are interpreted under the framework of the horse-race model (Logan and Cowan, 1984). This model proposes that on each stop trial, the presentation of the go stimulus triggers the go process, which races towards a threshold that results in a response. Upon the presentation of the stop signal, a stop process is similarly triggered, which races towards an independent threshold. Depending on whether the go or stop process finishes first, the response is, respectively, performed or inhibited. Performance on go trials and failed stop trials (where the participant makes an inappropriate response) is quantified by reaction time (RT). Inhibition performance in the SST as a whole is quantified by the stop-signal reaction time (SSRT), which estimates the speed of the latent stopping process (Verbruggen et al., 2019).

Contemporary models of response inhibition propose that inhibition is realized via three cortico-basal-ganglia pathways; the direct, indirect, and hyperdirect pathways (Diesburg and Wessel, 2021; Mink, 1996). While all three are involved in response inhibition and movement, the hyperdirect pathway has been theorized to be the pathway through which action is ultimately cancelled (Aron and Poldrack, 2006). The signalling cascade originates from the prefrontal cortex and is thought to implement stopping upon detection of a stop signal by inhibition of the thalamus (Tha) via the subthalamic nucleus (STN), substantia nigra (SN), and globus pallidus interna (GPi; Coudé et al., 2018; Diesburg and Wessel, 2021). This pathway was originally identified in rodents and non-human primates (Eagle et al., 2008; Schmidt et al., 2013), but its anatomical plausibility in humans was demonstrated by Chen et al., who measured firing in the frontal cortex 1–2 ms after stimulation of the STN (Chen et al., 2020). The connectivity of these cortico-basal-ganglia tracts has been shown to be correlated with stopping behaviour (Forstmann et al., 2012; Singh et al., 2021; Xu et al., 2016; Zhang and Iwaki, 2020). Clinical studies have also demonstrated the importance of subcortical regions, particularly the STN, in relation to stopping. A multitude of these studies provide electrophysiological support for the involvement of the STN in successful response inhibition (Alegre et al., 2013; Benis et al., 2014; Benis et al., 2016; Fischer et al., 2017; Mosher et al., 2021; Wessel et al., 2016), indicating that increased β-band activity induces global motor suppression. Evidence from Parkinson’s disease patients undergoing deep brain stimulation has also associated the STN with (successful) stopping behaviour (Mirabella et al., 2012; Ray et al., 2009; Ray et al., 2012; Swann et al., 2011; van den Wildenberg et al., 2021) and demonstrated that bilateral stimulation of this region can improve performance in the SST (Mancini et al., 2018).

Functional imaging research has also been used extensively to elucidate which regions are associated with response inhibition. These images are frequently acquired at 3 Tesla (T) and the BOLD responses interpreted by the use of contrast analyses, subtracting the activity of regions during different conditions. In the SST, these conditions are go (GO), failed stop (FS), and successful stop (SS) trials. Contrasts of interest are often FS > GO, SS > GO, and FS > SS. Cortically, three regions have been consistently implicated in successful inhibition: the right inferior frontal gyrus (rIFG), pre-supplementary motor area (preSMA), and anterior insula (Aron et al., 2014; de Hollander et al., 2017; Isherwood et al., 2023; Miletić et al., 2020; Swick et al., 2011). In the subcortex, functional evidence is relatively inconsistent. Some studies have found an increase in BOLD response in the STN in SS > GO contrasts (Aron and Poldrack, 2006; Coxon et al., 2016; Gaillard et al., 2020; Yoon et al., 2019), but others have failed to replicate this (Bloemendaal et al., 2016; Boehler et al., 2010; Chang et al., 2020; Xu et al., 2015). Moreover, some studies have actually found higher STN, SN, and thalamic activation in failed stop trials, not successful ones (de Hollander et al., 2017; Isherwood et al., 2023; Miletić et al., 2020).

Here, we reprocess and reanalyse five functional SST datasets to shed light on the discrepancies in subcortical BOLD responses. Canonical methods of meta-analysis have the tendency to lose information when compiling multiple sources of data, due to reliance on summary statistics and a lack of raw data accessibility. Taking advantage of the recent surge in open-access data, we aimed to improve upon these methods by using the raw data now available instead of relying on simple summary measures (e.g., MNI coordinates). Though computationally expensive, the gain in power from reanalysing multiple functional datasets without this loss of information is of huge benefit. In addition, using raw data as a starting point for datasets acquired separately allows one to minimize differences in preprocessing and analyses pipelines. We chose datasets that used similar go stimuli (left or right pointing arrows) to maintain as much consistency across the datasets as possible. Stop signals during the SST are generally either of the auditory or visual type; we opted to use both types in this study with the assumption that they rely on the same underlying inhibition network (Ramautar et al., 2006).

The functional network underlying response inhibition in the human brain has been a key research question in the cognitive neurosciences for decades. The basal ganglia specifically have been implicated in broad movement control since the early twentieth century (Wilson, 1912). However, the precise role of these subcortical structures in successful response inhibition is still unclear. Evidence for the role of the basal ganglia in response inhibition comes from a multitude of studies citing significant activation of either the SN, STN, or GPe during successful inhibition trials (Aron, 2007; Aron and Poldrack, 2006; Mallet et al., 2016; Nambu et al., 2002; Zhang and Iwaki, 2019). Here, we re-examined activation patterns in the subcortex across five different datasets, identifying differences in regional activation using both frequentist and Bayesian approaches. Broadly, the frequentist approach found significant differences between most ROIs in FS > GO and FS > SS contrasts, and limited differences in the SS > GO contrast. The Bayesian results were more conservative; while many of the ROIs showed moderate or strong evidence, some with small but significant z-scores were considered only anecdotal by the Bayesian analysis. In our discussion, where the findings between analytical approaches differ, we focus mainly on the more conservative Bayesian analysis.

Here, our multi-study results found limited evidence that the canonical inhibition pathways (the indirect and hyperdirect pathways) are recruited during successful response inhibition in the SST. We expected to find increased activation in the nodes of the indirect pathway (e.g., the preSMA, GPe, STN, SN, GPi, and thalamus; Diesburg and Wessel, 2021) during successful stop compared to go or failed stop trials. We found strong evidence for activation pattern differences in the preSMA, thalamus, and right GPi between the two stop types (failed and successful), and limited evidence, or evidence in favour of the null hypothesis, in the other regions, such as the GPe, STN, and SN. However, we did find recruitment of subcortical nodes (VTA, thalamus, STN, and caudate), as well as preSMA and IFG activation during failed stop trials. We suggest that these results indicate that failing to inhibit one’s action is a larger driver of the utilization of these nodes than action cancellation itself.

These results are in contention to many previous functional magnetic resonance imaging (fMRI) studies of the SST as well as research using other measurement techniques such as local field potential recordings, direct subcortical stimulation, and animal studies, where activation of particularly the STN has consistently been observed (Aron and Poldrack, 2006; Benis et al., 2014; Fischer et al., 2017; Mancini et al., 2018; Wessel et al., 2016). Attempting to reconcile these discrepancies leaves us with a few discussion points. The first is that of the reliability of studying the subcortex in vivo using fMRI. Due to its distance from the MR head coils, proximity of subregions and varying biophysical properties, the subcortex can have limited inter-regional contrast and a low signal-to-noise ratio (Bazin et al., 2020; de Hollander et al., 2017; Isaacs et al., 2018; Isherwood et al., 2021a; Keuken et al., 2018; Miletić et al., 2022). The subcortical regions are also relatively small structures. For example, the STN has a volume of approximately 82 mm³; 3 mm isotropic resolutions therefore provide only three to four voxels for analysis of a relatively complex structure (Alkemade et al., 2020). Attaining sufficient signal in the deep brain for accurate statistical analysis is particularly difficult at lower field strengths (Murphy et al., 2007). Even with ultra-high field MRI and optimized sequences, it is sometimes difficult to approximate the reliability of the signal we receive. Indeed, the mismatch of findings between fMRI studies seemingly examining the same task, begs the question of reliability. While studies using lower field strengths have found it difficult to consistently replicate activation profiles in the SST, higher field strengths have had yielded better success. The subcortical activation profiles of inhibition painted by the three optimized 7T studies (deHollander_7T, Isherwood_7T, and Miletic_7T) are very consistent. We can therefore suggest that fMRI of the subcortex at higher field strengths is at the very least reliable.

A second point of consideration is the haemodynamics of the BOLD response in the subcortex, and the validity of our modelling methods in this context. A fundamental assumption of fMRI is that BOLD activity is influenced by neural activity. Although multiple studies have at least partially confirmed the linearity of the relationship between the HRF and neural activity (Liu et al., 2010; Logothetis et al., 2001), these findings have almost exclusively focused on the cortex (Kim and Ress, 2017; Taylor et al., 2018). Few studies have attempted to characterize the effect of vasculature on the HRF in the deep brain, though differences between the cortex and subcortex have been found (Duvernoy, 1999; Lewis et al., 2018; Tatu et al., 1998; Wall et al., 2009). Subcortical BOLD responses appear to peak earlier than those observed in the cortex and the post-stimulus undershoot normally associated with the canonical HRF is not always seen (Kim et al., 2022). Physiological noise is also more of an issue in the subcortex due to its proximity to large vessels (Singh et al., 2018). The cardiac system produces artefacts due to changes in blood flow and physical pulsation of vessels (Dagli et al., 1999; Krüger and Glover, 2001), while the respiratory system produces artefacts due to arterial pressure changes and effects on B0 (Raj et al., 2001; Wise et al., 2004). Due to all of this, accurately imaging the subcortex requires numerous technical considerations. Moreover, an increase in BOLD signal most likely reflects underlying neural activation, but a lack of observed BOLD signal (especially in the subcortex) in no way suggests a lack of underlying neuronal activation (Lowe et al., 2000). So, while these results do not provide evidence towards involvement of the indirect or hyperdirect pathways in successful response inhibition, they also do not provide direct evidence against it.

As well as attempting to disentangle the pathways associated with inhibitory control specifically, we are also observing pathways related to attention and signal detection. This could explain the lack of subcortical findings for the SS > GO contrasts, but not the abundance of activation seen in the FS > GO contrast. From both the voxel- and ROI-wise analyses, it is clear that the VTA and STN play a role in the mechanisms underlying FS trials. Investigation into the specific role of the VTA and dopaminergic system in response inhibition has led to conflicting results (Aron et al., 2003; Boonstra et al., 2005). Inhibition of the VTA has been seen to increase the number of premature responses in a five-choice serial RT task in rats (Flores-Dourojeanni et al., 2021), the VTA appears to degenerate in Parkinson’s disease (Alberico et al., 2015), and is associated with reward uncertainty in response inhibition tasks (Tennyson et al., 2018). Feedback in the SST is inherent in its design; a failure to stop simultaneously triggers the realization that an error was made, without the need for explicit task feedback. Speculatively, a failure to stop could trigger nodes of the mesolimbic pathway in response to this action error, as the VTA is known to respond to reward prediction errors (Bayer and Neuron, 2005; Eshel et al., 2015; Schultz et al., 1997). For the STN, we know that this region responds to errors (Cavanagh et al., 2014; Siegert et al., 2014), which is likely due to exertion of additional motor inhibition afterwards (Guan and Wessel, 2022). Therefore, one explanation is that the FS > GO contrast reflects an error mechanism. This is perhaps why this is not the first aggregatory study that has found limited activation of basal ganglia regions when specifically looking at successful response inhibition (Hung et al., 2018; Isherwood et al., 2021b; Zhang et al., 2017).

While more direct measurements of neural activity (e.g., LFP recordings) may provide superior temporal resolution, studies that use them are often required to observe electrophysiological responses in clinical populations, due to the invasive nature of the method. How such findings can translate to the neurotypical population continues to be a complex topic. Similarly, much work on the role of cortico-basal-ganglia loop in response inhibition has come from animal studies. While we can acquire data that is excellent in terms of temporal and spatial resolution and number of trials, how well this translates to the general human population is uncertain. Furthermore, while the temporal resolution of fMRI is inherently slower, more efficient methods for acquiring analysing fast fMRI data, show promise for overcoming this apparent drawback of fMRI (Lewis et al., 2016; Polimeni and Lewis, 2021). The ability to extract relevant neuronal information from early phases of the BOLD response may help in identifying activation from overlapping processes in complex tasks such as this. Several studies have established nonlinear properties of the hemodynamic response that have significant implications for fast fMRI (Miller et al., 2001; Vazquez and Noll, 1998). Of course, we cannot rule out that the range of fMRI data analysed here lacks the temporal resolution or sensitivity to observe significant changes in subcortical activation during motor inhibition, engagement, and instantiation of inhibition. Instead, this study picks up on the role of the basal ganglia in error processing, something that more direct methods of measurement have thus far not focused on.

From our literature search, this is the first study to take advantage of an array of unprocessed open-access data. Now that it is becoming increasingly common for researchers to make such detailed data available, it is beneficial for the research field to move on from meta-analytical methods that use only summary measures of activation profiles. Although, it should be noted, that canonical methods of meta-analysis still provide advantages over more processing-intensive methods such as applied here. Firstly, on the scale of five datasets, this methodology was applied and completed relatively quickly, but standard meta-analyses can include tens if not hundreds of studies, a feat that would be difficult to manage for the method described here. Secondly, the sample reported here is somewhat biased, it includes only data that were openly accessible in full, it is likely there are many more studies that would be useful to answer the research question posited here. Simpler methods of meta-analyses, where only coordinates or summary measures are needed to aggregate data, benefit from having access to a much wider range of potential sources of data. What we have been able to do here, even with a limited number of datasets, is process all the data with the same set of criteria. We therefore benefit from sets of extremely well-vetted behavioural and functional data, that can have all aspects of the datasets compared to one another. This allowed us to tightly control aspects of the preprocessing pipeline that can affect later analyses steps, such as distortion correction and smoother kernel sizes.

The consequences of spatial smoothing on statistical analyses are well known and can have huge effects on group or subject-level inferences (Chen and Calhoun, 2018; Mikl et al., 2008). Here, we have shown again the substantial effect smoothing can have on the conclusions drawn from task-specific GLMs. In the absence of a ground truth, we are not able to fully justify the use of either larger or smaller kernels to analyse such data. On the one hand, aberrantly large smoothing kernels could lead to false positives in activation profiles, due to bleeding of observed activation into surrounding tissues. Conversely, too little smoothing could lead to false negatives, missing some true activity in surrounding regions. While we cannot concretely validate either choice, it should be noted that there is lower spatial uncertainty in the subcortex compared to the cortex, due to the lower anatomical variability. False positives from smoothing spatially unmatched signal, are therefore more likely than false negatives. It may be more prudent for studies to use a range of smoothing kernels, to assess the robustness of their fMRI activation profiles. Based on the results of the smoothing comparison and the differences in optimal kernel sizes for each dataset, ROI analyses may offer superior statistical testing to that of voxel-wise methods as you do not introduce a loss of specificity. ROI-wise methods also have the added benefit of not needing to warp images from individual space to common or group templates, which may also introduce a loss of specificity when looking at smaller structures, such as those in the subcortex. However, ROI-wise methods are only as good as the predefined atlases used in the analysis. MRI may benefit from high spatial resolution in comparison to other neuroimaging methods, but there are still subpopulations of nuclei, such as those within the STN, that may have different roles in response inhibition and are not easily distinguishable (Mosher et al., 2021). As already discussed in detail, MRI is of course also disadvantaged by its poor temporal resolution, a dimension that hinders the ability to dissociate different mechanisms occurring during the course of a trial (e.g., attention, detection of salient events; Benis et al., 2016). Methodologies with enhanced temporal resolution, such as electroencephalography, will also benefit from the wave of open-access data and can focus on research questions that MRI currently cannot, including disentangling the mixed cognitive processes underlying response inhibition.

This paper serves as a proof of concept for methods of meta-analysis that allow the unification of largely unprocessed or unreduced datasets and exemplifies the huge opportunities that open-access data sharing can bring to the research field. As more and more datasets are made publicly available, researchers will be able to perform meta-analyses not only on summary data, but datasets with a rich body of parameters and data points. Our results indicate that error processing is likely a large driver of subcortical activity, and that nodes of the indirect and hyperdirect pathways appear to respond to this non-motor inhibition process more than to motor inhibition itself. We do not find evidence for either pathways involvement in successful motor inhibition, which may be a consequence of the overlap of inhibition control, attention, signal detection, and error processing on sub-second timescales in this task. Adaptations of the classical SST are already being deployed and may aid in the disentangling of attention and signal detection in overall response inhibition (Boecker et al., 2013; Bryden and Roesch, 2015).

Data were aggregated from five extant, open-source fMRI datasets. All analysis code can be found at https://osf.io/3h6rc/.

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Author details

1. Scott Isherwood

   Integrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Amsterdam, Netherlands

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   Contributed equally with

   Sarah A Kemp

   For correspondence

   scott@leeclan.net

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2045-9268

2. Sarah A Kemp

   1. Integrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Amsterdam, Netherlands
   2. Sensorimotor Neuroscience and Ageing Research Lab, School of Psychological Sciences, University of Tasmania, Hobart, Australia

   Contribution

   Conceptualization, Data curation, Software, Validation, Methodology, Writing – original draft, Project administration, Writing – review and editing

   Contributed equally with

   Scott Isherwood

   Competing interests

   No competing interests declared

3. Steven Miletić

   1. Integrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Amsterdam, Netherlands
   2. Department of Psychology, Faculty of Social Sciences, Leiden University, Leiden, Netherlands

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7399-2926

4. Niek Stevenson

   Integrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Amsterdam, Netherlands

   Contribution

   Conceptualization, Validation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

5. Pierre-Louis Bazin

   Full brain picture Analytics, Leiden, Netherlands

   Contribution

   Conceptualization, Data curation, Software, Supervision, Validation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

6. Birte Forstmann

   Integrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Amsterdam, Netherlands

   Contribution

   Conceptualization, Supervision, Funding acquisition, Writing – review and editing

   Competing interests

   No competing interests declared

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