Abstract This study investigates the functional network underlying response inhibition in the human brain, particularly the role of the basal ganglia in successful response inhibition. We provide evidence that the canonical inhibition pathways may not be recruited during successful response inhibition during the stop signal task (SST). Instead, subcortical nodes including the substantia nigra, subthalamic nucleus, thalamus, and ventral tegmental area are more likely to be activated during failed stop trials, suggesting that successful inhibition does not rely on the recruitment of these nodes. The findings challenge previous functional magnetic resonance imaging (fMRI) studies of the SST and suggest the need to ascribe a separate function to these networks. We also highlight the substantial effect smoothing can have on the conclusions drawn from task-specific GLMs. This study presents a proof of concept for meta-analytical methods that enable the merging of extensive, unprocessed or unreduced datasets. It demonstrates the significant potential that open-access data sharing can offer to the research community. With an increasing number of datasets being shared publicly, researchers will have the ability to conduct meta-analyses on more than just summary data.

<p>This study investigates the functional network underlying response inhibition in the human brain, particularly the role of the basal ganglia in successful response inhibition. We provide evidence that the canonical inhibition pathways may not be recruited during successful response inhibition during the stop signal task (SST). Instead, subcortical nodes including the substantia nigra, subthalamic nucleus, thalamus, and ventral tegmental area are more likely to be activated during failed stop trials, suggesting that successful inhibition does not rely on the recruitment of these nodes. The findings challenge previous functional magnetic resonance imaging (fMRI) studies of the SST and suggest the need to ascribe a separate function to these networks. We also highlight the substantial effect smoothing can have on the conclusions drawn from task-specific GLMs. This study presents a proof of concept for meta-analytical methods that enable the merging of extensive, unprocessed or unreduced datasets. It demonstrates the significant potential that open-access data sharing can offer to the research community. With an increasing number of datasets being shared publicly, researchers will have the ability to conduct meta-analyses on more than just summary data.</p>

The canonical stopping network: Revisiting the role of the subcortex in response inhibition