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. Behavioural dynamics during the SST are interpreted under the framework of the horse race model ( Logan & Cowan, 1984).
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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). 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. In the SST, participants make a motor response as quickly as possible in response to a go signal. Response inhibition has been behaviourally examined using the stop signal task (SST) for more than four decades. 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).
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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. However, evidence for the authors' conclusions regarding the role of subcortical nodes in stopping is incomplete, due to the limitations of fMRI and a lack of theoretical synthesis. This study is valuable as a well-powered investigation of fMRI measures of stopping. This study aggregates across five fMRI datasets and reports that a network of brain areas previously associated with response inhibition processes, including several in the basal ganglia, are more active on failed stop than successful stop trials. With an increasing number of datasets being shared publicly, researchers will have the ability to conduct meta-analyses on more than just summary data. It demonstrates the significant potential that open-access data sharing can offer to the research community.
This study presents a proof of concept for meta-analytical methods that enable the merging of extensive, unprocessed or unreduced datasets. We also highlight the substantial effect smoothing can have on the conclusions drawn from task-specific GLMs. 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. 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. We provide evidence that the canonical inhibition pathways may not be recruited during successful response inhibition during the stop signal task (SST). This study investigates the functional network underlying response inhibition in the human brain, particularly the role of the basal ganglia in successful response inhibition.
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