A Functional and Structural Investigation of the Human Fronto-Basal Volitional Saccade Network

Almost all cortical areas are connected to the subcortical basal ganglia (BG) through parallel recurrent inhibitory and excitatory loops, exerting volitional control over automatic behavior. As this model is largely based on non-human primate research, we used high resolution functional MRI and diffusion tensor imaging (DTI) to investigate the functional and structural organization of the human (pre)frontal cortico-basal network controlling eye movements. Participants performed saccades in darkness, pro- and antisaccades and observed stimuli during fixation. We observed several bilateral functional subdivisions along the precentral sulcus around the human frontal eye fields (FEF): a medial and lateral zone activating for saccades in darkness, a more fronto-medial zone preferentially active for ipsilateral antisaccades, and a large anterior strip along the precentral sulcus activating for visual stimulus presentation during fixation. The supplementary eye fields (SEF) were identified along the medial wall containing all aforementioned functions. In the striatum, the BG area receiving almost all cortical input, all saccade related activation was observed in the putamen, previously considered a skeletomotor striatal subdivision. Activation elicited by the cue instructing pro or antisaccade trials was clearest in the medial FEF and right putamen. DTI fiber tracking revealed that the subdivisions of the human FEF complex are mainly connected to the putamen, in agreement with the fMRI findings. The present findings demonstrate that the human FEF has functional subdivisions somewhat comparable to non-human primates. However, the connections to and activation in the human striatum preferentially involve the putamen, not the caudate nucleus as is reported for monkeys. This could imply that fronto-striatal projections for the oculomotor system are fundamentally different between humans and monkeys. Alternatively, there could be a bias in published reports of monkey studies favoring the caudate nucleus over the putamen in the search for oculomotor functions

On the Role of the Striatum in Response Inhibition

BACKGROUND: Stopping a manual response requires suppression of the primary motor cortex (M1) and has been linked to activation of the striatum. Here, we test three hypotheses regarding the role of the striatum in stopping: striatum activation during successful stopping may reflect suppression of M1, anticipation of a stop-signal occurring, or a slower response build-up. METHODOLOGY/PRINCIPAL FINDINGS: Twenty-four healthy volunteers underwent functional magnetic resonance imaging (fMRI) while performing a stop-signal paradigm, in which anticipation of stopping was manipulated using a visual cue indicating stop-signal probability, with their right hand. We observed activation of the striatum and deactivation of left M1 during successful versus unsuccessful stopping. In addition, striatum activation was proportional to the degree of left M1 deactivation during successful stopping, implicating the striatum in response suppression. Furthermore, striatum activation increased as a function of stop-signal probability and was to linked to activation in the supplementary motor complex (SMC) and right inferior frontal cortex (rIFC) during successful stopping, suggesting a role in anticipation of stopping. Finally, trial-to-trial variations in response time did not affect striatum activation. CONCLUSIONS/SIGNIFICANCE: The results identify the striatum as a critical node in the neural network associated with stopping motor responses. As striatum activation was related to both suppression of M1 and anticipation of a stop-signal occurring, these findings suggest that the striatum is involved in proactive inhibitory control over M1, most likely in interaction with SMC and rIFC

A consensus guide to capturing the ability to inhibit actions and impulsive behaviors in the stop-signal task.

Response inhibition is essential for navigating everyday life. Its derailment is considered integral to numerous neurological and psychiatric disorders, and more generally, to a wide range of behavioral and health problems. Response-inhibition efficiency furthermore correlates with treatment outcome in some of these conditions. The stop-signal task is an essential tool to determine how quickly response inhibition is implemented. Despite its apparent simplicity, there are many features (ranging from task design to data analysis) that vary across studies in ways that can easily compromise the validity of the obtained results. Our goal is to facilitate a more accurate use of the stop-signal task. To this end, we provide 12 easy-to-implement consensus recommendations and point out the problems that can arise when they are not followed. Furthermore, we provide user-friendly open-source resources intended to inform statistical-power considerations, facilitate the correct implementation of the task, and assist in proper data analysis

Reliable estimation of inhibitory efficiency: To anticipate, choose, or simply react?

Response inhibition is an important executive process studied by clinical and experimental psychologists, neurophysiologists, and cognitive neuroscientists alike. Stop-signal paradigms are popular because they are grounded in a theory that provides methods to estimate the latency of an unobservable process: the stop-signal reaction time (SSRT). Critically, SSRT estimates can be biased by skew of the response time distribution and gradual slowing over the course of the experiment. Here, we present a series of experiments that directly compare three common stop-signal paradigms that differ in the distribution of response times. The results show that the widely used choice response (CR) and simple response (SR) time versions of the stop-signal paradigm are particularly susceptible to skew of the response time distribution and response slowing, and that using the anticipated response (AR) paradigm based on the Slater-Hammel task offers a viable alternative to obtain more reliable SSRT estimates. This article is protected by copyright. All rights reserved.status: publishe

The role of stop-signal probability and expectation in proactive inhibition

The subjective belief of what will happen plays an important role across many cognitive domains, including response inhibition. However, tasks that study inhibition do not distinguish between the processing of objective contextual cues indicating stop-signal probability and the subjective expectation that a stop-signal will or will not occur. Here we investigated the effects of stop-signal probability and the expectation of a stop-signal on proactive inhibition. Twenty participants performed a modified stop-signal anticipation task while being scanned with functional magnetic resonance imaging. At the beginning of each trial, the stop-signal probability was indicated by a cue (0% or > 0%), and participants had to indicate whether they expected a stop-signal to occur (yes/no/don't know). Participants slowed down responding on trials with a > 0% stop-signal probability, but this proactive response slowing was even greater when they expected a stop-signal to occur. Analyses were performed in brain regions previously associated with proactive inhibition. Activation in the striatum, supplementary motor area and left dorsal premotor cortex during the cue period was increased when participants expected a stop-signal to occur. In contrast, activation in the right inferior frontal gyrus and right inferior parietal cortex activity during the stimulus-response period was related to the processing of contextual cues signalling objective stop-signal probability, regardless of expectation. These data show that proactive inhibition depends on both the processing of objective contextual task information and the subjective expectation of stop-signals

The role of stop-signal probability and expectation in proactive inhibition

The subjective belief of what will happen plays an important role across many cognitive domains, including response inhibition. However, tasks that study inhibition do not distinguish between the processing of objective contextual cues indicating stop-signal probability and the subjective expectation that a stop-signal will or will not occur. Here we investigated the effects of stop-signal probability and the expectation of a stop-signal on proactive inhibition. Twenty participants performed a modified stop-signal anticipation task while being scanned with functional magnetic resonance imaging. At the beginning of each trial, the stop-signal probability was indicated by a cue (0% or > 0%), and participants had to indicate whether they expected a stop-signal to occur (yes/no/don't know). Participants slowed down responding on trials with a > 0% stop-signal probability, but this proactive response slowing was even greater when they expected a stop-signal to occur. Analyses were performed in brain regions previously associated with proactive inhibition. Activation in the striatum, supplementary motor area and left dorsal premotor cortex during the cue period was increased when participants expected a stop-signal to occur. In contrast, activation in the right inferior frontal gyrus and right inferior parietal cortex activity during the stimulus-response period was related to the processing of contextual cues signalling objective stop-signal probability, regardless of expectation. These data show that proactive inhibition depends on both the processing of objective contextual task information and the subjective expectation of stop-signals

Capturing the dynamics of response variability in the brain in ADHD

ADHD is characterized by increased intra-individual variability in response times during the performance of cognitive tasks. However, little is known about developmental changes in intra-individual variability, and how these changes relate to cognitive performance. Twenty subjects with ADHD aged 7-24 years and 20 age-matched, typically developing controls participated in an fMRI-scan while they performed a go-no-go task. We fit an ex-Gaussian distribution on the response distribution to objectively separate extremely slow responses, related to lapses of attention, from variability on fast responses. We assessed developmental changes in these intra-individual variability measures, and investigated their relation to no-go performance. Results show that the ex-Gaussian measures were better predictors of no-go performance than traditional measures of reaction time. Furthermore, we found between-group differences in the change in ex-Gaussian parameters with age, and their relation to task performance: subjects with ADHD showed age-related decreases in their variability on fast responses (sigma), but not in lapses of attention (tau), whereas control subjects showed a decrease in both measures of variability. For control subjects, but not subjects with ADHD, this age-related reduction in variability was predictive of task performance. This group difference was reflected in neural activation: for typically developing subjects, the age-related decrease in intra-individual variability on fast responses (sigma) predicted activity in the dorsal anterior cingulate gyrus (dACG), whereas for subjects with ADHD, activity in this region was related to improved no-go performance with age, but not to intra-individual variability. These data show that using more sophisticated measures of intra-individual variability allows the capturing of the dynamics of task performance and associated neural changes not permitted by more traditional measures

Frontostriatal activity and connectivity increase during proactive inhibition across adolescence and early adulthood

During adolescence, functional and structural changes in the brain facilitate the transition from childhood to adulthood. Because the cortex and the striatum mature at different rates, temporary imbalances in the frontostriatal network occur. Here, we investigate the development of the subcortical and cortical components of the frontostriatal network from early adolescence to early adulthood in 60 subjects in a cross-sectional design, using functional MRI and a stop-signal task measuring two forms of inhibitory control: reactive inhibition (outright stopping) and proactive inhibition (anticipation of stopping). During development, reactive inhibition improved: older subjects were faster in reactive inhibition. In the brain, this was paralleled by an increase in motor cortex suppression. The level of proactive inhibition increased, with older subjects slowing down responding more than younger subjects when anticipating a stop-signal. Activation increased in the right striatum, right ventral and dorsal inferior frontal gyrus, and supplementary motor area. Moreover, functional connectivity during proactive inhibition increased between striatum and frontal regions with age. In conclusion, we demonstrate that developmental improvements in proactive inhibition are paralleled by increases in activation and functional connectivity of the frontostriatal network. These data serve as a stepping stone to investigate abnormal development of the frontostriatal network in disorders such as schizophrenia and attention-deficit hyperactivity disorder