Measurements of response inhibition components of reactive inhibition and proactive inhibition within the stop-signal paradigm have been of particular interest to researchers since the 1980s. While frequentist nonparametric and Bayesian parametric methods have been proposed to precisely estimate the entire distribution of reactive inhibition, quantified by stop signal reaction times (SSRT), there is no method yet in the stop signal task literature to precisely estimate the entire distribution of proactive inhibition. We identify the proactive inhibition as the difference of go reaction times for go trials following stop trials versus those following go trials and introduce an Asymmetric Laplace Gaussian (ALG) model to describe its distribution. The proposed method is based on two assumptions of independent trial type (go/stop) reaction times and Ex-Gaussian (ExG) models. Results indicated that the four parametric ALG model uniquely describes the proactive inhibition distribution and its key shape features, and its hazard function is monotonically increasing, as are its three parametric ExG components. In conclusion, the four parametric ALG model can be used for both response inhibition components and its parameters and descriptive and shape statistics can be used to classify both components in a spectrum of clinical conditions.

BACKGROUND:Altered brain activity that has been observed in attention deficit hyperactivity disorder (ADHD) while performing cognitive control tasks like the stop signal task (SST) has generally been interpreted as reflecting either weak (under-active) or compensatory (over-active) versions of the same functions as in healthy controls. If so, then regional activities that correlate with the efficiency of inhibitory control (i.e. stop signal reaction time, SSRT) in healthy subjects should also correlate with SSRT in ADHD. Here we test the alternate hypothesis that BOLD (blood-oxygen-level-dependent) differences might instead reflect the redirection of neural processing resources normally used for task-directed inhibitory control, towards actively managing symptomatic behaviour. If so, then activities that correlate with SSRT in TD should instead correlate with inattentive and hyperactive symptoms in ADHD.METHODS:We used fMRI (functional magnetic resonance imaging) in 14 typically developing (TD) and 14 ADHD adolescents performing the SST, and in a replication sample of 14 healthy adults. First, we identified significant group BOLD differences during all phases of activity in the SST (i.e. warning, response, reactive inhibition, error detection and post-error slowing). Next, we correlated these phases of activity with SSRT in TD and with SSRT, inattentive and hyperactive symptom scores in ADHD. We then identified whole brain significant correlations in regions of significant group difference in activity.RESULTS:Only three regions of significant group difference were correlated with SSRT in TD and replication groups (left and right inferior frontal gyri (IFG) during error detection and hypothalamus during post-error slowing). Consistent with regions of altered activity managing symptomatic behaviour instead of task-directed behaviour, left IFG correlated with greater inattentive score, right IFG correlated with lower hyperactive score and hypothalamus correlated with greater inattentive score and oppositely correlated with SSRT compared to TD.CONCLUSIONS:Stimuli that elicit task-directed integration of neural processing in healthy subjects instead appear to be directing integrated function towards managing symptomatic behaviour in ADHD. The ability of the current approach to determine whether altered neural activities reflect comparable functions in ADHD and control groups has broad implications for the development and monitoring of therapeutic interventions.

Adjusting speed to maintain fast and accurate performance is critical to goal-directed behavior. This study examined development of response time adjustments in the stop signal task in 13,709 individuals aged 6–17 years (49.0% Caucasian) across four trial types: correct and incorrect go, successful (stop-inhibit), and failed (stop-respond) trials. People sped more after correct than incorrect go responses and slowed more after failed than successful stop trials. Greater slowing after stop-respond but less slowing after stop-inhibit trials was associated with better response inhibition. Response time adjustments were evident in children as young as age 6, developed throughout childhood, and plateaued by age 10. Results were consistent with the predictions of the error detection and shifting goal priority hypotheses for adjustments.

Patients with Attention Deficit/Hyperactivity Disorder (ADHD) often demonstrate abnormal brain functioning during the stop signal task, although previous work has typically used small samples thereby limiting conclusions.

ADHD is associated with altered dopamine regulated reinforcement learning on prediction errors. Despite evidence of categorically altered error processing in ADHD, neuroimaging advances have largely investigated models of normal reinforcement learning in greater detail. Further, although reinforcement leaning critically relies on ventral striatum exerting error magnitude related thresholding influences on substantia nigra (SN) and dorsal striatum, these thresholding influences have never been identified with neuroimaging. To identify such thresholding influences, we propose that error magnitude related activities must first be separated from opposite activities in overlapping neural regions during error detection. Here we separate error detection from magnitude related adjustment (post-error slowing) during inhibition errors in the stop signal task in typically developing (TD) and ADHD adolescents using fMRI. In TD, we predicted that: 1) deactivation of dorsal striatum on error detection interrupts ongoing processing, and should be proportional to right frontoparietal response phase activity that has been observed in the SST; 2) deactivation of ventral striatum on post-error slowing exerts thresholding influences on, and should be proportional to activity in dorsal striatum. In ADHD, we predicted that ventral striatum would instead correlate with heightened amygdala responses to errors. We found deactivation of dorsal striatum on error detection correlated with response-phase activity in both groups. In TD, post-error slowing deactivation of ventral striatum correlated with activation of dorsal striatum. In ADHD, ventral striatum correlated with heightened amygdala activity. Further, heightened activities in locus coeruleus (norepinephrine), raphe nucleus (serotonin) and medial septal nuclei (acetylcholine), which all compete for control of DA, and are altered in ADHD, exhibited altered correlations with SN. All correlations in TD were replicated in healthy adults. Results in TD are consistent with dopamine regulated reinforcement learning on post-error slowing. In ADHD, results are consistent with heightened activities in the amygdala and non-dopaminergic neurotransmitter nuclei preventing reinforcement learning.

Adjusting speed to maintain fast and accurate performance is critical to goal-directed behavior. This study examined development of response time adjustments in the stop signal task in 13,709 individuals aged 6-17 years (49.0% Caucasian) across four trial types: correct and incorrect go, successful (stop-inhibit), and failed (stop-respond) trials. People sped more after correct than incorrect go responses and slowed more after failed than successful stop trials. Greater slowing after stop-respond but less slowing after stop-inhibit trials was associated with better response inhibition. Response time adjustments were evident in children as young as age 6, developed throughout childhood, and plateaued by age 10. Results were consistent with the predictions of the error detection and shifting goal priority hypotheses for adjustments.

Often we must balance being prepared to act quickly with being prepared to suddenly stop. The stop signal task (SST) is widely used to study inhibitory control, and provides a measure of the speed of the stop process that is robust to changes in subjects' response strategy. Previous studies have shown that preparation affects inhibition. We used fMRI to separate activity that occurs after a brief (500 ms) warning stimulus (warning-phase) from activity that occurs during responses that follow (response-phase). Both of these phases could contribute to the preparedness to stop because they both precede stop signals. Warning stimuli activated posterior networks that signal the need for top-down control, whereas response phases engaged prefrontal and subcortical networks that implement top-down control. Regression analyses revealed that both of these phases affect inhibitory control in different ways. Warning-phase activity in the cerebellum and posterior cingulate predicted stop latency and accuracy, respectively. By contrast, response-phase activity in fronto-temporal areas and left striatum predicted go speed and stop accuracy, in pre-supplementary motor area affected stop accuracy, and in right striatum predicted stop latency and accuracy. The ability to separate hidden contributions to inhibitory control during warning-phases from those during response-phases can aid in the study of models of preparation and inhibitory control, and of disorders marked by poor top-down control.

Background: Deficient response inhibition in situations involving a trade-off between response execution and response stopping is a hallmark of attention deficit hyperactive disorder (ADHD). There are two key components of response inhibition; reactive inhibition where one attempts to cancel an ongoing response and prospective inhibition is when one withholds a response pending a signal to stop. Prospective inhibition comes into play prior to the presentation of the stop signal and reactive inhibition follows the presentation of a signal to stop a particular action. The aim of this study is to investigate the neural activity evoked by prospective and reactive inhibition in adolescents with and without ADHD. Methods: Twelve adolescents with ADHD and 12 age-matched healthy controls (age range 9-18) were imaged while performing the stop signal task (SST). Results: Reactive inhibition activated right inferior frontal gyrus (IFG) in both groups. ADHD subjects activated IFG bilaterally. In controls, prospective inhibition invoked preactivation of the same part of right IFG that activated during reactive inhibition. In ADHD subjects, prospective inhibition was associated with deactivation in this region. Controls also deactivated the medial prefrontal cortex (MPFC) during prospective inhibition, whereas ADHD subjects activated the same area. Discussion: This pattern of activity changes in the same structures, but in opposite directions, was also evident across all phases of the task in various task-specific areas like the superior and middle temporal gyrus and other frontal areas. Conclusion: Differences between ADHD and control participants in task-specific and default mode structures (IFG and MPFC) were evident during prospective, but not during reactive inhibition.

Previous error detection research has focused on error processing functions in the anterior cingulate cortex or on putative reinforcement learning roles of midbrain dopamine pathways. We studied error detection in 14 healthy adult volunteers using a novel fMRI design in the stop signal task (SST), a task which invokes numerous errors in performance and frequent instances of post-error slowing. The imaging design accommodated variability immediately before errors (handedness of response) and immediately after (degree of post-error slowing) using distinct within-trial regressors. This approach revealed a whole-brain view of error detection in a reinforcement-learning pathway. Error detection deactivated the midbrain in the vicinity of dorsal substantia nigra where dopamine neurons originate, and the primary targets of dopamine neurons: dorsal striatum and ventral anterior cingulate. Error detection also deactivated posterior hippocampus, which is highly sensitive to long-term synaptic plasticity effects of dopamine. Errors that led to slowed responses deactivated structures in the reciprocal pathway that are known to exert control over dopamine output, and which have been shown to encode error magnitude: ventral midbrain, ventral striatum, and caudal orbitofrontal cortex. Consistent with the role of these structures in modulating dopamine output, post-error slowing also increased activities in the same structures that deactivated on error detection. These results are consistent with the view that errors deactivate structures that receive input from dopamine neurons, followed by deactivations related to requisite behavioral adjustments in structures that exert control over dopamine output.

We examined the neural substrate of motor response inhibition and performance monitoring in the stop signal task (SST) using event-related functional magnetic resonance imaging (fMRI). The SST involves a go task and the occasional requirement to stop the go response. We posit that both the go and the stop phases of the SST involve components of inhibition and performance monitoring. The goal of this study was to determine whether inhibition and performance monitoring during go and stop phases of the task activated different networks. We isolated go-phase activities underlying response withholding, monitoring, and sensorimotor processing and contrasted these with successful inhibition to identify the substrate of response inhibition. Error detection activity was isolated using trials in which a stop signal appeared but the response was executed. These trials were modeled as a hand-specific go trial followed by error processing. Cognitive go-phase processes included response withholding and monitoring and activated right prefrontal and midline networks. Response withdrawal additionally activated right inferior frontal gyrus and basal ganglia (caudate). Error detection invoked by failed inhibition activated dorsal anterior cingulate cortex (dACC) and right middle frontal Brodmann's area 9. Our results confirm that there are distinct aspects of inhibition and performance monitoring functions which come into play at various phases within a given trial of the SST, and that these are separable using fMRI.