Children with ADHD symptoms show deficits in reactive but not proactive inhibition, irrespective of their formal diagnosis.

BACKGROUND: Attenuated inhibitory control is one of the most robust findings in the neuropsychology of attention-deficit/hyperactivity disorder (ADHD). However, it is unclear whether this represents a deficit in outright stopping (reactive inhibition), whether it relates to a deficit in anticipatory response slowing (proactive inhibition), or both. In addition, children with other development disorders, such as autism spectrum disorder (ASD), often have symptoms of inattention, impulsivity, and hyperactivity similar to children with ADHD. These may relate to similar underlying changes in inhibitory processing. METHODS: In this study, we used a modified stop-signal task to dissociate reactive and proactive inhibition. We included not only children with ADHD, but also children primarily diagnosed with an ASD and high parent-rated levels of ADHD symptoms. RESULTS: We replicated the well-documented finding of attenuated reactive inhibition in children with ADHD. In addition, we found a similar deficit in children with ASD and a similar level of ADHD symptoms. In contrast, we found no evidence for deficits in proactive inhibition in either clinical group. CONCLUSIONS: These findings re-emphasize the role of reactive inhibition in children with ADHD and ADHD symptoms. Moreover, our findings stress the importance of a trans-diagnostic approach to the relationship between behavior and neuropsychology.

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.

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.

Common and unique neural networks for proactive and reactive response inhibition revealed by independent component analysis of functional MRI data.

Response inhibition involves proactive and reactive modes. Proactive inhibition is goal-directed, triggered by warning cues, and serves to restrain actions. Reactive inhibition is stimulus-driven, triggered by salient stop-signals, and used to stop actions completely. Functional MRI studies have identified brain regions that activate during proactive and reactive inhibition. It remains unclear how these brain regions operate in functional networks, and whether proactive and reactive inhibition depend on common networks, unique networks, or a combination. To address this we analyzed a large fMRI dataset (N=65) of a stop-signal task designed to measure proactive and reactive inhibition, using independent component analysis (ICA). We found 1) three frontal networks that were associated with both proactive and reactive inhibition, 2) one network in the superior parietal lobe, which also included dorsal premotor cortex and left putamen, that was specifically associated with proactive inhibition, and 3) two right-lateralized frontal and fronto-parietal networks, including the right inferior frontal gyrus and temporoparietal junction as well as a bilateral fronto-temporal network that were uniquely associated with reactive inhibition. Overlap between networks was observed in dorsolateral prefrontal and parietal cortices. Taken together, we offer a new perspective on the neural underpinnings of inhibitory control, by showing that proactive inhibition and reactive inhibition are supported by a group of common and unique networks that appear to integrate and interact in frontoparietal areas.

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.

Transcranial magnetic stimulation and functional MRI reveal cortical and subcortical interactions during stop-signal response inhibition.

Stopping an action requires suppression of the primary motor cortex (M1). Inhibitory control over M1 relies on a network including the right inferior frontal cortex (rIFC) and the supplementary motor complex (SMC), but how these regions interact to exert inhibitory control over M1 is unknown. Specifically, the hierarchical position of the rIFC and SMC with respect to each other, the routes by which these regions control M1, and the causal involvement of these regions in proactive and reactive inhibition remain unclear. We used off-line repetitive TMS to perturb neural activity in the rIFC and SMC followed by fMRI to examine effects on activation in the networks involved in proactive and reactive inhibition, as assessed with a modified stop-signal task. We found repetitive TMS effects on reactive inhibition only. rIFC and SMC stimulation shortened the stop-signal RT (SSRT) and a shorter SSRT was associated with increased M1 deactivation. Furthermore, rIFC and SMC stimulation increased right striatal activation, implicating frontostriatal pathways in reactive inhibition. Finally, rIFC stimulation altered SMC activation, but SMC stimulation did not alter rIFC activation, indicating that rIFC lies upstream from SMC. These findings extend our knowledge about the functional organization of inhibitory control, an important component of executive functioning, showing that rIFC exerts reactive control over M1 via SMC and right striatum.

Expectations and violations: delineating the neural network of proactive inhibitory control.

The ability to stop a prepared response (reactive inhibition) appears to depend on the degree to which stopping is expected (proactive inhibition). Functional MRI studies have shown that activation during proactive and reactive inhibition overlaps, suggesting that the whole neural network for reactive inhibition becomes already activated in anticipation of stopping. However, these studies measured proactive inhibition as the effect of stop-signal probability on activation during go trials. Therefore, activation could reflect expectation of a stop-signal (evoked by the stop-signal probability cue), but also violation of this expectation because stop-signals do not occur on go trials. We addressed this problem, using a stop-signal task in which the stop-signal probability cue and the go-signal were separated in time. Hence, we could separate activation during the cue, reflecting expectation of the stop-signal, from activation during the go-signal, reflecting expectation of the stop-signal or violation of that expectation. During the cue, the striatum, the supplementary motor complex (SMC), and the midbrain activated. During the go-signal, the right inferior parietal cortex (IPC) and the right inferior frontal cortex (IFC) activated. These findings suggest that the neural network previously associated with proactive inhibition can be subdivided into two components. One component, including the striatum, the SMC, and the midbrain, activated during the cue, implicating this network in proactive inhibition. Another component, consisting of the right IPC and the right IFC, activated during the go-signal. Rather than being involved in proactive inhibition, this network appears to be involved in processes associated with violation of expectations.

Reduced functional coupling in the default-mode network during self-referential processing.

Activity within the default-mode network (DMN) is thought to be related to self-referential processing, such as thinking about one's preferences or personality traits. Although the DMN is generally considered to function as a network, evidence is starting to accumulate that suggests that areas of the DMN are each specialized for different subfunctions of self-referential processing. Here, we address the issue of functional specialization by investigating changes in coupling between areas of the DMN during self-referential processing. To this aim, brain activity was assessed during a task in which subjects had to indicate whether a trait adjective described their own personality (self-referential, Self condition), that of another person (other-referential, Other condition), or whether the trait was socially desirable (nonreferential, Control condition). To exclude confounding effects of cardiorespiratory processes on activity and functional coupling, we corrected the fMRI signal for these effects. Activity within areas of the DMN was found to be modulated by self-referential processing. More specifically, during the Self condition compared to the Other and Control condition, activity within the dorsal medial prefrontal cortex, ventral medial prefrontal cortex, and posterior cingulate cortex was increased. Moreover, coupling between areas of the DMN was reduced during the Self condition compared to the Other and Control condition, while coupling between regions of the DMN and regions outside the network was increased. As such, these results provide an indication for functional specialization within the DMN and support the notion that each area of the DMN is involved in different subfunctions of self-referential processing.

Countermanding Perceptual Decision-Making.

We investigated whether a task requiring concurrent perceptual decision-making and response control can be performed concurrently, whether evidence accumulation and response control are accomplished by the same neurons, and whether perceptual decision-making and countermanding can be unified computationally. Based on neural recordings in a prefrontal area of macaque monkeys, we present behavioral, neural, and computational results demonstrating that perceptual decision-making of varying difficulty can be countermanded efficiently, that single prefrontal neurons instantiate both evidence accumulation and response control, and that an interactive race between stochastic GO evidence accumulators for each alternative and a distinct STOP accumulator fits countermanding choice behavior and replicates neural trajectories. Thus, perceptual decision-making and response control, previously regarded as distinct mechanisms, are actually aspects of a common neuro-computational mechanism supporting flexible behavior.

Methylphenidate boosts choices of mental labor over leisure depending on striatal dopamine synthesis capacity.

The cognitive enhancing effects of methylphenidate are well established, but the mechanisms remain unclear. We recently demonstrated that methylphenidate boosts cognitive motivation by enhancing the weight on the benefits of a cognitive task in a manner that depended on striatal dopamine. Here, we considered the complementary hypothesis that methylphenidate might also act by changing the weight on the opportunity cost of a cognitive task, that is, the cost of foregoing alternative opportunity. To this end, 50 healthy participants (25 women) completed a novel cognitive effort-discounting task that required choices between task and leisure. They were tested on methylphenidate, placebo, as well as the selective D2-receptor agent sulpiride, the latter to strengthen inference about dopamine receptor selectivity of methylphenidate's effects. Furthermore, they also underwent an [18F]DOPA PET scan to quantify striatal dopamine synthesis capacity. Methylphenidate boosted choices of cognitive effort over leisure across the group, and this effect was greatest in participants with more striatal dopamine synthesis capacity. The effects of sulpiride did not reach significance. This study strengthens the motivational account of methylphenidate's effects on cognition, and suggests that methylphenidate reduces the cost of mental labor by increasing striatal dopamine.

Cardiorespiratory effects on default-mode network activity as measured with fMRI.

The default-mode network (DMN) consists of areas showing more activation during rest than during a task. Several authors propose some form of cognitive processing to underlie BOLD signal changes in the DMN as activity within the network is modulated by the level of effort required by the task and is positively correlated with self-referential processing. Alternatively, BOLD signal changes within the DMN may be caused by cardiorespiratory processes (CR) affecting BOLD signal measurements independent of neuronal activity. The goal of this study is to investigate whether BOLD signal changes within the DMN can be explained by CR effects. To this aim, brain activity, heartbeat, and respiration are measured during resting-state and while subjects perform a cognitive task with a high- and low-demand condition. To correct for CR effects we used RETROICOR (Glover et al., [2000]: Magn Reson Med 44:162-167) in combination with additive linear modeling of changes due to respiration volume, heart rate and heart rate variability. CR effects were present within the frequency-range of the DMN and were located in areas of the DMN, but equally so in other areas. After removal of CR effects, deactivation and resting-state connectivity between the areas of the DMN remained significant. In addition, DMN deactivation was still modulated by task demand. The same CR correction method did remove activation in task-related areas. We take these results to indicate that the BOLD signal within the DMN cannot be explained by CR effects alone and is possibly related to some form of cognitive neuronal processing.

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.

Within-subject variation in BOLD-fMRI signal changes across repeated measurements: quantification and implications for sample size.

Functional magnetic resonance imaging (fMRI) can be used to detect experimental effects on brain activity across measurements. The success of such studies depends on the size of the experimental effect, the reliability of the measurements, and the number of subjects. Here, we report on the stability of fMRI measurements and provide sample size estimations needed for repeated measurement studies. Stability was quantified in terms of the within-subject standard deviation (sigma(w)) of BOLD signal changes across measurements. In contrast to correlation measures of stability, this statistic does not depend on the between-subjects variance in the sampled group. Sample sizes required for repeated measurements of the same subjects were calculated using this sigma(w). Ten healthy subjects performed a motor task on three occasions, separated by one week, while being scanned. In order to exclude training effects on fMRI stability, all subjects were trained extensively on the task. Task performance, spatial activation pattern, and group-wise BOLD signal changes were highly stable over sessions. In contrast, we found substantial fluctuations (up to half the size of the group mean activation level) in individual activation levels, both in ROIs and in voxels. Given this large degree of instability over sessions, and the fact that the amount of within-subject variation plays a crucial role in determining the success of an fMRI study with repeated measurements, improving stability is essential. In order to guide future studies, sample sizes are provided for a range of experimental effects and levels of stability. Obtaining estimates of these latter two variables is essential for selecting an appropriate number of subjects.

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.

Working memory and default mode network abnormalities in unaffected siblings of schizophrenia patients.

BACKGROUND: Impaired working memory (WM) is a hallmark of schizophrenia. In addition to classical WM regions such as the dorsolateral prefrontal cortex (DLPFC) and the striatum, dysfunctions in the default-mode network (DMN) contribute to these WM deficits. Unaffected siblings of patients also show WM impairments. However, the nature of the functional deficits underlying these impairments is unclear, mainly because of impaired performance confounding neuroimaging results. METHODS: Here, we investigated WM and DMN activity in 23 unaffected siblings of schizophrenia patients and 24 healthy volunteers using fMRI and a Sternberg WM task. WM load was determined prior to scanning to ensure 90% accuracy for all subjects. RESULTS: Siblings showed hyperactivation during the encoding phase of WM in the right medial prefrontal cortex (MPFC) which is the anterior part of the DMN. No differences were found during the maintenance phase. During the retrieval phase, siblings showed hyperactivation in WM regions: DLPFC, inferior parietal cortex and the striatum. Siblings who showed hyperactivity in the MPFC during encoding showed DLPFC and striatum hyperactivation during retrieval. CONCLUSIONS: Our finding of hyperactivation in WM and DMN areas indicates that siblings fail to adequately inhibit DMN activity during demanding cognitive tasks and subsequently hyperactivate WM areas. This failure may reflect dopamine hyperactivity in the striatum which prevents adequate DMN suppression needed for effective WM. This study provides support for the notion that aberrant WM and DMN activation patterns may represent candidate endophenotypes for schizophrenia.

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.

Reduced proactive inhibition in schizophrenia is related to corticostriatal dysfunction and poor working memory.

BACKGROUND: Inhibitory control is central to executive functioning and appears deficient in schizophrenia. However, it is unclear how inhibitory control is affected, what the underlying neural mechanisms are, whether these deficits are related to the illness itself or to increased risk for the illness, and whether there is a relation to impairments in other executive functions. METHODS: We used functional magnetic resonance imaging to investigate two forms of inhibitory control: proactive inhibition (anticipation of stopping) and reactive inhibition (outright stopping). Twenty-four schizophrenia patients, 24 unaffected siblings, and 24 healthy control subjects performed a modified version of the stop-signal paradigm. To assess the relation between performance on inhibitory control and other executive functions, we correlated inhibitory control indices with working memory span. RESULTS: Compared with control subjects, proactive inhibition was reduced in patients and siblings. Reactive inhibition was unaffected. Reduced proactive inhibition was associated with a failure to activate the right striatum, the right inferior frontal cortex, and the left and right temporoparietal junction. Activation during reactive inhibition was unaffected. Those patients with the least proactive inhibition also showed the shortest working memory span. CONCLUSIONS: These results suggest that schizophrenia is associated with reduced proactive inhibition, probably resulting from corticostriatal dysfunction. This deficit is related to an increased risk for schizophrenia and likely reflects a general executive function deficit rather than a specific inhibitory control impairment.

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.