Bram B. Zandbelt

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.

Test-retest reliability of fMRI activation during prosaccades and antisaccades

Various studies have investigated reproducibility of fMRI results. Whereas group results can be highly reproducible, individual activity maps tend to vary across sessions. Individual reliability is of importance for the application of fMRI in endophenotype research, where brain activity is linked to genetic polymorphisms. In this study, the test-retest reliability of activation maps during the antisaccade paradigm was assessed for individual and group results. Functional MRI images were acquired during two sessions of prosaccades and antisaccades in twelve healthy subjects using an event-related fMRI design. Reliability was assessed for both individual and group-wise results. In addition, the reliability of differences between subjects was established in predefined regions of interest. The reliability of group activation maps was high for prosaccades and antisaccades, but only moderate for antisaccades vs. prosaccades, probably as a result of low statistical power of individual results. Reproducibility of individual subject maps was highly variable, indicating that reliable results can be obtained in some but not all subjects. Reliability of individual activity maps was largely explained by individual differences in the global temporal signal to noise ratio (SNR). As the global SNR was stable over sessions, it explained a large portion of the differences between subjects in regional brain activation. A low SNR in some subjects may be dealt with either by improving the statistical sensitivity of the fMRI procedure or by subject exclusion. Differences in the global SNR between subjects should be addressed before using regional brain activation as phenotype in genetic studies.

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. Hum Brain Mapp 34:2015–2024, 2013.

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. Hum Brain Mapp, 2009.

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.

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.

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.

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 ones 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. Hum Brain Mapp, 2010.

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. Hum Brain Mapp 35:4415–4427, 2014.

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.