Sara Jahfari

Function and Structure of the Right Inferior Frontal Cortex Predict Individual Differences in Response Inhibition: A Model-Based Approach

The ability to suppress ones impulses and actions constitutes a fundamental mechanism of cognitive control, thought to be subserved by the right inferior frontal cortex (rIFC). The neural bases of more selective inhibitory control when selecting between two actions have thus far remained articulated with less precision. Selective inhibition can be explored in detail by extracting parameters from response time (RT) distributions as derived from performance in the Simon task. Individual differences in RT distribution parameters not only can be used to probe the efficiency and temporal dynamics of selective response inhibition, but also allow a more detailed analysis of functional neuroimaging data. Such model-based analyses, which capitalize on individual differences, have demonstrated that selective response inhibition is subserved by the rIFC. The aim of the present study was to specify the relationship between model parameters of response inhibition and their functional and structural underpinnings in the brain. Functional magnetic resonance imaging (fMRI) data were obtained from healthy participants while performing a Simon task in which irrelevant information can activate incorrect responses that should be selectively inhibited in favor of selecting the correct response. In addition, structural data on the density of coherency of white matter tracts were obtained using diffusion tensor imaging (DTI). The analyses aimed at quantifying the extent to which RT distribution measures of response inhibition are associated with individual differences in both rIFC function and structure. The results revealed a strong correlation between the model parameters and both fMRI and DTI characteristics of the rIFC. In general, our results reveal that individual differences in inhibition are accompanied by differences in both brain function and structure.

Effective connectivity reveals important roles for both the hyperdirect (fronto-subthalamic) and the indirect (fronto-striatal-pallidal) fronto-basal ganglia pathways during response inhibition.

Fronto-basal ganglia pathways play a crucial role in voluntary action control, including the ability to inhibit motor responses. Response inhibition might be mediated via a fast hyperdirect pathway connecting the right inferior frontal gyrus (rIFG) and the presupplementary motor area (preSMA) with the subthalamic nucleus or, alternatively, via the indirect pathway between the cortex and caudate. To test the relative contribution of these two pathways to inhibitory action control, we applied an innovative quantification method for effective brain connectivity. Functional magnetic resonance imaging data were collected from 20 human participants performing a Simon interference task with an occasional stop signal. A single right-lateralized model involving both the hyperdirect and indirect pathways best explained the pattern of brain activation on stop trials. Notably, the overall connection strength of this combined model was highest on successfully inhibited trials. Inspection of the relationship between behavior and connection values revealed that fast inhibitors showed increased connectivity between rIFG and right caudate (rCaudate), whereas slow inhibitors were associated with increased connectivity between preSMA and rCaudate. In compliance, connection strengths from the rIFG and preSMA into the rCaudate were correlated negatively. If participants failed to stop, the magnitude of experienced interference (Simon effect), but not stopping latency, was predictive for the hyperdirect–indirect model connections. Together, the present results suggest that both the hyperdirect and indirect pathways act together to implement response inhibition, whereas the relationship between performance control and the fronto-basal ganglia connections points toward a top-down mechanism that underlies voluntary action control.

Responding with restraint: What are the neurocognitive mechanisms?

An important aspect of cognitive control is the ability to respond with restraint. Here, we modeled this experimentally by measuring the degree of response slowing that occurs when people respond to an imperative stimulus in a context where they might suddenly need to stop the initiated response compared with a context in which they do not need to stop. We refer to the RT slowing that occurs as the “response delay effect.” We conjectured that this response delay effect could relate to one or more neurocognitive mechanism(s): partial response suppression (i.e., “active braking”), prolonged decision time, and slower response facilitation. These accounts make different predictions about motor system excitability and brain activation. To test which neurocognitive mechanisms underlie the response delay effect, we performed two studies with TMS and we reanalyzed fMRI data. The results suggest that the response delay effect is at least partly explained by active braking, possibly involving a mechanism that is similar to that used to stop responses completely. These results further our understanding of how people respond with restraint by pointing to proactive recruitment of a neurocognitive mechanism heretofore associated with outright stopping.

Cortico-subthalamic white matter tract strength predicts interindividual efficacy in stopping a motor response

The subthalamic nucleus (STN) is a small but vitally important structure in the basal ganglia. Because of its small volume, and its localization in the basal ganglia, the STN can best be visualized using ultra-high resolution 7 Tesla (T) magnetic resonance imaging (MRI). In the present study, first we individually segmented 7 T MRI STN masks to generate atlas probability maps. Secondly, the individually segmented STN masks and the probability maps were used to derive cortico-subthalamic white matter tract strength. Tract strength measures were then taken to test two functional STN hypotheses which account for the efficiency in stopping a motor response: the right inferior fronto-subthalamic (rIFC-STN) hypothesis and the posterior medial frontal cortex-subthalamic (pMFC-STN) hypothesis. Results of two independent experiments show that increased white matter tract strength between the pMFC and STN results in better stopping behaviour.

How preparation changes the need for top–down control of the basal ganglia when inhibiting premature actions

Goal-oriented signals from the prefrontal cortex gate the selection of appropriate actions in the basal ganglia. Key nodes within this fronto-basal ganglia action regulation network are increasingly engaged when one anticipates the need to inhibit and override planned actions. Here, we ask how the advance preparation of action plans modulates the need for fronto-subcortical control when a planned action needs to be withdrawn. Functional magnetic resonance imaging data were collected while human participants performed a stop task with cues indicating the likelihood of a stop signal being sounded. Mathematical modeling of go trial responses suggested that participants attained a more cautious response strategy when the probability of a stop signal increased. Effective connectivity analysis indicated that, even in the absence of stop signals, the proactive engagement of the full control network is tailored to the likelihood of stop trial occurrence. Importantly, during actual stop trials, the strength of fronto-subcortical projections was stronger when stopping had to be engaged reactively compared with when it was proactively prepared in advance. These findings suggest that fronto-basal ganglia control is strongest in an unpredictable environment, where the prefrontal cortex plays an important role in the optimization of reactive control. Importantly, these results further indicate that the advance preparation of action plans reduces the need for reactive fronto-basal ganglia communication to gate voluntary actions.

Individual Differences in Heart Rate Variability Predict the Degree of Slowing during Response Inhibition and Initiation in the Presence of Emotional Stimuli.

Response inhibition is a hallmark of executive control and crucial to support flexible behavior in a constantly changing environment. Recently, it has been shown that response inhibition is influenced by the presentation of emotional stimuli (Verbruggen and De Houwer, 2007). Healthy individuals typically differ in the degree to which they are able to regulate their emotional state, but it remains unknown whether individual differences in emotion regulation (ER) may alter the interplay between emotion and response inhibition. Here we address this issue by testing healthy volunteers who were equally divided in groups with high and low heart rate variability (HRV) during rest, a physiological measure that serves as proxy of ER. Both groups performed an emotional stop-signal task, in which negative high arousing pictures served as negative emotional stimuli and neutral low arousing pictures served as neutral non-emotional stimuli. We found that individuals with high HRV activated and inhibited their responses faster compared to individuals with low HRV, but only in the presence of negative stimuli. No group differences emerged for the neutral stimuli. Thus, individuals with low HRV are more susceptible to the adverse effects of negative emotion on response initiation and inhibition. The present research corroborates the idea that the presentation of emotional stimuli may interfere with inhibition and it also adds to previous research by demonstrating that the aforementioned relationship varies for individuals differing in HRV. We suggest that focusing on individual differences in HRV and its associative ER may shed more light on the dynamic interplay between emotion and cognition.

Visual information shapes the dynamics of corticobasal ganglia pathways during response selection and inhibition

Action selection often requires the transformation of visual information into motor plans. Preventing premature responses may entail the suppression of visual input and/or of prepared muscle activity. This study examined how the quality of visual information affects frontobasal ganglia (BG) routes associated with response selection and inhibition. Human fMRI data were collected from a stop task with visually degraded or intact face stimuli. During go trials, degraded spatial frequency information reduced the speed of information accumulation and response cautiousness. Effective connectivity analysis of the fMRI data showed action selection to emerge through the classic direct and indirect BG pathways, with inputs deriving form both prefrontal and visual regions. When stimuli were degraded, visual and prefrontal regions processing the stimulus information increased connectivity strengths toward BG, whereas regions evaluating visual scene content or response strategies reduced connectivity toward BG. Response inhibition during stop trials recruited the indirect and hyperdirect BG pathways, with input from visual and prefrontal regions. Importantly, when stimuli were nondegraded and processed fast, the optimal stop model contained additional connections from prefrontal to visual cortex. Individual differences analysis revealed that stronger prefrontal-to-visual connectivity covaried with faster inhibition times. Therefore, prefrontal-to-visual cortex connections appear to suppress the fast flow of visual input for the go task, such that the inhibition process can finish before the selection process. These results indicate response selection and inhibition within the BG to emerge through the interplay of top–down adjustments from prefrontal and bottom–up input from sensory cortex.

Making Large-Scale Networks from fMRI Data

Pairwise correlations are currently a popular way to estimate a large-scale network (> 1000 nodes) from functional magnetic resonance imaging data. However, this approach generally results in a poor representation of the true underlying network. The reason is that pairwise correlations cannot distinguish between direct and indirect connectivity. As a result, pairwise correlation networks can lead to fallacious conclusions; for example, one may conclude that a network is a small-world when it is not. In a simulation study and an application to resting-state fMRI data, we compare the performance of pairwise correlations in large-scale networks (2000 nodes) against three other methods that are designed to filter out indirect connections. Recovery methods are evaluated in four simulated network topologies (small world or not, scale-free or not) in scenarios where the number of observations is very small compared to the number of nodes. Simulations clearly show that pairwise correlation networks are fragmented into separate unconnected components with excessive connectedness within components. This often leads to erroneous estimates of network metrics, like small-world structures or low betweenness centrality, and produces too many low-degree nodes. We conclude that using partial correlations, informed by a sparseness penalty, results in more accurate networks and corresponding metrics than pairwise correlation networks. However, even with these methods, the presence of hubs in the generating network can be problematic if the number of observations is too small. Additionally, we show for resting-state fMRI that partial correlations are more robust than correlations to different parcellation sets and to different lengths of time-series.

Spatial frequency information modulates response inhibition and decision-making processes.

We interact with the world through the assessment of available, but sometimes imperfect, sensory information. However, little is known about how variance in the quality of sensory information affects the regulation of controlled actions. In a series of three experiments, comprising a total of seven behavioral studies, we examined how different types of spatial frequency information affect underlying processes of response inhibition and selection. Participants underwent a stop-signal task, a two choice speed/accuracy balance experiment, and a variant of both these tasks where prior information was given about the nature of stimuli. In all experiments, stimuli were either intact, or contained only high-, or low- spatial frequencies. Overall, drift diffusion model analysis showed a decreased rate of information processing when spatial frequencies were removed, whereas the criterion for information accumulation was lowered. When spatial frequency information was intact, the cost of response inhibition increased (longer SSRT), while a correct response was produced faster (shorter reaction times) and with more certainty (decreased errors). When we manipulated the motivation to respond with a deadline (i.e., be fast or accurate), removal of spatial frequency information slowed response times only when instructions emphasized accuracy. However, the slowing of response times did not improve error rates, when compared to fast instruction trials. These behavioral studies suggest that the removal of spatial frequency information differentially affects the speed of response initiation, inhibition, and the efficiency to balance fast or accurate responses. More generally, the present results indicate a task-independent influence of basic sensory information on strategic adjustments in action control.

A focused information criterion for graphical models in fMRI connectivity with high-dimensional data

Connectivity in the brain is the most promising approach to explain human behavior. Here we develop a focused information criterion for graphical models to determine brain connectivity tailored to specific research questions. All efforts are concentrated on high-dimensional settings where the number of nodes in the graph is larger than the number of samples. The graphical models may include autoregressive times series components, they can relate graphs from different subjects, or pool data via random effects. The proposed method selects a graph with a small estimated mean squared error for a user-specified focus. The performance of the proposed method is assessed on simulated datasets and on a resting state functional magnetic resonance imaging (fMRI) dataset where often the number of nodes in the estimated graph is equal to, or larger than the number of samples.