Using subjective expectations to model the neural underpinnings of proactive inhibition

Lateralization of the cerebral network of inhibition in children before and after cognitive training

Inhibitory control (IC) plays a critical role in cognitive and socio-emotional development. IC relies on a lateralized cortico-subcortical brain network including the inferior frontal cortex, anterior parts of insula, anterior cingulate cortex, caudate nucleus and putamen. Brain asymmetries play a critical role for IC efficiency. In parallel to age-related changes, IC can be improved following training. The aim of this study was to (1) assess the lateralization of IC network in children (N = 60, 9-10 y.o.) and (2) examine possible changes in neural asymmetry of this network from anatomical (structural MRI) and functional (resting-state fMRI) levels after 5-week computerized IC vs. active control (AC) training. We observed that IC training, but not AC training, led to a leftward lateralization of the putamen anatomy, similarly to what is observed in adults, supporting that training could accelerate the maturation of this structure.

Self-regulation in the pre-adolescent brain

Self-regulation refers to the ability to monitor and modulate emotions, behavior, and cognition, which in turn allows us to achieve goals and adapt to ever changing circumstances. This trait develops from early infancy well into adulthood, and features both low-level executive functions such as reactive inhibition, as well as higher level executive functions such as proactive inhibition. Development of self-regulation is linked to brain maturation in adolescence and adulthood. However, how self-regulation in daily life relates to brain functioning in pre-adolescent children is not known. To this aim, we have analyzed data from 640 children aged 8–11, who performed a stop-signal anticipation task combined with functional magnetic resonance imaging, in addition to questionnaire data on self-regulation. We find that pre-adolescent boys and girls who display higher levels of self-regulation, are better able to employ proactive inhibitory control strategies, exhibit stronger frontal activation and more functional coupling between cortical and subcortical areas of the brain. Furthermore, we demonstrate that pre-adolescent children show significant activation in areas of the brain that were previously only associated with reactive and proactive inhibition in adults and adolescents. Thus, already in pre-adolescent children, frontal-striatal brain areas are active during self-regulatory behavior.

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Children with higher levels of self-regulation employ more proactive inhibition.

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During proactive inhibition, children aged 8–11 show activation in frontal-cortical areas.

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Children higher in self-regulation exhibit more cortical-subcortical coupling.

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Children aged 8–11 show similar brain activation as adults during inhibition.

Control of response interference: caudate nucleus contributes to selective inhibition

While the role of cortical regions in cognitive control processes is well accepted, the contribution of subcortical structures (e.g., the striatum), especially to the control of response interference, remains controversial. Therefore, the present study aimed to investigate the cortical and particularly subcortical neural mechanisms of response interference control (including selective inhibition). Thirteen healthy young participants underwent event-related functional magnetic resonance imaging while performing a unimanual version of the Simon task. In this task, successful performance required the resolution of stimulus–response conflicts in incongruent trials by selectively inhibiting interfering response tendencies. The behavioral results show an asymmetrical Simon effect that was more pronounced in the contralateral hemifield. Contrasting incongruent trials with congruent trials (i.e., the overall Simon effect) significantly activated clusters in the right anterior cingulate cortex, the right posterior insula, and the caudate nucleus bilaterally. Furthermore, a region of interest analysis based on previous patient studies revealed that activation in the bilateral caudate nucleus significantly co-varied with a parameter of selective inhibition derived from distributional analyses of response times. Our results corroborate the notion that the cognitive control of response interference is supported by a fronto-striatal circuitry, with a functional contribution of the caudate nucleus to the selective inhibition of interfering response tendencies.

A Time Series-Based Point Estimation of Stop Signal Reaction Times: More Evidence on the Role of Reactive Inhibition-Proactive Inhibition Interplay on the SSRT Estimations

The Stop Signal Reaction Time (SSRT) is a latency measurement for the unobservable human brain stopping process, and was formulated by Logan (1994) without consideration of the nature (go/stop) of trials that precede the stop trials. Two asymptotically equivalent and larger indices of mixture SSRT and weighted SSRT were proposed in 2017 to address this issue from time in task longitudinal perspective, but estimation based on the time series perspective has still been missing in the literature. A time series-based state space estimation of SSRT was presented and it was compared with Logan 1994 SSRT over two samples of real Stop Signal Task (SST) data and the simulated SST data. The results showed that time series-based SSRT is significantly larger than Logan’s 1994 SSRT consistent with former Longitudinal-based findings. As a conclusion, SSRT indices considering the after effects of inhibition in their estimation process are larger yielding to hypothesize a larger estimates of SSRT using information on the reactive inhibition, proactive inhibition and their interplay in the SST data.

Using subjective expectations to model the neural underpinnings of proactive inhibition

Proactive inhibition – the anticipation of having to stop a response – relies on objective information contained in cue‐related contingencies in the environment, as well as on the subjective interpretation derived from these cues. To date, most studies of brain areas underlying proactive inhibition have exclusively considered the objective predictive value of environmental cues, by varying the probability of stop‐signals. However, by only taking into account the effect of different cues on brain activation, the subjective component of how cues affect behavior is ignored. We used a modified stop‐signal response task that includes a measurement for subjective expectation, to investigate the effect of this subjective interpretation. After presenting a cue indicating the probability that a stop‐signal will occur, subjects were asked whether they expected a stop‐signal to occur. Furthermore, response time was used to retrospectively model brain activation related to stop‐expectation. We found more activation during the cue period for 50% stop‐signal probability, when contrasting with 0%, in the mid and inferior frontal gyrus, inferior parietal lobe and putamen. When contrasting expected vs. unexpected trials, we found modest effects in the mid frontal gyrus, parietal, and occipital areas. With our third contrast, we modeled brain activation during the cue with trial‐by‐trial variances in response times. This yielded activation in the putamen, inferior parietal lobe, and mid frontal gyrus. Our study is the first to use the behavioral effects of proactive inhibition to identify the underlying brain regions, by employing an unbiased task‐design that temporally separates cue and response.