Estrogen modulates inhibitory control in healthy human females: evidence from the stop-signal paradigm

Neuroscience ◽  
2010 ◽  
Vol 167 (3) ◽  
pp. 709-715 ◽  
Author(s):  
L.S. Colzato ◽  
G. Hertsig ◽  
W.P.M. van den Wildenberg ◽  
B. Hommel
Keyword(s):  
Inhibitory Control ◽  
Stop Signal ◽  
Healthy Human ◽  
Human Females

Adjustments to Proactive Motor Inhibition without Effector-Specific Foreknowledge Are Reflected in a Bilateral Upregulation of Sensorimotor β-Burst Rates

2021 ◽  
Vol 33 (5) ◽  
pp. 784-798 ◽  
Author(s):  
Cheol Soh ◽  
Megan Hynd ◽  
Benjamin O. Rangel ◽  
Jan R. Wessel
Keyword(s):  
Inhibitory Control ◽  
Motor System ◽  
Baseline Period ◽  
Proactive Inhibition ◽  
Stop Signal ◽  
Stop Signal Task ◽  
Movement Initiation ◽  
Healthy Human ◽  
Subsequent Work ◽  
Band Activity

Abstract Classic work using the stop-signal task has shown that humans can use inhibitory control to cancel already initiated movements. Subsequent work revealed that inhibitory control can be proactively recruited in anticipation of a potential stop-signal, thereby increasing the likelihood of successful movement cancellation. However, the exact neurophysiological effects of proactive inhibitory control on the motor system are still unclear. On the basis of classic views of sensorimotor β-band activity, as well as recent findings demonstrating the burst-like nature of this signal, we recently proposed that proactive inhibitory control is implemented by influencing the rate of sensorimotor β-bursts during movement initiation. Here, we directly tested this hypothesis using scalp EEG recordings of β-band activity in 41 healthy human adults during a bimanual RT task. By comparing motor responses made in two different contexts—during blocks with or without stop-signals—we found that premovement β-burst rates over both contralateral and ipsilateral sensorimotor areas were increased in stop-signal blocks compared to pure-go blocks. Moreover, the degree of this burst rate difference indexed the behavioral implementation of proactive inhibition (i.e., the degree of anticipatory response slowing in the stop-signal blocks). Finally, exploratory analyses showed that these condition differences were explained by a significant increase in β bursting that was already present during baseline period before the movement initiation signal. Together, this suggests that the strategic deployment of proactive inhibitory motor control is implemented by upregulating the tonic inhibition of the motor system, signified by increased sensorimotor β-bursting both before and after signals to initiate a movement.

scholarly journals Effect of obesity on inhibitory control in preadolescents during stop-signal task. An event-related potentials study

2021 ◽  
Author(s):  
Graciela C. Alatorre-Cruz ◽  
Heather Downs ◽  
Darcy Hagood ◽  
Seth T. Sorensen ◽  
D. Keith Williams ◽  
...  
Keyword(s):  
Inhibitory Control ◽  
Event Related Potentials ◽  
Stop Signal ◽  
Stop Signal Task ◽  
Related Potentials

scholarly journals Decomposing Decision Components in the Stop-signal Task: A Model-based Approach to Individual Differences in Inhibitory Control

2014 ◽  
Vol 26 (8) ◽  
pp. 1601-1614 ◽  
Author(s):  
Corey N. White ◽  
Eliza Congdon ◽  
Jeanette A. Mumford ◽  
Katherine H. Karlsgodt ◽  
Fred W. Sabb ◽  
...  
Keyword(s):  
Individual Differences ◽  
Inhibitory Control ◽  
Processing Speed ◽  
Execution Time ◽  
Stop Signal ◽  
Stop Signal Task ◽  
Motor Execution ◽  
Stimulus Processing ◽  
Model Based ◽  
The Right

The stop-signal task, in which participants must inhibit prepotent responses, has been used to identify neural systems that vary with individual differences in inhibitory control. To explore how these differences relate to other aspects of decision making, a drift-diffusion model of simple decisions was fitted to stop-signal task data from go trials to extract measures of caution, motor execution time, and stimulus processing speed for each of 123 participants. These values were used to probe fMRI data to explore individual differences in neural activation. Faster processing of the go stimulus correlated with greater activation in the right frontal pole for both go and stop trials. On stop trials, stimulus processing speed also correlated with regions implicated in inhibitory control, including the right inferior frontal gyrus, medial frontal gyrus, and BG. Individual differences in motor execution time correlated with activation of the right parietal cortex. These findings suggest a robust relationship between the speed of stimulus processing and inhibitory processing at the neural level. This model-based approach provides novel insight into the interrelationships among decision components involved in inhibitory control and raises interesting questions about strategic adjustments in performance and inhibitory deficits associated with psychopathology.

Transcranial Magnetic Stimulation and Functional MRI Reveal Cortical and Subcortical Interactions during Stop-signal Response Inhibition

2013 ◽  
Vol 25 (2) ◽  
pp. 157-174 ◽  
Author(s):  
Bram B. Zandbelt ◽  
Mirjam Bloemendaal ◽  
Janna Marie Hoogendam ◽  
René S. Kahn ◽  
Matthijs Vink
Keyword(s):  
Inhibitory Control ◽  
Primary Motor Cortex ◽  
Functional Organization ◽  
Stop Signal ◽  
Stop Signal Task ◽  
Reactive Inhibition ◽  
Reactive Control ◽  
Motor Complex ◽  
Repetitive Tms ◽  
The Right

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.

Estrogen modulates inhibition of return in healthy human females

2012 ◽  
Vol 50 (1) ◽  
pp. 98-103 ◽  
Author(s):  
Lorenza S. Colzato ◽  
Jay Pratt ◽  
Bernhard Hommel
Keyword(s):  
Inhibition Of Return ◽  
Healthy Human ◽  
Human Females

scholarly journals BOLD differences normally attributed to inhibitory control predict symptoms, not task-directed inhibitory control in ADHD

2019 ◽  
Author(s):  
Andre Chevrier ◽  
Russell J. Schachar
Keyword(s):  
Inhibitory Control ◽  
Error Detection ◽  
Healthy Subjects ◽  
Stop Signal ◽  
Therapeutic Interventions ◽  
Stop Signal Task ◽  
Neural Processing ◽  
Significant Group ◽  
Significant Group Difference ◽  
Symptomatic Behavior

AbstractBackgroundAltered 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 differences might instead reflect the redirection of neural processing resources normally used for task-directed inhibitory control, toward actively managing symptomatic behavior. If so, then activities that correlate with SSRT in TD should instead correlate with inattentive and hyperactive symptoms in ADHD.MethodsWe used fMRI 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.ResultsOnly 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 behavior instead of task-directed behavior, 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.ConclusionsResults are consistent with stimuli that elicit task-directed integration of neural processing in healthy subjects, instead directing integrated function towards managing symptomatic behavior 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.

scholarly journals Using Smartphone Sensor Data to Assess Inhibitory Control in the Wild: Longitudinal Study (Preprint)

2020 ◽  
Author(s):  
Vincent WS Tseng ◽  
Jean Dos Reis Costa ◽  
Malte F Jung ◽  
Tanzeem Choudhury
Keyword(s):  
Heart Rate ◽  
Inhibitory Control ◽  
Intelligent Systems ◽  
Well Being ◽  
Stop Signal ◽  
Sensor Data ◽  
Gradient Boosting ◽  
Negatively Associated ◽  
In The Wild ◽  
The Mean

BACKGROUND Inhibitory control, or inhibition, is one of the core executive functions of humans. It contributes to our attention, performance, and physical and mental well-being. Our inhibitory control is modulated by various factors and therefore fluctuates over time. Being able to continuously and unobtrusively assess our inhibitory control and understand the mediating factors may allow us to design intelligent systems that help manage our inhibitory control and ultimately our well-being. OBJECTIVE The aim of this study is to investigate whether we can assess individuals’ inhibitory control using an unobtrusive and scalable approach to identify digital markers that are predictive of changes in inhibitory control. METHODS We developed InhibiSense, an app that passively collects the following information: users’ behaviors based on their phone use and sensor data, the ground truths of their inhibition control measured with stop-signal tasks (SSTs) and ecological momentary assessments (EMAs), and heart rate information transmitted from a wearable heart rate monitor (Polar H10). We conducted a 4-week in-the-wild study, where participants were asked to install InhibiSense on their phone and wear a Polar H10. We used generalized estimating equation (GEE) and gradient boosting tree models fitted with features extracted from participants’ phone use and sensor data to predict their stop-signal reaction time (SSRT), an objective metric used to measure an individual’s inhibitory control, and identify the predictive digital markers. RESULTS A total of 12 participants completed the study, and 2189 EMAs and SST responses were collected. The results from the GEE models suggest that the top digital markers positively associated with an individual’s SSRT include phone use burstiness (<i>P</i>=.005), the mean duration between 2 consecutive phone use sessions (<i>P</i>=.02), the change rate of battery level when the phone was not charged (<i>P</i>=.04), and the frequency of incoming calls (<i>P</i>=.03). The top digital markers negatively associated with SSRT include the standard deviation of acceleration (<i>P</i>&lt;.001), the frequency of short phone use sessions (<i>P</i>&lt;.001), the mean duration of incoming calls (<i>P</i>&lt;.001), the mean decibel level of ambient noise (<i>P</i>=.007), and the percentage of time in which the phone was connected to the internet through a mobile network (<i>P</i>=.001). No significant correlation between the participants’ objective and subjective measurement of inhibitory control was found. CONCLUSIONS We identified phone-based digital markers that were predictive of changes in inhibitory control and how they were positively or negatively associated with a person’s inhibitory control. The results of this study corroborate the findings of previous studies, which suggest that inhibitory control can be assessed continuously and unobtrusively in the wild. We discussed some potential applications of the system and how technological interventions can be designed to help manage inhibitory control.

scholarly journals Negative Emotional Response Inhibition: Support for a cognitive mechanism underlying negative urgency in nonsuicidal self-injury

2018 ◽  
Author(s):  
Kenneth Javad Dale Allen ◽  
D.Phil. Jill Miranda Hooley
Keyword(s):  
Inhibitory Control ◽  
Response Inhibition ◽  
Emotional Response ◽  
Stop Signal ◽  
Stop Signal Task ◽  
Control Group ◽  
Late Response ◽  
Negative Urgency ◽  
Self Injury ◽  
Nonsuicidal Self Injury

Negative urgency, the self-reported tendency to act impulsively when distressed, increases risk for nonsuicidal self-injury (NSSI). Prior research also suggests that NSSI is associated with impaired negative emotional response inhibition (NERI), a cognitive process theoretically related to negative urgency. Specifically, individuals with a history of NSSI had difficulty inhibiting behavioral responses to negative affective images in an Emotional Stop-Signal Task, but not to those depicting positive or neutral content. The present study sought to replicate this finding, determine whether this deficit extends to an earlier stage of NERI, and explore whether impairment in these two stages of emotional inhibitory control helps explain the relationship between negative urgency and NSSI. To address these aims, 88 adults with NSSI histories (n = 45) and healthy control participants (n = 43) without NSSI history or psychopathology completed a clinical interview, symptom inventories, an impulsivity questionnaire, and behavioral impulsivity tasks measuring early and late emotional response inhibition. The NSSI group had worse late NERI than the control group on the Emotional Stop-Signal Task, but no group differences were observed in early NERI on an Emotional Go/no-go task. However, both early and late stages of NERI accounted for independent variance in negative urgency. We additionally found that late NERI explained variance in the association between negative urgency and NSSI. These results suggest that impulsive behavior in NSSI may involve specifically impaired inhibitory control over negative emotional impulses during late response inhibition, and that this cognitive deficit might reflect one mechanism or pathway to elevated negative urgency among people who self-injure.

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