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85 results for “Inhibitory control”
Learning, Inhibitory Control, and Perception
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Occipital Nerve Stimulation Selectively Modulates Top-down Inhibitory Control
<p><strong>Objective:</strong> Here we investigate the effect of occipital nerve stimulation using low-gamma range alternating current on goal-directed and stimulus-driven attention and inhibitory training and performance. We sought to determine if stimulation modulated performance over a two-day period. <strong>Methods</strong>: We studied this effect in 47 participants recruited in one of two experiments. The goal-directed task used the stop-signal reaction time task (SSRT) during stimulation and stop-change reaction time (SCRT) in a 24-hour follow-up. Stop-signal reaction time (SSRT) and Stop-change reaction time (SCRT) were recorded in seconds, calculated using a non-integration method. SSRT/SCRT and accuracy were used as outcome measures. The stimulus-driven task used a sustained-attention reaction time task (SART), and reaction time and inhibition (NoGo) accuracy were used as outcome measures. <strong>Results</strong>: Compared to the control group, the stimulation group had improved SCRT 24 hours after combined stimulation and training. No difference in accuracy on either day were present. No difference between groups arose in the SART during training or testing. </p>
Inhibitory Kcnip2 neurons of the spinal dorsal horn control behavioral sensitivity to environmental cold
<p>Excel file containing datasets for all Figures published in the article "Inhibitory Kcnip2 neurons of the spinal dorsal horn control behavioral sensitivity to environmental cold" by Albisetti et al.</p>
Inhibitory control, exploration behaviour and manipulated ecological context are associated with foraging flexibility in the great tit
<p class="MsoCommentText">Organisms are constantly under selection to respond effectively to diverse, sometimes rapid, changes in their environment, but not all individuals are equally plastic in their behaviour. Although cognitive processes and personality are expected to influence individual behavioural plasticity, the effects reported are highly inconsistent, which we hypothesise is because ecological context is usually not considered.</p> <p class="MsoCommentText">We explored how one type of behavioural plasticity, foraging flexibility, was associated with inhibitory control (assayed using a detour-reaching task) and exploration behaviour in a novel environment (a trait closely linked to the fast-slow personality axis). We investigated how these effects varied across two experimentally manipulated ecological contexts, food value and predation risk.</p> <p class="MsoCommentText">In the first phase of the experiment, we trained great tits <i>Parus major</i> to retrieve high value (preferred) food that was hidden in sand so that this became the familiar food source. In the second phase, we offered them the same familiar hidden food at the same time as a new alternative option that was visible on the surface, which was either high or low value, and under either high or low perceived predation risk. Foraging flexibility was defined as the proportion of choices made during four minute trials that were for the new alternative food source.</p> <p>Our assays captured consistent differences among individuals in foraging flexibility. Inhibitory control was associated with foraging flexibility - birds with high inhibitory control were more flexible when the alternative food was high value, suggesting they inhibited the urge to select the familiar food and instead selected the new food option. Exploration behaviour also predicted flexibility – fast explorers were more flexible, supporting the information gathering hypothesis. This tendency was especially strong under high predation risk, suggesting risk aversion also influenced the observed flexibility because fast explorers are risk prone and the new unfamiliar food was perceived to be the risky option. Thus, both behaviours predicted flexibility, and these links were at least partly dependent on ecological conditions.</p> <p class="MsoCommentText">Our results demonstrate that an executive cognitive function (inhibitory control) and a behavioural assay of a well-known personality axis are both associated with individual variation in the plasticity of a key functional behaviour. That their effects on foraging flexibility were primarily observed as interactions with food value or predation risk treatments also suggests that the population level consequences of some behavioural mechanisms may only be revealed across key ecological conditions.</p>
Inhibitory control, exploration behaviour and manipulated ecological context are associated with foraging flexibility in the great tit
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Data for: Genetic and context-specific effects on individual inhibitory control performance in the guppy (Poecilia reticulata)
<p>Among-individual variation in cognitive traits, widely assumed to have evolved under adaptive processes, is increasingly being demonstrated across animal taxa. As variation among individuals is required for natural selection, characterising individual differences and their heritability is important to understand how cognitive traits evolve. Here we use a quantitative genetic study of wild-type guppies repeatedly exposed to a 'detour task' to test for genetic variance in the cognitive trait of inhibitory control. We also test for genotype-by-environment interactions (GxE) by testing related fish under alternative experimental treatments (transparent vs. semi-transparent barrier in the detour-task). We find among-individual variation in detour task performance, consistent with differences in inhibitory control. However, analysis of GxE reveals that heritable factors only contribute to performance variation in one treatment. This suggests that the adaptive evolutionary potential of inhibitory control (and/or other latent variables contributing to task performance) may be highly sensitive to environmental conditions. The presence of GxE also implies that the plastic response of detour task performance to treatment environment is genetically variable. Our results are consistent with a scenario where variation in individual inhibitory control stems from complex interactions between heritable and plastic components.</p>
Fig 3 eYFP sustained inactivation controls, in Regulation of REM Sleep by Inhibitory Neurons in the Dorsomedial Medulla
<p>Sleep recording data eYFP controls. Associated with Figure 3. for "Regulation of REM Sleep by Inhibitory Neurons in the Dorsomedial Medulla". Associated with Figure 3.</p>
Dynamic targeting enables domain-general inhibitory control over action and thought by the prefrontal cortex (data & code)
<p><strong>Data and code for:</strong></p> <p>Apšvalka, D., Ferreira, C. S., Schmitz, T. W., Rowe, J. B., & Anderson, M. C. (2022). Dynamic targeting enables domain-general inhibitory control over action and thought by the prefrontal cortex. <em>Nature Communications, </em> <strong>13, </strong>274<em>.</em> <a href="https://doi.org/10.1038/s41467-021-27926-w"> https://doi.org/10.1038/s41467-021-27926-w</a></p> <blockquote> <p>Over the last two decades, inhibitory control has featured prominently in accounts of how humans and other organisms regulate their behaviour and thought. Previous work on how the brain stops actions and thoughts, however, has emphasised distinct prefrontal regions supporting these functions, suggesting domain-specific mechanisms. Here we show that stopping actions and thoughts recruits common regions in the right dorsolateral and ventrolateral prefrontal cortex to suppress diverse content, via dynamic targeting. Within each region, classifiers trained to distinguish action-stopping from action-execution also identify when people are suppressing their thoughts (and vice versa). Effective connectivity analysis reveals that both prefrontal regions contribute to action and thought stopping by targeting the motor cortex or the hippocampus, depending on the goal, to suppress their task-specific activity. These findings support the existence of a domain-general system that underlies inhibitory control and establish Dynamic Targeting as a mechanism enabling this ability.</p> </blockquote>
REXCO Project :Physical exercise increases overall brain oscillatory activity but does not influence inhibitory control in young adults
<p><strong>Methods and design</strong></p> <p><em>Participants</em></p> <p>We recruited 20 young males (19-32 years old, average age 23.8 years old) from the University of Granada (Spain). All participants met the inclusion criteria of normal or corrected to normal vision, reported no neurological, cardiovascular or musculoskeletal disorders, were taking no medication and reporting less than 3 hours of moderate exercise per week. Participants were required to maintain regular sleep-wake cycle for at least one day before each experimental session and to abstain from stimulating beverages or any intense exercise 24 hours before each session. From the 20 participants, one was excluded from the analyses because he did not attend to the last experimental session and another one because of technical issues. Thus, only data from the remaining 18 participants are reported. All subjects gave written informed consent before the study and received 20 euros for their participation. The protocol was approved in accordance with both the ethical guidelines of the University of Granada and the Declaration of Helsinki of 1964.</p> <p><em>Apparatus and materials</em></p> <p>All participants were fitted with a Polar RS800 CX monitor (Polar Electro Öy, Kempele, Finland) to record their heart rate (HR) during the incremental exercise test. We used a ViaSprint 150 P cycle ergometer (Ergoline GmbH, Germany) to induce physical effort and to obtain power values, and a JAEGER Master Screen gas analyser (CareFusion GmbH, Germany) to provide a measure of gas exchange during the effort test. Flanker task stimuli were presented on a 21-inch BENQ screen maintaining a fixed distance of 50 cm between the head of participants and the center of the screen. E-Prime software (Psychology Software Tools, Pittsburgh, PA, USA) was used for stimulus presentation and behavioural data collection.</p> <p><em>Procedure</em></p> <p>Participants completed two counterbalanced experimental sessions of approximately 120 min each. Sessions were scheduled on different days allowing a time interval of 48–72 hours between them to avoid possible fatigue and/or training effects. On each experimental session (see Fig. 1), participants completed a 15’ resting state period sitting in a comfortable chair with closed eyes. Subsequently, they performed 10’ warm-up on a cycle-ergometer at a power load of 20% of their individual VO<sub>2</sub> VAT, following by 30’ exercise at 80% (moderate-intensity exercise session) or at 20% (light intensity exercise session) of their VO<sub>2</sub> VAT (see Table 1). Upon completion of the exercise, a 10’ cool down period at 20% VO<sub>2</sub> VAT of intensity followed. Each participant set his preferred cadence (between 60-90 rpm • min-1) before the warm-up and was asked to maintain this cadence throughout the session in order to match conditions in terms of dual-task demands. Later, participants waited sitting in a comfortable chair until their heart rate returned to within their 130% of heart rate at resting (average waiting time 5’ 44’’). The first flanker task was then performed for 6’, followed by a 15’ resting period with closed eyes. Finally, they again completed the 6’ flanker task.</p> <p><em>Flanker task</em></p> <p>We used a modified version of the Eriksen flanker task based on that reported in Eriksen and Eriksen (1974). The task consisted of a random presentation of a set arrows flanked by other arrows that faced the same or the opposite direction. In the congruent trials, the central arrow is flanked by arrows in the same direction (e.g., <<<<< or >>>>>), while in the incongruent trials, the central arrow is flanked by arrows in the opposite direction (e.g., <<><< or >><>>). Stimuli were displayed sequentially on the center of the screen on a black background. Each trial started with the presentation of a white fixation cross in a black background with random duration between 1000 and 1500 ms. Then, the stimulus was presented during 150 ms and a variable interstimulus interval (1000–1500 ms). Participants were instructed to respond by pressing the left tab button with their left index finger when the central arrow (regardless of condition) faced to the left and the right tab button with their right index finger when the central arrow faced to the right. Participants were encouraged to respond as quick as possible, being accurate. A total of 120 trials were randomly presented (60 congruent and 60 incongruent trials) in each task. Each task lasted for 6 minutes approximately without breaks.</p> <p><em>EEG recording and analysis</em></p> <p>EEG data were recorded at 1000 Hz using a 30-channel actiCHamp System (Brain Products GmbH, Munich, Germany) with active electrodes positioned according to the 10-20 EEG International System and referenced to the Cz electrode. The cap was adapted to individual head size, and each electrode was filled with Signa Electro-Gel (Parker Laboratories, Fairfield, NJ). Participants were instructed to avoid body movements as much as possible, and to keep their gaze on the center of the screen during the exercise. Electrode impedances were kept below 10 kΩ. EEG preprocessing was conducted using custom Matlab scripts and the EEGLAB (Delorme & Makeig, 2004) and Fieldtrip (Oostenveld et al., 2011) Matlab toolboxes. EEG data were resampled at 500 Hz, bandpass filtered offline from 1 and 40 Hz to remove signal drifts and line noise, and re-referenced to a common average reference. Horizontal electrooculograms (EOG) were recorded by bipolar external electrodes for the offline detection of ocular artifacts. The potential influence of electromyographic (EMG) activity in the EEG signal was minimized by using the available EEGLAB routines (Delorme & Makeig, 2004). Independent component analysis was used to detect and remove EEG components reflecting eye blinks (Hoffmann and Falkenstein, 2008). Abnormal spectra epochs which spectral power deviated from the mean by +/-50 dB in the 0-2 Hz frequency window (useful for catching eye movements) and by +25 or -100 dB in the 20-40 Hz frequency window (useful for detecting muscle activity) were rejected. On average, 5.1% of epochs per participant were rejected.</p> <p><em>Spectral power analysis</em>. Processed EEG data from each experimental period (Resting 1, Warm-up, Exercise, Cool Down, Flanker Task 1, Resting 2, Flanker Task 2) were subsequently segmented to 1-s epochs. The spectral decomposition of each epoch was computed using Fast Fourier Transformation (FFT) applying a symmetric Hamming window and the obtained power values were averaged across experimental periods.</p> <p><em>Event-Related Spectral Perturbation (ERSP) analysis.</em> Task-evoked spectral EEG activity was assessed by computing ERSP in epochs extending from –500 ms to 500 ms time-locked to stimulus onset for frequencies between 4 and 40 Hz. Spectral decomposition was performed using sinusoidal wavelets with 3 cycles at the lowest frequency and increasing by a factor of 0.8 with increasing frequency. Power values were normalized with respect to a −300 ms to 0 ms pre-stimulus baseline and transformed into the decibel scale.</p>
REXCO Project :Physical exercise increases overall brain oscillatory activity but does not influence inhibitory control in young adults
<p><strong>Methods and design</strong></p> <p><em>Participants</em></p> <p>We recruited 20 young males (19-32 years old, average age 23.8 years old) from the University of Granada (Spain). All participants met the inclusion criteria of normal or corrected to normal vision, reported no neurological, cardiovascular or musculoskeletal disorders, were taking no medication and reporting less than 3 hours of moderate exercise per week. Participants were required to maintain regular sleep-wake cycle for at least one day before each experimental session and to abstain from stimulating beverages or any intense exercise 24 hours before each session. From the 20 participants, one was excluded from the analyses because he did not attend to the last experimental session and another one because of technical issues. Thus, only data from the remaining 18 participants are reported. All subjects gave written informed consent before the study and received 20 euros for their participation. The protocol was approved in accordance with both the ethical guidelines of the University of Granada and the Declaration of Helsinki of 1964.</p> <p><em>Apparatus and materials</em></p> <p>All participants were fitted with a Polar RS800 CX monitor (Polar Electro Öy, Kempele, Finland) to record their heart rate (HR) during the incremental exercise test. We used a ViaSprint 150 P cycle ergometer (Ergoline GmbH, Germany) to induce physical effort and to obtain power values, and a JAEGER Master Screen gas analyser (CareFusion GmbH, Germany) to provide a measure of gas exchange during the effort test. Flanker task stimuli were presented on a 21-inch BENQ screen maintaining a fixed distance of 50 cm between the head of participants and the center of the screen. E-Prime software (Psychology Software Tools, Pittsburgh, PA, USA) was used for stimulus presentation and behavioural data collection.</p> <p><em>Procedure</em></p> <p>Participants completed two counterbalanced experimental sessions of approximately 120 min each. Sessions were scheduled on different days allowing a time interval of 48–72 hours between them to avoid possible fatigue and/or training effects. On each experimental session (see Fig. 1), participants completed a 15’ resting state period sitting in a comfortable chair with closed eyes. Subsequently, they performed 10’ warm-up on a cycle-ergometer at a power load of 20% of their individual VO<sub>2</sub> VAT, following by 30’ exercise at 80% (moderate-intensity exercise session) or at 20% (light intensity exercise session) of their VO<sub>2</sub> VAT (see Table 1). Upon completion of the exercise, a 10’ cool down period at 20% VO<sub>2</sub> VAT of intensity followed. Each participant set his preferred cadence (between 60-90 rpm • min-1) before the warm-up and was asked to maintain this cadence throughout the session in order to match conditions in terms of dual-task demands. Later, participants waited sitting in a comfortable chair until their heart rate returned to within their 130% of heart rate at resting (average waiting time 5’ 44’’). The first flanker task was then performed for 6’, followed by a 15’ resting period with closed eyes. Finally, they again completed the 6’ flanker task.</p> <p><em>Flanker task</em></p> <p>We used a modified version of the Eriksen flanker task based on that reported in Eriksen and Eriksen (1974). The task consisted of a random presentation of a set arrows flanked by other arrows that faced the same or the opposite direction. In the congruent trials, the central arrow is flanked by arrows in the same direction (e.g., <<<<< or >>>>>), while in the incongruent trials, the central arrow is flanked by arrows in the opposite direction (e.g., <<><< or >><>>). Stimuli were displayed sequentially on the center of the screen on a black background. Each trial started with the presentation of a white fixation cross in a black background with random duration between 1000 and 1500 ms. Then, the stimulus was presented during 150 ms and a variable interstimulus interval (1000–1500 ms). Participants were instructed to respond by pressing the left tab button with their left index finger when the central arrow (regardless of condition) faced to the left and the right tab button with their right index finger when the central arrow faced to the right. Participants were encouraged to respond as quick as possible, being accurate. A total of 120 trials were randomly presented (60 congruent and 60 incongruent trials) in each task. Each task lasted for 6 minutes approximately without breaks.</p> <p><em>EEG recording and analysis</em></p> <p>EEG data were recorded at 1000 Hz using a 30-channel actiCHamp System (Brain Products GmbH, Munich, Germany) with active electrodes positioned according to the 10-20 EEG International System and referenced to the Cz electrode. The cap was adapted to individual head size, and each electrode was filled with Signa Electro-Gel (Parker Laboratories, Fairfield, NJ). Participants were instructed to avoid body movements as much as possible, and to keep their gaze on the center of the screen during the exercise. Electrode impedances were kept below 10 kΩ. EEG preprocessing was conducted using custom Matlab scripts and the EEGLAB (Delorme & Makeig, 2004) and Fieldtrip (Oostenveld et al., 2011) Matlab toolboxes. EEG data were resampled at 500 Hz, bandpass filtered offline from 1 and 40 Hz to remove signal drifts and line noise, and re-referenced to a common average reference. Horizontal electrooculograms (EOG) were recorded by bipolar external electrodes for the offline detection of ocular artifacts. The potential influence of electromyographic (EMG) activity in the EEG signal was minimized by using the available EEGLAB routines (Delorme & Makeig, 2004). Independent component analysis was used to detect and remove EEG components reflecting eye blinks (Hoffmann and Falkenstein, 2008). Abnormal spectra epochs which spectral power deviated from the mean by +/-50 dB in the 0-2 Hz frequency window (useful for catching eye movements) and by +25 or -100 dB in the 20-40 Hz frequency window (useful for detecting muscle activity) were rejected. On average, 5.1% of epochs per participant were rejected.</p> <p><em>Spectral power analysis</em>. Processed EEG data from each experimental period (Resting 1, Warm-up, Exercise, Cool Down, Flanker Task 1, Resting 2, Flanker Task 2) were subsequently segmented to 1-s epochs. The spectral decomposition of each epoch was computed using Fast Fourier Transformation (FFT) applying a symmetric Hamming window and the obtained power values were averaged across experimental periods.</p> <p><em>Event-Related Spectral Perturbation (ERSP) analysis.</em> Task-evoked spectral EEG activity was assessed by computing ERSP in epochs extending from –500 ms to 500 ms time-locked to stimulus onset for frequencies between 4 and 40 Hz. Spectral decomposition was performed using sinusoidal wavelets with 3 cycles at the lowest frequency and increasing by a factor of 0.8 with increasing frequency. Power values were normalized with respect to a −300 ms to 0 ms pre-stimulus baseline and transformed into the decibel scale.</p>
Data base for the paper "Inhibitory control and temporal perception in cerebral palsy" submitted to Child Neuropsychology
<p>Data base for the paper “Inhibitory control and temporal perception in cerebral palsy” submitted to “Child Neuropsychology” Review</p>
Effects of Coordinative Exercise on Physical Fitness, Motor Competence, and Inhibitory Control in Preschoolers
ClinicalTrials.gov study NCT06631248. IPD Sharing: YES. Countries: 1. Publications: 3.
Examining the Effects of Neural Stimulation on Inhibitory Control and Cigarette Smoking
ClinicalTrials.gov study NCT03960138. IPD Sharing: NO. Countries: 1. Publications: 2.
Enhancement of Brain Circuit of Inhibitory Control in Obese Patients Undergoing Gastric Banding
ClinicalTrials.gov study NCT01632280. IPD Sharing: Not stated. Countries: 1. Publications: 20.
Data for: Genetic and context-specific effects on individual inhibitory control performance in the guppy (Poecilia reticulata)
Open the record for dataset details and reuse information.
Data from: Heritability and correlations among learning and inhibitory control traits
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Right inferior frontal gyrus implements motor inhibitory control via beta-band oscillations in humans
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Data from: Lateralisation correlates with individual differences in inhibitory control in zebrafish
<p class="MsoNoSpacing"><span><span><span><span><span><span><span><span><span><span><span>The study investigated the relationship between cerebral lateralisation and inhibitory control in zebrafish. Lateralisation was assessed using a mirror test. Fish were observed in an apparatus with mirror walls and eye preference for observing the mirror image was recorded. This preference was used to calculate the relative and the absolute lateralisation index. Inhibitory control was assessed using a foraging task. Fish were exposed to live prey enclosed inside a transparent tube. The number of attacks and its reduction over time were used as measures of inhibition.</span></span></span></span></span></span></span></span></span></span></span></p>
Fig S4 mCherry controls, in Regulation of REM Sleep by Inhibitory Neurons in the Dorsomedial Medulla
<p>Sleep recordings for mCherry controls. Associated with Figure S4.</p>
Fig 2 eYFP closed loop inactivation controls, in Regulation of REM Sleep by Inhibitory Neurons in the Dorsomedial Medulla
<p>Sleep recordings for eYFP controls for Swichr++ control experiments. Associated with Figure 2D.</p>
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