scholarly journals Development of Inhibitory Control in Children With Attention Deficit/Hyperactivity Disorder by the Modified Stop-Signal Task

2006 ◽  
Vol 43 (6) ◽  
pp. 473-484
Author(s):  
Chie SAKAJIRI ◽  
Hisao MAEKAWA
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Inhibitory Control ◽  
Attention Deficit ◽  
Stop Signal ◽  
Stop Signal Task ◽  
Hyperactivity Disorder

scholarly journals Cognitive Modeling Suggests That Attentional Failures Drive Longer Stop-Signal Reaction Time Estimates in Attention Deficit/Hyperactivity Disorder

2019 ◽  
Vol 7 (4) ◽  
pp. 856-872 ◽  
Author(s):  
Alexander Weigard ◽  
Andrew Heathcote ◽  
Dóra Matzke ◽  
Cynthia Huang-Pollock
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Reaction Time ◽  
Attention Deficit ◽  
Cognitive Modeling ◽  
Stop Signal ◽  
Stop Signal Task ◽  
Unbiased Estimation ◽  
Children With Adhd ◽  
Hyperactivity Disorder ◽  
Stop Signal Reaction Time

Mean stop-signal reaction time (SSRT) is frequently employed as a measure of response inhibition in cognitive neuroscience research on attention deficit/hyperactivity disorder (ADHD). However, this measurement model is limited by two factors that may bias SSRT estimation in this population: (a) excessive skew in “go” RT distributions and (b) trigger failures, or instances in which individuals fail to trigger an inhibition process in response to the stop signal. We used a Bayesian parametric approach that allows unbiased estimation of the shape of entire SSRT distributions and the probability of trigger failures to clarify mechanisms of stop-signal task deficits in ADHD. Children with ADHD displayed greater positive skew than their peers in both go RT and SSRT distributions. However, they also displayed more frequent trigger failures, which appeared to drive ADHD-related stopping difficulties. Results suggest that performance on the stop-signal task among children with ADHD reflects impairments in early attentional processes, rather than inefficiency in the stop process.

scholarly journals Computer-based inhibitory control training in children with Attention-Deficit/Hyperactivity Disorder (ADHD): Evidence for behavioral and neural impact

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0241352
Author(s):  
Kristin N. Meyer ◽  
Rosario Santillana ◽  
Brian Miller ◽  
Wes Clapp ◽  
Marcus Way ◽  
...  
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Inhibitory Control ◽  
Attention Deficit ◽  
Psychological Disorder ◽  
Stop Signal Task ◽  
Control Group ◽  
Children With Adhd ◽  
Theta Power ◽  
Hyperactivity Disorder ◽  
Computerized Training

Attention-deficit hyperactivity disorder (ADHD) is the most commonly diagnosed psychological disorder of childhood. Medication and cognitive behavioral therapy are effective treatments for many children; however, adherence to medication and therapy regimens is low. Thus, identifying effective adjunct treatments is imperative. Previous studies exploring computerized training programs as supplementary treatments have targeted working memory or attention. However, many lines of research suggest inhibitory control (IC) plays a central role in ADHD pathophysiology, which makes IC a potential intervention target. In this randomized control trial (NCT03363568), we target IC using a modified stop-signal task (SST) training designed by NeuroScouting, LLC in 40 children with ADHD, aged 8 to 11 years. Children were randomly assigned to adaptive treatment (n = 20) or non-adaptive control (n = 20) with identical stimuli and task goals. Children trained at home for at least 5 days a week (about 15m/day) for 4-weeks. Relative to the control group, the treatment group showed decreased relative theta power in resting EEG and trending improvements in parent ratings of attention (i.e. decreases in inattentive behaviors). Both groups showed improved SST performance. There was not evidence for treatment effects on hyperactivity or teacher ratings of symptoms. Results suggest training IC alone has potential to positively impact symptoms of ADHD and provide evidence for neural underpinnings of this impact (change in theta power; change in N200 latency). This shows promising initial results for the use of computerized training of IC in children with ADHD as a potential adjunct treatment option for children with ADHD.

scholarly journals P2-14: Noise Effect to Cross-Modality Stop Signal Task in Patients with Attention-Deficit / Hyperactivity Disorder

i-Perception ◽  
10.1068/if674 ◽  
2012 ◽  
Vol 3 (9) ◽  
pp. 674-674
Author(s):  
Hisn-Wei Wu ◽  
Jeng-Yi Tyan ◽  
Li-Chin Lin ◽  
Yi-Min Tien ◽  
Tun-Shin Lo ◽  
...  
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Stop Signal ◽  
Stop Signal Task ◽  
Noise Effect ◽  
Hyperactivity Disorder

Dimensions of Impulsivity in Healthy People, Patients with Borderline Personality Disorder, and Patients with Attention-Deficit/Hyperactivity Disorder

2019 ◽  
Vol 25 (4) ◽  
pp. 584-595 ◽  
Author(s):  
Pavla Linhartová ◽  
Jan Širůček ◽  
Anastasia Ejova ◽  
Richard Barteček ◽  
Pavel Theiner ◽  
...  
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Borderline Personality Disorder ◽  
Personality Disorder ◽  
Attention Deficit ◽  
Latent Variables ◽  
Borderline Personality ◽  
Healthy People ◽  
Stop Signal Task ◽  
Impulsive Behavior ◽  
Hyperactivity Disorder

Objective: Impulsivity, observed in patients with various psychiatric disorders, is a heterogeneous construct with different behavioral manifestations. Through confirmatory factor analysis (CFA), this study tests hypotheses about relationships between dimensions of impulsivity measured using personality questionnaires and behavioral tests. Method: The study included 200 healthy people, 40 patients with borderline personality disorder, and 26 patients with attention-deficit/hyperactivity disorder (ADHD) who underwent a comprehensive impulsivity test battery including the Barratt Impulsiveness Scale (BIS), UPPS-P Impulsive Behavior Scale, a Go-NoGo task, a stop-signal task, and a delay discounting task. Results: A CFA model comprising three self-reported and three behavioral latent variables reached a good fit. Both patient groups scored higher in the self-reported dimensions and impulsive choice; only the ADHD patients displayed impaired waiting and stopping impulsivity. Conclusions: Using the developed CFA model, it is possible to describe relations between impulsivity dimensions and show different impulsivity patterns in patient populations.

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