The impact of the COMT genotype and cognitive demands on facets of intra-subject variability

Salunkhe, Gayatri; Feige, Bernd; Saville, Christopher W. N.; Lancaster, Thomas; Stefanou, Maria Elena; Bender, Stephan; Berger, Andrea; Smyrnis, Nikolaos; Biscaldi, Monica; Linden, David; Klein, Christoph

doi:10.1016/j.bandc.2019.03.002

Cited by 7 publications

(4 citation statements)

References 49 publications

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“…A common explanation for elevated τ in patients with ADHD is that it reflects frequent attentional lapses due to poor suppression of the Default Mode Network [DMN; ( 5 , 25 )]. By contrast, increased σ, if given in patients with ADHD, may be an index of poor neuro-modulation or neural “noise” ( 26 ).…”

Section: Discussionmentioning

confidence: 99%

Dissociating Slow Responses From Slow Responding

et al. 2020

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Increased Intra-Subject Variability (ISV) is a candidate endophenotype of ADHD. ISV’s relationship with response speed is highly relevant for ADHD as patients are highly variable but typically no slower than controls. This brief report addresses the relationship between variability and speed by employing dimensional analyses for differentiated performance measures, with a particular focus on the ex-Gaussian measures, across relevant ADHD studies and in young healthy adults (N = 70). For both patients with ADHD and healthy adults, we found that reaction time standard deviation and mean reaction time were strongly correlated, thus failing to dissociate, but ex-Gaussian tau (τ) shared only little variance with Gaussian mu (μ), thus dissociating slow responses (τ) from response speed or—if given—slow responding (μ). Our results highlight the utility of employing the ex-Gaussian measures to disentangle ISV and speed, particularly for ADHD data as patients make more slow responses but are not overall slower than typical controls.

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Section: Discussionmentioning

confidence: 99%

Dissociating Slow Responses From Slow Responding

et al. 2020

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“…), but the principal component analysis performed by Salunkhe et al (2020) showed that σ and errors load on different components. The same authors argued elsewhere (Salunkhe et al, 2019) that σ may be related to neuromodulation or neural "noise".…”

Section: Cognitive Interpretations For Ex-gaussian Parametersmentioning

confidence: 79%

Differences in Ex-Gaussian Parameters from Response Time Distributions Between Individuals with and Without Attention Deficit/Hyperactivity Disorder: A Meta-analysis

Bella-Fernández

Martín-Moratinos

et al. 2023

Neuropsychol Rev

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Attention Deficit Hyperactivity Disorder (ADHD) is one of the most prevalent neurodevelopmental disorders in childhood and adolescence. Differences in reaction times (RT) in cognitive tasks have been consistently observed between ADHD and typical participants. Instead of estimating means and standard deviations, fitting non-symmetrical distributions like the ex-Gaussian, characterized by three parameters (µ, σ, and τ), account for the whole RT distributions. A meta-analysis is performed with all the available literature using ex-Gaussian distributions for comparisons between individuals with ADHD and controls. Results show that τ and σ are generally greater for ADHD samples, while µ tends to be larger for typical groups but only for younger ages. Differences in τ are also moderated by ADHD subtypes. τ and σ show, respectively, quadratic and linear relationships with inter-stimulus intervals from Continuous Performance Test and Go/No Go tasks. Furthermore, tasks and cognitive domains influence the three parameters. Interpretations of ex-Gaussian parameters and clinical implications of these findings are also discussed. Fitting ex-Gaussian distributions to RT data is a useful way to explore differences between individuals with ADHD and healthy controls.

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“…R (Version 4.0.0) for Windows (R Core Team, 2015) was used for data preparation and basic analyses. A data-driven approach, described in Salunkhe et al (2019), was applied to identify a threshold for every task, above which correct reactions appeared significantly more often than incorrect reactions (compared via χ² tests, p < .05). In line with differences in the difficulties of the CRTs (see means in Table 1) the calculated thresholds were: 250 ms for CRT above-below, 300 ms for CRT up-down, 350 ms for CRT large-small, and 400 ms for CRT odd-even.…”

Section: Methodsmentioning

confidence: 99%

Intra-Subject Variability, Intelligence, and ADHD Traits in a Community-Based Sample

et al. 2022

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Objective: The present study investigates the predictive validity of intra-subject variability (ISV) for ADHD traits in a community-based sample and the stability of the relationship between ISV and fluid intelligence (gf) across the continuum of ADHD traits. Method: Age-residualized data from 426 participants (8–18 years, 6% ADHD) was used to investigate whether ex-Gaussian and DDM parameters derived from simple choice-reaction-time tasks can predict continuously assessed ADHD traits. Multiple-Group-Analyses and Latent-Moderated-Structural-Equations were used to test whether ADHD traits moderate the relationship between ISV and gf. Results: σ and μ of the ex-Gaussian model as well as DDM parameters drift rate ( v) and boundary separation ( a) significantly predicted general ADHD traits, while τ predicted attention difficulties specifically. Across the ADHD continuum, σ and v were significant predictors of gf. Conclusion: The results confirm the link between ISV and ADHD. The relationship between ISV and gf appears stable across the ADHD continuum.

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The impact of the COMT genotype and cognitive demands on facets of intra-subject variability

Cited by 7 publications

References 49 publications

Dissociating Slow Responses From Slow Responding

Dissociating Slow Responses From Slow Responding

Differences in Ex-Gaussian Parameters from Response Time Distributions Between Individuals with and Without Attention Deficit/Hyperactivity Disorder: A Meta-analysis

Intra-Subject Variability, Intelligence, and ADHD Traits in a Community-Based Sample

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