A toolbox for residue iteration decomposition (RIDE)—A method for the decomposition, reconstruction, and single trial analysis of event related potentials

Ouyang, Guang; Sommer, Werner; Zhou, Changsong

doi:10.1016/j.jneumeth.2014.10.009

Cited by 146 publications

(291 citation statements)

References 25 publications

Supporting

Mentioning

283

Contrasting

Order By: Relevance

“…RIDE is able to utilize the latency variability and time markers to separate ERPs components into a stimuluslocked component cluster (named S), a response-locked component cluster (named R), and an intermediate component cluster (named C) (Stürmer et al 2013). The core procedures of RIDE followed the work of Stürmer et al (2013) and Ouyang et al (2015a).…”

Section: Electroencephalogram Recording and Data Analysismentioning

confidence: 99%

Event-related lateralized readiness potential correlates of the emotion-priming Simon effect

Shang

Qiu

et al. 2016

Exp Brain Res

View full text Add to dashboard Cite

The Simon effect indicates that the reaction time (RT) is shorter when the stimulus and response locations are congruent than when they are not. This study used a priming-target paradigm to explore the emotion-priming Simon effect with event-related potential techniques. The technique of residue iteration decomposition was employed to analyze the lateralized readiness potential (LRP) component, which contributed to disentangling the overlap between LRP and N2 central contralateral in the Simon task with horizontal stimulus-response arrangements. The behavioral result revealed significant Simon effect in RT. In the neural process, the Simon effect was reflected by both the stimulus-locked LRP (S-LRP) and the response-locked LRP (R-LRP), with the incongruent condition showing longer onset latency, larger Gratton-dip, and smaller negative-going deflection of S-LRP and smaller negative-going deflection of R-LRP. These findings suggest that the interference of irrelevant location information is located at the perceptual-encoding (indicated by S-LRP) and response-execution stages (indicated by R-LRP), providing evidence for both the perceptual-interference and response-interference accounts. However, the further linear regression result signaled that the Simon effect might be more closely related to the response-execution stage than the perceptual-encoding stage. In addition, the influence of emotion on the Simon effect was salient only in the incongruent condition, showing longer onset latency of S-LRP and larger Gratton-dip of R-LRP in the negative emotion-priming condition than in the neutral emotion-priming condition, which revealed that the emotional interference effect arose from the stages of perceptual encoding and early response execution only when the locations of a stimulus and the corresponding response were incongruent.

show abstract

Section: Electroencephalogram Recording and Data Analysismentioning

confidence: 99%

Event-related lateralized readiness potential correlates of the emotion-priming Simon effect

Shang

Qiu

et al. 2016

Exp Brain Res

View full text Add to dashboard Cite

show abstract

“…This is due to the fact that a similar behavioral outcome may emerge from dysfunctions at different processing levels. While it is possible to examine the neurophysiology of these different processing stages in ADD and ADHD-C using classic event-related potential (ERP) methods (Johnstone and Clarke, 2009; Gong et al, 2014; Mazaheri et al, 2014), it is important to consider that this and related methods can only yield a true reflection of the neural activity when there is little intra-individual variability (Ouyang et al, 2011, 2015a; Mückschel et al, 2017). This however, is unlikely to be the case in ADHD because a high behavioral intra-individual variability is a core aspect also discussed as an endophenotype of this disorder (Henríquez-Henríquez et al, 2014; Lin et al, 2015; Saville et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…To achieve this, we use residue iteration decomposition (RIDE; Ouyang et al, 2011, 2015a,b) applied on single-trial ERP data in combination with source localization techniques to examine neurophysiological changes at different levels in the information processing stream in ADD and ADHD-C. For this, participants performed a classical flanker task which reliably measures conflict and interference control (Keye et al, 2013; Chmielewski et al, 2014; Bluschke et al, 2016b) and has already been extensively used in ADHD research (e.g., Jonkman et al, 1999; van Meel et al, 2007; Albrecht et al, 2008; Mullane et al, 2009; Iannaccone et al, 2015). However, to the best of our knowledge, no study has so far actually statistically accounted for alterations in intra-individual variability when interpreting results.…”

Section: Introductionmentioning

confidence: 99%

Neuronal Intra-Individual Variability Masks Response Selection Differences between ADHD Subtypes—A Need to Change Perspectives

Bluschke

Chmielewski

Mückschel

et al. 2017

Front. Hum. Neurosci.

View full text Add to dashboard Cite

Due to the high intra-individual variability in attention deficit/hyperactivity disorder (ADHD), there may be considerable bias in knowledge about altered neurophysiological processes underlying executive dysfunctions in patients with different ADHD subtypes. When aiming to establish dimensional cognitive-neurophysiological constructs representing symptoms of ADHD as suggested by the initiative for Research Domain Criteria, it is crucial to consider such processes independent of variability. We examined patients with the predominantly inattentive subtype (attention deficit disorder, ADD) and the combined subtype of ADHD (ADHD-C) in a flanker task measuring conflict control. Groups were matched for task performance. Besides using classic event-related potential (ERP) techniques and source localization, neurophysiological data was also analyzed using residue iteration decomposition (RIDE) to statistically account for intra-individual variability and S-LORETA to estimate the sources of the activations. The analysis of classic ERPs related to conflict monitoring revealed no differences between patients with ADD and ADHD-C. When individual variability was accounted for, clear differences became apparent in the RIDE C-cluster (analog to the P3 ERP-component). While patients with ADD distinguished between compatible and incompatible flanker trials early on, patients with ADHD-C seemed to employ more cognitive resources overall. These differences are reflected in inferior parietal areas. The study demonstrates differences in neuronal mechanisms related to response selection processes between ADD and ADHD-C which, according to source localization, arise from the inferior parietal cortex. Importantly, these differences could only be detected when accounting for intra-individual variability. The results imply that it is very likely that differences in neurophysiological processes between ADHD subtypes are underestimated and have not been recognized because intra-individual variability in neurophysiological data has not sufficiently been taken into account.

show abstract

“…Consequently, the effects of measurement error remain subject to variation which—as shown—affects the validity of the model selection. Several methods have been suggested for cleaning data (e.g., Turetsky et al, 1989; Effern et al, 2000; Quiroga, 2000; He et al, 2004; Gonzalez-Moreno et al, 2014; Ouyang et al, 2015). However, even though the average data quality can be improved, this does not compensate the insufficiency of the data in potentially many studies.…”

Section: Discussionmentioning

confidence: 99%

Mind the Noise When Identifying Computational Models of Cognition from Brain Activity

Kolossa

Kopp

2016

Front. Neurosci.

View full text Add to dashboard Cite

The aim of this study was to analyze how measurement error affects the validity of modeling studies in computational neuroscience. A synthetic validity test was created using simulated P300 event-related potentials as an example. The model space comprised four computational models of single-trial P300 amplitude fluctuations which differed in terms of complexity and dependency. The single-trial fluctuation of simulated P300 amplitudes was computed on the basis of one of the models, at various levels of measurement error and at various numbers of data points. Bayesian model selection was performed based on exceedance probabilities. At very low numbers of data points, the least complex model generally outperformed the data-generating model. Invalid model identification also occurred at low levels of data quality and under low numbers of data points if the winning model's predictors were closely correlated with the predictors from the data-generating model. Given sufficient data quality and numbers of data points, the data-generating model could be correctly identified, even against models which were very similar to the data-generating model. Thus, a number of variables affects the validity of computational modeling studies, and data quality and numbers of data points are among the main factors relevant to the issue. Further, the nature of the model space (i.e., model complexity, model dependency) should not be neglected. This study provided quantitative results which show the importance of ensuring the validity of computational modeling via adequately prepared studies. The accomplishment of synthetic validity tests is recommended for future applications. Beyond that, we propose to render the demonstration of sufficient validity via adequate simulations mandatory to computational modeling studies.

show abstract

A toolbox for residue iteration decomposition (RIDE)—A method for the decomposition, reconstruction, and single trial analysis of event related potentials

Cited by 146 publications

References 25 publications

Event-related lateralized readiness potential correlates of the emotion-priming Simon effect

Event-related lateralized readiness potential correlates of the emotion-priming Simon effect

Neuronal Intra-Individual Variability Masks Response Selection Differences between ADHD Subtypes—A Need to Change Perspectives

Mind the Noise When Identifying Computational Models of Cognition from Brain Activity

Contact Info

Product

Resources

About