Dimensions of anxiety and depression and neurophysiological indicators of error‐monitoring: Relationship with delta and theta oscillatory power and error‐related negativity amplitude

Muir, Alexandra M.; Hedges-Muncy, Ariana; Clawson, Ann; Carbine, Kaylie A.; Larson, Michael J.

doi:10.1111/psyp.13595

Cited by 18 publications

(7 citation statements)

References 78 publications

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“…An association with anxious apprehension was further supported by a study that compared groups with symptom profiles of OCD, worry, and anxiety symptoms and linked increased ERN amplitudes specifically to worry (Zambrano‐Vazquez & Allen, 2014). However, other studies failed to replicate the association between anxious apprehension and ERN (Härpfer et al, 2020; Muir et al, 2020) and it has been suggested that the anxiety‐ERN relationship might be stronger or even specific to clinical samples (Härpfer et al, 2022; Saunders & Inzlicht, 2020; Seow et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Associations of neural error‐processing with symptoms and traits in a dimensional sample recruited across the obsessive–compulsive spectrum

et al. 2022

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The error‐related negativity (ERN), a neural response to errors, has been associated with several forms of psychopathology and assumed to represent a neural risk marker for obsessive–compulsive disorder (OCD) and anxiety disorders. Yet, it is still unknown which specific symptoms or traits best explain ERN variation. This study investigated performance‐monitoring in participants (N = 100) recruited across a spectrum of obsessive–compulsive characteristics (n = 26 patients with OCD; n = 74 healthy participants including n = 24 with low, n = 24 with medium, and n = 26 with high OC‐characteristics). Several compulsivity‐ and anxiety‐associated characteristics were assessed and submitted to exploratory principal axis factor analysis. Associations of raw measures and derived factors with ERN and correct‐related negativity (CRN) were examined. Patients with OCD showed increased ERN amplitudes compared to healthy participants. The ERN was associated with a variety of traits related to anxiety and negative affect. Factor analysis results revealed a most prominent association of the ERN with a composite measure of anxiety and neuroticism, whereas the CRN was specifically associated with compulsivity. Results support differential associations for the ERN and CRN and demonstrate that a dimensional recruitment approach and use of composite measures can improve our understanding of characteristics underlying variation in neural performance monitoring.

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Section: Introductionmentioning

confidence: 99%

Associations of neural error‐processing with symptoms and traits in a dimensional sample recruited across the obsessive–compulsive spectrum

et al. 2022

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“…In many areas of neuroscience research, including ERP studies, a common approach for calculating difference scores is to subtract activity during one condition ( X , the subtrahend) from activity during another condition ( Y , the minuend; Y − X ). Another approach increasing in popularity is a residualized difference score (e.g., Crane et al., 2021; Hanna et al., 2020; Klawohn et al., 2020; Macatee et al., 2020; Meyer et al., 2017; Muir et al., 2020; Sandre et al., 2020; Schroder et al., 2020; Suor et al, in press). A residualized difference score is the difference between the actual score ( Y ) and predicted score (

\overset{Y}{true}

) using a linear regression equation (

Y ‐ \hat{Y}

), wherein Y scores are regressed on X .…”

Section: Introductionmentioning

confidence: 99%

Evaluating the internal consistency of subtraction‐based and residualized difference scores: Considerations for psychometric reliability analyses of event‐related potentials

2021

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In studies of event‐related brain potentials (ERPs), difference scores between conditions in a task are frequently used to isolate neural activity for use as a dependent or independent variable. Adequate score reliability is a prerequisite for studies examining relationships between ERPs and external correlates, but there is no extensive treatment on the suitability of the various available approaches to estimating difference score reliability that focus on ERP research. In the present study, we provide formulas from classical test theory and generalizability theory for estimating the internal consistency of subtraction‐based and residualized difference scores. These formulas are then applied to error‐related negativity (ERN) and reward positivity (RewP) difference scores from the same sample of 117 participants. Analyses demonstrate that ERN difference scores can be reliable, which supports their use in studies of individual differences. However, RewP difference scores yielded poor reliability due to the high correlation between the constituent reward and non‐reward ERPs. Findings emphasize that difference score reliability largely depends on the internal consistency of constituent scores and the correlation between those scores. Furthermore, generalizability theory yields more suitable estimates of internal consistency for subtraction‐based difference scores than classical test theory. We conclude that ERP difference scores can show adequate reliability and be useful for isolating neural activity in studies of individual differences.

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“…Next, the data were lowpass filtered using the same sixth-order IIR Butterworth filter, and the three lowpass cutoffs used were 15 Hz, 20 Hz, and 30 Hz. These highpass filter choices were based on studies examining the impact of filter settings on ERPs related to language and cognition (e.g., Duncan-Johnson & Donchin, 1979;Tanner, Morgan-Short, & Luck, 2015) as well as on ERN/Pe work that has used these settings (e.g., Banica, Sandre, & Weinberg, 2019;Härpfer, Carsten, Spychalski, Kathmann, & Riesel, 2020;Muir, Hedges-Muncy, Clawson, Carbine, & Larson, 2020). Continuous EEG data were then epoched from -500 ms prior to the participant's response to 800 ms following the participant's response.…”

Section: Electrophysiological Data Recording and Reductionmentioning

confidence: 99%

The Data-Processing Multiverse of Event-Related Potentials (ERPs): A Roadmap for the Optimization and Standardization of ERP Processing and Reduction Pipelines

Clayson¹,

Baldwin²,

Rocha³

et al. 2021

Preprint

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In studies of event-related brain potentials (ERPs), numerous decisions about data processing are required to extract ERP scores from continuous data. Unfortunately, the systematic impact of these choices on the data quality and psychometric reliability of ERP scores or even ERP scores themselves is virtually unknown, which is a barrier to the standardization of ERPs. The aim of the present study was to optimize processing pipelines for the error-related negativity (ERN) and error positivity (Pe) by considering a multiverse of data processing choices. A multiverse analysis of a data processing pipeline examines the impact of a large set of different reasonable choices to determine the robustness of effects, such as the impact of different decisions on between-trial standard deviations (i.e., data quality) and between-condition differences (i.e., experimental effects). ERN and Pe data from 298 healthy young adults were used to determine the impact of different methodological choices on data quality and experimental effects (correct vs. error trials) at several key stages: highpass filtering, lowpass filtering, ocular artifact correction, reference, baseline adjustment, scoring sensors, and measurement procedure. This multiverse analysis yielded 3,456 ERN scores and 576 Pe scores per person. An optimized pipeline for ERN included a .01 Hz highpass filter, 15 Hz lowpass filter, ICA-based ocular artifact correction, and a region of interest (ROI) approach to scoring. For Pe, the optimized pipeline included a .10 Hz highpass filter, 30 Hz lowpass filter, regression-based ocular artifact correction, a -200 to 0 ms baseline adjustment window, and an ROI approach to scoring. The multiverse approach can be used to optimize pipelines for eventual standardization, which would support efforts toward establishing normative ERP databases. The proposed process of analyzing the data-processing multiverse of ERP scores paves the way for better refinement, identification, and selection of data processing parameters, ultimately improving the precision and utility of ERPs.

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Dimensions of anxiety and depression and neurophysiological indicators of error‐monitoring: Relationship with delta and theta oscillatory power and error‐related negativity amplitude

Cited by 18 publications

References 78 publications

Associations of neural error‐processing with symptoms and traits in a dimensional sample recruited across the obsessive–compulsive spectrum

Associations of neural error‐processing with symptoms and traits in a dimensional sample recruited across the obsessive–compulsive spectrum

Evaluating the internal consistency of subtraction‐based and residualized difference scores: Considerations for psychometric reliability analyses of event‐related potentials

The Data-Processing Multiverse of Event-Related Potentials (ERPs): A Roadmap for the Optimization and Standardization of ERP Processing and Reduction Pipelines

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