Inconsistent analytic strategies reduce robustness in fear extinction via skin conductance response

Ney, Luke J.; Laing, Patrick A.F.; Steward, Trevor; Zuj, Daniel V.; Dymond, Simon; Felmingham, Kim L.

doi:10.1111/psyp.13650

Cited by 30 publications

(29 citation statements)

References 62 publications

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“…Demographics and genotypes were compared with univariate ANOVAs or chi‐square tests, where appropriate. Data analysis of SCRs was synonymous with our recent approaches (Ney, Vicario, et al, 2021), so as to conform to the standardization of fear extinction analysis (Ney et al, 2018; Ney et al, 2020). Specifically, linear mixed‐effects were used to analyze SCR data during habituation, fear conditioning, early extinction learning, and late extinction learning phases separately.…”

Section: Methodsmentioning

confidence: 99%

Cannabinoid polymorphisms interact with plasma endocannabinoid levels to predict fear extinction learning

Ney

Matthews

Hsu

et al. 2021

Depression and Anxiety

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Background The endocannabinoid system is gaining increasing attention as a favorable target for improving posttraumatic stress disorder (PTSD) treatments. Exposure therapy is the gold‐standard treatment for PTSD, and fear extinction learning is a key concept underlying successful exposure. Methods This study examined the role of genetic endocannabinoid polymorphisms in a fear extinction paradigm with PTSD compared to healthy participants (N = 220). Participants provided saliva for genotyping, completed a fear conditioning and extinction task, with blood samples taken before and after the task (n = 57). Skin conductance was the outcome and was analyzed using mixed models. Results Results for cannabinoid receptor type 1 polymorphisms suggested that minor alleles of rs2180619 and rs1049353 were associated with poorer extinction learning in PTSD participants. The minor allele of the fatty acid amide hydrolase (FAAH) polymorphism rs324420 was associated with worse extinction in PTSD participants. Subanalysis of healthy participants (n = 57) showed the FAAH rs324420 genotype effect was dependent on plasma arachidonoyl ethanolamide (AEA) level, but not oleoylethanolamide or 2‐arachidonoyl glycerol. Specifically, higher but not lower AEA levels in conjunction with the minor allele of FAAH rs324420 were associated with better extinction learning. Conclusions These findings provide translational evidence that cannabinoid receptor 1 and AEA are involved in extinction learning in humans. FAAH rs324420's effect on fear extinction is moderated by AEA plasma level in healthy controls. These findings imply that FAAH inhibitors may be effective for targeting anxiety in PTSD, but this effect needs to be explored further in clinical populations.

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Section: Methodsmentioning

confidence: 99%

Cannabinoid polymorphisms interact with plasma endocannabinoid levels to predict fear extinction learning

Ney

Matthews

Hsu

et al. 2021

Depression and Anxiety

Self Cite

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“…For Ledalab and PsPM, data used for i, ii and iii were derived from the same model as the full phase. Decision to include these additional phases was guided by the fact that the specific number of trials included in the statistical models to analyze the success of fear acquisition training is heterogeneous in the literature as revealed by the systematic literature search (T. Lonsdorf et al, 2021) and as illustrated for fear extinction (T. Lonsdorf et al, 2021;Ney et al, 2020).…”

Section: Scr Response Quantification Approaches Employedmentioning

confidence: 99%

Navigating the manifold of skin conductance response quantification approaches – a direct comparison of Trough-to-Peak, Baseline-correction and model-based approaches in Ledalab and PsPM

Lonsdorf¹,

Kuhn²,

Gerlicher³

2021

Preprint

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Scientific work rests fundamentally upon data, their measurement, processing, analysis, illustration and interpretation. Raw data have to be processed to be ready for statistical analyses and interpretation. While these processing pipelines can be well defined and standardized, they are often characterized by substantial heterogeneity. Here, we present results from a systematic literature search on the different SCR response quantification approaches used in the literature by using fear conditioning research as a case example. Next, we applied seven of the identified approaches (trough-to-peak scoring, script-based baseline-correction, Ledalab as well as four different models implemented in the software PsPM) to two fear conditioning datasets differing in key procedural specifications (i.e., CS duration, reinforcement rate, number of trials). This can be viewed as a set of robustness analyses (i.e., same data subjected to different methods) aiming to investigate if and to what extent these methods yield comparable results. To our knowledge, no formal framework for the evaluation of robustness analyses exists to date, but we may borrow some criteria from a framework suggested for the evaluation of ‘replicability’ in general. Our results from seven different SCR response quantification approaches applied to two datasets suggest that there may be no single approach that consistently yields larger effect sizes across both datasets. Yet, at least some of the approaches employed show consistent effect sizes within each dataset indicating comparability. Finally, we highlight substantial heterogeneity also within most quantification approaches and discuss implications and potential remedies.

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“…Yet, many decisions are required also when planning statistical analyses of a fear MULTIVERSE ANALYSES 4 conditioning study (Ney et al, 2020) which involves questions, such as: Shall I run a t-test or an Analysis of Variance (ANOVA)? Shall I use p-values or Bayes factors?…”

Section: Multiverse Analysesmentioning

confidence: 99%

“…Yet, this verbal theory is ill-defined as it does not specify for instance a) how high those responses will be (e.g., 10, 20, 50 point differences), and b) how many pairings between the CS+ and the US are required for differential responses will be expressed (e.g., after 2, 3, or 10 trials). This imprecision in 5 theory results in a multitude of different statistical models that may be used (Muthukrishna & Henrich, 2019) on idiosyncratic criteria about how large CS discrimination needs to be , or to consider different amounts of trials in their analyses (Lonsdorf, Klingelhöfer-Jens, et al, 2019;Ney et al, 2020). The decisions to choose a specific statistical analysis from a set of plausible analyses can be considered to occur mostly in good faith.…”

Section: Multiverse Analysesmentioning

confidence: 99%

“…Recently, (Ney et al, 2020) described inconsistent statistical strategies when analyzing skin conductance data in fear extinction training. Their results suggest unsatisfying correlations between the different analysis approaches as applied to the same data-set which were mainly attributable to the selection of trials from different stages of the experimental phases and employment of trial-by-trial analyses vs. averaged scores (Ney et al, 2020). This may not be particularly surprising as different analytic strategies may not test exactly the same underlying hypothesis but may -intentionally or unintentionally -test different hypotheses.…”

Section: Multiverse Analysesmentioning

confidence: 99%

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Multiverse analyses in fear conditioning research

Lonsdorf¹,

Gerlicher²,

Klingelhöfer-Jens³

et al. 2021

Preprint

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There is heterogeneity in and a lack of consensus on the preferred statistical analyses foranalyzing fear conditioning effects in light of a multitude of potentially equally justifiablestatistical approaches. Here, we introduce the concept of multiverse analysis for fearconditioning research. We also present a model multiverse approach specifically tailored tofear conditioning research and introduce the novel and easy to use R package ‘multifear’ thatallows to run all the models though a single line of code. Model specifications and datareduction approaches employed in the ‘multifear’ package were identified through arepresentative systematic literature search. The heterogeneity of statistical models identifiedincluded Bayesian ANOVA and t-tests as well as frequentist ANOVA, t-test as well as mixedmodels with a variety of data reduction approaches (i.e., number of trials, trial blocks,averages) as input. We illustrate the power of a multiverse analysis for fear conditioning databased on two pre-existing data sets with partial (data set 1) and 100% reinforcement rate(data set 2) by using CS discrimination in skin conductance responses (SCRs) during fearacquisition and extinction training as case examples. Both the effect size and the direction ofeffect was impacted by choice of the model and data reduction techniques. We anticipatethat an increase in multiverse-type of studies in the field of fear conditioning research andtheir extension to other outcome measures as well as data and design multiverse analyseswill aid the development of formal theories through the accumulation of empirical evidence.This may contribute to facilitated and more successful clinical translation.

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Inconsistent analytic strategies reduce robustness in fear extinction via skin conductance response

Cited by 30 publications

References 62 publications

Cannabinoid polymorphisms interact with plasma endocannabinoid levels to predict fear extinction learning

Cannabinoid polymorphisms interact with plasma endocannabinoid levels to predict fear extinction learning

Navigating the manifold of skin conductance response quantification approaches – a direct comparison of Trough-to-Peak, Baseline-correction and model-based approaches in Ledalab and PsPM

Multiverse analyses in fear conditioning research

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