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Exposure to adverse childhood experiences (ACEs) is a major risk factor for the development of behavioral, somatic and psychopathological conditions. Learning is the central mechanism through which environmental inputs shape emotional and cognitive processes as well as behavior. In this review, we lay out a systematic and methodological overview and integration of the impact of ACEs on threat and reward learning processes in experimental paradigms based on systematic literature search (following PRISMA guidelines) which resulted in a total of 65 publications (threat: N=37, reward: N=28). Across the threat and reward learning fields, we observed a converging pattern of blunted learning in individuals exposed to ACEs independent of sample characteristics, ACE subtypes and outcome measures. Specifically, blunted threat learning was reflected in reduced discrimination between threat and safety cues, primarily driven by blunted responding to threat cues, while attenuated reward learning manifested in reduced accuracy and learning rate. Importantly, this pattern emerged despite substantial heterogeneity in ACE assessment and operationalization. we conclude that blunted threat and reward learning may represent two mechanistic routes by which ACEs may become biologically embedded and ultimately increase the risk for psychopathology. In closing, we discuss potentially fruitful future directions for the research field, including methodological and assessment considerations., The zip-folder contains the text, code, code, figures and tables necessary to create the manuscript with the title as indicated above. Please download the zip-folder and follow the instructions of the ReadMe-file to render the manuscript. This work was funded by the German Research foundation (DFG, Deutsche Forschungsgemeinschaft) - GRK 2753/1 - Projektnummer 449640848 and DFG LO1980/4-1.

Raw data are typically required to be processed to be ready for statistical analyses, and processing pipelines are often characterized by substantial heterogeneity. Here, we applied seven different approaches (trough-to-peak scoring by two different raters, script-based baseline correction, Ledalab as well as four different models implemented in the software PsPM) to two fear conditioning data sets. Selection of the approaches included was guided by a systematic literature search by using fear conditioning research as a case example. Our approach can be viewed as a set of robustness analyses (i.e., same data subjected to different processing pipelines) aiming to investigate if and to what extent these different quantification approaches yield comparable results given the same data. 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 quantification approaches applied to two data sets with different paradigms suggest that there may be no single approach that consistently yields larger effect sizes and could be universally considered "best." Yet, at least some of the approaches employed show consistent effect sizes within each data set indicating comparability. Finally, we highlight substantial heterogeneity also within most quantification approaches and discuss implications and potential remedies.

Since the so-called “replication crisis”, the reproducibility of findings as a major quality criterion in natural science is the target of methodological discussions in many research areas (Munafò et al., 2017; Open Science Colloboration., 2015). To address this topic, the scientific community requires strategies to increase transparency. When preparing their data, researchers inevitably take more or less arbitrary decisions by picking a processing pipeline out of a large pool of (potentially) equally justifiable data processing and analysis choices (Brand, Dixon, & McBee, 2019). Usually there is more than one suitable path without a clear empirical evidence for the superiority of appropriateness of one specific path (Dragicevic, Jansen, Sarma, Kay, & Chevalier, 2019). These researchers’ degrees of freedom provoke a garden of forking paths (Gelman & Loken, 2014) which has been criticized in the current literature. Further, the lack of consensus about data processing and analysis pipelines used even within an area of research leads to difficulties in comparing findings across different studies. Consequently, the result of a single statistical model applied to a single data set generated via a specific preprocessing pipeline - as typically reported in research papers - is limited, since it doesn’t inform on the robustness of the result. To address the problem of choosing one data processing path while multiple suitable paths may exist, multiverse analysis has been proposed (Steegen, Tuerlinckx, Gelman, & Vanpaemel, 2016). Thereby researchers generate a ‘data multiverse’ from all or many possible decision paths in processing a single data set. The underlying idea of a data multiverse is that at every point the researchers have to take a decision, the multiverse grows by one more possible data set or by a whole branch of single data sets. This naturally leads to a multiverse of results based on this data multiverse even when applying an identical statistical model to all data sets (i.e., ‘data multiverse’). A similar approach is to set up a ‘model multiverse’ by applying different statistical models to a single data set (Donnelly, Brooks, & Homer, 2019; McBee, Brand, & Wallace E. Dixon, 2019; Patel, Burford, & Ioannidis, 2015). As a consequence, a data multiverse analysis illustrates the existence of possible outcomes based on equally justifiable choices within different preprocessing or transformation pipelines of a single data set (Stern, Arslan, Gerlach, & Penke, 2019). In case findings show homogeneity of results across the different forking paths, the robustness of an effect independent of the used preprocessing pipeline (for a data multiverse) or statistical pipeline (for a model multiverse), can be assumed. If inhomogeneity, however, is observed, this may inform us on potential boundary conditions (for instance inclusion of specific covariates). In this sense, we want to address a topical problem concerning skin conductance response (SCR) quantification approaches which arises from recent methodological discussions about comparability of methods and procedures. In human fear conditioning research the most commonly used psychophysiological index to infer fear learning are SCR. In practice, different methods to quantify SCR exist: First of all, the trough-to-peak (TTP) approach consists of quantifying the SCR amplitude as the difference of the peak score of the response and the preceding trough within specific time-windows and criteria (Boucsein, 1992). It can be conducted manually or semi-manually (i.e., computer-assisted) through trained raters and is supported in a fully computerized (though manually modifiable) way in the software Autonomate, which implements the TTP criteria (Boucsein, 1992) in computer-based algorithms (Green, Kragel, Fecteau, & LaBar, 2014). Second, computational approaches for SCR quantification exist with software programs such as Ledalab or PsPM providing computerized model-based SCR quantification (Bach, 2014; Staib, Castegnetti, & Bach, 2015). A third SCR quantification approach is what we here refer to as baseline correction approach (Pineles, Orr, & Orr, 2009): The baseline of each SCR is defined as the mean in a pre-CS period (conditioned stimulus). This mean is subtracted from the subsequent peak response in a post-CS period. Of note, the pre- and post-CS periods vary in the literature. In sum, there is no standard quantification approach for SCR, but all approaches mentioned are currently used interchangeably. As part of previous work, we conducted a systemic literature research (described in detail in: Lonsdorf et al., 2019) covering all publications (including e-pubs ahead of print) in PubMed from the 22nd September 2018 to the 22nd March 2019 to identify records which measure SCRs in human fear conditioning research. From 854 records identified through database searching, 50 records remained after the exclusion of review articles/ meta-analysis, rodent work, irrelevant topics, duplicates and no SCR measurement (for further information on screening and exclusion of records see the supplemental flow chart according to PRISMA guidelines (Liberati et al., 2009)). In these articles, TTP and baseline correction for SCR quantification were most common in the field with few studies employing computational approaches (an overview can be found in the additional bar chart). Yet, the “baseline correction category” is itself a particularly heterogeneous category as we identified 16 different ways to perform SCR quantification through baseline correction through our systematic literature search. These differed in the duration and length of the time windows used (yet, only partly explained by different experimental timings of the employed paradigm). Because TTP SCR quantification approaches are conducted semi-manually by independent raters they may be affected by the raters’ decision even with extensive rater-training. Here, we aim to investigate whether differences in the operationalization of SCR quantification through baseline correction and semi-manual TTP scoring as used in the field (described in detail in: Lonsdorf et al., 2019) result in divergent or convergent outcomes. Hence, we employ these different definitions in a multiverse analysis by applying all of them to a single preexisting data set to generate a data multiverse. Beside the frequentist approach we implement a Bayesian inference approach and compute Bayes Factors. Bayes Factors quantify the evidence for the absence of an effect or the absence of evidence for or against an effect. As our primary aim, we want to test for CS+/CS- discrimination within fear acquisition training (effect of main interest) as a prime example in this data multiverse based on different SCR quantification approaches. Our secondary aim is to use trait anxiety, as assessed by the STAI-Questionnaire (Spielberger, Gorsuch, & Lushene, 1983), as a case example for individual differences in fear conditioning to test robustness of a possible association between CS+/CS- discrimination in fear acquisition training and trait anxiety regarding different baseline correction approaches and TTP scoring in a data multiverse approach. This is important as individual difference effects are much more subtle and hence potentially more susceptible to data processing differences (Eysenck, 1963; Lonsdorf & Merz, 2017; Naveteur & Freixa i Baque, 1987). Findings regarding the association between trait anxiety as a criterion variable and differential SCRs in fear conditioning research show inconsistent results and were mainly assessed in small samples fear conditioning experiments (Indovina, Robbins, Núñez-Elizalde, Dunn, & Bishop, 2011; Lissek et al., 2005; Mineka & Oehlberg, 2008; Plehn & Peterson, 2002; Torrents-Rodas et al., 2013; larger sample: Sjouwerman, Scharfenort, & Lonsdorf, preprint). Hence we want to investigate the agreement between the baseline correction variants and TTP scoring from the multiverse approach using the Bland-Altman approach for method comparison (Bland & Altman, 1986, 1995). References Bach, D. R. (2014). 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The relevance of recent developments in classical conditioning to understanding the etiology and maintenance of anxiety disorders. Acta Psychologica, 127(3), 567–580. https://doi.org/10.1016/j.actpsy.2007.11.007 Morey, R. D., & Rouder, J. N. (2018). BayesFactor 0.9.12-4.2. Comprehensive R Archive Network. Retrieved from http://cran.r-project.org/web/packages/ BayesFactor/index.html Munafò, M. R., Nosek, B. A., Bishop, D. V. M., Button, K. S., Chambers, C. D., Percie Du Sert, N., … Ioannidis, J. P. A. (2017). A manifesto for reproducible science. Nature Human Behaviour, 1(1), 1–9. https://doi.org/10.1038/s41562-016-0021 Naveteur, J., & Freixa i Baque, E. (1987). Individual differences in electrodermal activity as a function of subjects’ anxiety. Personality and Individual Differences, 8(5), 615–626. https://doi.org/10.1016/0191-8869(87)90059-6 Open Science Colloboration. (2015). Estimating the reproducibility of psychological science. 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Individual differences in predicting fear: Specificity to trait anxiety and mediation via amygdala activation (in revision). https://doi.org/https://www.biorxiv.org/content/10.1101/233528v2 Spielberger, C., Gorsuch, R., & Lushene, R. (1983). Manual for the State-Trait Anxiety Inventory. In Consulting Psychologists Press. Palo Alto, CA. Staib, M., Castegnetti, G., & Bach, D. R. (2015). Optimising a model-based approach to inferring fear learning from skin conductance responses. Journal of Neuroscience Methods, 255, 131–138. https://doi.org/10.1016/j.jneumeth.2015.08.009 Steegen, S., Tuerlinckx, F., Gelman, A., & Vanpaemel, W. (2016). Increasing Transparency Through a Multiverse Analysis. Perspectives on Psychological Science, 11(5), 702–712. https://doi.org/10.1177/1745691616658637 Stern, J., Arslan, R. C., Gerlach, T. M., & Penke, L. (2019). 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code for figure 1 is shared under CC0.1 and is based on publicly available code by Tom Hardwicke et al., (2022) DOI: 10.1177/1745691620979806

The prevalence of being exposed to a traumatic event lies between approximately 54 and 60.6% in Europe (Carmassi et al., 2014; Ferry et al., 2014; Olaya et al., 2014). In many cases, the experience of such a traumatic event can lead to the development of pathological posttraumatic distress. It is not exactly known how exposure to a traumatic event develops into a disorder, but interindividual differences in cerebral morphology and baseline cortisol levels are discussed in the literature as potential risk factors. The hippocampus, the amygdala and the anterior cingulate cortex (ACC) (Rauch et al., 2003; Kitayama et al., 2005) have been extensively investigated in this context. A meta-analysis involving 89 structural magnetic resonance imaging (MRI) studies (Bromis et al., 2018), concluded that size differences of these three brain regions are a common finding among subjects suffering from PTSD when compared to control subjects. Nevertheless, it is still unclear whether these structural differences in PTSD patients are a pre-existing vulnerability factor affecting the severity of their symptoms after trauma exposure or whether they are a consequence of the experience. Only a handful of studies have employed longitudinal designs to study potential changes of the hippocampus and other regions in trauma victims and have related them to the development of PTSD. Critically, their results are inconsistent. Bonne et al. (2001) and Xie et al. (2018) looked exclusively at trauma victims with structural images taken immediately after a trauma and some months later. A longitudinal effect of traumatic experiences on hippocampal volume was neither found between one week and three months post-trauma (Bonne et al., 2001) nor between two weeks and six months after the trauma (Xie et al., 2018). However, Xie et al. (2018) report a significant negative association between left hippocampal volume and posttraumatic stress symptom severity. Another study by Quidé et al. (2018) compared women who suffered from sexual violence one month prior to structural imaging and a control group of untraumatized women in a longitudinal design. Again, hippocampal volume and amygdala volume did not vary between the two measurements six months apart. However, trauma victims who developed PTSD after six months had a significantly smaller hippocampal volume at the first measurement compared to the control subjects. Lastly, in a recent study by Dark et al. 2021 a longitudinal design was employed as well, however, only involving a structural MRI at the first time point and an assessment of PTSD symptoms at both time points (one month and three months post trauma). Investigating trauma victims and control subjects, the authors found a significantly lower volume of the left hippocampi of the trauma group and a significant negative association of bilateral hippocampal volume with posttraumatic stress symptom severity after one month but not after three months. Despite the inconsistencies of the results of these studies, hippocampal volume appears to rather be a risk factor for posttraumatic stress symptoms and not a consequence of traumatic experiences, since none of them found any longitudinal changes on the brain morphology. In addition to brain morphology, saliva and plasma cortisol measures might also have predictive potential towards determining the onset of posttraumatic stress symptoms after exposure to a traumatic event. It appears to be a common finding that the cortisol levels of individuals suffering from PTSD are blunted, meaning that they show lower levels of circulating cortisol than healthy control subjects, especially in the morning (Rauch et al., 2020; Pan et al., 2018). However, not much is known about how cortisol levels react to acute traumas and there are only few studies that investigated the cortisol response a few weeks after subjects experienced a trauma. Mouthaan et al. (2014) have shown in a large sample that lower levels of cortisol six weeks after a trauma predicted more severe posttraumatic stress symptoms six months later. A comparable finding has been made by McFarlane et al. (2011), who reported that lower saliva morning cortisol levels one month after sexual trauma predicted more severe PTSD symptoms six months later. While these studies seem to show that low levels of cortisol right after a trauma could possibly predict the severity of posttraumatic outcomes, there is a need for studies including a group comparison with control subjects to see how cortisol levels might be affected by the experience of the trauma. This research project aims to fill this gap by conducting a group comparison of both plasma and saliva morning cortisol levels between recently traumatized subjects and healthy controls, by investigating how cortisol levels develop in both groups over time and whether posttraumatic stress symptoms could be predicted from cortisol levels. Furthermore, this project will look at the predictive potential of the cerebral morphology of traumatized subjects. We will also include group comparisons with control subjects to check for potential longitudinal effects of the trauma on brain morphology, which previous studies have not found so far. Lastly, we also want to look at the interaction of both factors and see whether together they might serve to predict PTSD symptoms more accurately.

Affective Startle Modulation and Life Adversity

Anxiety disorders are amongst the most diagnosed mental diseases and tend to have a chronic course. Women are twice more likely to be affected (Bandelow & Michaelis, 2015). There are effective interventions, but also high relapse rates after successful treatment (Yonkers et al., 2003). What is still to be determined are the individual factors that facilitate relapse and how to prevent them. Fear conditioning protocols are used to simulate the development of anxiety disorders, their treatment and relapse in an experimental setting (Mineka & Oehlberg, 2008). In differential fear conditioning protocols, participants are confronted with two initially neutral stimuli. During fear acquisition training, one of these (CS+) is paired with an aversive stimulus (US), provoking a conditioned response (CR). The other stimulus (CS-) is never paired with the US. The difference in responses to CS+ and CS- (i.e., CS discrimination) is taken to indicate discriminatory learning between stimuli signaling danger (i.e., CS+) and safety (i.e., CS-). When the CS+ is no longer paired with the US during extinction training, the CR gradually wanes. Research suggests that extinction training, however, does not lead to erasure of the fear memory to the CS+, but instead generates a new, inhibitory memory trace (Bouton, 2004), both of which co-exist after successful extinction training. Thus, a subsequent return of fear (ROF) test can be used to determine which memory trace is longer lasting or stronger. Return of fear can for instance be experimentally induced by ‘reinstatement’: After extinction training, participants get confronted with the US unexpectedly and the response to both conditioned stimuli (CS+ and CS-) is subsequently tested. If the CR does not re-occur, extinction memory is considered dominant, which is called extinction recall. If the CR re-occurs, the fear memory is considered dominant, which is called return of fear. This is considered an experimental analogue of relapse. The return of fear can appear in different forms. Some participants show the CR in the reinstatement test phase specifically to the CS+, but not to the CS- which is referred to as differential reinstatement. Other participants show the CR to the CS+ as well as to the CS-. This is called generalized reinstatement and may be related to overgeneralization of fear, which is often observed in patients with anxiety disorders (Dymond et al., 2015). Other return of fear phenomena are also used in experimental settings such as return of fear induced by a change in context (i.e., renewal). Studies show that acute stress can prevent renewal-induced return of fear (Drexler et al., 2018, Merz et al., 2014). Drexler et al. treated one group of participants with the socially evaluated cold-pressor test (SECPT, Schwabe et al., 2008) directly before extinction training. This group showed significantly less return of fear (in form of renewal) in the SCRs than the control group. Jackson et al. (2006) showed in a differential fear conditioning paradigm without ROF manipulation that acute administration of stress before fear acquisition training has different effects on women and men: After being confronted with a social stressor (slightly modified version of the Trier Social Stress Test, Kirschbaum et al., 1993), only male participants showed enhanced SCRs to the CS+ in all phases of the experiment, in contrast to unstressed controls. Stressed women showed reduced SCRs on a trend level in acquisition and early extinction phase as compared to controls, though this difference was not significant. The study also showed elevated salivary cortisol levels in male, but not in female participants after the acute stress exposure. Apart of sex and timing, different types of stress seem to matter. For example, the experience of a life event, which can be viewed as a stressor: Scharfenort et al. (2016) showed that one or more aversive life events in a twelve-month period prior to the experiment facilitate return of fear in healthy adults. Participants exposed to life events showed a generalized fear recall and reinstatement-induced return of fear with reduced SCRs to CS+ than participants without adverse events. What has not been determined yet are the influences of chronic stress on return of fear in humans. Chronic stress is defined as a prolonged exposure to (internal or external) stressors resulting in an ergotropic response, lasting longer than is adaptive. It is still to be determined whether chronic stress has an impact on reinstatement and fear recall and if this influence is affected by the sex of participants. Due to the severe consequences of chronic stress, an experimental induction in humans is ethically impossible. The assessment of chronic stress is therefore mostly implemented via self-report questionnaires including life events and measurement of hair cortisol concentration (HCC) as a proxy (Russell et al., 2012; Stalder et al., 2017). Studies show that HCC and self-report measurements show a small correlation (r