Exposure to childhood adversity - particularly in early life - is a strong predictor for developing somatic, psychological, and psychopathological conditions (Anda et al., 2006; Baldwin et al., 2024; Danese et al., 2007; Danese and Widom, 2023; Felitti, 2002; Gilbert et al., 2009; Green et al., 2010; Heim and Nemeroff, 2002; Hosseini-Kamkar et al., 2023; Klauke et al., 2010; McLaughlin et al., 2012; Moffitt et al., 2007; Samaey et al., 2024; Teicher et al., 2022) and is hence associated with substantial individual suffering as well as societal costs (Hughes et al., 2021). Exposure to childhood adversity is rather common, with nearly two-thirds of individuals experiencing one or more traumatic events prior to their 18^th birthday (McLaughlin et al., 2013). While not all trauma-exposed individuals develop psychopathological conditions, there is some evidence of a dose-response relationship (Danese et al., 2009; Smith and Pollak, 2021; Young et al., 2019). As this potential relationship is not yet fully clear, understanding the mechanisms by which childhood adversity becomes biologically embedded and contributes to the pathogenesis of stress-related somatic and mental disorders is central to the development of targeted intervention and prevention programmes. Learning is a core mechanism through which environmental inputs shape emotional and cognitive processes and ultimately behavior. Thus, learning mechanisms are key candidates potentially underlying the biological embedding of exposure to childhood adversity and their impact on development and risk for psychopathology (McLaughlin and Sheridan, 2016).

Fear conditioning is a prime translational paradigm for testing potentially altered (threat) learning mechanisms following exposure to childhood adversity under laboratory conditions. The fear conditioning paradigm typically consists of different experimental phases (Lonsdorf et al., 2017). During fear acquisition training, a neutral cue is paired with an aversive event such as an electrotactile stimulation or a loud aversive human scream (unconditioned stimulus, US). Through these pairings, an association between both stimuli is formed and the previously neutral cue becomes a conditioned stimulus (CS+) that elicits conditioned responses. In human differential conditioning experiments, typically a second neutral cue is never paired with the US and serves as a control or safety stimulus (i.e. CS-). During a subsequent fear extinction training phase, both the CS+ and the CS- are presented without the US which leads to a gradual waning of conditioned responding. A fear generalization phase includes additional stimuli (i.e. generalization stimuli; GSs) that are perceptually or conceptually similar to the CS+ and CS- (e.g. generated through merging perceptual properties of the CS+ and CS-) which allows for the investigation of the degree to which conditioned responding generalizes to similar cues.

Fear acquisition as well as extinction are considered as experimental models of the development and exposure-based treatment of anxiety- and stress-related disorders. Fear generalization is in principle adaptive in ensuring survival (‘better safe than sorry’), but broad overgeneralization can become burdensome for patients. Accordingly, maintaining the ability to distinguish between signals of danger (i.e. CS+) and safety (i.e. CS-) under aversive circumstances is crucial, as it is assumed to be beneficial for healthy functioning (Hölzel et al., 2016) and predicts resilience to life stress (Craske et al., 2012), while reduced discrimination between the CS+ and the CS- has been linked to pathological anxiety (Duits et al., 2015; Lissek et al., 2005): Meta-analyses suggest that patients suffering from anxiety- and stress-related disorders show enhanced responding to the safe CS- during fear acquisition (Duits et al., 2015). During extinction, patients exhibit stronger defensive responses to the CS+ and a trend toward increased discrimination between the CS+ and CS- compared to controls, which may indicate delayed and/or reduced extinction (Duits et al., 2015). Furthermore, meta-analytic evidence also suggests stronger generalization to cues similar to the CS+ in patients and more linear generalization gradients (Cooper et al., 2022; Dymond et al., 2015; Fraunfelter et al., 2022). Hence, aberrant fear acquisition, extinction, and generalization processes may provide clear and potentially modifiable targets for intervention and prevention programs for stress-related psychopathology (McLaughlin and Sheridan, 2016).

In sharp contrast to these threat learning patterns observed in patient samples, a recent review provided converging evidence that exposure to childhood adversity is linked to reduced CS discrimination, driven by blunted responding to the CS+ during experimental phases characterized through the presence of threat (i.e. acquisition training and generalization, Ruge et al., 2024). Of note, this pattern was observed in mixed samples (healthy, at risk, patients), in pediatric samples, and in adults exposed to childhood adversity as children. The latter suggests that recency of exposure or developmental timing may not play a major role, even though there is some evidence pointing towards accelerated pubertal and neural (connectivity) development in exposed children (Machlin et al., 2019; Silvers et al., 2016). There is, however, no evidence pointing towards differences in extinction learning or generalization gradients between individuals exposed and unexposed to childhood adversity (for a review, see Ruge et al., 2024).

Ruge et al., 2024 also highlighted operationalization as a key challenge in the field hampering the interpretation of findings across studies and consequently cumulative knowledge generation. Operationalization of exposure to childhood adversity, and hence the translation of theoretical accounts of the role of childhood adversity into statistical tests, is an ongoing discussion in the field (McLaughlin et al., 2021; Pollak and Smith, 2021; Smith and Pollak, 2021). Historically, childhood adversity has been conceptualized rather broadly considering different adversity types lumped into a single category. This follows from the (implicit) assumption that any exposure to an adverse event will have similar and additive effects on the individual and its (neuro-biological) development (Smith and Pollak, 2021). Accordingly, childhood adversity has often been considered as a cumulative measure (‘cumulative risk approach;’ McLaughlin et al., 2021; Smith and Pollak, 2021). An alternative approach posits that different types of adverse events have a distinct impact on individuals and their (neuro-biological) development through distinct mechanisms (‘specificity approach;’ McLaughlin et al., 2021; Sheridan and McLaughlin, 2014; Smith and Pollak, 2021). Currently, distinguishing between threat and deprivation exposure represents the prevailing approach (McLaughlin et al., 2019a), which has been formalized in the (two-)dimensional model of adversity and psychopathology (DMAP; Machlin et al., 2019; McLaughlin and Sheridan, 2016; McLaughlin et al., 2021; McLaughlin et al., 2014; Sheridan and McLaughlin, 2014; McLaughlin et al., 2016; Sheridan and McLaughlin, 2016). To this end, exposure to threat-related childhood adversity has been suggested to be specifically linked to altered emotional and fear learning (Sheridan and McLaughlin, 2014).

Yet, there is converging evidence from different fields of research suggesting that the effects of exposure to childhood adversity are cumulative, non-specific, and rather unlikely to be tied to specific types of adverse events (Danese et al., 2009; Smith and Pollak, 2021; Young et al., 2019) - with few exceptions (Colich et al., 2020; McLaughlin et al., 2019b). This is also supported by the recent review of Ruge et al., 2024. However, the different theoretical accounts have not yet been directly compared in a single fear conditioning study.

Here, we aim to fill this gap in an extraordinarily large sample of healthy adults (N=1402) recruited in the context of a multi-centric study conducted at the Universities of Münster, Würzburg, and Hamburg, Germany. Participants underwent a differential fear conditioning paradigm including a fear acquisition and generalization phase using female faces as CSs and GSs and a female scream as US, while we measured skin conductance responses (SCRs) and different ratings types (i.e. arousal, valence, and US contingency). For SCRs and fear ratings, we calculated three different outcomes: CS discrimination (i.e. the difference between CS+ and CS- responses), the linear deviation score (LDS) as an index of the linearity of the generalization gradient (Kaczkurkin et al., 2017), and the general reactivity which was defined as the reactivity across all phases (for more details, see Materials and methods section). We also performed manipulation checks to verify whether the experimental manipulations had the intended effect.

We operationalized childhood adversity exposure through different approaches: Our main analyses employ the approach adopted by most publications in the field (see Ruge et al., 2024 for a review) - dichotomization of the sample into exposed vs. unexposed individuals based on published cut-offs for the Childhood Trauma Questionnaire (CTQ; Bernstein et al., 2003; Wingenfeld et al., 2010). Individuals were classified as exposed to childhood adversity if at least one CTQ subscale met the published cut-offs (Bernstein and Fink, 1998; Häuser et al., 2011) for at least moderate exposure (i.e. emotional abuse ≥ 13, physical abuse ≥ 10, sexual abuse ≥ 8, emotional neglect ≥ 15, physical neglect ≥ 10).

In addition, we provide exploratory analyses that attempt to translate dominant (verbal) theoretical accounts (McLaughlin et al., 2021; Pollak and Smith, 2021) on the impact of exposure to childhood adversity into statistical tests. At the same time, we acknowledge that such a translation is not unambiguous and these exploratory analyses should be considered as showcasing a set of plausible solutions. With this, we aim to facilitate comparability, replicability, and cumulative knowledge generation in the field as well as providing a solid base for hypothesis generation (Ruge et al., 2024) and refinement of theoretical accounts. More precisely, we attempted to exploratively translate (a) the cumulative risk approach, which is based on the assumed key role of cumulative childhood adversity exposure, (b) the specificity model, which considers specific types of exposure (in the present study: abuse and neglect), and (c) the dimensional model, which also considers specific exposure types but controls for the effects of one another, into statistical tests applied to our dataset. Furthermore, we compiled challenges that arise in the practical implementation of these verbal theories into statistical models (for more details, see Table 1).

Based on the recently reviewed literature (Ruge et al., 2024), we expected less discrimination between signals of danger (CS+) and safety (CS-) in exposed individuals as compared to those unexposed to childhood adversity - primarily due to reduced CS+ responses - during both the fear acquisition and the generalization phase. Based on the literature (Ruge et al., 2024), we did not expect group differences in generalization gradients.

Exposed and unexposed participants were equally distributed across data recording sites (${\chi }^2$(3)=3.72, p=0.293).

Association between different outcomes and exposure to childhood adversity

During both the acquisition training and generalization phase, CS discrimination in SCRs was significantly lower in individuals exposed to childhood adversity as compared to unexposed individuals (see Table 2 and Figure 1; for trial-by-trial responses, see Appendix 1—figure 4). Post hoc analyses (i.e. ANOVAs) revealed that childhood adversity exposure significantly interacted with stimulus type (acquisition training: F(1, 1400)=5.42, p=0.020, ${\eta }_p^2$<0.01; generalization test: F(1, 1051)=5.37, p=0.021): SCRs to the CS + during both acquisition training and the generalization phase were significantly lower in exposed as compared to unexposed individuals (acquisition training: t(1400)=2.54, p=0.011, d=0.14; generalization test: t=(194.1)=3.51, p=0.001, explanatory measure of effect size=0.179; see Figure 2) but not for the CS- (acquisition training: t(1400)=0.75, p=0.452, d=0.04; generalization test: t(178.9)=1.63, p=0.104, explanatory measure of effect size=0.09). For ratings, no significant effects of exposure to childhood adversity were observed in CS discrimination (see Table 2).

Table 2

Outcome	Phase	Measure	t	df	p	Cohen’s d	LL (95% CI)	UL (95% CI)
CS discrimination	ACQ	SCR	2.33	1,400	0.020	–0.18	–0.33	–0.03
		Arousal ratings	–1.52	1,400	0.128	0.12	–0.03	0.26
		Valence ratings	0.20	1,400	0.845	–0.01	–0.16	0.13
		Contigency ratings	0.70	1,400	0.484	–0.05	–0.20	0.10
	GEN	SCR	2.34	1,400	0.020	–0.18	–0.33	–0.03
		Arousal ratings	–0.28	1,400	0.777	0.02	–0.13	0.17
		Valence ratings	0.06	1,400	0.953	0.00	–0.15	0.14
		Contigency ratings	0.58	1,400	0.560	–0.04	–0.19	0.10
LDS	GEN	SCR	1.41	295	0.158	–0.10	–0.25	0.05
		Arousal ratings	–0.62	1,400	0.538	0.05	–0.10	0.20
		Valence ratings	0.30	1,400	0.765	–0.02	–0.17	0.13
		Contigency ratings	–0.95	1,400	0.344	0.07	–0.08	0.22
General reactivity	ALL	SCR	2.06	1,400	0.040	–0.16	–0.31	–0.01
		Arousal ratings	–0.10	1,400	0.920	0.01	–0.14	0.16
		Valence ratings	0.83	1,400	0.408	–0.06	–0.21	0.09
		Contigency ratings	–0.97	250	0.334	0.07	–0.09	0.24

1. Note. ACQ = acquisition training, GEN = generalization phase, LDS = linear deviation score. Bold numbers indicate significant results (p<0.05).

Figure 1

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Figure 2

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No significant effect of exposure to childhood adversity in either SCRs or ratings was observed for generalization gradients (see Table 2 and Figure 3). It is, however, also evident from the generalization gradients that both groups differ specifically in reactivity to the CS+ (see above and Figure 3).

Figure 3

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In addition, general physiological reactivity in SCRs (i.e. raw amplitudes) was significantly lower in participants exposed to childhood adversity compared to unexposed participants (see Table 2 and Figure 4) while there were no differences between both groups in general rating response levels (see Table 2).

Figure 4

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At the request of a reviewer, we repeated our main analyses by using linear mixed models including age, sex, school degree (i.e. to approximate socioeconomic status, SES), and exposure to childhood adversity as fixed effects as well as site as random effect. These analyses yielded comparable results demonstrating a significant effect of childhood adversity on CS discrimination during acquisition training and the generalization phase as well as on general reactivity, but not on the generalization gradients in SCRs (see Appendix 1—table 2A). Consistent with the results of the main analyses reported in our manuscript, we did not observe any significant effects of childhood adversity on the different types of ratings when using mixed models (see Appendix 1—table 2B–D). Some of the mixed model analyses showed significantly lower CS discrimination during acquisition training and generalization, and lower general reactivity in males compared to females (see Appendix 1—table 2 for details).

Exploratory analyses

The cumulative risk model operationalized through the different cut-offs for no, low, moderate, and severe exposure (Bernstein and Fink, 1998) did not yield any significant results for any outcome measure and experimental phase (see Appendix 1—table 3). However, on a descriptive level (see Figure 5), it seems that indeed exposure to an at least moderate cut-off level may induce behavioral and physiological changes (see main analysis, Bernstein and Fink, 1998). This might suggest that the cut-off for exposure commonly applied in the literature (see Ruge et al., 2024) may indeed represent a reasonable approach.

Figure 5

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Cumulative risk operationalized as the number of CTQ subscales exceeding the moderate cut-off (Bernstein and Fink, 1998), however, revealed that a higher number of subscales exceeding the cut-off predicted lower CS discrimination in SCRs (F(1, 1400)=6.86, p=0.009, R²=0.005) and contingency ratings (F(1, 1400)=4.08, p=0.044, R²=0.003) during acquisition training (see Appendix 1—table 4 and Figure 6 for an exemplary illustration of SCRs during acquisition training). This was driven by significantly lower SCRs to the CS+ (F(1, 1400)=5.42, p=0.02, R²=0.004) while for contingency ratings no significant post hoc tests were identified (all p>0.05). For an illustration of how the different adversity types (i.e. subscales) are distributed among the different numbers of subscales, see Appendix 1—figure 5.

Figure 6

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The operationalization of childhood adversity in the context of the specificity model tests the association between exposure to abuse and neglect experiences on conditioned responding statistically independently, while the dimensional model controls for each other’s impact (see Table 2 for details and Figure 7 for an exemplary illustration of SCRs during acquisition training). Despite these conceptual and operationalizational differences, results are converging. More precisely, no significant effect of exposure to abuse was observed on CS discrimination, the strength of generalization (i.e. LDS), or general reactivity in any of the outcome measures and in any experimental phase (see Appendix 1—table 5; Appendix 1—table 7). In contrast, a significant negative association between exposure to neglect and CS discrimination in SCRs was observed during acquisition training (specificity model: F(1, 1400)=6.4, p=0.012, R²=0.005; dimensional model: F(3, 1398)=2.91, p=0.234, R²=0.006), which is contrary to the predictions of the dimensional model, that posits a specific role for abuse but not neglect (Machlin et al., 2019; McLaughlin et al., 2021). Post hoc tests revealed that in both models, effects were driven by significantly lower SCRs to the CS+ (specificity model: F(1, 1400)=6.13, p=0.013, R²=0.004, dimensional model: ß=–0.004, t(1398)=–1.97, p=0.049, r=–0.07). Within the dimensional model framework, the issue of multicollinearity among predictors (i.e. different childhood adversity types) is frequently discussed (McLaughlin et al., 2021; Smith and Pollak, 2021). If we apply the rule of thumb of a variance inflation factor (VIF) >10, which is often used in the literature to indicate concerning multicollinearity (e.g. Hair et al., 1995; Mason et al., 1989; Neter et al., 1989), we can assume that multicollinearity was not a concern in our study (abuse: VIF=8.64; neglect: VIF=7.93). However, some authors state that VIFs should not exceed a value of 5 (e.g. Akinwande et al., 2015), while others suggest that these rules of thumb are rather arbitrary (O’brien, 2007).

Figure 7

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Furthermore, the statistical analyses of the specificity model additionally revealed that greater exposure to neglect significantly predicted a generally lower SCR reactivity (F(1, 1400)=4.3, p=0.038, R²=0.003) as well as a lower CS discrimination in contingency ratings during both acquisition training (F(1, 1400)=5.58, p=0.018, R²=0.004) and the generalization test (F(1, 1400)=6.33, p=0.012, R²=0.005; see Appendix 1—table 6). These were driven by significantly higher CS- responding in contingency ratings (acquisition training: F(1, 1400)=4.62, p=0.032, R²=0.003; generalization test: F(1, 1400)=8.38, p=0.004, R²=0.006) in individuals exposed to neglect.

To explore the explanatory power of different theories, we exemplarily compared the absolute values of Cohen’s d of all exploratory analyses including CS discrimination in SCRs during acquisition training with the absolute values of the Cohen’s d confidence intervals of our main analyses. We chose CS discrimination during fear acquisition training for this test, because the most convergent results across theories were observed during this experimental phase. None of the effect sizes from the exploratory analyses (cumulative risk, severity groups: d=0.14; cumulative risk, number of subscales exceeding an at least moderate cut-off: d=0.20; specificity model, abuse: d=0.10; specificity model, neglect: d=0.19; dimensional model: d=0.18) fell outside the confidence intervals of our main results (i.e. an at least moderate childhood adversity exposure: [0.03; 0.33]). Hence, we found no evidence of differential explanatory strengths among theories.

The objective of this study was to examine the relationship between the exposure to childhood adversity and conditioned responding using a large community sample of healthy participants. This relationship might represent a potential mechanistic route linking experience-dependent plasticity in the nervous system and behavior related to risk of and resilience to psychopathology. In additional exploratory analyses, we examined these associations through different approaches by translating key theories in the literature into statistical models. In line with the conclusion of a recent systematic literature review (Ruge et al., 2024), individuals exposed to (an at least moderate level of) childhood adversity exhibited reduced CS discrimination in SCRs during both acquisition training and the generalization phase compared to those classified as unexposed (i.e. no or low exposure). Generalization gradients themselves were, however, comparable between exposed and unexposed individuals.

The systematic literature search by Ruge et al., 2024 revealed that the pattern of decreased CS discrimination, driven primarily by reduced CS+ responding, was observed despite substantial heterogeneity in childhood adversity assessment and operationalization, and despite differences in the experimental paradigms. Although both individuals without mental disorders exposed to childhood adversity and patients suffering from anxiety- and stress-related disorders (e.g. Duits et al., 2015) show reduced CS discrimination, it is striking that the response pattern of individuals exposed to childhood adversity (i.e. reduced responding to the CS+) is remarkably different from what is typically observed in patients (i.e. enhanced responding to the CS-). It should be noted, however, that childhood adversity exposure status was not considered in this meta-analysis (Duits et al., 2015). As exposure to childhood adversity represents a particularly strong risk factor for the development of later psychopathology, these seemingly contrary findings warrant an explanation. In this context, it is important to note that all individuals included in the present study were mentally healthy - at least up to the assessment. Hence, it may be an obvious explanation that reduced CS discrimination driven by decreased CS+ responding may represent a resilience rather than a risk factor because individuals exposed to childhood adversity in our sample are mentally healthy despite being exposed to a strong risk factor.

In fact, there is substantial heterogeneity in individual trajectories and profiles in the aftermath of such exposures in humans and rodents (Russo et al., 2012). While some individuals remain resilient despite exposure, others develop psychopathological conditions (Galea et al., 2005). Consequently, the sample of the present study may represent a specific subsample of exposed individuals who are developing along a resilient trajectory. Thus, it can be speculated that reduced physiological reactivity to a signal of threat (e.g. CS+) may protect the individual from overwhelming physiological and/or emotional responses to potentially recurrent threats (for a discussion, see Ruge et al., 2024). Similar concepts have been proposed as ‘emotional numbing’ in post-traumatic stress disorders (for a review, see e.g. Litz and Gray, 2002).

While this seems a plausible theoretical explanation, decreased CS discrimination driven by reduced CS+ responding is observed rather consistently and most importantly irrespective of whether the investigated samples were healthy, at risk, or included patients (Ruge et al., 2024). Thus, this response pattern which was also observed in our study might be a specific characteristic of childhood adversity exposure distinct from the response pattern generally observed in patients suffering from anxiety- and stress-related disorders (i.e. increased responding to the CS-) - even though individuals exposed to childhood adversity in this sample indeed showed significantly higher anxiety and depression despite being free of any categorical diagnoses (see Appendix 1) which was also previously reported by e.g., Spinhoven et al., 2011 and Kuhn et al., 2016. Interestingly, in our study, trait anxiety and depression scores were mostly unrelated to SCRs, defined by CS discrimination and generalization gradients based on SCRs as well as general SCR reactivity, with the exception of a significant - albeit minute - relationship between CS discrimination during acquisition training and depression scores (see above). Although reported associations in the literature are heterogeneous (Lonsdorf et al., 2017), we may speculate that they may be mediated by childhood adversity. We conducted additional mediation analyses (data not shown) which, however, did not support this hypothesis. As the potential links between reduced CS discrimination in individuals exposed to childhood adversity and the developmental trajectories of psychopathological symptoms are still not fully understood, future work should investigate these further in - ideally - prospective studies.

In addition to reduced CS discrimination in SCRs, a generally blunted electrodermal responding was observed, which may, however, be mainly driven by substantially reduced CS+ responses. Yet, it is noteworthy that reduced skin conductance in children exposed to childhood adversity was also observed during other tasks such as attention regulation during interpersonal conflict (Pollak et al., 2005), or passively viewing slides with emotional or cognitive content (Carrey et al., 1995), whereas other studies did not find such an association (Ben-Amitay et al., 2016). Moreover, in various threat-related studies, also enhanced responding or no significant differences were observed across outcome measures (Estrada et al., 2020; Huskey et al., 2022; Jovanovic et al., 2009; Jovanovic et al., 2022; Kreutzer and Gorka, 2021; Lis et al., 2020; Pole et al., 2007; Rowland et al., 2022; Thome et al., 2018; Young et al., 2018). While generally blunted responding might be particularly related to decreased CS+ responding in the present study, differences in general reactivity need to be taken into account for data analyses and interpretation - in particular as the exclusion of physiological non-responders or so-called ‘non-learners’ (i.e. individuals not showing a minimum discrimination score between SCRs to the CS+ and CS-) has been common in the field until recently (for a critical discussion, see Lonsdorf et al., 2019). Future work should also investigate reactivity to the unconditioned stimulus, which was not implemented here and may shed light on potential differences in general reactivity unaffected by associative learning processes (see e.g. Harnett et al., 2019; Machlin et al., 2019).

Contrary to the association between CS discrimination as well as general (electrodermal) reactivity and exposure to childhood adversity, no such relationship was found for generalization gradients. In a subsample of this study, it was previously observed that fear generalization phenotypes explained less variance as compared to CS discrimination and general reactivity (Stegmann et al., 2019), and CS discrimination as well as general reactivity but not fear generalization predicted increases in anxiety and depression scores during the COVID-19 pandemic (Imholze et al., 2023). The lack of associations with fear generalization measures (i.e. LDS) may be specific to the paradigm and sample used in these studies, but it may also be an interesting lead for future work to disentangle the relationship between CS discrimination, general reactivity, and generalization gradients, as they have been suggested to be interrelated (Imholze et al., 2023; Stegmann et al., 2019).

In sum, the current results converge with the literature in identifying reduced CS discrimination and decreased CS+ responding as key characteristics in individuals exposed to childhood adversity. As highlighted recently (see Koppold et al., 2023; Ruge et al., 2024), the various operationalizations of childhood adversity as well as general trauma (Karstoft and Armour, 2023) represent a challenge for integrating the current results into the existing literature. Hence, future studies should focus on in-depth phenotyping (Ruge et al., 2024), an elaborate classification of adversity subtypes (Pollak and Smith, 2021), and methodological considerations (Ruge et al., 2024). Besides optimizing the operationalization of childhood adversity, there are also initiatives to advance the field in data processing and analysis, such as developing methods to address multicollinearity in childhood adversity data (Brieant et al., 2024).

Several proposed (verbal) theories describe the association between (specific) childhood adversity types and behavioral as well as physiological consequences differently and there is currently a heated debate rather than consensus on this issue (McLaughlin et al., 2021; Pollak and Smith, 2021; Smith and Pollak, 2021). In the field, most often a dichotomization in exposed vs. unexposed individuals is used (for a review, see Ruge et al., 2024). We adopted this typical approach of an at least moderate exposure cut-off from the literature for our main analyses, despite the well-known statistical disadvantages of artificially dichotomizing variables that are (presumably) dimensional in nature (Cohen, 1983). It is noteworthy, however, that this cut-off appears to map rather well onto the psychophysiological response patterns observed here (see Figure 5). More precisely, our exploratory results of applying different exposure cut-offs (low, moderate, severe, no exposure) seem to indicate that indeed a moderate exposure level is ‘required’ for the manifestation of physiological differences, suggesting that childhood adversity exposure may not have a linear or cumulative effect.

Of note, comparing individuals exposed vs. unexposed to an at least moderate level of childhood adversity is not derived from any of the existing theories, but rather from practices in the literature (see Ruge et al., 2024). For this reason, we aimed at an exploratory translation of key (verbal) theories into statistical models (see Table 1). Several important topical and methodological take-home messages can be drawn from this endeavor: First, the translation of these verbal theories into precise statistical tests proved to be a rather challenging task paved by operationalizational ambiguity. We have collected some key challenges in Table 1 and conclude that current verbal theories are, at least to a certain degree, ill-defined, as our attempt has disclosed a multiverse of different, equally plausible ways to test them - even though we provide only a limited number of exemplary tests. Second, despite these challenges, the results of most tests converged in identifying an effect of childhood adversity on reduced CS discrimination in SCRs during acquisition training, which is reassuring when aiming to integrate results based on different operationalizations. Third, none of the theories appears to be explanatorily superior. Fourth, our results are not in line with predictions of the dimensional model (Machlin et al., 2019; McLaughlin et al., 2021) which posits a specific association between exposure to threat- but not deprivation-related childhood adversity and fear conditioning performance. If anything, our results point in the opposite direction.

Taken together, neither considering childhood adversity as a broad category, nor different subtypes have consistently shown to strongly map onto biological mechanisms (for an in-depth discussion, see Smith and Pollak, 2021). Hence, even though it is currently the dominant view in the field, that considering the potentially distinct effects of dissociable adversity types holds promise to provide mechanistic insights into how childhood adversity becomes biologically embedded (Berens et al., 2017; Kuhlman et al., 2017; Smith and Pollak, 2021), we emphasize the urgent need for additional exploration, refinement, and testing of current theories. This is particularly important in light of diverging evidence pointing towards different conclusions.

Some limitations of this work are worth noting: First, despite our observation of significant associations between exposure to childhood adversity and fear conditioning performance in a large sample, it should be noted that effect sizes were small. Second, we cannot provide a comparison of potential group differences in unconditioned responding to the US. This is, however, important as this comparison may explain group differences in conditioned responding - a mechanism that remains unexplored to date (Ruge et al., 2024). Third, the use of the CTQ, which is the most commonly used questionnaire in the field (see Ruge et al., 2024), comes with a number of disadvantages. Most prominently, the CTQ focuses exclusively on the presence or absence of exposure without consideration of individual and exposure characteristics that have been shown to be of crucial relevance (Danese and Widom, 2023; see Smith and Pollak, 2021), such as controllability, burdening, exposure severity, duration, and developmental timing. These characteristics are embedded in the framework of the topological approach (Smith and Pollak, 2021), another important model linking childhood adversity exposure to negative outcomes, which, however, was not evaluated in the present work. Testing this model requires an extremely large dataset including in-depth phenotyping, which was not available here, but may be an important avenue for future work. Fourth, across all theories, significant effects of childhood adversity have been shown primarily on physiological reactivity (i.e. SCR). Whether these findings are specific to SCRs or might generalize to other physiological outcome measures such as fear-potentiated startle, heart rate, or local changes in neural activation, remains an open question for future studies.

In sum, when ultimately aiming to understand the impact of exposure to adversity on the development of psychopathological symptoms (Anda et al., 2006; Felitti, 2002; Gilbert et al., 2009; Green et al., 2010; Heim and Nemeroff, 2001; McLaughlin et al., 2012; Moffitt et al., 2007; Teicher et al., 2022), it is crucial to understand the biological mechanisms through which exposure to adversity ‘gets under the skin.’ To achieve this, emotional-associative learning can serve as a prime translational model for fear and anxiety disorders: One plausible mechanism is the ability to distinguish threat from safety, which is key to an individual’s ability to dynamically adapt to changing environmental demands (Craske et al., 2012; Vervliet et al., 2013) - an ability that appears to be impaired in individuals with a history of childhood adversity. This mechanism is of particular relevance to the development of stress- and anxiety-related psychopathology, as the identification of risk but also resilience factors following exposure to childhood adversity is essential for the development of effective intervention and prevention programs.

The data will be made available to editors and reviewers only, as publicly sharing of individual-level data was not included in the informed consent forms. Instead, the forms specified that the data would be published anonymously as a collective dataset. At the time the study was planned, data sharing was not a common practice. Therefore, participants were not asked to consent to individual-level data sharing and were assured that their data would be used exclusively for the purposes specified in the consent forms. This restriction also applies to de-identified and processed versions of the individual-level data. R Markdown files that include the code for all analyses and generate this manuscript are openly available at Zenodo (https://doi.org/10.5281/zenodo.14851004; Klingelhöfer-Jens et al., 2023).

1. Preprint

   1. Ahlmann-Eltze C
   2. Patil I

   (2021) Ggsignif: R package for displaying significance brackets for “Ggplot2”

   PsyArXiv.

   https://doi.org/10.31234/osf.io/7awm6

   • Google Scholar
2. 1. Akinwande MO
   2. Dikko HG
   3. Samson A

   (2015) Variance inflation factor: as a condition for the inclusion of suppressor variable(s) in regression analysis

   Open Journal of Statistics 05:754–767.

   https://doi.org/10.4236/ojs.2015.57075

   • Google Scholar
3. 1. Anda RF
   2. Felitti VJ
   3. Bremner JD
   4. Walker JD
   5. Whitfield C
   6. Perry BD
   7. Dube SR
   8. Giles WH

   (2006) The enduring effects of abuse and related adverse experiences in childhood. A convergence of evidence from neurobiology and epidemiology

   European Archives of Psychiatry and Clinical Neuroscience 256:174–186.

   https://doi.org/10.1007/s00406-005-0624-4

   • PubMed
   • Google Scholar
4. Software

   1. Andri S

   (2021) DescTools: tools for descriptive statistics, version 0.99.58

   Github.

   https://cran.r-project.org/package=DescTools
5. Software

   1. Attali D
   2. Baker C

   (2022) GgExtra: add marginal histograms to ’ggplot2’, and more ’ggplot2’ enhancements, version 0.10.1

   Package.

   https://CRAN.R-project.org/package=ggExtra
6. Software

   1. Auguie B

   (2017) GridExtra: miscellaneous functions for “grid” graphics, version 2.3

   Package.

   https://CRAN.R-project.org/package=gridExtra
7. Software

   1. Aust F
   2. Barth M

   (2020) Papaja: prepare reproducible APA journal articles with R markdown, version 7591bf3

   Github.

   https://github.com/crsh/papaja
8. 1. Baldwin JR
   2. Coleman O
   3. Francis ER
   4. Danese A

   (2024) Prospective and retrospective measures of child maltreatment and their association with psychopathology: a systematic review and meta-analysis

   JAMA Psychiatry 81:769–781.

   https://doi.org/10.1001/jamapsychiatry.2024.0818

   • PubMed
   • Google Scholar
9. Software

   1. Barth M

   (2022) Tinylabels: lightweight variable labels, version 0.2.4

   Package.

   https://cran.r-project.org/package=tinylabels
10. 1. Bates D
    2. Mächler M
    3. Bolker B
    4. Walker S

    (2015) Fitting linear mixed-effects models using lme4

    Journal of Statistical Software 67:1–48.

    https://doi.org/10.18637/jss.v067.i01

    • Google Scholar
11. Software

    1. Bates D
    2. Maechler M

    (2021) Matrix: sparse and dense matrix classes and methods, version 1.7-1

    Package.

    https://CRAN.R-project.org/package=Matrix
12. Software

    1. Behrendt S

    (2014) Lm.beta: add standardized regression coefficients to linear-model-objects, version 1.7-2

    Package.

    https://CRAN.R-project.org/package=lm.beta
13. 1. Ben-Amitay G
    2. Kimchi N
    3. Wolmer L
    4. Toren P

    (2016) Psychophysiological reactivity in child sexual abuse

    Journal of Child Sexual Abuse 25:185–200.

    https://doi.org/10.1080/10538712.2016.1124309

    • PubMed
    • Google Scholar
14. 1. Ben-Shachar MS
    2. Lüdecke D
    3. Makowski D

    (2020) effectsize: estimation of effect size indices and standardized parameters

    Journal of Open Source Software 5:2815.

    https://doi.org/10.21105/joss.02815

    • Google Scholar
15. 1. Berens AE
    2. Jensen SKG
    3. Nelson CA

    (2017) Biological embedding of childhood adversity: from physiological mechanisms to clinical implications

    BMC Medicine 15:135.

    https://doi.org/10.1186/s12916-017-0895-4

    • PubMed
    • Google Scholar
16. 1. Bernstein DP
    2. Ahluvalia T
    3. Pogge D
    4. Handelsman L

    (1997) Validity of the childhood trauma questionnaire in an adolescent psychiatric population

    Journal of the American Academy of Child & Adolescent Psychiatry 36:340–348.

    https://doi.org/10.1097/00004583-199703000-00012

    • Google Scholar
17. Book

    1. Bernstein DP
    2. Fink L

    (1998)

    Childhood Trauma questionnaire: a retrospective self-report manual

    The Psychological Corporation.

    • Google Scholar
18. 1. Bernstein DP
    2. Stein JA
    3. Newcomb MD
    4. Walker E
    5. Pogge D
    6. Ahluvalia T
    7. Stokes J
    8. Handelsman L
    9. Medrano M
    10. Desmond D
    11. Zule W

    (2003) Development and validation of a brief screening version of the childhood trauma questionnaire

    Child Abuse & Neglect 27:169–190.

    https://doi.org/10.1016/S0145-2134(02)00541-0

    • Google Scholar
19. 1. Boucsein W
    2. Fowles DC
    3. Grimnes S
    4. Ben-Shakhar G
    5. roth WT
    6. Dawson ME
    7. Filion DL
    8. Society for Psychophysiological Research Ad Hoc Committee on Electrodermal Measures

    (2012) Publication recommendations for electrodermal measurements

    Psychophysiology 49:1017–1034.

    https://doi.org/10.1111/j.1469-8986.2012.01384.x

    • PubMed
    • Google Scholar
20. 1. Brieant A
    2. Sisk LM
    3. Keding TJ
    4. Cohodes EM
    5. Gee DG

    (2024) Leveraging multivariate approaches to advance the science of early-life adversity

    Child Abuse & Neglect 106754:106754.

    https://doi.org/10.1016/j.chiabu.2024.106754

    • Google Scholar
21. 1. Carozza S
    2. Holmes J
    3. Astle DE

    (2022) Testing deprivation and threat: a preregistered network analysis of the dimensions of early adversity

    Psychological Science 33:1753–1766.

    https://doi.org/10.1177/09567976221101045

    • PubMed
    • Google Scholar
22. 1. Carrey NJ
    2. Butter HJ
    3. Persinger MA
    4. Bialik RJ

    (1995) Physiological and cognitive correlates of child abuse

    Journal of the American Academy of Child and Adolescent Psychiatry 34:1067–1075.

    https://doi.org/10.1097/00004583-199508000-00017

    • PubMed
    • Google Scholar
23. 1. Cohen J

    (1983) The cost of dichotomization

    Applied Psychological Measurement 7:249–253.

    https://doi.org/10.1177/014662168300700301

    • Google Scholar
24. 1. Colich NL
    2. Rosen ML
    3. Williams ES
    4. McLaughlin KA

    (2020) Biological aging in childhood and adolescence following experiences of threat and deprivation: a systematic review and meta-analysis

    Psychological Bulletin 146:721–764.

    https://doi.org/10.1037/bul0000270

    • PubMed
    • Google Scholar
25. 1. Contractor AA
    2. Brown LA
    3. Weiss NH

    (2018) Relation between lifespan polytrauma typologies and post-trauma mental health

    Comprehensive Psychiatry 80:202–213.

    https://doi.org/10.1016/j.comppsych.2017.10.005

    • PubMed
    • Google Scholar
26. 1. Cooper SE
    2. van Dis EAM
    3. Hagenaars MA
    4. Krypotos AM
    5. Nemeroff CB
    6. Lissek S
    7. Engelhard IM
    8. Dunsmoor JE

    (2022) A meta-analysis of conditioned fear generalization in anxiety-related disorders

    Neuropsychopharmacology 01:1652–1661.

    https://doi.org/10.1038/s41386-022-01332-2

    • PubMed
    • Google Scholar
27. 1. Cooper SE
    2. Dunsmoor JE
    3. Koval KA
    4. Pino ER
    5. Steinman SA

    (2023) TEST-RETEST reliability of human threat conditioning and generalization across a 1-TO-2-WEEK interval

    Psychophysiology 60:e14242.

    https://doi.org/10.1111/psyp.14242

    • PubMed
    • Google Scholar
28. 1. Craske MG
    2. Wolitzky-Taylor KB
    3. Mineka S
    4. Zinbarg R
    5. Waters AM
    6. Vrshek-Schallhorn S
    7. Epstein A
    8. Naliboff B
    9. Ornitz E

    (2012) Elevated responding to safe conditions as a specific risk factor for anxiety versus depressive disorders: evidence from a longitudinal investigation

    Journal of Abnormal Psychology 121:315–324.

    https://doi.org/10.1037/a0025738

    • PubMed
    • Google Scholar
29. 1. Danese A
    2. Pariante CM
    3. Caspi A
    4. Taylor A
    5. Poulton R

    (2007) Childhood maltreatment predicts adult inflammation in a life-course study

    PNAS 104:1319–1324.

    https://doi.org/10.1073/pnas.0610362104

    • Google Scholar
30. 1. Danese A
    2. Moffitt TE
    3. Harrington H
    4. Milne BJ
    5. Polanczyk G
    6. Pariante CM
    7. Poulton R
    8. Caspi A

    (2009) Adverse childhood experiences and adult risk factors for age-related disease: depression, inflammation, and clustering of metabolic risk markers

    Archives of Pediatrics & Adolescent Medicine 163:1135–1143.

    https://doi.org/10.1001/archpediatrics.2009.214

    • PubMed
    • Google Scholar
31. 1. Danese A
    2. Widom CS

    (2023) Associations between objective and subjective experiences of childhood maltreatment and the course of emotional disorders in adulthood

    JAMA Psychiatry 80:1009–1016.

    https://doi.org/10.1001/jamapsychiatry.2023.2140

    • PubMed
    • Google Scholar
32. 1. Diedenhofen B
    2. Musch J

    (2015) cocor: A comprehensive solution for the statistical comparison of correlations

    PLOS ONE 10:e0121945.

    https://doi.org/10.1371/journal.pone.0121945

    • PubMed
    • Google Scholar
33. Software

    1. Dowle M
    2. Srinivasan A

    (2020) Data.table: extension of “data.frame”, version 1.16.4

    Package.

    https://CRAN.R-project.org/package=data.table
34. 1. Duits P
    2. Cath DC
    3. Lissek S
    4. Hox JJ
    5. Hamm AO
    6. Engelhard IM
    7. van den Hout MA
    8. Baas JMP

    (2015) Updated meta-analysis of classical fear conditioning in the anxiety disorders

    Depression and Anxiety 32:239–253.

    https://doi.org/10.1002/da.22353

    • PubMed
    • Google Scholar
35. 1. Dunn OJ
    2. Clark V

    (1969) Correlation coefficients measured on the same individuals

    Journal of the American Statistical Association 64:366–377.

    https://doi.org/10.1080/01621459.1969.10500981

    • Google Scholar
36. 1. Dymond S
    2. Dunsmoor JE
    3. Vervliet B
    4. Roche B
    5. Hermans D

    (2015) Fear generalization in humans

    Systematic Review and Implications for Anxiety Disorder Research. Behavior Therapy 46:561–582.

    https://doi.org/10.1016/j.beth.2014.10.001

    • Google Scholar
37. Software

    1. Ebbert D

    (2019) Chisq.posthoc.test: a post hoc analysis for pearson’s chi-squared test for count data, version 0.1.2

    Package.

    https://CRAN.R-project.org/package=chisq.posthoc.test
38. 1. Ehlers MR
    2. Nold J
    3. Kuhn M
    4. Klingelhöfer-Jens M
    5. Lonsdorf TB

    (2020) Revisiting potential associations between brain morphology, fear acquisition and extinction through new data and a literature review

    Scientific Reports 10:19894.

    https://doi.org/10.1038/s41598-020-76683-1

    • PubMed
    • Google Scholar
39. 1. Estrada S
    2. Richards C
    3. Gee DG
    4. Baskin-Sommers A

    (2020) Exposure to violence and nonassociative learning capability confer risk for violent behavior

    Journal of Abnormal Psychology 129:748–759.

    https://doi.org/10.1037/abn0000579

    • PubMed
    • Google Scholar
40. 1. Evans GW
    2. Li D
    3. Whipple SS

    (2013) Cumulative risk and child development

    Psychological Bulletin 139:1342–1396.

    https://doi.org/10.1037/a0031808

    • PubMed
    • Google Scholar
41. Software

    1. Fc M
    2. Davis TL

    (2022) Ggpattern: ’ggplot2’ pattern geoms, version 1.1.3

    Package.

    https://CRAN.R-project.org/package=ggpattern
42. 1. Felitti VJ

    (2002) The relationship of adverse childhood experiences to adult health: Turning gold into lead/ Belastungen in der Kindheit und Gesundheit im Erwachsenenalter: die Verwandlung von Gold in Blei

    Zeitschrift Für Psychosomatische Medizin Und Psychotherapie 48:359–369.

    https://doi.org/10.13109/zptm.2002.48.4.359

    • Google Scholar
43. Book

    1. Fox J
    2. Weisberg S

    (2019)

    An R Companion to Applied Regression (3rd)

    Sage.

    • Google Scholar
44. Software

    1. Fox J
    2. Weisberg S
    3. Price B

    (2020) CarData: companion to applied regression data sets, version 3.0-5

    Package.

    https://CRAN.R-project.org/package=carData
45. 1. Fraunfelter L
    2. Gerdes ABM
    3. Alpers GW

    (2022) Fear one, fear them all: A systematic review and meta-analysis of fear generalization in pathological anxiety

    Neuroscience and Biobehavioral Reviews 139:104707.

    https://doi.org/10.1016/j.neubiorev.2022.104707

    • PubMed
    • Google Scholar
46. 1. Fredrikson M
    2. Annas P
    3. Georgiades A
    4. Hursti T
    5. Tersman Z

    (1993) Internal consistency and temporal stability of classically conditioned skin conductance responses

    Biological Psychology 35:153–163.

    https://doi.org/10.1016/0301-0511(93)90011-v

    • PubMed
    • Google Scholar
47. 1. Galea S
    2. Nandi A
    3. Vlahov D

    (2005) The epidemiology of post-traumatic stress disorder after disasters

    Epidemiologic Reviews 27:78–91.

    https://doi.org/10.1093/epirev/mxi003

    • PubMed
    • Google Scholar
48. 1. Gilbert R
    2. Widom CS
    3. Browne K
    4. Fergusson D
    5. Webb E
    6. Janson S

    (2009) Burden and consequences of child maltreatment in high-income countries

    The Lancet 373:68–81.

    https://doi.org/10.1016/S0140-6736(08)61706-7

    • Google Scholar
49. Software

    1. Gohel D

    (2021) Flextable: functions for tabular reporting, version 0.9.7

    Package.

    https://CRAN.R-project.org/package=flextable
50. Software

    1. Gohel D
    2. Ross N

    (2022) Officedown: enhanced ’r markdown’ format for ’word’ and 'Powerpoint’, version 0.3.3

    Package.

    https://CRAN.R-project.org/package=officedown
51. 1. Green JG
    2. McLaughlin KA
    3. Berglund PA
    4. Gruber MJ
    5. Sampson NA
    6. Zaslavsky AM
    7. Kessler RC

    (2010) Childhood adversities and adult psychiatric disorders in the national comorbidity survey replication I

    Archives of General Psychiatry 67:113.

    https://doi.org/10.1001/archgenpsychiatry.2009.186

    • Google Scholar
52. Software

    1. Gromer D

    (2020) APA: format outputs of statistical tests according to APA guidelines, version 0.3.4

    Package.

    https://CRAN.R-project.org/package=apa
53. Book

    1. Hair JF
    2. Anderson RE
    3. Tatham RL
    4. Black WC

    (1995)

    Multivariate Data Analysis

    Macmillan.

    • Google Scholar
54. 1. Harnett NG
    2. Wheelock MD
    3. Wood KH
    4. Goodman AM
    5. Mrug S
    6. Elliott MN
    7. Schuster MA
    8. Tortolero S
    9. Knight DC

    (2019) Negative life experiences contribute to racial differences in the neural response to threat

    NeuroImage 202:116086.

    https://doi.org/10.1016/j.neuroimage.2019.116086

    • PubMed
    • Google Scholar
55. 1. Häuser W
    2. Schmutzer G
    3. Brähler E
    4. Glaesmer H

    (2011) Maltreatment in childhood and adolescence: results from a survey of a representative sample of the German population

    Deutsches Arzteblatt International 108:287–294.

    https://doi.org/10.3238/arztebl.2011.0287

    • PubMed
    • Google Scholar
56. Book

    1. Hautzinger M
    2. Bailer M

    (1993)

    Allgemeine Depressions-Skala (ADS) [German Version of the Center for Epidemiological Studies Depression Scale (CES-D)

    Belz.

    • Google Scholar
57. 1. Hayes AF
    2. Cai L

    (2007) Using heteroskedasticity-consistent standard error estimators in OLS regression: an introduction and software implementation

    Behavior Research Methods 39:709–722.

    https://doi.org/10.3758/bf03192961

    • PubMed
    • Google Scholar
58. 1. Heim C
    2. Nemeroff CB

    (2001) The role of childhood trauma in the neurobiology of mood and anxiety disorders: preclinical and clinical studies

    Biological Psychiatry 49:1023–1039.

    https://doi.org/10.1016/s0006-3223(01)01157-x

    • PubMed
    • Google Scholar
59. 1. Heim C
    2. Nemeroff CB

    (2002) Neurobiology of early life stress: clinical studies

    Seminars in Clinical Neuropsychiatry 7:147–159.

    https://doi.org/10.1053/scnp.2002.33127

    • PubMed
    • Google Scholar
60. Software

    1. Henry L
    2. Wickham H

    (2020) Purrr: functional programming tools, version 1.0.2

    Package.

    https://CRAN.R-project.org/package=purrr
61. 1. Herzog K
    2. Andreatta M
    3. Schneider K
    4. Schiele MA
    5. Domschke K
    6. Romanos M
    7. Deckert J
    8. Pauli P

    (2021) Reducing generalization of conditioned fear: beneficial impact of fear relevance and feedback in discrimination training

    Frontiers in Psychology 12:665711.

    https://doi.org/10.3389/fpsyg.2021.665711

    • PubMed
    • Google Scholar
62. 1. Ho DE
    2. Imai K
    3. King G
    4. Stuart EA

    (2011) MatchIt: nonparametric preprocessing for parametric causal inference

    Journal of Statistical Software 42:1–28.

    https://doi.org/10.18637/jss.v042.i08

    • Google Scholar
63. 1. Hölzel BK
    2. Brunsch V
    3. Gard T
    4. Greve DN
    5. Koch K
    6. Sorg C
    7. Lazar SW
    8. Milad MR

    (2016) Mindfulness-based stress reduction, fear conditioning, and the uncinate fasciculus: a pilot study

    Frontiers in Behavioral Neuroscience 10:124.

    https://doi.org/10.3389/fnbeh.2016.00124

    • PubMed
    • Google Scholar
64. 1. Hosseini-Kamkar N
    2. Varvani Farahani M
    3. Nikolic M
    4. Stewart K
    5. Goldsmith S
    6. Soltaninejad M
    7. Rajabli R
    8. Lowe C
    9. Nicholson AA
    10. Morton JB
    11. Leyton M

    (2023) Adverse life experiences and brain function: a meta-analysis of functional magnetic resonance imaging findings

    JAMA Network Open 6:e2340018.

    https://doi.org/10.1001/jamanetworkopen.2023.40018

    • PubMed
    • Google Scholar
65. 1. Hughes K
    2. Ford K
    3. Bellis MA
    4. Glendinning F
    5. Harrison E
    6. Passmore J

    (2021) Health and financial costs of adverse childhood experiences in 28 European countries: a systematic review and meta-analysis

    The Lancet. Public Health 6:e848–e857.

    https://doi.org/10.1016/S2468-2667(21)00232-2

    • PubMed
    • Google Scholar
66. 1. Huskey A
    2. Taylor DJ
    3. Friedman BH

    (2022) “Generalized unsafety” as fear inhibition to safety signals in adults with and without childhood trauma

    Developmental Psychobiology 64:e22242.

    https://doi.org/10.1002/dev.22242

    • PubMed
    • Google Scholar
67. 1. Imholze C
    2. Hutterer K
    3. Gall D
    4. Dannlowski U
    5. Domschke K
    6. Leehr EJ
    7. Lonsdorf TB
    8. Lueken U
    9. Reif A
    10. Rosenkranz K
    11. Schiele MA
    12. Zwanzger P
    13. Pauli P
    14. Gamer M

    (2023) Prediction of changes in negative affect during the covid-19 pandemic by experimental fear conditioning and generalization measures

    Zeitschrift Für Psychologie 231:137–148.

    https://doi.org/10.1027/2151-2604/a000523

    • Google Scholar
68. 1. Infantolino ZP
    2. Luking KR
    3. Sauder CL
    4. Curtin JJ
    5. Hajcak G

    (2018) Robust is not necessarily reliable: From within-subjects fMRI contrasts to between-subjects comparisons

    NeuroImage 173:146–152.

    https://doi.org/10.1016/j.neuroimage.2018.02.024

    • Google Scholar
69. 1. Jovanovic T
    2. Blanding NQ
    3. Norrholm SD
    4. Duncan E
    5. Bradley B
    6. Ressler KJ

    (2009) Childhood abuse is associated with increased startle reactivity in adulthood

    Depression and Anxiety 26:1018–1026.

    https://doi.org/10.1002/da.20599

    • Google Scholar
70. 1. Jovanovic T
    2. Wiltshire CN
    3. Reda MH
    4. France J
    5. Wanna CP
    6. Minton ST
    7. Davie W
    8. Grasser LR
    9. Winters S
    10. Schacter H
    11. Marusak HA
    12. Stenson AF

    (2022) Uncertain in the face of change: Lack of contingency shift awareness during extinction is associated with higher fear-potentiated startle and PTSD symptoms in children

    International Journal of Psychophysiology 178:90–98.

    https://doi.org/10.1016/j.ijpsycho.2022.06.008

    • Google Scholar
71. 1. Kaczkurkin AN
    2. Burton PC
    3. Chazin SM
    4. Manbeck AB
    5. Espensen-Sturges T
    6. Cooper SE
    7. Sponheim SR
    8. Lissek S

    (2017) Neural substrates of overgeneralized conditioned fear in PTSD

    The American Journal of Psychiatry 174:125–134.

    https://doi.org/10.1176/appi.ajp.2016.15121549

    • PubMed
    • Google Scholar
72. 1. Karstoft KI
    2. Armour C

    (2023) What we talk about when we talk about trauma: Content overlap and heterogeneity in the assessment of trauma exposure

    Journal of Traumatic Stress 36:71–82.

    https://doi.org/10.1002/jts.22880

    • PubMed
    • Google Scholar
73. Software

    1. Kassambara A

    (2020) Ggpubr: “ggplot2” based publication ready plots, version 0.6.0

    Package.

    https://CRAN.R-project.org/package=ggpubr
74. Software

    1. Kassambara A

    (2021) Rstatix: pipe-friendly framework for basic statistical tests, version 0.7.2

    Package.

    https://CRAN.R-project.org/package=rstatix
75. 1. Klauke B
    2. Deckert J
    3. Reif A
    4. Pauli P
    5. Domschke K

    (2010) Life events in panic disorder-an update on “candidate stressors”

    Depression and Anxiety 27:716–730.

    https://doi.org/10.1002/da.20667

    • PubMed
    • Google Scholar
76. 1. Klingelhöfer-Jens M
    2. Ehlers MR
    3. Kuhn M
    4. Keyaniyan V
    5. Lonsdorf TB

    (2022) Robust group- but limited individual-level (longitudinal) reliability and insights into cross-phases response prediction of conditioned fear

    eLife 11:e78717.

    https://doi.org/10.7554/eLife.78717

    • PubMed
    • Google Scholar
77. Software

    1. Klingelhöfer-Jens M
    2. Hutterer K
    3. Schiele MA
    4. Leehr EJ
    5. Schümann D
    6. Rosenkranz K
    7. Böhnlein J
    8. Repple J
    9. Deckert J
    10. Domschke K
    11. Dannlowski U
    12. Lueken U
    13. Reif A
    14. Romanos M
    15. Zwanzger P
    16. Pauli P
    17. Gamer M
    18. Lonsdorf TB

    (2023) Reduced discrimination between signals of danger and safety but not overgeneralization is linked to exposure to childhood adversity in healthy adults, version v5

    Zenodo.

    https://doi.org/10.5281/zenodo.14851004
78. 1. Koppold A
    2. Kastrinogiannis A
    3. Kuhn M
    4. Lonsdorf TB

    (2023) Watching with Argus eyes: Characterization of emotional and physiological responding in adults exposed to childhood maltreatment and/or recent adversity

    Psychophysiology 60:e14253.

    https://doi.org/10.1111/psyp.14253

    • PubMed
    • Google Scholar
79. 1. Kreutzer KA
    2. Gorka SM

    (2021) Impact of trauma type on startle reactivity to predictable and unpredictable threats

    Journal of Nervous & Mental Disease 209:899–904.

    https://doi.org/10.1097/NMD.0000000000001394

    • Google Scholar
80. 1. Kuhlman KR
    2. Chiang JJ
    3. Horn S
    4. Bower JE

    (2017) Developmental psychoneuroendocrine and psychoneuroimmune pathways from childhood adversity to disease

    Neuroscience and Biobehavioral Reviews 80:166–184.

    https://doi.org/10.1016/j.neubiorev.2017.05.020

    • PubMed
    • Google Scholar
81. 1. Kuhn M
    2. Mertens G
    3. Lonsdorf TB

    (2016) State anxiety modulates the return of fear

    International Journal of Psychophysiology 110:194–199.

    https://doi.org/10.1016/j.ijpsycho.2016.08.001

    • PubMed
    • Google Scholar
82. 1. Kuznetsova A
    2. Brockhoff PB
    3. Christensen RHB

    (2017) lmerTest package: tests in linear mixed effects models

    Journal of Statistical Software 82:1–26.

    https://doi.org/10.18637/jss.v082.i13

    • Google Scholar
83. 1. Lau JYF
    2. Lissek S
    3. Nelson EE
    4. Lee Y
    5. Roberson-Nay R
    6. Poeth K
    7. Jenness J
    8. Ernst M
    9. Grillon C
    10. Pine DS

    (2008) Fear conditioning in adolescents with anxiety disorders: results from a novel experimental paradigm

    Journal of the American Academy of Child and Adolescent Psychiatry 47:94–102.

    https://doi.org/10.1097/chi.0b01e31815a5f01

    • PubMed
    • Google Scholar
84. Book

    1. Laux L
    2. Spielberger CD

    (1981)

    Das State-Trait-Angstinventar: STAI

    Beltz.

    • Google Scholar
85. Software

    1. Lawrence MA

    (2016) Ez: easy analysis and visualization of factorial experiments, version 4.4-0

    Package.

    https://CRAN.R-project.org/package=ez
86. 1. LeBel EP
    2. McCarthy RJ
    3. Earp BD
    4. Elson M
    5. Vanpaemel W

    (2018) A unified framework to quantify the credibility of scientific findings

    Advances in Methods and Practices in Psychological Science 1:389–402.

    https://doi.org/10.1177/2515245918787489

    • Google Scholar
87. 1. Lis S
    2. Thome J
    3. Kleindienst N
    4. Mueller-Engelmann M
    5. Steil R
    6. Priebe K
    7. Schmahl C
    8. Hermans D
    9. Bohus M

    (2020) Generalization of fear in post-traumatic stress disorder

    Psychophysiology 57:e13422.

    https://doi.org/10.1111/psyp.13422

    • PubMed
    • Google Scholar
88. 1. Lissek S
    2. Powers AS
    3. McClure EB
    4. Phelps EA
    5. Woldehawariat G
    6. Grillon C
    7. Pine DS

    (2005) Classical fear conditioning in the anxiety disorders: a meta-analysis

    Behaviour Research and Therapy 43:1391–1424.

    https://doi.org/10.1016/j.brat.2004.10.007

    • PubMed
    • Google Scholar
89. 1. Litz BT
    2. Gray MJ

    (2002) Emotional numbing in posttraumatic stress disorder: current and future research directions

    The Australian and New Zealand Journal of Psychiatry 36:198–204.

    https://doi.org/10.1046/j.1440-1614.2002.01002.x

    • PubMed
    • Google Scholar
90. 1. Lonsdorf TB
    2. Menz MM
    3. Andreatta M
    4. Fullana MA
    5. Golkar A
    6. Haaker J
    7. Heitland I
    8. Hermann A
    9. Kuhn M
    10. Kruse O
    11. Meir Drexler S
    12. Meulders A
    13. Nees F
    14. Pittig A
    15. Richter J
    16. Römer S
    17. Shiban Y
    18. Schmitz A
    19. Straube B
    20. Vervliet B
    21. Wendt J
    22. Baas JMP
    23. Merz CJ

    (2017) Don’t fear ‘fear conditioning’: Methodological considerations for the design and analysis of studies on human fear acquisition, extinction, and return of fear

    Neuroscience & Biobehavioral Reviews 77:247–285.

    https://doi.org/10.1016/j.neubiorev.2017.02.026

    • Google Scholar
91. 1. Lonsdorf TB
    2. Klingelhöfer-Jens M
    3. Andreatta M
    4. Beckers T
    5. Chalkia A
    6. Gerlicher A
    7. Jentsch VL
    8. Meir Drexler S
    9. Mertens G
    10. Richter J
    11. Sjouwerman R
    12. Wendt J
    13. Merz CJ

    (2019) Navigating the garden of forking paths for data exclusions in fear conditioning research

    eLife 8:e52465.

    https://doi.org/10.7554/eLife.52465

    • PubMed
    • Google Scholar
92. 1. Lüdecke D
    2. Ben-Shachar MS
    3. Patil I
    4. Waggoner P
    5. Makowski D

    (2021) performance: An R Package for assessment, comparison and testing of statistical models

    Journal of Open Source Software 6:3139.

    https://doi.org/10.21105/joss.03139

    • Google Scholar
93. Software

    1. Lüdecke D

    (2024) SjPlot: data visualization for statistics in social science, version 2.8.17

    Package.

    https://CRAN.R-project.org/package=sjPlot
94. 1. Lykken DT
    2. Venables PH

    (1971) Direct measurement of skin conductance: A proposal for standardization

    Psychophysiology 8:656–672.

    https://doi.org/10.1111/j.1469-8986.1971.tb00501.x

    • PubMed
    • Google Scholar
95. 1. Lykken DT

    (1972) Range correction applied to heart rate and to GSR data

    Psychophysiology 9:373–379.

    https://doi.org/10.1111/j.1469-8986.1972.tb03222.x

    • PubMed
    • Google Scholar
96. 1. Lynam DR
    2. Hoyle RH
    3. Newman JP

    (2006) The perils of partialling: cautionary tales from aggression and psychopathy

    Assessment 13:328–341.

    https://doi.org/10.1177/1073191106290562

    • PubMed
    • Google Scholar
97. 1. Machlin L
    2. Miller AB
    3. Snyder J
    4. McLaughlin KA
    5. Sheridan MA

    (2019) Differential associations of deprivation and threat with cognitive control and fear conditioning in early childhood

    Frontiers in Behavioral Neuroscience 13:80.

    https://doi.org/10.3389/fnbeh.2019.00080

    • PubMed
    • Google Scholar
98. 1. Mair P
    2. Wilcox R

    (2020) Robust statistical methods in R using the WRS2 package

    Behavior Research Methods 52:464–488.

    https://doi.org/10.3758/s13428-019-01246-w

    • Google Scholar
99. Book

    1. Mason RL
    2. Gunst RF
    3. Hess JL

    (1989)

    Statistical Design and Analysis of Experiments: Applications to Engineering and Science

    Wiley.

    • Google Scholar
100. 1. McEwen BS

     (2003) Mood disorders and allostatic load

     Biological Psychiatry 54:200–207.

     https://doi.org/10.1016/S0006-3223(03)00177-X

     • Google Scholar
101. 1. McLaughlin KA
     2. Greif Green J
     3. Gruber MJ
     4. Sampson NA
     5. Zaslavsky AM
     6. Kessler RC

     (2012) Childhood adversities and first onset of psychiatric disorders in a national sample of US adolescents

     Archives of General Psychiatry 69:1151–1160.

     https://doi.org/10.1001/archgenpsychiatry.2011.2277

     • PubMed
     • Google Scholar
102. 1. McLaughlin KA
     2. Koenen KC
     3. Hill ED
     4. Petukhova M
     5. Sampson NA
     6. Zaslavsky AM
     7. Kessler RC

     (2013) Trauma exposure and posttraumatic stress disorder in a national sample of adolescents

     Journal of the American Academy of Child and Adolescent Psychiatry 52:815–830.

     https://doi.org/10.1016/j.jaac.2013.05.011

     • PubMed
     • Google Scholar
103. 1. McLaughlin KA
     2. Sheridan MA
     3. Lambert HK

     (2014) Childhood adversity and neural development: Deprivation and threat as distinct dimensions of early experience

     Neuroscience & Biobehavioral Reviews 47:578–591.

     https://doi.org/10.1016/j.neubiorev.2014.10.012

     • Google Scholar
104. 1. McLaughlin KA
     2. Sheridan MA

     (2016) Beyond cumulative risk: a dimensional approach to childhood adversity

     Current Directions in Psychological Science 25:239–245.

     https://doi.org/10.1177/0963721416655883

     • PubMed
     • Google Scholar
105. 1. McLaughlin KA
     2. Sheridan MA
     3. Gold AL
     4. Duys A
     5. Lambert HK
     6. Peverill M
     7. Heleniak C
     8. Shechner T
     9. Wojcieszak Z
     10. Pine DS

     (2016) Maltreatment exposure, brain structure, and fear conditioning in children and adolescents

     Neuropsychopharmacology 41:1956–1964.

     https://doi.org/10.1038/npp.2015.365

     • PubMed
     • Google Scholar
106. 1. McLaughlin KA
     2. DeCross SN
     3. Jovanovic T
     4. Tottenham N

     (2019a) Mechanisms linking childhood adversity with psychopathology: learning as an intervention target

     Behaviour Research and Therapy 118:101–109.

     https://doi.org/10.1016/j.brat.2019.04.008

     • PubMed
     • Google Scholar
107. 1. McLaughlin KA
     2. Weissman D
     3. Bitrán D

     (2019b) Childhood adversity and neural development: a systematic review

     Annual Review of Developmental Psychology 1:277–312.

     https://doi.org/10.1146/annurev-devpsych-121318-084950

     • PubMed
     • Google Scholar
108. 1. McLaughlin KA
     2. Sheridan MA
     3. Humphreys KL
     4. Belsky J
     5. Ellis BJ

     (2021) The value of dimensional models of early experience: thinking clearly about concepts and categories

     Perspectives on Psychological Science 16:1463–1472.

     https://doi.org/10.1177/1745691621992346

     • PubMed
     • Google Scholar
109. 1. McMahon SD
     2. Grant KE
     3. Compas BE
     4. Thurm AE
     5. Ey S

     (2003) Stress and psychopathology in children and adolescents: is there evidence of specificity?

     Journal of Child Psychology and Psychiatry, and Allied Disciplines 44:107–133.

     https://doi.org/10.1111/1469-7610.00105

     • PubMed
     • Google Scholar
110. 1. Moffitt TE
     2. Caspi A
     3. Harrington H
     4. Milne BJ
     5. Melchior M
     6. Goldberg D
     7. Poulton R

     (2007) Generalized anxiety disorder and depression: childhood risk factors in a birth cohort followed to age 32

     Psychological Medicine 37:441.

     https://doi.org/10.1017/S0033291706009640

     • Google Scholar
111. 1. Moriarity DP
     2. Alloy LB

     (2021) Back to basics: the importance of measurement properties in biological psychiatry

     Neuroscience and Biobehavioral Reviews 123:72–82.

     https://doi.org/10.1016/j.neubiorev.2021.01.008

     • PubMed
     • Google Scholar
112. Software

     1. Müller K

     (2020) Here: A simpler way to find your files, version 1.0.1

     Package.

     https://CRAN.R-project.org/package=here
113. Software

     1. Müller K
     2. Wickham H

     (2021) Here: a simpler way to find your files, version 3.2.1

     Package.

     https://CRAN.R-project.org/package=tibble
114. Book

     1. Neter J
     2. Wasserman W
     3. Kutner MH

     (1989)

     Applied Linear Regression Models

     Irwin.

     • Google Scholar
115. 1. O’brien RM

     (2007) A caution regarding rules of thumb for variance inflation factors

     Quality & Quantity 41:673–690.

     https://doi.org/10.1007/s11135-006-9018-6

     • Google Scholar
116. Software

     1. Pedersen TL

     (2020) DescTools: tools for descriptive statistics, version 0.99.58

     Package.

     https://CRAN.R-project.org/package=patchwork
117. 1. Pole N
     2. Neylan TC
     3. Otte C
     4. Metzler TJ
     5. Best SR
     6. Henn-Haase C
     7. Marmar CR

     (2007) Associations between childhood trauma and emotion-modulated psychophysiological responses to startling sounds: A study of police cadets

     Journal of Abnormal Psychology 116:352–361.

     https://doi.org/10.1037/0021-843X.116.2.352

     • PubMed
     • Google Scholar
118. 1. Pollak SD
     2. Cicchetti D
     3. Hornung K
     4. Reed A

     (2000) Recognizing emotion in faces: developmental effects of child abuse and neglect

     Developmental Psychology 36:679–688.

     https://doi.org/10.1037/0012-1649.36.5.679

     • PubMed
     • Google Scholar
119. Book

     1. Pollak SD
     2. Tolley-Schell S

     (2004)

     Attention, emotion, and the development of psychopathology

     In: Posner MI, editors. Cognitive Neuroscience of Attention. The Guilford Press. pp. 357–368.

     • Google Scholar
120. 1. Pollak SD
     2. Vardi S
     3. Putzer Bechner AM
     4. Curtin JJ

     (2005) Physically abused children’s regulation of attention in response to hostility

     Child Development 76:968–977.

     https://doi.org/10.1111/j.1467-8624.2005.00890.x

     • PubMed
     • Google Scholar
121. 1. Pollak SD
     2. Smith KE

     (2021) Thinking clearly about biology and childhood adversity: next steps for continued progress

     Perspectives on Psychological Science 16:1473–1477.

     https://doi.org/10.1177/17456916211031539

     • PubMed
     • Google Scholar
122. Software

     1. R Development Core Team

     (2022a) Foreign: read data stored by ’minitab’, ’s’, ’SAS’, ’SPSS’, ’stata’, ’systat’, ’weka’, ’dbase’

     R Foundation for Statistical Computing, Vienna, Austria.

     https://CRAN.R-project.org/package=foreign
123. Software

     1. R Development Core Team

     (2022b) R: a language and environment for statistical computing

     R Foundation for Statistical Computing, Vienna, Austria.

     https://www.r-project.org
124. Software

     1. Revelle W

     (2020) Psych: procedures for psychological, psychometric, and personality research, version 2.4.6.26

     Package.

     https://CRAN.R-project.org/package=psych
125. 1. Richter-Levin G
     2. Sandi C

     (2021) Title: “Labels Matter: is it stress or is it Trauma?”

     Translational Psychiatry 11:385.

     https://doi.org/10.1038/s41398-021-01514-4

     • PubMed
     • Google Scholar
126. 1. Rowland GE
     2. Mekawi Y
     3. Michopoulos V
     4. Powers A
     5. Fani N
     6. Bradley B
     7. Ressler KJ
     8. Jovanovic T
     9. Stevens JS

     (2022) Distinctive impacts of sexual trauma versus non-sexual trauma on PTSD profiles in highly trauma-exposed, Black women

     Journal of Affective Disorders 317:329–338.

     https://doi.org/10.1016/j.jad.2022.08.099

     • Google Scholar
127. 1. Ruge J
     2. Ehlers MR
     3. Kastrinogiannis A
     4. Klingelhöfer-Jens M
     5. Koppold A
     6. Abend R
     7. Lonsdorf TB

     (2024) How adverse childhood experiences get under the skin: A systematic review, integration and methodological discussion on threat and reward learning mechanisms

     eLife 13:e92700.

     https://doi.org/10.7554/eLife.92700

     • Google Scholar
128. 1. Russo SJ
     2. Murrough JW
     3. Han MH
     4. Charney DS
     5. Nestler EJ

     (2012) Neurobiology of resilience

     Nature Neuroscience 15:1475–1484.

     https://doi.org/10.1038/nn.3234

     • PubMed
     • Google Scholar
129. 1. Samaey C
     2. Lecei A
     3. Achterhof R
     4. Hagemann N
     5. Hermans K
     6. Hiekkaranta AP
     7. Kirtley OJ
     8. Reininghaus U
     9. Boets B
     10. Myin-Germeys I
     11. van Winkel R

     (2024) Anticipating future threats: evidence for association, but not interaction, of childhood adversity and identity development with threat anticipation in adolescence

     Early Intervention in Psychiatry 18:750–757.

     https://doi.org/10.1111/eip.13511

     • PubMed
     • Google Scholar
130. 1. Scharfenort R
     2. Menz M
     3. Lonsdorf TB

     (2016) Adversity-induced relapse of fear: neural mechanisms and implications for relapse prevention from a study on experimentally induced return-of-fear following fear conditioning and extinction

     Translational Psychiatry 6:e858.

     https://doi.org/10.1038/tp.2016.126

     • PubMed
     • Google Scholar
131. 1. Schiele MA
     2. Reinhard J
     3. Reif A
     4. Domschke K
     5. Romanos M
     6. Deckert J
     7. Pauli P

     (2016a) Developmental aspects of fear: comparing the acquisition and generalization of conditioned fear in children and adults

     Developmental Psychobiology 58:471–481.

     https://doi.org/10.1002/dev.21393

     • PubMed
     • Google Scholar
132. 1. Schiele MA
     2. Ziegler C
     3. Holitschke K
     4. Schartner C
     5. Schmidt B
     6. Weber H
     7. Reif A
     8. Romanos M
     9. Pauli P
     10. Zwanzger P
     11. Deckert J
     12. Domschke K

     (2016b) Influence of 5-HTT variation, childhood trauma and self-efficacy on anxiety traits: a gene-environment-coping interaction study

     Journal of Neural Transmission 123:895–904.

     https://doi.org/10.1007/s00702-016-1564-z

     • PubMed
     • Google Scholar
133. 1. Schiele MA
     2. Herzog K
     3. Kollert L
     4. Schartner C
     5. Leehr EJ
     6. Böhnlein J
     7. Repple J
     8. Rosenkranz K
     9. Lonsdorf TB
     10. Dannlowski U
     11. Zwanzger P
     12. Reif A
     13. Pauli P
     14. Deckert J
     15. Domschke K

     (2020) Extending the vulnerability-stress model of mental disorders: three-dimensional NPSR1 × environment × coping interaction study in anxiety

     The British Journal of Psychiatry 217:645–650.

     https://doi.org/10.1192/bjp.2020.73

     • PubMed
     • Google Scholar
134. Software

     1. Schloerke B
     2. Cook D
     3. Larmarange J
     4. Briatte F
     5. Marbach M
     6. Thoen E
     7. Crowley J

     (2021) GGally: extension to “ggplot2”, version 2.2.1

     Package.

     https://CRAN.R-project.org/package=GGally
135. 1. Sheehan DV
     2. Lecrubier Y
     3. Sheehan KH
     4. Amorim P
     5. Janavs J
     6. Weiller E
     7. Hergueta T
     8. Baker R
     9. Dunbar GC

     (1998)

     The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10

     The Journal of Clinical Psychiatry 59 Suppl 20:22–33.

     • PubMed
     • Google Scholar
136. 1. Sheridan MA
     2. McLaughlin KA

     (2014) Dimensions of early experience and neural development: deprivation and threat

     Trends in Cognitive Sciences 18:580–585.

     https://doi.org/10.1016/j.tics.2014.09.001

     • Google Scholar
137. 1. Sheridan MA
     2. McLaughlin KA

     (2016) Neurobiological models of the impact of adversity on education

     Current Opinion in Behavioral Sciences 10:108–113.

     https://doi.org/10.1016/j.cobeha.2016.05.013

     • PubMed
     • Google Scholar
138. 1. Sheridan MA
     2. Peverill M
     3. Finn AS
     4. McLaughlin KA

     (2017) Dimensions of childhood adversity have distinct associations with neural systems underlying executive functioning

     Development and Psychopathology 29:1777–1794.

     https://doi.org/10.1017/S0954579417001390

     • PubMed
     • Google Scholar
139. 1. Silvers JA
     2. Lumian DS
     3. Gabard-Durnam L
     4. Gee DG
     5. Goff B
     6. Fareri DS
     7. Caldera C
     8. Flannery J
     9. Telzer EH
     10. Humphreys KL
     11. Tottenham N

     (2016) Previous institutionalization is followed by broader amygdala-hippocampal-PFC network connectivity during aversive learning in human development

     The Journal of Neuroscience 36:6420–6430.

     https://doi.org/10.1523/JNEUROSCI.0038-16.2016

     • PubMed
     • Google Scholar
140. 1. Sjouwerman R
     2. Niehaus J
     3. Lonsdorf TB

     (2015) Contextual change after fear acquisition affects conditioned responding and the time course of extinction learning-implications for renewal research

     Frontiers in Behavioral Neuroscience 9:337.

     https://doi.org/10.3389/fnbeh.2015.00337

     • PubMed
     • Google Scholar
141. 1. Sjouwerman R
     2. Lonsdorf TB

     (2020) Experimental boundary conditions of reinstatement-induced return of fear in humans: Is reinstatement in humans what we think it is?

     Psychophysiology 57:e13549.

     https://doi.org/10.1111/psyp.13549

     • PubMed
     • Google Scholar
142. 1. Sjouwerman R
     2. Illius S
     3. Kuhn M
     4. Lonsdorf TB

     (2022) A data multiverse analysis investigating non-model based SCR quantification approaches

     Psychophysiology 59:e14130.

     https://doi.org/10.1111/psyp.14130

     • PubMed
     • Google Scholar
143. 1. Smith KE
     2. Pollak SD

     (2021) Rethinking concepts and categories for understanding the neurodevelopmental effects of childhood adversity

     Perspectives on Psychological Science 16:67–93.

     https://doi.org/10.1177/1745691620920725

     • Google Scholar
144. Book

     1. Spielberger CD

     (1983)

     Manual for the State-Trait Inventory STAI (Form Y)

     Palo Alto, CA: Mind Garden.

     • Google Scholar
145. 1. Spinhoven P
     2. Elzinga BM
     3. Hovens J
     4. Roelofs K
     5. van Oppen P
     6. Zitman FG
     7. Penninx B

     (2011) Positive and negative life events and personality traits in predicting course of depression and anxiety

     Acta Psychiatrica Scandinavica 124:462–473.

     https://doi.org/10.1111/j.1600-0447.2011.01753.x

     • PubMed
     • Google Scholar
146. 1. Stegmann Y
     2. Schiele MA
     3. Schümann D
     4. Lonsdorf TB
     5. Zwanzger P
     6. Romanos M
     7. Reif A
     8. Domschke K
     9. Deckert J
     10. Gamer M
     11. Pauli P

     (2019) Individual differences in human fear generalization-pattern identification and implications for anxiety disorders

     Translational Psychiatry 9:307.

     https://doi.org/10.1038/s41398-019-0646-8

     • PubMed
     • Google Scholar
147. 1. Teicher MH
     2. Gordon JB
     3. Nemeroff CB

     (2022) Recognizing the importance of childhood maltreatment as a critical factor in psychiatric diagnoses, treatment, research, prevention, and education

     Molecular Psychiatry 27:1331–1338.

     https://doi.org/10.1038/s41380-021-01367-9

     • PubMed
     • Google Scholar
148. 1. Thome J
     2. Hauschild S
     3. Koppe G
     4. Liebke L
     5. Rausch S
     6. Herzog JI
     7. Müller-Engelmann M
     8. Steil R
     9. Priebe K
     10. Hermans D
     11. Schmahl C
     12. Bohus M
     13. Lis S

     (2018) Generalisation of fear in PTSD related to prolonged childhood maltreatment: an experimental study

     Psychological Medicine 48:2223–2234.

     https://doi.org/10.1017/S0033291717003713

     • PubMed
     • Google Scholar
149. Software

     1. Tiedemann F

     (2020) Gghalves: compose half-half plots using your favourite geoms, version 0.1.4

     Package.

     https://CRAN.R-project.org/package=gghalves
150. Software

     1. Torchiano M

     (2020) Effsize - a package for efficient effect size computation, version 2

     Zenodo.

     https://doi.org/10.5281/zenodo.1480624
151. 1. Vervliet B
     2. Craske MG
     3. Hermans D

     (2013) Fear extinction and relapse: state of the art

     Annual Review of Clinical Psychology 9:215–248.

     https://doi.org/10.1146/annurev-clinpsy-050212-185542

     • PubMed
     • Google Scholar
152. 1. Wickham H

     (2007) Reshaping data with the reshape package

     Journal of Statistical Software 01:1–20.

     https://doi.org/10.18637/jss.v021.i12

     • Google Scholar
153. Software

     1. Wickham H

     (2016) Ggplot2: elegant graphics for data analysis, version 3.5.1

     Package.

     https://ggplot2.tidyverse.org
154. Software

     1. Wickham H

     (2019) Stringr: simple, consistent wrappers for common string operations, version 1.5.1

     Package.

     https://CRAN.R-project.org/package=stringr
155. 1. Wickham H
     2. Averick M
     3. Bryan J
     4. Chang W
     5. McGowan L
     6. François R
     7. Grolemund G
     8. Hayes A
     9. Henry L
     10. Hester J
     11. Kuhn M
     12. Pedersen T
     13. Miller E
     14. Bache S
     15. Müller K
     16. Ooms J
     17. Robinson D
     18. Seidel D
     19. Spinu V
     20. Takahashi K
     21. Vaughan D
     22. Wilke C
     23. Woo K
     24. Yutani H

     (2019) Welcome to the Tidyverse

     Journal of Open Source Software 4:1686.

     https://doi.org/10.21105/joss.01686

     • Google Scholar
156. Software

     1. Wickham H

     (2020) Forcats: tools for working with categorical variables (factors), version 1.0.0

     Package.

     https://CRAN.R-project.org/package=forcats
157. Software

     1. Wickham H
     2. Miller E

     (2020) Haven: import and export ’SPSS’, ’stata’ and ’SAS’ files, version 2.5.4

     Package.

     https://CRAN.R-project.org/package=haven
158. Software

     1. Wickham H
     2. François R
     3. Henry L
     4. Müller K

     (2022) Dplyr: A grammar of data manipulation, version 1.1.4

     Package.

     https://CRAN.R-project.org/package=dplyr
159. Software

     1. Wickham H
     2. Girlich M

     (2022) Tidyr: tidy messy data, version 1.3.1

     Package.

     https://CRAN.R-project.org/package=tidyr
160. 1. Wingenfeld K
     2. Spitzer C
     3. Mensebach C
     4. Grabe H
     5. Hill A
     6. Gast U
     7. Schlosser N
     8. Höpp H
     9. Beblo T
     10. Driessen M

     (2010) Die deutsche version des childhood trauma questionnaire (CTQ): Erste Befunde zu den psychometrischen Kennwerten

     PPmP - Psychotherapie · Psychosomatik · Medizinische Psychologie 60:442–450.

     https://doi.org/10.1055/s-0030-1247564

     • Google Scholar
161. Book

     1. Xie Y

     (2015) Dynamic documents with R and Knitr

     Chapman.

     https://doi.org/10.1201/b15166

     • Google Scholar
162. Software

     1. Yasumoto A

     (2023) FtExtra: extensions for ’flextable’, version 0.6.4

     Package.

     https://CRAN.R-project.org/package=ftExtra
163. 1. Young DA
     2. Neylan TC
     3. O’Donovan A
     4. Metzler T
     5. Richards A
     6. Ross JA
     7. Inslicht SS

     (2018) The interaction of BDNF Val66Met, PTSD, and child abuse on psychophysiological reactivity and HPA axis function in a sample of Gulf War Veterans

     Journal of Affective Disorders 235:52–60.

     https://doi.org/10.1016/j.jad.2018.04.004

     • PubMed
     • Google Scholar
164. 1. Young ES
     2. Farrell AK
     3. Carlson EA
     4. Englund MM
     5. Miller GE
     6. Gunnar MR
     7. Roisman GI
     8. Simpson JA

     (2019) The dual impact of early and concurrent life stress on adults’ diurnal cortisol patterns: a prospective study

     Psychological Science 30:739–747.

     https://doi.org/10.1177/0956797619833664

     • PubMed
     • Google Scholar
165. 1. Zeileis A
     2. Hothorn T

     (2002)

     Diagnostic checking in regression relationships

     R News 01:7–10.

     • Google Scholar
166. 1. Zeileis A

     (2004) Econometric computing with HC and HAC covariance matrix estimators

     Journal of Statistical Software 11:1–17.

     https://doi.org/10.18637/jss.v011.i10

     • Google Scholar
167. 1. Zeileis A
     2. Grothendieck G

     (2005) Zoo: S3 infrastructure for regular and irregular time series

     Journal of Statistical Software 14:1–27.

     https://doi.org/10.18637/jss.v014.i06

     • Google Scholar
168. 1. Zeileis A

     (2006) Object-oriented computation of sandwich estimators

     Journal of Statistical Software 16:1–16.

     https://doi.org/10.18637/jss.v016.i09

     • Google Scholar
169. 1. Zeileis A
     2. Köll S
     3. Graham N

     (2020) Various versatile variances: an object-oriented implementation of clustered covariances in R

     Journal of Statistical Software 95:1–36.

     https://doi.org/10.18637/jss.v095.i01

     • Google Scholar
170. Software

     1. Zhu H

     (2020) KableExtra: construct complex table with ’kable’ and pipe syntax, version 1.4.0

     Package.

     https://CRAN.R-project.org/package=kableExtra
171. 1. Zuo XN
     2. Xu T
     3. Milham MP

     (2019) Harnessing reliability for neuroscience research

     Nature Human Behaviour 3:768–771.

     https://doi.org/10.1038/s41562-019-0655-x

     • PubMed
     • Google Scholar