6 Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany 7 Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Frankfurt am Main,

8 Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital of Würzburg, University of Würzburg, Würzburg, Germany 9 Kbo Inn Salzach Hospital Clinical Center for Psychiatry, Wasserburg am Inn, Germany,

10 Department of Psychiatry, Ludwig-Maximilian-University Munich, Munich, Germany 11 Department of Psychology and Center of Mental Health, Julius Maximilians University of

12 Department of Psychology, Biological Psychology and Cognitive Neuroscience, University of

The authors made the following contributions. Maren Klingelhöfer-Jens: Conceptualization, Software, Validation, Formal analysis, Writing - Original Draft, Writing

Correspondence concerning this article should be addressed to Maren Klingelhöfer-Jens,

Exposure to adverse childhood experiences (ACEs) is a strong predictor for developing behavioral, somatic and psychopathological conditions. Exposure to threat-related early adversity has been suggested to be specifically linked to altered emotional learning as well as changes in neural circuits involved in emotional responding and fear. Learning mechanisms are particularly interesting as they are central mechanisms through which environmental inputs shape emotional and cognitive processes and ultimately behavior. Multiple theories on the mechanisms underlying this association have been suggested which, however, differ in the operationalization of ACEs. 1,402 physically and mentally healthy participants underwent a fear conditioning paradigm including a fear acquisition and generalization phase while skin conductance responses (SCRs) and different subjective ratings were acquired. ACEs were retrospectively assessed through the childhood trauma questionnaire and participants were assigned to individuals exposed or unexposed to at least moderate adverse childhood experiences according to established cut-off criteria. In addition, we provide exploratory analyses aiming to shed light on different theoretical accounts on how ACE9s impact individual risk profiles (i.e., cumulative risk account, specificity model, dimensional model). During fear acquisition training and generalization, we observed reduced discrimination in SCRs between the CS+ and the CS-, primarily due to reduced CS+ responding in exposed individuals. During fear generalization, no differences in generalization gradients were observed between exposed and unexposed individuals but generally blunted physiological responses in exposed individuals. No differences between the groups were observed in ratings in any of the experimental phases. The lower CS discrimination in SCRs in exposed individuals was evident across operationalizations according to the cumulative risk account, specificity as well as dimensional model. However, none of these theories showed clear explanatory superiority. Our results stand in stark contrast to typical patterns observed in patients suffering from anxiety and stress-related disorders (i.e., reduced CS discrimination due to increased responses to safety signals). Thus, reduced CS+ responding in individuals exposed to ACEs, yet not showing signs of psychopathology, may represent a specific characteristic of this resilient subgroup that warrants further investigation with respect to its relation to risk and resilience. In addition, we conclude that theories linking ACEs to psychopathology need refinement. Reduced discrimination between signals of danger and safety but not overgeneralization is linked to exposure to childhood adversity in healthy adults

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 2015; Fraunfelter, Gerdes, & Alpers, 2022).

In sharp contrast to these threat learning patterns observed in patient samples, a recent review provided converging evidence that exposure to ACEs 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. (2023)]. Of note, this pattern was observed in mixed samples (healthy, at risk, patients) and in pediatric samples and adults exposed to ACEs 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, Miller, Snyder, McLaughlin, & Sheridan, 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 ACEs (for a review, see Ruge et al., 2023).

Ruge et al. (2023) also highlighted operationalization as a key challenge in the field hampering interpretation of findings across studies and consequently cumulative knowledge generation. Operationalization of exposure to ACEs, and hence translation of theoretical accounts of the role of ACEs into statistical tests, is an ongoing and current discussion in the field (McLaughlin, Sheridan, Humphreys, Belsky, & Ellis, 2021; Pollak & Smith, 2021; Smith & Pollak, 2021). Historically, ACEs have 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 & Pollak, 2021). Accordingly, ACEs have often been considered as a cumulative measure [8cumulative risk approach9; Smith and Pollak (2021); McLaughlin et al. (2021)]. An alternative approach Sheridan & McLaughlin (2014) posits that different types of adverse events have a distinct impact on individuals and their (neurobiological) development through distinct mechanisms [8specificity approach9; Smith and Pollak (2021); McLaughlin et al. (2021)]. Currently, distinguishing between threat and deprivation exposure represents the prevailing approach (McLaughlin, DeCross, Jovanovic, & Tottenham, 2019), which has been formalized in the (two-)dimensional model of adversity and psychopathology [DMAP; Sheridan and McLaughlin (2014); McLaughlin, Sheridan, and Lambert (2014); Sheridan and McLaughlin (2016); McLaughlin and Sheridan (2016b); Machlin et al. (2019); McLaughlin et al. (2021)]. To this end, exposure to threat-related ACEs has been suggested to be specifically linked to altered emotional and fear learning (Sheridan &

Yet, there is converging evidence from different fields of research suggesting that the effects of exposure to ACEs are cumulative, non-specific and rather unlikely to be tied to specific types of adverse events (Danese et al., 2009; Smith & Pollak, 2021; D. A. Young et al., 2019) with few exceptions (Colich, Rosen, Williams, & McLaughlin, 2020; McLaughlin, Weissman, & Bitrán, 2019), which is also supported by a recent review on the association between threat and reward learning with exposure to ACEs (Ruge et al., 2023). Yet, 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).

We operationalize ACE exposure through different approaches: Our main analyses employ the approach adopted by most publications in the field (see Ruge et al., 2023 for a review) - dichotomization of the sample into exposed vs. unexposed based on published cut-offs for the Childhood Trauma Questionnaire (CTQ). In addition, we provide exploratory analyses that attempt to translate dominant (verbal) theoretical accounts (McLaughlin et al., 2021; Pollak & Smith, 2021) on the impact of exposure to ACEs into statistical tests while acknowledging 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., 2023) and refinement of theoretical accounts. More precisely, we attempted to exemplarily and exploratively translate a) the cumulative risk approach, b) the specificity model, and c) the dimensional model into statistical tests applied to our dataset, while also compiling challenges encountered when aiming to translate these verbal theories into statistical models in practice.

Based on the recently reviewed literature (Ruge et al., 2023), we expect less discrimination between signals of danger (CS+) and safety (CS-) in exposed individuals as compared to those unexposed to ACEs - primarily due to reduced responses to the CS+ - during both the fear acquisition and the generalization phase. Based on the literature (Ruge et al., 2023), we do not expect group differences in generalization gradients.

At the end of each experimental phase (habituation, acquisition training and generalization) as well as after half of the total acquisition and generalization trials, participants provided ratings of the faces with regards to valence, arousal (9-point Likert-scales; from 1 = very unpleasant/very calm to 9 = very pleasant/very arousing) and US contingencies (11-point Likert-scale; from 0 to 100% in 10% increments). As the US did not occur during the habituation phase, contingency ratings were not provided after this phase. For reasons of comparability, valence ratings were inverted.

Approach name and reference Main analyses Moderate exposure based on CTQ (exposed vs. unexposed)

Short description: At least one subscale met the published cut-off for at least moderate exposure (Bernstein & Fink,1998; Häuser et al., 2011) Procedure: emotional abuse >= 13, physical abuse >= 10, sexual abuse >= 8, emotional neglect >= 15, physical neglect >= 10), a cut-off employed in previous work by our team (Koppold et al., 2023) and in the literature (Ruge et al., 2023) Statistical test: See <Methods: Statistical analyses= Exploratory analyses Cumulative risk model Evans et al., 2013; McEwen 2003)

Short description: Based on the assumed key role of cumulative exposure (exposure intensity and frequency) Procedure a): classification into the four severity groups (no, low, moderate, severe exposure) based on cut-offs published by Bernstein & Fink (1998) Statistical test a): comparison of conditioned responding of the four severity groups by using one-way ANOVAs

Challenges in translating theory into a statistical model ● Not based on an existing theory but on what is commonly used in

the literature (Ruge et al., 2023) ● Different cut-offs published (for a discussion, see Ruge et al.,

2023) ● (Statistical) Challenges linked to dichotomization of an inherently

continuous variable ● Problem with CTQ sum score: it assigns the same <value= to all

CM types (see also <General operationalizational challenges= below) ● Number of subscales exceeding cut-off: calculate ANOVA or

regression? ● Cumulative risk scores are based on the implicit assumption that different types of adverse events affect the same mechanisms and are of equal impact

Specificity model (McMahon et al., 2003; Pollak et al., 2000; Pollak & Tolley-Schell, 2004) Dimensional model

Procedure b): number of subscales exceeding an at least moderate cut-off based on Bernstein & Fink (1998) and Häuser et al.,(2011) Statistical test b): number of sub-scales exceeding an at least moderate cut-off as predictor and conditioned responding as criterion in simple linear regression models Short description: Consideration of specific exposure types (abuse vs. neglect) Procedure: summing up the CTQ subscales emotional abuse, physical abuse and sexual abuse yielding a composite score for exposure to <abuse= and summing up the subscales emotional neglect and physical neglect to yield a composite score for <neglect= (or threat vs. deprivation as done by Sheridan et al., 2017) Statistical test: the abuse and neglect composite score is tested for associations with conditioned responding in separate regression models In our sample n = 52 and n = 96 individuals were exposed to abuse only and neglect only, respectively, while n = 55 reported to have experienced both abuse and neglect. We included all participants in all analyses as done previously (Sheridan et al., 2017) Short description: consideration of specific exposure types (i.e., abuse and neglect) that are assumed to co-occur and be controlled for the ● What qualifies as a specific exposure type (i.e., subscales or

composite scales for neglect vs. abuse?) ● Which exposure subcategories are <too specific= or <too broad=? (A heterogeneous category may obscure potentially relevant discrete associations) ● Include only participants who experienced only one specific type but not any other types despite this being rather artificial due to high co-occurrences of different exposure types and requiring extremely large samples? Which cut-off should be used then to define exposure? We decided to include all participants in the analyses as done in previous studies (Sheridan et al., 2017) ● Lack of specificity of exposure subtypes (e.g., sexual abuse also

has an emotional component) ● Ongoing debate on multicollinearity of multiple ACEs in one model (McLaughlin et al., 2021; Pollak & Smith, 2021) (McLaughlin2016 et al. effect of one another (as opposed to the specificity 2016; McLaughlin et al., model) 2021)

Procedure: see specificity model Statistical test: abuse and neglect scores are tested for associations with conditioned responding in a single linear regression model in which the influence of the other type is controlled for General operationalizational challenges ● Non-comparability of dimensional and categorical approaches: CTQ sum score assumes an equal contribution of all items which contradicts different thresholds for being considered as exposed for different subscales (e.g., lower cut-off for sexual abuse as compared to emotional neglect) ● Associations in a full sample may differ from associations in the group of exposed individuals only which is a challenge for interpretation of data ● Multiple cut-offs published (Bernstein et al. 1997; Bernstein & Fink, 1998) ● Specific challenges relating to abuse and neglect: They ○ often co-occur ○ are not the only relevant dimensions (e.g., unpredictability, loss) ○ are not strongly supported as distinct dimensions in the literature (Carozza et al., 2022; Smith & Pollak, 2021) ● Heterogeneity in the assessment of childhood adversity across studies - both with respect to the assessment tools (e.g., questionnaires, interview) as well as with respect to the operationalization of adversity (i.e., definition) ● Different response formats (yes/no vs. specification of duration and frequency) and the number of trauma types/events included in assessment tools impact on prevalence rates and potentially also associations between the number of adverse experiences and symptom severity [e.g., Contractor et al., 2018) ● Distinction between stressful events and trauma is often unclear (Richter-Levin & Sandi, 2021)

Contractor, A. A., Brown, L. A., & Weiss, N. H. (2018). Relation between lifespan polytrauma typologies and post-trauma mental health. Comprehensive Psychiatry, 80, 202–213.

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