ABSTRACT Title of Thesis: UNDERSTANDING THE RELEVANCE OF EXTENDED AMYGDALA REACTIVITY TO DISPOSITIONAL NEGATIVITY Shannon E. Grogans, Master of Science, 2021 Thesis Directed By: Dr. Alexander J. Shackman, Associate Professor, Department of Psychology Elevated dispositional negativity (DN; i.e., neuroticism/negative emotionality) is associated with a range of deleterious outcomes, including mental illness. Yet, DN?s neurobiology remains incompletely understood. Prior work suggests that DN reflects heightened threat-elicited reactivity in the extended amygdala (EAc), a circuit encompassing the central nucleus (Ce) and the bed nucleus of the stria terminalis (BST), and that this association may be intensified for uncertain threat. We utilized a multi-trait, multi-occasion DN composite and neuroimaging assays of threat anticipation and perception to demonstrate that individuals with elevated DN show heightened BST activation during threat anticipation. Analyses revealed that DN is uniquely predicted by BST reactivity to uncertain threat. DN was unrelated to Ce activation during threat anticipation or EAc activation during ?threatening?-face presentation. Follow-up analyses revealed that the threat paradigms are not interchangeable probes of EAc function. These observations lay the foundation for future studies necessary to determine causation and improve interventions. UNDERSTANDING THE RELEVANCE OF EXTENDED AMYGDALA REACTIVITY TO DISPOSITIONAL NEGATIVITY by Shannon E. Grogans Thesis submitted to the Faculty of the Graduate School of the University of Maryland, College Park, in partial fulfillment of the requirements for the degree of Master of Science 2021 Advisory Committee: Professor Alexander Shackman, Chair Professor Lea Dougherty Professor Arianna Gard ? Copyright by Shannon E. Grogans 2021 Table of Contents Table of Contents .......................................................................................................... ii List of Tables ............................................................................................................... iii List of Figures .............................................................................................................. iv Introduction ................................................................................................................... 1 Methods....................................................................................................................... 17 Results ......................................................................................................................... 37 Discussion ................................................................................................................... 42 Tables .......................................................................................................................... 47 Figures......................................................................................................................... 49 Supplement ................................................................................................................. 53 References ................................................................................................................... 75 This Table of Contents is automatically generated by MS Word, linked to the Heading formats used within the Chapter text. ii List of Tables Table 1. Human studies of dispositional negativity and distress-eliciting neuroimaging paradigms. iii List of Figures Figure 1. The 4 reinforcer conditions comprising the Maryland Threat Countdown paradigm used in the present study. Figure 2. Coronal slices depicting the locations of the BST and Ce ROIs used in the present study. Figure 3. Threat anticipation robustly increased subjective symptoms and objective signs of anxiety. Figure 4. The coronal slices above depict voxels showing significantly increased activity within the BST and the dorsal amygdala/Ce for various contrasts of interest. Figure 5. Individuals with a more negative disposition show increased BST reactivity to Uncertain Threat. iv Introduction Dispositional negativity (i.e., neuroticism or negative emotionality)?the propensity to experience and express more intense, frequent, or persistent negative affect?is a fundamental dimension of mammalian temperament (Boissy, 1995; Hur, Stockbridge, Fox, & Shackman, 2019; Shackman, Stockbridge, LeMay, & Fox, 2018; Shackman et al., 2016). Elevated levels of dispositional negativity are associated with a wide range of practically important outcomes, from marital stability and socioeconomic attainment to mental illness and premature death. Despite this, our understanding of the brain bases of dispositional negativity remains far from complete. The Nature of Dispositional Negativity Dispositional negativity encompasses a range of overlapping measures and traits, including neuroticism, negative affectivity/emotionality, anxious temperament, behavioral inhibition, harm avoidance, and trait anxiety (Caspi, Roberts, & Shiner, 2005; Knowles & Olatunji, in press; Shackman et al., 2016; Watson, Stanton, & Clark, 2017; Zentner & Shiner, 2012). This extended family of distress-promoting phenotypes first emerges early in development, persists into adulthood, and reflects a combination of genetic and environmental factors (Cheesman et al., 2020; Hur et al., 2019; Kendler et al., 2019; Valk et al., 2020). The structure of dispositional negativity is relatively invariant across cultures, languages, and ages?at least from elementary school onward (Hur et al., 2019). Core features of this family of traits, including hypervigilance and behavioral inhibition, manifest similarly across mammalian species, enabling mechanistic (e.g., focal perturbation) research to be performed in rodents and monkeys 1 (Fox & Kalin, 2014; Fox & Shackman, 2019; Fox et al., in press; Kalin et al., 2016; Kenwood & Kalin, 2020; Oler, Fox, Shackman, & Kalin, 2016). Individual differences in dispositional negativity are highly reliable, show substantial agreement across instruments and informants, and are lawfully associated with behavior in the laboratory and in the real world (Geukes et al., 2019; Gross, Sutton, & Ketelaar, 1998; Hur et al., 2019; Lenhausen, van Scheppingen, & Bleidorn, 2020; Oltmanns, Jackson, & Oltmanns, 2020; Thake & Zelenski, 2013). The Deleterious Consequences of Dispositional Negativity Individual differences in dispositional negativity have important consequences for health, wealth, and wellbeing?drawing the attention of social scientists, biomedical researchers, clinicians, and policy makers (Barlow, Sauer-Zavala, Carl, Bullis, & Ellard, 2013; Bleidorn et al., 2019; Lahey, 2009; Rapee & Bayer, 2018; Tackett & Lahey, 2017; Widiger & Oltmanns, 2017). General Wellbeing and Health. Elevated dispositional negativity is prospectively associated with lower levels of educational attainment, occupational success, and annual income (Beck, 2020; Hoff, Einarsd?ttir, Chu, Briley, & Rounds, in press; Hur et al., 2019; Kajonius & Carlander, 2017; Leckelt et al., 2019; C. J. Soto, in press; von Soest, Wagner, Hansen, & Gerstorf, 2018). Individuals with a more negative disposition report lower levels of social support and decreased well-being, and they are less satisfied with their jobs, sexual experiences, friends, romantic partners, family members, and lives (M. S. Allen & E. E. Walter, 2018; Anglim, Horwood, Smillie, Marrero, & Wood, 2020; Barelds, 2005; Baselmans et al., 2019; Bolger & Eckenrode, 2 1991; Caughlin, Huston, & Houts, 2000; Finn, Mitte, & Neyer, 2013; Hansson et al., 2020; Hanzal & Segrin, 2009; Heller, Watson, & Ilies, 2004; Hur et al., 2019; Joel et al., in press; Judge, Heller, & Mount, 2002; Kajonius & Carlander, 2017; Lavner, Weiss, Miller, & Karney, 2018; Leskela et al., 2009; Li et al., 2019; McHugh & Lawlor, 2012; Mueller, Wagner, Smith, Voelkle, & Gerstorf, 2018; Newton-Howes, Horwood, & Mulder, 2015; O'Meara & South, 2019; Roohafza et al., 2016; R?ysamb, Nes, Czajkowski, & Vassend, 2018; Shaver & Brennan, 1992; Slatcher & Vazire, 2009; C. J. Soto, 2019, in press; Swickert, Hittner, & Foster, 2010). Cross-sectional and longitudinal studies show that dispositionally negative individuals are prone to experiences that confer risk for emotional illness, including heightened feelings of loneliness, burnout, and emotional exhaustion; greater difficulties adjusting to major life transitions, such as college, moving abroad, and retirement; and more frequent work-family conflict, marital discord, unemployment, and divorce (Abdellaoui, Chen, et al., 2019; Abdellaoui et al., 2018; Abdellaoui, Sanchez-Roige, et al., 2019; T. D. Allen et al., 2012; Altschul, Iveson, & Deary, in press; Asselmann & Specht, 2020; Baselmans et al., 2019; Beck, 2020; Brock, Dindo, Simms, & Clark, 2016; Buecker, Maes, Denissen, & Luhmann, 2020; Cred? & Niehorster, 2012; Day, Ong, & Perry, 2018; Hansson et al., 2020; Harari, Reaves, Beane, Laginess, & Viswesvaran, 2018; Hur et al., 2019; Klimstra, Noftle, Luyckx, Goossens, & Robins, 2018; C. J. Soto, in press; Swider & Zimmerman, 2010; You, Huang, Wang, & Bao, 2015). They are more likely to engage in unhealthy behaviors; to suffer from sleep problems, chronic pain, and subjective health complaints; to develop physical illnesses; and to die prematurely (Adams et al., in press; M. S. Allen & Robson, 2018; M. S. Allen & Emma E. Walter, 3 2018; Baselmans et al., 2019; B. P. Chapman & Goldberg, 2017; B. P Chapman et al., 2019; B. P. Chapman et al., 2020; Charles, Gatz, Kato, & Pedersen, 2008; Gale et al., 2017; E. K. Graham et al., 2017; Huang et al., 2017; Hur et al., 2019; Jokela, Airaksinen, Kivimaki, & Hakulinen, 2018; Jokela et al., in press; K??ts-Ausmees et al., 2016; Kornadt, Hagemeyer, Neyer, & Kandler, 2018; M. Liu et al., 2019; Meng et al., 2020; Puterman et al., 2020; Quach et al., 2020; Sallis, Davey Smith, & Munafo, 2019; Spengler, Roberts, Ludtke, Martin, & Brunner, 2016; Sutin et al., 2016; Tackman et al., in press; Valk et al., 2020; Weston & Jackson, 2018; Wettstein, Wahl, & Siebert, in press). Mental Illness. There is ample cross-sectional, longitudinal, and genetic evidence that individuals with a negative disposition are more likely to develop a variety of psychiatric illnesses?including anxiety disorders, depression, and alcohol abuse? and, among those who do, to experience more severe, recurrent, and treatment-resistant symptoms (Adams et al., in press; Akingbuwa et al., 2020; Beck, 2020; Boe, Holgersen, & Holen, 2011; Brislin et al., 2020; Brouwer et al., 2019; Bucher, Suzuki, & Samuel, 2019; Class et al., 2019b; Cohen, Thakur, Young, & Hankin, in press; Coryell, Mills, Dindo, & Calarge, in press; du Pont, Rhee, Corley, Hewitt, & Friedman, 2019; Ejova, Milojev, Worthington, Bulbulia, & Sibley, 2020; Fullana et al., 2020; B. L. Goldstein, Greg Perlman, Nicholas R. Eaton, Roman Kotov, & D. N. Klein, in press; B. L. Goldstein, G. Perlman, N. R. Eaton, R. Kotov, & D. N. Klein, in press; Hur et al., 2019; Jones et al., 2018; Katz, Matanky, Aviram, & Yovel, 2020; Khoo, Stanton, Clark, & Watson, 2020; Kostyrka-Allchorne, Wass, & Sonuga-Barke, 2020; Kroencke, Kuper, Bleidorn, & Denissen, in press; Levin-Aspenson, Khoo, & Kotelnikova, 2019; 4 Michelini et al., 2020; Mineka et al., in press; Mumper, Dyson, Finsaas, Olino, & Klein, 2020; Sadeh, Miller, Wolf, & Harkness, 2015; Sen et al., 2010; A. Tang et al., 2020; Thorp et al., 2020; van Eeden et al., 2019). Likewise, dimensional approaches to psychopathology indicate that dispositional negativity is associated with the p-factor, a superordinate dimension that encompasses both internalizing and externalizing symptoms (Brandes, Herzhoff, Smack, & Tackett, 2019; Caspi & Moffitt, 2018; Jones et al., 2018; Levin-Aspenson et al., 2019; Mann, Atherton, DeYoung, Krueger, & Robins, in press). Furthermore, there is evidence that relations between dispositional negativity and mental illness remain evident, albeit attenuated, after eliminating overlapping item content (Class et al., 2019a; Uliaszek et al., 2009) or controlling for baseline symptomatology (Jeronimus, Kotov, Riese, & Ormel, 2016). Given this panoply of adverse, often co-morbid outcomes, dispositional negativity imposes a tremendous burden on healthcare providers and the global economy (Cuijpers et al., 2010; Goodwin, Hoven, Lyons, & Stein, 2002; ten Have, Oldehinkel, Vollebergh, & Ormel, 2005). Despite its profound consequences for wellbeing and disease, the neural systems underlying trait-like individual differences in dispositional negativity remain incompletely understood. Relevance of the Extended Amygdala to Dispositional Negativity Theoretical Foundations. The neural circuits governing trait-like individual differences in dispositional negativity have only recently started to come into focus. Decades ago, the influential personality theorist, Gordon Allport, wrote that, ?traits are cortical [or] subcortical ? dispositions having the capacity to gate or guide 5 specific phasic reactions? (Allport, 1966, p. 3). Today, most models remain firmly rooted in the idea that dispositional negativity reflects a neurobiological tendency to overreact to conflict, criticism, novelty, punishment, threat, and other kinds of acute ?trait-relevant? challenges (Eysenck, 1967; Goldsmith et al., 1987; Kagan, Reznick, & Snidman, 1988; Lahey, 2009; Reiss, 1997; Shackman et al., 2016; Spielberger, 1966; Zuckerman, 1976). This tendency has been linked to altered function in a number of brain regions?including the anterior insula/frontal operculum, extended amygdala, mid-cingulate cortex, and periaqueductal gray (Cavanagh & Shackman, 2015; Fox & Kalin, 2014; Hur et al., 2019; Kalin, 2017; Kirlic et al., 2019; Lowery-Gionta, DiBerto, Mazzone, & Kash, 2018; Shackman et al., 2011; Shackman et al., 2016; Sjouwerman, Scharfenort, & Lonsdorf, 2020; Somerville, Whalen, & Kelley, 2010). Among these, the extended amygdala has received the most intense empirical scrutiny and occupies the most privileged position in neurobiological models of dispositional negativity and pathological fear and anxiety (e.g., Davis, Walker, Miles, & Grillon, 2010; Fox, Oler, Tromp, Fudge, & Kalin, 2015; Grupe & Nitschke, 2013; Kagan et al., 1988; Kalin, 2017; Shackman et al., 2016). Anatomy of the Extended Amygdala. The extended amygdala encompasses a heterogeneous collection of subcortical nuclei along the borders of the amygdala and the ventral striatum (Yilmazer-Hanke, 2012). Classical studies of anatomical connectivity first suggested that the central division of the extended amygdala? including the dorsal amygdala in the region of the central nucleus (Ce) and the lateral division of the neighboring bed nucleus of the stria terminalis (BST)?represents an integrative macrocircuit (Alheid & Heimer, 1988). More recent axonal tracing studies 6 in monkeys have confirmed that the Ce and BST are densely interconnected (deCampo & Fudge, 2013; Fudge et al., 2017; Oler et al., 2017). In parallel, magnetic resonance imaging (MRI) studies in humans have revealed evidence of robust anatomical (Avery et al., 2014; Kamali et al., 2016; Kamali et al., 2015) and functional connectivity between the Ce and BST (Avery et al., 2014; Berry, Wise, Lawrence, & Lancaster, 2021; R. M. Birn et al., 2014; Cano et al., 2018; Gorka, Torrisi, Shackman, Grillon, & Ernst, 2018; Oler et al., 2012; Oler et al., 2017; Pedersen et al., 2020; Tillman et al., 2018; Torrisi et al., 2018; Torrisi et al., 2015), reinforcing the hypothesis that they represent a functionally meaningful circuit (Alheid & Heimer, 1988; Fox, Oler, Tromp, et al., 2015). From an anatomical perspective, the extended amygdala is poised to integrate divergent sources of potentially threat-relevant information and assemble states of fear and anxiety. The Ce and BST receive direct and indirect projections from brain regions that encode sensory, contextual, and regulatory information (Freese & Amaral, 2009), and both regions are anatomically poised to trigger somatomotor and neuroendocrine responses via dense projections to brainstem and subcortical effector regions (Fox, Oler, Tromp, et al., 2015; Freese & Amaral, 2009; Fudge et al., 2017). Other work shows that the Ce and BST contain cells with similar architectonic and neurochemical features and that the two regions show similar patterns of gene expression (for a detailed review, see Fox, Oler, Tromp, et al., 2015). Collectively, these anatomical observations suggest that the Ce and the BST represent an evolutionarily conserved circuit that is poised to use potentially threat-relevant information to trigger a range of fear- and anxiety-related defensive responses. 7 Mechanistic Evidence. A growing body of perturbation and recording studies in rodents demonstrates that the extended amygdala is critical for orchestrating adaptive defensive responses to a wide variety of threatening stimuli (Ahrens et al., 2018; Calhoon & Tye, 2015; Fadok, Markovic, Tovote, & L?thi, 2018; Fox & Shackman, 2019; Glover et al., 2020; Griessner et al., in press; Gungor & Par?, 2016; Pomrenze, Giovanetti, et al., 2019; Pomrenze, Tovar-Diaz, et al., 2019; Ressler, Goode, Evemy, & Maren, 2020; Tovote, Fadok, & Luthi, 2015). Other work suggests a role in dispositional negativity (Ahrens et al., 2018; Glover et al., 2020). For example, Ahrens and colleagues showed that anxious, behaviorally inhibited mice are marked by tonically elevated activity in a specific type of Ce neurons?cells within the lateral division that express somatostatin and project to the BST (Ahrens et al., 2018)? consistent with the much coarser results revealed by positron emission tomography (PET) and perfusion fMRI studies of tonic amygdala activity in humans and monkeys (Abercrombie et al., 1998; Canli et al., 2006; Fox, Oler, Shackman, et al., 2015; Fox, Shelton, Oakes, Davidson, & Kalin, 2008; Kaczkurkin et al., 2016). In an elegant series of experiments, Ahrens and colleagues demonstrated that these neurons are sensitive to uncertain danger (i.e., unpredictable shock) and that they are both necessary and sufficient for heightened defensive responses (e.g., freezing) to novelty and diffuse threat (e.g., a brightly lit open field). While our understanding of the primate extended amygdala lags far behind that of rodents, work in monkeys and humans motivates the hypothesis that the dorsal amygdala (Ce) is crucial for dispositional negativity. In monkeys, fiber-sparing excitotoxic lesions of the Ce have been shown to attenuate defensive behaviors and 8 neuroendocrine responses to a range of learned and innate threats (Davis, Antoniadis, Amaral, & Winslow, 2008; Kalin et al., 2016; Oler et al., 2016). These findings are well aligned with observations of humans with circumscribed amygdala damage (Bechara et al., 1995; Feinstein, Adolphs, Damasio, & Tranel, 2011; Feinstein, Adolphs, & Tranel, 2016; Klumpers, Morgan, Terburg, Stein, & van Honk, 2015; Korn et al., 2017). Patient SM, for example, shows a profound lack of fear and anxiety? whether measured objectively or subjectively?to both diffusely threatening contexts (e.g., a haunted house) and acute threat cues, including exotic spiders and snakes, horror film clips, and conditioned threat cues (Feinstein et al., 2011). Notably, she also endorses atypically low levels of dispositional negativity on standard psychometric measures (Feinstein et al., 2011), consistent with clinical assessments of her temperament (Tranel, Gullickson, Koch, & Adolphs, 2006). Other research has examined the consequences of amplifying extended amygdala activity. Work in monkeys shows that genetic manipulations that increase dorsal amygdala (Ce) metabolism potentiate defensive responses to uncertain threat (Kalin et al., 2016), consistent with rodent studies (Ahrens et al., 2018). Electrical stimulation work in humans has revealed a broadly consistent pattern of results, with stimulation delivered in the region of the dorsal amygdala (Ce) eliciting intense feelings of fear and anxiety and elevated signs of arousal (Inman et al., 2020). Although the causal contribution of the BST to individual differences in fear and anxiety has yet to be explored in humans or other primates, the existing body of mechanistic work reinforces the hypothesis that circuits centered on the extended amygdala are a critical substrate for trait-like individual differences in dispositional negativity. 9 Functional Neuroimaging Findings. Functional neuroimaging studies in monkeys and humans demonstrate that the dorsal amygdala and the BST both respond to a broad spectrum of fear- and anxiety-eliciting challenges, including intruder threat (Fox, Oler, Shackman, et al., 2015), aversive photographs (Brinkmann et al., 2018; Sabatinelli et al., 2011), an unpredictably approaching tarantula (Mobbs et al., 2010), horror film clips (Hudson et al., in press; https://neurovault.org/collections/6237), and the anticipation of noxious stimuli (Hur, Smith, et al., 2020). Furthermore, there is evidence that extended amygdala responses to such challenges are associated with concurrent changes in subjective experience and objective arousal (Fox & Shackman, 2019; Hur et al., 2019; Orem et al., 2019). Despite this progress, it remains remarkably unclear whether more trait-like individual differences in dispositional negativity reflect heightened extended amygdala reactivity to threat. To date, the vast majority of human neuroimaging studies have relied on emotional-face paradigms. While faces are potent triggers of amygdala activity (Hur et al., 2019) and are widely used in a variety of on-going biobank projects1, they do not elicit robust negative affect and, as such, do not represent a ?trait-relevant? challenge in the traditional sense. In emotional-face paradigms, ?threat? is operationalized as the difference in activity elicited by negative expressions (e.g., anger, fear) and either neutral expressions or simple control stimuli (e.g., geometric shapes, houses). While there is some evidence that dispositionally negative individuals show increased 1 Emotional-face paradigms are used in the ABCD Study (Casey et al., 2018), Duke Neurogenetics Study (Elliott et al., 2019), Human Connectome Project and follow-up studies (Barch et al., 2013; Siless et al., 2020; Somerville et al., 2018; Tozzi et al., 2020), IMAGEN (Albaugh et al., 2019), Philadelphia Neurodevelopmental Cohort (Satterthwaite et al., 2016), and UK Biobank (Miller et al., 2016). 10 amygdala reactivity to negative expressions (Calder, Ewbank, & Passamonti, 2011; Fonzo et al., 2015; Fox & Kalin, 2014; Stein, Simmons, Feinstein, & Paulus, 2007), recent large-scale studies of middle-aged (Minnesota Twin Study: n = 548) and young adults (Duke Neurogenetics Study: n = 1,256) failed to detect credible relations (MacDuffie, Knodt, Radtke, Strauman, & Hariri, 2019; Silverman et al., 2019). On balance, this body of research suggests that relations between dispositional negativity and amygdala reactivity to emotional faces are negligible in magnitude, conditional on moderator variables2, or simply non-existent. To date, comparatively few human studies have used demonstrably distress-eliciting challenges to examine relations between dispositional negativity and extended amygdala function, and most of these have predominantly focused on the role of the amygdala (Table 1). Studies using aversive photographs have not found evidence of heightened amygdala reactivity in convenience samples of either young (Brinkmann et al., 2018; West, Burgess, Dust, Kandala, & Barch, 2021) or middle-aged adults (Schuyler et al., 2012). Studies of Pavlovian threat conditioning provide mixed evidence, with some reporting a positive association between dispositional negativity and dorsal amygdala reactivity to cues or contexts predictive of shock delivery (CS+; Indovina, Robbins, N??ez-Elizalde, Dunn, & Bishop, 2011; Sjouwerman et al., 2020), and others reporting null effects (Kirlic et al., 2019; Klumpers, Kroes, Baas, & 2 Potentially important moderators include the degree to which the faces are task-relevant or masked (Calder et al., 2011; G?nther et al., 2020; D. M. Stout, A. J. Shackman, W. S. Pedersen, T. A. Miskovich, & C. L. Larson, 2017), the control condition (Ball et al., 2012), the analytic approach (Blackford, Avery, Cowan, Shelton, & Zald, 2011; Blackford, Avery, Shelton, & Zald, 2009), the degree of ambient stress (Everaerd, Klumpers, van Wingen, Tendolkar, & Fern?ndez, 2015), and the range of dispositional negativity (Ball et al., 2012; Greenberg et al., 2017). 11 Fernandez, 2017). Much less is known about the role of the BST, the other major division of the extended amygdala. To date, only one human neuroimaging study has directly addressed this question, showing that individuals with a more negative disposition show heightened engagement of the BST during the anticipation of temporally uncertain shock (Somerville et al., 2010). Taken with the available strands of mechanistic work, these observations motivate the hypothesis that dispositional negativity reflects heightened recruitment of the BST, and possibly the dorsal amygdala, during the anticipation of genuinely threatening stimuli, and that this association may be more evident when threat is uncertain. Convergent Validity Across Threat Assays The extended amygdala is exquisitely sensitive to a broad spectrum of emotionally and motivationally salient stimuli (Chase, Eickhoff, Laird, & Hogarth, 2011; Costafreda, Brammer, David, & Fu, 2008; Fried, MacDonald, & Wilson, 1997; Fusar-Poli et al., 2009; Gothard, Battaglia, Erickson, Spitler, & Amaral, 2007; Hoffman, Gothard, Schmid, & Logothetis, 2007; Hur, Smith, et al., 2020; Kuhn & Gallinat, 2011; Lindquist, Wager, Kober, Bliss-Moreau, & Barrett, 2012; Sabatinelli et al., 2011; Sergerie, Chochol, & Armony, 2008; Sescousse, Caldu, Segura, & Dreher, 2013; D. W. Tang, Fellows, Small, & Dagher, 2012; Wang et al., 2014), and there is ample evidence that it plays a mechanistically critical role in orchestrating defensive responses to a variety of threats (Fox & Shackman, 2019; Hur et al., 2019). This makes it tempting to treat a variety of so-called ?threat? paradigms?from viewing photographs of fearful faces to anticipating the delivery of a painful electric shock?as interchangeable probes of individual differences in extended amygdala reactivity. 12 Although this muddling of emotion perception and expression is common (e.g., Tozzi et al., in press), the underlying assumption of convergent validity has rarely been examined empirically. In the only study aimed at addressing this fundamental question, Villalta-Gill and colleagues reported negligible relations (r < 0.17) between amygdala reactivity to threat-related faces and aversive scenes in 32 young adults (Villalta-Gil et al., 2017)?a sample much too small to allow decisive inferences (i.e., the 95% CI for r = 0.17 ranges from -0.19 to +0.49). Overview of the Present Approach Here, we used a suite of neuroimaging techniques to determine the degree to which trait-like individual differences in dispositional negativity are associated with extended amygdala reactivity to well-established ?threat-of-shock? and emotional-face paradigms in an existing longitudinal sample of more than 200 young adults (Table 1). We focused on young adulthood because it is a time of profound, often stressful developmental transitions (e.g., moving away from home, forging new identities and relationships; Alloy & Abramson, 1999; Arnett, 2000; Hays & Oxley, 1986; Pancer, Hunsberger, Pratt, & Alisat, 2000). In fact, more than half of undergraduate students report overwhelming feelings of anxiety and more than a third report severe feelings of depression (American College Health Association, 2019), with many experiencing the first onset of anxiety disorders and depression during this period (Auerbach et al., 2016; Auerbach et al., 2018; Beesdo, Pine, Lieb, & Wittchen, 2010; Binkley & Fenn, 2019; Fava et al., 2010; Global Burden of Disease Collaborators, 2016; Kessler et al., 2007; Kessler, Petukhova, Sampson, Zaslavsky, & Wittchen, 2012; Lipson, Lattie, & 13 Eisenberg, 2019; Liu, Stevens, Wong, Yasui, & Chen, 2019; Slee, Nazareth, Freemantle, & Horsfall, in press; Substance Abuse and Mental Health Services Administration, 2019; Twenge, Cooper, Joiner, Duffy, & Binau, 2019). Subjects were selectively recruited from a much larger pool of previously screened individuals (N = 6,594), enabling us to examine a broad spectrum of dispositional negativity. A multiband pulse sequence, advanced co-registration and spatial normalization techniques, and anatomically-defined regions-of-interest (ROIs) made it possible to examine variation in Ce and BST reactivity to the two tasks in an unbiased manner (J. F. Smith, Hur, Kaplan, & Shackman, 2018; Theiss, Ridgewell, McHugo, Heckers, & Blackford, 2017; Tillman et al., 2018). To further enhance anatomical specificity, analyses were conducted using spatially unsmoothed data and newly developed extended amygdala seeds. To facilitate open and reproducible science, the study hypotheses and approach were pre-registered using tools provided by the Open Science Foundation (Botvinik-Nezer et al., 2020; Fox, Lapate, Davidson, & Shackman, 2018; Munaf? et al., 2017; Shackman & Fox, 2018). Understanding the neural systems underlying individual differences in dispositional negativity and determining the degree to which different experimental probes of threat reactivity are interchangeable is conceptually and practically important. Dispositional negativity is a central dimension of childhood temperament and adult personality, and individuals with a more negative disposition are at risk for a range of adverse outcomes. Although a range of mechanistic and observational evidence suggests that the extended amygdala mediates the heightened threat reactivity that characterizes individuals with a negative disposition, this hypothesis has rarely been subjected to a stringent test. 14 Likewise, emotional-face paradigms are widely used to probe the neural systems that orchestrate responses to threat, but the degree to which ?threatening? faces adequately capture individual differences in extended amygdala reactivity to a genuinely distress- eliciting challenge remains largely unknown. In short, addressing these aims has the potential to significantly refine scientific theory and practice, and accelerate the development of improved intervention strategies for extreme dispositional negativity (Barlow et al., 2017; S. E. Sauer-Zavala et al., in press). Specific Aims Aim 1: Leverage a large sample and cutting-edge neuroimaging techniques to determine the relevance of trait-like individual differences in dispositional negativity to extended amygdala activation during an anxiety-eliciting experimental challenge (?threat-of-shock?). We anticipated that individuals with a more negative disposition would show heighted reactivity in the dorsal amygdala (Ce) and BST, and explored the possibility that this association would be most evident during the anticipation of uncertain threat. To clarify specificity, we performed a parallel set of analyses using data from an emotional-faces paradigm that, while widely used as a probe of individual differences in amygdala reactivity, does not elicit robust anxiety. Whole-brain voxelwise analyses enabled us to explore the potential relevance of other, less intensively scrutinized regions. Aim 2: Determine whether neuroimaging probes of ?threat? reactivity are interchangeable. It is widely assumed that different experimental tasks that target a common function (e.g., ?emotion?) are quasi-interchangeable probes of individual 15 differences in regional function (e.g., amygdala). Yet this has rarely been examined empirically, never in a large sample, and never in the BST. Here, we tested whether assays of emotion perception (fearful and angry emotional faces) and emotion elicitation (?threat-of-shock?) show evidence of ?convergent validity? in the extended amygdala. Based on prior work (Villalta-Gil et al., 2017), we anticipated that measures of dorsal amygdala (Ce) and BST reactivity to the two tasks would show little-to-no evidence of convergence, as indexed by negligible between-assay correlations and moderate-to-strong Bayesian evidence for the null hypothesis (i.e., Bayes Factor <.33) (Kelter, 2020; Quintana & Williams, 2018; van Doorn et al., in press). 16 Methods Overview of the Larger Longitudinal Study As part of an on-going prospective-longitudinal study focused on individuals at risk for the development of internalizing disorders, we used a well-established psychometric measure of dispositional negativity to screen 6,594 first-year university students (57.1% female; 59.0% White, 19.0% Asian, 9.9% African American, 6.3% Hispanic, 5.8% Multiracial/Other; M=19.2 years, SD=1.1 years) (Shackman, Weinstein, et al., 2018). Screening data were stratified into quartiles (top quartile, middle quartiles, bottom quartile), separately for males and females. Individuals who met preliminary inclusion criteria were independently and randomly recruited via email from each of the resulting six strata. Because of our focus on psychiatric risk, approximately half the participants were recruited from the top quartile, with the remainder split between the middle and bottom quartiles (i.e., 50% high, 25% medium, and 25% low). This enabled us to sample a broad spectrum of dispositional negativity without gaps or discontinuities, while balancing the inclusion of men and women. Simulation work suggests that this over-sampling (?enrichment?) approach does not bias statistical tests to a degree that would compromise their validity (Hauner, Zinbarg, & Revelle, 2014). All subjects had normal or corrected-to-normal color vision, and reported the absence of lifetime neurological symptoms, pervasive developmental disorder, very premature birth, medical conditions that would contraindicate MRI, and prior experience with noxious electrical stimulation. All subjects were free from a lifetime history of psychotic and bipolar disorders; a current diagnosis of a mood, anxiety, or trauma 17 disorder (past 2 months); severe substance abuse; active suicidality; and on-going psychiatric treatment as determined by an experienced, masters-level diagnostician using the Structured Clinical Interview for DSM-5 (First, Williams, Karg, & Spitzer, 2015). Subjects provided informed written consent and all procedures were approved by the Institutional Review Board at the University of Maryland, College Park. Data from this study were featured in prior work focused on the development and validation of the threat-anticipation paradigm (Hur, Smith, et al., 2020) and relations between social anxiety and momentary mood (Hur, DeYoung, et al., 2020), but have not yet been used to address the two central aims proposed here. Power Analyses Sample size was determined a priori as part of the application for the award that supported this research (R01-MH107444). The target sample size (N ? 240) was chosen to afford acceptable power and precision given available resources. At the time of study design, G-power 3.1.9.2 (http://www.gpower.hhu.de) indicated >99% power to detect a benchmark medium-sized effect (r = 0.30) with up to 20% planned attrition (N = 192 usable datasets) using ? = 0.05 (two-tailed). Participants A total of 241 subjects were recruited and scanned. Of these, 6 withdrew due to excess distress in the scanner, 1 withdrew from the study after the imaging session, and 4 were excluded due to incidental neurological findings. 18 Threat-anticipation task. One subject was excluded from fMRI analyses due to gross susceptibility artifacts in the echoplanar imaging (EPI) data, 2 were excluded due to insufficient usable data (<2 usable scans; see below), 6 were excluded due to excessive movement artifact (i.e., the variance of the volume-to-volume displacement of a selected voxel at the center of the brain was >2 SDs above the mean), and 1 was excluded due to task timing issues, yielding a final sample of 220 subjects (49.5% female; 61.4% White, 18.2% Asian, 8.6% African American, 4.1% Hispanic, 7.3% Multiracial/Other; M = 18.8 years, SD = 0.4 years). Of these, 2 individuals were excluded from skin conductance analyses due to insufficient usable data (<2 usable ?scans?; see below). Emotional faces task. Three subjects were excluded due to gross susceptibility artifacts in the EPI data, 1 was excluded due to insufficient usable data (<2 scans), 7 were excluded due to excessive motion artifact, and 6 subjects for inadequate behavioral performance (i.e., both runs had accuracy less than 2 SD from the mean), yielding a final sample of 213 subjects (49.3% female; 61.0% White, 17.8% Asian, 8.5% African American, 4.2% Hispanic, 7.0% Multiracial/Other; M = 18.8 years, SD = 0.3 years). Dispositional Negativity As in prior work (Hur, DeYoung, et al., 2020; Hur, Smith, et al., 2020; Shackman, Weinstein, et al., 2018), we used psychometrically sound measures of neuroticism (Big Five Inventory-Neuroticism; John, Naumann, & Soto, 2008) and trait anxiety (International Personality Item Pool-Trait Anxiety; Goldberg, 1999; Goldberg et al., 2006) to quantify individual differences in dispositional negativity. Participants used a 19 1 (disagree strongly) to 5 (agree strongly) scale to rate themselves on a total of 18 items (e.g., depressed or blue, tense, worry, nervous, get distressed easily, fear for the worst, afraid of many things). At screening, the neuroticism and anxiety scales were strongly correlated (?s > .85) and reliable (?s > .85, ??s > .80). To minimize the influence of occasion-specific fluctuations in responding, hypothesis testing employed a composite measure of dispositional negativity. The composite was created by standardizing the neuroticism and trait anxiety scales (z-transformation using the mean and SD from the screening sample), and then averaging across the 2 scales and 3 assessments (screening, enrollment, and 6-month follow-up). The resulting composite is intended to capture a sizable range of the dispositional negativity spectrum. Threat-Anticipation Paradigm Paradigm Structure and Design Considerations. The Maryland Threat Countdown paradigm is a well-established, fMRI-optimized version of temporally uncertain-threat assays that have been validated using fear-potentiated startle and acute anxiolytic administration (e.g., benzodiazepine) in mice (Daldrup et al., 2015; Lange et al., 2017), rats (Miles, Davis, & Walker, 2011), and humans (Hefner, Moberg, Hachiya, & Curtin, 2013), enhancing its translational relevance. The MTC paradigm takes the form of a 2 (Valence: Threat/Safety) ? 2 (Temporal Certainty: Uncertain/Certain) randomized event-related design (3 scans; 6 trials/condition/scan). Simulations were used to optimize the detection and deconvolution of task-related hemodynamic signals (variance inflation factors <1.54). Stimulus presentation and ratings acquisition were 20 controlled using Presentation software (version 19.0, Neurobehavioral Systems, Berkeley, CA). On Certain Threat trials, subjects saw a descending stream of integers (?count-down;? e.g., 30, 29, 28...3, 2, 1) for 18.75 s. To ensure robust emotion, this anticipatory epoch always culminated with the delivery of a noxious electric shock, unpleasant photographic image (e.g., mutilated body), and thematically related audio clip (e.g., scream, gunshot). Uncertain Threat trials were similar, but the integer stream was randomized and presented for an uncertain and variable duration (8.75-30.00 s; M = 18.75 s). Here, subjects knew that something aversive was going to occur, but they had no way of knowing precisely when it would occur. Consistent with recent recommendations (Shackman & Fox, 2016), the average duration of the anticipatory epoch was identical across conditions, ensuring an equal number of measurements (TRs/condition). Mean duration was chosen to enhance detection of task-related differences in the blood oxygen level-dependent (BOLD) signal (Henson, 2007b), and to enable dissection of onset from genuinely sustained responses. Safety trials were similar, but terminated with the delivery of benign reinforcers (see below). Valence was continuously signaled during the anticipatory epoch by the background color of the display. Temporal certainty was signaled by the nature of the integer stream. Certain trials always began with the presentation of the number 30 (Fig. 1). On Uncertain trials, integers were randomly drawn from a near-uniform distribution ranging from 1 to 45 to reinforce the impression that Uncertain trials could be much longer than Certain ones and to minimize incidental temporal learning (?time-keeping?). To mitigate potential confusion and eliminate mnemonic demands, a lower-case ?c? or ?u? was presented at 21 the lower edge of the display throughout the anticipatory epoch. White-noise visual masks (3.2 s) were presented between trials to minimize persistence of the visual reinforcers in iconic memory. Subjects provided ratings of anticipatory fear/anxiety for each trial type during each scan using an MRI-compatible response pad (MRA, Washington, PA). Subjects were instructed to rate the intensity of the fear/anxiety experienced during the prior anticipatory (?countdown?) epoch using a 1 (minimal) to 4 (maximal) scale. Subjects were prompted to rate each trial type once per scan. Premature ratings (< 300 ms) were censored. All subjects provided at least 6 usable ratings, and rated each condition at least once. A total of 6 additional echo-planar imaging (EPI) volumes were acquired at the beginning and end of each scan. Procedures. Prior to scanning, subjects practiced an abbreviated version of the paradigm?without electrical stimulation?until they indicated and staff confirmed that they understood the task. Benign and aversive electrical stimulation levels were individually titrated. Benign Stimulation. Subjects were asked whether they could ?reliably detect? a 20 V stimulus and whether it was ?at all unpleasant.? If the subject could not detect the stimulus, the voltage was increased by 4 V and the process repeated. If the subject indicated that the stimulus was unpleasant, the voltage was reduced by 4V and the process was repeated. The final level chosen served as the benign electrical stimulation during the imaging assessment (M = 21.06 V, SD = 4.98 V). Aversive Stimulation. Subjects received a 100 V stimulus and were asked whether it was ?as unpleasant as you are willing to tolerate.? If the subject indicated that they were willing to tolerate more intense stimulation, the voltage was increased by 10 V and the process repeated. If the subject indicated that the stimulus was too intense, the 22 voltage was reduced by 5 V and the process repeated. The final level chosen served as the aversive electrical stimulation during the imaging assessment (M = 118.02, SD = 26.09). Following each scan of the MTC paradigm, we re-assessed whether stimulation was sufficiently intense and re-calibrated as necessary. Electrical Stimuli. Electrical stimuli (100 ms; 2 ms pulses every 10 ms) were generated using an MRI-compatible constant-voltage stimulator system (STMEPM-MRI; Biopac Systems, Inc., Goleta, CA). Stimuli were delivered using MRI-compatible, disposable carbon electrodes (Biopac) attached to the fourth and fifth phalanges of the non- dominant hand. Visual Stimuli. Visual stimuli (1.8 s) were digitally back-projected (Powerlite Pro G5550, Epson America, Inc., Long Beach, CA) onto a semi-opaque screen mounted at the head-end of the scanner bore and viewed using a mirror mounted on the head-coil. A total of 72 photographs were selected from the International Affective Picture System (IAPS identification numbers)?Benign: 1670, 2026, 2038, 2102, 2190, 2381, 2393, 2397, 2411, 2850, 2870, 2890, 5390, 5471, 5510, 5740, 7000, 7003, 7004, 7014, 7020, 7026, 7032, 7035, 7050, 7059, 7080, 7090, 7100, 7140, 7187, 7217, 7233, 7235, 7300, 7950. Aversive: 1300, 3000, 3001, 3010, 3015, 3030, 3051, 3053, 3061, 3062, 3063, 3069, 3100, 3102, 3150, 3168, 3170, 3213, 3400, 3500, 6022, 6250, 6312, 6540, 8230, 9042, 9140, 9253, 9300, 9405, 9410, 9414, 9490, 9570, 9584, 9590 (Lang, Bradley, & Cuthbert, 2008). Based on normative ratings, the aversive images were significantly more negative and arousing than the benign images, t(70) > 24.3, p < 0.001. On a 1 (negative/low-arousal) to 9 (positive/high-arousal) scale, the mean 23 valence and arousal scores were 2.2 (SD = 0.6) and 6.3 (SD = 0.6) for the aversive images, and 5.2 (SD = 0.4) and 2.8 (SD = 0.3) for the benign images. Auditory Stimuli. Auditory stimuli (0.80 s) were delivered using an amplifier (PA-1 Whirlwind) with in-line noise-reducing filters and ear buds (S14; Sensimetrics, Gloucester, MA) fitted with noise-reducing ear plugs (Hearing Components, Inc., St. Paul, MN). A total of 72 auditory stimuli (half aversive, half benign) were adapted from open-access online sources. Skin Conductance Data Collection. To confirm the validity of the threat-anticipation paradigm, skin conductance was continuously assessed during each scan of the task using a Biopac system (MP-150; Biopac Systems, Inc., Goleta, CA). Skin conductance (250 Hz; 0.05 Hz high-pass) was measured using MRI-compatible disposable electrodes (EL507) attached to the second and third fingers of the non-dominant hand. Emotional Faces Paradigm Building on work by our group (Daniel M Stout, Alexander J Shackman, Walker S Pedersen, Tara A Miskovich, & Christine L Larson, 2017) and many others (Albaugh et al., 2019; Barch et al., 2013; Casey et al., 2018; Elliott et al., 2019; Miller et al., 2016; Satterthwaite et al., 2016; Siless et al., 2020; Somerville et al., 2018; Tozzi et al., 2020) demonstrating the utility of emotional-faces paradigms for probing extended amygdala reactivity, subjects viewed alternating blocks of either faces (21 blocks) or places (7 blocks) in a pseudo-randomized order. The use of a block design mitigates potential concerns about alcohol-induced changes in the shape of the hemodynamic response function (HRF). Block length (20 s) was chosen to maximize our power to 24 detect a difference in the blood oxygen level-dependent (BOLD) signal elicited by the two conditions (Henson, 2007a; Maus, van Breukelen, Goebel, & Berger, 2010). To maximize signal strength and homogeneity and minimize potential neural habituation (Henson, 2007a; Maus et al., 2010; Plichta et al., 2014), each block consisted of 10 brief photographs of faces or places (1.5 s/image) separated by a fixation cross (0.5 s) Face blocks included photographs of prototypical angry, fearful, or happy facial expressions (7 blocks/expression). Face stimuli were taken from prior work by Gamer and colleagues (Gamer, Schmitz, Tittgemeyer, & Schilbach, 2013; Scheller, B?chel, & Gamer, 2012) and included standardized images of unfamiliar male and female adults displaying unambiguous fearful or neutral expressions. To maximize the number of models and mitigate potential habituation, images were derived from several well- established databases: Ekman and Friesen?s Pictures of Facial Affect (Ekman & Friesen, 1976), the FACES database (Ebner, Riediger, & Lindenberger, 2010), the Karolinska Directed Emotional Faces database (http://www.emotionlab.se/resources/kdef), and the NimStim Face Stimulus Set (https://www.macbrain.org/resources.htm). Colored images were converted to grayscale, brightness normalized, and masked to occlude non-facial features (e.g., ears, hair). Place blocks included photographs of residential and commercial buildings. Grayscale building stimuli were adapted from prior work (Choi, Padmala, & Pessoa, 2012, 2015). To ensure engagement, subjects indicated whether each image matched that presented on the prior trial (i.e., a 1-back continuous performance task). MRI Data Acquisition 25 MRI data were acquired using a Siemens Magnetom TIM Trio 3 Tesla scanner (32- channel head-coil). Foam inserts were used to immobilize the participant?s head within the head-coil and mitigate potential motion artifact. Subjects were continuously monitored from the control room using an MRI-compatible eye-tracker (Eyelink 1000; SR Research, Ottawa, Ontario, Canada). Head motion was monitored using the AFNI real-time plugin (Cox, 1996). Sagittal T1-weighted anatomical images were acquired using a magnetization prepared rapid acquisition gradient echo (MPRAGE) sequence (TR=2,400 ms; TE=2.01 ms; inversion time=1060 ms; flip angle=8?; sagittal slice thickness=0.8 mm; in-plane=0.8 ? 0.8 mm; matrix=300 ? 320; field-of-view=240 ? 256). A T2-weighted image was collected co-planar to the T1-weighted image (TR=3,200 ms; TE=564 ms; flip angle=120?). To enhance resolution, a multi-band sequence was used to collect oblique-axial echo planar imaging (EPI) volumes (multiband acceleration=6; TR=1,250 ms; TE=39.4 ms; flip angle=36.4?; slice thickness=2.2 mm, number of slices=60; in-plane resolution=2.1875 ? 2.1875 mm; matrix=96 ? 96). Images were collected in the oblique axial plane (approximately ?20? relative to the AC-PC plane) to minimize potential susceptibility artifacts. For the threat-anticipation task, three 478-volume EPI scans were acquired. For the emotional- faces task, two 454-volume EPI scans were acquired. The scanner automatically discarded 7 volumes prior to the first recorded volume. To enable fieldmap correction, two oblique-axial spin echo (SE) images were collected in each of two opposing phase- encoding directions (rostral-to-caudal and caudal-to-rostral) at the same location and resolution as the functional volumes (i.e., co-planar; TR=7,220 ms; TE=73 ms). 26 Following the last scan, subjects were removed from the scanner, debriefed, compensated, and discharged. Skin Conductance Pipeline Skin conductance data were processed using PsPM (version 4.0.2) and in-house MATLAB code (Dominik R Bach et al., 2018; Dominik R Bach, Friston, & Dolan, 2013). For those subjects with usable fMRI data for the threat-anticipation task, skin conductance data from each scan were outlier-interpolated (>3 median absolute deviations; linear-interpolation), regressed to remove pulse and respiration signals, band-pass filtered (0.008-0.2 Hz), resampled to match the TR used for fMRI data acquisition (1.25 s), and median-centered. Subject-specific SCR functions were derived using a four-parameter model (D. R. Bach, Flandin, Friston, & Dolan, 2010) and a boxcar function corresponding to the period of reinforcer presentation and the subsequent visual white noise mask. A robust regression framework was used to residualize signals associated with the presentation of reinforcers, the white noise mask, and the rating prompts for each subject. Skin conductance levels were computed for the anticipatory epoch of each condition by averaging the studentized residuals, separately for each scan. To ensure data validity, scans that did not show numerically positive skin conductance responses to reinforcer delivery were censored. Subjects with <2 usable scans were excluded from analyses (n = 2). MRI Data Pipeline 27 Methods have been optimized to minimize spatial normalization error and other potential sources of noise. Structural and functional MRI data were visually inspected before and after processing for quality assurance. Anatomical Data Processing. Methods are similar to those described in other recent reports by our group (Hur et al., 2018; Hur, Smith, et al., 2020; Tillman et al., 2018). T1-weighted images were inhomogeneity corrected using N4 (Tustison et al., 2010) and filtered using the denoise function in ANTS (Avants et al., 2011). The brain was then be extracted using a variant of the BEaST algorithm (Eskildsen et al., 2012) with brain-extracted and normalized reference brains from the IXI database (https://brain- development.org/ixi-dataset). Brain-extracted T1 images were normalized to a version of the brain-extracted 1-mm T1-weighted MNI152 (version 6) template (Grabner et al., 2006) modified to remove extracerebral tissue. This was motivated by evidence that brain-extracted T1 images and a brain-extracted template enhance the quality of spatial normalization (Acosta-Cabronero, Williams, Pereira, Pengas, & Nestor, 2008; Fein et al., 2006; Fischmeister et al., 2013). Normalization was performed using the diffeomorphic approach implemented in SyN (version 1.9.x.2017-09.11; Avants et al., 2011; Klein et al., 2009). T2-weighted images were rigidly co-registered with the corresponding T1 prior to normalization and the brain extraction mask from the T1 were then applied. Tissue priors (Lorio et al., 2016) were unwarped to the native space of each T1 using the inverse of the diffeomorphic transformation. Brain-extracted T1 and T2 images were simultaneously segmented using native-space priors generated using FAST (FSL version 5.0.9) (Zhang, Brady, & Smith, 2001) for use in T1-EPI co- registration (see below). 28 Fieldmap Data Processing. SE images were used to create a fieldmap in topup (Andersson, Skare, & Ashburner, 2003; Graham, Drobnjak, & Zhang, 2017; S. M. Smith et al., 2004). Fieldmaps were converted to radians, median-filtered, and smoothed (2-mm). The average of the distortion-corrected SE images was inhomogeneity corrected using N4, and brain-masked using 3dSkullStrip in AFNI (version 17.2.10; Cox, 1996). Functional Data Processing. EPI files were de-spiked using 3dDespike and slice-time corrected (to the center of the TR) using 3dTshift, and motion corrected to the first volume using a 12-parameter affine transformation implemented in ANTs. During motion correction, data were inhomogeneity corrected using N4. Recent work indicates that de-spiking is more effective than ?scrubbing? for attenuating motion-related artifacts (Jo et al., 2013; Power, Schlaggar, & Petersen, 2015; Siegel et al., 2014). Transformations were saved in ITK-compatible format for subsequent use. The first volume was extracted for EPI-T1 co-registration. The reference EPI volume was simultaneously co-registered with the corresponding T1-weighted image in native space and corrected for geometric distortions using boundary-based registration (Greve & Fischl, 2009). This step incorporated the previously created fieldmap, undistorted SE, T1, white matter (WM) image, and masks. The spatial transformations necessary to transform each EPI volume from native space to the reference EPI, from the reference EPI to the T1, and from the T1 to the template were concatenated and applied to the processed (de-spiked and slice-time corrected) EPI data in a single step to minimize incidental spatial blurring. Normalized EPI data were resampled to 2-mm isotopic voxels using fifth-order b-splines. To maximize spatial resolution, no 29 additional spatial filters were applied, consistent with recent recommendations (Stelzer, Lohmann, Mueller, Buschmann, & Turner, 2014; Turner & Geyer, 2014). Data Exclusions. To assess residual motion artifact, we computed the variance of volume-to-volume displacement of a selected voxel in the center of the brain (x = 5, y = 34, z = 28) using the motion-corrected EPI data. Scans with excess artifact (>2 SD above the mean) were discarded. Subjects who lacked sufficient usable fMRI data (<2 scans of the threat-anticipation task or <1 scan of the emotional-faces task) or showed inadequate performance on the emotional-faces task (see above; accuracy >2 SD) were excluded from analyses. Canonical First-Level (Single-Subject) fMRI Modeling. First-level modeling was performed using SPM12 (version 6678; https://www.fil.ion.ucl.ac.uk/spm), with the band-pass set to the hemodynamic response function (HRF) and 128 s for low and high pass, respectively. Regressors were convolved with a canonical HRF and its temporal derivative. The autoregressive model at the first level was set to the default of AR 0.2. EPI volumes collected before the first trial, during intertrial intervals, and following the final trial were unmodeled, and contributed to the baseline estimate. Clusters and local maxima were labeled using a combination of the Allen Institute, Harvard?Oxford, and Mai atlases (Desikan et al., 2006; Frazier et al., 2005; Hawrylycz et al., 2012; Mai, Majtanik, & Paxinos, 2015; Makris et al., 2006) and a recently established consensus nomenclature (ten Donkelaar, Tzourio-Mazoyer, & Mai, 2018). Brain figures were created using MRIcroGL (version 1.2; https://www.nitrc.org/projects/mricrogl). 30 Threat-Anticipation Task. The threat-anticipation paradigm was modeled using variable-duration rectangular (?box-car?) regressors time-locked to the anticipation epochs of the Uncertain Threat, Certain Threat, and Uncertain Safety trials. The anticipation epochs of Certain Safety trials were treated as the implicit baseline. The periods corresponding to the delivery of each of the four reinforcer types, the white noise mask following each trial, and rating trials were modeled using a similar approach (Fig. 1). Volume-to-volume displacement and motion parameters (including 1- and 2- volume lagged versions) were be included, similar to other recent work (Reddan, Wager, & Schiller, 2018). To further attenuate potential noise, cerebrospinal fluid (CSF) time-series, instantaneous pulse and respiration rates, and their estimated effect on the BOLD time-series were also be included as nuisance variates (R. M. Birn, Smith, Jones, & Bandettini, 2008; Chang, Cunningham, & Glover, 2009). ICA-AROMA (Pruim et al., 2015) was used to model several other potential sources of noise (e.g., brain-edge, CSF-edge, WM). These and the single ICA component showing the strongest correlation with motion estimates were included as additional nuisance variates. EPI volumes with excessive volume-to-volume displacement (>0.5 mm), as well as those during and immediately following the delivery of aversive reinforcers, were also censored. Emotional-Faces Task. Hemodynamic activity associated with each emotional expression (angry, fearful, and happy) was modeled using rectangular functions time- locked to block on- and offset, with place blocks treated as an implicit baseline. 31 Extended Amygdala Regions of Interest (ROIs). Consistent with past work by our group (Tillman et al., 2018), task-related Ce and BST activity was quantified using well-established anatomically-defined ROIs and spatially unsmoothed functional data (Theiss et al., 2017; Tillman et al., 2018) (Fig. 2). Analyses were performed using standardized regression coefficients extracted and averaged for each combination of task contrast (e.g., Threat vs. Safety anticipation, ?Threatening? Faces vs. Places), ROI, and subject. Data Analytic Plan All analyses were performed in R version 4.0.2 (Team, 2020). As a precursor to hypothesis testing, we first confirmed that the threat-anticipation paradigm had the intended effects on subjective distress (in-scanner fear/anxiety ratings) and anxious arousal (skin conductance response). To do so, we used 2 (Valence: Threat, Safety) x 2 (Certainty: Uncertain, Certain) repeated-measures generalized linear models, implemented using the ?lme4? package (Bates, M?chler, Bolker, & Walker, 2015). To inform interpretation of predicted brain- behavior associations, dispositional negativity was included in the model. dispositional negativity was indexed using the mean-centered, multi-assessment composite. Preliminary analyses indicated that the ratings data for the Safety condition were positively skewed. Accordingly, we modelled the data with a gamma distribution using a log link function. For skin conductance analyses, we used an ordinary least-squares model. For simplicity, non-significant three-way interaction terms were pruned from the final model. Significance was determined using a parametric bootstrapping 32 approach (1,000 samples), implemented using the ?afex? package (Singmann, Bolker, Westfall, & Aust, 2021). Significant interactions were then decomposed using simple slopes, implemented using the ?interactions? package (Long, 2019). Next, we confirmed that the threat-anticipation and emotional-faces tasks had the predicted consequences for brain function. A series of whole-brain voxelwise GLMs was used to confirm significant BST and dorsal amygdala activation during threat anticipation (Threat vs. Safety). The same approach was used to examine extended amygdala reactivity to temporally Uncertain Threat (vs. Baseline) and Certain Threat (vs. Baseline) anticipation. For the emotional- faces task, a parallel approach was used to confirm significant extended amygdala reactivity to ?threatening? faces (Angry and Fearful Faces vs. Places) presentation. Significance was assessed using FDR q < .05, whole-brain corrected. All analyses employed unsmoothed data to maximize spatial resolution. The first major aim of the present study was to test whether individuals with a more negative disposition show heightened extended amygdala reactivity to threat. To do so, we extracted regression coefficients for the Threat vs. Safety contrast, separately for the anatomically-defined BST and Ce ROIs. Exploratory one-sample t-tests, implemented using the ?stats? package (Team, 2020), confirmed significant activation in both ROIs (ps < 0.001). To maximize power and ensure strong inferences, brain-disposition relations were tested using robust regression models (Tukey?s bi-weight), which mitigate the 33 influence of outliers and other departures from conventional model assumptions (Wager, Keller, Lacey, & Jonides, 2005). To provide an unbiased estimate of model performance, we used a repeated cross- validation approach (5-fold, 1,000 repetitions) (Yarkoni & Westfall, 2017). The dataset was randomly subdivided into 5 ?folds? of approximately equal size. Then, the regression model was trained using 4 folds of the data (80%) and tested on the ?held- out? fold (20%). This was iteratively repeated 4 times using a different fold for testing each time. This method was then repeated 1,000 times, randomly re-allocating the data to a new set of 5 folds on each repetition. Model estimates were averaged across repetitions. A similar analytic framework was used to test whether relations between dispositional negativity and extended amygdala reactivity are more evident during the anticipation of Uncertain Threat (vs. Baseline) compared to Certain Threat (vs. Baseline). To determine the unique contribution of each contrast, this was tested using a simultaneous model. Follow-up analyses?focused on relations with concurrent measures of anxious distress and arousal?were used to inform the interpretation of significant brain- disposition associations. To clarify specificity, we performed parallel analyses using data acquired during the presentation of ?threatening? faces (Angry and Fearful Faces vs. Places). Again, exploratory one-sample t-tests confirmed significant activation in both ROIs (ps < 0.001). Hypothesis testing used spatially unsmoothed data, and significance was assessed using p = 0.05 (two-tailed, uncorrected). To inform the interpretation of significant brain- 34 disposition associations, we performed follow-up tests to explore potential relations between brain function and concurrent measures of in-scanner distress and arousal. Finally, standard voxelwise GLMs (random effects) were used to explore relations between mean-centered dispositional negativity and activation in less intensely scrutinized regions. The second major aim of the present study was to determine whether the threat- anticipation and emotional-faces tasks are interchangeable probes of extended amygdala reactivity (i.e., show ?convergent validity?). Using the analytic framework described above, we computed the correlation between individual differences in threat-anticipation and emotional-faces activation, separately for the BST and Ce ROIs. To quantify the strength of the evidence for the null and alternative hypotheses, Bayes Factors were computed using the ?bayesTestR? package (Makowski, Ben Shachar, & L?decke, 2019). This approach quantified the odds in favor of one hypothesis (e.g., alternative hypothesis, H1; ? > 0; tasks show evidence of convergence) relative to another (i.e., null hypothesis, H0; ? = 0; tasks show no evidence of convergence). This approach integrates prior parameter information with model likelihood to obtain the posterior distribution of the parameters of interest (e.g., the between-task correlation coefficient for BST reactivity). Here, the Bayes Factor (BF10) quantifies the change in relative belief in favor of a given hypothesis, using the following equation (Kelter, 2020; Quintana & Williams, 2018; van Doorn et al., in press): 35 ?(?|?1) ?(?1|?) ?(?0) = ? ?(?|?0) ?(?0|?) ?(?1) Posterior Prior BF10 Odds Odds A BF10 < .33 is often interpreted as moderate-to-strong support for the null, whereas a BF10 >3 is often interpreted as moderate-to-strong support for the alternative. 36 Results Threat anticipation increases anxious distress and arousal As a precursor to hypothesis testing, we used a series of repeated-measures generalized linear models to confirm that the threat-anticipation task had the intended effects on behavior (Fig. 3). To inform interpretation of predicted brain-behavior associations, dispositional negativity was included in the model. Results revealed that subjects experienced significantly greater distress when anticipating aversive outcomes (Valence: t = 56.65, p < 0.001), and when anticipating outcomes with uncertain timing (Certainty: t = 11.54, p < 0.001). Furthermore, individuals with higher levels of dispositional negativity showed indiscriminately elevated distress across conditions (dispositional negativity: t = 4.12, p < 0.001). None of the interactions were significant, ps > 0.126. A similar pattern was evident for anxious arousal. Skin conductance levels were significantly elevated when anticipating aversive outcomes (Valence: t = 38.00, p < 0.001), and when anticipating outcomes with uncertain timing (Certainty: t = 8.84, p < 0.001). The impact of threat on skin conductance was potentiated by temporal uncertainty (Valence x Certainty: t = 20.54, p < 0.001), such that the difference in skin conductance levels during Threat and Safety conditions was significantly greater when timing was uncertain (? = 0.09, t = 41.24, p < 0.001) than when it was predictable (? = 0.03, t = 12.30, p < 0.001). Taken together, these observations confirm that the threat-anticipation task elicits robust anxiety across multiple response channels. 37 Threat anticipation and ?threatening? faces recruit the extended amygdala We used a series of whole-brain voxelwise GLMs to confirm that the threat- anticipation and the emotional-faces tasks both engaged the extended amygdala. Results revealed significant BST and dorsal amygdala activation during threat anticipation (FDR q < 0.05, whole-brain corrected; see Fig. 4 and Supplementary Table 1). The same general pattern was evident for the anticipation of Uncertain Threat and Certain Threat, relative to the implicit baseline (see Fig. 4 and Supplementary Tables 2-3). Analyses focused on the presentation of ?threatening? faces also revealed significant activation in the BST and the dorsal amygdala (see Fig. 4 and Supplementary Table 4). Collectively, these findings demonstrate that both threat anticipation and emotional- face presentation are valid probes of extended amygdala function. Dispositionally negative individuals show increased BST reactivity to Uncertain Threat The first major aim of the present study was to test whether individuals with a more negative disposition show heightened extended amygdala reactivity during threat anticipation. To test this, we extracted contrast coefficients (Threat vs. Safety), separately for each subject and ROI, and computed robust regressions with dispositional negativity. Models were trained and tested using a repeated cross- validation approach, providing unbiased estimates of brain-disposition relations (5- folds, 1,000 repetitions). 38 Results revealed that individuals with a more negative disposition exhibited significantly greater activation in the BST during threat anticipation (? = 0.12, t(218) = 1.67, p = 0.049; see Fig. 5). This association was marginally significant when controlling for Ce reactivity (? = 0.11, t(217) = 1.58, p = 0.057). Ce activation during threat anticipation was not significantly related to individual differences in dispositional negativity (? = 0.02, t(218) = 0.34, p = 0.366; see Fig. 5). Prior work raises the possibility that relations between dispositional negativity and extended amygdala function will be magnified when threat is uncertain. To test this, we computed robust regressions between dispositional negativity and extended amygdala reactivity to temporally uncertain threat, separately for each ROI. To clarify specificity, models controlled for activation during the anticipation of certain threat. Results revealed that individuals with a more negative disposition showed significantly greater activation in the BST during Uncertain-Threat anticipation, controlling for Certain-Threat (? = 0.24, t(217) = 2.71, p = 0.004; see Fig. 5). This association remained significant in models that included Ce reactivity to Uncertain Threat (? = 0.26, t(216) = 2.84, p = 0.002), or excluded BST reactivity to Certain Threat (? = 0.19, t(218) = 2.73, p = 0.003). Individual differences in dispositional negativity were not significantly related to BST reactivity to Certain Threat (? = -0.09, t(217) = -1.04, p = 0.151; see Fig. 5). Dispositional negativity was also unrelated to Ce reactivity for both types of Threat (Uncertain: ? = -0.10, t(217) = -1.04, p = 0.150; Certain: ? = 0.04, t(217) = 0.48, p = 0.317; see Fig. 5). In short, relations between dispositional negativity and extended amygdala reactivity are only evident for the BST and unique to Uncertain- Threat anticipation. 39 To inform interpretation of the observed association between dispositional negativity and BST function, we performed a series of follow-up analyses. The first examined relations between BST reactivity to Uncertain-Threat anticipation and concurrent measures of anxious distress and arousal. BST activation was associated with elevated levels of physiological arousal during Uncertain-Threat anticipation (? = 0.16, t(216) = 2.31, p = 0.011), but was unrelated to the intensity of subjective anxiety (? = 0.07, t(218) = 1.03, p = 0.152). A second set of analyses examined relations between dispositional negativity and threat-elicited distress and arousal. Results mirrored the first set. Here, higher levels of dispositional negativity were associated with more intense anxiety (? = 0.35, t(218) = 5.27, p < 0.001), but were unrelated to the degree of physiological arousal elicited by Uncertain-Threat anticipation (? = 0.00, t(216)=0.05, p = 0.482). Individual differences in anxious distress and arousal were marginally associated, ? = 0.095, t(216) = 1.36, p = 0.087. These results suggest that relations between dispositional negativity?the propensity to experience heightened negative affect?and BST reactivity to Uncertain Threat are indirect. BST reactivity to Uncertain Threat is associated with heightened physiological arousal, consistent with prior work, but not increased feelings of distress. To date, the vast majority of human neuroimaging studies of dispositional negativity have relied on emotional-face paradigms. While emotional faces are widely used and evoke robust extended amygdala activation, they do not elicit meaningful distress or arousal in typical populations. Here, we leveraged the same ROI-based analytic approach used to interrogate relations with threat anticipation to test relations between dispositional negativity and extended amygdala reactivity to ?threatening? faces (Angry 40 and Fearful Faces vs. Places). Results failed to reveal significant relations with either the BST (? = 0.03, t(211) = 0.41, p = 0.342) or Ce (? = 0.03, t(211) = 0.39, p = 0.348). Likewise, a series of exploratory voxelwise analyses did not detect significant relations between (mean-centered) dispositional negativity and extended amygdala reactivity to either the threat-anticipation or the emotional-faces tasks (FDR q < .05, whole-brain corrected). Individual differences in extended amygdala reactivity to the threat-anticipation and emotional-faces tasks show inconsistent evidence of convergent validity Implicit in much of the literature is the assumption that different fMRI paradigms targeting a common function (e.g., ?emotion?) are exchangeable probes of individual differences in brain function (e.g., amygdala). Yet, this assumption of ?convergent validity? has rarely been examined. Here, we used robust regressions with repeated cross-validation to test whether individual differences in BST and Ce reactivity to the anticipation of threat and the presentation of ?threatening? faces co-vary. Consistent with prior work (Villalta-Gil et al., 2017), robust regression showed no evidence of convergence in Ce reactivity between tasks, ? = -0.01, t(207) = -0.19, p = 0.424. From a Bayesian perspective, this corresponds to inconclusive evidence of convergent validity (BF10 = 1.02). In contrast to the Ce, robust regression yielded marginally significant evidence of between-task convergence in the BST, ? = 0.11, t(207) = 1.57, p = 0.059, with moderate Bayesian evidence of convergent validity, BF10 = 3.47. 41 Discussion Elevated levels of dispositional negativity confer increased risk for anxiety disorders, depression, and a variety of other adverse outcomes, but the underlying neurobiology has remained incompletely understood (Boissy, 1995; Hur, Stockbridge, Fox, & Shackman, 2019; Shackman, Stockbridge, LeMay, & Fox, 2018; Shackman et al., 2016). The present results demonstrate that individuals with a more negative disposition show heightened BST activation during threat anticipation, and this association is uniquely evident when threat is temporally uncertain. In fact, BST reactivity to Uncertain Threat remained predictive of dispositional negativity after controlling for either BST reactivity to Certain Threat or Ce reactivity to Uncertain Threat. Our results further suggest that relations between dispositional negativity?the propensity to experience heightened negative affect?and BST function are indirect. BST reactivity to Uncertain Threat was associated with heightened signs of threat- elicited arousal, but not increased feelings of distress. Dispositional negativity was unrelated to Ce activation during threat anticipation and to extended amygdala (BST/Ce) activation during ?threatening? face presentation. While it is tempting to treat different ?threat? paradigms?from viewing photographs of ?threatening? faces to anticipating the delivery of aversive stimulation?as interchangeable probes of individual differences in extended amygdala function, the underlying assumption of convergent validity has rarely been examined. The present results provide no evidence of between-task convergence in the Ce, consistent with prior work (Villalta-Gil et al., 2017), and marginal evidence in the BST. 42 The present study provides new evidence that individual differences in dispositional negativity are associated with heightened BST activation during Uncertain-Threat anticipation. This is consistent with anatomical evidence that the BST sends dense projections to the subcortical and brainstem regions that proximally mediate behavioral and physiological signs of negative affect (Hur et al., 2019). While the mechanistic relevance of the BST to dispositional negativity remains under-explored, perturbation studies in rodents suggest that it is crucial for some forms of anxiety (Duvarci, Bauer, & Par?, 2009; Glover et al., 2020). For example, excitotoxic lesions of the BST attenuate defensive responses (freezing) to diffuse, uncertain threat (elevated-plus maze; Duvarci, Bauer, & Par?, 2009). These mechanistic observations are consistent with neuroimaging evidence that the BST is sensitive to a range of noxious and threatening stimuli, including aversive photographs (Brinkmann et al., 2018; Sabatinelli et al., 2011), horror film clips (Hudson et al., in press; https://neurovault.org/collections/6237), and the uncertain anticipation of aversive stimuli (Hur, Smith, et al., 2020; Mobbs et al., 2010). With regard to dispositional negativity, PET studies in monkeys demonstrate that BST metabolism is phenotypically and genetically correlated with trait anxiety and behavioral inhibition (Fox, Oler, Shackman, et al., 2015; Shackman et al., 2017). Likewise, fMRI work in humans shows that individuals with a more negative disposition are characterized by heightened BST engagement during the anticipation of temporally uncertain shock (Somerville et al., 2010). The present results reinforce and extend this work by showing that BST reactivity to Uncertain-Threat anticipation is uniquely associated with individual differences in dispositional negativity, over and above variation in BST 43 reactivity to Certain Threat, and dorsal amygdala (Ce) reactivity to Uncertain Threat. Together, these observations reinforce the hypothesis that the BST is a central component of the distributed neural system governing dispositional negativity. A key challenge for the future will be to clarify causation. There is compelling evidence that dispositional negativity can be dampened through both psychological and pharmacological interventions (Roberts et al., 2017; S. Sauer-Zavala et al., 2020; Stieger et al., 2021; Zemestani, Ommati, Rezaei, & Gallagher, 2021). It would be fruitful to test whether these effects reflect attenuated BST reactivity to uncertain threat. The present results have implications for understanding how dispositional negativity confers risk for anxiety disorders and depression. Our findings show that individuals who, by virtue of their more negative disposition, are at risk for developing internalizing disorders are marked by heightened BST reactivity to Uncertain Threat. This observation is consistent with conceptual models that emphasize the central role of threat uncertainty to the development and maintenance of pathological anxiety (Davis et al., 2010; Grupe & Nitschke, 2013; Shackman et al., 2016). It is also consistent with recent meta-analytic evidence that individuals with anxiety disorders show exaggerated BST reactivity to threat (Chavanne & Robinson, 2021; Shackman & Fox, 2021). Collectively, this work motivates the hypothesis that exaggerated BST reactivity to uncertain threat is an active ingredient (i.e., diathesis) that helps mediate the association between dispositional negativity and internalizing illnesses. Prospective-longitudinal studies in more nationally representative, diverse populations will be a key step to addressing this hypothesis. 44 Our findings also demonstrate that BST reactivity to Uncertain-Threat anticipation is associated with elevated physiological arousal, but not the intensity of threat-elicited anxiety. This result is broadly consistent with the theoretical model articulated by LeDoux and colleagues, who argue that the BST is primarily responsible for orchestrating behavioral and physiological responses to uncertain threat, and that it only indirectly contributes to anxious feelings (LeDoux, 2015; LeDoux & Pine, 2016; Mobbs et al., 2019). The present results reinforce the possibility that relations between BST function and dispositional negativity?the tendency to experience heightened negative emotions?are implicit and indirect. Our findings also have implications for the interpretation and design of neuroimaging studies of psychiatric risk and disease. Much of this work relies on emotional-faces tasks as the sole probe of negative valence systems. Yet, the present results demonstrate that extended amygdala reactivity to emotional faces is unrelated to the risk-conferring dispositional negativity phenotype. Moreover, analyses of convergent validity revealed modest between-task convergence in the BST, and negligible convergence in the dorsal amygdala (Ce), in broad accord with prior work (Villalta-Gil et al., 2017). These observations caution against relying on a single task to understand the role of individual differences in extended amygdala function in internalizing illness (Holmes & Patrick, 2018). To the extent that uncertain-threat anticipation is key, it may be necessary to devise new paradigms that are more suitable for community and biobank samples?for instance, using aversive auditory stimuli or film clips. 45 It is important to acknowledge the modest size of the BST-disposition associations observed in the present study. This is not surprising; it is, in fact, entirely consistent with theoretical expectation and prior work focused on the extended amygdala and other isolated brain regions (LeDoux & Pine, 2016; Shackman & Fox, 2018). In order to predict additional variance in dispositional negativity, it will be necessary to adopt multivoxel or multivariate machine learning approaches at the expense of neuroanatomical specificity (Woo, Chang, Lindquist, & Wager, 2017). In addition, recent psychometric work makes it clear that dispositional negativity can be fractionated into more specific facets (e.g., anxious, depressive, irritable) (Christopher J Soto & John, 2017). It will be fruitful to determine whether these specific facets are equally related to BST function. Understanding the neural systems governing individual differences in dispositional negativity is important. Elevated dispositional negativity confers risk for a range of deleterious outcomes spanning health, wealth, and well-being. The present findings highlight the relevance of threat-elicited BST function to individual differences in dispositional negativity, particularly when threat is uncertain. A relatively large and carefully phenotyped sample, well-controlled tasks, and a pre-registered, best-practices approach (e.g., spatially unsmoothed data, a priori anatomical ROIs, and repeated cross-validation framework) bolster confidence in the robustness and translational relevance of these results. These observations lay the groundwork for the kinds of prospective-longitudinal and mechanistic studies that will be necessary to determine causation and, ultimately, to develop improved interventions for extreme dispositional negativity. 46 Tables Relations Between MRI EPI Voxel Extended Dispositional Field Trait-Relevant Study N (% Male) Size Normalization Amygdala Negativity Strength Challenge (mm3) Reactivity and (T) Dispositional Negativity 220 (49.5%), enriched for Multi-Scale, BBR and See the main Present Study extreme Multi-Assessment 3 10.5 Diffeomorphic report See the main dispositional Composite report negativity 93 (35.5%), screened to Brinkmann et exclude any Affine and Manual Aversive STAI 3 15.3 al., 2018 emotional TT Photographs N.S. ROI illness in the past five years Positive relations 23 (43.48%), between amygdala screened to reactivity and Indovina et al., Conditioned exclude l/t STAI 3 18.0 ?SPM5? dispositional 2011 Aversive Cue emotional negativity; ROI & illness ROI-based FIR *BST not examined 83 (42.2%), 43 of whom had Conditioned Kirlic et al., depression or ?AFNI? and Aversive STAI 3 18.1 2019 anxiety Diffeomorphic Context N.S. Voxelwise disorder (Instructed) diagnoses 108 (100%), screened to Klumpers et al., Conditioned exclude l/t STAI 1.5 38.1 ?SPM8? 2017 Aversive Cue emotional N.S. Voxelwise illness 127 (36.22%), N.S. ROI screened to exclude Positive relations Schuyler et al., emotional Aversive between amygdala BFI-N 3 56.25 Affine 2011 illness in the Photographs reactivity and past year, & l/t dispositional serious mental negativity; ROI- illness based FIR *BST not examined Positive relations 113 (61.06%), between amygdala screened to Sjouwerman et Conditioned reactivity and exclude l/t STAI 3 8.0 ?SPM8? al., 2020 Aversive Cue dispositional emotional negativity; ROI illness *BST not examined Positive relations 50 (44%), between BST Anticipation of screened to reactivity and Somerville et Multi-Scale Temporally exclude l/t 3 31.5 ?SPM2? dispositional al., 2010 Composite Uncertain emotional negativity; ROI & Shock illness Voxelwise N.S. Amygdala; ROI & Voxelwise 319 (53.29%), screen to West et al., Multi-Scale Aversive exclude l/t 3 8.0 ?FNIRT? N.S. ROI 2021 Composite Photographs emotional *BST not illness examined 47 Table 1. Human studies of dispositional negativity and distress-eliciting neuroimaging paradigms. 1Older normalization techniques (e.g., affine, manual TT) can introduce substantial spatial smoothing and registration error, which is a concern for work focused on small subcortical structures, such as the Ce and BST. Abbreviations?BBR, boundary-based registration of the T1- and T2-weighted images; FIR, Finite Impulse Response modeling; l/t, lifetime; NR, not reported; NS, not significant; STAI, State- Trait Anxiety Inventory. 48 Figures Figure 1. The 4 reinforcer conditions comprising the Maryland Threat Countdown (MTC) paradigm used in the present study. The task takes on a 2 ? 2 design, using threat and benign reinforcers presented on a temporally certain or uncertain scale. Figure 2. Coronal slices depicting the locations of the BST and Ce ROIs used in the present study. Analyses employed bilateral masks. 49 a b Figure 3. As shown in a and b, threat anticipation robustly increased subjective symptoms (in-scanner ratings) and objective signs (skin conductance) of anxiety, and this was particularly evident when the timing of aversive stimulation was uncertain (Threat > Safety, ps < 0.001; Uncertain Threat > Certain Threat, ps < 0.001). Skin conductance results also revealed a Valence x Certainty interaction, such that the difference in skin conductance levels during Threat and Safety conditions was significantly greater when timing was uncertain, p < 0.001. Data (black points; individual participants), Bayesian 95% highest density interval (gray bands), and mean (bars) for each condition. Highest density intervals permit population-generalizable visual inferences about mean differences and were estimated using 1000 samples from a posterior Gaussian distribution. 50 Figure 4. The coronal slices above depict voxels showing significantly increased activity within the BST (left column) and the dorsal amygdala/Ce (right column) for various contrasts of interest. All images are masked to highlight significant voxels in the extended amygdala. Together, these observations suggest that these regions are sensitive to both temporally certain and uncertain threat, as well as to threat-related face stimuli. For additional details, see Supplementary Tables 1-4; FDR, false discovery rate; WB, whole-brain-corrected. 51 Figure 5. Individuals with a more negative disposition show increased BST reactivity to Uncertain Threat. Figure depicts standardized, cross-validated robust regression coefficients for threat-anticipation and emotional-faces contrasts of interest. The left side of the bar graph show findings for the BST. The right side of the bar graph show findings for the Ce. Error bars indicate the SE. Inset depicts the scatterplot corresponding to the key significant finding?that BST reactivity to Uncertain Threat is associated with heightened dispositional negativity when controlling for Certain Threat. 52 Supplement Supplemental Table 1. Descriptive statistics for clusters and local extrema showing greater activity during the anticipation of Threat relative to Safety (FDR q < 0.05, whole-brain corrected). mm3 t x y z Cluster 1 666,016 L Frontal Operculum Cortex 13.64 -32 20 10 L Frontal Orbital Cortex 11.77 -30 28 0 L Paracingulate Gyrus 11.56 -8 12 38 L Cingulate Gyrus, anterior division 11.50 -6 10 40 L Central Opercular Cortex 11.10 -42 8 2 L Caudate 11.05 -8 0 8 L Putamen 10.88 -20 10 -2 L Temporal Occipital Fusiform Cortex 10.67 -26 -60 -16 L Brain-Stem 10.30 -2 -28 -2 L Superior Frontal Gyrus 10.25 -14 -2 66 L Precentral Gyrus 10.07 -32 -6 50 L Juxtapositional Lobule Cortex 10.05 -2 6 46 L Cingulate Gyrus, posterior division 10.04 -14 -28 38 L Thalamus 10.01 -8 -4 12 L Occipital Fusiform Gyrus 9.48 -30 -68 -18 L Supramarginal Gyrus, posterior 9.45 -56 -50 38 division L Bed Nucleus of the Stria Terminalis 9.43 -8 2 4 L Middle Frontal Gyrus 9.16 -42 -2 60 L Supramarginal Gyrus, anterior 8.89 -54 -38 32 division L Inferior Frontal Gyrus, pars 8.85 -54 10 10 opercularis L Lingual Gyrus 8.85 -4 -74 -12 L Frontal Pole 8.83 -36 44 30 L Bed Nucleus of the Stria Terminalis 8.55 -6 4 0 L Parietal Operculum Cortex 7.71 -56 -34 24 L Angular Gyrus 7.67 -56 -56 46 L Superior Parietal Lobule 7.55 -18 -52 64 L Precuneus Cortex 7.49 -10 -78 40 L Lateral Occipital Cortex, superior 7.35 -34 -60 60 division L Postcentral Gyrus 7.27 -42 -32 44 53 L Cuneal Cortex 6.34 -16 -78 34 L Occipital Pole 5.90 -20 -96 10 L Hippocampus 5.56 -34 -28 -8 L Insular Cortex 5.55 -34 18 -4 L Lateral Occipital Cortex, inferior 5.50 -56 -64 10 division L Pallidum 5.50 -20 -10 -4 L Inferior Frontal Gyrus, pars 5.46 -54 22 6 triangularis L Inferior Temporal Gyrus, 5.46 -42 -56 -10 temporooccipital part L Heschls Gyrus (includes H1 and H2) 5.31 -38 -28 12 L Middle Temporal Gyrus, 5.13 -62 -56 6 temporooccipital part L Planum Polare 5.06 -40 -14 -8 L Temporal Fusiform Cortex, posterior 5.01 -30 -32 -28 division L Amygdala 4.74 -24 -14 -14 L Superior Temporal Gyrus, posterior 4.47 -54 -24 -4 division L Middle Temporal Gyrus, posterior 4.44 -48 -28 -6 division L Planum Temporale 4.37 -62 -20 4 L Intracalcarine Cortex 4.34 -18 -66 6 L Amygdala (central nucleus) 4.14 -24 -12 -12 L Subcallosal Cortex 4.12 -12 16 -14 L Temporal Pole 4.04 -52 14 -12 L Accumbens 3.65 -6 12 -6 L Parahippocampal Gyrus, posterior 3.33 -14 -36 -6 division L Superior Temporal Gyrus, anterior 3.12 -54 2 -14 division L Amygdala (central nucleus) 2.92 -22 -6 -12 L Inferior Temporal Gyrus, posterior 2.34 -50 -38 -16 division L Supracalcarine Cortex 2.32 -22 -62 20 R Cingulate Gyrus, anterior division 14.08 10 12 38 R Paracingulate Gyrus 14.06 10 20 34 R Frontal Operculum Cortex 13.18 34 24 8 R Bed Nucleus of the Stria 12.87 10 2 8 Terminalis/Caudate R Thalamus 12.44 10 -2 12 R Precentral Gyrus 12.08 42 -2 46 R Juxtapositional Lobule Cortex 11.49 10 6 44 R Putamen 11.44 22 6 4 54 R Brain-Stem 11.39 4 -28 -2 R Supramarginal Gyrus, posterior 11.32 60 -42 24 division R Central Opercular Cortex 11.15 44 8 2 R Superior Temporal Gyrus, posterior 10.61 48 -24 -4 division R Superior Frontal Gyrus 10.41 14 10 62 R Pallidum 9.87 20 4 0 R Superior Parietal Lobule 9.69 24 -46 62 R Frontal Orbital Cortex 9.57 34 22 -8 R Frontal Pole 9.56 34 46 30 R Bed Nucleus of the Stria 9.51 8 6 0 Terminalis/Caudate R Cingulate Gyrus, posterior division 9.49 10 -26 42 R Parietal Operculum Cortex 9.40 56 -30 26 R Supramarginal Gyrus, anterior 9.33 56 -32 34 division R Angular Gyrus 9.16 64 -46 28 R Precuneus Cortex 8.97 12 -54 54 R Middle Frontal Gyrus 8.84 52 8 42 R Postcentral Gyrus 8.43 48 -28 50 R Inferior Frontal Gyrus, pars 8.05 50 22 2 triangularis R Temporal Occipital Fusiform Cortex 8.03 26 -56 -16 R Middle Temporal Gyrus, posterior 7.83 52 -30 -4 division R Inferior Frontal Gyrus, pars 7.67 54 12 4 opercularis R Lateral Occipital Cortex, superior 7.50 18 -74 40 division R Middle Temporal Gyrus, 6.94 56 -40 4 temporooccipital part R Inferior Temporal Gyrus, 6.69 52 -60 -12 temporooccipital part R Occipital Fusiform Gyrus 6.45 20 -74 -16 R Occipital Pole 6.41 28 -94 12 R Hippocampus 6.38 34 -28 -6 R Lateral Occipital Cortex, inferior 6.29 42 -86 -8 division R Lingual Gyrus 6.27 0 -74 -8 R Cuneal Cortex 6.00 14 -76 36 R Amygdala 5.99 30 -8 -14 R Insular Cortex 5.32 42 -2 -12 R Temporal Pole 5.17 54 16 -10 R Accumbens 4.77 6 10 -6 55 R Planum Polare 4.50 44 -8 -6 R Intracalcarine Cortex 4.35 24 -60 6 R Amygdala (central nucleus) 4.16 22 -6 -12 R Temporal Fusiform Cortex, posterior 4.07 40 -32 -16 division R Inferior Temporal Gyrus, anterior 3.99 42 -2 -38 division R Supracalcarine Cortex 3.89 22 -64 14 R Parahippocampal Gyrus, anterior 3.66 26 -4 -32 division R Planum Temporale 3.54 40 -28 12 R Parahippocampal Gyrus, posterior 3.32 16 -34 -6 division R Heschls Gyrus (includes H1 and H2) 2.95 48 -8 4 R Superior Temporal Gyrus, anterior 2.66 56 4 -14 division R Temporal Fusiform Cortex, anterior 2.33 38 -8 -38 division Cluster 2 1,368 L Middle Temporal Gyrus, anterior 4.11 -48 -2 -32 division L Inferior Temporal Gyrus, anterior 3.97 -46 0 -34 division L Inferior Temporal Gyrus, posterior 3.94 -56 -20 -32 division L Temporal Pole 3.61 -42 10 -34 L Temporal Fusiform Cortex, anterior 3.44 -34 -6 -34 division Cluster 3 920 R Frontal Pole 4.64 26 52 -12 Cluster 4 392 R Frontal Pole 3.88 30 68 2 Cluster 5 104 L Frontal Pole 3.22 -24 66 -6 Cluster 6 96 R Occipital Pole 3.88 6 -96 -8 Cluster 7 88 R Paracingulate Gyrus 3.14 12 46 -2 Cluster 8 88 L Insular Cortex 4.28 -42 -14 2 Cluster 9 80 L Hippocampus 3.51 -30 -14 -24 L Parahippocampal Gyrus, anterior 2.44 -30 -10 -30 division Cluster 10 80 56 R Frontal Pole 2.40 18 44 44 Cluster 11 64 L Temporal Pole 3.21 -44 16 -26 Cluster 12 64 L Temporal Pole 4.57 -50 16 -18 Cluster 13 56 L Middle Temporal Gyrus, posterior 2.61 -66 -34 -18 division Cluster 14 56 L Frontal Pole 2.95 -46 38 4 L Inferior Frontal Gyrus, pars 2.32 -52 36 6 triangularis Cluster 15 56 R Frontal Pole 3.45 10 62 10 Cluster 16 56 L Postcentral Gyrus 3.97 -34 -26 52 Cluster 17 48 L Frontal Medial Cortex 2.25 -6 40 -22 Cluster 18 48 L Frontal Pole 3.18 -28 62 -10 Cluster 19 48 L Middle Temporal Gyrus, posterior 2.84 -68 -38 2 division Cluster 20 40 L Brain-Stem 3.26 -14 -26 -38 Cluster 21 40 R Subcallosal Cortex 3.06 0 24 -4 Cluster 22 40 L Planum Temporale 3.42 -52 -24 4 Supplemental Table 2. Descriptive statistics for clusters and local extrema showing greater activity during the anticipation of Uncertain Threat relative to Predictable Safety (FDR q < 0.05, whole-brain corrected). mm3 t x y z Cluster 1 360,632 L Frontal Operculum Cortex 12.24 -32 20 10 L Frontal Orbital Cortex 11.50 -30 28 0 L Cingulate Gyrus, anterior division 9.86 -2 22 34 L Central Opercular Cortex 9.53 -42 6 2 57 L Paracingulate Gyrus 8.61 -8 22 32 L Supramarginal Gyrus, posterior 8.59 -56 -50 36 division L Superior Frontal Gyrus 8.58 -12 -6 68 L Juxtapositional Lobule Cortex 8.35 -2 6 46 L Angular Gyrus 8.33 -60 -54 36 L Putamen 8.06 -24 6 -4 L Brain-Stem 7.94 -2 -28 -2 L Frontal Pole 7.58 -36 48 30 L Cingulate Gyrus, posterior division 7.50 -12 -24 38 L Precentral Gyrus 7.37 -32 -6 50 L Occipital Fusiform Gyrus 7.37 -14 -90 -12 L Bed Nucleus of the Stria 7.35 -8 2 6 Terminalis/Caudate L Inferior Frontal Gyrus, pars 7.27 -56 14 0 opercularis L Parietal Operculum Cortex 7.21 -62 -30 20 L Temporal Occipital Fusiform Cortex 7.20 -34 -56 -22 L Inferior Frontal Gyrus, pars 7.18 -54 22 4 triangularis L Occipital Pole 7.01 -22 -98 14 L Supramarginal Gyrus, anterior 7.00 -56 -38 32 division L Precuneus Cortex 6.81 -10 -70 38 L Lingual Gyrus 6.76 -8 -72 -12 L Postcentral Gyrus 6.54 -66 -22 22 L Middle Frontal Gyrus 6.49 -34 34 38 L Superior Parietal Lobule 6.30 -18 -52 64 L Thalamus 6.23 -8 -4 12 L Lateral Occipital Cortex, superior 6.12 -38 -58 58 division L Bed Nucleus of the Stria 5.99 -8 2 -6 Terminalis/Anterior Commissure L Lateral Occipital Cortex, inferior 5.42 -42 -84 -10 division L Insular Cortex 4.49 -38 2 -2 L Temporal Pole 4.26 -58 6 -8 L Temporal Fusiform Cortex, posterior 4.20 -40 -42 -18 division L Inferior Temporal Gyrus, 4.11 -46 -62 -10 temporooccipital part L Pallidum 4.05 -26 -16 -2 L Bed Nucleus of the Stria Terminalis 3.95 -6 2 0 R Frontal Operculum Cortex 11.68 34 24 10 58 R Paracingulate Gyrus 11.60 10 20 34 R Precentral Gyrus 11.14 40 -2 46 R Thalamus 10.72 10 0 10 R Cingulate Gyrus, anterior division 10.68 10 12 38 R Juxtapositional Lobule Cortex 10.45 10 4 46 R Frontal Orbital Cortex 10.30 32 22 -8 R Putamen 10.12 32 -2 -6 R Central Opercular Cortex 10.02 46 8 0 R Parietal Operculum Cortex 9.85 52 -30 28 R Brain-Stem 9.84 4 -28 -2 R Lateral Occipital Cortex, inferior 9.61 42 -86 -8 division R Middle Frontal Gyrus 9.37 46 2 56 R Angular Gyrus 9.25 64 -46 28 R Supramarginal Gyrus, posterior 9.23 62 -46 34 division R Superior Parietal Lobule 9.02 22 -46 64 R Supramarginal Gyrus, anterior 8.94 54 -30 32 division R Occipital Pole 8.92 26 -94 12 R Superior Frontal Gyrus 8.88 18 -8 70 R Temporal Pole 8.87 50 10 -4 R Occipital Fusiform Gyrus 8.62 38 -68 -12 R Superior Temporal Gyrus, posterior 8.19 48 -24 -4 division R Inferior Frontal Gyrus, pars 8.10 52 22 2 triangularis R Cingulate Gyrus, posterior division 7.79 10 -20 42 R Caudate 7.60 12 -2 16 R Inferior Frontal Gyrus, pars 7.44 54 10 6 opercularis R Postcentral Gyrus 7.40 34 -38 62 R Frontal Pole 7.15 26 48 24 R Bed Nucleus of the Stria Terminalis 6.87 8 2 4 R Middle Temporal Gyrus, posterior 6.61 54 -30 -4 division R Inferior Temporal Gyrus, 6.50 46 -50 -14 temporooccipital part R Insular Cortex 6.49 38 4 0 R Middle Temporal Gyrus, 5.93 46 -56 6 temporooccipital part R Lingual Gyrus 5.68 10 -80 -4 R Lateral Occipital Cortex, superior 5.65 34 -66 28 division 59 R Temporal Occipital Fusiform Cortex 5.63 38 -48 -18 R Accumbens 5.11 10 6 -6 R Pallidum 5.08 18 2 2 R Precuneus Cortex 4.92 4 -48 54 R Planum Polare 4.66 46 -8 -6 R Thalamus 4.35 0 -10 0 R Temporal Fusiform Cortex, 3.80 32 -34 -24 posterior division R Hippocampus 3.79 34 -30 -6 R Amygdala 3.66 18 -2 -14 R Heschls Gyrus (includes H1 and H2) 3.08 48 -22 8 Cluster 2 2,352 L Middle Temporal Gyrus, temporooccipital part 4.52 -62 -48 6 L Supramarginal Gyrus, posterior division 4.23 -54 -48 14 L Middle Temporal Gyrus, posterior division 3.97 -60 -26 -4 L Angular Gyrus 3.19 -64 -50 10 Cluster 3 1,432 R Precuneus Cortex 5.83 16 -64 36 R Cuneal Cortex 2.71 12 -78 38 Cluster 4 920 R Temporal Pole 4.24 42 8 -38 R Middle Temporal Gyrus, anterior 3.99 division 48 2 -32 Cluster 5 448 L Inferior Temporal Gyrus, anterior 3.76 division -46 -6 -32 L Temporal Pole 3.69 -38 4 -36 L Middle Temporal Gyrus, anterior 3.67 division -50 0 -32 L Middle Temporal Gyrus, posterior 3.24 division -58 -12 -28 Cluster 6 256 L Frontal Pole 4.73 -32 54 -12 Cluster 7 248 L Planum Polare 4.80 -40 -14 -8 Cluster 8 224 R Right Hippocampus 4.60 30 -38 2 Cluster 9 176 L Brain-Stem 3.86 -6 -46 -52 Cluster 10 136 60 R Frontal Pole 3.93 40 46 6 Cluster 11 128 L Thalamus 3.45 -22 -34 -2 L Hippocampus 2.95 -28 -36 -2 Cluster 12 120 L Precentral Gyrus 3.82 -36 -16 42 L Postcentral Gyrus 3.51 -38 -18 40 Cluster 13 112 L Frontal Pole 4.01 -16 52 -16 Cluster 14 104 L Heschls Gyrus (includes H1 and H2) 4.35 -38 -28 12 Cluster 15 96 L Middle Frontal Gyrus 4.42 -46 14 30 Cluster 16 88 L Middle Temporal Gyrus, posterior 3.06 division -56 -34 -10 Cluster 17 64 L Middle Temporal Gyrus, posterior 2.96 -68 -42 -2 division Cluster 18 56 L Temporal Pole 3.13 -46 8 -34 Cluster 19 56 L Paracingulate Gyrus 2.99 -12 50 6 Cluster 20 56 L Inferior Frontal Gyrus, pars 2.83 opercularis -38 16 26 L Middle Frontal Gyrus 2.48 -36 14 28 Cluster 21 48 L Inferior Temporal Gyrus, posterior 3.65 -56 -20 -30 division Cluster 22 48 L Hippocampus 3.60 -22 -34 -8 Cluster 23 40 L Hippocampus 3.76 -28 -18 -12 Cluster 24 40 L Precuneus Cortex 2.80 -8 -42 44 Note: Suprathreshold activation was also evident in the R Dorsal Amygdala in the region of the Ce. 61 Supplemental Table 3. Descriptive statistics for clusters and local extrema showing greater activity during the anticipation of Certain Threat relative to Predictable Safety (FDR q < 0.05, whole-brain corrected). mm3 t x y z Cluster 1 67,208 L Cingulate Gyrus, anterior division 8.15 -8 16 34 L Superior Frontal Gyrus 7.25 -14 -2 68 L Paracingulate Gyrus 6.75 -4 12 42 L Juxtapositional Lobule Cortex 6.25 -6 4 56 L Precentral Gyrus 5.97 -28 -8 46 L Middle Frontal Gyrus 4.88 -40 0 50 L Postcentral Gyrus 3.49 -40 -18 40 R Cingulate Gyrus, anterior division 8.37 10 12 38 R Paracingulate Gyrus 7.52 12 20 34 R Superior Frontal Gyrus 7.07 14 -6 68 R Juxtapositional Lobule Cortex 6.68 8 6 58 R Precentral Gyrus 6.45 48 2 46 R Supramarginal Gyrus, posterior division 6.14 64 -40 26 R Lateral Occipital Cortex, superior division 6.00 18 -76 42 R Superior Temporal Gyrus, posterior division 5.87 48 -24 -4 R Postcentral Gyrus 5.82 44 -28 42 R Superior Parietal Lobule 5.78 34 -42 58 R Cingulate Gyrus, posterior division 5.68 14 -28 40 R Angular Gyrus 5.46 60 -46 32 R Precuneus Cortex 5.34 14 -78 42 R Middle Temporal Gyrus, temporooccipital part 5.26 50 -42 8 R Middle Frontal Gyrus 5.20 42 0 54 R Parietal Operculum Cortex 5.15 56 -28 24 R Middle Temporal Gyrus, posterior division 4.66 48 -22 -8 R Supramarginal Gyrus, anterior division 4.59 58 -24 32 R Occipital Pole 4.31 18 -90 34 R Cuneal Cortex 4.13 12 -80 38 R Planum Temporale 3.36 62 -32 20 Cluster 2 41,944 L Putamen 6.71 -20 12 -2 62 L Thalamus 5.98 -2 -2 4 L Lingual Gyrus 5.83 -10 -72 -10 L Bed Nucleus of the Stria Terminalis 5.81 -6 2 0 L Pallidum 5.68 -20 2 -4 L Occipital Fusiform Gyrus 5.57 -30 -68 -18 L Temporal Occipital Fusiform Cortex 5.54 -26 -60 -16 L Caudate 5.35 -8 0 8 L Brain-Stem 5.18 -2 -28 -2 L Insular Cortex 2.95 -32 6 8 L Accumbens 2.83 -6 12 -6 R Temporal Occipital Fusiform Cortex 5.86 24 -54 -16 R Thalamus 5.83 2 -16 -2 R Lingual Gyrus 5.46 12 -70 -12 R Brain-Stem 5.24 4 -28 -2 R Occipital Fusiform Gyrus 4.71 20 -68 -14 R Thalamus 3.35 0 -10 2 Cluster 3 20,704 L Supramarginal Gyrus, posterior division 6.43 -56 -50 40 L Precuneus Cortex 5.43 -10 -70 36 L Supramarginal Gyrus, anterior division 5.29 -58 -34 40 L Lateral Occipital Cortex, superior division 5.08 -10 -64 62 L Angular Gyrus 5.07 -60 -54 36 L Cuneal Cortex 4.96 -12 -80 38 L Parietal Operculum Cortex 4.88 -58 -36 22 L Superior Parietal Lobule 4.80 -36 -48 48 L Postcentral Gyrus 4.68 -40 -32 48 L Superior Temporal Gyrus, posterior division 2.77 -66 -34 14 Cluster 4 15,232 R Putamen 7.94 24 10 -4 R Caudate 7.33 10 2 10 R Pallidum 7.19 20 4 0 R Bed Nucleus of the Stria 6.50 Terminalis/Caudate 8 6 0 R Bed Nucleus of the Stria Terminalis 6.36 8 2 4 R Central Opercular Cortex 6.17 48 8 -2 R Frontal Operculum Cortex 6.07 34 24 8 R Precentral Gyrus 5.61 62 4 12 63 R Thalamus 5.56 10 -2 12 R Insular Cortex 5.47 34 16 8 R Frontal Orbital Cortex 5.25 32 30 -2 R Temporal Pole 4.38 54 10 -2 R Accumbens 4.34 10 12 -6 R Frontal Pole 4.32 50 34 -4 R Inferior Frontal Gyrus, pars 4.17 triangularis 54 26 6 R Inferior Frontal Gyrus, pars 3.66 opercularis 56 10 6 Cluster 5 7,880 L Frontal Pole 6.03 -30 52 28 L Middle Frontal Gyrus 5.73 -34 34 38 Cluster 6 5,616 R Frontal Pole 6.39 34 44 28 R Middle Frontal Gyrus 6.35 30 32 34 Cluster 7 5,536 L Frontal Operculum Cortex 7.52 -36 12 6 L Central Opercular Cortex 5.93 -42 8 2 L Insular Cortex 5.57 -30 26 6 L Inferior Frontal Gyrus, pars 5.03 opercularis -52 14 2 L Frontal Orbital Cortex 4.65 -30 28 0 L Temporal Pole 4.21 -52 12 -6 L Precentral Gyrus 3.35 -52 8 4 L Inferior Frontal Gyrus, pars 2.82 triangularis -52 24 -4 Cluster 8 1,224 L Cingulate Gyrus, posterior division 4.52 -14 -28 38 L Precuneus Cortex 4.22 -10 -44 50 L Precentral Gyrus 4.20 -16 -32 42 Cluster 9 856 R Thalamus 4.39 16 -18 6 Cluster 10 832 R Lateral Occipital Cortex, inferior division 4.57 44 -62 -12 R Inferior Temporal Gyrus, temporooccipital part 3.97 52 -60 -12 R Middle Temporal Gyrus, temporooccipital part 3.22 48 -56 2 Cluster 11 664 R Putamen 5.23 30 -18 2 64 R Amygdala (basolateral nucleus) 5.11 30 -8 -14 R Hippocampus 3.34 26 -16 -12 Cluster 12 424 R Occipital Pole 4.07 24 -92 10 R Lateral Occipital Cortex, superior division 3.30 30 -84 18 Cluster 13 376 L Cingulate Gyrus, posterior division 3.69 -2 -28 26 R Cingulate Gyrus, posterior division 3.83 8 -26 28 Cluster 14 360 L Lingual Gyrus 4.50 -10 -84 -8 L Occipital Fusiform Gyrus 3.42 -16 -78 -10 Cluster 15 304 L Putamen 5.68 -32 -12 -10 L Amygdala (central nucleus) 4.14 -24 -12 -10 L Hippocampus 3.58 -30 -16 -14 Cluster 16 296 L Hippocampus 4.17 -20 -42 2 L Thalamus 3.83 -22 -36 0 Cluster 17 296 L Postcentral Gyrus 3.53 -66 -22 22 Cluster 18 264 R Hippocampus 3.98 24 -36 4 R Thalamus 3.75 20 -34 6 Cluster 19 184 L Frontal Pole 4.18 -30 50 -14 Cluster 20 176 L Brain-Stem 3.74 -6 -38 -44 Cluster 21 168 R Brain-Stem 3.87 12 -26 -14 Cluster 22 168 L Lateral Occipital Cortex, inferior 4.32 division -42 -80 -4 Cluster 23 160 R Cingulate Gyrus, anterior division 3.93 4 -8 40 Cluster 24 144 L Postcentral Gyrus 3.85 -56 -18 30 Cluster 25 136 R Superior Frontal Gyrus 4.34 4 56 34 Cluster 26 128 65 R Hippocampus 4.83 34 -34 -6 Cluster 27 128 L Supramarginal Gyrus, posterior 3.60 division -52 -48 12 Cluster 28 128 R Inferior Frontal Gyrus, pars 3.33 opercularis 38 12 26 Cluster 29 120 L Paracingulate Gyrus 3.64 -10 50 8 Cluster 30 112 R Lateral Occipital Cortex, inferior 3.84 division 40 -80 -4 Cluster 31 104 R Brain-Stem 3.62 8 -38 -48 Cluster 32 104 R Lateral Occipital Cortex, inferior 2.99 division 50 -66 6 Cluster 33 96 R Lateral Occipital Cortex, inferior division 3.78 42 -68 10 R Lateral Occipital Cortex, superior division 3.60 40 -66 22 Cluster 34 88 R Temporal Pole 4.02 36 8 -36 Cluster 35 88 L Heschls Gyrus (includes H1 and H2) 4.22 -38 -28 12 Cluster 36 88 L Central Opercular Cortex 3.38 -54 -20 18 Cluster 37 88 R Postcentral Gyrus 3.27 22 -36 70 Cluster 38 88 L Superior Frontal Gyrus 3.57 -4 20 64 Cluster 39 80 L Middle Temporal Gyrus, 3.49 temporooccipital part -66 -50 6 Cluster 40 80 L Lateral Occipital Cortex, inferior 3.25 division -40 -70 14 Cluster 41 80 L Cingulate Gyrus, posterior division 3.62 -4 -18 28 R Cingulate Gyrus, posterior division 3.38 2 -22 30 Cluster 42 80 L Superior Frontal Gyrus 3.85 -12 32 52 66 Cluster 43 72 L Hippocampus 3.64 -34 -30 -8 Cluster 44 72 L Middle Temporal Gyrus, 2.95 temporooccipital part -58 -50 -4 Cluster 45 72 R Frontal Pole 3.24 42 50 2 Cluster 46 72 L Precentral Gyrus 5.19 -56 0 14 Cluster 47 64 L Putamen 4.08 -32 -20 -4 Cluster 48 64 L Lingual Gyrus 3.68 -22 -50 -2 Cluster 49 64 R Lateral Occipital Cortex, inferior 3.28 division 58 -62 6 Cluster 50 64 L Occipital Pole 3.25 -20 -94 10 Cluster 51 64 L Central Opercular Cortex 4.40 -44 0 12 Cluster 52 64 L Precentral Gyrus 3.65 -60 2 20 Cluster 53 64 R Postcentral Gyrus 3.50 52 -16 32 Cluster 54 56 R Brain-Stem 3.43 2 -34 -48 Cluster 55 56 L Brain-Stem 3.39 -10 -38 -30 Cluster 56 56 R Brain-Stem 3.27 4 -26 -22 Cluster 57 56 L Lateral Occipital Cortex, inferior 3.40 division -48 -74 -10 Cluster 58 56 L Hippocampus 3.74 -34 -22 -10 Cluster 59 56 L Frontal Pole 3.79 -32 38 -12 Cluster 60 56 R Middle Temporal Gyrus, 3.10 temporooccipital part 56 -56 6 Cluster 61 56 67 L Frontal Pole 3.88 -38 60 6 Cluster 62 56 L Lateral Occipital Cortex, inferior division 2.96 -56 -66 8 Cluster 63 56 R Occipital Pole 3.61 20 -98 10 Cluster 64 56 L Angular Gyrus 3.11 -46 -56 14 Cluster 65 56 R Middle Temporal Gyrus, 3.48 temporooccipital part 54 -52 10 Cluster 66 56 R Cingulate Gyrus, anterior division 3.52 6 28 18 Cluster 67 56 L Cingulate Gyrus, anterior division 3.85 -2 -6 38 Cluster 68 56 R Precentral Gyrus 3.36 24 -18 74 Cluster 69 48 L Inferior Temporal Gyrus, 3.45 temporooccipital part -44 -48 -12 Cluster 70 48 R Middle Temporal Gyrus, posterior 3.59 division 68 -20 -4 Cluster 71 48 L Lateral Occipital Cortex, inferior 3.65 division -48 -66 2 Cluster 72 48 R Lateral Occipital Cortex, inferior 3.69 division 40 -88 2 Cluster 73 48 L Supramarginal Gyrus, posterior 2.93 division -64 -46 8 Cluster 74 48 L Central Opercular Cortex 2.88 -34 4 14 Cluster 75 48 R Inferior Frontal Gyrus, pars 3.21 opercularis 48 14 20 Cluster 76 48 R Inferior Frontal Gyrus, pars opercularis 4.38 56 16 20 R Precentral Gyrus 2.80 56 10 20 Cluster 77 48 R Superior Frontal Gyrus 4.36 12 28 60 Cluster 78 40 68 R Brain-Stem 4.06 8 -46 -54 Cluster 79 40 L Brain-Stem 3.36 -2 -32 -24 Cluster 80 40 L Middle Temporal Gyrus, posterior 3.29 division -62 -34 -18 Cluster 81 40 L Lateral Occipital Cortex, inferior 3.13 division -38 -72 -4 Cluster 82 40 R Paracingulate Gyrus 3.56 14 52 6 Cluster 83 40 R Middle Temporal Gyrus, 3.16 temporooccipital part 62 -54 12 Cluster 84 40 R Thalamus 3.73 8 -20 16 Cluster 85 40 L Caudate 3.67 -18 -16 24 Cluster 86 40 R Frontal Pole 2.80 14 58 28 Cluster 87 40 R Angular Gyrus 3.20 52 -52 46 Note: Suprathreshold activation was also evident in the L Dorsal Amygdala in the region of the Ce. Supplemental Table 4. Descriptive statistics for clusters and local extrema showing greater activity during the anticipation of Angry/Fearful Faces relative to Houses (FDR q < 0.05, whole-brain corrected). mm3 t x y z Cluster 1 648,072 L Postcentral Gyrus 15.78 -34 -28 60 L Amygdala (cortical, amygdalohippocampal area) 14.14 -16 -4 -16 L Precentral Gyrus 13.73 -12 -32 68 L Superior Parietal Lobule 12.92 -36 -42 64 L Intracalcarine Cortex 12.78 -6 -72 12 L Superior Frontal Gyrus 12.55 -6 -8 74 L Supramarginal Gyrus, anterior division 11.40 -54 -30 52 L Temporal Occipital Fusiform Cortex 11.40 -44 -48 -20 69 L Lateral Occipital Cortex, inferior division 11.18 -56 -70 10 L Middle Frontal Gyrus 10.76 -44 0 58 L Juxtapositional Lobule Cortex 10.56 -2 -8 66 L Supracalcarine Cortex 10.37 -2 -84 10 L Middle Temporal Gyrus, temporooccipital part 9.39 -58 -60 8 L Angular Gyrus 9.33 -60 -52 12 L Thalamus 9.29 -8 -2 10 L Lingual Gyrus 9.10 -8 -78 -14 L Cingulate Gyrus, posterior division 8.97 -6 -52 32 L Lateral Occipital Cortex, superior division 8.69 -36 -58 58 L Supramarginal Gyrus, posterior division 8.65 -52 -48 10 L Paracingulate Gyrus 8.39 -4 16 50 L Caudate 8.20 -12 -6 18 L Cingulate Gyrus, anterior division 8.05 -8 16 36 L Precuneus Cortex 7.83 -6 -56 56 L Parietal Operculum Cortex 7.74 -58 -40 24 L Cuneal Cortex 7.71 -10 -80 30 L Insular Cortex 7.36 -40 0 -16 L Occipital Pole 7.23 -6 -90 16 L Pallidum 7.02 -16 2 0 L Temporal Pole 6.97 -36 4 -20 L Central Opercular Cortex 6.95 -56 -18 20 L Frontal Orbital Cortex 6.72 -38 30 -16 L Bed Nucleus of the Stria Terminalis 6.38 -4 4 -2 L Planum Polare 6.28 -42 -2 -14 L Inferior Frontal Gyrus, pars triangularis 6.21 -50 24 4 L Inferior Frontal Gyrus, pars opercularis 6.16 -58 14 24 L Putamen 5.96 -30 -22 2 L Frontal Medial Cortex 5.94 -2 54 -12 L Frontal Pole 5.87 -18 40 42 L Middle Temporal Gyrus, posterior division 5.76 -66 -32 0 L Temporal Fusiform Cortex, posterior division 5.60 -42 -30 -18 L Planum Temporale 5.52 -42 -32 8 L Frontal Operculum Cortex 5.30 -42 22 4 L Heschls Gyrus (includes H1 and H2) 5.27 -52 -24 10 70 L Temporal Fusiform Cortex, anterior division 5.17 -30 0 -36 L Accumbens 5.05 -8 16 -6 L Occipital Fusiform Gyrus 4.89 -40 -70 -16 L Superior Temporal Gyrus, posterior division 4.89 -62 -30 0 L Hippocampus 4.85 -26 -34 -6 L Brainstem 4.35 -6 -18 -20 L Inferior Temporal Gyrus, posterior division 4.24 -46 -24 -22 L Subcallosal Cortex 4.10 -2 30 -10 L Superior Temporal Gyrus, anterior division 3.30 -62 0 -8 L Middle Temporal Gyrus, anterior division 3.00 -62 0 -22 R Amygdala (cortical, amygdalohippocampal area) 16.14 16 -4 -18 R Intracalcarine Cortex 14.97 10 -72 14 R Precentral Gyrus 12.96 14 -32 64 R Postcentral Gyrus 12.85 16 -30 68 R Supracalcarine Cortex 12.40 2 -74 12 R Middle Frontal Gyrus 12.18 44 0 58 R Middle Temporal Gyrus, temporooccipital part 12.10 48 -58 6 R Supramarginal Gyrus, posterior division 12.05 48 -40 10 R Cingulate Gyrus, posterior division 11.79 6 -52 24 R Superior Parietal Lobule 11.39 26 -42 66 R Lateral Occipital Cortex, inferior division 11.31 56 -62 6 R Central Opercular Cortex 10.80 40 -2 14 R Superior Frontal Gyrus 10.79 18 -8 72 R Inferior Temporal Gyrus, temporooccipital part 10.68 46 -42 -20 R Juxtapositional Lobule Cortex 10.17 4 4 56 R Precuneus Cortex 10.11 2 -62 34 R Cuneal Cortex 10.08 6 -78 22 R Lingual Gyrus 10.07 6 -66 2 R Temporal Occipital Fusiform Cortex 9.69 48 -48 -24 R Supramarginal Gyrus, anterior division 9.41 62 -28 24 R Thalamus 9.38 10 -2 12 R Paracingulate Gyrus 8.97 0 16 48 R Caudate 8.69 14 -8 20 R Angular Gyrus 8.58 52 -56 22 71 R Thalamus 8.56 0 -8 10 R Putamen 8.21 30 -18 0 R Cingulate Gyrus, anterior division 8.04 6 -8 30 R Inferior Frontal Gyrus, pars opercularis 7.88 52 16 32 R Middle Temporal Gyrus, posterior division 7.66 66 -36 2 R Insular Cortex 7.63 40 4 -16 R Parietal Operculum Cortex 7.53 54 -32 28 R Frontal Pole 7.46 2 56 -12 R Superior Temporal Gyrus, posterior division 7.24 52 -16 -6 R Frontal Medial Cortex 6.91 2 50 -16 R Inferior Frontal Gyrus, pars triangularis 6.88 54 26 -6 R Lateral Occipital Cortex, superior division 6.69 58 -60 38 R Hippocampus 6.36 24 -36 -6 R Temporal Fusiform Cortex, anterior division 6.15 38 -2 -38 R Temporal Pole 6.11 32 8 -24 R Pallidum 6.07 20 2 2 R Frontal Orbital Cortex 5.91 48 22 -12 R Planum Temporale 5.80 38 -32 14 R Heschls Gyrus (includes H1 and H2) 5.57 54 -14 8 R Frontal Operculum Cortex 5.48 42 26 0 R Parahippocampal Gyrus, anterior division 5.19 30 -4 -34 R Bed Nucleus of the Stria Terminalis 5.08 6 4 0 R Inferior Temporal Gyrus, posterior division 5.06 44 -28 -18 R Middle Temporal Gyrus, anterior division 4.40 60 2 -16 R Accumbens 4.16 6 16 -2 R Planum Polare 4.13 42 -8 -10 R Brainstem 4.00 4 -42 -22 R Superior Temporal Gyrus, anterior division 3.88 62 0 -12 R Subcallosal Cortex 3.53 4 30 -10 Cluster 2 552 L Brainstem 3.75 -10 -36 -44 R Brainstem 3.86 2 -30 -48 Cluster 3 296 L Brainstem 3.60 -16 -30 -30 72 Cluster 4 248 L Middle Temporal Gyrus, anterior division 4.22 -50 -4 -28 L Temporal Pole 3.00 -50 6 -26 Cluster 5 240 R Brainstem 3.82 12 -26 -42 Cluster 6 224 L Temporal Pole 3.85 -52 4 -16 Cluster 7 160 R Brainstem 3.48 12 -22 -36 Cluster 8 136 L Brainstem 3.31 -8 -28 -36 R Brainstem 2.68 2 -32 -36 Cluster 9 104 L Frontal Pole 4.09 -26 54 -14 Cluster 10 96 L Temporal Pole 3.94 -44 12 -28 Cluster 11 96 R Inferior Temporal Gyrus, posterior division 3.48 62 -30 -18 R Middle Temporal Gyrus, posterior division 3.18 64 -32 -16 Cluster 12 88 L Temporal Pole 3.13 -48 4 -22 L Superior Temporal Gyrus, anterior division 2.44 -48 0 -18 Cluster 13 80 R Precentral Gyrus 4.44 62 2 12 Cluster 14 72 L Brainstem 3.27 -8 -38 -32 Cluster 15 72 L Subcallosal Cortex 2.16 -2 24 -26 R Subcallosal Cortex 3.54 4 24 -26 Cluster 16 72 R Frontal Pole 4.48 22 42 -16 Cluster 17 64 R Brainstem 3.45 6 -36 -48 Cluster 18 64 R Inferior Temporal Gyrus, posterior 2.86 division 46 -12 -32 Cluster 19 64 73 L Frontal Pole 3.23 -14 68 -2 Cluster 20 64 L Caudate 2.84 -18 20 10 Cluster 21 56 L Frontal Pole 2.80 -42 52 0 Cluster 22 48 R Brainstem 3.46 0 -18 -38 Cluster 23 48 L Inferior Temporal Gyrus, posterior 2.97 division -60 -36 -20 Cluster 24 48 L Middle Temporal Gyrus, posterior 2.51 division -64 -28 -18 L Inferior Temporal Gyrus, posterior 2.33 division -62 -28 -22 Cluster 25 48 L Frontal Pole 3.45 -18 52 -16 Cluster 26 40 L Temporal Pole 3.43 -46 16 -38 Cluster 27 40 R Brainstem 2.96 0 -18 -32 Cluster 28 40 L Frontal Pole 2.11 -26 50 0 Cluster 29 40 L Heschls Gyrus (includes H1 and H2) 2.68 -42 -20 4 Note: Suprathreshold activation was also evident bilaterally in the Dorsal Amygdala in the region of the Ce. 74 References Abdellaoui, A., Chen, H. 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