Discriminative fear conditioning is one of the most common methods of studying fear learning in laboratory settings (LeDoux, 2012; Davis and Whalen, 2001 and Grillon, 2002). It refers to the pairing of an initially neutral stimulus (CS+) with an aversive unconditioned stimulus (US), thereby imbuing the CS+ with the capacity to elicit increased autonomic responses relative to an unpaired neutral stimulus (CS-). Acquisition of conditioned fear forms an important part of theories of how anxiety and stress disorders develop from experiencing aversive events (Mineka and Oehlberg, 2008; Bouton et al., 2001; Craske et al., 2014). A more precise understanding of why some individuals are more prone than others to developing anxiety and stress disorders may therefore be achieved by studying individual differences in fear conditioning. Ultimately, such knowledge could inform the development of more effective treatments for subgroups of patients whose responses to fear conditioning are related to the disorder (see e.g. Insel, 2014). Consequently, it has been argued that fear conditioning research should increasingly focus on understanding the sources of individual differences. One way that individuals vary during fear conditioning is in their autonomic responses (Lonsdorf and Merz, 2017).

The most commonly used autonomic measure of conditioned fear is the differential Skin Conductance Response (SCR; Lonsdorf et al., 2017), a label referring to the changes in skin conductance induced by sympathetic activation (Dawson et al., 2007; Wallin, 1981). A plethora of studies have investigated SCR in fear conditioning to determine its associations with psychiatric disorders. A meta-analysis of studies in patients with anxiety disorders found that SCR to the control cue (CS-) during the acquisition of conditioned fear was elevated in patients relative to controls (Duits et al., 2015). Duits et al. have proposed that increased SCR to the CS- was an effect of fear generalization from the threat cue to the control cue, or of reduced inhibition of threat responses to the control cue. Also, in individuals with schizophrenia, an increased SCR to the CS- has been reported across four independent fear conditioning studies (Tuominen et al., 2022). In OCD, however, a systematic review found associations to be less clear when considering SCR during the acquisition of conditioned fear and point to stronger OCD-related differences in SCR during the extinction phase of conditioned fear (Cooper and Dunsmoor, 2021). Taken together, these studies demonstrate increased SCR during fear conditioning to the control cue in patients with anxiety disorders and schizophrenia, relative to control participants, which gives credence to the notion that fear conditioning is an experimental model that can inform research on autonomic regulation in these disorders (Lonsdorf and Merz, 2017).

Previous studies of the neural correlates of individual differences in conditioned SCR, generally defined as the difference in average SCR score between CS+ and CS- presentations during acquisition (see Lonsdorf and Merz, 2017, for a discussion of definitions), have focused on either one or a few brain regions, using region of interest analyses. Many of these studies have found positive correlations with neural responses in the amygdala (LaBar et al., 1998; Phelps et al., 2004; Dunsmoor et al., 2011; Petrovic et al., 2008; MacNamara et al., 2015; Marin et al., 2020). Neuroimaging studies of within-subject variation in conditioned SCR have also generally found positive correlations to amygdala responses (Cheng et al., 2003; Knight et al., 2005; Cheng et al., 2006), although exceptions exist (Sjouwerman et al., 2020; Savage et al., 2021). The findings from studies that report a positive relationship between SCR and amygdala activity are in line with the general understanding of fear conditioning from animal models, where a neural circuitry centered on the amygdala is responsible for the acquisition of conditioned fear responses (LeDoux, 2000; Davis, 2000). They also complement those human lesion studies demonstrating either diminished or absent conditioned SCR following amygdala damage (LaBar et al., 1995; Bechara et al., 1995), although not all studies have found such an effect (Ahs et al., 2010; for a review see Ojala and Bach, 2020). Further, the involvement of the amygdala in human fear conditioning has been questioned based on the results of a meta-analysis of fMRI studies investigating fear conditioning (Fullana et al., 2016) and based on studies showing unexpected, increased amygdala responses to the CS- compared to the CS+ (see e.g. Visser et al., 2021). Such results could arise from distributed representations of the CS+ and CS- in the amygdala (Bach et al., 2011; Reijmers et al., 2007) or from a need for larger sample sizes to detect differential responses in the amygdala. Speaking to the latter idea, two independent studies, each including hundreds of participants, have recently reported increased CS+, relative to CS-, activation in the amygdala (Kastrati et al., 2022; Wen et al., 2022). Amygdala activation to the CS+ was primarily detected during the first trials of acquisition, whereas CS- activity was larger in the end of acquisition (Wen et al., 2022). The results of these two large and independent neuroimaging studies, together with the fairly consistent findings of correlated individual differences in conditioned SCR and amygdala activation (LaBar et al., 1998; Phelps et al., 2004; Dunsmoor et al., 2011; Petrovic et al., 2008; MacNamara et al., 2015; Marin et al., 2020), support the hypothesis that amygdala activation should be positively correlated with SCR.

A limiting factor of previous studies of the neural correlates of individual differences in conditioned SCR is that they generally have been based on small sample sizes (N ≤ 27) and have reported results from region of interest analyses (ROIs). Notably, most previous studies have sample sizes that fall below the minimum guidelines for correlation analysis in fMRI research (Yarkoni, 2009; Yarkoni and Braver, 2010), which recommend a sample size of at least N = 40 for determination of inter-individual correlations. Studies that do not comply with this minimum recommendation are highly susceptible to type II errors (i.e. not detecting real effects, even within ROIs) as well as gross inflation of reported effect sizes (Yarkoni, 2009). Furthermore, some of the reported studies also use non-standard statistical procedures, such as reporting results from uncorrected whole brain analyses (Petrovic et al., 2008), or initially use an uncorrected whole brain analysis followed up by stringent FWE-correction within masks only targeting regions implicated in the prior uncorrected analysis (Dunsmoor et al., 2011), which raises the risk for type I error (see e.g. Poldrack et al., 2017) and could reduce the reliability of the results. Only two previous studies that investigated the association between individual differences in conditioned SCR and neural activity have met the minimum requirements for sample size suggested by Yarkoni and Braver, 2010: MacNamara et al., 2015 (N = 49) and Marin et al., 2020 (N = 60). However, both of these studies have still reported results based on uncorrected statistics in pre-defined ROIs (See Table 1 for a comparison of previous studies investigating neural correlates of SCR).

The previous focus on ROIs excludes activations in many parts of the brain that are potentially important for explaining individual differences in conditioned SCR. Fear conditioning is known to activate a large set of cortical, subcortical, and brainstem areas other than the ROIs that have so far been investigated for their correlation with SCR (Fullana et al., 2016). In their meta-analysis, Fullana et al., 2016 proposed that neural regions consistently activated during fear conditioning collectively constitute a large-scale neural network, centered on the dACC and anterior insula, that represents autonomic-interoceptive processing in response to conditioned stimuli (Fullana et al., 2016; based on findings by e.g. Cameron, 2009; Craig, 2009; Critchley and Harrison, 2013; Medford and Critchley, 2010). Based on this proposal, one would expect individual differences in autonomic conditioned responding, such as those measured by SCR (Dawson et al., 2007), to correlate with conditioning-related activity within this broader network.

Because previous studies only studied correlations to SCR in a handful of brain regions and in relatively small samples, the primary aim of this study was to investigate the whole brain correlations of individual differences in conditioned SCR by analyzing data from a large twin sample performing a fear conditioning task (N = 285 individuals). Similar to previous studies (e.g. Phelps et al., 2004; Dunsmoor et al., 2011; MacNamara et al., 2015), the present study used differential SCR as a between-subjects regressor of CS+ > CS- BOLD activation. Previous studies have not found whole-brain correlations surviving correction for multiple comparisons. In the current study, the sample size of N = 285 individuals provided sufficient statistical power to detect correlations of medium effect size and above (r > .251) throughout the whole brain (see section 4.3.3 in the Materials and methods section for details), thereby substantially improving upon the power of previous whole brain analyses of smaller sample sizes. A family-wise error (FWE) corrected alpha level of α = .05 was used to constrain the risk of type I errors to an acceptable level (see e.g. Poldrack et al., 2017), which was not done in previous studies.

Based on findings from previous studies of individual differences in conditioned SCR (LaBar et al., 1998; Phelps et al., 2004; Dunsmoor et al., 2011; Petrovic et al., 2008; MacNamara et al., 2015; Marin et al., 2020), we hypothesized a positive correlation between SCR and amygdala activation. Again, our sample size yielded substantially improved power in comparison to previous studies while securing an alpha level of α = .05 (see section 4.3.3 in the Materials and methods section for details). Finally, a last aim of the present study was to determine whether areas whose activity explained significant individual variation in conditioned SCR did so independently of one other. A comparison of the relative contributions of different brain areas to conditioned SCR has the potential to elucidate separate neural pathways mediating individual differences in conditioned autonomic responding. Ultimately, such findings may aid the understanding of autonomic regulation in pathological fear and anxiety.

Individual differences in SCR during discriminative fear conditioning are common (Lonsdorf and Merz, 2017) and have been associated with psychopathology (Duits et al., 2015; Nees et al., 2015; Lonsdorf and Merz, 2017). In this study, we examined the whole brain correlates of conditioned SCR in a large sample of twins (N = 285) during discriminative fear conditioning with concomitant SCR and fMRI recordings. As expected, we found a correlation between individual differences in conditioned SCR and amygdala activity in line with previous reports (LaBar et al., 1998; Phelps et al., 2004; Petrovic et al., 2008; Dunsmoor et al., 2011; MacNamara et al., 2015; Marin et al., 2020). Our analysis also implicated the dACC and insula, two regions which have previously been found to show increased responses in high vs. low conditioners (Marin et al., 2020). Importantly, we also identified correlations in novel regions including the bilateral temporoparietal junction/superior temporal gyri, right frontal operculum, bilateral dorsal premotor cortex, right superior parietal lobe, and midbrain. All correlations between brain responses and conditioned SCR were positive, meaning that individuals who respond more strongly to the CS+ relative to the CS- on the physiological level (SCR) also showed greater neural activation to the CS+ relative to the CS-. Furthermore, all regional activations demonstrated a stronger correlation with SCR to the CS+ alone compared to the CS- alone, although a few correlations did not survive Bonferroni correction. This indicated that neural activity was primarily associated with heightened, conditioned SCR to the CS+ (i.e. as opposed to inhibited SCR to the CS-).

An important research question is whether the neural network associated with individual differences in conditioned SCR is embedded in the network of regions that is generally activated during fear conditioning. The whole brain correlates of SCR found in the present study belong to the set of regions consistently activated during human neuroimaging studies of fear conditioning (Fullana et al., 2016). This indicates that these regions not only respond to the CS+ relative to the CS- in general, but that the magnitude of this activation also co-varies with individual differences in the magnitude of conditioned responding indexed by SCR. This is consistent with the proposal by Fullana et al., 2016; based on findings by e.g. Cameron, 2009; Craig, 2009; Critchley and Harrison, 2013; Medford and Critchley, 2010 that these regions, especially the dACC and anterior insula, are part of a large-scale neural network regulating autonomic responding (SCR). Reasonably, increased autonomic activation would correlate with larger responses in neural regions regulating autonomic processing. To better understand the potentially unique contributions of different brain regions to SCR, we performed a hierarchical regression analysis. Results suggested that cortical areas together with midbrain regions contributed to individual differences in conditioned SCR. No unique contribution from any of the regions could be proven. These results are consistent with the idea that the regions identified in the whole brain analysis form part of one functional network, rather than separate independent networks. Indeed, the functional connectivity and network-structure between the regions reported in the present study have been examined by several research groups previously (see e.g. Uddin et al., 2019, for a review). These research groups have proposed a more general function for this network, beyond autonomic-interoceptive processing and autonomic regulation, in detecting and preparing responses to salient events across homeostatic, affective, and cognitive domains (see e.g. Seeley et al., 2007; Menon and Uddin, 2010; Uddin, 2015; Menon, 2015; Uddin et al., 2019). Uddin et al., 2019 refer to this network as the ‘midcingulo-insular network’ to reflect the anatomy of the network, rather than using functional labels that can be dependent on context (e.g. ‘salience network’, Menon, 2015; ‘ventral attention network’, Corbetta et al., 2008). Within this network, the dACC/aMCC (see Vogt, 2016, for a discussion regarding the naming of this region) and anterior insula, constitute the major input-output nodes (Uddin et al., 2019; Menon, 2015). While, the insula is thought to integrate cognitive, affective, interoceptive, and homeostatic information, the dACC is believed to represent this summarized information in order to determine autonomic, behavioral, and cognitive responding (Menon, 2015; Medford and Critchley, 2010). Efferent autonomic output from the dACC is proposed to be mediated by the periaqueductal gray (Menon, 2015), which was another region identified in our whole brain analysis (labeling consistent with a previous definition of the periaqueductal gray, see Linnman et al., 2012). Notably, the midcingulo-insular network also includes the bilateral temporoparietal junction/superior temporal gyri (Uddin et al., 2019; see e.g. Yeo et al., 2011; Seeley et al., 2007; Bzdok et al., 2013), whose activity we found to be correlated with SCR. The right frontal operculum, which was another region we found to be positively correlated to SCR, is part of what has previously been called the ‘cingulo-opercular network’ or the ‘ventral attention network’ (Uddin et al., 2019; see e.g. Corbetta et al., 2008). More specifically, the right lateralized temporoparietal junction and frontal operculum appear to be recruited particularly during exogenous salience detection (Uddin et al., 2019), which fits well with the stimulus-driven salience detection in the context of fear conditioning. Finally, the amygdala has also been proposed to constitute a major subcortical node within this network (Menon, 2015; Uddin et al., 2019). Thus, most of the regions whose activity we found to correlate with SCR belong to this functional network, consistent with its role in regulating autonomic responses, the only exception being the bilateral dorsal premotor cortex. However, the premotor cortex is known to be a major cortical elicitor of the SCR (Dawson et al., 2007; Boucsein, 2012), making it a plausible link between midcingulo-insular activity and peripheral skin conductance. Consequently, based on our findings and on previous evidence-based theory, we propose that individual differences in conditioned SCR may originate from activity within the midcingulo-insular network (Uddin et al., 2019) in conjunction with the dorsal premotor cortex.

It has been suggested that understanding sources of individual differences in fear conditioning may uncover individual risk and resilience factors with respect to fear and anxiety that may ultimately aid the understanding and treatment of fear-related psychopathology (Lonsdorf and Merz, 2017). Our proposal that individual differences in conditioned SCR co-vary with activity in a large-scale neural network substantiating autonomic-interoceptive processing and salience detection may highlight such a source. Indeed, overactivity within the midcingulo-insular network has previously been suggested to underlie pathological anxiety (Menon, 2011; Menon, 2015), as patients with anxiety disorders show altered functional connectivity within this network (Peterson et al., 2014) as well as hyperactivity within the anterior insula (Paulus and Stein, 2006; Stein et al., 2007) and amygdala (Etkin and Wager, 2007), which are important nodes of the network. Based on these findings, it has been suggested that anxiety disorders and neuroticism may result from excessive processing of emotion-related salient cues (Menon, 2011) and/or alterations in autonomic interoceptive-autonomic processing (e.g. Paulus and Stein, 2006; Medford and Critchley, 2010). As anxiety disorders have also been associated with altered discriminative fear learning (Nees et al., 2015), and as our results indicate that individual differences in discriminative fear learning covary with midcingulo-insular activity, our results are consistent with this anxiety model. Specifically, in reviewing the relationship between discriminative fear learning and anxiety disorders, Nees et al., 2015 note that while discriminative fear learning does not appear to be impaired across all anxiety disorders and across all stimuli types (see also Duits et al., 2015, for a meta-analysis confirming this observation), studies comparing anxiety disorder patients to controls with regard to disorder-specific stimuli have found increased discriminatory fear learning in patients (in specific phobia, see Schweckendiek et al., 2011; in social phobia, see Lissek et al., 2008; in PTSD, see Wessa and Flor, 2007), suggesting that discriminatory fear learning may be a mechanism underlying these disorders. The present finding, that individual differences in discriminatory fear learning covary with differences in midcingulo-insular activity, suggests that alterations in such neural activity may also be contributing to, or resulting from, anxiety, consistent with the anxiety model proposed by Menon, 2011. However, it should be noted that the present study only used SCR as an outcome measure of discriminative fear learning while the previously cited studies considered other outcome measures as well (e.g. fear potentiated startle in Lissek et al., 2008; subjective ratings of valence and arousal in Wessa and Flor, 2007), thus somewhat limiting the generalizability of our findings (for further details regarding methodological differences and similarities we refer to the review by Nees et al., 2015). We recommend that future studies continue exploring the midcingulo-insular network and its relationship to fear and anxiety disorders.

One limitation of the present study is the use of social stimuli as CSs. While the general CS+> CS- BOLD contrast analyzed in the present study largely demonstrated a pattern of activation typical to fear conditioning (Fullana et al., 2016; see Appendix 6), the potential influence of social threat processing cannot be entirely ruled out. Therefore, we recommend reproducing our results using non-social stimuli as CSs. Another limitation is the use of a twin sample, which may affect both the generalizability and validity of our findings. Regarding generalizability, small to moderate differences between twins and the normal population have been reported for some measures of fear and anxiety (Munn-Chernoff et al., 2013; Kendler et al., 1995) but not others (Pulkkinen et al., 2003). To what extent twins diverge from the normal population with regards, specifically, to neural and physiological responding during fear conditioning is largely unknown, and, while we know of no reason to assume a significant difference between the two populations, ideally our results should be extended to a random sample from the normal population. Furthermore, regarding the validity of our findings, there is evidence demonstrating moderate heritability of SCR (Hettema et al., 2003) and BOLD-responses (Kastrati et al., 2022) during fear conditioning, meaning that twin pairs may be more similar to each other than expected by chance and therefore making data points less independent than normally assumed. To what extent this affects the reported associations to neural activity is difficult to determine. In order to test for any biases, we repeated our analysis using only single-twins, consequently cutting our sample size in half (see Appendix 7). All implicated regions evidenced correlation coefficient strengths similar, or only slightly lower, than those resulting from use of the full sample. This indicated that our results from the full sample were also applicable to unrelated individuals (see Appendix 7). In summary, while the use of a twin sample is a limitation of the present study, we contend that there is no reason to conclude that it invalidates the reported results. Finally, it should be noted that only a few studies to date have examined the longitudinal (test-retest) reliability of individual differences in discriminatory fear learning measured by SCR (Cooper et al., 2022, Preprint; Klingelhöfer-Jens et al., 2022, Preprint). While one study found evidence of fair within-person stability in 51 participants across a 9-day period (Cooper et al., 2022, Preprint), another study found evidence of poor individual-level reliability in 120 participants across a 6-month period (Klingelhöfer-Jens et al., 2022, Preprint). Similarly, previous studies examining the individual-level reliability of fMRI BOLD responding during fear conditioning have reported low to moderate reliability (Ridderbusch et al., 2021; Klingelhöfer-Jens et al., 2022, Preprint). Taken together, this means that it is currently unclear to what extent our results reflect stable individual traits, as opposed to participants’ particular states at the time of measurement, which limits the interpretation of our findings. In line with Klingelhöfer-Jens et al., 2022 (Preprint) we encourage future research on individual differences in fear conditioning to explore new ways of improving the reliability of measurement.

In summary, the present study is the largest study to date on the neural correlates of autonomic fear conditioning and identified several novel areas whose activations predict individual differences in conditioned SCR. Previous findings from smaller studies could also be confirmed. Our results are consistent with the activation of a large-scale midcingulo-insular network substantiating autonomic-interoceptive processing and salience detection. We propose that altered activity within this network underlies individual differences in conditioned SCR and possibly autonomic regulation in pathological fear and anxiety. Future research should continue investigating this network as well as its possible relationship to fear and anxiety. Ultimately, such efforts may uncover the mechanisms of fear-related psychopathology and aid in its treatment.

The present study reports data from human participants that did not explicitly consent to their raw neuroimaging and physiological data being made public. Therefore, raw neuroimaging and physiological data from the present study cannot currently be made publicly available. Requests for the anonymized raw neuroimaging and physiological data should be made to Fredrik Åhs (fredrik.ahs@miun.se) and will be reviewed by an independent data access committee, taking into account the research proposal and the intended use of the data. Requestors are required to sign a data transfer agreement to ensure participants' confidentiality is maintained prior to release of any data and that procedures conform with the EU legislation on the general data protection regulation and local ethical regulations. While access to raw source data is thus limited, processed data and standardized statistical images sufficient to reproduce the reported results and figures are publicly and freely available at https://osf.io/7dz9p/. Specifically, we provide statistical brain images in NIfTI file format used to render Figure 1a, Figure 2a, Table 2, Appendix 3—figure 1, Appendix 4—figure 1 and Appendix 6—figure 1 of the present study. We also provide brief explanations of the software used to produce all source data files, along with the SPM job files used for neuroimaging analyses. In the event that ethical approval to publicly share the raw neuroimaging data of the present study is obtained at a later stage; this data will also be made publicly available on the OSF site. In the present journal we have included Figure 1—source data 1, which provides source data for Figure 1b, Figure 2b and statistical analyses reported in section 2.2.3 as well as for Appendix 1—figure 1, Appendix 2—figure 1 and Appendix 5—table 1.

1. 1. Rosén J
   2. Åhs F
   3. Vinberg K
   4. Kastrati G

   (2022) Open Science Framework

   Genetic influence on proximal, social and conditioned threat responses.

   https://doi.org/10.17605/OSF.IO/7DZ9P

1. 1. Ahs F
   2. Frans O
   3. Tibblin B
   4. Kumlien E
   5. Fredrikson M

   (2010) The effects of medial temporal lobe resections on verbal threat and fear conditioning

   Biological Psychology 83:41–46.

   https://doi.org/10.1016/j.biopsycho.2009.10.002

   • PubMed
   • Google Scholar
2. 1. Åhs F
   2. Kragel PA
   3. Zielinski DJ
   4. Brady R
   5. LaBar KS

   (2015) Medial prefrontal pathways for the contextual regulation of extinguished fear in humans

   NeuroImage 122:262–271.

   https://doi.org/10.1016/j.neuroimage.2015.07.051

   • PubMed
   • Google Scholar
3. 1. Bach DR
   2. Daunizeau J
   3. Friston KJ
   4. Dolan RJ

   (2010) Dynamic causal modelling of anticipatory skin conductance responses

   Biological Psychology 85:163–170.

   https://doi.org/10.1016/j.biopsycho.2010.06.007

   • PubMed
   • Google Scholar
4. 1. Bach DR
   2. Weiskopf N
   3. Dolan RJ

   (2011) A stable sparse fear memory trace in human amygdala

   The Journal of Neuroscience 31:9383–9389.

   https://doi.org/10.1523/JNEUROSCI.1524-11.2011

   • PubMed
   • Google Scholar
5. 1. Bach DR
   2. Friston KJ

   (2013) Causal models in psychophysiology

   Psychophysiol 50:15–22.

   https://doi.org/10.1111/j.1469-8986.2012.01483.x

   • PubMed
   • Google Scholar
6. 1. Bach DR

   (2014) A head-to-head comparison of scralyze and ledalab, two model-based methods for skin conductance analysis

   Biological Psychology 103:63–68.

   https://doi.org/10.1016/j.biopsycho.2014.08.006

   • PubMed
   • Google Scholar
7. 1. Bechara A
   2. Tranel D
   3. Damasio H
   4. Adolphs R
   5. Rockland C
   6. Damasio AR

   (1995) Double dissociation of conditioning and declarative knowledge relative to the amygdala and hippocampus in humans

   Science 269:1115–1118.

   https://doi.org/10.1126/science.7652558

   • PubMed
   • Google Scholar
8. 1. Benedek M
   2. Kaernbach C

   (2010) A continuous measure of phasic electrodermal activity

   Journal of Neuroscience Methods 190:80–91.

   https://doi.org/10.1016/j.jneumeth.2010.04.028

   • PubMed
   • Google Scholar
9. 1. BenShakhar G

   (1985) Standardization within individuals: A simple method to neutralize individual differences in skin conductance

   Psychophysiology 22:292–299.

   https://doi.org/10.1111/j.1469-8986.1985.tb01603.x

   • PubMed
   • Google Scholar
10. Book

    1. Boucsein W

    (2012) Electrodermal Activity

    springer.

    https://doi.org/10.1007/978-1-4614-1126-0

    • Google Scholar
11. 1. Bouton ME
    2. Mineka S
    3. Barlow DH

    (2001) A modern learning theory perspective on the etiology of panic disorder

    Psychological Review 108:4–32.

    https://doi.org/10.1037/0033-295x.108.1.4

    • PubMed
    • Google Scholar
12. 1. Bzdok D
    2. Langner R
    3. Schilbach L
    4. Jakobs O
    5. Roski C
    6. Caspers S
    7. Laird AR
    8. Fox PT
    9. Zilles K
    10. Eickhoff SB

    (2013) Characterization of the temporo-parietal junction by combining data-driven parcellation, complementary connectivity analyses, and functional decoding

    NeuroImage 81:381–392.

    https://doi.org/10.1016/j.neuroimage.2013.05.046

    • Google Scholar
13. 1. Cameron OG

    (2009) Visceral brain-body information transfer

    NeuroImage 47:787–794.

    https://doi.org/10.1016/j.neuroimage.2009.05.010

    • PubMed
    • Google Scholar
14. 1. Cheng DT
    2. Knight DC
    3. Smith CN
    4. Stein EA
    5. Helmstetter FJ

    (2003) Functional MRI of human amygdala activity during Pavlovian fear conditioning: stimulus processing versus response expression

    Behavioral Neuroscience 117:3–10.

    https://doi.org/10.1037/0735-7044.117.1.3

    • Google Scholar
15. 1. Cheng DT
    2. Knight DC
    3. Smith CN
    4. Helmstetter FJ

    (2006) Human amygdala activity during the expression of fear responses

    Behavioral Neuroscience 120:1187–1195.

    https://doi.org/10.1037/0735-7044.120.5.1187

    • PubMed
    • Google Scholar
16. Book

    1. Cohen J

    (1988)

    Statistical Power Analysis for the Behavioral Sciences

    Routledge.

    • Google Scholar
17. 1. Cohen J

    (1992) A power primer

    Psychological Bulletin 112:155–159.

    https://doi.org/10.1037//0033-2909.112.1.155

    • PubMed
    • Google Scholar
18. 1. Cooper SE
    2. Dunsmoor JE

    (2021) Fear conditioning and extinction in obsessive-compulsive disorder: a systematic review

    Neuroscience and Biobehavioral Reviews 129:75–94.

    https://doi.org/10.1016/j.neubiorev.2021.07.026

    • PubMed
    • Google Scholar
19. Preprint

    1. Cooper SE
    2. Dunsmoor JE
    3. Koval K
    4. Pino E
    5. Steinman S

    (2022) Test-Retest Reliability of Human Threat Conditioning and Generalization

    PsyArXiv.

    https://doi.org/10.31234/osf.io/84uqz

    • Google Scholar
20. 1. Corbetta M
    2. Patel G
    3. Shulman GL

    (2008) The reorienting system of the human brain: from environment to theory of mind

    Neuron 58:306–324.

    https://doi.org/10.1016/j.neuron.2008.04.017

    • PubMed
    • Google Scholar
21. 1. Craig ADB

    (2009) How do you feel -- now? the anterior insula and human awareness

    Nature Reviews. Neuroscience 10:59–70.

    https://doi.org/10.1038/nrn2555

    • PubMed
    • Google Scholar
22. 1. Craske MG
    2. Treanor M
    3. Conway CC
    4. Zbozinek T
    5. Vervliet B

    (2014) Maximizing exposure therapy: an inhibitory learning approach

    Behaviour Research and Therapy 58:10–23.

    https://doi.org/10.1016/j.brat.2014.04.006

    • PubMed
    • Google Scholar
23. 1. Critchley HD
    2. Harrison NA

    (2013) Visceral influences on brain and behavior

    Neuron 77:624–638.

    https://doi.org/10.1016/j.neuron.2013.02.008

    • PubMed
    • Google Scholar
24. Book

    1. Davis M

    (2000)

    The role of the amygdala in conditioned and unconditioned fear and anxiety

    In: Aggleton JP, editors. The Amygdala. Oxford University Press. pp. 213–288.

    • Google Scholar
25. 1. Davis M
    2. Whalen PJ

    (2001) The amygdala: vigilance and emotion

    Molecular Psychiatry 6:13–34.

    https://doi.org/10.1038/sj.mp.4000812

    • PubMed
    • Google Scholar
26. Book

    1. Dawson ME
    2. Schell AM
    3. Filion DL

    (2007)

    The electrodermal system

    In: Cacioppo JT, Tassinary LG, Berntson GG, editors. Handbook of Psychophysiology. Cambridge University Press. pp. 159–181.

    • Google Scholar
27. 1. Duits P
    2. Cath DC
    3. Lissek S
    4. Hox JJ
    5. Hamm AO
    6. Engelhard IM
    7. van den Hout MA
    8. Baas JMP

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

    Depression and Anxiety 32:239–253.

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

    • PubMed
    • Google Scholar
28. 1. Dunsmoor JE
    2. Prince SE
    3. Murty VP
    4. Kragel PA
    5. LaBar KS

    (2011) Neurobehavioral mechanisms of human fear generalization

    NeuroImage 55:1878–1888.

    https://doi.org/10.1016/j.neuroimage.2011.01.041

    • PubMed
    • Google Scholar
29. 1. Esteban O
    2. Birman D
    3. Schaer M
    4. Koyejo OO
    5. Poldrack RA
    6. Gorgolewski KJ

    (2017) MRIQC: advancing the automatic prediction of image quality in MRI from unseen sites

    PLOS ONE 12:e0184661.

    https://doi.org/10.1371/journal.pone.0184661

    • PubMed
    • Google Scholar
30. 1. Etkin A
    2. Wager TD

    (2007) Functional neuroimaging of anxiety: a meta-analysis of emotional processing in PTSD, social anxiety disorder, and specific phobia

    The American Journal of Psychiatry 164:1476–1488.

    https://doi.org/10.1176/appi.ajp.2007.07030504

    • PubMed
    • Google Scholar
31. Book

    1. Fox J

    (2015)

    Applied Regression Analysis and Generalized Linear Models

    SAGE Publications, Inc.

    • Google Scholar
32. 1. Fullana MA
    2. Harrison BJ
    3. Soriano-Mas C
    4. Vervliet B
    5. Cardoner N
    6. Àvila-Parcet A
    7. Radua J

    (2016) Neural signatures of human fear conditioning: an updated and extended meta-analysis of fmri studies

    Molecular Psychiatry 21:500–508.

    https://doi.org/10.1038/mp.2015.88

    • PubMed
    • Google Scholar
33. 1. Grillon C

    (2002) Startle reactivity and anxiety disorders: aversive conditioning, context, and neurobiology

    Biological Psychiatry 52:958–975.

    https://doi.org/10.1016/s0006-3223(02)01665-7

    • PubMed
    • Google Scholar
34. 1. Hettema JM
    2. Annas P
    3. Neale MC
    4. Kendler KS
    5. Fredrikson M

    (2003) A twin study of the genetics of fear conditioning

    Archives of General Psychiatry 60:702.

    https://doi.org/10.1001/archpsyc.60.7.702

    • Google Scholar
35. 1. Insel TR

    (2014) The NIMH research domain criteria (rdoc) project: precision medicine for psychiatry

    The American Journal of Psychiatry 171:395–397.

    https://doi.org/10.1176/appi.ajp.2014.14020138

    • PubMed
    • Google Scholar
36. 1. Kastrati G
    2. Rosén J
    3. Fredrikson M
    4. Chen X
    5. Kuja-Halkola R
    6. Larsson H
    7. Jensen KB
    8. Åhs F

    (2022) Genetic influences on central and peripheral nervous system activity during fear conditioning

    Translational Psychiatry 12:01861.

    https://doi.org/10.1038/s41398-022-01861-w

    • Google Scholar
37. 1. Kendler KS
    2. Martin NG
    3. Heath AC
    4. Eaves LJ

    (1995) Self-report psychiatric symptoms in twins and their nontwin relatives: are twins different?

    American Journal of Medical Genetics 60:588–591.

    https://doi.org/10.1002/ajmg.1320600622

    • PubMed
    • Google Scholar
38. Preprint

    1. Klingelhöfer-Jens M
    2. Ehlers MR
    3. Kuhn M
    4. Keyaniyan V
    5. Lonsdorf TB

    (2022) Robust Group- but Limited Individual-Level (Longitudinal) Reliability and Insights into Cross-Phases Response Prediction of Conditioned Fear

    bioRxiv.

    https://doi.org/10.1101/2022.03.15.484434

    • Google Scholar
39. 1. Knight DC
    2. Nguyen HT
    3. Bandettini PA

    (2005) The role of the human amygdala in the production of conditioned fear responses

    NeuroImage 26:1193–1200.

    https://doi.org/10.1016/j.neuroimage.2005.03.020

    • PubMed
    • Google Scholar
40. 1. Kuhn M
    2. Gerlicher AMV
    3. Lonsdorf TB

    (2022) Navigating the manyverse of skin conductance response quantification approaches-a direct comparison of trough-to-peak, baseline correction, and model-based approaches in ledalab and pspm

    Psychophysiology 59:e14058.

    https://doi.org/10.1111/psyp.14058

    • PubMed
    • Google Scholar
41. 1. LaBar KS
    2. LeDoux JE
    3. Spencer DD
    4. Phelps EA

    (1995) Impaired fear conditioning following unilateral temporal lobectomy in humans

    The Journal of Neuroscience 15:6846–6855.

    https://doi.org/10.1523/JNEUROSCI.15-10-06846.1995

    • PubMed
    • Google Scholar
42. 1. LaBar KS
    2. Gatenby JC
    3. Gore JC
    4. LeDoux JE
    5. Phelps EA

    (1998) Human amygdala activation during conditioned fear acquisition and extinction: a mixed-trial fMRI study

    Neuron 20:937–945.

    https://doi.org/10.1016/s0896-6273(00)80475-4

    • PubMed
    • Google Scholar
43. 1. LeDoux JE

    (2000) Emotion circuits in the brain

    Annual Review of Neuroscience 23:155–184.

    https://doi.org/10.1146/annurev.neuro.23.1.155

    • PubMed
    • Google Scholar
44. 1. LeDoux J

    (2012) Rethinking the emotional brain

    Neuron 73:653–676.

    https://doi.org/10.1016/j.neuron.2012.02.004

    • PubMed
    • Google Scholar
45. Software

    1. Lee IA
    2. Preacher KJ

    (2013) Calculation for the test of the difference between two dependent correlations with one variable in common

    Computer Software.

    http://quantpsy.org
46. 1. Linnman C
    2. Moulton EA
    3. Barmettler G
    4. Becerra L
    5. Borsook D

    (2012) Neuroimaging of the periaqueductal gray: state of the field

    NeuroImage 60:505–522.

    https://doi.org/10.1016/j.neuroimage.2011.11.095

    • PubMed
    • Google Scholar
47. 1. Lissek S
    2. Levenson J
    3. Biggs AL
    4. Johnson LL
    5. Ameli R
    6. Pine DS
    7. Grillon C

    (2008) Elevated fear conditioning to socially relevant unconditioned stimuli in social anxiety disorder

    The American Journal of Psychiatry 165:124–132.

    https://doi.org/10.1176/appi.ajp.2007.06091513

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

    (2017) Do ’’t fear “ fear conditioning”: methodological considerations for the design and analysis of studies on human fear acquisition, extinction, and return of fear

    Neuroscience and Biobehavioral Reviews 77:247–285.

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

    • PubMed
    • Google Scholar
49. 1. Lonsdorf TB
    2. Merz CJ

    (2017) More than just noise: inter-individual differences in fear acquisition, extinction and return of fear in humans-biological, experiential, temperamental factors, and methodological pitfalls

    Neuroscience and Biobehavioral Reviews 80:703–728.

    https://doi.org/10.1016/j.neubiorev.2017.07.007

    • PubMed
    • Google Scholar
50. 1. MacNamara A
    2. Rabinak CA
    3. Fitzgerald DA
    4. Zhou XJ
    5. Shankman SA
    6. Milad MR
    7. Phan KL

    (2015) Neural correlates of individual differences in fear learning

    Behavioural Brain Research 287:34–41.

    https://doi.org/10.1016/j.bbr.2015.03.035

    • PubMed
    • Google Scholar
51. 1. Marin MF
    2. Barbey F
    3. Rosenbaum BL
    4. Hammoud MZ
    5. Orr SP
    6. Milad MR

    (2020) Absence of conditioned responding in humans: a bad measure or individual differences?

    Psychophysiology 57:e13350.

    https://doi.org/10.1111/psyp.13350

    • PubMed
    • Google Scholar
52. 1. Medford N
    2. Critchley HD

    (2010) Conjoint activity of anterior insular and anterior cingulate cortex: awareness and response

    Brain Structure & Function 214:535–549.

    https://doi.org/10.1007/s00429-010-0265-x

    • PubMed
    • Google Scholar
53. 1. Menon V
    2. Uddin LQ

    (2010) Saliency, switching, attention and control: a network model of insula function

    Brain Structure & Function 214:655–667.

    https://doi.org/10.1007/s00429-010-0262-0

    • PubMed
    • Google Scholar
54. 1. Menon V

    (2011) Large-scale brain networks and psychopathology: a unifying triple network model

    Trends in Cognitive Sciences 15:483–506.

    https://doi.org/10.1016/j.tics.2011.08.003

    • PubMed
    • Google Scholar
55. Book

    1. Menon V

    (2015)

    Salience network

    In: Toga AW, editors. Brain Mapping: An Encyclopedic Reference. Academic Press. pp. 597–611.

    • Google Scholar
56. 1. Mineka S
    2. Oehlberg K

    (2008) The relevance of recent developments in classical conditioning to understanding the etiology and maintenance of anxiety disorders

    Acta Psychologica 127:567–580.

    https://doi.org/10.1016/j.actpsy.2007.11.007

    • PubMed
    • Google Scholar
57. 1. Munn-Chernoff MA
    2. von Ranson KM
    3. Culbert KM
    4. Larson CL
    5. Burt SA
    6. Klump KL

    (2013) An examination of the representativeness assumption for twin studies of eating pathology and internalizing symptoms

    Behavior Genetics 43:427–435.

    https://doi.org/10.1007/s10519-013-9603-0

    • PubMed
    • Google Scholar
58. Book

    1. Myers L
    2. Sirois MJ

    (2006) Spearman correlation coefficients, differences between

    In: Kotz S, Balakrishnan N, Read CB, Vidakovic B, editors. Encyclopedia of Statistical Sciences. John Wiley and Sons. pp. 7901–7903.

    https://doi.org/10.1002/0471667196.ess5050.pub2

    • Google Scholar
59. 1. Nees F
    2. Heinrich A
    3. Flor H

    (2015) A mechanism-oriented approach to psychopathology: the role of Pavlovian conditioning

    International Journal of Psychophysiology 98:351–364.

    https://doi.org/10.1016/j.ijpsycho.2015.05.005

    • PubMed
    • Google Scholar
60. 1. Ojala KE
    2. Bach DR

    (2020) Measuring learning in human classical threat conditioning: translational, cognitive and methodological considerations

    Neuroscience and Biobehavioral Reviews 114:96–112.

    https://doi.org/10.1016/j.neubiorev.2020.04.019

    • PubMed
    • Google Scholar
61. 1. Paulus MP
    2. Stein MB

    (2006) An insular view of anxiety

    Biological Psychiatry 60:383–387.

    https://doi.org/10.1016/j.biopsych.2006.03.042

    • PubMed
    • Google Scholar
62. Software

    1. Penny W

    (2004) Message posted at

    Jiscmail.

    https://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2=spm;1423d6ac.04
63. 1. Peterson A
    2. Thome J
    3. Frewen P
    4. Lanius RA

    (2014) Resting-State neuroimaging studies: a new way of identifying differences and similarities among the anxiety disorders?

    Canadian Journal of Psychiatry. Revue Canadienne de Psychiatrie 59:294–300.

    https://doi.org/10.1177/070674371405900602

    • PubMed
    • Google Scholar
64. 1. Petrovic P
    2. Kalisch R
    3. Pessiglione M
    4. Singer T
    5. Dolan RJ

    (2008) Learning affective values for faces is expressed in amygdala and fusiform gyrus

    Social Cognitive and Affective Neuroscience 3:109–118.

    https://doi.org/10.1093/scan/nsn002

    • PubMed
    • Google Scholar
65. 1. Peyron R
    2. Laurent B
    3. García-Larrea L

    (2000) Functional imaging of brain responses to pain. A review and meta-analysis (2000)

    Neurophysiologie Clinique = Clinical Neurophysiology 30:263–288.

    https://doi.org/10.1016/s0987-7053(00)00227-6

    • PubMed
    • Google Scholar
66. 1. Phelps EA
    2. Delgado MR
    3. Nearing KI
    4. LeDoux JE

    (2004) Extinction learning in humans: role of the amygdala and vmpfc

    Neuron 43:897–905.

    https://doi.org/10.1016/j.neuron.2004.08.042

    • PubMed
    • Google Scholar
67. 1. Poldrack RA
    2. Baker CI
    3. Durnez J
    4. Gorgolewski KJ
    5. Matthews PM
    6. Munafò MR
    7. Nichols TE
    8. Poline JB
    9. Vul E
    10. Yarkoni T

    (2017) Scanning the horizon: towards transparent and reproducible neuroimaging research

    Nature Reviews. Neuroscience 18:115–126.

    https://doi.org/10.1038/nrn.2016.167

    • PubMed
    • Google Scholar
68. 1. Pulkkinen L
    2. Vaalamo I
    3. Hietala R
    4. Kaprio J
    5. Rose RJ

    (2003) Peer reports of adaptive behavior in twins and singletons: is twinship a risk or an advantage?

    Twin Research 6:106–118.

    https://doi.org/10.1375/136905203321536236

    • PubMed
    • Google Scholar
69. 1. Razali NM
    2. Wah YB

    (2011)

    Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests

    Journal of Statistical Modeling and Analytics 2:21–33.

    • Google Scholar
70. 1. Reijmers LG
    2. Perkins BL
    3. Matsuo N
    4. Mayford M

    (2007) Localization of a stable neural correlate of associative memory

    Science 317:1230–1233.

    https://doi.org/10.1126/science.1143839

    • PubMed
    • Google Scholar
71. 1. Ridderbusch IC
    2. Wroblewski A
    3. Yang Y
    4. Richter J
    5. Hollandt M
    6. Hamm AO
    7. Wittchen HU
    8. Ströhle A
    9. Arolt V
    10. Margraf J
    11. Lueken U
    12. Herrmann MJ
    13. Kircher T
    14. Straube B

    (2021) Neural adaptation of cingulate and insular activity during delayed fear extinction: A replicable pattern across assessment sites and repeated measurements

    NeuroImage 237:118157.

    https://doi.org/10.1016/j.neuroimage.2021.118157

    • Google Scholar
72. Software

    1. Ridgeway GR

    (2012) Message posted at

    SPM.

    https://www.researchgate.net/post/How-to-perform-a-region-of-interest-ROI-analysis-with-statistical-parametric-mapping-SPM
73. 1. Rosén J
    2. Kastrati G
    3. Åhs F

    (2017) Social, proximal and conditioned threat

    Neurobiology of Learning and Memory 142:236–243.

    https://doi.org/10.1016/j.nlm.2017.05.014

    • PubMed
    • Google Scholar
74. 1. Rosén J
    2. Kastrati G
    3. Reppling A
    4. Bergkvist K
    5. Åhs F

    (2019) The effect of immersive virtual reality on proximal and conditioned threat

    Scientific Reports 9:17407.

    https://doi.org/10.1038/s41598-019-53971-z

    • Google Scholar
75. 1. Savage HS
    2. Davey CG
    3. Wager TD
    4. Garfinkel SN
    5. Moffat BA
    6. Glarin RK
    7. Harrison BJ

    (2021) Neural mediators of subjective and autonomic responding during threat learning and regulation

    NeuroImage 245:118643.

    https://doi.org/10.1016/j.neuroimage.2021.118643

    • PubMed
    • Google Scholar
76. 1. Schweckendiek J
    2. Klucken T
    3. Merz CJ
    4. Tabbert K
    5. Walter B
    6. Ambach W
    7. Vaitl D
    8. Stark R

    (2011) Weaving the (neuronal) web: fear learning in spider phobia

    NeuroImage 54:681–688.

    https://doi.org/10.1016/j.neuroimage.2010.07.049

    • PubMed
    • Google Scholar
77. 1. Seeley WW
    2. Menon V
    3. Schatzberg AF
    4. Keller J
    5. Glover GH
    6. Kenna H
    7. Reiss AL
    8. Greicius MD

    (2007) Dissociable intrinsic connectivity networks for salience processing and executive control

    The Journal of Neuroscience 27:2349–2356.

    https://doi.org/10.1523/JNEUROSCI.5587-06.2007

    • PubMed
    • Google Scholar
78. 1. Shapiro SS
    2. Wilk MB

    (1965) An analysis of variance test for normality (complete samples)

    Biometrika 52:591–611.

    https://doi.org/10.1093/biomet/52.3-4.591

    • Google Scholar
79. 1. Sjouwerman R
    2. Scharfenort R
    3. Lonsdorf TB

    (2020) Individual differences in fear acquisition: multivariate analyses of different emotional negativity scales, physiological responding, subjective measures, and neural activation

    Scientific Reports 10:15283.

    https://doi.org/10.1038/s41598-020-72007-5

    • PubMed
    • Google Scholar
80. 1. Staib M
    2. Castegnetti G
    3. Bach DR

    (2015) Optimising a model-based approach to inferring fear learning from skin conductance responses

    Journal of Neuroscience Methods 255:131–138.

    https://doi.org/10.1016/j.jneumeth.2015.08.009

    • PubMed
    • Google Scholar
81. 1. Steiger JH

    (1980) Tests for comparing elements of a correlation matrix

    Psychological Bulletin 87:245–251.

    https://doi.org/10.1037/0033-2909.87.2.245

    • Google Scholar
82. 1. Stein MB
    2. Simmons AN
    3. Feinstein JS
    4. Paulus MP

    (2007) Increased amygdala and insula activation during emotion processing in anxiety-prone subjects

    The American Journal of Psychiatry 164:318–327.

    https://doi.org/10.1176/ajp.2007.164.2.318

    • PubMed
    • Google Scholar
83. 1. Tuominen L
    2. Romaniuk L
    3. Milad MR
    4. Goff DC
    5. Hall J
    6. Holt DJ

    (2022) Impairment in acquisition of conditioned fear in schizophrenia

    Neuropsychopharmacology 47:681–686.

    https://doi.org/10.1038/s41386-021-01193-1

    • PubMed
    • Google Scholar
84. 1. Tzourio-Mazoyer N
    2. Landeau B
    3. Papathanassiou D
    4. Crivello F
    5. Etard O
    6. Delcroix N
    7. Mazoyer B
    8. Joliot M

    (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain

    NeuroImage 15:273–289.

    https://doi.org/10.1006/nimg.2001.0978

    • PubMed
    • Google Scholar
85. 1. Uddin LQ

    (2015) Salience processing and insular cortical function and dysfunction

    Nature Reviews Neuroscience 16:55–61.

    https://doi.org/10.1038/nrn3857

    • PubMed
    • Google Scholar
86. 1. Uddin LQ
    2. Yeo BTT
    3. Spreng RN

    (2019) Towards A universal taxonomy of macro - scale functional human brain networks how many functional brain networks are A fundamental construct in neuroscience is the definition

    Brain Topography 32:926–942.

    https://doi.org/10.1007/s10548-019-00744-6

    • Google Scholar
87. 1. Visser RM
    2. Bathelt J
    3. Scholte HS
    4. Kindt M

    (2021) Robust BOLD responses to faces but not to conditioned threat: challenging the amygdala’s reputation in human fear and extinction learning

    The Journal of Neuroscience 41:10278–10292.

    https://doi.org/10.1523/JNEUROSCI.0857-21.2021

    • PubMed
    • Google Scholar
88. 1. Vogt BA

    (2016) Midcingulate cortex: structure, connections, homologies, functions and diseases

    Journal of Chemical Neuroanatomy 74:28–46.

    https://doi.org/10.1016/j.jchemneu.2016.01.010

    • PubMed
    • Google Scholar
89. 1. Wallin BG

    (1981) Sympathetic nerve activity underlying electrodermal and cardiovascular reactions in man

    Psychophysiology 18:470–476.

    https://doi.org/10.1111/j.1469-8986.1981.tb02483.x

    • PubMed
    • Google Scholar
90. 1. Wen Z
    2. Raio CM
    3. Pace-Schott EF
    4. Lazar SW
    5. LeDoux JE
    6. Phelps EA
    7. Milad MR

    (2022) Temporally and anatomically specific contributions of the human amygdala to threat and safety learning

    PNAS 119:e2204066119.

    https://doi.org/10.1073/pnas.2204066119

    • PubMed
    • Google Scholar
91. 1. Wessa M
    2. Flor H

    (2007) Failure of extinction of fear responses in posttraumatic stress disorder: evidence from second-order conditioning

    The American Journal of Psychiatry 164:1684–1692.

    https://doi.org/10.1176/appi.ajp.2007.07030525

    • PubMed
    • Google Scholar
92. 1. Yarkoni T

    (2009) Big correlations in little studies: inflated fMRI correlations reflect low statistical power-commentary on vul et al. (2009)

    Perspectives on Psychological Science 4:294–298.

    https://doi.org/10.1111/j.1745-6924.2009.01127.x

    • PubMed
    • Google Scholar
93. Book

    1. Yarkoni T
    2. Braver TS

    (2010) Cognitive neuroscience approaches to individual differences in working memory and executive control: conceptual and methodological issues

    In: Gruszka A, Matthews G, Szymura B, editors. Handbook of Individual Differences in Cognition. Springer. pp. 87–107.

    https://doi.org/10.1007/978-1-4419-1210-7_6

    • Google Scholar
94. 1. Yeo BTT
    2. Krienen FM
    3. Sepulcre J
    4. Sabuncu MR
    5. Lashkari D
    6. Hollinshead M
    7. Roffman JL
    8. Smoller JW
    9. Zöllei L
    10. Polimeni JR
    11. Fischl B
    12. Liu H
    13. Buckner RL

    (2011) The organization of the human cerebral cortex estimated by intrinsic functional connectivity

    Journal of Neurophysiology 106:1125–1165.

    https://doi.org/10.1152/jn.00338.2011

    • PubMed
    • Google Scholar

Author details

1. Kevin Vinberg

   Department of Psychology and Social Work, Mid Sweden University, Östersund, Sweden

   Contribution

   Conceptualization, Formal analysis, Visualization, Methodology, Writing - original draft, Writing - review and editing

   For correspondence

   kevinvinberg@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4848-3174

2. Jörgen Rosén

   1. Department of Psychology and Social Work, Mid Sweden University, Östersund, Sweden
   2. Department of Psychology, Uppsala University, Uppsala, Sweden

   Contribution

   Data curation, Software, Formal analysis, Supervision, Investigation, Visualization, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3688-3859

3. Granit Kastrati

   1. Department of Psychology and Social Work, Mid Sweden University, Östersund, Sweden
   2. Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden

   Contribution

   Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9092-4335

4. Fredrik Ahs

   Department of Psychology and Social Work, Mid Sweden University, Östersund, Sweden

   Contribution

   Conceptualization, Supervision, Funding acquisition, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6355-660X

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