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. Author manuscript; available in PMC: 2016 Aug 1.
Published in final edited form as: Curr Opin Psychol. 2015 Aug;4:6–12. doi: 10.1016/j.copsyc.2015.01.004

Integrating NIMH Research Domain Criteria (RDoC) into Depression Research

Mary L Woody 1, Brandon E Gibb 1
PMCID: PMC4306327  NIHMSID: NIHMS655064  PMID: 25642446

Abstract

The NIMH Research Domain Criteria (RDoC) initiative grew out of the agency’s goal to develop “new ways of classifying mental disorders based on behavioral dimensions and neurobiological measures” (NIMH, 2008). In this article, we review how depression research can be meaningfully conducted within an RDoC framework, with a particular focus on the negative valence systems construct of Loss. New efforts to understand depression within the context of RDoC must seek an integrative understanding of the disorder across multiple units of analysis from genes to neural circuits to behavior. In addition, the constructs or processes must be understood within the context of specific environmental and developmental influences. Key concepts are discussed and we end by highlighting research on rumination as a prime example of research that is consistent with RDoC.

Strategy 1.4 of the 2008 National Institute of Mental Health (NIMH) Strategic Plan was to “Develop, for research purposes, new ways of classifying mental disorders based on behavioral dimensions and neurobiological measures” (NIMH, 2008). The Research Domain Criteria (RDoC) initiative grew directly out of this aim, with the explicit goal of linking classification of psychopathology to recent advances in genetics and neuroimaging, which often suggest core features or influences that occur across traditional diagnostic boundaries (Cuthbert & Insel, 2013). Depression, as currently defined, spans two of the RDoC domains: the Loss construct within the Negative Valence Systems domain and various Reward constructs within the Positive Valence Systems domain. This article focuses specifically on the Loss construct (for a RDoC-oriented discussion of reward processing and depression, see Dillon et al., 2014).

Constructs within RDoC are defined across multiple units of analysis, with developmental and environmental/contextual influences seen as additional dimensions within a broader four-dimensional matrix (Casey, Oliveri, & Insel, 2014; Cuthbert, 2014). This four-dimensional model is presented in Figure 1. The key departure of this figure from the traditional two-dimensional RDoC matrix (http://www.nimh.nih.gov/research-priorities/rdoc/research-domain-criteria-matrix.shtml) is that it explicitly highlights how the traditional axes of domains/constructs and units of analysis must be understood within the context of specific environmental and contextual influences. In addition, the constructs and processes featured in RDoC change over time, both in terms of the development of the individual and the development or progression of the disease. Three developmental windows are highlighted in the figure to emphasize the fact that any current presentation by an individual or disease has a developmental history and a future trajectory that must be taken into account if we are to truly understand the disorder. Therefore, within RDoC, forms of psychopathology are viewed as neurodevelopmental disorders with core disruptions in specific brain circuits that are linked to influences and disruptions across units of analysis ranging from genes and molecules to physiology, behavior, and self-report, which must be understood in terms of specific environmental and contextual influences (Cuthbert & Insel, 2013).

Figure 1.

Figure 1

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Expanded four-dimensional RDoC matrix.

The goal of this article is to discuss how to integrate RDoC into depression research, with a particular focus on the measurement of depression. Prior to RDoC, the “measurement of depression” was easy. One simply administered a standardized self-report or clinician-administered measure of depressive symptoms or the relevant section(s) of a structured clinical interview. With RDoC, however, comes an increasing focus on the heterogeneity not only of depression but also of other disorders. Therefore, the measurement of depression at the symptom level must become more focused, perhaps focusing specifically on affective, cognitive, or somatic symptoms of depression. In contrast, however, measurements within the RDoC Loss construct become much richer, and assessments across each unit of analysis become more salient. Therefore, researchers are given greater flexibility in designing their studies (and seeking NIMH funding) to focus specifically on the processes or mechanisms they want to study rather than always having to link them to DSM diagnoses (Cuthbert, 2014). In addition, there is an explicit focus on the full range of functioning from normal to abnormal (Cuthbert & Insel, 2013) reflecting clear priorities that have formed a cornerstone of developmental psychopathology research since its inception (Cicchetti, 1989; Franklin, Jamieson, Glenn, & Nock, in press).

The Negative Valence System Construct of Loss

Within the current RDoC matrix, every construct is defined across multiple units of analysis. The central feature within this organization is disruptions in a specific (set of) neural circuit(s) (cf. Cuthbert, 2014). For the Loss construct (see Figure 2), the key disruptions are within cortico-limbic circuitry (heightened limbic reactivity to affectively-salient stimuli, reduced activation in prefrontal areas, and reduced functional connectivity between these regions) as well as increased activity in the default mode network, disruptions that have been highlighted in depression research more generally (for reviews, see Disner, Beevers, Haigh, & Beck, 2011; Gibb, 2014; Hamilton et al., 2012). At the genetic level of the Loss construct, the current iteration of the RDoC matrix focuses primarily on genes known to regulate neurotransmission of monoamines including serotonin and dopamine (e.g., 5-HTTLPR, 5-HT receptor genes, MAOA, and COMT), though these have not been identified in genome-wide association studies (GWAS; e.g., Major Depressive Disorder Working Group of the Psychiatric GWAS Consortium, 2013), a point to which we return shortly. The molecular level of the Loss construct highlights the roles of glucocorticoids, sex hormones (estrogen and androgen), oxytocin, vasopressin, and cytokines. At the physiological unit of analysis are peripheral measures of autonomic nervous system (ANS), hypothalamic-pituitary-adrenal (HPA) axis, and neuroimmune dysregulation. To this list, we may add pupil dilation, which is greater in depressed adults (Siegle, Granholm, Ingram, & Matt, 2001) as well as at-risk children (Burkhouse, Siegle, & Gibb, 2014) compared to controls following exposure to affectively-salient stimuli, and which also predicts remission following cognitive therapy for depression (Siegle, Steinhauer, Friedman, Thompson, & Thase, 2011). At the behavioral unit of analysis, Loss is categorized by a heterogeneous list of features, many which are congruent with current DSM criteria for major depressive disorder (e.g., sadness, anhedonia, guilt, morbid thoughts, psychomotor retardation, deficits in executive function, and disruptions in sleep, appetite, and libido) as well as rumination and biases in attention and memory. Finally, the self-report level of analysis highlights attributional styles and hopelessness.

Figure 2.

Figure 2

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Negative Valence Systems Construct of Loss.

Loss in the Context of Environment and Development

As noted above and in descriptions of RDoC (e.g., Cuthbert, 2014), environmental influences are considered a separate dimension of the RDoC matrix. These influences are arguably more salient to researchers examining depression and the RDoC Loss construct than for any other construct within RDoC. Severe negative life events, particularly those characterized by potential or actual loss of relationships or status, are the strongest individual predictors of depression onset (Monroe, Slavich, & Giorgiades, 2014). The relation is bi-directional in that depressed and at-risk individuals also contribute to the generation of additional negative events in their lives, particularly interpersonal events (Feurer, Hammen, & Gibb, in press; Liu & Alloy, 2010). Most major theories of depression, including cognitive and genetic theories, present vulnerability-stress or diathesis-stress models of risk in which the focus is factors that influence a person’s level of depression risk following the occurrence of negative life events. Supporting these models, there is growing evidence that risk for depression following negative life events is greater among individuals exhibiting various forms of cognitive vulnerability including negative attributional/inferential styles, rumination, and biases in attention and memory (for reviews, see Gibb, 2014; Joormann, & Arditte, 2014). There is also evidence for gene × environment (G × E) models of risk for depression involving genes associated with serotonergic or HPA axis functioning (for a review, see Heim & Binder, 2012), though these studies have largely focused on single candidate genes and there is some question about the replicability of the findings (e.g., Karg, Burmeister, Shedden, & Sen, 2011; Risch et al., 2009). Indeed, researchers now recognize that psychiatric disorders such as depression, as well as intermediate phenotypes or endophenotypes associated with depression, are likely impacted by the combined influence of multiple genes operating within specific biological pathways. Given this, researchers have begun to examine aggregate levels of influence across multiple genes and there is a call to scale this up to GWAS × environment analyses (e.g., Peyrot et al., 2014; Power et al., 2013; Thomas, 2010), which may help to resolve the previous null GWAS findings. In combination, what these studies suggest is that depression-relevant influences across multiple units of analysis cannot be understood without considering the environmental context. To this, we would also add the “mood context” as we know that various forms of vulnerability to depression may remain latent until “activated” by a negative mood (cf. Disner, McGeary, Wells, Ellis, & Beevers, in press; Steidtmann, Ingram, & Siegle, 2010).

Like environmental influences, development is viewed as an additional dimension to the RDoC matrix. These considerations are also central to depression research. For example, there appear to be developmental shifts in the direction of (i) amygdala-prefrontal connectivity (Gee et al., 2013), (ii) cortisol reactivity to stress (Hankin, Badanes, Abela, & Watamura, 2010), and (iii) attentional biases and pupillary reactivity to affectively-salient stimuli (Harrison & Gibb, in press; Kellough, Beevers, Ellis, & Wells, 2008; Silk et al., 2009), as well as genetic influences on neural development (i.e., gene × development interactions; Lenroot et al., 2009; Schmitt et al., 2014). There are also developmental increases in the stability of cognitive vulnerabilities to depression (Cole et al., 2008; Hankin, 2008) as well as in the magnitude of cognitive vulnerability × stress interactions (Cole et al., 2008; Lakdawalla, Hankin, & Mermelstein, 2007). Finally, there is evidence for sensitive periods in which the effects of environmental stressors may have a stronger impact on the development and functioning of neural and physiological systems (Heim, & Binder, 2012). Therefore, researchers examining depression and the RDoC Loss construct must understand how specific processes or influences change across development, both in terms of the development of the individual and development of the disease.

The Study of Rumination as an Example of a Multiple-Levels-of-Analysis Approach

Within the Loss construct, perhaps the best example of the multiple-units-of-analysis approach to research advocated by RDoC has been for rumination. Rumination, defined as the tendency to passively contemplate the causes and consequences of one’s negative mood, is cross-sectionally correlated with levels of depressive symptoms and predicts prospective changes in depressive symptoms and onset of depressive diagnoses (Gibb, 2014; Joormann & Arditte, 2014). In line with the RDoC aim of identifying mechanisms that may cut across traditional diagnostic boundaries, we should note that rumination predicts prospective changes not only in depression, but also anxiety, alcohol abuse, disordered eating, and self-harm (Nolen-Hoeksema, Wisco, & Lyubormirsky, 2008).

Rumination has been linked to disruptions in the same neural circuits as those highlighted in the Loss construct, including disruptions in cortico-limbic circuitry (e.g., Cooney, Joormann, Eugène, Dennis, & Gotlib, 2010; Mandell, Siegle, Shutt, Feldmiller, & Thase, 2014; Ray et al., 2005; Vanderhasselt, Kuehn, & De Raedt, 2011; Vanderhasselt et al., 2013). At the physiological level, disruptions in this cortico-limbic circuit are tied to levels of heart rate variability (HRV), with higher levels reflecting better physiological capacity for flexible emotion regulation in response to stress (Thayer, Åhs, Fredrikson, Sollers, & Wager, 2012). Importantly, higher levels of rumination are associated with lower levels of HRV at rest (Woody, McGeary, & Gibb, 2014) as well as greater reductions in HRV following a laboratory-based interpersonal stressor (Woody, Burkhouse, Birk, & Gibb, in press). Similarly, both state and trait levels of rumination are positively correlated with higher levels of basal cortisol and cortisol reactivity (for a review, see Zoccola & Dickerson, 2012). Behaviorally, rumination is significantly associated with other forms of cognitive vulnerability to depression including attention and memory biases (working memory and overgeneral autobiographical memory; for a review, see Gotlib & Joormann, 2010).

There are also clear genetic, environmental, and developmental influences on rumination. Rumination is moderately heritable (h2 = .20–.41) and exhibits shared genetic variability with depression (Chen & Li, 2013; Johnson et al., 2014; Moore et al., 2013). Evidence for the impact of specific candidate genes is limited and mixed but there is at least some evidence for the role of a polymorphism in the brain-derived neurotrophic factor (BDNF) gene (e.g., Beevers, Wells, & McGeary, 2009; Hilt, Sander, Nolen-Hoeksema, & Simen, 2007; but see also Gibb, Grassia, Stone, Uhrlass, & McGeary, 2012). Rumination is thought to develop during childhood (Nolen-Hoeksema, 1991), stabilizing into a relatively trait-like influence during adolescence (Hankin, 2008). There is evidence that negative life events and negative family environment contributes to the development of rumination (Gibb et al., 2012; Hilt, Armstrong, & Essex, 2012; Michl, McLaughlin, Shepherd, & Nolen-Hoeksema, 2013), effects that may be moderated by variation in genes that influence stress sensitivity (Clasen, Wells, McGeary, Knopik, & Beevers, 2011) and HPA axis reactivity (Woody et al., in press).

These findings highlight rumination as a dynamic process that is likely a major driving force in the neurodevelopmental progression of depression. Notably, however, our focus on rumination brings up important considerations that will need to be addressed by investigators seeking to integrate RDoC into depression research. First, women are more likely to ruminate than men (Hilt & Nolen-Hoeksema, 2014; Johnson & Whisman, 2013) and are more likely to experience depression, but only once they enter adolescence (Hilt & Nolen-Hoeksema, 2014). However, the role of sex differences are not highlighted anywhere in the current RDoC matrix for any of the domains. Second, although RDoC encourages research examining the full range of functioning and processes that may cut across current diagnostic boundaries, the majority of imaging studies have focused on rumination solely within the context of a current MDD diagnosis (for exceptions, see Vanderhasselt et al., 2011, 2013). Third, future research is needed to determine whether each of the influences identified in this article is a cause, correlate, or consequence of depressive symptoms (or some mixture of these three), which is an essential step toward developing more targeted and effective intervention and prevention programs. Finally, we should note that recent efforts to integrate cognitive, genetic, and neural models of depression risk (Beck, 2008; Disner et al., 2011; Gibb, Beevers, & McGeary, 2013) are just the types of multiple-levels-of-analysis research that RDoC is trying to promote.

Conclusion

In summary, we are reminded of a quote from Stephen King’s The Dark Tower series, “There are other worlds than these” (King, 2003, p. 266). We try to keep these words in mind during our studies so that each time we find ourselves focused on a particular measure at a specific unit of analysis, we remember that this measurement is connected to, and only makes sense in relation to, constructs at other units of analysis both micro and macro and within a specific environmental and developmental context.

Highlights.

  • -

    Within RDoC, forms of psychopathology are viewed as neurodevelopmental disorders

  • -

    RDoC domains and constructs are defined across multiple units of analysis

  • -

    Development and environment are additional dimensions within a broader 4-D matrix

  • -

    Research must explicitly seek to integrate findings across multiple units of analysis

  • -

    Research on rumination is highlighted as a model program of RDoC research

Acknowledgments

This project was supported by National Institute of Mental Health grant MH098060 awarded to B. E. Gibb.

Footnotes

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References

* denotes papers of special interest

** denotes papers of outstanding interest

  1. Beck AT. The evolution of the cognitive model of depression and its neurobiological correlates. American Journal of Psychiatry. 2008;8:969–977. doi: 10.1176/appi.ajp.2008.08050721. [DOI] [PubMed] [Google Scholar]
  2. Beevers CG, Wells TT, McGeary JE. The BDNF Val66Met polymorphism is associated with rumination in healthy adults. Emotion. 2009;9:579–584. doi: 10.1037/a0016189. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Burkhouse KL, Siegle GJ, Gibb BE. Pupillary reactivity to emotional stimuli in children of depressed mothers. Journal of Child Psychology and Psychiatry. 2014;55:1009–1016. doi: 10.1111/jcpp.12225. This study examined group differences in maternal history of depression and anxiety on children’s physiological reactivity to emotional stimuli via pupillometry. Children of depressed mothers, compared to children of nondepressed mothers, exhibited greater pupil dilation to sad faces but not angry or happy faces. Similarly, children of anxious mothers, compared to children of nonanxious mothers, displayed greater pupil dilation to angry faces but not happy or sad faces. These results suggest that pupillary response to emotional faces may be an important biomarker in children’s risk for depression and anxiety.
  4. Casey BJ, Oliveri ME, Insel T. A neurodevelopmental perspective on the research domain criteria (RDoC) framework. Biological Psychiatry. 2014;76:350–353. doi: 10.1016/j.biopsych.2014.01.006. [DOI] [PubMed] [Google Scholar]
  5. Chen J, Li X. Genetic and environmental influences on adolescent rumination and its association with depressive symptoms. Journal of Abnormal Child Psychology. 2013;41:1289–1298. doi: 10.1007/s10802-013-9757-5. [DOI] [PubMed] [Google Scholar]
  6. Cicchetti D. Developmental psychopathology: Some thoughts on its evolution. Development and Psychopathology. 1989;1:1–4. [Google Scholar]
  7. Clasen PC, Wells TT, McGeary JE, Knopik V, Beevers CG. 5-HTTLPR and BDNF Val66Met polymorphisms moderate effects of stress on rumination. Genes, Brain, and Behavior. 2011;10:740–746. doi: 10.1111/j.1601-183X.2011.00715.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Cole DA, Ciesla JA, Dallaire DH, Jacquez FM, Pineda AQ, LaGrange B, Truss AE, Folmer AS, Tilghman-Osborne C, Felton JW. Emergence of attributional style and its relation to depressive symptoms. Journal of Abnormal Psychology. 2008;117:16–31. doi: 10.1037/0021-843X.117.1.16. [DOI] [PubMed] [Google Scholar]
  9. Cooney RE, Joormann J, Eugène F, Dennis EL, Gotlib IH. Neural correlates of rumination in depression. Cognitive, Affective, & Behavioral Neuroscience. 2010;10:470–478. doi: 10.3758/CABN.10.4.470. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Cuthbert BN. The RDoC framework: Facilitating transition from ICD/DSM to dimensional approaches that integrate neuroscience and psychopathology. World Psychiatry. 2014;13:28–35. doi: 10.1002/wps.20087. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Cuthbert BN, Insel TR. Toward the future of psychiatric diagnosis: The seven pillars of RDoC. BMC Medicine. 2013;11:126. doi: 10.1186/1741-7015-11-126. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Dillon DG, Rosso IM, Pechtel P, Killgore WDS, Rauch SL, Pizzagalli DA. Peril and pleasure: An RDoC-inspired examination of threat responses and reward processing in anxiety and depression. Depression and Anxiety. 2014;31:233–249. doi: 10.1002/da.22202. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Disner SG, Beevers CG, Haigh EAP, Beck AT. Neural mechanisms of the cognitive model of depression. Nature Reviews Neuroscience. 2011;12:467–477. doi: 10.1038/nrn3027. [DOI] [PubMed] [Google Scholar]
  14. Disner SG, McGeary JE, Wells TT, Ellis AJ, Beevers CG. 5-HTTPR, HTR1A, and HTR2A cumulative genetic score interacts with mood reactivity to predict mood-congruent gaze bias. Cognitive, Affective, and Behavioral Neuroscience. doi: 10.3758/s13415-014-0267-x. (in press). Using a cumulative genetic score focused on variation in three genes associated with serotonergic functioning (5-HTTLPR, HTR1A rs6295, and HTR2A rs6311), the authors demonstrated that genetic score moderated the impact of a sad mood induction on changes in gaze bias to dysphoric and positive images among healthy participants. Specifically, individuals with a higher number of “risk” alleles were more likely to look toward dysphoric images and away from positive images following the negative mood induction. Notably, these findings were observed only when focused on the cumulative genetic score and were not observed for variation in any single gene considered individually. This study highlights the need to examine aggregate levels of influence across multiple genes and the importance of environmental context (e.g., mood) in the presentation of cognitive vulnerabilities related to depression
  15. Feurer C, Hammen CL, Gibb BE. Chronic and episodic stress in children of depressed mothers. Journal of Clinical Child and Adolescent Psychology. doi: 10.1080/15374416.2014.963859. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Franklin JC, Jamieson JP, Glenn CR, Nock MK. How developmental psychopathology theory and research can inform the research domain criteria (RDoC) project. Journal of Clinical Child and Adolescent Psychology. doi: 10.1080/15374416.2013.873981. [DOI] [PubMed] [Google Scholar]
  17. Gee DG, Humphreys KL, Flannery J, Goff B, Telzer EH, Shapiro M, Hare TA, Bookheimer SY, Tottenham N. A developmental shift from positive to negative connectivity in human amygdala-prefrontal circuitry. Journal of Neuroscience. 2013;33:4584–4593. doi: 10.1523/JNEUROSCI.3446-12.2013. This study examined trajectories of amygdala reactivity and amygdala-medial prefrontal cortex (mPFC) functional connectivity in a sample of healthy youth and adults (4–22 years old). Prior research has shown that more negative amygdala-mPFC connectivity is associated with better emotion regulation. Current findings revealed a switch from positive to negative functional connectivity in the transition from childhood to adolescence. Similarly, amygdala reactivity to emotional stimuli was negatively correlated with age. This research highlights the importance of examining the development of neural circuitry in healthy samples as these findings may serve as an essential reference for understanding deviations in the development of neural circuitry and its relation to depression.
  18. Gibb BE. Depression in children. In: Gotlib IH, Hammen CL, editors. Handbook of depression. 3rd ed. New York: Guilford; 2014. pp. 374–390. [Google Scholar]
  19. Gibb BE, Beevers CG, McGeary JE. Toward an integration of cognitive and genetic models of risk for depression. Cognition and Emotion. 2013;27:193–216. doi: 10.1080/02699931.2012.712950. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Gibb BE, Grassia M, Stone LB, Uhrlass DJ, McGeary JE. Brooding rumination and risk for depressive disorders in children of depressed mothers. Journal of Abnormal Child Psychology. 2012;40:317–326. doi: 10.1007/s10802-011-9554-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Gotlib IH, Joormann J. Cognition and depression: Current status and future directions. Annual Review of Clinical Psychology. 2010;6:285–312. doi: 10.1146/annurev.clinpsy.121208.131305. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Hamilton JP, Etkin A, Furman DJ, Lemus MG, Johnson RF, Gotlib IH. Functional neuroimaging of major depressive disorder: A meta-analysis and new integration of baseline activation and neural response data. American Journal of Psychiatry. 2012;169:693–703. doi: 10.1176/appi.ajp.2012.11071105. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Hankin BL. Stability of cognitive vulnerabilities to depression: A short-term prospective multi-wave study. Journal of Abnormal Psychology. 2008;117:324–333. doi: 10.1037/0021-843X.117.2.324. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Hankin BL, Badanes LS, Abela JRZ, Watamura SE. Hypothalamic-pituitary- adrenal axis dysregulation in dysphoric children and adolescents: Cortisol reactivity to psychosocial stress from preschool through middle adolescence. Biological Psychiatry. 2010;68:484–490. doi: 10.1016/j.biopsych.2010.04.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Harrison AJ, Gibb BE. Attentional biases in currently depressed children: An eye-tracking study of biases in sustained attention to emotional stimuli. Journal of Clinical Child and Adolescent Psychology. doi: 10.1080/15374416.2014.930688. (in press). [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Heim C, Binder EB. Current research trends in early life stress and depression: Review of human studies on sensitive periods, gene-environment interactions, and epigenetics. Experimental Neurology. 2012;233:12–111. doi: 10.1016/j.expneurol.2011.10.032. [DOI] [PubMed] [Google Scholar]
  27. Hilt LM, Amrstrong JM, Essex MJ. Early family environment and development of adolescent ruminative style: Moderation by temperament. Cognition and Emotion. 2012;26:916–926. doi: 10.1080/02699931.2011.621932. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Hilt LM, Nolen-Hoeksema S. Gender differences in depression. In: Gotlib IH, Hammen CL, editors. Handbook of depression. 3rd ed. New York: Guilford; 2014. pp. 355–373. [Google Scholar]
  29. Hilt LM, Sander LC, Nolen-Hoeksema S, Simen AA. The BDNF Val66Met polymorphism predicts rumination and depression differently in adolescent girls and their mothers. Neuroscience Letters. 2007;429:13–16. doi: 10.1016/j.neulet.2007.09.053. [DOI] [PubMed] [Google Scholar]
  30. Johnson DP, Whisman MA. Gender differences in rumination: A meta-analysis. Personality and Individual Differences. 2013;55:367–374. doi: 10.1016/j.paid.2013.03.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Johnson DP, Whisman MA, Corley RP, Hewitt JK, Friedman NP. Genetic and environmental influences on rumination and its covariation with depression. Cognition and Emotion. 2014;28:2170–1286. doi: 10.1080/02699931.2014.881325. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Johnstone T, van Reekum CM, Urry HL, Kalin NH, Davison RJ. Failure to regulate: Counterproductive recruitment of top-down prefrontal-subcortical circuitry in major depression. Journal of Neuroscience. 2007;27:8877–8884. doi: 10.1523/JNEUROSCI.2063-07.2007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Joormann J, Arditte K. Cognitive Aspects of Depression. In: Gotlib IH, Hammen CL, editors. Handbook of depression ( 3rd ed. New York: Guilford; 2014. pp. 259–276. [Google Scholar]
  34. Karg K, Burmeister M, Shedden K, Sen S. The serotonin transporter variant (5- HTTLPR), stress, and depression meta-analysis revisited: Evidence of genetic moderation. Archives of General Psychiatry. 2011;68:444–454. doi: 10.1001/archgenpsychiatry.2010.189. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Kellough JL, Beevers CG, Ellis AJ, Wells TT. Time course of selective attention in clinically depressed young adults: An eye tracking study. Behaviour Research and Therapy. 2008;46:1238–1243. doi: 10.1016/j.brat.2008.07.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. King S. The gunslinger: The dark tower I (revised edition) New York: Signet; 2003. [Google Scholar]
  37. Lakdawalla Z, Hankin BL, Mermelstein R. Cognitive theories of depression in children and adolescents: A conceptual and quantitative review. Clinical Child and Family Psychology Review. 2007;10:1–24. doi: 10.1007/s10567-006-0013-1. [DOI] [PubMed] [Google Scholar]
  38. Lenroot RK, Schmitt JE, ORdaz SJ, Wallace GL, Neale MC, Lerch JP, Kendler KS, Evans AC, Giedd JN. Differences in genetic and environmental influences on the human cerebral cortex associated with development during childhood and adolescence. Human Brain Mapping. 2009;30:163–174. doi: 10.1002/hbm.20494. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Liu RT, Alloy LB. Stress generation in depression: A systematic review of the empirical literature and recommendations for future study. Clinical Psychology Review. 2010;30:582–593. doi: 10.1016/j.cpr.2010.04.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Major Depressive Disorder Working Group of the Psychiatric GWAS Consortium. A mega-analysis of genome-wide association studies for major depressive disorder. Molecular Psychiatry. 2013;18:497–511. doi: 10.1038/mp.2012.21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Mandell D, Siegle GJ, Shutt L, Feldmiller J, Thase ME. Neural substrates of trait ruminations in depression. Journal of Abnormal Psychology. 2014;123:35–48. doi: 10.1037/a0035834. This study examined multiple measures of self-reported trait rumination and their relation to neural activity during an alternating emotion-processing/executive control task. All three measures of rumination were positively correlated with increased amygdala reactivity during the executive-control task; however, only “constructive rumination” was associated with amygdala reactivity during the appraisal of negative words. In addition, rumination was associated with activity in the bilateral hippocampus, right anterior insula, medial prefrontal cortex/BA10, and posterior cingulate. However, after controlling for amygdala reactivity, only the association between rumination and activity in the hippocampus was maintained. Importantly, this study conducted a multi-measure examination of the Loss construct at the neural (fMRI) and behavioral (self-reported rumination) levels, and these results suggest reliable neural correlates of rumination where increased reactivity to negative information promotes sustained processing of the information
  42. Michl LC, McLaughlin KA, Shepherd K, Nolen-Hoeksema S. Rumination as a mechanism linking stressful life events to symptoms of depression and anxiety: Longitudinal evidence in early adolescents and adults. Journal of Abnormal Psychology. 2013;122:339–352. doi: 10.1037/a0031994. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Monroe SM, Slavich GM, Georgiades K. The social environment and depression: The roles of life stress. In: Gotlib IH, Hammen CL, editors. Handbook of depression. 3rd ed. New York: Guilford; 2014. pp. 296–314. [Google Scholar]
  44. Moore MN, Salk RH, Van Hulle CA, Abramson LY, Hyde JS, Lemery-Chalfant K, Goldsmith HH. Genetic and environmental influences on rumination, distraction, and depressed mood in adolescence. Clinical Psychological Science. 2013;1:316–322. doi: 10.1177/2167702612472884. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. National Institute of Mental Health. The National Institute of Mental Health strategic plan. 2008 (NIH Publication No. 08-6368). Retrieved from http://www.nimh.nih.gov/about/strategic-planning-reports/index.shtml.
  46. Nolen-Hoeksema S. Responses to depression and their effects on the duration of depressive episodes. Journal of Abnormal Psychology. 1991;100:569–582. doi: 10.1037//0021-843x.100.4.569. [DOI] [PubMed] [Google Scholar]
  47. Nolen-Hoekseman S, Wisco BE, Lyubomirsky S. Rethinking rumination. Perspectives on Psychological Science. 2008;3:400–424. doi: 10.1111/j.1745-6924.2008.00088.x. [DOI] [PubMed] [Google Scholar]
  48. Peyrot WJ, Milaneschi Y, Abdellaoui A, Sullivan PF, Hottenga JJ, Boomsma DI, Penninx BWJH. Effect of polygenic risk scores on depression in childhood trauma. British Journal of Psychiatry. 2014;205:113–119. doi: 10.1192/bjp.bp.113.143081. This study examined a gene (polygenic risk score) × environment (history of childhood trauma) interaction predicting a history of MDD. Polygenic risk scores were calculated based on GWAS meta-analytic results from the Psychiatric Genomics Consortium. In the context of childhood trauma, the effects of the polygenic risk scores were increased such that individuals with higher risk scores who had experienced a history of childhood abuse were more likely to have a history of MDD. This study is the first of its kind in that it provides evidence for a GWAS x environment interaction predicting depression, which may help remedy our understanding of the previous null findings from GWAS studies of depression. Importantly, this suggests that biological influences on depression may not be well understood without considering an individual’s environmental context.
  49. Power RA, Cohen-Woods S, Ng MY, Butler AW, Craddock N, Korszun A, Uher R. Genome-wide association analysis accounting for environmental factors through propensity-score matching: Application to stressful life events in major depressive disorder. American Journal of Medical Genetics Part B Neuropsychiatric Genetics. 2013;162B:521–529. doi: 10.1002/ajmg.b.32180. [DOI] [PubMed] [Google Scholar]
  50. Ray RD, Ochsner KN, Cooper JC, Robertson ER, Gabrieli JDE, Gross J. Individual differences in trait rumination and the neural systems supporting cognitive reappraisal. Cognitive, Affective, & Behavioral Neuroscience. 2005;5:156–168. doi: 10.3758/cabn.5.2.156. [DOI] [PubMed] [Google Scholar]
  51. Risch N, Herrell R, Lehner T, Liang K-Y, Eaves L, Hoh J, Greim A, Kovacs M, Ott J, Merikangas KR. Interaction between the serotonin transporter gene (5- HTTLPR), stressful life events, and risk of depression: A meta-analysis. Journal of the American Medical Association. 2009;301:2462–2471. doi: 10.1001/jama.2009.878. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Schmitt JE, Neale MC, Fassassi B, Perez J, Lenroot RK, Wells EM, Giedd JN. The dynamic role of genetics on cortical patterning during childhood and adolescence. Proceedings of the National Academy of Sciences. 2014;111:6774–6779. doi: 10.1073/pnas.1311630111. This longitudinal study examined genetic contributions to cortical patterning in an unselected sample of child and adolescent twins and siblings. Results indicated that genetic variance in cortical thickness is dynamic throughout the first two decades of development, with the greatest genetically mediated changes occurring in the first decade. By mid-childhood, genetic contributions to cortical thickness stabilized and environmental influences decreased. Notably, regions of the frontal lobes demonstrated the greatest heritability. Finally, results suggested that genes associated with myelination and synaptic pruning may play an essential role in developmental abnormalities in brain volume. Given prior research associating MDD with abnormalities in cortical thickness in frontal regions, these findings suggest future directions for research in the neural development of depression.
  53. Siegle GJ, Granholm E, Ingram RE, Matt GE. Pupillary and reaction time measures of sustained processing of negative information in depression. Biological Psychiatry. 2001;49:624–636. doi: 10.1016/s0006-3223(00)01024-6. [DOI] [PubMed] [Google Scholar]
  54. Siegle GJ, Steinhauer SR, Friedman ES, Thompson WS, Thase ME. Remission prognosis for cognitive therapy for recurrent depression using the pupil: Utility and neural correlates. Biological Psychiatry. 2011;69:726–733. doi: 10.1016/j.biopsych.2010.12.041. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Silk JS, Siegle GJ, Whalen DJ, Ostapenko LJ, Ladouceur CD, Dahl RE. Pubertal changes in emotional information processing: Pupillary, behavioral, and subjective evidence during emotional word identification. Development and Psychopathology. 2009;21:7–26. doi: 10.1017/S0954579409000029. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Steidtmann D, Ingram RE, Siegle GJ. Pupil response to negative emotional information in individuals at risk for depression. Cognition and Emotion. 2010;24:480–493. [Google Scholar]
  57. Thayer JF, Åhs F, Fredrikson M, Sollers JJ, Wager TD. Heart rate variability and neuroimaging studies: Implications for heart rate variability as a marker of stress and health. Neuroscience and Biobehavioral Reviews. 2012;36:747–756. doi: 10.1016/j.neubiorev.2011.11.009. [DOI] [PubMed] [Google Scholar]
  58. Thomas D. Gene-environment-wide association studies: Emerging approaches. Nature Reviews Genetics. 2010;11:259–272. doi: 10.1038/nrg2764. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Vanderhasselt MA, Baeken C, Van Schuerbeek P, Luypaert R, De Mey J, De Raedt R. How brooding minds inhibit negative material: An event-related fMRI study. Brain and Cognition. 2013;81:352–359. doi: 10.1016/j.bandc.2013.01.007. [DOI] [PubMed] [Google Scholar]
  60. Vanderhasselt MA, Kuehn S, De Raedt R. Healthy brooders employ more attentional resources when disengaging from the negative: An event related fMRI study. Cognitive, Affective, and Behavioral Neuroscience. 2011;11:207–216. doi: 10.3758/s13415-011-0022-5. [DOI] [PubMed] [Google Scholar]
  61. Woody ML, Burkhouse KL, Birk SL, Gibb BE. Brooding rumination and cardiovascular reactivity to a laboratory-based interpersonal stressor. Psychophysiology. doi: 10.1111/psyp.12397. (in press). [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Woody ML, Kudinova AY, McGeary JE, Knopik VS, Palmer RHC, Gibb BE. Influence of maternal depression on children’s brooding rumination: Moderation by CRHR1TAT haplotype. Cognition and Emotion. doi: 10.1080/02699931.2014.998631. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Woody M, McGeary J, Gibb B. Brooding rumination and heart rate variability in women at high and low risk for depression: Group differences and moderation by COMT genotype. Journal of Abnormal Psychology. 2014;123:61–67. doi: 10.1037/a0035450. This study examined vulnerabilities thought to underlie the RDoC construct of Loss at the behavioral (brooding rumination), physiological (HRV), and genetic (COMT val158met genotype) levels among women with a past history of MDD and never-depressed controls. Brooding and low HRV, two measures thought to reflect similar deficits in the same cortico-limbic circuit, were negatively correlated with one another. However, different factors predicted how women exhibited vulnerability on both measures. Specifically, a history of past MDD predicted brooding independent of the COMT genotype. In contrast, the link between MDD history and low HRV was stronger among women homozygous for the COMT met allele than among val carriers. This study suggests that brooding and low HRV may be parallel but unique mechanisms underlying depression risk.
  64. Zoccola PM, Dickerson SS. Assessing the relationship between rumination and cortisol: A review. Journal of Psychosomatic Research. 2012;73:1–9. doi: 10.1016/j.jpsychores.2012.03.007. [DOI] [PubMed] [Google Scholar]

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