Hostname: page-component-78c5997874-g7gxr Total loading time: 0 Render date: 2024-11-19T07:34:45.416Z Has data issue: false hasContentIssue false
  • English
  • Français

Appreciating methodological complexity and integrating neurobiological perspectives to advance the science of resilience

Published online by Cambridge University Press:  02 September 2015

Birgit Kleim  and
Isaac R. Galatzer-Levy
Birgit Kleim
Affiliation:
Department of Psychiatry, University of Zurich; 8006 Zurich, [email protected]
Isaac R. Galatzer-Levy
Affiliation:
School of Medicine, New York University, New York, NY 10016. [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Kalisch and colleagues identify several routes to a better understanding of mechanisms underlying resilience and highlight the need to integrate findings from neuroscience and animal learning. We argue that appreciating methodological complexity and integrating neurobiological perspectives will advance the science of resilience and ultimately help improve the lives of those exposed to stress and adversity.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 38 , 2015 , e108
Copyright
Copyright © Cambridge University Press 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aliferis, C. F., Statnikov, A., Tsamardinos, I., Mani, S. & Koutsoukos, X. D. (2010) Local causal and Markov blanket induction for causal discovery and feature selection for classification. Part I: Algorithms and empirical evaluation. Journal of Machine Learning Research 11:171–234.Google Scholar
Berntsen, D., Johannessen, K. B., Thomsen, Y. D., Bertelsen, M., Hoyle, R. H. & Rubin, D. C. (2012) Peace and war: Trajectories of posttraumatic stress disorder symptoms before, during, and after military deployment in Afghanistan. Psychological Science 23(12):1557–65. doi: 10.1177/0956797612457389.Google Scholar
Bonanno, G. A. (2005) Resilience in the face of potential trauma. Current Directions in Psychological Science 14(3):135–38.Google Scholar
Bonanno, G. A. & Diminich, E. D. (2013) Annual research review: Positive adjustment to adversity – Trajectories of minimal-impact resilience and emergent resilience. Journal of Child Psychology and Psychiatry 54(4):378–401.Google Scholar
Bonanno, G. A., Galea, S., Bucciarelli, A. & Vlahov, D. (2006) Psychological resilience after disaster: New York City in the aftermath of the September 11th terrorist attack. Psychological Science 17(3):181–86.Google Scholar
Bonanno, G. A., Mancini, A. D., Horton, J. L., Powell, T. M., LeardMann, C. A., Boyko, E. J., Wells, T. S., Hooper, T. I., Gackstetter, G. D. & Smith, T. C. (2012) Trajectories of trauma symptoms and resilience in deployed US military service members: Prospective cohort study. The British Journal of Psychiatry 200(4):317–23. doi: 10.1192/bjp.bp.111.096552.CrossRefGoogle ScholarPubMed
Galatzer-Levy, I. R. (2014) Empirical characterization of heterogeneous posttraumatic stress responses is necessary to improve the science of posttraumatic stress. Journal of Clinical Psychiatry,75(9):950–52. doi: 10.4088/JCP.14com09372 Google Scholar
Galatzer-Levy, I. R. & Bonanno, G. A. (2012) Beyond normality in the study of bereavement: Heterogeneity in depression outcomes following loss in older adults. Social Science and Medicine 74(12):1987–94. doi: 10.1016/j.socscimed.2012.02.022.CrossRefGoogle Scholar
Galatzer-Levy, I. R. & Bonanno, G. A. (2014) Optimism and death: Predicting the course and consequences of depression trajectories in response to heart attack. Psychological Science 25(12):2177–88. doi: 10.1177/0956797614551750.Google Scholar
Galatzer-Levy, I. R., Bonanno, G. A., Bush, D. E. & LeDoux, J. E. (2013) Heterogeneity in threat extinction learning: Substantive and methodological considerations for identifying individual difference in response to stress. Frontiers in Behavioral Neuroscience 7:55. doi: 10.3389/fnbeh.2013.00055.Google Scholar
Galatzer-Levy, I. R., Karstoft, K.-I., Statnikov, A. & Shalev, A. Y. (2014a) Quantitative forecasting of PTSD from early trauma responses: A machine learning application. Journal of Psychiatric Research 59:68–76.Google Scholar
Galatzer-Levy, I. R., Moscarello, J., Blessing, E. M., Klein, J., Cain, C. K. & LeDoux, J. E. (2014b) Heterogeneity in signaled active avoidance learning: Substantive and methodological relevance of diversity in instrumental defensive responses to threat cues. Frontiers in Systems Neuroscience 8:179. doi: 10.3389/fnsys.2014.00179.Google Scholar
Lebron-Milad, K. & Milad, M. R. (2012) Sex differences, gonadal hormones and the fear extinction network: Implications for anxiety disorders. Biology of Mood and Anxiety Disorders 2(1):3. doi: 10.1186/2045-5380-2-3.Google Scholar
LeDoux, J. E. (2014) Coming to terms with fear. Proceedings of the National Academy of Sciences of the United States of America 111(8):2871–78. doi: 10.1073/pnas.1400335111.Google Scholar
Shanksy, R. M. (2015) Sex differences in PTSD resilience and susceptibility: Challenges for animal models of fear learning. Neurobiology of Stress 1:60–65.Google Scholar
Stoeckel, L. E., Garrison, K.A., Ghosh, S., Wighton, P., Hanlon, C. A., Gilman, J. M., Greer, S., Turk-Browne, N. B., deBettencourt, M. T., Scheinost, D., Craddock, C., Thompson, T., Calderon, V., Bauer, C. C., George, M, Breitner, H. C., Whitfield-Gabrieli, S., Gabrieli, J. D., LaConte, S. M., Hirshberg, L., Brewer, J. A., Hampson, M., Van der Kouwe, A., Mackey, S. & Evans, A. E. (2014) Optimizing real time fMRI neurofeedback for therapeutic discovery and development. Neuroimage Clinics 10(5):245–55.Google Scholar