Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-22T19:58:04.695Z Has data issue: false hasContentIssue false
  • English
  • Français

The challenges of forecasting resilience

Published online by Cambridge University Press:  02 September 2015

Luke J. Chang ,
Marianne Reddan ,
Yoni K. Ashar ,
Hedwig Eisenbarth  and
Tor D. Wager
Luke J. Chang
Affiliation:
Department of Psychology & Neuroscience, University of Colorado, Boulder, CO 80309. [email protected]@[email protected]@[email protected]://cosanlab.comhttp://wagerlab.colorado.edu
Marianne Reddan
Affiliation:
Department of Psychology & Neuroscience, University of Colorado, Boulder, CO 80309. [email protected]@[email protected]@[email protected]://cosanlab.comhttp://wagerlab.colorado.edu
Yoni K. Ashar
Affiliation:
Department of Psychology & Neuroscience, University of Colorado, Boulder, CO 80309. [email protected]@[email protected]@[email protected]://cosanlab.comhttp://wagerlab.colorado.edu
Hedwig Eisenbarth
Affiliation:
Department of Psychology & Neuroscience, University of Colorado, Boulder, CO 80309. [email protected]@[email protected]@[email protected]://cosanlab.comhttp://wagerlab.colorado.edu
Tor D. Wager
Affiliation:
Department of Psychology & Neuroscience, University of Colorado, Boulder, CO 80309. [email protected]@[email protected]@[email protected]://cosanlab.comhttp://wagerlab.colorado.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

Developing prospective models of resilience using the translational and transdiagnostic framework proposed in the target article is a challenging endeavor and will require large-scale data sets with dense intraindividual temporal sampling and innovative analytic methods.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 38 , 2015 , e98
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

Allen, N. E., Sudlow, C., Peakman, T., Collins, R., Dal-Ré, R., Ioannidis, J. P. & Franco, E. L. (2014) UK Biobank data: Come and get it. Science Translational Medicine 6:224ed4. doi: 10.1126/scitranslmed.3008601.Google Scholar
Bollen, J., Mao, H. & Zeng, X. (2011) Twitter mood predicts the stock market. Journal of Computational Science 2(1):1–8.Google Scholar
Butler, E. A. & Randall, A. K. (2013) Emotional coregulation in close relationships. Emotion Review 5(2):202–10.Google Scholar
Coan, J. A., Schaefer, H. S. & Davidson, R. J. (2006) Lending a hand: Social regulation of the neural response to threat. Psychological Science 17(12):1032–39.Google Scholar
Fowler, J. H. & Christakis, N. A. (2008) Dynamic spread of happiness in a large social network: Longitudinal analysis over 20 years in the Framingham Heart Study. BMJ 337:a2338. doi: 10.1136/bmj.a2338.Google Scholar
Hastie, T., Tibshirani, R. & Friedman, J. (2009) The elements of statistical learning: Data mining, inference, and prediction, second edition. Springer.Google Scholar
House, J. S., Landis, K. R. & Umberson, D. (1988) Social relationships and health. Science 241(4865):540–45.Google Scholar
Kanner, A. D., Coyne, J. C., Schaefer, C. & Lazarus, R. S. (1981) Comparison of two modes of stress measurement: Daily hassles and uplifts versus major life events. Journal of Behavioral Medicine 4(1):1–39.Google Scholar
Kessler, R. C., Chiu, W. T., Demler, O., Merikangas, K. R. & Walters, E. E. (2005) Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication. Archives of General Psychiatry 62(6):617–27.Google Scholar
Kessler, R. C., Sonnega, A., Bromet, E., Hughes, M. & Nelson, C. B. (1995) Posttraumatic stress disorder in the National Comorbidity Survey. Archives of General Psychiatry 52(12):1048–60.Google Scholar
Killingsworth, M. A. & Gilbert, D. T. (2010) A wandering mind is an unhappy mind. Science 330(6006):932. doi: 10.1126/science.1192439.Google Scholar
King, G. & Zeng, L. (2001) Logistic regression in rare events data. Political Analysis 9(2):137–63.Google Scholar
Lindquist, M. A. & McKeague, I. W. (2009) Logistic regression with Brownian-like predictors. Journal of the American Statistical Association 104(488)1575–85.Google Scholar
Master, S. L., Eisenberger, N. I., Taylor, S. E., Naliboff, B. D., Shirinyan, D. & Lieberman, M. D. (2009) A picture's worth: Partner photographs reduce experimentally induced pain. Psychological Science 20(11):1316–18. doi: 10.1111/j.1467-9280.2009.02444.x.Google Scholar
Rutledge, R. B., Skandali, N., Dayan, P. & Dolan, R. J. (2014) A computational and neural model of momentary subjective well-being. Proceedings of the National Academy of Sciences 111(33):12252–57.Google Scholar
Schapire, R. E. (1990) The strength of weak learnability. Machine Learning 5(2):197–227.Google Scholar
Schilbach, L., Timmermans, B., Reddy, V., Costall, A., Bente, G., Schlicht, T. & Vogeley, K. (2013) Toward a second-person neuroscience. Behavioral and Brain Sciences 36(4):393–414. doi: 10.1017/S0140525X12000660.CrossRefGoogle Scholar
Uchino, B. N., Cacioppo, J. T. & Kiecolt-Glaser, J. K. (1996) The relationship between social support and physiological processes: A review with emphasis on underlying mechanisms and implications for health. Psychological Bulletin 119(3):488–531.Google Scholar
Wager, T. D., Davidson, M. L., Hughes, B. L., Lindquist, M. A. & Ochsner, K. N. (2008) Prefrontal-subcortical pathways mediating successful emotion regulation. Neuron 59(6):1037–50. doi: 10.1016/j.neuron.2008.09.006.Google Scholar
Weiss, G. M. (2004) Mining with rarity: A unifying framework. ACM SIGKDD Explorations 6(1):7–19.Google Scholar
Zaki, J. & Williams, W. C. (2013) Interpersonal emotion regulation. Emotion 13(5):803–10. doi: 10.1037/a0033839.Google Scholar